Lignin, a major component of plant cell walls, plays a critical role in structural support and stress resistance. Despite its importance, the transport and deposition dynamics of the glycosylated lignin monomer during lignification in living cells remain poorly understood, hindering advances in biomass utilization. To address this challenge, alkyne-labeled glycosylated lignin precursors (pGCA ALK and CF ALK ) were synthesized by introducing propargyl groups at the ortho position of aromatic rings. These precursors were successfully incorporated into lignin polymers in flax (a herbaceous plant) and ginkgo (a gymnosperm), enabling the real-time tracking of lignification via fluorescent click chemistry. Quantitative imaging revealed that lignification initiates at cell corners and the middle lamella and then progressively extends into secondary cell walls. Distinct deposition patterns were observed: parenchyma cells exhibited continuous lignin accumulation, whereas fiber tracheids underwent rapid lignification, followed by cell death. Specialized pit structures displayed "tunnel-like" lignin deposition in longitudinal pits and unilateral patterns in transverse pits. In vitro synthesis of dehydrogenation polymers (DHP) and extraction of the cellulolytic enzyme lignin (CEL) from ginkgo confirmed the biocompatibility of labeled monomers. LC-MS analysis further demonstrated that alkynyl groups formed oxygen-containing cyclic structures without disrupting natural β-O-4 and β-5 lignin linkages. Application of this labeling method in biomass utilization indicated that lower overall fluorescence intensity correlates with more efficient lignin removal during pretreatment. These results provide new insights into the spatiotemporal dynamics of lignification and establish a bioorthogonal platform for lignin research, offering promising strategies for optimizing plant biomass in industrial applications.
Why it matches plant phenotyping methods蛍光クリック化学による生細胞内リグニン形成の時空間追跡と定量イメージング手法を開発・適用しており、植物状態の取得方法が研究の中心である。
abstractenabling the real-time tracking of lignification via fluorescent click chemistry.
Protein, oil content, stearic acid, linolenic acid, and linoleic acid are key indicators for evaluating the quality of flaxseed in order to optimize the detection method of nutritional quality of flaxseed and to improve the efficiency of the screening of high-quality flax germplasm resources. This study integrated visible near-infrared (Vis-NIR) and near-infrared (NIR) hyperspectral imaging to determine protein, oil, stearic acid, linolenic acid, and linoleic acid contents in diverse flaxseed varieties, along with conducting correlation analyses. After seven data preprocessing methods and three feature selection methods, quantitative prediction models were developed using partial least squares regression (PLSR), principal component regression (PCR), support vector regression (SVR), and multiple linear regression (MLR). Experimental results demonstrated that NIR and fused spectral data outperformed Vis-NIR data across all five quality indices. NIR spectroscopy showed optimal performance for predicting oil content (R p 2 = 0.9671, RMSEP = 0.4364 %), linolenic acid (R p 2 = 0.9517, RMSEP = 0.8795 %), and linoleic acid (R p 2 = 0.9458, RMSEP = 0.3037 %). Fused spectral data achieved superior predictions for protein content (R p 2 = 0.9712, RMSEP = 0.2360 %) and stearic acid (R p 2 = 0.9195, RMSEP = 0.3454 %). And the spatial distribution of flaxseed's internal nutrient contents was also visualized by map. The results showed that the NIR and fusion spectral sets could be successfully used to evaluate multiple nutritional qualities of flaxseed, which provides a new option for nondestructive determination of the nutritional qualities of flaxseed in the future.
Why it matches plant phenotyping methodsVis-NIR/NIRハイパースペクトル画像とデータ融合・回帰モデルを開発し、アマ種子の栄養形質を非破壊推定・可視化する方法が研究の中心である。
abstractThis study integrated visible near-infrared (Vis-NIR) and near-infrared (NIR) hyperspectral imaging to determine protein, oil, stearic acid, linolenic acid, and linoleic acid contents in diverse flaxseed varieties
The growing interest in the rapid measurement of seed ingredients using single‐kernel NIR (SKNIR) spectroscopy as a nondestructive measurement technique allows fast analysis of sample seed variance that can have effects on breeding and end‐use processing. Flax (Linum usitatissimum), an oilseed crop grown in the Northwest United States and worldwide, is highly beneficial for human health, food, and fiber. Its health benefits include its high protein and omega‐3 fatty acids content. Therefore, seed composition profiles are an important aspect of breeding. The goals of this research were the development of single seed NIR calibration models for protein, oil, and weight of intact flax seeds. In this study, SKNIR spectroscopy was used on a diverse set of flax accessions comprising of 306 samples to create prediction models on a custom built SKNIR instrument. Spectra data and reference protein, oil, and weight were used to build partial least squares (PLS) models. Calibration models provided reasonable prediction of these traits and could be used for screening purposes. PLS statistics were oil (R² = 0.82, SEP = 1.72), weight (R² = 0.74, SEP = 0.71), and protein (R² = 0.62, SEP = 0.96) for validation data sets comprising of one‐third of the total samples. In conclusion, prediction models showed that SKNIR spectroscopy could be a very beneficial nondestructive technique to determine oil and weight as well as rapid screening of protein in single flax seeds while not requiring extensive preparation as compared to traditional techniques.
Why it matches plant phenotyping methods単粒NIR分光による種子の油・タンパク質・重量形質の非破壊推定モデルを開発・検証しており、植物形質取得法が研究の中心である。
abstractThe goals of this research were the development of single seed NIR calibration models for protein, oil, and weight of intact flax seeds.
Flax, as a functional crop with rich essential fatty acids and nutrients, is important in nutrition and industrial applications. However, the current process of flax seed detection relies mainly on manual operation, which is not only inefficient but also prone to error. The development of computer vision and deep learning techniques offers a new way to solve this problem. In this study, based on RT-DETR, we introduced the RepNCSPELAN4 module, ADown module, Context Aggregation module, and TFE module, and designed the HWD-ADown module, HiLo-AIFI module, and DSSFF module, and proposed an improved model, called LEHP-DETR. Experimental results show that LEHP-DETR achieves significant performance improvement on the flax dataset and comprehensively outperforms the comparison model. Compared to the base model, LEHP-DETR reduces the number of parameters by 67.3%, the model size by 66.3%, and the FLOPs by 37.6%. the average detection accuracy mAP50 and mAP50:95 increased by 2.6% and 3.5%, respectively.
Why it matches plant phenotyping methodsアマ種子ではなくフラックスの莢という植物器官を画像から検出するモデルを新規設計し、比較実験で性能を検証しており、器官表現型の取得・抽出法が中心である。
titleLEHP-DETR: A model with backbone improved and hybrid encoding innovated for flax capsule detection.
Reproduction assets foundThe paper's authors publicly release the LEHP-DETR analysis code (the improved RT-DETR model used for flax capsule detection) on GitHub. The paper-specific FLAX dataset is only available upon request from the lead contact, so it does not qualify as a public asset. DOTA is a cited third-party dataset, not paper-specificCode · publicAX dataset reported in this paper is available from the lead contact upon request.
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The DOTA dataset has been published in a publicly accessible repository. The access address is listed in the key resources table . Datasets are publicly accessible.
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All code associated with this paper can be freely accessed and downloaded via https://github.com/ShawnWang04/LEHP-DETR .
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Any additional information required to reanalyze the data reported in this paper is available from the lead contact upon request.
Acknowledgments
Thanks to the National Natural Science Foundation of China (No. 32360437) and the Innovation Fund for Higher Education of Gansu Province (No. 2021A-056), and the National IndustriOpen asset ↗ShawnWang04/LEHP-DETRlines:594-657Plant phenotyping relevance match · UnverifiedEurope PMC · checked 7 Sept 2026
Accurate detection and counting of flax plant organs are crucial for obtaining phenotypic data and are the cornerstone of flax variety selection and management strategies. In this study, a Flax-YOLOv5 model is proposed for obtaining flax plant phenotypic data. Based on the solid foundation of the original YOLOv5x feature extraction network, the network structure was extended to include the BiFormer module, which seamlessly integrates bi-directional encoders and converters, enabling it to focus on key features in an adaptive query manner. As a result, this improves the computational performance and efficiency of the model. In addition, we introduced the SIoU function to compute the regression loss, which effectively solves the problem of mismatch between predicted and actual frames. The flax plants grown in Lanzhou were collected to produce the training, validation, and test sets, and the detection results on the validation set showed that the average accuracy (mAP@0.5) was 99.29%. In the test set, the correlation coefficients (R) of the model's prediction results with the manually measured number of flax fruits, plant height, main stem length, and number of main stem divisions were 99.59%, 99.53%, 99.05%, and 92.82%, respectively. This study provides a stable and reliable method for the detection and quantification of flax phenotypic characteristics. It opens up a new technical way of selecting and breeding good varieties.
Why it matches plant phenotyping methodsFlax-YOLOv5による器官検出・計数と、草丈・果実数などの形質推定手法を開発・検証しており、植物フェノタイピング手法が研究の中心である。
abstracta Flax-YOLOv5 model is proposed for obtaining flax plant phenotypic data
Protein, oil content, linoleic acid, and lignan are several key indicators for evaluating the quality of flaxseed. In order to optimize the testing methods for flaxseed's nutritional quality and enhance the efficiency of screening high-quality flax germplasm resources, we selected 30 flaxseed species widely cultivated in Northwest China as the subjects of our study. Firstly, we gathered hyperspectral information regarding the seeds, along with data on protein, oil content, linoleic acid, and lignan, and utilized the SPXY algorithm to classify the sample set. Subsequently, the spectral data underwent seven distinct preprocessing methods, revealing that the PLSR model exhibited superior performance after being processed with the SG smoothing method. Feature wavelength extraction was carried out using the Successive Projections Algorithm (SPA) and the Competitive Adaptive Reweighted Sampling (CARS). Finally, four quantitative analysis models, namely Partial Least Squares Regression (PLSR), Support Vector Regression (SVR), Multiple Linear Regression (MLR), and Principal Component Regression (PCR), were individually established. Experimental results demonstrated that among all the models for predicting protein content, the SG-CARS-MLR model predicted the best, with and of 0.9563 and 0.9336, with the corresponding Root Mean Square Error Correction (RMSEC) and Root Mean Square Error Prediction (RMSEP) of 0.4892 and 0.5616, respectively. In the optimal prediction models for oil content, linoleic acid and lignan, the Rp2 was 0.8565, 0.8028, 0.9343, and the RMSEP was 0.8682, 0.5404, 0.5384, respectively. The study results show that hyperspectral imaging technology has excellent potential for application in the detection of quality characteristics of flaxseed and provides a new option for the future non-destructive testing of the nutritional quality of flaxseed.
Why it matches plant phenotyping methodsハイパースペクトル画像から flaxseed の種子成分形質を非破壊推定するモデルを開発・比較し、品質評価と遺伝資源スクリーニングへの応用可能性を検証しており、形質取得法が中心である。
abstractwe gathered hyperspectral information regarding the seeds, along with data on protein, oil content, linoleic acid, and lignan
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"
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:1-63Code / dataset availability confirmedEurope PMC · checked 14 Sept 2026
A collection of flax accessions from Russian Federal Research Center for Bast Fiber Crops was characterised to evaluate its phenotypic diversity. 406 samples representing different morphotypes were selected for thorough quantitative assessment of various agronomic traits. We measured height, length of technical part of the stem, technical part weight, inflorescence length, number of bolls and seeds per plant, 1000 seed weight, the diameter of the stem, the number of internodes and finally, distance between internodes. The fiber quality was estimated by calculating stem slenderness, stem taperingness and elementary fiber length. The dataset was produced in a framework of a project focused on characterization of diversity of flax genotypes and phenotypes, as well as on identification of genomic regions associated with various traits, it is hosted on Figshare.
Why it matches plant phenotyping methods植物遺伝資源の多形質表現型を体系的に収集したデータセットであり、表現型データセットとして中心的な対象である。
titleA comprehensive dataset of flax ( Linum uitatissimum L.) phenotypes.
Reproduction assets foundThe paper is a Data in Brief article describing a flax phenotype dataset (406 accessions, agronomic and fiber quality traits) hosted publicly on Figshare. The Figshare link is explicitly given as the direct URL to the data and matches an allowed URL, making it a paper-specific, publicly accessible phenotype dataset.Dataset · publicear of phenotyping.
Data source location
Institution: Federal Research Center for Bast Fiber Crops
City/Town/Region: Torzhok/Tver Region
Country:Russia
Latitude and longitude for collected samples/data: 57°02′N, 34°58′E; Altitude: 165 m
Data accessibility
Repository name: Figshare
Data identification number:
Direct URL to data: https://figshare.com/s/86a68ecfacf6872ef239
Value of the Data
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The data on flax phenotypic diversity provides insight into flax domestication history and facilitates flax breeding efforts.
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Flax raw material has multiple uses in various sectors of the economy including textile, medical, food and chemical industries as a source of fiber, linseed and oil. This data haOpen asset ↗Figsharelines:47-144Plant phenotyping relevance match · UnverifiedEurope PMC · checked 15 Sept 2026
Published19 Feb 2019Luminescence : the journal of biological and chemical luminescenceCited by 19 · OpenAlex ↗
In the present study a new luminescent dye 3-N-(2-pyrrolidinylacetamido)benzanthrone (AZR) was synthesized. Spectroscopic measurements of the novel benzanthrone 3-aminoderivative were performed in seven organic solvents showing strong fluorescence. The capability of the prepared dye for visualization has been tested on flax, red clover and alfalfa to determinate the embryo in plant callus tissue cultures. Callus cells were stained with AZR and further analysed utilizing confocal laser scanning fluorescence microscopy. Performed experiments show high visualization effectiveness of newly synthesized fluorescent dye AZR that is efficient in fast and relatively inexpensive diagnostics of callus embryos that are problematic due to in vitro culture specificity.
Why it matches plant phenotyping methods新規蛍光色素と共焦点顕微鏡を用いて植物カルス中の胚を可視化・診断する手法が研究の中心であり、植物の発生状態を取得する画像ベースのフェノタイピング手法に該当する。
abstractThe capability of the prepared dye for visualization has been tested on flax, red clover and alfalfa to determinate the embryo in plant callus tissue cultures.
In this paper, we propose a method of automated data processing allowing to detect weeds and assess crop sprouts quality and quantity based on RGB images obtained by unmanned aerial vehicles (UAVs). The process consists of four main stages: 1) vegetation map generation with the use of modified Triangular Greenness Index (TGI); the index is defined as the area of a triangle formed by 3 points on a spectral curve with wavelengths of 480, 550 and 670 nm and estimates leaf chlorophyll content based on RGB images; 2) determination of the position of crop rows and spaces between rows based on the vegetation map; 3) detection of weeds and generation of an appropriate weed map; 4) division of crop rows into non-intersecting fragments and calculating vegetation density in each (the ratio of vegetation area to the total fragment area). By changing the empirically defined parameters of map thresholds of fragment density, one can obtain a map that describes quality of crop sprouts. Unlike existing methods, the proposed scheme does not require presence of infrared data and can be applied to usual RGB images with the use of wide-spread types of UAVs. The method was tested on RGB images of flax and sunflower sprouts collected with SONY ILCE6000 camera in June, 2017 in Altai Territory. The images were taken at the height of 150 m, spatial resolution was 1.5 cm/pixel. The size of each image was 6000x4000 pixels. Test results confirmed high efficiency of the proposed method.
Why it matches plant phenotyping methodsRGB画像から作物の発芽個体群の品質・量を評価する画像解析手法を提案・検証しており、植物状態の抽出が中心的です。雑草検出も含みますが、作物の植生密度による品質評価が明示されています。
abstractwe propose a method of automated data processing allowing to detect weeds and assess crop sprouts quality and quantity based on RGB images obtained by unmanned aerial vehicles (UAVs).
The mucilage is a model to study the polysaccharide biosynthesis since it is produced in large amounts and composed of complex polymers. In addition, it is of great economic interest for its technical and nutritional value. A fast method for phenotyping the released mucilage and the seed morphometric parameters will be useful for fundamental, food, pharmaceutical and breeding researches. Current strategies to phenotype soluble mucilage are restricted to visual evaluations or are highly time-consuming. Here, we developed a high-throughput phenotyping method for the simultaneous measurement of the soluble mucilage content released on a gel and the seed morphometric parameters. Within this context, we combined a biochemical assay and an open-source computer-aided image analysis tool, MuSeeQ. The biochemical assay consists in sowing seeds on an agarose medium containing the dye toluidine blue O, which specifically stains the mucilage once it is released on the gel. The second part of MuSeeQ is a macro developed in ImageJ allowing to quickly extract and analyse 11 morphometric data of seeds and their respective released mucilages. As an example, MuSeeQ was applied on a flax recombinant inbred lines population (previously screened for fatty acids content.) and revealed significant correlations between the soluble mucilage shape and the concentration of some fatty acids, e.g. C16:0 and C18:2. Other fatty acids were also found to correlate with the seed shape parameters, e.g. C18:0 and C18:2. MuSeeQ was then showed to be used for the analysis of other myxospermous species, including Arabidopsis thaliana and Camelina sativa. MuSeeQ is a low-cost and user-friendly method which may be used by breeders and researchers for phenotyping simultaneously seeds of specific cultivars, natural variants or mutants and their respective soluble mucilage area released on a gel. The script of MuSeeQ and video tutorials are freely available at http://MuSeeQ.free.fr .
Why it matches plant phenotyping methods種子形態と放出粘液を画像から同時測定する高スループット手法およびImageJツールを開発・適用しており、植物表現型取得が研究の中心である。
abstractHere, we developed a high-throughput phenotyping method for the simultaneous measurement of the soluble mucilage content released on a gel and the seed morphometric parameters.
Reproduction assets foundThe paper's MuSeeQ ImageJ macro (the authors' phenotyping analysis code) is explicitly stated to be freely available, with video tutorials, at the authors' dedicated public website http://MuSeeQ.free.fr, which appears in the allowed URLs.Code · publicThe script of MuSeeQ and video tutorials are freely available at http://MuSeeQ.free.fr .Open asset ↗MuSeeQ.free.frlines:1-73Plant phenotyping relevance match · UnverifiedEurope PMC · checked 15 Sept 2026
This paper describes a method for optical projection tomography for the 3D in situ characterization of micrometric plant fibers. The proposed approach is based on digital holographic microscopy, the holographic capability being convenient to compensate for the runout of the fiber during rotations. The setup requires a telecentric alignment to prevent from the changes in the optical magnification, and calibration results show the very good experimental adjustment. Amplitude images are obtained from the set of recorded and digitally processed holograms. Refocusing of blurred images and correction of both runout and jitter are carried out to get appropriate amplitude images. The 3D data related to the plant fiber are computed from the set of images using a dedicated numerical processing. Experimental results exhibit the internal and external shapes of the plant fiber. These experimental results constitute the first attempt to obtain 3D data of flax fiber, about 12 μm×17 μm in apparent diameter, with a full-field optical tomography approach using light in the visible range.
Why it matches plant phenotyping methods植物繊維の内部・外部形状を3D計測する光学トモグラフィー手法の開発と校正・実証が中心であり、植物器官の形態表現型を取得する方法論研究に該当する。
abstractThis paper describes a method for optical projection tomography for the 3D in situ characterization of micrometric plant fibers.