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.
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this version posted September 3, 2026.
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https://doi.org/10.64898/2026.08.30.747465
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bioRxiv preprintOpen asset ↗jingwanglab/BioIMApdf-raw-page:4 lines:1-60Plant phenotyping relevance match · UnverifiedCrossref · checked 15 Sept 2026
Abstract Living tissues contain dynamic biochemical information that is difficult to capture with conventional hyperspectral microscopes because sequential spectral acquisition is poorly matched to in vivo molecular processes that evolve during measurement. Here we introduce a task-specific optical encoding framework for video-rate molecular inference in living plant tissue. The system integrates a passive spectral encoder, implemented here as a low-angle scattering LDPE layer, into a 22-mm miniaturized probe and learns a supervised mapping from ultraviolet-excited autofluorescence measurements to biomolecular abundance maps. Unlike conventional pipelines that first reconstruct hyperspectral datacubes and then perform spectral unmixing, the deployed system directly estimates endogenous molecular contrast associated primarily with lignin and chlorophyll in poplar tissue, with additional suberin-associated contrast evaluated in suberin-rich tissue. This reframing makes the measurement task biomolecular inference rather than spectral reconstruction, enabling biochemical mapping under low-photon autofluorescence conditions while reducing data burden and computational latency. In living poplar stems, the platform captures autofluorescence-derived videos of embolism propagation and wound-induced biochemical remodeling, dynamic processes for which sequential spectral acquisition can introduce temporal mixing because the molecular contrast evolves during the scan itself. The system also resolves genotype-dependent reductions in lignin-associated autofluorescence in engineered poplar lines. Direct molecular inference improves biomolecular estimation relative to a reconstruction-based pipeline, while probabilistic decoding provides uncertainty estimates. These results show that compact passive spectral encoding, when optimized for biological inference rather than datacube recovery, enables deployable, label-free molecular videography of living plant tissue dynamics after task-specific calibration.
Why it matches plant phenotyping methods生体植物組織の生化学的状態を動画取得・推定する光学センシング手法を開発し、校正、比較評価、不確実性推定まで行っており、表現型取得法が中心である。
abstractHere we introduce a task-specific optical encoding framework for video-rate molecular inference in living plant tissue.
Accurate stem-volume estimation is fundamental for urban tree inventory and management, but equations developed for forest-grown trees may not be directly suitable for open-grown urban trees with altered stem form and height–diameter relationships. This study developed species-specific, model-assisted stem-volume equations for four dominant urban broad-leaved species in Beijing, China: Quercus mongolica, Sophora japonica, Ginkgo biloba, and Populus davidiana. A total of 2679 standing trees from 535 plots were used for model development and evaluation. The diameter at breast height and ground diameter were field-measured, whereas tree height was obtained as a photogrammetry-derived non-destructive measurement using a handheld tree-measurement superstation. Bivariate DBH–height models, DBH-based linked models, and ground-diameter-based chained models were fitted using weighted nonlinear least squares. Model performance was assessed using validation statistics, 10-fold cross-validation, Monte Carlo uncertainty propagation, and an independent destructive reference dataset of 55 felled trees with section-measured stem volume. Across species, the bivariate models performed best, with mean percent standard errors of 8.68%–16.24%, compared with 9.76%–20.25% for DBH-based linked models and 15.13%–28.56% for ground-diameter-based models. Destructive reference validation showed acceptable agreement within the available validation dataset, with relative RMSE values of 2.30%–5.03% and relative bias values of 0.51%–2.51%. Monte Carlo simulation indicated species-specific propagation of photogrammetric height error, with the lowest average volume fluctuation in Ginkgo biloba. These results suggest that handheld photogrammetry combined with species-specific modelling provides a practical and uncertainty-aware basis for urban stem-volume estimation. This study directly estimates stem volume rather than biomass or carbon stock, and the equations may support future biomass- and carbon-related assessments when combined with appropriate conversion parameters.
Why it matches plant phenotyping methods携帯型フォトグラメトリによる樹高取得と、幹体積推定モデルの開発・交差検証・伐倒木による独立検証が研究の中心であり、樹木の形態形質を定量化する実質的なフェノタイピング手法である。
abstractThis study developed species-specific, model-assisted stem-volume equations for four dominant urban broad-leaved species in Beijing, China
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:108-129Plant phenotyping relevance match · UnverifiedEurope PMC · checked 5 Sept 2026
Poplar (Populus) trees are indispensable to various industries and environmental sustainability efforts. They are widely utilized for paper production, timber, and windbreaks, while also playing a significant role in carbon sequestration. Given their economic and ecological importance, the effective management of diseases is crucial. Convolutional Neural Networks (CNNs), renowned for their ability to process visual data, are pivotal in accurately detecting and classifying plant diseases. This study presents a domain-specific dataset of manually collected images of diseased poplar leaves from Uzbekistan and South Korea, ensuring geographic diversity and broader applicability. The dataset includes four disease classes, i.e., " Parsha (Scab) ," " Brown spotting ," " White-Gray spotting ," and " Rust ," which represent common afflictions in these regions. To advance research efforts, this dataset will be made publicly accessible, providing a valuable resource for the scientific community. Leveraging the cutting-edge YOLOv9c model, a state-of-the-art CNN architecture, we applied the Histogram Equalization technique as a preprocessing step to enhance the image quality to increase the accuracy of disease detection. This method not only improves the diagnostic performance of the model but also provides a scalable solution for monitoring and managing poplar diseases. By ensuring the health of poplar trees, this approach supports the sustainability of these critical resources. To our knowledge, this is the first publicly available dataset specifically focused on diseased poplar leaves, making it a significant contribution to global research efforts. It offers an invaluable resource for researchers and practitioners, enabling further advancements in early disease detection and sustainable forestry management.
Why it matches plant phenotyping methodsポプラ葉の病徴を画像から検出・分類する手法と公開データセットが研究の中心であり、植物の病害状態を直接推定するため、植物フェノタイピング手法として採択。
abstractThis study presents a domain-specific dataset of manually collected images of diseased poplar leaves from Uzbekistan and South Korea
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:163-201Plant phenotyping relevance match · UnverifiedEurope PMC · checked 5 Sept 2026
Introduction Populus pruinosa is the key foundation tree species in desert riparian forests in arid areas of northwestern China. Timely and accurate monitoring of the physiological status of P. pruinosa is crucial for restoring the damaged ecosystem. Anthocyanins are one of the important physiological indicators that reflect the environmental adaptability of P. pruinosa under stress. Existing studies have extensively applied hyperspectral technology for the quantitative prediction of crop leaf pigments. However, research on hyperspectral prediction of anthocyanin concentration in woody halophytes is still lacking, particularly in the integration of spectral preprocessing, species-specific vegetation index construction, and machine learning modeling. Methods In this study, the hyperspectral technology was used to estimate the anthocyanin concentration of P. pruinosa leaves collected in five months (June - October) under five groundwater depth conditions (0-2, 2-4, 4-6, 6-8, and 8-10 m). Based on first-order (FD) and second-order (SD) derivative processing, competitive adaptive reweighted sampling (CARS), https://xueshu.baidu.com/usercenter/paper/show?paperid=bea4d6371f19161f21aac22941cc4408&site=xueshu_se shuffled frog leaping algorithm (SFLA), and recursive feature elimination with cross-validation (RFECV) were used to extract spectral features of P. pruinosa leaves to construct the anthocyanin reflectance index, composite index, difference vegetation index, and normalized anthocyanin reflectance index. After that, the top 10 sets of data with high correlation with anthocyanin concentration were selected from each vegetation index to form a total data set (40 sets in total) for modeling. Twelve models were constructed using support vector machine (SVM) and one-dimensional convolutional neural network (1D-CNN) methods. Results The FD and SD derivative transformations of the spectral reflectance significantly enhanced the correlation with anthocyanin concentration. The feature extraction methods SFLA and RFECV were superior in extracting the bands highly related to anthocyanin concentration, and the vegetation indices constructed based on these two methods had a high correlation with anthocyanin concentration in the red and near-infrared regions. The optimal prediction model was FD-SFLA-SVM (R 2 = 0.852, RMSE = 86.851 mg m -2 , RPD = 2.596). Discussion Unlike existing vegetation index-based studies, the research develops a systematic approach to construct vegetation indices and models for estimating the anthocyanin concentration in the woody halophyte P. pruinose in deserts. The research will provide technical support for non-destructive monitoring of the physiological status of P. pruinosa , and also contribute to the restoration of desert riparian ecosystems.
Why it matches plant phenotyping methods葉のハイパースペクトルからアントシアニン濃度という植物生理形質を推定する手法を開発・比較しており、形質取得とモデル化が研究の中心である。
abstractthe hyperspectral technology was used to estimate the anthocyanin concentration of P. pruinosa leaves
Intelligent forest tree breeding has advanced plant phenotyping, yet existing research largely focuses on large-leaf agricultural crops, with limited attention to fine-grained leaf analysis of sapling trees in open-field environments. Natural scenes introduce challenges including scale variation, illumination changes, and irregular leaf morphology. To address these issues, we collected UAV RGB imagery of field-grown saplings and constructed the Poplar-leaf dataset, containing 1,202 branches and 19,876 pixel-level annotated leaf instances. To our knowledge, this is the first instance segmentation dataset specifically designed for forestry leaves in open-field conditions. We propose LeafInst, a novel segmentation framework tailored for irregular and multi-scale leaf structures. The model integrates an Asymptotic Feature Pyramid Network (AFPN) for multi-scale perception, a Dynamic Asymmetric Spatial Perception (DASP) module for irregular shape modeling, and a dual-residual Dynamic Anomalous Regression Head (DARH) with Top-down Concatenation decoder Feature Fusion (TCFU) to improve detection and segmentation performance. On Poplar-leaf, LeafInst achieves 68.4 mAP, outperforming YOLOv11 by 7.1 percent and MaskDINO by 6.5 percent. On the public PhenoBench benchmark, it reaches 52.7 box mAP, exceeding MaskDINO by 3.4 percent. Additional experiments demonstrate strong generalization and practical utility for large-scale leaf phenotyping.
Why it matches plant phenotyping methods森林葉の個体分割と表現型解析のためのUAV画像データセットおよび新規セグメンテーション手法を開発・評価しており、植物表現型取得が中心である。
abstractwe collected UAV RGB imagery of field-grown saplings and constructed the Poplar-leaf dataset, containing 1,202 branches and 19,876 pixel-level annotated leaf instances.
Extracting poplar seed morphological phenotypes is a core task in modern poplar breeding research. Accurate seed image segmentation is crucial for phenotype extraction and data quality. However, the small size of poplar seeds and their tendency to form dense clusters challenge the accuracy of current segmentation methods. Unlike current approaches that struggle with small-target segmentation and boundary delineation, this study develops the MP-Seed semantic segmentation algorithm, which combines a small-target attention module (based on Layer Across Feature Map Attention) with a multi-task learning mechanism that integrates boundary features. This novel integration targets small-seed key regions, fuses boundary features, and refines predictions to precisely segment densely clustered seeds, achieving superior accuracy and fine-grained delineation compared to current single-task methods. To address low efficiency and accuracy in poplar seed morphological phenotype extraction, this study further proposes a high-throughput extraction method leveraging the MP-Seed algorithm. To analyse the phenotypic data, an SVM classification model classifies eight types of poplar seeds. Experimental validation shows that the MP-Seed algorithm outperforms current methods on the test set, achieving Seed_IoU of 94.1 %, mIoU of 97.2 %, and Reference_IoU of 97.6 %. The high-throughput phenotyping method measures seed length and width with relative errors within 2.72 % versus manual measurements and extracts ten morphological traits at about 18.3 seeds per second. The overall classification accuracy reaches 91.1 %. Overall, this study provides technical support for accurate poplar seed segmentation and efficient morphological phenotype extraction, offering a valuable reference for other seed morphological phenotype research and analysis.
Why it matches plant phenotyping methodsポプラ種子画像のセグメンテーションアルゴリズムと高スループット形態形質抽出法を開発・検証しており、植物フェノタイピング手法が研究の中心である。
abstractthis study develops the MP-Seed semantic segmentation algorithm
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:192-218Plant phenotyping relevance match · UnverifiedEurope PMC · checked 5 Sept 2026
Introduction Leaf water potential (Ψ leaf ) is a fundamental physiological metric quantifying tree water status and forest drought stress, yet its measurement remains labor-intensive and destructive. Hyperspectral techniques show great promise for retrieving plant physiological traits; however, robust Ψ leaf estimation remains limited by three critical factors: unbalanced data distributions, the need for global-local feature synergy, and inherent uncertainty in point-based regression. Methods Here, we propose a deep learning framework (CIDL) that integrates: (1) a conditional generative adversarial network (CGAN) to generate balanced synthetic samples across the full Ψ leaf domain; (2) a feature extractor that combines Inception-ResNet with ACmix (IRAC) to capture local absorption features and long-range spectral dependencies jointly; and (3) a distribution-aware regression network (DARN) to explicitly model the target-variable distribution, thereby enhancing predictive reliability. The model was trained and evaluated using a dataset derived from dehydration experiments on leaves of young Populus euramericana 'I-214' trees, comprising 229 paired Ψ leaf and hyperspectral reflectance measurements, which were further augmented with 500 CGAN-generated synthetic samples to improve model robustness. Results CIDL achieved a prediction accuracy of R 2 = 0.78 and RMSE = 0.27 MPa on the test set, clearly outperforming traditional machine learning methods (mean R 2 = 0.66, mean RMSE = 0.34 MPa) and yielding a modest yet consistent improvement over mainstream deep learning approaches (mean R 2 = 0.76, mean RMSE = 0.28 MPa). Discussion These results demonstrate that the proposed CIDL framework provides a generalizable solution for small-sample physiological hyperspectral analysis and offers a reliable, non-destructive pathway for tree water-stress monitoring, with strong potential for applications in smart forestry management.
Why it matches plant phenotyping methods葉のハイパースペクトル反射から葉水ポテンシャルという植物生理形質を推定する深層学習フレームワークを開発し、既存手法と比較検証しているため、植物フェノタイピング手法が中心である。
abstractHere, we propose a deep learning framework (CIDL)
PoplarMicroscopyCell / cellular structureRootVisualization / data management
Abstract Background Cortical microtubules (CMTs), one of the components of cytoskeleton, control the orientation and localization of newly deposited cellulose microfibrils in cell walls, and thereby determine the shape, size, and structure of plant cells. Imaging of CMTs in plant tissues is generally performed using fluorescently labeled specimens under an optical fluorescence or confocal laser scanning microscope. However, optical microscopy has insufficient resolution to visualize individual CMTs, and its observation range is limited to superficial tissue layers that light can penetrate. In contrast, transmission electron microscopy offers high-resolution visualization of CMTs in plant cells but is restricted to slightly oblique ultrathin sections with an approximate thickness of 70–100 nm. Results Herein, we introduce a technique for visualizing CMTs within unstained plant tissues by combining cryofracture techniques with field emission scanning electron microscopy (FE-SEM). We successfully observed the arrangement of CMTs in several plant specimens, including young branches of ginkgo ( Ginkgo biloba ), calli from the leaves of hybrid poplar ( Populus sieboldii × P. grandidentata ), and root tips of the adzuki bean ( Vigna angularis ). CMTs were visualized on the protoplasmic fracture face using both cryo-FE-SEM and conventional room-temperature FE-SEM. Conclusions The combination of freeze-fracture techniques with FE-SEM enables the visualization of CMT arrangement in plant tissues at a high resolution and across a broad area without the need for staining or extraction of cellular components. This technique is applicable to various plant tissues and allows for detailed observation of CMTs within these tissues, providing valuable insights into the role of microtubules in the division and differentiation of plant cells.
Why it matches plant phenotyping methods植物組織内の微小管配列を高解像度で可視化するFE-SEMと凍結割断の新規画像取得法を開発しており、植物細胞状態の観察手法が研究の中心です。
abstractHerein, we introduce a technique for visualizing CMTs within unstained plant tissues by combining cryofracture techniques with field emission scanning electron microscopy (FE-SEM).
Seeds of major Populus cultivars were collected from across China in 2024 to build the image-data bank of over 1187000 images of singular seeds for the National Forestry and Grassland Science Data Center (NFGSDC). An innovative vibration-assisted machine-vision system was built with alternating back-lit and front-lit illumination, which incorporated a flexible vibratory panel (FVP) to manipulate the multitude of seeds to minimize the occurrence of butting or overlapping, and the lighting from alternating directions to capture phenotypic features both in silhouettes and in vivid color images. To investigate how illumination directions would affect phenotyping, morphological and chromatic metrics were measured, respectively from only the common front-lit images and through the combined use with back-lit images, and applied to distinguish different cultivars and harvest-batches. Results verified that back-lit excelled for reliable segmentation for feature images and accurate morphological metrics, especially when the closeness was clearly revealed in the clustering dendrogram between Nanlin 895 and Zhonglin 46, which shared a common genetic sourcing from P. Euramericana. In contrast, front-lit images were prone to occasional segmentation defects leading to inaccurate morphological measurements due to the highly dynamic range of seed colors, which caused the clustering to lose the genetic relevance. The power of the image-dataset of alternating illuminations was further demonstrated when a decent accuracy of 0.819 yielded from the simple support-vector-machine classification while working on only the back-lit morphological measurements, and the increase to 0.856 with statistical significance if with the addition of chromatic metrics from corresponding front-lit color images, while other image characteristics had been strictly held back. The vibration-assisted alternating illumination protocol established in this work to capture delicate seed-features of Populus cultivars may also be applied to other small grains facing similar imaging challenges, laying a sturdy step-stone of high-throughput phenotyping for large-scale breeding programs and genetic studies. Keywords: Populus seed, machine vision, camera calibration, flexible vibratory plate, back-lit and front-lit illumination DOI: 10.25165/j.ijabe.20261901.9850 Citation: Wang X W, Horly M M, Li Z P, Zhao M C, Wu B, Wang M M, et al. High-throughput seed phenotyping of Populus cultivars in China using vibration-assisted machine vision with alternating back-lit and front-lit illuminations. Int J Agric & Biol Eng, 2026; 19(1): 197–212.
Why it matches plant phenotyping methodsポプラ種子の形態・色形質を高 throughput に取得する画像計測システムと照明・振動プロトコルを開発・検証しており、植物フェノタイピング手法が研究の中心である。
abstractAn innovative vibration-assisted machine-vision system was built with alternating back-lit and front-lit illumination
Abstract has not been obtained from indexed metadata or an accessible article page.
Why it matches plant phenotyping methodsポプラ種子の形態的表現型を画像セグメンテーションで抽出・分類する手法が題名上の中心であり、植物表現型計測法に該当する。
titleFrom segmentation to classification: Morphological phenotype extraction and classification analysis of tiny poplar seeds using the MP-Seed segmentation algorithm
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:268-286Plant phenotyping relevance match · UnverifiedCrossref · checked 15 Sept 2026
Accurate and early detection of rapid Woody Plant Encroachment (rWPE) in grasslands is critical for management and conservation. However, this task remains challenging due to the spectral and spatial complexities of multi-species grassland ecosystems. This study evaluates the potential of UAV-RGB imagery and deep learning algorithms for early detection and classification of three dominant woody species (Wolf Willow – Elaeagnus commutata , Western Snowberry – Symphoricarpos occidentalis , and Trembling Aspen - Populus tremuloides ) in the Canadian Prairies. Five semantic segmentation models, including three CNNs (PSPNet, DeepLabV3+, UNet) and two Transformers (SegFormer and Mask2Former), were assessed in Foam Lake Community Pasture. The results indicate that Transformers outperformed CNNs, with the largest SegFormer model (MIT-B5) achieving the highest overall accuracy (92.5 %), mean IoU (68.2 %), and F1-score (79.8 %). Transfer learning improved the model performance in SegFormer by more than 5 % in the mF1-score and 7 % in mIoU. A lightweight variant (MIT-B1) balanced high accuracy (79.2 % F1-score) with high speed (17.4 fps). Spatial resolution degradation (from 0.73 cm to 3 cm) reduced detection accuracy for small shrub patches (diameters ∼10–20 cm), while showing minimal impact on larger patches (diameters >1 m). SegFormer exhibited superior capability in distinguishing woody species using high resolution imagery, even at early growth stages. Our findings highlight the effectiveness of Transformers and high-resolution UAV imagery for precise woody species mapping, offering scalable solutions for grassland conservation and monitoring. • Vision Transformers outperform CNNs significantly in early woody species detection. • SegFormer achieves 92.5 % accuracy for grassland woody encroachment detection. • Transfer learning boosts SegFormer accuracy by 5–7 % with limited training data. • Spatial resolution <3 cm/pixel critical for small shrub detection (diameter < 20 cm). • Framework aids scalable grassland conservation via UAV monitoring.
Why it matches plant phenotyping methodsUAV画像と深層学習によって植物個体・群落の侵入状態および樹種を直接推定し、複数モデルの性能比較、転移学習、空間解像度の影響を評価しており、植物状態の取得・抽出手法が中心である。
abstractThis study evaluates the potential of UAV-RGB imagery and deep learning algorithms for early detection and classification of three dominant woody species
Photosystem II (PSII) is among the most thermally sensitive components of photosynthesis, and emerging evidence suggests that that plants in diverse biomes face increasing risk of PSII damage under future climate change. However, uncertainties in the distribution and drivers of PSII thermal tolerance (Tcrit) limit our ability to predict thermal risk in plant communities across spatial scales. Here, we evaluate whether intraspecific variation in Tcrit corresponds to leaf reflectance spectra (400-2500nm) to identify mechanisms associated with Tcrit in field conditions and evaluate the potential of its remote estimation using novel remote sensing platforms. We measured Tcrit using temperature response curves of minimal fluorescence (Fo) along with corresponding leaf reflectance spectra in two foundation tree species: Populus fremontii (US Southwest) and Metrosideros polymorpha (Hawai‘i). P. fremontii was sampled under both moderate ( 45ºC) heat. Consistent spectral signatures of Tcrit emerged across species and sampling conditions, with the strongest signatures in P. fremontii under extreme heat. These signatures allowed Tcrit estimation (R²=0.24-0.30; RMSE<1.0ºC) and classification of high- versus low-Tcrit (71-77% accuracy) in P. fremontii. Across both species, Tcrit tended to increase with spectral indices reflecting higher chlorophyll content and lower carotenoids, nonphotochemical quenching, and leaf water content. These findings suggest that variation in PSII thermal tolerance is linked to fundamental biochemical properties of leaves, which are reflected in their optical traits. As climate extremes intensify, spectral screening and scaling of Tcrit via remote sensing may support improved conservation, management, and thermal risk assessment in vulnerable ecosystems.
Why it matches plant phenotyping methods葉の反射スペクトルからPSII熱耐性（Tcrit）を推定・分類する方法を評価しており、植物生理形質の取得・リモート推定が研究の中心である。
abstractHere, we evaluate whether intraspecific variation in Tcrit corresponds to leaf reflectance spectra (400-2500nm) to identify mechanisms associated with Tcrit in field conditions and evaluate the potential of its remote estimation using novel remote sensing platforms.
Poplar trees are widely cultivated for their ecological and economic benefits. Studying the phenotypes of poplar seedlings can enable the selection of optimal cultivation methods to enhance yield and quality. UAV-based low-altitude remote sensing with optical sensors captures images and spectral data for such studies. However, deep learning in UAV plant phenotyping faces the challenge of requiring substantial time and effort to label image samples for model training. This paper aims to assess the efficiency of using Grounding DINO-SAM2 for zero-shot instance segmentation of individual poplar seedlings across multiple genotypes. An automatic program calculates image features from RGB and multispectral mask areas, including canopy projection, color, texture, and spectral reflectance, which are then used to establish a biomass estimation model based on two years of data. The study obtained the following results: (1) The Grounding DINO-SAM2 model was used to implement zero-labelled sample instance segmentation of 400 image data. After modifying the sample with incorrect target recognition quantity in less than 15 min, the total model took only 0.5 h, with a precision of 0.943, which greatly saved time and computing cost compared with mainstream fully-supervised segmentation models. (2) A poplar seedling biomass estimation model based on multimodal image features was established. After comparing and optimizing single-sensor and multi-sensor combined with different modelling algorithms, it was found that the CNN test set accuracy (R²) reached 0.823. This research provides a lightweight, cost-effective approach for plant image segmentation and feature extraction, promoting advances in intelligent management and monitoring for agriculture and forestry.
Why it matches plant phenotyping methodsUAV画像による個体セグメンテーションと特徴抽出を開発・評価し、ポプラ苗のバイオマスを推定する方法が研究の中心である。
abstractThis paper aims to assess the efficiency of using Grounding DINO-SAM2 for zero-shot instance segmentation of individual poplar seedlings across multiple genotypes.
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:1-59Plant phenotyping relevance match · UnverifiedCrossref · checked 15 Sept 2026
PoplarField / plotStem / branchCalibration / preprocessingWater status / transpiration
In ecohydrology, stable water isotopes (δ2H and δ18O) are valuable tools for investigating the water’s movement through the soil-plant-atmosphere continuum. Recent tracer-based studies using stable water isotopes showed that different methods for extracting water from plant tissues can return different isotopic compositions due to the presence of organic contaminants and because these methods extract different plant water domains. While Cryogenic Vacuum Distillation (CVD) is widely recognized as a standard method of plant water extraction for isotopic analysis, its indiscriminate water extraction has proven problematic. Various other techniques have been developed and tested for plant water extraction, such as direct vapour equilibration, mechanical squeezing and centrifugation. However, there remains a necessity to develop a cost and time efficient method to discriminately extract xylem water, which better represents the source waters used by plants for transpiration.In this work, we evaluated the viability of Vacuum Extraction (VAC) - a method previously used in ecophysiology for chemical analysis - for the extraction of plant water for isotopic analysis. The specific objectives were to i) assess the likely influence of organic contaminants (glucose, fructose, sucrose, ethanol and methanol) in water samples extracted by VAC, ii) determine whether there is a significant difference in the isotopic signature of plant water extracted by VAC from lignified samples with and without bark, iii) compare the isotopic composition of plant water extracted by VAC and CVD.The comparison tests were carried out in late March and early July 2024 on trees or shrubs of Cornus sanguinea, Carpinus orientalis, Prunus cerasifera, Photinia serratifolia, and Populus canadensis, located in a village close to Padua (Italy). In March, samples were taken from lignified twigs, and we prepared replicates with and without bark for extraction by VAC. In July, twig samples were collected for extraction by VAC and by CVD. Given the negligible presence of organic contaminants in VAC samples, we performed their isotopic analysis by laser spectroscopy. Conversely, CVD samples were analysed by isotope-ratio mass spectrometry. Our results showed no significant differences in the sugar levels of samples with and without bark, and no clear relation between the sugar content and the isotopic composition of plant water extracted by VAC. Additionally, when comparing CVD and VAC, the δ18O values were similar, but there were significant differences in the δ2H between the two methods, with VAC samples plotting significantly closer to the Local Meteoric Water Line compared to CVD samples. These first results indicate that VAC is a promising and effective method for the extraction of plant water for isotopic analysis. However, further tests should be performed for other species and under different environmental conditions. Acknowledgements: This study was carried out within the Agritech National Research Center and received funding from the European Union Next-Generation EU (PIANO NAZIONALE DI RIPRESA E RESILIENZA (PNRR) – MISSIONE 4 COMPONENTE 2, INVESTIMENTO 1.4 – D.D. 1032 17/06/2022, CN00000022). This abstract reflects only the authors’ views and opinions, neither the European Union nor the European Commission can be considered responsible for them.
Why it matches plant phenotyping methods植物組織から木部水を抽出し、その同位体組成を測定する手法の開発・比較検証が研究の中心であり、植物の水分状態に関する生理的表現型を取得する方法に該当する。
abstractthere remains a necessity to develop a cost and time efficient method to discriminately extract xylem water
Abstract Context Climate change is causing landscape shifts and locally-adapted plants are becoming increasingly maladapted. As a foundation species, Fremont cottonwood facilitates adaptation to changing climate for the whole community. Populations within this species, however, have varying adaptive responses and facilitative capacity due to genetic variation. It is important to identify these differences to inform landscape restoration and management. Objectives UAV hyperspectral, thermal, and lidar images might reveal genetic trait differences within a single tree species. This study tests and demonstrates: (1) UAV hyperspectral images in detecting differences among populations in canopy leaf area, water content, carbon, and nitrogen content as indicators of population-level productivity, fitness, adaptability, and biodiversity they can support, and (2) UAV hyperspectral-thermal-lidar fusion in detecting and classifying 16 populations sourced from different environments across Arizona, USA. Methods UAV hyperspectral, thermal, and lidar images were acquired from a common garden with 16 different Fremont cottonwood populations growing together. The UAV hyperspectral image was used to calculate spectral indices for canopy leaf area (LAI), canopy water content, nitrogen, carbon, and carbon-to-nitrogen ratio (C:N). The hyperspectral indices (EVI, LAI, PRI, MSI, NDWI, NDNI, NDLI, and C:N) were also examined with the UAV thermal image-derived canopy temperature data for potential correlations. Finally, all hyperspectral bands (n = 487 bands), thermal image-derived canopy temperature, and lidar-derived maximum canopy height estimates were stacked into a single image and then classified to detect 16 different populations of Fremont cottonwood using a random forest classification. Results The UAV hyperspectral indices and canopy temperature were significantly different among populations suggesting that the productivity, fitness, and adaptability of varying populations are significantly different. Many of the UAV hyperspectral indices were strongly correlated with canopy temperature. Populations with greater canopy cover, lower canopy temperature, and greater canopy height were well detected in the UAV hyperspectral-thermal-lidar fusion-based classification (producer’s accuracies of > 75%), whereas populations at low abundance were poorly classified (producer’s accuracies of Conclusions This study demonstrates the first application of UAV hyperspectral-thermal-lidar data fusion in phenotyping. The machine learning-based classification detects various populations within a single tree species. Future studies can use similar UAV data sources, derived variables, and data fusion to detect populations that have better fitness and adaptability to changing environments. Such populations can be strategically managed to sustain healthy landscapes that support diverse communities and species.
Why it matches plant phenotyping methodsUAVのハイパースペクトル・熱・LiDAR融合により、樹冠形態・生理形質を推定し、集団差を分類する手法を中心的に開発・実証しているため。
abstractThis study tests and demonstrates: (1) UAV hyperspectral images in detecting differences among populations in canopy leaf area, water content, carbon, and nitrogen content
O_LIUnderstanding how vegetation responds to drought is fundamental for understanding the broader implications of climate change on foundation tree species that support high biodiversity. Leveraging remote sensing technology provides a unique vantage point to explore these responses across and within species. C_LIO_LIWe investigated interspecific drought responses of two Populus species (P. fremontii, P. angustifolia) and their naturally occurring hybrids using leaf-level visible through shortwave infrared (VSWIR; 400-2500 nm) reflectance. As F1 hybrids backcross with either species, resulting in a range of backcross genotypes, we heretofore refer to the two species and their hybrids collectively as "cross types." We additionally explored intraspecific variation in P. fremontii drought response at the leaf and canopy levels using reflectance data and thermal unmanned aerial vehicle (UAV) imagery. We employed several analyses to assess genotype-by-environment (GxE) interactions concerning drought, including principal component analysis, support vector machine, and spectral similarity index. C_LIO_LIFive key findings emerged: (1) Spectra of all three cross types shifted significantly in response to drought. The magnitude of these reaction norms can be ranked from hybrids>P. fremontii>P. angustifolia, suggesting differential variation in response to drought; (2) Spectral space among cross types constricted under drought, indicating spectral--and phenotypic--convergence; (3) Experimentally, populations of P. fremontii from cool regions had different responses to drought than populations from warm regions, with source population mean annual temperature driving the magnitude and direction of change in VSWIR reflectance. (4) UAV thermal imagery revealed that watered, warm-adapted populations maintained lower leaf temperatures and retained more leaves than cool-adapted populations, but differences in leaf retention decreased when droughted. (5) These findings are consistent with patterns of local adaptation to drought and temperature stress, demonstrating the ability of leaf spectra to detect ecological and evolutionary responses to drought as a function of adaptation to different environments. C_LIO_LISynthesis. Leaf-level spectroscopy and canopy-level UAV thermal data captured inter- and intraspecific responses to water stress in cottonwoods, which are widely distributed in arid environments. This study demonstrates the potential of remote sensing to monitor and predict the impacts of drought on scales varying from leaves to landscapes. C_LI
Why it matches plant phenotyping methods葉面分光とUAV熱画像を用いて、植物の乾燥応答や葉温・葉保持を測定し、リモートセンシングによる表現型評価の有効性を実証しているため。
abstractWe additionally explored intraspecific variation in P. fremontii drought response at the leaf and canopy levels using reflectance data and thermal unmanned aerial vehicle (UAV) imagery.
Low-cost, minimally invasive microscopy for tracking cellular dynamics in living plants within their natural ecosystems is crucial for addressing fundamental questions in plant ecology and biology. However, existing solutions are constrained by coarse resolution, limited field-of-view (FoV), and poor deployability in natural settings. Here, we utilize a compact, portable microscope ("miniscope") for label-free (autofluorescence) imaging in living poplar wood. We systematically implement and evaluate multiple computational methods to enhance resolution and FoV. Our optimal computational pipeline, comprising maximal intensity projection, deconvolution, and flat-field correction, increases resolution by up to 39% on-axis and up to 49% at the field edges, resolving features of 2.87 μm, averaged over a FoV of ∼1 mm (diameter), compared with a 4.34 μm baseline. We demonstrate microscopy within the tissue of a living poplar plant in our greenhouse, observing the embolism of vessel elements, wound response, and tissue deformation from moisture evaporation.
Why it matches plant phenotyping methods生体ポプラ組織の細胞動態・木部塞栓・創傷応答を観察する携帯型顕微鏡と画像処理パイプラインを開発・評価しており、植物状態の取得手法が中心である。
abstractWe systematically implement and evaluate multiple computational methods to enhance resolution and FoV.
Accurately predicting drought tolerance in woody perennial bioenergy crops is critical for sustainable biomass production under fluctuating precipitation. Hyperspectral imaging (HSI) in the visible-near-infrared (VNIR) and shortwave-infrared (SWIR) ranges offers a promising approach for predicting plant biochemical traits, yet its application in metabolite profiling remains underexplored. We integrated VNIR+SWIR HSI with untargeted metabolomics to investigate drought-induced metabolic shifts in Populus leaves from eight Populus genotypes. Metabolite profiling identified 127 compounds, with 73 showing significant drought responses spanning amino acids (AA), carbohydrates (CHO), phenolic glycosides (PG), organic acids (OA), fatty acids and alcohols (FA), terpenes (T), phenolic metabolites (P), and unclassified metabolites. Spectral analysis revealed consistently higher reflectance across VNIR and SWIR wavelengths in drought-stressed plants, corresponding with increased accumulation of AA and reduced CHO and PG levels. Least absolute shrinkage and selection operator (LASSO) regression modeling identified robust spectral predictors of metabolite concentrations, associating VNIR wavelengths (500–700nm) predominantly with AA and P, whereas SWIR wavelengths (1680–1700nm) reliably predicted CHO, OA, and S. Several stable spectral-metabolite associations persisted across the two watering regimes (drought vs. well-watered), highlighting their potential as spectral biomarkers for non-destructive stress monitoring. Minimal genotype-specific variation suggests that observed spectral and metabolic responses were driven primarily by environmental factors, likely reflecting limited genetic diversity among the commercial Populus genotypes examined. This work establishes VNIR+SWIR hyperspectral imaging as a powerful, non-destructive phenotyping tool for precision monitoring and targeted improvement of drought resilience in bioenergy crops.
Why it matches plant phenotyping methodsVNIR+SWIRハイパースペクトル画像と回帰モデルにより、葉の代謝物濃度および干ばつストレスを非破壊推定する方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明示されています。
abstractHyperspectral imaging (HSI) in the visible-near-infrared (VNIR) and shortwave-infrared (SWIR) ranges offers a promising approach for predicting plant biochemical traits
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:1-66Plant phenotyping relevance match · UnverifiedOpenAlex · checked 15 Sept 2026
Dimensional characterization of trees plays important roles in phytoremediation project of precision agriculture and environmental protection. The dimensional characterization can be evaluated by using UAV-based geomatic surveys. The work in this study applies low-cost UAV photogrammetry for tree height estimation, particularly for a phytoremediation project on contaminated soils. Two locations that had differing mean tree heights (7m and 4m) were used for the purpose of study. Three different UAV flights were carried out at 40m, 50m, and 60m altitudes in Area 1, an olive grove, and two different flights at 45m and 52m altitudes in Area 2, which has poplar species. The Structure from Motion (SfM) method, Vegetation Filter (VF), Digital Surface Models (DSMs), and Raster computational tool were used to process the UAV point clouds in order to produce Canopy Height Models (CHMs) for a local maximum driven extraction of tree height. Relatively, the results obtained from the tree height estimation experiment for the locations using UAV are higher than the results obtained using in-field measurement, thereby justifying the suitability of UAV photogrammetric data for tree height estimation.
Why it matches plant phenotyping methodsUAVフォトグラメトリとSfM、CHM、画像点群処理を用いて樹高を推定し、圃場測定と比較検証しているため、植物形質取得法が研究の中心です。
abstractThe work in this study applies low-cost UAV photogrammetry for tree height estimation
Drought has become a major climate threat affecting the growth and yield of agricultural and forestry crops. Rapid evaluation of drought tolerance, response, and recovery plays an important role in the cultivation and management of forestry seedlings. In this study, the response of poplar (Populus L.) seedlings under different drought stress levels was analyzed using two-stage machine learning. Two varieties of poplars differing in their drought tolerance were used for experiment. Three groups of phenotypic traits were measured. The first group was the morphological traits of plant height, ground diameter, crown width, and leaf number, collected via manual measurement. The second group was the physiological and biochemical traits of chlorophyll content, leaf water content, specific leaf weight, and equivalent water thickness, measured by destructive leaf sampling. The third group was the nondestructive spectral traits captured by a RedEdge-MX multispectral camera mounted on a custom-made phenotyping platform, including B (blue), G (green), R (red), NIR (near-infrared), RedEdge (red edge), RVI (ratio vegetation index), NDVI (normalized difference vegetation index), SIPI (structure insensitive pigment index), GI (green index), VDVI (visible-band difference vegetation index), GNDVI (green NDVI), and SRI (simple ratio index). Random forest (RF) was employed in a two-stage modeling scheme, with the first stage to classify poplar varieties, and the predicted variety information was added to the second stage for drought tolerance classification. The results showed that the accuracy of variety classification (Stage 1) reached 100%. Moreover, the classification of drought stress (Stage 2) was more accurate with the addition of the predicted variety information. The average accuracy, recall, and precision of the best drought classification model were 95.6%, 90.2%, and 92.1%, respectively. This study constructed a drought stress detection and grading system for poplar seedlings, which would be further applied for accurate and rapid evaluation of drought-tolerance and forestry irrigation management.
Why it matches plant phenotyping methodsポプラの形態・生理・スペクトル形質を取得するカスタム表現型計測プラットフォームと二段階機械学習を用い、干ばつストレスの検出・分類手法を構築しており、表現型取得と解析が研究の中心である。
titleEvaluating drought stress response of poplar seedlings using a proximal sensing platform via multi-parameter phenotyping and two-stage machine learning
Drought is a main abiotic stress facing agriculture and forestry production and its impacts are exacerbated by climate change. Accurately and effectively monitoring the drought stress levels of crop and tree species is crucial for their efficient management and the selection of drought-resistant varieties. This study used four poplar seedlings with differing drought tolerance and conducted five drought stress level tests. A self-propelled phenotyping platform was constructed and equipped with an Intel RealSense D435i RGB-D (Red-Green- Blue-Depth) camera and a RedEdge-MX multispectral camera. The side-view RGB and depth images and five-channel top-view multispectral images of poplar seedlings were collected by this platform; and the color, texture, depth, and spectral features were extracted through image processing. In addition, plant height, ground diameter, leafstalk angle, chlorophyll content and water content were collected from the poplar seedlings. Using long short-term memory (LSTM), a multi-output classification was performed on the varieties and drought stress levels of the four poplar seedlings based on the 50 parameters obtained through manual and image processing. By combining ResNet18 and the improved ResNet18 embedded in the convolutional block attention module (CBAM) with the LSTM model, the resulting ResNet18-LSTM and ResNet18-CBAM-LSTM models were used to perform multi-output grading on the varieties and drought stress levels. As for varieties, the classification accuracy of LSTM, ResNet18-LSTM, and ResNet18-CBAM-LSTM models were 96.56 %, 98.12 %, and 99.69 %, respectively. As for drought stress levels, the classification accuracy of the three models were 83.44 %, 85.62 %, and 90.94 %, respectively. The ResNet18-CBAM-LSTM model performed the best in the classification of the two parameters. This study comprehensively and continuously monitored the dynamic response of multiple varieties of poplar seedlings under different drought conditions, and a new perspective for the classification of drought stress levels and the breeding of better varieties are provided.
Why it matches plant phenotyping methods自走式フェノタイピングプラットフォーム、RGB-D・マルチスペクトル画像、画像処理による形質抽出、深層学習分類が研究の中心であり、ポプラの品種と干ばつストレス状態を評価する実質的なフェノタイピング手法研究である。
abstractA self-propelled phenotyping platform was constructed and equipped with an Intel RealSense D435i RGB-D (Red-Green- Blue-Depth) camera and a RedEdge-MX multispectral camera.
Plant phenotyping relevance match · UnverifiedCrossref · Europe PMC · checked 7 Sept 2026
Abstract The geometric shape and arrangement of individual cells play a role in shaping organ functions. However, analyzing multicellular features and exploring their connectomes in centimeter-scale plant organs remain challenging. Here, we established a set of frameworks named large-volume fully automated cell reconstruction (LVACR), enabling the exploration of 3D cytological features and cellular connectivity in plant tissues. Through benchmark testing, our framework demonstrated superior efficiency in cell segmentation and aggregation, successfully addressing the inherent challenges posed by light sheet fluorescence microscopy imaging. Using LVACR, we successfully established a cell atlas of different plant tissues. Cellular morphology analysis revealed differences of cell clusters and shapes in between different poplar (Populus simonii Carr. and Populus canadensis Moench.) seeds, whereas topological analysis revealed that they maintained conserved cellular connectivity. Furthermore, LVACR spatiotemporally demonstrated an initial burst of cell proliferation, accompanied by morphological transformations at an early stage in developing the shoot apical meristem of Pinus tabuliformis Carr. seedlings. During subsequent development, cell differentiation produced anisotropic features, thereby resulting in various cell shapes. Overall, our findings provided valuable insights into the precise spatial arrangement and cellular behavior of multicellular organisms, thus enhancing our understanding of the complex processes underlying plant growth and differentiation.
Why it matches plant phenotyping methods植物組織の3D細胞形態・接続性を画像から抽出するLVACRを開発し、ベンチマーク検証と実データ適用を行っており、植物フェノタイピング手法が中心である。
abstractwe established a set of frameworks named large-volume fully automated cell reconstruction (LVACR), enabling the exploration of 3D cytological features and cellular connectivity in plant tissues.
Background Dissection of complex plant cell wall structures demands a sensitive and quantitative method. FTIR is used regularly as a screening method to identify specific linkages in cell walls. However, quantification and assigning spectral bands to particular cell wall components is still a major challenge, specifically in crop species. In this study, we addressed these challenges using ATR-FTIR spectroscopy as it is a high throughput, cost-effective and non-destructive approach to understand the plant cell wall composition. This method was validated by analysing different varieties of mungbean which is one of the most important legume crops grown widely in Asia. Results Using standards and extraction of a specific component of cell wall components, we assigned 1050-1060 cm -1 and 1390-1420 cm -1 wavenumbers that can be widely used to quantify cellulose and lignin, respectively, in Arabidopsis, Populus, rice and mungbean. Also, using KBr as a diluent, we established a method that can relatively quantify the cellulose and lignin composition among different tissue types of the above species. We further used this method to quantify cellulose and lignin in field-grown mungbean genotypes. The ATR-FTIR-based study revealed the cellulose content variation ranges from 27.9% to 52.3%, and the lignin content variation ranges from 13.7% to 31.6% in mungbean genotypes. Conclusion Multivariate analysis of FT-IR data revealed differences in total cell wall (600-2000 cm -1 ), cellulose (1000-1100 cm -1 ) and lignin (1390-1420 cm -1 ) among leaf and stem of four plant species. Overall, our data suggested that ATR-FTIR can be used for the relative quantification of lignin and cellulose in different plant species. This method was successfully applied for rapid screening of cell wall composition in mungbean stem, and similarly, it can be used for screening other crops or tree species.
Why it matches plant phenotyping methodsATR-FTIRと多変量解析による植物細胞壁成分（セルロース・リグニン）の定量法を開発・検証し、作物遺伝子型の表現型スクリーニングに適用しているため、測定法が中心的である。
abstractIn this study, we addressed these challenges using ATR-FTIR spectroscopy as it is a high throughput, cost-effective and non-destructive approach to understand the plant cell wall composition.
Poplar ( Populus ) trees play a vital role in various industries and in environmental sustainability. They are widely used for paper production, timber, and as windbreaks, in addition to their significant contributions to carbon sequestration. Given their economic and ecological importance, effective disease management is essential. Convolutional Neural Networks (CNNs), particularly adept at processing visual information, are crucial for the accurate detection and classification of plant diseases. This study introduces a novel dataset of manually collected images of diseased poplar leaves from Uzbekistan and South Korea, enhancing the geographic diversity and application of the dataset. The disease classes consist of "Parsha (Scab)", "Brown-spotting", "White-Gray spotting", and "Rust", reflecting common afflictions in these regions. This dataset will be made publicly available to support ongoing research efforts. Employing the advanced YOLOv8 model, a state-of-the-art CNN architecture, we applied a Contrast Stretching technique prior to model training in order to enhance disease detection accuracy. This approach not only improves the model's diagnostic capabilities but also offers a scalable tool for monitoring and treating poplar diseases, thereby supporting the health and sustainability of these critical resources. This dataset, to our knowledge, will be the first of its kind to be publicly available, offering a valuable resource for researchers and practitioners worldwide.
Why it matches plant phenotyping methodsポプラ葉の病害状態を画像から検出・分類するYOLOv8手法とコントラスト伸張、公開データセットが研究の中心であり、植物病害フェノタイピングに該当する。
abstractThis study introduces a novel dataset of manually collected images of diseased poplar leaves from Uzbekistan and South Korea
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:269-294Plant phenotyping relevance match · UnverifiedEurope PMC · bioRxiv · checked 15 Sept 2026
Roots are important in agricultural and natural systems for determining plant productivity and soil carbon inputs. The collection of root samples from the field and their subsequent cleaning and scanning in a water-filled tray ranging in size from 5 to 20 cm, followed by digital image analysis has been commonly used since the 1990s for measuring root length, volume, area, and diameter. However, one common issue has been neglected. Sometimes, the amount of roots for a sample is too much to fit into a single scanned image, so the sample is divided among several scans. There is no standard method to aggregate the root measurements across the scans of the same sample. Here, we describe and validate two methods for standardizing measurements across multiple scans: image concatenation and statistical aggregation. Both methods rely on standardizing file naming conventions to identify scans that belong to the same sample. Image concatenation refers to combining digital images into a single larger image while maintaining the original resolution. We developed a Python script that identifies which images belong to the same sample and returns a single, larger concatenated image for every set of images in a directory. These concatenated images (combining up to 10 scans) and the original images were processed with RhizoVision Explorer, a free and open-source software developed for estimating root traits from images, with the same settings. An R script was developed that can identify the rows of data belonging to the same sample in RhizoVision Explorer data files and apply correct statistical methods such as summation, weighted average by length, and average to the appropriate measurement types 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. Overall, the new methods accomplished the goal of standardizing measurement aggregation. Most root measurements were nearly identical except median diameter, which can not 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根画像から根形質を抽出する複数スキャン統合手法を開発・検証し、Python/Rスクリプトとして実装しているため、植物フェノタイピング手法が研究の中心である。
abstractHere, we describe and validate two methods for standardizing measurements across multiple scans: image concatenation and statistical aggregation.
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:1-94Plant phenotyping relevance match · UnverifiedEurope PMC · bioRxiv · checked 15 Sept 2026
The ionome represents elemental composition in plant tissues and can be an indicator of nutrient status as well as overall plant performance. Thus, identifying genetic determinants governing elemental uptake and storage is an important goal in plant breeding and engineering. In this study, we coupled high-throughput ionome characterization with high-resolution genome-wide association studies (GWAS) to uncover genetic loci that modulate ionomic composition in leaves of 584 black cottonwood poplar ( Populus trichocarpa ) genotypes. Congruence of alternate ionomic profiling platforms, i.e., inductively coupled plasma-mass spectrometry (ICP-MS), neutron activation analysis (NAA) and laser-induced breakdown spectroscopy (LIBS), was performed on leaf samples from a subset of the population. Significant agreement was observed across the three platforms with some notable exceptions for individual elements. Subsequently, we used the ICP-MS platform to profile the 584 genotypes focusing on 20 elements. GWAS performed using a set of high-density (>8.2 million) single nucleotide polymorphisms (SNP), identified multiple loci significantly associated with variations in these mineral elements. The potential causal genes for variations in the ionome were significantly enriched in genes whose homologs were previously associated to ion homeostasis in other species. Notably, a polymorphic copy of the high-affinity molybdenum transporter MOT1 was found directly associated to molybdenum content in leaf tissues. The results of the GWAS also provided evidence of physiological and genetic interactions between mineral elements in poplar. The new candidate genes predicted to play a key role in cross-homeostasis of multiple elements are new targets for engineering a variety of traits of interest in tree species.
Why it matches plant phenotyping methods葉の元素組成という植物状態を測定する複数の高スループット計測プラットフォームを比較・検証しており、GWASだけでなく表現型取得法の技術的評価が明示されています。
abstractwe coupled high-throughput ionome characterization with high-resolution genome-wide association studies (GWAS)
Quantitative analysis of vessel characteristics at the cellular scale is of great significance for understan-ding plant adaptation strategies to environment. The direct grinding combined with stereo-microscope imaging is one of the main approaches to examine the anatomical structure of xylem (conifer tracheid and hardwood vessel) wood structure, which inevitably damages xylem cells, hindering the accurate understanding of anatomical structures. In this study, we applied X-ray micro-computed tomography (μCT) and stereo-microscope technology to quantitatively measure the diameter and area of vessels of seven Canadian broadleaved tree species ( Acer saccharum , Betula papyrifera , Fraxinus americana , Ostrya virginiana , Populus grandidentata , Quercus rubra , and Carya cordiformis ). We fitted the results by linear model and tested the feasibility of μCT technology in quantifying the vessel size of broadleaved species. We found that the results of the two methods for measuring vessel size were highly similar ( R 2 =0.98). The goodness of fit of the vessel diameter results measured by the two methods for the ring-porous wood species ( C. cordiformis , R 2 =0.98; F. americana , R 2 =0.96; Q. rubra , R 2 =0.99) was higher than that of the diffuse-porous wood species ( B. papyrifera , R 2 =0.88; O. virginiana , R 2 =0.73; A. saccharum , R 2 =0.68; P. grandiden-tata , R 2 =0.88). The goodness of fit of small vessels (diameter≤200 μm, R 2 =0.94) measured by the two methods was higher than that of large vessels (diameter>200 μm, R 2 =0.92). Thus, the μCT technique provided a new non-destructive detection method for quantifying xylem vessels of broadleaved tree species.
Why it matches plant phenotyping methodsμCTを用いた木部道管サイズ測定法をステレオ顕微鏡法と比較検証し、非破壊的な植物形質取得法として実証しているため。
abstractWe fitted the results by linear model and tested the feasibility of μCT technology in quantifying the vessel size of broadleaved species.
Plant phenotyping relevance match · UnverifiedOpenAlex · Europe PMC · checked 14 Sept 2026
Wide variation in amenability to transformation and regeneration (TR) among many plant species and genotypes presents a challenge to the use of genetic engineering in research and breeding. To help understand the causes of this variation, we performed association mapping and network analysis using a population of 1204 wild trees of Populus trichocarpa (black cottonwood). To enable precise and high-throughput phenotyping of callus and shoot TR, we developed a computer vision system that cross-referenced complementary red, green, and blue (RGB) and fluorescent-hyperspectral images. We performed association mapping using single-marker and combined variant methods, followed by statistical tests for epistasis and integration of published multi-omic datasets to identify likely regulatory hubs. We report 409 candidate genes implicated by associations within 5 kb of coding sequences, and epistasis tests implicated 81 of these candidate genes as regulators of one another. Gene ontology terms related to protein-protein interactions and transcriptional regulation are overrepresented, among others. In addition to auxin and cytokinin pathways long established as critical to TR, our results highlight the importance of stress and wounding pathways. Potential regulatory hubs of signaling within and across these pathways include GROWTH REGULATORY FACTOR 1 (GRF1), PHOSPHATIDYLINOSITOL 4-KINASE β1 (PI-4Kβ1), and OBF-BINDING PROTEIN 1 (OBP1).
Why it matches plant phenotyping methodsRGB画像と蛍光ハイパースペクトル画像を統合したコンピュータビジョンシステムを開発し、カルスおよびシュートの形質転換・再生を高精度かつハイスループットに表現型解析することが中心的な方法論的貢献である。
abstractTo enable precise and high-throughput phenotyping of callus and shoot TR, we developed a computer vision system that cross-referenced complementary red, green, and blue (RGB) and fluorescent-hyperspectral images.
Abstract Background Dissection of complex plant cell wall structures demands a sensitive and quantitative method. FTIR is used regularly as a screening method to identify specific linkages in cell walls. However, quantification and assigning spectral bands to particular cell wall components is still a major challenge, specifically in crop species. In this study, we addressed these challenges using ATR-FTIR spectroscopy as it is a high throughput, cost-effective and non-destructive approach to understand plant cell wall composition. This method was validated by analysing different varieties of mungbean which is one of the most important legume crop grown widely in Asia. Results Using standards and extraction of a specific component of cell wall components, we assigned 1050-1060 cm -1 and 1390-1420 cm -1 wavenumbers that can be widely used to quantify cellulose and lignin, respectively, in Arabidopsis, Populus , rice and mungbean. Also, using KBr as a diluent, we established a method which can relatively quantify the cellulose and lignin composition among different tissue types of the above species. We further used this method to quantify cellulose and lignin in field-grown mungbean genotypes. The ATR-FTIR-based study revealed the cellulose content variation ranges from 27.9% to 52.37%, and the lignin content variation ranges from 13.77% to 31.6% in mungbean genotypes. Conclusion Cell wall composition in different mungbean genotypes was determined by the developed FT-IR-based method, which was cross-validated using canonical wet-chemistry methods. Overall, our data suggested that ATR-FTIR can be used for the relative quantification of lignin and cellulose in different plant species. This method can be used for rapid screening of cell wall composition in large number of germplasms of different crops including mungbean.
Why it matches plant phenotyping methodsATR-FTIRによる植物細胞壁組成（セルロース・リグニン）の定量法を開発し、標準物質・湿式化学法で検証した研究であり、植物形質の取得手法が中心である。
abstractIn this study, we addressed these challenges using ATR-FTIR spectroscopy as it is a high throughput, cost-effective and non-destructive approach to understand plant cell wall composition.
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:318-364Plant phenotyping relevance match · UnverifiedOpenAlex · checked 13 Sept 2026
In the context of precision agriculture (PA), geomatic surveys exploiting UAV (unmanned aerial vehicle) platforms allow the dimensional characterization of trees. This paper focuses on the use of low-cost UAV photogrammetry to estimate tree height, as part of a project for the phytoremediation of contaminated soils. Two study areas with different characteristics in terms of mean tree height (5 m; 0.7 m) are chosen to test the procedure even in a challenging context. Three campaigns are performed in an olive grove (Area 1) at different flying altitudes (30 m, 40 m, and 50 m), and one UAV flight is available for Area 2 (42 m of altitude), where three species are present: oleander, lentisk, and poplar. The workflow involves the elaboration of the UAV point clouds through the SfM (structure from motion) approach, digital surface models (DSMs), vegetation filtering, and a GIS-based analysis to obtain canopy height models (CHMs) for height extraction based on a local maxima approach. UAV-derived heights are compared with in-field measurements, and promising results are obtained for Area 1, confirming the applicability of the procedure for tree height extraction, while the application in Area 2 (shorter tree seedlings) is more problematic.
Why it matches plant phenotyping methodsUAV画像・SfM・DSM/CHMを用いて樹高を抽出する手法を開発・検証しており、植物形質の取得が研究の中心です。
abstractThis paper focuses on the use of low-cost UAV photogrammetry to estimate tree height
Understanding and overcoming the resistance of plant cell wall to enzymatic deconstruction is crucial to achieve a sustainable and economical conversion of plant biomass to bio-based products as alternatives to petroleum-based products. Despite the significant scientific advances over the past decades, the plant cell wall deconstruction at cell and tissue scales has remained under-investigated. In this study, to quantitatively characterize plant cell wall deconstruction, we set up an original imaging pipeline by combining time-lapse 4D (space + time) fluorescence confocal imaging, and a novel computational tool, to track and quantify cell wall deconstruction at cell and tissue scales offering a digital representation of cell wall deconstruction. Using this pipeline on poplar wood sections, we computed dynamics of several cellular parameters (e.g. cell wall volume, surface area, and number of cell neighbors) while measuring cellulose conversion. The results showed that the effect of enzymatic deconstruction at the cell scale is predominantly noticeable in terms of cell wall volume reduction rather than a significant decrease in surface area and accessible surface area. The results also revealed a negative correlation between pre-hydrolysis 3D cell wall compactness measures and volumetric cell wall deconstruction. The strength of this correlation was modulated by enzymatic activity. Combining cell wall compactness with the number of neighboring cells as a tissue-scale parameter yielded a stronger correlation. Our results also revealed a strong positive correlation between average volumetric cell wall deconstruction and cellulose conversion, thus establishing a link between key parameters and bridging the gap between nano and micro scales.
Why it matches plant phenotyping methods植物細胞壁の分解状態を定量化する4D蛍光画像パイプラインと計算ツールの開発が研究の中心であり、細胞壁体積・表面積・細胞隣接数などの植物組織形質を抽出している。
abstractwe set up an original imaging pipeline by combining time-lapse 4D (space + time) fluorescence confocal imaging, and a novel computational tool, to track and quantify cell wall deconstruction at cell and tissue scales
PoplarMultispectral / hyperspectralLeafPhysiological trait estimationPhotosynthesis / fluorescenceWater status / transpiration
Effectively evaluating and estimating the photosynthetic capacities of different poplar genotypes is essential for selecting and breeding poplars with high productivity. This study measured leaf hyperspectral reflectance, net photosynthetic rate (Pn), transpiration rate (Tr), intercellular CO2 concentration (Ci), and stomatal conductance (Gs) across the upper-, middle- and lower-layer leaves of six poplar genotypes. Photosynthetic capacities and spectral differences were assessed among these genotypes. By analyzing the correlation of photosynthetic parameters and spectral characteristics, the photosynthetic parameters were also estimated from hyperspectral parameters using BP neural networks. Significant differences were observed in the photosynthetic parameters among six poplar genotypes. Populus tremula × P. alba exhibited the highest photosynthetic rate, while Populus hopeiensis showed the lowest. Leaves in the middle layer demonstrated greater photosynthetic capacities than those in the other layers. Leaf reflectance among the six poplar genotypes differed significantly in the ranges of 400−760 nm, 800−1,300 nm, 1,500−1,800 nm, and 1,900−2,000 nm. Values for MTCI, WI, REP, PRI, and first-order derivative at 891 nm also showed significant differences. Hyperspectral parameters, including first-order derivative spectra (FDS), raw spectral reflectance, and photosynthetic parameters, showed strong correlations in the red light (670 nm), near-infrared (760−940 nm), and short-wave infrared (1,800−2,500 nm). Four photosynthetic parameters including Pn, Tr, Ci, and Gs were estimated using BP neural network models and R2 were 0.56, 0.44, 0.35, and 0.35, respectively. The present results indicate that hyperspectral reflectance can effectively distinguish between different poplar genotypes and estimate photosynthetic parameters, highlighting its great potential for studying plant phenomics.
Why it matches plant phenotyping methods葉のハイパースペクトル反射から光合成形質を推定する手法とBPニューラルネットワークモデルを中心に扱っており、植物フェノタイピング手法の開発・適用に該当する。
abstractphotosynthetic parameters were also estimated from hyperspectral parameters using BP neural networks
This dataset contains the phenotypic measurements of Populus genotypes selected for genomic selection and breeding program. Populus species in this study includes P. trichocarpa, P. deltoides and a hybrid between these two species.
Why it matches plant phenotyping methodsPopulus遺伝資源の表現型測定データセットを提供する研究であり、植物フェノタイピングデータ自体が中心的な成果である。
titlePhenotypic data of Populus species selected for genomic selection and breeding program using the Advanced Plant Phenotyping Laboratory at ORNL
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
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RhizoVision explorer: Open-source software for root image analysis and measurement standardization
. AoB PLANTS . 2021 ; 13 ( 6 ): pOpen asset ↗Zenodo · 10.5281/zenodo.8083525lines:196-345Plant phenotyping relevance match · UnverifiedOpenAlex · checked 13 Sept 2026
As global temperatures warm, drought reduces plant yields and is one of the most serious abiotic stresses causing plant losses. The early identification of plant drought is of great significance for making improvement decisions in advance. Chlorophyll is closely related to plant photosynthesis and nutritional status. By tracking the changes in chlorophyll between plant strains, we can identify the impact of drought on a plant’s physiological status, efficiently adjust the plant’s ecosystem adaptability, and achieve optimization of planting management strategies and resource utilization efficiency. Plant three-dimensional reconstruction and three-dimensional character description are current research hot spots in the development of phenomics, which can three-dimensionally reveal the impact of drought on plant structure and physiological phenotypes. This article obtains visible light multi-view images of four poplar varieties before and after drought. Machine learning algorithms were used to establish the regression models between color vegetation indices and chlorophyll content. The model, based on the partial least squares regression (PLSR), reached the best performance, with an R2 of 0.711. The SFM-MVS algorithm was used to reconstruct the plant’s three-dimensional point cloud and perform color correction, point cloud noise reduction, and morphological calibration. The trained PLSR chlorophyll prediction model was combined with the point cloud color information, and the point cloud color was re-rendered to achieve three-dimensional digitization of plant chlorophyll content. Experimental research found that under natural growth conditions, the chlorophyll content of poplar trees showed a gradient distribution state with gradually increasing values from top to bottom; after being given a short period of mild drought stress, the chlorophyll content accumulated. Compared with the value before stress, it has improved, but no longer presents a gradient distribution state. At the same time, after severe drought stress, the chlorophyll value decreased as a whole, and the lower leaves began to turn yellow, wilt and fall off; when the stress intensity was consistent with the duration, the effect of drought on the chlorophyll value was 895 < SY-1 < 110 < 3804. This research provides an effective tool for in-depth understanding of the mechanisms and physiological responses of plants to environmental stress. It is of great significance for improving agricultural and forestry production and protecting the ecological environment. It also provides decision-making for solving plant drought problems caused by global climate change.
Why it matches plant phenotyping methodsSFM-MVSによる3次元再構成とPLSRを組み合わせ、ポプラ個体のクロロフィル含量を3次元的に推定・可視化する手法が研究の中心である。
abstractPlant three-dimensional reconstruction and three-dimensional character description are current research hot spots in the development of phenomics
PoplarLaboratory / benchtopX-ray / CTLeafTissuePhysiological trait estimationWater status / transpiration
Plant hydraulic traits related to leaf drought tolerance, like the water potential at turgor loss point (TLP) and the water potential inducing 50% loss of hydraulic conductance (P50), are extremely useful to predict the potential impacts of drought on plants. While novel techniques have allowed the inclusion of TLP in studies targeting a large group of species, fast and reliable protocols to measure leaf P50 are still lacking. Recently, the optical method coupled with the gas injection (GI) technique has been proposed as a possibility to speed up the P50 estimation. Here, we present a comparison of leaf optical vulnerability curves (OVcs) measured in three woody species, namely Acer campestre (Ac), Ostrya carpinifolia (Oc) and Populus nigra (Pn), based on bench dehydration (BD) or GI of detached branches. For Pn, we also compared optical data with direct micro-computed tomography (micro-CT) imaging in both intact saplings and cut shoots subjected to BD. Based on the BD procedure, Ac, Oc and Pn had P50 values of -2.87, -2.47 and -2.11 MPa, respectively, while the GI procedure overestimated the leaf vulnerability (-2.68, -2.04 and -1.54 MPa for Ac, Oc and Pn, respectively). The overestimation was higher for Oc and Pn than for Ac, likely reflecting the species-specific vessel lengths. According to micro-CT observations performed on Pn, the leaf midrib showed none or very few embolized conduits at -1.2 MPa, consistent with the OVcs obtained with the BD procedure but at odds with that derived on the basis of GI. Overall, our data suggest that coupling the optical method with GI might not be a reliable technique to quantify leaf hydraulic vulnerability since it could be affected by the 'open-vessel' artifact. Accurate detection of xylem embolism in the leaf vein network should be based on BD, preferably of intact up-rooted plants.
Why it matches plant phenotyping methods葉の木部エンボリズム脆弱性を測定する光学法・ガス注入法を比較し、マイクロCTで検証しており、植物生理形質の取得法の技術評価が中心である。
abstractfast and reliable protocols to measure leaf P50 are still lacking.
Populus euphratica and Tamarix chinensis hold significant importance in wind prevention, sand fixation, and biodiversity conservation. The precise extraction of these species can offer technical assistance for vegetation studies. This paper focuses on the Populus euphratica and Tamarix chinensis located within Daliyabuyi, utilizing PointCNN as the primary research method. After decorrelating and stretching the images, deep learning techniques were applied, successfully distinguishing between various vegetation types, thereby enhancing the precision of vegetation information extraction. On the validation dataset, the PointCNN model showcased a high degree of accuracy, with the respective regular accuracy rates for Populus euphratica and Tamarix chinensis being 92.106% and 91.936%. In comparison to two-dimensional deep learning models, the classification accuracy of the PointCNN model is superior. Additionally, this study extracted individual tree information for the Populus euphratica, such as tree height, crown width, crown area, and crown volume. A comparative analysis with the validation data attested to the accuracy of the extracted results. Furthermore, this research concluded that the batch size and block size in deep learning model training could influence classification outcomes. In summary, compared to 2D deep learning models, the point cloud deep learning approach of the PointCNN model exhibits higher accuracy and reliability in classifying and extracting information for poplars and tamarisks. These research findings offer valuable references and insights for remote sensing image processing and vegetation study domains.
Why it matches plant phenotyping methodsPointCNNによる点群解析を中核として、個体樹木の樹高・樹冠幅・樹冠面積・樹冠体積を抽出し、検証データで精度評価しているため、植物表現型取得手法の開発・検証に該当する。
abstractAdditionally, this study extracted individual tree information for the Populus euphratica, such as tree height, crown width, crown area, and crown volume.
Image-based morphometric technology is broadly applicable to generate large-scale phenomic datasets in ecological, genetic and morphological studies. However, little is known about the performance of image-based measuring methods on plant morphological characters. In this study, we presented an automatic image-based workflow to obtain the accurate estimations for basic leaf characteristics (e.g., ratio of length/width, length, width, and area) from a hundred Populus simonii pictures, which were captured on Colony counter Scan1200. The image-based workflow was implemented with Python and OpenCV, and subdivided into three parts, including image pre-processing, image segmentation and object contour detection. Six image segmentation methods, including Chan-Vese, Iterative threshold, K-Mean, Mean, OSTU, and Watershed, differed in the running time, noise sensitivity and accuracy. The image-based estimates and measured values for leaf morphological traits had a strong correlation coefficient (r2 > 0.9736), and their residual errors followed a Gaussian distribution with a mean of almost zero. Iterative threshold, K-Mean, OSTU, and Watershed overperformed the other two methods in terms of efficiency and accuracy. This study highlights the high-quality and high-throughput of autonomous image-based phenotyping and offers a guiding clue for the practical use of suitable image-based technologies in biological and ecological research.
Why it matches plant phenotyping methods植物葉の形態形質を画像から自動推定するワークフローを開発し、複数のセグメンテーション手法の精度・効率を比較検証しており、フェノタイピング手法が研究の中心です。
abstractwe presented an automatic image-based workflow to obtain the accurate estimations for basic leaf characteristics
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 to segment the leaf body and visible venation of 2,906 Populus trichocarpa leaf images obtained in the field. In contrast to previous methods, our approach (a) does not require experimental or image preprocessing, (b) uses the raw RGB images at full resolution, and (c) requires very few samples for training (e.g., just 8 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 (a) methods for fast and accurate image-based feature extraction that require minimal training data and (b) a new population-scale dataset, 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画像解析法を開発し、実測値で検証するとともに、再利用可能なデータセットを提供しており、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 to segment the leaf body and visible venation of 2,906 Populus trichocarpa leaf images obtained in the field.
The detection of stem water content is necessary as it is an important indicator for measuring woody plant vitality. However, the relationship between stem water content, determined by non‐destructive, real‐time, and long‐term monitoring, and woody plant vitality remains undefined. In this study, the response of woody plant vitality to stem water content under different stress (freeze–thaw, pest, or drought) was analysed by mining the dynamic characteristics of the stem water content in different woody plants at the temporal scales of year, month, and day. Compared with unstressed trees, stressed trees had contrasting diurnal patterns. The stem water content in Populus koreana Rehd. during the freeze period was much lower than that during the thaw period, and opposite diurnal variation trends were observed during the freeze and thaw periods. The stem water content in infected Lagerstroemia indica was lower than that in uninfected L. indica, and the amplitude of the diurnal variation curve was lower in infected than in uninfected L. indica. Under drought stress, the more severe the water shortage, the lower the stem water content in Malus micromalus. When it was below a certain threshold, the diurnal variation trend was opposite to that without water shortage. In conclusion, stem water content dynamics can be used to evaluate the cold, pest, and drought response of trees, which could monitor tree health and guide forest assessment.
Why it matches plant phenotyping methods樹木の活力・ストレス状態を評価するため、茎水分量を非破壊・リアルタイム・長期測定する方法を中心に適用しており、植物の生理状態のフェノタイピングに該当する。
titleVitality characterization of stressed trees based on non‐destructive and real‐time monitoring of stem water content
Plant phenotyping relevance match · UnverifiedbioRxiv · Europe 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. While 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, the power of these methods relies on the accuracy and scale of phenotypic data used. To enable a largescale GWAS of in planta regeneration in model tree Populus, we implemented a workflow involving semantic segmentation to quantify regenerating plant tissues (callus and shoot) over time. We found the resulting statistics are of highly non-normal distributions, which necessitated transformations or permutations to avoid violating assumptions of linear models used in GWAS. While transformations can lead to a loss of statistical power, we demonstrate that this can be mitigated by the application of the Augmented Rank Truncation method, or avoided altogether using the Multi-Threaded Monte Carlo SNP-set (Sequence) Kernel Association Test to compute empirical p-values in GWAS. We report over 200 statistically supported candidate genes, with top candidates including regulators of cell adhesion, stress signaling, and hormone signaling pathways, as well as other diverse functions. We demonstrate that sensitive genetic discovery for complex developmental traits can be enabled by a workflow based on computer vision and adaptation of several statistical approaches necessitated by to the complexity of regeneration trait expression and distribution.
Why it matches plant phenotyping methods再生組織（カルスとシュート）を意味的セグメンテーションで時系列定量する画像ベース表現型ワークフローが、GWAS用データ取得の中心的手法として明示されているため。
abstractwe implemented a workflow involving semantic segmentation to quantify regenerating plant tissues (callus and shoot) over time.
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:249-258Plant phenotyping relevance match · UnverifiedCrossref · checked 8 Sept 2026
Proanthocyanidins (PAs) are polymeric phenolic compounds found in plants and used in many industrial applications. Despite strong evidence of herbivore and pathogen resistance-related properties of PAs, their in planta function is not fully understood. Determining the location and dynamics of PAs in plant tissues and cellular compartments is crucial to understand their mode of action. Such an approach requires microscopic localization with fluorescent dyes that specifically bind to PAs. Such dyes have hitherto been lacking. Here, we show that 4-dimethylaminocinnamaldehyde (DMACA) can be used as a PA-specific fluorescent dye that allows localization of PAs at high resolution in cell walls and inside cells using confocal microscopy, revealing features of previously unreported wall-bound PAs. We demonstrate several novel usages of DMACA as a fluorophore by taking advantage of its double staining compatibility with other fluorescent dyes. We illustrate the use of the dye alone and its co-localization with cell wall polymers in different Populus root tissues. The easy-to-use fluorescent staining method, together with its high photostability and compatibility with other fluorogenic dyes, makes DMACA a valuable tool for uncovering the biological function of PAs at a cellular level in plant tissues. DMACA can also be used in other plant tissues than roots, however care needs to be taken when tissues contain compounds that autofluoresce in the red spectral region which can be confounded with the PA-specific DMACA signal.
Why it matches plant phenotyping methods植物組織中のプロアントシアニジンを高解像度で可視化・局在化する蛍光染色法を開発し、共焦点顕微鏡で検証・適用しているため、植物フェノタイピング手法が中心である。
abstractHere, we show that 4-dimethylaminocinnamaldehyde (DMACA) can be used as a PA-specific fluorescent dye that allows localization of PAs at high resolution in cell walls and inside cells using confocal microscopy
The xylem network, the water conduction system in wood determines the ability of trees to avoid hydraulic failure during drought stress. The capability to withstand embolisms, disruptions of the water column by gas bubbles that contribute to hydraulic failure, is mainly determined by the anatomical arrangement and connectedness (topology) of xylem vessels. However, the quantification of xylem network characteristics has been difficult, so that relating network properties and topology to hydraulic vulnerability and predicting xylem function remains challenging. We studied the xylem vessel networks of three diffuse- ( Fagus sylvatica, Liriodendron tulipifera, Populus x canadensis ) and three ring-porous ( Carya ovata, Fraxinus pennsylvatica, Quercus montana ) tree species using volumetric images of xylem from laser ablation tomography (LATscan). Using convolutional neural networks for image segmentation, we generated three-dimensional, high-resolution maps of xylem vessels, with detailed measurements of morphology and topology. We studied the network topologies by incorporating multiple network metrics into a multidimensional analysis and simulated the robustness of these networks against the loss of individual vessel elements that mimic the obstruction of water flow from embolisms. This analysis suggested that networks in Populus x canadensis and Carya ovata are quite similar despite being different wood types. Similar networks had comparable experimental measurements of P50 values (pressure inducing 50% hydraulic conductivity loss) obtained from hydraulic vulnerability curves, a common tool to quantify the cavitation resistance of xylem networks. This work produced novel data on plant xylem vessel networks and introduces new methods for analyzing the biological impact of these network structures. Significance statement The resilience of fluid transport networks such as xylem vessels that conduct water in trees depends on both the structure of the network and features of the individual network elements. High-resolution reconstruction of xylem networks from six tree species provided novel, three-dimensional, structural data which enabled the xylem networks to be described using graph theory. Using an array of network metrics as multidimensional descriptors, we compared the xylem networks between species and showed relationships to simulated and experimental measures of drought resistance. In addition to providing insight on drought resistance, these approaches offer new ways for comparative analysis of networks applicable to many systems.
Why it matches plant phenotyping methodsCNNによる画像セグメンテーションで木部道管を三次元再構成し、形態・トポロジー形質を定量化する手法を開発・適用しており、植物フェノタイピングが中心。
abstractUsing convolutional neural networks for image segmentation, we generated three-dimensional, high-resolution maps of xylem vessels, with detailed measurements of morphology and topology.
In the forest stand-wise inventory of Serbia, data is obtained using terrestrial methods - using complete (total) and partial measurement. Foreign experiences show that part of the information about forests can be obtained on the basis of aerial images - using aerial photogrammetry methods. In this sense, the goal of this work was to assess the possibility of applying aerial photogrammetry in the process of collecting information in poplar plantations, planting spacing 5 x 5 m, aged 10, 15, 20 and 25 years. The reliability of data obtained on the basis of air images was determined by comparison with data obtained by terrestrial measurement. The results of the research only partially confirmed foreign experiences about the possibility of applying aerial photogrammetry in forest inventory. A comparative analysis of the numerical elements of the stand read directly from the images, such as the number of trees, height and crown area, and derived elements - diameter on breast height, basal area and volume of the stand in relation to the values obtained by terrestrial surveying methods, indicated the possibility of limited use of aerial photogrammetry in the inventory of these forests. The differences were reflected in the reading of lower values of crown areas and tree heights, which is why mathematical models predicted lower values of diameters on breast height. This was reflected in the displacement of the tree distribution to lower diameter degrees, consequently leading to differences in the basal area and the volume of the researched plantations in relation to the values obtained by terrestrial surveying. Therefore, as an optimal solution in specific conditions, a combined inventory is imposed, which sublimates the positive characteristics of the tested methods.
Why it matches plant phenotyping methodsポプラ植林地で航空写真測量から樹高・樹冠面積・本数などの植物形質を抽出し、地上測定と比較して信頼性と適用可能性を検証しており、フェノタイピング手法が中心である。
abstractThe reliability of data obtained on the basis of air images was determined by comparison with data obtained by terrestrial measurement.
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:272-401Plant phenotyping relevance match · UnverifiedOpenAlex · checked 13 Sept 2026
(1) Objective: The opacity of soils complicates studies of root infection. An example of this is the infection of Armillaria solidipes on poplar (Populus davidiana × Populus alba var. pyramidalis Louche) roots systems, which risks damaging trees. (2) Methods: Only one of the four tested substrates for tree species was shown to be suitable to perform X-ray computed tomography (CT). Three-dimensional (3D) imaging was used to reconstruct the root system of poplar seedlings and the changes caused by the infection. (3) Results: We developed a protocol to efficiently grow poplar on a synthetic matrix, vermiculite, that allows for monitoring the root system by X-ray CT. Poplar 3D reconstruction of the root system was automated using the software Win-RHIZO, and various infection parameters were identified. (4) Conclusions: Our procedure allows for monitoring the infection of root systems and provides new opportunities to characterize the complex Armillaria solidipes poplar interaction using X-ray CT.
Why it matches plant phenotyping methodsポプラ根系の感染状態をX線CTと3D再構成で取得・定量するプロトコルを開発しており、植物フェノタイピング手法が研究の中心である。
abstractPoplar 3D reconstruction of the root system was automated using the software Win-RHIZO, and various infection parameters were identified.
With the development of water detection instruments, it is feasible to detect the stem liquid water content. However, real-time and non-destructive monitoring of liquid water and ice content of plant stems in winter remains challenging. Here, we developed a living wood freeze–thaw detection (LWFTD) sensor to detect the liquid water and ice content of plant stems in situ, in real time, and micro-destructively. First, based on the latent heat effect and using a retractable ring-type shrapnel probe, we monitored the freeze–thaw front of the stem water in real time and micro-destructively. Second, using calibration data and infrared detection data, the reliability of the LWFTD sensor and the feasibility of stem freeze–thaw detection based on the latent heat effect were proven. Finally, by simulating a freeze–thaw cycle and analyzing the changes in the stem liquid water content and stem temperature, an ice content model was constructed to calculate the ice content and freeze–thaw fronts. In field experiments, we recorded stem water content and freeze–thaw data of Pachira glabra, Populus tomentosa, and Lagerstroemia indica during an overwintering period. The results showed that the LWFTD sensor can effectively detect the changes in water-related physiological parameters during plant freeze–thawing in real time and that the ice content in the stem volume exhibits diurnal changes, with a single-peak, single-valley wave pattern. Our study provides a reference for evaluating the effects of freeze–thaw on plant metabolism and vitality. Furthermore, this study provides an advanced technical tool for in situ, real-time, and micro-destructive monitoring of the water and ice content in plant stems, which will help further our understanding of the water transport process and freeze–thaw-induced embolism of woody plants during winter.
Why it matches plant phenotyping methods植物茎内の液水・氷含量と凍結融解前線を測定するセンサーを開発し、校正・赤外検出による信頼性検証と実地適用を行っており、植物生理状態の取得手法が中心である。
abstractwe developed a living wood freeze–thaw detection (LWFTD) sensor to detect the liquid water and ice content of plant stems in situ, in real time, and micro-destructively.
The measurement of tree diameter at breast height (DBH) is the basis for estimating forest timber volume, biomass, and carbon fluxes. The traditional contact methods of measuring DBH are time-consuming and labor-intensive. Thus, it is important to realize a low-cost and rapid method for measuring DBH. In this paper, a non-contact method was proposed by integrating passive (a smartphone) and active optical sensors (a laser ranger). With this device, the horizontal distance from the sensor to the tree trunk acquired by the laser ranger and the image of the target tree acquired by the smartphone were collected simultaneously. An autodetection algorithm was employed to identify the tree trunk within the image, and the diameter of the tree was then measured in combination with the horizontal distance based on the photogrammetry principle. The performance of the proposed method was validated using measuring tapes across 371 trees, the main species of which were Italian Poplar (Populus euramevicana) and Pine (Pinus tabuliformis) with diameters ranging from 6 to 51 cm. To investigate the factors that might affect the method, the results were further analyzed under four different conditions, i.e., varied illumination conditions, urban and natural forest conditions and different tree species with varied surface texture features. The results suggested that the measurements using the proposed device were in good agreement with those of the traditional contact method, with an absolute mean error (MAE) of 1.12 cm and RMSE of 1.55 cm. The attraction of the proposed method is that it is low-cost, portable, easy to use and sufficiently accurate. It is also expected that the proposed method can facilitate the measurement of DBH-related canopy structure parameters, such as tree volume, and other parameters, such as tree height, with little adaptation to the current version.
Why it matches plant phenotyping methods樹木のDBHという明示的な植物形態形質を、スマートフォン画像とレーザーセンサーで非接触測定する手法を開発し、371本で従来法と検証しているため、方法が中心的です。
abstractIn this paper, a non-contact method was proposed by integrating passive (a smartphone) and active optical sensors (a laser ranger).
PoplarGreenhouseThermalStem / branchPhysiological trait estimationWater status / transpiration
Sap flow is frequently measured with probes inserted in larger plants to quantify transpiration and irrigation needs, to monitor stress, or to investigate the coupling between plant hydraulic function and growth, but sap flow measurements remain challenging when working with small dimensions as probe insertion causes relatively more damage to the plant tissue. An external sap flow sensor, with external temperature measurements and heat application according to the heat ratio method (HRM), is therefore more suitable for small-diameter plant organs. In this study, the sensor design was optimized to limit measurement errors through an accurate and precise positioning of the electronic components by 3D-printing the sensor casing. Also, a suitable spacing of 3 mm to measure stems ranging from 2 to 4 mm is provided for stem guidance and to ensure a proper stem-sensor contact. A spacing of 5 mm is available for stems ranging from 4 to 6 mm. The 3D-printed HRM-sensor (ExoBeat) is evaluated on 1- to 5-months-old Ficus benjamina L. stems and 1-year-old Populus tremula L. branches. Thermal imaging showed a significant amount of heat loss through the sensor material, which is inherent to the heat-based sensor design. A wide range of heater powers (0.09–0.49 W) resulted in robust heat pulse velocity (vₕ) patterns after temperature gradient correction. Temperature gradient correction is particularly important for ExoBeat measurements as the heat pulse induced temperature rise is relatively small. The conventional temperature gradient correction by subtracting the extrapolated temperature gradient before the heat pulse resulted in slightly less stable sap flow calculations than subtracting the interpolated temperature from a few seconds before to 150 s after the heat pulse. The frequently used 60–100 s time interval for vₕ calculations was confirmed. Furthermore, two calibration methods were evaluated with the calibration on an intact stem under variable greenhouse temperature conditions being more accurate than the one using cut stems. With their broad range of suitable stem diameters, sap flow and ambient temperature stability, ExoBeat sensors show great potential for many applications.
Why it matches plant phenotyping methods小径植物器官の蒸散・茎流を測定する外部センサーを設計最適化し、熱画像、校正、測定条件、精度を評価しており、植物生理状態の取得手法が研究の中心です。
abstractAn external sap flow sensor, with external temperature measurements and heat application according to the heat ratio method (HRM), is therefore more suitable for small-diameter plant organs.
Poplar ( Populus spp.) plantations are globally widespread in the Northern Hemisphere, and provide a wide range of benefits and products, including timber, carbon sequestration and phytoremediation. Because of poplar specific features (fast growth, short rotation) the information needs require frequent updates, which exceed the traditional scope of National Forest Inventories, implying the need for ad-hoc monitoring solutions. Here we presented a regional-level multi-scale monitoring system developed for poplar plantations, which is based on the integration of different remotely-sensed informations at different spatial scales, developed in Lombardy (Northern Italy) region. The system is based on three levels of information: 1) At plot scale, terrestrial laser scanning (TLS) was used to develop non-destructive tree stem volume allometries in calibration sites; the produced allometries were then used to estimate plot-level stand parameters from field inventory; additional canopy structure attributes were derived using field digital cover photography. 2) At farm level, unmanned aerial vehicles (UAVs) equipped with multispectral sensors were used to upscale results obtained from field data. 3) Finally, both field and unmanned aerial estimates were used to calibrate a regional-scale supervised continuous monitoring system based on multispectral Sentinel-2 imagery, which was implemented and updated in a Google Earth Engine platform. The combined use of multi-scale information allowed an effective management and monitoring of poplar plantations. From a top-down perspective, the continuous satellite monitoring system allowed the detection of early warning poplar stress, which are suitable for variable rate irrigation and fertilizing scheduling. From a bottom-up perspective, the spatially explicit nature of TLS measurements allows better integration with remotely sensed data, enabling a multiscale assessment of poplar plantation structure with different levels of detail, enhancing conventional tree inventories, and supporting effective management strategies. Finally, use of UAV is key in poplar plantations as their spatial resolution is suited for calibrating metrics from coarser remotely-sensed products, reducing or avoiding the need of ground measurements, with a significant reduction of time and costs.
Why it matches plant phenotyping methodsTLS、UAVマルチスペクトル、衛星画像を統合し、樹幹体積・林分構造・樹木ストレスを推定する多尺度モニタリング手法が研究の中心であるため、植物フェノタイピング基盤として適格。
abstractHere we presented a regional-level multi-scale monitoring system developed for poplar plantations
Leaf margins are complex plant morphological features and contribute to the diversity of leaf shapes which effect on plant structure, yield and adaptation. Although several regulators of leaf margins have been identified, the genetic basis of natural variation therein has not been fully elucidated. We first profiled two distinct types (serration and smooth) of leaf morphology using the persistent homology mathematical framework (PHMF) in poplar. Combined genome-wide association studies (GWAS) and expression quantitative trait nucleotide (eQTN) mapping to create a module of leaf morphology controlling using data from Populus tomentosa and P. simonii association population, respectively. Natural variation of leaf margins is associated with transcript abundances of YABBY11 ( YAB11 ) in poplar. In P. tomentosa , PtoYAB11 carries premature stop codon ( PtoYAB11 PSC ) resulting in lost its positive regulation in PtoNGAL-1 , PtoRBCL , PtoATPA , PtoATPE , and PtoPSBB . Overexpression of PtoYAB11 PSC serrated leaf margin, enlarged leaves, promoted photosynthesis and increased biomass. Overexpression of PsiYAB11 in P. tomentosa could rescue leaf margin serration and increase stomatal density and light damage repair ability. In poplar , YAB11 - NGAL1 is sensitive to environmental conditions and play positive regulator of leaf margin serration. It might be important regulator which bridge environment signaling to leaf morphological plasticity.
Why it matches plant phenotyping methods葉形態（鋸歯・平滑）をpersistent homology数学フレームワークでプロファイリングしており、葉の形態形質を計算的に抽出する手法が研究のGWAS解析の基盤として明示されています。
abstractWe first profiled two distinct types (serration and smooth) of leaf morphology using the persistent homology mathematical framework (PHMF) in poplar.
Plant phenotyping relevance match · UnverifiedOpenAlex · bioRxiv · Europe PMC · checked 15 Sept 2026
Abstract The abilities of plant biologists and breeders to characterize the genetic basis of physio-logical traits is limited by their abilities to obtain quantitative data representing precise details of trait variation, and particularly to collect this data at a high-throughput scale at low cost. Although deep learning methods have demonstrated unprecedented potential to automate plant phenotyping, these methods commonly rely on large training sets that can be time-consuming to generate. Intelligent algorithms have therefore been proposed to enhance the productivity of these annotations and reduce human efforts. We propose a high-throughput phenotyping system which features a Graphical User Interface (GUI) and a novel interactive segmentation algorithm: Semantic-Guided Interactive Object Segmentation (SGIOS). By providing a user-friendly interface and intelligent assistance with annotation, this system offers potential to streamline and accelerate the generation of training sets, reducing the effort required by the user. Our evaluation shows that our proposed SGIOS model requires fewer user inputs compared to the state-of-art models for interactive segmentation. As a case study in the use of the GUI applied for genetic discovery in plants, we present an example of results from a preliminary genome-wide association study (GWAS) of in planta regeneration in Populus trichocarpa (poplar). We further demonstrate that the inclusion of semantic prior map with SGIOS can accelerate the training process for future GWAS, using a sample of a dataset extracted from a poplar GWAS of in vitro regeneration. The capabilities of our phenotyping system surpass those of humans unassisted to rapidly and precisely phenotype our traits of interest. The scalability of this system enables large-scale phenomic screens that would otherwise be time-prohibitive, thereby providing increased power for GWAS, mutant screens, and other studies relying on large sample sizes to characterize the genetic basis of trait variation. Our user-friendly system can be used by researchers lacking a computational background, thus helping to democratize the use of deep segmentation as a tool for plant phenotyping.
Why it matches plant phenotyping methods植物フェノタイピング用のGUIと対話型画像セグメンテーション手法を開発・評価しており、表現型取得ワークフロー自体が中心である。
abstractWe propose a high-throughput phenotyping system which features a Graphical User Interface (GUI) and a novel interactive segmentation algorithm: Semantic-Guided Interactive Object Segmentation (SGIOS).
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:159-169Plant phenotyping relevance match · UnverifiedCrossref · checked 8 Sept 2026
Floods, as one of the most common disasters in the natural environment, have caused huge losses to human life and property. Predicting the flood resistance of poplar can effectively help researchers select seedlings scientifically and resist floods precisely. Using machine learning algorithms, models of poplar’s waterlogging tolerance were established and evaluated. First of all, the evaluation indexes of poplar’s waterlogging tolerance were analyzed and determined. Then, significance testing, correlation analysis, and three feature selection algorithms (Hierarchical clustering, Lasso, and Stepwise regression) were used to screen photosynthesis, chlorophyll fluorescence, and environmental parameters. Based on this, four machine learning methods, BP neural network regression (BPR), extreme learning machine regression (ELMR), support vector regression (SVR), and random forest regression (RFR) were used to predict the flood resistance of poplar. The results show that random forest regression (RFR) and support vector regression (SVR) have high precision. On the test set, the coefficient of determination (R 2 ) is 0.8351 and 0.6864, the root mean square error (RMSE) is 0.2016 and 0.2780, and the mean absolute error (MAE) is 0.1782 and 0.2031, respectively. Therefore, random forest regression (RFR) and support vector regression (SVR) can be given priority to predict poplar flood resistance.
Why it matches plant phenotyping methodsポプラの湛水耐性という植物状態を、光合成・クロロフィル蛍光などの観測値から機械学習で推定するモデルを開発・評価しており、表現型推定手法が研究の中心である。
abstractUsing machine learning algorithms, models of poplar’s waterlogging tolerance were established and evaluated.
This article describes a methodology for detailed mapping of the lignification capacity of plant cell walls that we have called "REPRISAL" for REPorter Ratiometrics Integrating Segmentation for Analyzing Lignification. 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 labeling strategy based on the sequential use of three main bioorthogonal chemical reactions. In the second step, an automatic parametric and/or artificial intelligence segmentation algorithm is developed that assigns fluorescent image pixels to three distinct cell wall zones corresponding to cell corners, compound middle lamella and secondary cell walls. The last step corresponds to the exploitation of a ratiometric approach enabling statistical analyses of differences in monolignol reporter distribution (ratiometric method [RM] 1) and proportions (RM 2) within the different cell wall zones. We first describe the use of this methodology to map developmentally related changes in the lignification capacity of wild-type Arabidopsis (Arabidopsis thaliana) interfascicular fiber cells. We then apply REPRISAL to analyze the Arabidopsis peroxidase (PRX) mutant prx64 and provide further evidence for the implication of the AtPRX64 protein in floral stem lignification. In addition, we also demonstrate the general applicability of REPRISAL by using it to map lignification capacity in poplar (Populus tremula × Populus alba), flax (Linum usitatissimum), and maize (Zea mays). Finally, we show that the methodology can be used to map the incorporation of a fucose reporter into noncellulosic cell wall polymers.
Why it matches plant phenotyping methods植物細胞壁のリグニン化能力を、蛍光レポーター、画像セグメンテーション、比率解析で空間的にマッピングするREPRISAL法を開発・適用しており、植物状態の取得・抽出法が研究の中心である。
abstractThis article describes a methodology for detailed mapping of the lignification capacity of plant cell walls that we have called "REPRISAL"
Plant phenotyping relevance match · UnverifiedOpenAlex · Europe PMC · Crossref · checked 15 Sept 2026
The abilities of plant biologists and breeders to characterize the genetic basis of physiological traits are limited by their abilities to obtain quantitative data representing precise details of trait variation, and particularly to collect this data at a high-throughput scale with low cost. Although deep learning methods have demonstrated unprecedented potential to automate plant phenotyping, these methods commonly rely on large training sets that can be time-consuming to generate. Intelligent algorithms have therefore been proposed to enhance the productivity of these annotations and reduce human efforts. We propose a high-throughput phenotyping system which features a Graphical User Interface (GUI) and a novel interactive segmentation algorithm: Semantic-Guided Interactive Object Segmentation (SGIOS). By providing a user-friendly interface and intelligent assistance with annotation, this system offers potential to streamline and accelerate the generation of training sets, reducing the effort required by the user. Our evaluation shows that our proposed SGIOS model requires fewer user inputs compared to the state-of-art models for interactive segmentation. As a case study of the use of the GUI applied for genetic discovery in plants, we present an example of results from a preliminary genome-wide association study (GWAS) of in planta regeneration in Populus trichocarpa (poplar). We further demonstrate that the inclusion of a semantic prior map with SGIOS can accelerate the training process for future GWAS, using a sample of a dataset extracted from a poplar GWAS of in vitro regeneration. The capabilities of our phenotyping system surpass those of unassisted humans to rapidly and precisely phenotype our traits of interest. The scalability of this system enables large-scale phenomic screens that would otherwise be time-prohibitive, thereby providing increased power for GWAS, mutant screens, and other studies relying on large sample sizes to characterize the genetic basis of trait variation. Our user-friendly system can be used by researchers lacking a computational background, thus helping to democratize the use of deep segmentation as a tool for plant phenotyping.
Why it matches plant phenotyping methods植物形質の高スループット取得を目的に、GUIと新規インタラクティブ画像セグメンテーション手法を開発・評価しており、フェノタイピング手法が研究の中心である。
abstractWe propose a high-throughput phenotyping system which features a Graphical User Interface (GUI) and a novel interactive segmentation algorithm: Semantic-Guided Interactive Object Segmentation (SGIOS).
Generating renewable bioenergy crops requires varietals that are suited to grow under varying environmental conditions necessitating the development and testing of a wide range of poplar (Populus) genotypes. Meanwhile, there is an increasing demand for refining the selection process of high-performing poplars. However, a cost-effective method is still needed to predict the productivity of various poplar genotypes. Photosynthetic capacity and leaf nitrogen are important growth-related physicochemical traits, but measuring them in the field and laboratory is expensive and time-consuming. Alternatively, remote sensing of hyperspectral leaf spectra may serve as a proxy to rapidly estimate these traits, which are associated with absorption, reflection, and transmission of solar radiation. To quantify photosynthetic traits, CO 2 response curves were used to estimate Rubisco-limited carboxylation rate (V cmax ), maximum electron transport rate (J max ), and triose phosphate utilization (TPU). From the same leaves measured for photosynthesis, leaf reflectance was measured with a handheld spectroradiometer. We measured a total of 105 leaf samples, including 6 taxa with 61 different poplar genotypes. For data analyses, Least Absolute Shrinkage and Selection Operator and Principal Component Analysis were used to determine the wavelengths that were the most useful for capturing the variability in the physicochemical data. Results showed that leaf reflectance at 758 nm and 936 nm were crucial wavelengths for predicting V cmax (RMSPE = 31%) and J max (RMSPE = 32%), while 687 nm and 757 nm were important predictors for TPU (RMSPE = 31%), and 709 nm and 927 nm were important predictors for leaf nitrogen (RMSPE = 22%). The wavelengths near 687 nm and 760 nm are the oxygen absorption bands, and also overlap with the chlorophyll fluorescence emission of plants. Therefore, it is possible to apply hyperspectral reflectance models for rapid clonal screening and high-throughput field phenotyping of photosynthetic capacity parameters and leaf nitrogen of various poplar genotypes.
Why it matches plant phenotyping methodsハイパースペクトル反射スペクトルからポプラの光合成能力と葉窒素を推定するモデルを開発・評価しており、植物形質取得手法が中心です。
abstractAlternatively, remote sensing of hyperspectral leaf spectra may serve as a proxy to rapidly estimate these traits
Individual tree structural parameters are vital for precision silviculture in planted forests. This study used near-field LiDAR (light detection and ranging) data (i.e., unmanned aerial vehicle laser scanning (ULS) and ground backpack laser scanning (BLS)) to extract individual tree structural parameters and fit volume models in subtropical planted forests in southeastern China. To do this, firstly, the tree height was acquired from ULS data and the diameter at breast height (DBH) was acquired from BLS data by using individual tree segmentation algorithms. Secondly, point clouds of the complete forest canopy were obtained through the combination of ULS and BLS data. Finally, five tree taper models were fitted using the LiDAR-extracted structural parameters of each tree, and then the optimal taper model was selected. Moreover, standard volume models were used to calculate the stand volume; then, standing timber volume tables were created for dawn redwood and poplar. The extraction of individual tree structural parameters exhibited good performance. The volume model had a good performance in calculating the standing volume for dawn redwood and poplar. Our results demonstrate that near-field LiDAR has a strong capability of extracting tree structural parameters and creating volume tables for subtropical planted forests.
Why it matches plant phenotyping methods近場LiDARと個体木セグメンテーションにより樹高・胸高直径などの植物構造形質を抽出する手法が研究の中心であり、体積モデル作成まで技術的に評価している。
abstractThis study used near-field LiDAR (light detection and ranging) data (i.e., unmanned aerial vehicle laser scanning (ULS) and ground backpack laser scanning (BLS)) to extract individual tree structural parameters and fit volume models in subtropical planted forests in southeastern China.
Abstract Background: Frost stress is an abiotic stressor for plant growth that impacts the health and the regional distribution of plants. The freeze-thaw characteristics of plants during the overwintering period help to understand relevant issues in plant physiology, including plant cold resistance and cold acclimation. Therefore, we aimed to develop a non-invasive instrument and method for accurate in situ detection of changes in stem freeze-thaw characteristics during the overwintering period. Results: A sensor was designed based on standing wave ratio method (SWR) to measure stem volume water content (StVWC). We were able to measure stem volume ice content (StVIC) and stem freeze-thaw rate of ice (StFTRI) during the overwintering period. The resolution of the StVWC sensor is less than 0.05 %, the mean absolute error and root mean square error are less than 1 %, and the dynamic response time is 0.296 s. The peak point of the daily change rate of the lower envelope of the StVWC sequence occurs when the plant enters and exits the overwintering period. The peak point can be used to determine the moment of freeze-thaw occurrence, whereas the time point corresponding to the moment of freeze-thaw coincides with the rapid transition between high and low ambient temperatures. In the field, the StVIC and StFTRI of Juniperus virginiana L., Lagerstroemia indica L . and Populus alba L. gradually increased at the beginning, fluctuated steadily during, and then gradually decreased by the end of the overwintering period. The StVIC and StFTRI also showed significant variability due to differences among the tree species and latitude. Conclusions: The StVWC sensor has good resolution, accuracy, stability, and sensitivity. The envelope changes of the StVWC sequence and the correspondence between the freeze-thaw moment and the ambient temperature indicate that the determination of the freeze-thaw moment based on the peak point of the daily change rate of the lower envelope is reliable. The results show that the sensor is able to monitor changes in the freeze-thaw characteristics of plants and effectively characterize freeze-thaw differences and cold resistance of different tree species. Furthermore, this is a cost-effective tool for monitoring freeze-thaw conditions during the overwintering period.
Why it matches plant phenotyping methods植物の茎内水分・氷量と凍結融解特性を非侵襲的に測定するセンサーと解析法を開発・検証しており、植物の生理状態を取得する方法が研究の中心である。
abstractwe aimed to develop a non-invasive instrument and method for accurate in situ detection of changes in stem freeze-thaw characteristics
Plant phenotyping relevance match · UnverifiedOpenAlex · Europe PMC · checked 15 Sept 2026
Stomata are integral to plant performance, enabling the exchange of gases between the atmosphere and the plant. The anatomy of stomata influences conductance properties with the maximal conductance rate, gsmax, calculated from density and size. However, current calculations of stomatal dimensions are performed manually, which are time-consuming and error prone. Here, we show how automated morphometry from leaf impressions can predict a functional property: the anatomical gsmax. A deep learning network was derived to preserve stomatal morphometry via semantic segmentation. This forms part of an automated pipeline to measure stomata traits for the estimation of anatomical gsmax. The proposed pipeline achieves accuracy of 100% for the distinction (wheat vs. poplar) and detection of stomata in both datasets. The automated deep learning-based method gave estimates for gsmax within 3.8 and 1.9% of those values manually calculated from an expert for a wheat and poplar dataset, respectively. Semantic segmentation provides a rapid and repeatable method for the estimation of anatomical gsmax from microscopic images of leaf impressions. This advanced method provides a step toward reducing the bottleneck associated with plant phenotyping approaches and will provide a rapid method to assess gas fluxes in plants based on stomata morphometry.
Why it matches plant phenotyping methods葉面印象画像から気孔形態を自動抽出し、解剖学的gsmaxを推定する深層学習パイプラインの開発・精度検証が中心であり、植物表現型計測手法に該当する。
abstractA deep learning network was derived to preserve stomatal morphometry via semantic segmentation.
Plant phenotyping relevance match · UnverifiedbioRxiv · Europe PMC · checked 8 Sept 2026
Important structures and functions within living organisms rely on naturally fluorescent polymeric molecules such as collagen, keratin, elastin, resilin, or lignin. Theoretical physics predict that fluorescence lifetime of these polymers is related to their chemical composition. We verified this prediction for lignin, a major structural element in plant cell walls and one of the most abundant components of wood. Lignin is composed of different types of phenylpropanoid units, and its composition affects its properties, biological functions, and the utilization of wood biomass. We carried out fluorescence lifetime imaging microscopy (FLIM) measurements of wood cell wall lignin in a population of 90 hybrid aspen trees genetically engineered to display differences in cell wall chemistry and structure. We also measured wood cell wall composition by classical analytical methods in the wood cell walls of these trees. Using statistical modelling and machine learning algorithms, we identified parameters of fluorescence lifetime that predict the content of S-type and G-type lignin units, the two main types of units in the lignin of angiosperm plants. Finally, we show how quantitative measurements of lignin chemical composition by FLIM can reveal the dynamics of lignin biosynthesis in two different biological contexts, including in vivo while lignin is being synthesized in the walls of living cells.
Why it matches plant phenotyping methodsFLIMを用いて生体内の木質細胞壁リグニン組成を非破壊・定量推定する手法を開発し、古典的分析法との比較および機械学習による予測性能検証を行っており、植物フェノタイピング手法が中心である。
abstractWe verified this prediction for lignin, a major structural element in plant cell walls and one of the most abundant components of wood.
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
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fluorescence mean values were extracted for the 9 newly-created images. A recapitulative
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montage image was then created to quickly estimate segmentation quality. The imageJ macro
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and sample images are available in the Zenodo repository,
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http://doi.org/10.5281/zenodo.4809980.573
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AI Segmentation
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The Machine learning approach is based on the “Waikato Environment for Knowledge
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Analysis” (WEKA) implemented in ImageJ (Witten et al., 2016). We first defined a
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classification based on four categories: i) secondary cell wall, ii) cell corners, iii) compound
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middle lamella and iv) backgroOpen asset ↗zenodo · 10.5281/zenodo.4809980pdf-raw-page:21 lines:1-63Plant phenotyping relevance match · UnverifiedEurope PMC · checked 15 Sept 2026
High-throughput mapping of latent heat flux (λET) is critical to efforts to optimize water resources management and to accelerate forest tree breeding for improved drought tolerance. Ideally, investigation of the energy response at the tree level may promote tailored irrigation strategies and, thus, maximize crop biomass productivity. However, data availability is limited and planning experimental campaigns in the field can be highly operationally complex. To this end, a multi-platform multi-sensor observational approach is herein developed to dissect the λET signature of a black poplar (Populus nigra) breeding population (“POP6”) at the canopy level. POP6 comprised more than 4600 trees representing 503 replicated genotypes, whose parents were derived from contrasting environmental conditions. Trees were trialed in two adjacent plots where different irrigation treatments (moderate drought [mDr] and well-watered [WW]) were applied. Data collected from satellite and unmanned aerial vehicles (UAVs) remote sensing as well as from ground-based proximal sensors were integrated at consistent spatial aggregation and combined to compute the surface energy balance of the trees through a modified Priestley-Taylor method. Here, we demonstrated that λET response was significantly different between WW and mDr trees, whereby genotypes in mDr conditions exhibited larger standard deviations. Importantly, genotypes classified as drought tolerant based on the stress susceptibility index (SSI) presented λET values significantly higher than the rest of the population. This study confirmed that water limitation in mDr settings led to reduced soil moisture in the tree root zone and, thus, to lower λET. These results pave the way to breeding poplar and other bioenergy crops with this underexploited trait for higher λET. Most notably, the illustrated work demonstrates a multi-platform multi-sensor data fusion approach to tackle the global challenge of monitoring landscape-scale ecosystem processes at fine resolution.
Why it matches plant phenotyping methods黒ポプラ個体・遺伝型の干ばつ応答を推定するマルチプラットフォーム・マルチセンサー融合手法を開発・適用しており、潜熱フラックスという生理形質の取得が研究の中心である。
abstracta multi-platform multi-sensor observational approach is herein developed to dissect the λET signature of a black poplar (Populus nigra) breeding population (“POP6”) at the canopy level.
Abstract Background Recent interest in Populus as a source of renewable energy, combined with its numerous available pretreatment methods, has enabled further research on structural modification and hydrolysis. To improve the biodegradation efficiency of biomass, a better understanding of the relationship between its macroscopic structures and enzymatic process is important. Results This study investigated mutant cell wall structures compared with wild type on a molecular level. Furthermore, a novel insight into the structural dynamics occurring on mutant biomass was assessed in situ and in real time by functional Atomic Force Microscopy (AFM) imaging. High-resolution AFM images confirmed that genetic pretreatment effectively inhibited the production of irregular lignin. The average roughness values of the wild type are 78, 60, and 30 nm which are much higher than that of the mutant cell wall, approximately 10 nm. It is shown that the action of endoglucanases would expose pure crystalline cellulose with more cracks for easier hydrolysis by cellobiohydrolase I (CBHI). Throughout the entire CBHI hydrolytic process, when the average roughness exceeded 3 nm, the hydrolysis mode consisted of a peeling action. Conclusion Functional AFM imaging is helpful for biomass structural characterization. In addition, the visualization of the enzymatic hydrolysis process will be useful to explore the cell wall structure–activity relationships.
Why it matches plant phenotyping methods機能的AFMによる植物細胞壁の構造・粗さをリアルタイムに取得し、酵素加水分解過程を可視化することが研究の中心であり、植物組織の形態状態を測定する手法として適格。
abstracta novel insight into the structural dynamics occurring on mutant biomass was assessed in situ and in real time by functional Atomic Force Microscopy (AFM) imaging.
Effective segmentation of plant leaves is very necessary for non-contact extraction of plant leaf phenotype, especially leaf phenotype under environmental stress. However, the phenotype of leaves will change due to the influence of the environment, which increases the difficulty of detection. In this study, we proposed an accurate automatic segmentation method that combines Mask R-CNN with Density-Based Spatial Clustering of Applications with Noise (DBSCAN) clustering algorithm based on RGB-D camera to segment overlapped poplar seedling leaves under heavy metal stress. Firstly, an effective encoding method of depth information was used to facilitate the feature extraction of depth information. Next, we deployed Mask R-CNN to train the RGB-D data and fuse their features in the FPN structure to obtain more accurate leaf areas. Based on the detected leaf areas and depth data, DBSCAN based on manifold distance was then applied to segment a single leaves from overlapping leaves in the detected areas. Several analyses were performed to evaluate the performance of the proposed method, including the comparison of our network with classic Mask R-CNN and the comparison of DBSCAN based on manifold distance with other classic clustering methods. We used the pixel-wise Intersection over Union (p-IoU) to evaluate the detection results more accurately. In the experiments, the obtained p-IoU of normal and stressed leaves was 0.885 and 0.874, respectively, with corresponding mean accuracy values of 0.897 and 0.888. From our experimental results, it can be concluded that the proposed method can automatically detect leaves with high accuracy, which can be applied to 3-D leaf phenotype research and automatic plant de-leafing.
Why it matches plant phenotyping methodsRGB-D画像、Mask R-CNN、DBSCANを組み合わせ、重なったポプラ葉のセグメンテーション手法を開発・評価しており、葉形質抽出が中心である。
abstractwe proposed an accurate automatic segmentation method that combines Mask R-CNN with Density-Based Spatial Clustering of Applications with Noise (DBSCAN) clustering algorithm based on RGB-D camera to segment overlapped poplar seedling leaves under heavy metal stress.
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:1-42Plant phenotyping relevance match · UnverifiedEurope PMC · checked 15 Sept 2026
Background The increasing number of novel approaches for large-scale, multi-dimensional imaging of cells has created an unprecedented opportunity to analyze plant morphogenesis. However, complex image processing, including identifying specific cells and quantitating parameters, and high running cost of some image analysis softwares remains challenging. Therefore, it is essential to develop an efficient method for identifying plant complex multicellularity in raw micrographs in plants. Results Here, we developed a high-efficiency procedure to characterize, segment, and quantify plant multicellularity in various raw images using the open-source software packages ImageJ and SR-Tesseler. This procedure allows for the rapid, accurate, automatic quantification of cell patterns and organization at different scales, from large tissues down to the cellular level. We validated our method using different images captured from Arabidopsis thaliana roots and seeds and Populus tremula stems, including fluorescently labeled images, Micro-CT scans, and dyed sections. Finally, we determined the area, centroid coordinate, perimeter, and Feret's diameter of the cells and harvested the cell distribution patterns from Voronoï diagrams by setting the threshold at localization density, mean distance, or area. Conclusions This procedure can be used to determine the character and organization of multicellular plant tissues at high efficiency, including precise parameter identification and polygon-based segmentation of plant cells.
Why it matches plant phenotyping methods植物画像から細胞形態・配置を自動抽出する画像解析手法を開発し、複数植物種・画像 modality で検証しており、表現型取得が研究の中心です。
abstractwe developed a high-efficiency procedure to characterize, segment, and quantify plant multicellularity in various raw images using the open-source software packages ImageJ and SR-Tesseler.
The negative impact of water stress on forest and tree plantation productivity has been the focus of many investigations. However, moderate water stress that can decrease productivity and is difficult to detect, has received less attention. Therefore, we designed a greenhouse experiment where the main objective was to test the efficacy of published biochemical and water stress indices along with physiological traits in detecting moderate water stress at leaf scale. Potted saplings of three hybrid poplars ((Populus × canadensis) × P. maximowiczii, Populus × canadensis and Populus × generosa ‘Boelare’) were subjected to moderate water stress. During the experiment we recorded (i) the biomass accumulation and allocation to leaves, stem, and roots; (ii) Net CO2 assimilation rate (A) and stomatal conductance (gs) and (iii) Spectral biochemical and water indices. Results indicate that moderate water deficit had a negative impact on biomass accumulation, but no change was detected in physiological traits. Nitrogen and chlorophyll contents also remained unaffected. Spectral biochemical indices failed to detect differences between treatments, whereas water stress indices succeeded. We also have identified unique wavelengths in the shortwave infrared region of the spectrum that proved sufficiently sensitive on their own to detect moderate water deficit. The results were not influenced by a genotype effect, suggesting that these unique wavelengths could be responding to a general rather than a species-specific leaf feature, thus widening opportunities for the early detection of plant stress, particularly moderate water deficit.
Why it matches plant phenotyping methods葉のスペクトル指標と波長を用いた中程度の水ストレス検出性能を検証し、感度の高い波長を特定しており、植物表現型取得法が中心である。
abstractWe also have identified unique wavelengths in the shortwave infrared region of the spectrum that proved sufficiently sensitive on their own to detect moderate water deficit.
Plant phenotyping relevance match · UnverifiedOpenAlex · Europe PMC · checked 9 Sept 2026
Wood is a complex tissue that fulfils three major functions in trees: water conduction, mechanical support and nutrient storage. In Angiosperm trees, vessels, fibres and parenchyma rays are respectively assigned to these functions. Cell wall composition and structure strongly varies according to cell type, developmental stages and environmental conditions. This complexity can therefore hinder the study of the molecular mechanisms of wood formation, underlying the construction of its properties. However, this can be circumvented thanks to the development of cell-specific approaches and microphenotyping. Here, we present a non-destructive microphenotyping method based on attenuated total reflectance – Fourier transformed infrared (ATR-FTIR) microspectroscopy. We applied this technique to three types of poplar wood: normal wood of staked trees (NW), tension and opposite wood of artificially tilted trees (TW, OW). TW is produced by angiosperm trees in response to mechanical strains and is characterised by the presence of G fibres, exhibiting a thick gelatinous extra-layer, named G-layer, located in place of the usual S2 and/or S3 layers. By contrast, OW located on the opposite side of the trunk is totally deprived of fibres with G-layers. We developed a workflow for hyperspectral image analysis with both automatic pixel clustering according to cell wall types and identification of differentially absorbed wavenumbers (DAWNs). As pixel clustering failed to assign pixels to ray S-layers with sufficient efficiency, the IR profiling and identification of DAWNs were restricted to fibre and vessel cell walls. As reported elsewhere, this workflow identified cellulose as the main component of the G-layers, while the amount in acetylated xylans and lignins were shown to be reduced. These results validate ATR-FTIR technique for in situ characterization of G layers. In addition, this study brought new information about IR profiling of S-layers in TW, OW and NW. While OW and NW exhibited similar profiles, TW fibres S-layers combined characteristics of TW G-layers and of regular fibre S-layers. Unexpectedly, vessel S-layers of the three kinds of wood showed significant differences in IR profiling. In conclusion, ATR-FTIR microspectroscopy offers new possibilities for studying cell wall composition at the cell level.
Why it matches plant phenotyping methods植物細胞壁の化学状態を細胞レベルで取得する非破壊ATR-FTIRマイクロスペクトロスコピーと画像解析ワークフローを開発・検証しており、植物フェノタイピング手法が中心である。
abstractwe present a non-destructive microphenotyping method based on attenuated total reflectance – Fourier transformed infrared (ATR-FTIR) microspectroscopy.
Background As an essential component in reducing anthropogenic CO2 emissions to the atmosphere, tree planting is the key to keeping carbon dioxide emissions under control. In 1992, the United Nations agreed to take action at the Earth Summit to stabilize and reduce net zero global anthropogenic CO2 emissions. Tree planting was identified as an effective method to offset CO2 emissions. A high net photosynthetic rate (Pn) with fast-growing trees could efficiently fulfill the goal of CO2 emission reduction. Net photosynthetic rate model can provide refernece for plant's stability of photosynthesis productivity. Methods and results Using leaf phenotype data to predict the Pn can help effectively guide tree planting policies to offset CO2 release into the atmosphere. Tree planting has been proposed as one climate change solution. One of the most popular trees to plant are poplars. This study used a Populus simonii (P. simonii) dataset collected from 23 artificial forests in northern China. The samples represent almost the entire geographic distribution of P. simonii. The geographic locations of these P. simonii trees cover most of the major provinces of northern China. The northwestern point reaches (36°30'N, 98°09'E). The northeastern point reaches (40°91'N, 115°83'E). The southwestern point reaches (32°31'N, 108°90'E). The southeastern point reaches (34°39'N, 113°74'E). The collected data on leaf phenotypic traits are sparse, noisy, and highly correlated. The photosynthetic rate data are nonnormal and skewed. Many machine learning algorithms can produce reasonably accurate predictions despite these data issues. Influential outliers are removed to allow an accurate and precise prediction, and cluster analysis is implemented as part of a data exploratory analysis to investigate further details in the dataset. We select four regression methods, extreme gradient boosting (XGBoost), support vector machine (SVM), random forest (RF) and generalized additive model (GAM), which are suitable to use on the dataset given in this study. Cross-validation and regularization mechanisms are implemented in the XGBoost, SVM, RF, and GAM algorithms to ensure the validity of the outputs. Conclusions The best-performing approach is XGBoost, which generates a net photosynthetic rate prediction that has a 0.77 correlation with the actual rates. Moreover, the root mean square error (RMSE) is 2.57, which is approximately 35 percent smaller than the standard deviation of 3.97. The other metrics, i.e., the MAE, R2, and the min-max accuracy are 1.12, 0.60, and 0.93, respectively. This study demonstrates the ability of machine learning models to use noisy leaf phenotype data to predict the net photosynthetic rate with significant accuracy. Most net photosynthetic rate prediction studies are conducted on herbaceous plants. The net photosynthetic rate prediction of P. simonii, a kind of woody plant, illustrates significant guidance for plant science or environmental science regarding the predictive relationship between leaf phenotypic characteristics and the Pn for woody plants in northern China.
Why it matches plant phenotyping methods葉の表現型データから純光合成速度という植物生理形質を予測する複数の機械学習モデルを比較・検証しており、表現型推定手法が研究の中心である。
abstractUsing leaf phenotype data to predict the Pn can help effectively guide tree planting policies to offset CO2 release into the atmosphere.
Epigenomes have remarkable potential for the estimation of plant traits. This study tested the hypothesis that natural variation in DNA methylation can be used to estimate industrially important traits in a genetically diverse population of Populus balsamifera L. (balsam poplar) trees grown at two common garden sites. Statistical learning experiments enabled by deep learning models revealed that plant traits in novel genotypes can be modelled transparently using small numbers of methylated DNA predictors. Using this approach, tissue type, a nonheritable attribute, from which DNA methylomes were derived was assigned, and provenance, a purely heritable trait and an element of population structure, was determined. Significant proportions of phenotypic variance in quantitative wood traits, including total biomass (57.5%), wood density (40.9%), soluble lignin (25.3%) and cell wall carbohydrate (mannose: 44.8%) contents, were also explained from natural variation in DNA methylation. Modelling plant traits using DNA methylation can capture tissue-specific epigenetic mechanisms underlying plant phenotypes in natural environments. DNA methylation-based models offer new insight into natural epigenetic influence on plants and can be used as a strategy to validate the identity, provenance or quality of agroforestry products.
Why it matches plant phenotyping methodsDNAメチル化データと深層学習を用いて、木質形質や由来などの植物形質を推定するモデルを開発・検証しており、形質推定手法が研究の中心である。
abstractStatistical learning experiments enabled by deep learning models revealed that plant traits in novel genotypes can be modelled transparently using small numbers of methylated DNA predictors.
Coupling microfludics with microscopy has emerged as a powerful approach to study at cellular resolution the dynamics in plant physiology and root-microbe interactions. Most devices have been designed to study the model plant Arabidopsis thaliana at higher throughput than conventional methods. However, there is a need for microfluidic devices which enable in vivo studies of root development and root-microbe interactions in woody plants. Here, we developed the RMI-chip, a simple microfluidic setup in which Populus tremuloides (aspen tree) seedlings can grow for over a month, allowing continuous microscopic observation of interactions between live roots and rhizobacteria. We find that the colonization of growing aspen roots by Pseudomonas fluorescens in the RMI-chip involves dynamic biofilm formation and dispersal, in keeping with previous observations in a different experimental set-up. Also, we find that whole-cell biosensors based on the rhizobacterium Bacillus subtilis can be used to monitor compositional changes in the rhizosphere but that the application of these biosensors is limited by their efficiency at colonizing aspen roots and persisting. These results indicate that functional imaging of dynamic root-bacteria interactions in the RMI-chip requires careful matching between the host plant and the bacterial root colonizer.
Why it matches plant phenotyping methodsアスペン根の成長と根—細菌相互作用を長期間ライブ観察するマイクロ流体・顕微鏡システム自体を開発しており、植物表現型の取得が研究の中心である。
abstractHere, we developed the RMI-chip, a simple microfluidic setup in which Populus tremuloides (aspen tree) seedlings can grow for over a month, allowing continuous microscopic observation of interactions between live roots and rhizobacteria.
Background The stomata of plants mainly regulate gas exchange and water dispersion between the interior and external environments of plants and play a major role in the plants' health. The existing methods of stomata segmentation and measurement are mostly for specialized plants. The purpose of this research is to develop a generic method for the fully automated segmentation and measurement of the living stomata of different plants. The proposed method utilizes level set theory and image processing technology and can outperform the existing stomata segmentation and measurement methods based on threshold and skeleton in terms of its versatility. Results The single stomata images of different plants were the input of the method and a level set based on the Chan-Vese model was used for stomatal segmentation. This allowed the morphological features of the stomata to be measured. Contrary to existing methods, the proposed segmentation method does not need any prior information about the stomata and is independent of the plant types. The segmentation results of 692 living stomata of black poplars show that the average measurement accuracies of the major and minor axes, area, eccentricity and opening degree are 95.68%, 95.53%, 93.04%, 99.46% and 94.32%, respectively. A segmentation test on dayflower ( Commelina benghalensis ) stomata data available in the literature was completed. The results show that the proposed method can effectively segment the stomata images (181 stomata) of dayflowers using bright-field microscopy. The fitted slope of the manually and automatically measured aperture is 0.993, and the R 2 value is 0.9828, which slightly outperforms the segmentation results that are given in the literature. Conclusions The proposed automated segmentation and measurement method for living stomata is superior to the existing methods based on the threshold and skeletonization in terms of versatility. The method does not need any prior information about the stomata. It is an unconstrained segmentation method, which can accurately segment and measure the stomata for different types of plants (woody or herbs). The method can automatically discriminate whether the pore region is independent or not and perform pore region extraction. In addition, the segmentation accuracy of the method is positively correlated with the stomata's opening degree.
Why it matches plant phenotyping methods植物の気孔画像から形態形質と開口度を自動抽出する汎用セグメンテーション・測定法を開発し、複数植物種で精度検証しており、表現型取得手法が研究の中心である。
abstractThe purpose of this research is to develop a generic method for the fully automated segmentation and measurement of the living stomata of different plants.
The frequencies of free oscillations of plants, or plant parts, depend on their geometries, stiffnesses, and masses. Besides direct biomechanical interest, free frequencies also provide insights into plant properties that can usually only be measured destructively or with low-throughput techniques (e.g., change in mass, tissue density, or stiffness over development or with stresses). We propose here a new high-throughput method based on the noncontact measurements of the free frequencies of the standing plant. The plant is excited by short air pulses (typically 100 ms). The resulting motion is recorded by a high speed video camera (100 fps) and processed using fast space and time correlation algorithms. In less than a minute the mechanical behavior of the plant is tested over several directions. The performance and versatility of this method has been tested in three contrasted species: tobacco (Nicotiana benthamian), wheat (Triticum aestivum L.), and poplar (Populus sp.), for a total of more than 4000 data points. In tobacco we show that water stress decreased the free frequency by 15%. In wheat we could detect variations of less than 1 g in the mass of spikes. In poplar we could measure frequencies of both the whole stem and leaves. The work provides insight into new potential directions for development of phenotyping.
Why it matches plant phenotyping methods植物の自由振動を非接触・高速に測定し、質量や水ストレスなどの植物形質を推定する高スループット手法を開発・検証しており、フェノタイピング手法が研究の中心です。
abstractWe propose here a new high-throughput method based on the noncontact measurements of the free frequencies of the standing plant.
More frequently occurring, drought waves call for a deeper understanding of tree hydraulics and fast and easily applicable methods to measure drought stress. The aim of this study was to establish empirical relationships between the percent loss of hydraulic conductivity (PLC) and the relative water loss (RWL) in woody stem axes with different P₅₀, i.e. the water potential (Ψ) that causes 50% conductivity loss. Branches and saplings of temperate conifer (Picea abies, Larix decidua) and angiosperm species (Acer campestre, Fagus sylvatica, Populus x canescens, Populus tremula, Sorbus torminalis) and trunk wood of mature P. abies trees were analyzed. P₅₀ was calculated from hydraulic measurements following bench top dehydration or air injection. RWL and PLC were fitted by linear, quadratic or cubic equations. Species‐ or age‐specific RWLs at P₅₀ varied between 10 and 25% and P₈₈, the Ψ that causes 88% conductivity loss, between 18 and 44%. P₅₀ was predicted from the relationship between Ψ and the RWL. The predictive quality for P₅₀ across species was almost 1:1 (r² = 0.99). The approach presented allows thus reliable and fast prediction of PLC from RWL. Branches and saplings with high hydraulic vulnerability tended to have lower RWLs at P₅₀ and at P₈₈. The results are discussed with regard to the different water storage capacities in sapwood and survival strategies under drought stress. Potential applications are screening trees for drought sensitivity and a fast interpretation of diurnal, seasonal or drought induced changes in xylem water content upon their impact on conductivity loss.
Why it matches plant phenotyping methods相対含水損失から木部の水力伝導度損失を迅速に推定する測定・予測手法を確立し、複数樹種で予測性能を検証しているため、植物の乾燥ストレス状態を評価する方法研究として中心的である。
abstractThe aim of this study was to establish empirical relationships between the percent loss of hydraulic conductivity (PLC) and the relative water loss (RWL) in woody stem axes with different P₅₀
Forest canopy height plays an important role in forest management and ecosystem modeling. There are a variety of techniques employed to map forest height using remote sensing data but it is still necessary to explore the use of new data and methods. In this study, we demonstrate an approach for mapping canopy heights of poplar plantations in plain areas through a combination of stereo and multispectral data from China’s latest civilian stereo mapping satellite ZY3-02. First, a digital surface model (DSM) was extracted using photogrammetry methods. Then, canopy samples and ground samples were selected through manual interpretation. Canopy height samples were obtained by calculating the DSM elevation differences between the canopy samples and ground samples. A regression model was used to correlate the reflectance of a ZY3-02 multispectral image with the canopy height samples, in which the red band and green band reflectance were selected as predictors. Finally, the model was extrapolated to the entire study area and a wall-to-wall forest canopy height map was obtained. The validation of the predicted canopy height map reported a coefficient of determination (R2) of 0.72 and a root mean square error (RMSE) of 1.58 m. This study demonstrates the capacity of ZY3-02 data for mapping the canopy height of pure plantations in plain areas.
Why it matches plant phenotyping methods衛星ステレオ・マルチスペクトルデータからポプラ植林地の樹冠高を推定し、予測結果を検証しており、植物形質の取得・推定手法が中心である。
abstractwe demonstrate an approach for mapping canopy heights of poplar plantations in plain areas through a combination of stereo and multispectral data
Estimating forest structural attributes of planted forests plays a key role in managing forest resources, monitoring carbon stocks, and mitigating climate change. High-resolution and low-cost remote-sensing data are increasingly available to measure three-dimensional (3D) canopy structure and model forest structural attributes. In this study, we compared two suites of point cloud metrics and the accuracies of predictive models of forest structural attributes using unmanned aerial vehicle (UAV) light detection and ranging (LiDAR) and digital aerial photogrammetry (DAP) data, in a subtropical coastal planted forest of East China. A comparison between UAV-LiDAR and UAV-DAP metrics was performed across plots among different tree species, heights, and stem densities. The results showed that a higher similarity between the UAV-LiDAR and UAV-DAP metrics appeared in the dawn redwood plots with greater height and lower stem density. The comparison between the UAV-LiDAR and DAP metrics showed that the metrics of the upper percentiles (r for dawn redwood = 0.95–0.96, poplar = 0.94–0.95) showed a stronger correlation than the lower percentiles (r = 0.92–0.93, 0.90–0.92), whereas the metrics of upper canopy return density (r = 0.21–0.24, 0.14–0.15) showed a weaker correlation than those of lower canopy return density (r = 0.32–0.68, 0.31–0.52). The Weibull α parameter indicated a higher correlation (r = 0.70–0.72) than that of the Weibull β parameter (r = 0.07–0.60) for both dawn redwood and poplar plots. The accuracies of UAV-LiDAR (adjusted (Adj)R2 = 0.58–0.91, relative root-mean-square error (rRMSE) = 9.03%–24.29%) predicted forest structural attributes were higher than UAV-DAP (Adj-R2 = 0.52–0.83, rRMSE = 12.20%–25.84%). In addition, by comparing the forest structural attributes between UAV-LiDAR and UAV-DAP predictive models, the greatest difference was found for volume (ΔAdj-R2 = 0.09, ΔrRMSE = 4.20%), whereas the lowest difference was for basal area (ΔAdj-R2 = 0.03, ΔrRMSE = 0.86%). This study proved that the UAV-DAP data are useful and comparable to LiDAR for forest inventory and sustainable forest management in planted forests, by providing accurate estimations of forest structural attributes.
Why it matches plant phenotyping methodsUAV-LiDARとUAV-DAPによる森林キャノピー構造・林分属性推定を比較し、予測精度を検証することが研究の中心であるため。
abstractwe compared two suites of point cloud metrics and the accuracies of predictive models of forest structural attributes using unmanned aerial vehicle (UAV) light detection and ranging (LiDAR) and digital aerial photogrammetry (DAP) data
The evolution of disease resistance in plants occurs within a framework of interacting phenotypes, balancing natural selection for life-history traits along a continuum of fast-growing and poorly defended, or slow-growing and well-defended lifestyles. Plant populations connected by gene flow are physiologically limited to evolving along a single axis of the spectrum of the growth-defense trade-off, and strong local selection can purge phenotypic variance from a population or species, making it difficult to detect variation linked to the trade-off. Hybridization between two species that have evolved different growth-defense trade-off optima can reveal trade-offs hidden in either species by introducing phenotypic and genetic variance. Here, I investigated the phenotypic and genetic basis for variation of disease resistance in a set of naturally formed hybrid poplars. The focal species of this dissertation were the balsam poplar (Populus balsamifera), black balsam poplar (P. trichocarpa), narrowleaf cottonwood (P. angustifolia), and eastern cottonwood (P. deltoides). Vegetative cuttings of samples were collected from natural populations and clonally replicated in a common garden. Ecophysiology and stomata traits, and the severity of poplar leaf rust disease (Melampsora medusae) were collected. To overcome the methodological bottleneck of manually phenotyping stomata density for thousands of cuticle micrographs, I developed a publicly available tool to automatically identify and count stomata. To identify stomata, a deep con- volutional neural network was trained on over 4,000 cuticle images of over 700 plant species. The neural network had an accuracy of 94.2% when applied to new cuticle images and phenotyped hundreds of micrographs in a matter of minutes. To understand how disease severity, stomata, and ecophysiology traits changed as a result of hybridization, statistical models were fit that included the expected proportion of the genome from either parental species in a hybrid. These models in- dicated that the ratio of stomata on the upper surface of the leaf to the total number of stomata was strongly linked to disease, was highly heritable, and wass sensitive to hybridization. I further investigated the genomic basis of stomata-linked disease variation by performing an association genetic analysis that explicitly incorporated admixture. Positive selection in genes involved in guard cell regulation, immune sys- tem negative regulation, detoxification, lipid biosynthesis, and cell wall homeostasis were identified. Together, my dissertation incorporated advances in image-based phenotyping with evolutionary theory, directed at understanding how disease frequency changes when hybridization alters the genomes of a population.
Why it matches plant phenotyping methods数千枚の葉表皮画像から気孔を自動検出・計数する画像ベース表現型解析ツールを開発し、新規画像で精度検証しているため、方法が中心的です。
abstractTo overcome the methodological bottleneck of manually phenotyping stomata density for thousands of cuticle micrographs, I developed a publicly available tool to automatically identify and count stomata.
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:66-74Plant phenotyping relevance match · UnverifiedEurope PMC · checked 10 Sept 2026
More frequently occurring, drought waves call for a deeper understanding of tree hydraulics and fast and easily applicable methods to measure drought stress. The aim of this study was to establish empirical relationships between the percent loss of hydraulic conductivity (PLC) and the relative water loss (RWL) in woody stem axes with different P 50 , i.e. the water potential (Ψ) that causes 50% conductivity loss. Branches and saplings of temperate conifer (Picea abies, Larix decidua) and angiosperm species (Acer campestre, Fagus sylvatica, Populus x canescens, Populus tremula, Sorbus torminalis) and trunk wood of mature P. abies trees were analyzed. P 50 was calculated from hydraulic measurements following bench top dehydration or air injection. RWL and PLC were fitted by linear, quadratic or cubic equations. Species- or age-specific RWLs at P 50 varied between 10 and 25% and P 88 , the Ψ that causes 88% conductivity loss, between 18 and 44%. P 50 was predicted from the relationship between Ψ and the RWL. The predictive quality for P 50 across species was almost 1:1 (r 2 = 0.99). The approach presented allows thus reliable and fast prediction of PLC from RWL. Branches and saplings with high hydraulic vulnerability tended to have lower RWLs at P 50 and at P 88 . The results are discussed with regard to the different water storage capacities in sapwood and survival strategies under drought stress. Potential applications are screening trees for drought sensitivity and a fast interpretation of diurnal, seasonal or drought induced changes in xylem water content upon their impact on conductivity loss.
Why it matches plant phenotyping methods相対含水損失から水理伝導度損失を予測する経験的測定・推定法を開発し、その予測性能を検証している。乾燥ストレスや樹木の脆弱性評価への再利用可能な手法が中心である。
abstractThe aim of this study was to establish empirical relationships between the percent loss of hydraulic conductivity (PLC) and the relative water loss (RWL) in woody stem axes
Empirical models of plant drought responses rely on parameters that are difficult to specify a priori. We test a trait- and process-based model to predict environmental responses from an optimization of carbon gain vs hydraulic risk. We applied four drought treatments to aspen (Populus tremuloides) saplings in a research garden. First we tested the optimization algorithm by using predawn xylem pressure as an input. We then tested the full model which calculates root-zone water budget and xylem pressure hourly throughout the growing season. The optimization algorithm performed well when run from measured predawn pressures. The per cent mean absolute error (MAE) averaged 27.7% for midday xylem pressure, transpiration, net assimilation, leaf temperature, sapflow, diffusive conductance and soil-canopy hydraulic conductance. Average MAE was 31.2% for the same observations when the full model was run from irrigation and rain data. Saplings that died were projected to exceed 85% loss in soil-canopy hydraulic conductance, whereas surviving plants never reached this threshold. The model fit was equivalent to that of an empirical model, but with the advantage that all inputs are specific traits. Prediction is empowered because knowing these traits allows knowing the response to climatic stress.
Why it matches plant phenotyping methods植物の干ばつ応答を予測する最適化モデルを開発・検証し、複数の生理形質を定量的に予測しているため、方法が研究の中心です。
abstractWe test a trait- and process-based model to predict environmental responses from an optimization of carbon gain vs hydraulic risk.
Detecting how genes regulate biological shape has become a multidisciplinary research interest because of its wide application in many disciplines. Despite its fundamental importance, the challenges of accurately extracting information from an image, statistically modeling the high-dimensional shape and meticulously locating shape quantitative trait loci (QTL) affect the progress of this research. In this article, we propose a novel integrated framework that incorporates shape analysis, statistical curve modeling and genetic mapping to detect significant QTLs regulating variation of biological shape traits. After quantifying morphological shape via a radius centroid contour approach, each shape, as a phenotype, was characterized as a high-dimensional curve, varying as angle θ runs clockwise with the first point starting from angle zero. We then modeled the dynamic trajectories of three mean curves and variation patterns as functions of θ. Our framework led to the detection of a few significant QTLs regulating the variation of leaf shape collected from a natural population of poplar, Populus szechuanica var tibetica. This population, distributed at altitudes 2000-4500 m above sea level, is an evolutionarily important plant species. This is the first work in the quantitative genetic shape mapping area that emphasizes a sense of 'function' instead of decomposing the shape into a few discrete principal components, as the majority of shape studies do.
Why it matches plant phenotyping methods葉形状を画像から定量化し、形状表現と統計的遺伝解析を統合する新規フレームワークが研究の中心であり、植物表現型の取得・抽出手法に該当する。
abstractwe propose a novel integrated framework that incorporates shape analysis, statistical curve modeling and genetic mapping to detect significant QTLs regulating variation of biological shape traits.
ABSTRACT 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. ARTICLE SUMMARY Despite its widely adopted interest in breeding, genomic selection - the prediction of breeding values using DNA polymorphisms - remains difficult to implement for many species because of genotyping costs. As an alternative or complement depending on the context, we propose “phenomic selection” (PS) as the use of low-cost and high-throughput phenotypic records to reconstruct similarities between genotypes and predict their performances. As a proof of concept of PS, we made use of near infrared spectroscopy applied to different tissues in poplar and wheat to predict various key traits and showed that PS could reach predictions as accurate as with molecular markers.
Why it matches plant phenotyping methodsNIRSを用いた低コスト・高スループットな表現型記録から遺伝型間類似性を再構成し、複雑形質を予測する手法を提案・実証しており、表現型取得と解析ワークフローが研究の中心である。
abstractAs 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).
Lignocellulosic biomass is a complex network of polymers making up the cell walls of plants. It represents a feedstock of sustainable resources to be converted into fuels, chemicals, and materials. Because of its complex architecture, lignocellulose is a recalcitrant material that requires some pretreatments and several types of catalysts to be transformed efficiently. Gaining more knowledge in the architecture of plant cell walls is therefore important to understand and optimize transformation processes. For the first time, super-resolution imaging of poplar wood samples has been performed using the Stimulated Emission Depletion (STED) technique. In comparison to standard confocal images, STED reveals new details in cell wall structure, allowing the identification of secondary walls and middle lamella with fine details, while keeping open the possibility to perform topochemistry by the use of relevant fluorescent nano-probes. In particular, the deconvolution of STED images increases the signal-to-noise ratio so that images become very well defined. The obtained results show that the STED super-resolution technique can be easily implemented by using cheap commercial fluorescent rhodamine-PEG nano-probes which outline the architecture of plant cell walls due to their interaction with lignin. Moreover, the sample preparation only requires easily-prepared plant sections of a few tens of micrometers, in addition to an easily-implemented post-treatment of images. Overall, the STED super-resolution technique in combination with a variety of nano-probes can provide a new vision of plant cell wall imaging by filling in the gap between classical photon microscopy and electron microscopy.
Why it matches plant phenotyping methods植物細胞壁の構造を可視化・抽出する超解像STED画像法と蛍光ナノプローブを中心に開発・実証しており、植物形態特性の取得方法が主題である。
abstractFor the first time, super-resolution imaging of poplar wood samples has been performed using the Stimulated Emission Depletion (STED) technique.
High-throughput techniques for the compositional analysis of lignocellulosic biomass are essential to allow the genetic analysis and genetic improvement of bioenergy feedstocks. In this study, we investigated the feasibility of using near-infrared (NIR) spectroscopy for rapid assessment of wood chemical traits in a large sample of Populus nigra L. individuals evaluated in clonal trials at two contrasting sites. Spectra were acquired from 5799 wood samples collected in 3 harvests corresponding to two coppice rotations at one site and one coppice rotation at the second. Calibrations were developed and validated using 120 reference samples, representing spectral and chemical variations in the samples. The resulting global and site specific calibrations for most of the traits were at least good enough for ranking of genotypes, demonstrating the usefulness of NIR analysis for phenotyping the studied population. Clonal repeatability (Hc2) estimates of the studied traits based on all samples were moderate to high (Hc2 ranging from 0.57 to 0.89 in the 3 harvests). When data were pooled over coppice rotations or sites, the genotype×environment interaction was more evident across sites than across rotations. However, the interaction was smaller than the genotype main effect for all traits, except for glucose and extractives contents. Importantly, the interaction resulted mainly from re-ranking of a few genotypes leaving a substantial amount of stable and performant genetic material, which may encourage breeding for improved main wood components. Optimization of the NIR analysis for assessing clonal trials would facilitate the exploitation of standing genetic variation of energy or chemical related traits in tree breeding program.
Why it matches plant phenotyping methodsNIR分光法による木材化学形質の迅速推定を開発・検証し、大規模集団の表現型解析への有用性を評価しており、形質取得法が研究の中心である。
abstractinvestigated the feasibility of using near-infrared (NIR) spectroscopy for rapid assessment of wood chemical traits
Plant phenotyping relevance match · UnverifiedCrossref · Europe PMC · OpenAlex · checked 10 Sept 2026
Poplars are fast-growing, high-yielding forest tree species, whose cultivation as second-generation biofuel crops is of increasing interest and can efficiently meet emission reduction goals. Yet, breeding elite poplar trees for drought resistance remains a major challenge. Worldwide breeding programs are largely focused on intra/interspecific hybridization, whereby Populus nigra L. is a fundamental parental pool. While high-throughput genotyping has resulted in unprecedented capabilities to rapidly decode complex genetic architecture of plant stress resistance, linking genomics to phenomics is hindered by technically challenging phenotyping. Relying on unmanned aerial vehicle (UAV)-based remote sensing and imaging techniques, high-throughput field phenotyping (HTFP) aims at enabling highly precise and efficient, non-destructive screening of genotype performance in large populations. To efficiently support forest-tree breeding programs, ground-truthing observations should be complemented with standardized HTFP. In this study, we develop a high-resolution (leaf level) HTFP approach to investigate the response to drought of a full-sib F 2 partially inbred population (termed here 'POP6'), whose F 1 was obtained from an intraspecific P. nigra controlled cross between genotypes with highly divergent phenotypes. We assessed the effects of two water treatments (well-watered and moderate drought) on a population of 4603 trees (503 genotypes) hosted in two adjacent experimental plots (1.67 ha) by conducting low-elevation (25 m) flights with an aerial drone and capturing 7836 thermal infrared (TIR) images. TIR images were undistorted, georeferenced, and orthorectified to obtain radiometric mosaics. Canopy temperature ( T c ) was extracted using two independent semi-automated segmentation techniques, eCognition- and Matlab-based, to avoid the mixed-pixel problem. Overall, results showed that the UAV platform-based thermal imaging enables to effectively assess genotype variability under drought stress conditions. T c derived from aerial thermal imagery presented a good correlation with ground-truth stomatal conductance ( g s ) in both segmentation techniques. Interestingly, the HTFP approach was instrumental to detect drought-tolerant response in 25% of the population. This study shows the potential of UAV-based thermal imaging for field phenomics of poplar and other tree species. This is anticipated to have tremendous implications for accelerating forest tree genetic improvement against abiotic stress.
Why it matches plant phenotyping methodsUAV熱画像による高スループット圃場フェノタイピング手法を開発し、画像補正・セグメンテーション・樹冠温度抽出を検証しており、植物形質取得法が研究の中心である。
abstractwe develop a high-resolution (leaf level) HTFP approach to investigate the response to drought
Weather conditions can affect sensors' readings when sampling outdoors. Although sensors are usually set up covering a wide range of conditions, their operational range must be established. In recent years, depth cameras have been shown as a promising tool for plant phenotyping and other related uses. However, the use of these devices is still challenged by prevailing field conditions. Although the influence of lighting conditions on the performance of these cameras has already been established, the effect of wind is still unknown. This study establishes the associated errors when modeling some tree characteristics at different wind speeds. A system using a Kinect v2 sensor and a custom software was tested from null wind speed up to 10 m·s -1 . Two tree species with contrasting architecture, poplars and plums, were used as model plants. The results showed different responses depending on tree species and wind speed. Estimations of Leaf Area (LA) and tree volume were generally more consistent at high wind speeds in plum trees. Poplars were particularly affected by wind speeds higher than 5 m·s -1 . On the contrary, height measurements were more consistent for poplars than for plum trees. These results show that the use of depth cameras for tree characterization must take into consideration wind conditions in the field. In general, 5 m·s -1 (18 km·h -1 ) could be established as a conservative limit for good estimations.
Why it matches plant phenotyping methodsKinect RGB-Dによる樹木形質推定を風速条件下で評価し、測定誤差と運用限界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。
abstractThis study establishes the associated errors when modeling some tree characteristics at different wind speeds.
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:1-25Plant phenotyping relevance match · UnverifiedEurope PMC · checked 14 Sept 2026
Elevated forest mortality has been attributed to climate change-induced droughts, but prediction of spatial mortality patterns remains challenging. We evaluated whether introducing plant hydraulics and topographic convergence-induced soil moisture variation to land surface models (LSM) can help explain spatial patterns of mortality. A scheme predicting plant hydraulic safety loss from soil moisture was developed using field measurements and a plant physiology-hydraulics model, TREES. The scheme was upscaled to Populus tremuloides forests across Colorado, USA, using LSM-modeled and topography-mediated soil moisture, respectively. The spatial patterns of hydraulic safety loss were compared against aerial surveyed mortality. Incorporating hydraulic safety loss raised the explanatory power of mortality by 40% compared to LSM-modeled soil moisture. Topographic convergence was mostly influential in suppressing mortality in low and concave areas, explaining an additional 10% of the variations in mortality for those regions. Plant hydraulics integrated water stress along the soil-plant continuum and was more closely tied to plant physiological response to drought. In addition to the well-recognized topo-climate influence due to elevation and aspect, we found evidence that topographic convergence mediates tree mortality in certain parts of the landscape that are low and convergent, likely through influences on plant-available water.
Why it matches plant phenotyping methods植物の水理的安全性喪失という生理状態を推定するスキームを開発・広域適用し、航空調査による死亡と比較検証しているため、方法が研究の中心です。
abstractA scheme predicting plant hydraulic safety loss from soil moisture was developed using field measurements and a plant physiology-hydraulics model, TREES.