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:594-657Code / 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:1-63Code / 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:47-144Code / dataset availability confirmedOpenAlex · Europe PMC · checked 10 Sept 2026
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:1-73