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
97
currently including SAM (Kirillov et al., 2023) and mobile SAM (Zhang et al., 2023),
98
which are executed locally through ONNX Runtime for efficient inference without
99
internet connectivity. Source code, documentation, example datasets, and a user manual
100
are publicly available on GitHub (https://github.com/jingwanglab/BioIMA).101
preprint (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission.
The copyright holder for this
this version posted September 3, 2026.
;
https://doi.org/10.64898/2026.08.30.747465
doi:
bioRxiv preprintOpen asset ↗jingwanglab/BioIMApdf-raw-page:4 lines:1-60Code / dataset availability confirmedEurope PMC · bioRxiv · checked 5 Sept 2026
ABSTRACT Leaf shape is a fundamental trait of plant ecological strategies, influencing biotic interactions and ecosystem functioning. However, established quantitative metrics fail to capture subtle variations and irregularities, require user-based reference points or are challenging to compare among taxa with broadly different leaf shapes. In addition, established metrics typically conflate (aggregate) leaf edge complexity and macro-shape complexity, despite their independent functional significance and genetic foundations. Here, we introduce an entropy-based framework to quantify two new complexity metrics: edge complexity and macro-shape complexity. Based on three case studies, we show that these metrics outperform aggregate metrics in predicting Quercus robur chemical traits, provide more intuitive interspecific classifications, and strongly align with human perception. In addition, edge and macro-shape complexity show high complementarity, while aggregate metrics are highly redundant and typically strongly related to leaf area. Emerging as the strongest predictor of leaf chemistry and key visual cue for complexity as perceived by humans, the effects of edge complexity highlight the under-appreciated functional significance of leaf margins. Our framework and the proposed entropy-based complexity metrics thus promise to help unlock the potential of growing digital image archives of leaves, including images from herbaria and fossils, and are technically readily applicable to shapes of algae, bacteria, pollen, and beyond. The accompanying package ShapeComplexity enables the broad application of entropy-based metrics, providing a powerful tool to explore how the shape of organisms and biological structures influences ecological strategies, biotic interactions, and ecosystem functioning while tracking spatial and temporal variation.
Why it matches plant phenotyping methods葉の画像からエッジ複雑性とマクロ形状複雑性を定量化する新規指標とソフトウェアを開発しており、植物形質抽出法が研究の中心である。
abstractHere, we introduce an entropy-based framework to quantify two new complexity metrics: edge complexity and macro-shape complexity.
Reproduction assets foundThe paper's authors publicly release their ShapeComplexity analysis code (Rust) on GitHub, used to compute the paper's leaf edge- and macro-shape complexity metrics. Supplementary data/analysis code are on Dryad, but that URL is not in the allowed list. RMBG is a generic third-party background-removal model, not a phenCode · publicThe complete, open-source Rust-code (The Rust Team, 2025 ) is publicly available on GitHub ( https://github.com/Thornbach/ShapeComplexity ), ensuring transparency and reproducibilityOpen asset ↗Thornbach/ShapeComplexitylines:86-94Code / dataset availability confirmedbioRxiv · checked 5 Sept 2026
Biomolecular condensates that persist through cell division must be reorganized and inherited, yet it remains unclear whether subtle defects before division are associated with later organelle or growth phenotypes. We examined the Chlamydomonas reinhardtii pyrenoid, a liquid-like condensate that concentrates ribulose-1,5-bisphosphate carboxylase/oxygenase (Rubisco), the photosynthetic CO2-fixing enzyme. As part of the algal CO2-concentrating mechanism, the pyrenoid raises CO2 availability around Rubisco. We generated an RBCS1-mGold Rubisco reporter and developed an unsupervised image-analysis pipeline combining a convolutional autoencoder and a one-class support vector machine. Using 4,905 wild-type single-cell images, augmented 22-fold to 107,910 image instances, we defined the range of normal pyrenoid morphology. A combined machine-learning and visual screen of approximately 21,000 insertional mutants yielded 17 pyrenoid integrity mutants (pim1-pim17). Differential reconstruction-error maps highlighted local deviations from the wild-type reference, including phenotypes difficult to classify by eye. Four-dimensional live imaging showed defects in matrix dispersal, partitioning of Rubisco-containing foci, or pyrenoid recondensation in multiple pim strains. Growth assays identified broad defects and phenotypes that became more apparent as CO2 supply decreased. Insertion-site mapping nominated candidate loci, including STT7, which encodes a chloroplast kinase best known for regulating photosynthetic light harvesting. Independent STT7-edited lines lacked detectable STT7 accumulation and showed pyrenoid-region reconstruction-error patterns, supporting an association between impaired STT7 function and altered pyrenoid morphology. These findings show that unsupervised image screening can extend forward genetics to subtle pyrenoid phenotypes accompanied by mitotic remodeling or growth defects.
Why it matches plant phenotyping methods藻類細胞のピレノイド形態を対象に、画像解析と教師なし機械学習パイプラインを開発し、正常範囲の定義・変異体スクリーニング・検出性能の実証を行っており、表現型取得手法が研究の中心である。
abstractdeveloped an unsupervised image-analysis pipeline combining a convolutional autoencoder and a one-class support vector machine
Reproduction assets foundThe paper's custom machine-learning analysis scripts (CAE–OC-SVM pyrenoid screening pipeline) are explicitly stated to be publicly available on the authors' GitHub repository. Other data (microscopy files, anomaly scores) are only available upon request, so they do not qualify as public assets.Code · publicCustom scripts used for the machine-learning analyses are publicly available at https://github.com/Yamano-Lab/2025_Machine_Learning-based_screening .Open asset ↗Yamano-Lab/2025_Machine_Learning-based_screeninglines:103-119Code / dataset availability confirmedEurope PMC · bioRxiv · checked 11 Sept 2026
Solitary bee species that use artificial trap nests are important for agricultural crop production and as indicators of habitat quality. Quantifying cavity-nesting solitary bee foraging and nesting behavior is essential for real-time analysis of population numbers and pollination activity, as well as understanding how environmental conditions shape reproductive success and population dynamics. However, manual observation is labor-intensive, prone to observer bias, and unable to deliver continuous data. Existing automated systems either require individual bee marking or detect presence without resolving nest-tube-level entry and exit events. We developed BeeMonitor, an integrated hardware and computer-vision pipeline that detects nest entry and exit events in cavity-nesting solitary bees from continuous video, using Osmia cornifrons (the horn-faced mason bee) as a model system. A low-cost Raspberry Pi handles solar-powered field recording, while the software combines object detection (YOLOv26), a custom multiple-object tracker (BeeTrack), and a Random Forest classifier trained on trajectory-derived features to distinguish genuine events from incidental detections. Over a 29-day deployment, hardware reliability averaged 97.5% recording coverage. The pipeline achieved 91.3% precision and 87.3% recall (F1 = 0.893), generalizing robustly under leave-one-video-out cross-validation (mean F1 = 0.904). Detected foraging trips correlated strongly with brood cell counts (R2 = 0.849, p < 0.001, n = 19), and a Random Forest model (AUC = 0.820) identified solar radiation as the dominant driver of foraging activity, followed by temperature. BeeMonitor demonstrates that automated computer vision can reliably extract ecologically relevant behavioral data from continuous video, enabling real-time analysis of pollinator behavior and abundance at a temporal and spatial resolution unattainable through manual observation. Its modular design supports adaptation to other species and monitoring contexts.
Why it matches plant phenotyping methods植物ではなく昆虫を対象とするが、映像から採餌・営巣行動を抽出する技術開発として中心的であり、指定スコープの植物表現型ではないため除外。
abstractWe developed BeeMonitor, an integrated hardware and computer-vision pipeline that detects nest entry and exit events in cavity-nesting solitary bees from continuous video
Reproduction assets foundThe paper explicitly states that source code, 3D STL files, and validation datasets/code are publicly available on the authors' GitHub repository and ScholarSphere. These directly support reproducing the paper's behavioral-event detection pipeline and its evaluation (annotated videos, classifier training/LOVO cross-vaCode · publicSource code for
software and 3D stl files can be found on the official GitHub repository here
https://github.com/Team-Insect-Net/BeeMonitor.Open asset ↗Team-Insect-Net/BeeMonitorpdf-page:2 lines:1-57Dataset · publicValidation datasets and code are available on
Scholars Sphere here
https://scholarsphere.psu.edu/resources/55f1f34b-959f-4c60-8dd3-9b33fb09357f.Open asset ↗Scholars Sphere · 55f1f34b-959f-4c60-8dd3-9b33fb09357fpdf-page:2 lines:1-57Code / dataset availability confirmedEurope PMC · bioRxiv · checked 6 Sept 2026
0. Morphological traits such as floral area and body size are fundamental to ecological research, serving as inputs for studies of pollinator–plant interactions, habitat quality, and biodiversity monitoring. However, accurately measuring these traits from images remains challenging, particularly in complex field conditions where existing tools exhibit reduced accuracy and limited generalizability across taxa. We present EcoMorph, a modular morphological measurement system that leverages the Segment Anything Model 3 (SAM3) to quantify traits across diverse ecological contexts. Unlike task-specific segmentation models requiring domain-specific training data, SAM3’s prompt-based architecture enables segmentation of arbitrary biological structures from natural-language prompts, using the same underlying model across flowers, insects, and other targets without retraining. From the resulting segmentations, EcoMorph extracts three classes of measurement: area, linear dimensions, and object counts. We validated EcoMorph across two ecological scales. At the intermediate scale, EcoMorph-derived floral area agreed closely with manual ImageJ measurements (R 2 = 0.935, n = 74) under simple-background conditions and (R 2 = 0.928, n = 58) under complex-background conditions, with valid predictions for 95% of images. At the fine scale, EcoMorph-derived insect body area was strongly correlated with hand-measured intertegular distance (r = 0.810, n = 349), capturing body-size variation across species from the small Bombus impatiens to the large Xylocopa virginica . Object counts matched manual counts almost exactly for well-separated insects in an insect box (R 2 = 0.9997, n = 12). By combining prompt-based segmentation with modular measurement, EcoMorph enables high-throughput quantification of area, size, and abundance from heterogeneous image sources without taxon-specific training. This generality supports a broad range of ecological applications, including pollinator and plant trait research, biodiversity and abundance monitoring, and allometric biomass estimation.
Why it matches plant phenotyping methods画像から花の面積など植物形態形質を抽出する汎用システムを開発し、手動測定との一致で検証しており、植物フェノタイピング手法が中心である。
abstractWe present EcoMorph, a modular morphological measurement system that leverages the Segment Anything Model 3 (SAM3) to quantify traits across diverse ecological contexts.
Reproduction assets foundThe paper's Data and code availability statement provides a public Zenodo deposit containing the validation datasets and code used for the EcoMorph phenotyping measurements (floral area, insect morphometrics, counts), plus a public web deployment of the EcoMorph software itself.Code · publicValidation datasets and code are available here on Zenodo
https://zenodo.org/records/20980236.Open asset ↗Zenodo · 20980236pdf-page:2 lines:1-54Code / dataset availability confirmedEurope PMC · bioRxiv · checked 15 Sept 2026
Accurate segmentation of plant disease symptoms is essential for crop monitoring and phenotyping, yet it typically requires costly pixel-level annotations. Weakly supervised semantic segmentation (WSSS) alleviates this burden using image-level labels, but its performance depends on the quality of spatial priors such as class activation maps (CAMs). We investigate whether text-guided segmentation with the Segment Anything Model 3 (SAM3) can serve as an alternative weak supervision signal. Three pseudo-mask generation strategies are compared: (i) CAMs refined with SAM or SAM3, (ii) zero-shot text-guided SAM3, and (iii) a hybrid approach combining weak spatial cues with text prompts. The resulting pseudo-masks are used to train a DeepLabV3 model. Text guidance alone matches or outperforms conventional WSSS, achieving up to 0.46 IoU without spatial supervision and 0.61 IoU on a public dataset, although performance is sensitive to text prompt formulation. The hybrid strategy improves robustness, reaching 0.50 IoU on the primary dataset and 0.58 IoU on the additional dataset while reducing prompt sensitivity. Overall, text guidance is a promising alternative to conventional weak supervision, while hybrid approaches provide a more robust solution for plant disease segmentation.
Why it matches plant phenotyping methods植物病害症状の画像セグメンテーション手法を開発・比較し、病斑の疑似マスク生成とセグメンテーション性能を評価しているため、植物フェノタイピング手法が中心です。
abstractAccurate segmentation of plant disease symptoms is essential for crop monitoring and phenotyping
Reproduction assets foundThe paper's own oilseed rape leaf disease image dataset (2,540 RGB images, 601 pixel-level annotations) is explicitly made publicly available on Recherche Data Gouv under DOI 10.57745/R3HZOQ, which is an allowed URL. A companion dataset describing the foliar pathogens (DOI 10.57745/UCEKI8) is also public. The pixel-annDataset · publicAn original dataset of 2,540 RGB images of oilseed rape leaves affected by
7 fungal and bacterial diseases (Table 1) was built to develop and evaluate
the proposed pipelines, and is made publicly available [25].Open asset ↗pdf-raw-page:5 lines:1-43Code / dataset availability confirmedEurope PMC · OpenAlex · bioRxiv · Crossref · checked 15 Sept 2026
Background Cell geometry plays a central role in determining division orientation and body axis formation during early embryogenesis in Arabidopsis thaliana . However, quantitative analysis of dynamic three-dimensional (3D) morphology remains challenging because live-imaging studies often rely on two-dimensional (2D) projections, while existing 3D reconstruction approaches, including mesh-based methods, often lose the original orientation information relative to the ovule and require labor-intensive mesh correction. In addition, embryo positional fluctuation caused by floating in liquid medium and continuous growth makes it difficult to analyze temporal morphological changes within a common coordinate system. Results We developed a robust framework for quantitative 3D and four-dimensional (4D; 3D + time) analysis of embryo initial cell (apical cell) morphology. The method first establishes a standardized 3D coordinate system by normalizing cell orientation based on the bottom plane and the optical axis of the observation. Cell morphology is then reconstructed through ellipse-based approximation of serial cross-sections extracted from stacked imaging data, enabling accurate geometric characterization without the need for complex surface mesh reconstruction. To evaluate shape anisotropy, we quantified the apical cell shape in 3D. The framework further supports the characterization of volumetric features of subsequent division, providing a basis for quantifying 3D embryogenesis. Conclusion Our framework provides a simple and noise-reduced approach for quantitative analysis of living cell morphology in 3D. We named the integrated method of combining coordinate normalization with elliptical cross-section-based reconstruction Apical3DTip. This method enables consistent comparison of cell shapes without extensive manual corrections. The method overcomes key limitations of 2D projection-based and mesh-dependent analyses and offers a practical platform for quantifying cell shape and daughter cell shapes in 3D. More broadly, it provides a quantitative foundation for exploring the relationship between cell geometry, morphodynamics, and developmental patterning in living plant embryos.
Why it matches plant phenotyping methods植物胚の細胞形態を3D・4D画像から定量化する再構成手法を開発しており、表現型取得・抽出法が研究の中心である。
abstractWe developed a robust framework for quantitative 3D and four-dimensional (4D; 3D + time) analysis of embryo initial cell (apical cell) morphology.
Reproduction assets foundThe paper's Methods availability statement explicitly deposits the Apical3DTip analysis code and associated datasets on two public GitHub repositories (main implementation and ImageJ plugin). These are paper-specific author assets for the 3D/4D apical cell reconstruction and phenotyping analysis. No separate phenotype/Code · publicctor of the fitted vertical plane:
R ! .
Because the fitted plane passes through the centroid s, the offset e was calculated as
N
MQ.
Then, the fitted plane was represented as
O G N O
MQ 0.
Availability of data and materials
The code for Apical3DTip, along with all associated datasets, is available on Github:
https://github.com/blues0910/Apical3DTip.
Apical3DTip is also available as an ImageJ plugin:
https://github.com/YusukeKimata-Moo/Apical3DTip.
Ethics approval and consent to participate
Not applicable.
Consent for publication
Not applicable.
Competing interests
The authors declare no competing interests.
Funding
This work was supported by a Japan Society for the PromotOpen asset ↗https://github.com/blues0910/Apical3DTippdf-layout-page:12 lines:1-49Code · publictroid s, the offset e was calculated as
N
MQ.
Then, the fitted plane was represented as
O G N O
MQ 0.
Availability of data and materials
The code for Apical3DTip, along with all associated datasets, is available on Github:
https://github.com/blues0910/Apical3DTip.
Apical3DTip is also available as an ImageJ plugin:
https://github.com/YusukeKimata-Moo/Apical3DTip.
Ethics approval and consent to participate
Not applicable.
Consent for publication
Not applicable.
Competing interests
The authors declare no competing interests.
Funding
This work was supported by a Japan Society for the Promotion of Science (JSPS) KAKENHI
Grant (No. JP22K15135 to H.M., JP25H01809 to Y.K., JP26K02023 tOpen asset ↗https://github.com/YusukeKimata-Moo/Apical3DTippdf-layout-page:12 lines:1-49Code / dataset availability confirmedEurope PMC · bioRxiv · checked 14 Sept 2026
Hyperspectral imaging is an imaging technique that allows for acquisition of high-resolution spectral information beyond that of the visible spectrum. When applied to plants, it effectively enables non-invasive characterization of physiological status and has been widely used in agricultural settings. Marchantia is a model bryophyte species whose flat morphology and visually distinct stress-response phenotypes makes it an ideal candidate for imaging studies. Here, we provide a comprehensive protocol for hyperspectral imaging for Marchantia plants, which encompasses hardware configuration, data acquisition, and computations processing. This protocol features a streamlined data processing pipeline hosted on a web-based development platform that automates 1) the segmentation of plant area into spatially distinct regions for localized analysis of intra-specimen physiological gradients, and 2) classification of plant pixels based on their spectral signatures. All results are exported as structured CSV files for ease of further analysis as desired by the user.
Why it matches plant phenotyping methodsマーチャンティアを対象としたハイパースペクトル撮像プロトコルと、植物領域のセグメンテーション・スペクトル分類を含む処理パイプラインを開発しており、植物の生理状態取得が中心的な方法論的貢献である。
abstractHere, we provide a comprehensive protocol for hyperspectral imaging for Marchantia plants, which encompasses hardware configuration, data acquisition, and computations processing.
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。Dataset · publicExample images used in this protocol have previously been published by Krishnamoorthi et al. (2024) 4 and can be downloaded from https://github.com/dr-daisuke-urano/PlantHyperspectralSVDOpen asset ↗PlantHyperspectralSVDlines:47-85Code / dataset availability confirmedEurope PMC · bioRxiv · Crossref · checked 15 Sept 2026
Plant disease detection using deep learning is essential for precision agriculture, enabling early and automated crop health monitoring. This study proposes an end-to-end transfer learning pipeline, LeafyVGG-16, for multi-class classification of plant diseases and nutrient deficiencies using a tomato leaf dataset. The framework integrates data preprocessing, augmentation, and a VGG-16 backbone with a two-stage fine-tuning strategy. The proposed model is evaluated against CNN, DenseNet-121, Inception-V3, EfficientNetB0, and ResNet-50, achieving an accuracy of 0.93 with precision, recall, and F1-scores of 0.93, 0.90, and 0.92, respectively. These results demonstrate the effectiveness of transfer learning for fine-grained plant disease recognition. We further evaluate model robustness under adversarial cyber attacks to assess deployment reliability in agricultural systems. Under Fast Gradient Sign Method (FGSM) attacks ( ϵ = 0.01– 0.05), the model shows an accuracy drop of 1%–7.5%, while Projected Gradient Descent (PGD) attacks ( ϵ = 0.05, step size = 0.005, 10 iterations) produce similar degradation, highlighting the model’s vulnerability to adversarial perturbations. These findings highlight potential security and reliability risks in AI-based agricultural decision-making systems. Future work will focus on improving robustness and cyber-resilience and extending this framework to other crops for secure and context-aware deployment in resource-constrained environments.
Why it matches plant phenotyping methods植物葉の画像から病害・栄養欠乏状態を分類する深層学習パイプラインが研究の中心であり、複数モデルとの比較評価と敵対的攻撃下での頑健性検証も実施しているため。
abstractThis study proposes an end-to-end transfer learning pipeline, LeafyVGG-16, for multi-class classification of plant diseases and nutrient deficiencies using a tomato leaf dataset.
Reproduction assets foundThe paper's plant-phenotyping input is the publicly available Tomato-Village Variant-a dataset (4,525 tomato leaf images across 8 disease/deficiency classes), which the authors explicitly cite with a public Kaggle URL. No author analysis code, trained model checkpoints, or supplementary data deposits are mentioned. TheDataset · publicThis study uses the publicly available Tomato-Village
dataset [18], which is designed for real-world tomato disease
detection in agricultural environments.Open asset ↗pdf-raw-page:3 lines:1-59Code / dataset availability confirmedEurope PMC · bioRxiv · checked 15 Sept 2026
Accurate and reproducible assessment of foliar disease severity is essential for evaluating the performance of heterogeneous plant communities and understanding host-pathogen interactions. However, traditional visual scoring methods remain subjective, with limited precision, and difficult to scale in large phenotyping experiments. Here, we present a semi-automated image analysis workflow designed to quantify multiple foliar disease symptoms simultaneously on wheat flag leaves sampled from varietal mixtures. The workflow combines three methodological components: (i) a standardized protocol for leaf sampling and imaging, (ii) supervised machine learning segmentation using Random Forest implemented in Ilastik to classify multiple symptoms (powdery mildew and yellow rust), and (iii) a graphical user interface facilitating pipeline deployment by non-specialist operators. To evaluate the influence of image representation on classification performance, four color spaces (RGB, HSV, HLS, LAB) were systematically compared. The approach was validated using images of durum wheat flag leaves collected from a field experiment assessing eight-way varietal mixtures under natural fungal pressure. Cross-validation against manually annotated images demonstrated high segmentation accuracy across all symptom. Comparison among color spaces revealed only minor differences in performance. Overall, this workflow offers a cost-effective, annotation-efficient and reproducible alternative to deep learning approaches, leveraging open-source and actively maintained tools while requiring limited training data and enabling objective, reproducible and scalable disease phenotyping.
Why it matches plant phenotyping methods葉の病害症状を画像解析で定量化するワークフローを開発し、色空間比較と手動アノテーションによる検証を行っており、植物表現型取得法が中心である。
abstractwe present a semi-automated image analysis workflow designed to quantify multiple foliar disease symptoms simultaneously
Reproduction assets foundThe paper's authors explicitly state that all code implementing the leaf disease quantification workflow (SegLeaf, including the graphical interface and documentation) is hosted in a public GitHub repository. No separate public phenotype dataset or trained model checkpoint is described in the supplied blocks.Code · publicted by the Agence Nationale de
la Recherche (ANR) (project SCOOP, grant no. ANR-19-CE32-0011; and project MOBIDIV,
grant no. ANR-20-PCPA-0006).
Code and Data Availability
The method and associated scripts developed in this work are freely available to the re-
search community. All code is hosted in a public GitHub repository at https://github.com/titouanlegourrierec/SegLeaf, which includes the full implementation of the method includ-
ing the graphical interface and documentation to guide users through the analysis pipeline.
15
.
CC-BY 4.0 International license
made available under a
(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display tOpen asset ↗titouanlegourrierec/SegLeafpdf-raw-page:15 lines:1-39Code / dataset availability confirmedEurope PMC · bioRxiv · Crossref · checked 5 Sept 2026
Premise: Herbarium specimens are increasingly used to extract morphological traits for ecological and evolutionary studies, yet the effects of tissue desiccation on trait measurements remain poorly understood. Here, we tested whether higher tissue water content leads to greater measurement changes after herborization (H1) and whether fresh trait values can be reliably predicted from herbarium measurements (H2). Methods: We evaluated the reliability of herbarium-based measurements by comparing fresh and dried traits of leaves, flowers, fleshy fruits, and seeds across 262 individuals representing 133 Neotropical Myrtaceae species. Phylogenetic least square models and machine-learning regressions were used to test H1 and H2. Results: Leaves and flowers generally shrank after herborization, fruits size metrics tended to increase, and seeds were largely unaffected. Water content was significantly associated with the magnitude of herborization effects in flowers and some leaf and seed traits. Fresh trait values were accurately predicted from herbarium measurements. Prediction errors were lowest for leaf traits, followed by fruits, flowers, and seeds. Discussion: These results partially support H1 and support H2, indicating that herbarium specimens can be reliably used for trait analyses when organ-specific responses are considered, providing a practical framework to account for potential desiccation bias in functional trait research.
Why it matches plant phenotyping methodsハーバリウム標本による植物形態形質測定の信頼性評価と、生鮮形質の予測手法が研究の中心であり、植物フェノタイピング手法の検証に該当する。
abstractWe evaluated the reliability of herbarium-based measurements by comparing fresh and dried traits of leaves, flowers, fleshy fruits, and seeds across 262 individuals representing 133 Neotropical Myrtaceae species.
Reproduction assets foundThe authors explicitly state that the code used for the PGLS and machine-learning analyses is publicly available in a GitHub repository; raw phenotype data is promised only upon acceptance, so the code asset qualifies while the dataset is not yet actionable.Code · publicSupporting Information and the code used to perform the analyses are available at
https://github.com/ykilsztajn/fresh_dry_myrtaceae. All raw data will be made available in the
same repository upon acceptance for publication.Open asset ↗ykilsztajn/fresh_dry_myrtaceaepdf-page:9 lines:1-48Code / dataset availability confirmedEurope PMC · bioRxiv · checked 5 Sept 2026
Grapevine Trunk diseases (GTDs) represent a major threat for the wine industry. Despite several break-through, their etiology remains unclear and no curative treatment is currently available. Wood anatomy and water transport contribute to the symptoms of young plant decline. This study investigates wood anatomical alterations in two Alsatian grapevine cultivars presenting different susceptibility to GTDs, focusing on wood structure over six months of vegetative growth and in response to infection. Using a validated FasGa staining protocol, wood sections from transverse, tangential, and radial directions were stained to differentiate lignified and cellulosic tissues. Microscopic analysis was performed at x4, x10, and x40 magnifications, yielding a dataset of 4771 images. To support this high-throughput quantitative analysis of microscopy images, a computational model was developed, enabling reliable and efficient assessment of anatomical traits. Pre-established woody tissues presented higher xylem vessels diameter in Gewurztraminer than Riesling, with a dorsoventral arrangement whereas the number of vessels remained the same all over the cross section. No significant anatomical changes were observed in established woody tissues, whereas newly formed xylem anatomy showed a possible rearrangement during infection, especially in Gewurztraminer cultivar. Furthermore, colorimetric analysis quantified the lignification of woody tissues in response to wounding damage compared to un-treated plants. While definitive conclusions remain limited due to the experimental timeframe and sample variability, the findings highlight the need for longer-term studies and broader cultivar evaluation. Code and microscopy images have been made publicly available, providing a scalable digital tool for future research in plant vascular systems.
Why it matches plant phenotyping methods植物組織画像から木部解剖形質と木化を定量する計算モデルを開発・検証し、大規模画像データセットと公開コードを提供しており、表現型取得手法が研究の中心である。
abstractTo support this high-throughput quantitative analysis of microscopy images, a computational model was developed, enabling reliable and efficient assessment of anatomical traits.
Reproduction assets foundThe paper's microscopy image dataset (4771 grapevine wood images) is publicly deposited on Zenodo with an explicit DOI matching an allowed URL. The authors also state their Python analysis pipeline is available at github.com/courbot/vineside, but that URL is not among the allowed URLs, so only the Zenodo image dataset,Dataset · publicThis database can benefit the research community, and is publicly available
online at https://doi.org/10.5281/zenodo.18850060 [35].Open asset ↗Zenodo · 10.5281/zenodo.18850060pdf-page:4 lines:1-56Code / dataset availability confirmedEurope PMC · bioRxiv · Crossref · checked 5 Sept 2026
A central problem in soft and biological physics is how molecular-scale activity and remodelling coarse-grain into emergent mechanical laws at larger scales. In growing cell walls (polymeric composite materials that surround 90% of living organisms’ cells) irreversible deformation is not controlled by elastic stress alone. Instead, growth depends on the interplay between energy storage, dissipation, and the local timing of viscoelastic relaxation. Although dynamic atomic force microscopy (AFM) resolves storage and loss moduli ( E′, E″) of living walls at nanometre resolution, these observables have remained phenomenological and disconnected from constitutive field variables. Here we introduce a physics-based inversion framework that converts AFM measurements of epidermal cells of living Arabidopsis plants into spatially resolved fields of stiffness k , viscosity η , and relaxation time τ . By analysing the spatial gradients of E′ and E″, we uncover organized mechanical heterogeneities governed by cellular confinement and stress focusing. We demonstrate that the local relaxation time is encoded directly in the coupling between storage and dissipation, yielding the pointwise relation τ = (1/ ω ) ∂ E ’/∂ E ’’, where ω is the indentation frequency. This relation enables model-independent extraction of mechanical timescales and establishes a general route from nanoscale non-equilibrium rheology to continuum descriptions of growth in living and active soft materials. Significance How molecular-scale activity gives rise to tissue-scale form is a central challenge in biological physics. Although growth is fundamentally a non-equilibrium mechanical process, experimental measurements at the nanoscale have not been directly connected to the constitutive parameters that govern morphogenesis. We introduce a framework that converts dynamic atomic force microscopy maps of storage and loss moduli into spatially resolved fields of stiffness, viscosity, and relaxation time in living cell walls. By revealing that mechanical relaxation is encoded in the local coupling between elastic storage and viscous dissipation, our work provides a route from nanoscale rheology to growth-relevant mechanical timing. This establishes a quantitative bridge between molecular remodeling and continuum mechanics, enabling direct experimental constraints on multiscale theories of morphogenesis.
Why it matches plant phenotyping methods生きたArabidopsis細胞のAFM測定を物理ベースで反転し、剛性・粘性・緩和時間という植物細胞壁の機械的形質を空間的に抽出する新規フレームワークが研究の中心である。
abstractHere we introduce a physics-based inversion framework that converts AFM measurements of epidermal cells of living Arabidopsis plants into spatially resolved fields of stiffness k , viscosity η , and relaxation time τ .
Reproduction assets foundThe paper's custom AFM viscoelastic analysis code is explicitly deposited and publicly available on GitHub (ForceMetric). The underlying AFM phenotype/measurement data are only available upon request, not publicly.Code · publicAFM data were analysed in Python 3.5 using previously described routines [34] (code
available at https://github.com/jcbs/ForceMetric ).Open asset ↗jcbs/ForceMetricpdf-page:14 lines:1-56Code / dataset availability confirmedEurope PMC · bioRxiv · Crossref · checked 14 Sept 2026
Lettuce ( Lactuca sativa ) is an important field crop, but our understanding of its phenotypic variation and underlying genetics under natural field conditions remains limited, posing challenges for identifying effective crop breeding targets. Longitudinal hyperspectral phenotyping allows for non-invasive monitoring of crop performance under diverse agricultural conditions. In this study, we used hyperspectral imaging to assess the phenotypic variation of almost 200 different field-grown lettuce varieties, following the same plants from just after seedling- to flowering-stage. With automated image processing, we extracted a wide range of spectral phenotypes related to metabolite content, growth efficiency, and environmental stress responses, creating a multi-dimensional time-resolved data set. Principal component analysis (PCA) revealed the major axes of spectral variation over time, and highlighted differences in spectral patterns among lettuce genotypes. Integrating on-site weather data, we modelled G×E interactions of reflectance, revealing regions of the lettuce vegetation spectrum that are primarily shaped by genotype and/or environment. We estimated phenotypic plasticity in response to time, temperature and rainfall using best linear unbiased predictions (BLUPs), capturing genotype-specific developmental trajectories and responses to the environment. We used genome-wide association studies (GWAS) to identify quantitative trait loci (QTLs) of PC-based, single and BLUP-based phenotypes, disentangling the genetic architecture of spectral lettuce phenotypes from major axes of variation down to single wavelength spectral plasticity. These findings provide new insights into the genome-wide genetic regulation and dynamics of spectral phenotypes in field grown lettuce.
Why it matches plant phenotyping methods圃場レタスを対象に、縦断ハイパースペクトル画像と自動画像処理でスペクトル形質を抽出するフェノタイピング手法・データセットが研究の中心である。
abstractLongitudinal hyperspectral phenotyping allows for non-invasive monitoring of crop performance under diverse agricultural conditions.
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 · publicScripts used in this study can be found at https://github.com/SnoekLab/Hyperspec_Mehrem_etal_2025.Open asset ↗SnoekLab/Hyperspec_Mehrem_etal_2025pdf-page:9 lines:1-31Code / dataset availability confirmedEurope PMC · bioRxiv · checked 15 Sept 2026
1 Summary Zinc (Zn) deficiency limits rice productivity and poses a risk to human health, particularly in populations reliant on rice-based diets. Although rice germplasm exhibits wide variation in Zn-deficiency tolerance, the underlying physiological mechanisms remain poorly resolved. Evidence across the literature for Zn-deficiency–induced secretion of 2′-deoxymugineic acid (DMA) is inconsistent. This study clarifies the role of DMA secretion as a Zn-deficiency stress response. We developed and validated a sensitive LC–ESI–Q–TOF–MS method for selective detection of DMA in rice root exudates. Five rice genotypes with contrasting Zn-deficiency tolerance were grown hydroponically and DMA secretion measured. Zn-deficiency increased DMA exudation across all genotypes, with sensitive genotypes also showing higher secretion compared with control, supporting DMA’s role as a general response to Zn stress rather than being restricted to efficient genotypes. Fold-change responses exceeded previous studies, likely due to more severe stress exposure. Our results confirm that DMA secretion is induced under Zn-deficiency in rice as part of the micronutrient stress response. However, the lack of increased Zn uptake indicates that additional tolerance mechanisms are involved. These findings reconcile inconsistencies in the literature and position DMA secretion as an important, but not exclusive, component of Zn-deficiency adaptation in rice.
Why it matches plant phenotyping methodsイネ根滲出液中のDMAを選択的に検出するLC–MS法を開発・検証し、亜鉛欠乏応答という植物生理状態を測定しているため、化学分析が単なる付随測定ではなく中心的な方法貢献である。
abstractWe developed and validated a sensitive LC–ESI–Q–TOF–MS method for selective detection of DMA in rice root exudates.
Reproduction assets foundThe paper's Data availability statement points to a public Zenodo deposit containing the datasets generated and analysed in this study (DMA exudation and Zn uptake measurements in rice).Dataset · publicthe experiments, developed the
525
methods and analysed the results. The experimental data were collected by C.R. assisted by
526
G.L.M., C.T. and D.J.W. Data analysis and writing of paper by all authors.
527
528
Data availability
529
The data sets generated and/or analysed during the current study are available on Zenodo,
530
https://zenodo.org/uploads/18184803
531
532
533
.
CC-BY 4.0 International license
perpetuity. It is made available under a
preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in
The copyright holder for this
this version posted March 18, 2026.
;
https://doi.org/10.64898/2026.03.16.71158Open asset ↗Zenodo · 18184803pdf-raw-page:21 lines:1-47Code / dataset availability confirmedEurope PMC · bioRxiv · Crossref · checked 15 Sept 2026
Quantitative studies of plant growth and environmental responses increasingly rely on time-series imaging, yet automated segmentation remains challenging due to continuous growth, large non-rigid morphological change, and frequent self-occlusion. Traditional image-processing pipelines and task-specific deep learning models often require extensive annotated datasets and retraining, limiting portability across species, developmental stages, and imaging conditions. Here we present SAP (Segment Any Plant), a plant-focused framework that leverages the pretrained Segment Anything Model 2 (SAM2) to enable few-shot, training-free segmentation of plant time-series imagery. SAP integrates interactive prompting, automated temporal mask propagation, and centerline extraction within a web-based interface, allowing users to move from raw images to quantitative descriptors of organ shape and dynamics without programming expertise. Across multiple systems, including Arabidopsis thaliana rosette development, root growth, sunflower gravitropism, and confocal root microscopy, SAP achieves high segmentation accuracy (mean IoU 0.89–0.93) and sub-pixel centerline precision from single-frame prompting. By reducing the need for task-specific retraining, SAP provides a transferable framework for reproducible time-series phenotyping across diverse experimental contexts.
Why it matches plant phenotyping methods植物の時系列画像から器官形状・動態を抽出するセグメンテーション手法とWeb基盤を開発し、複数系で精度検証しているため、植物フェノタイピング手法が中心である。
abstractHere we present SAP (Segment Any Plant), a plant-focused framework that leverages the pretrained Segment Anything Model 2 (SAM2) to enable few-shot, training-free segmentation of plant time-series imagery.
Reproduction assets foundThe paper's authors publicly release both the SAP analysis code (GitHub repository) and the datasets generated/analyzed in the study (Zenodo), including raw images, ground-truth and SAP-generated segmentation masks, centerline validation data, and supplementary videos. Both are paper-specific, public, and directly cit.Dataset · publicCode Availability. The code is available at
https://github.com/merozlab/plant-segmentation-app.Data Availability. The datasets generated and an-
alyzed during this study are available on Zenodo at
https://doi.org/10.5281/zenodo.18732705. This includes
raw images and segmentation masks for the sunflower
gravitropism and Arabidopsis root growth experiments,
SAP-generated masks for the Lee et al. (9) and Strauss
et al. (13) datasets, centerline validation data, and supple-
mentary videos.
Funding. Y.M. acknowledges support from the Israel Sci-
ence Foundation ResOpen asset ↗zenodo · 10.5281/zenodo.18732705pdf-raw-page:9 lines:1-74Code / dataset availability confirmedbioRxiv · Europe PMC · checked 5 Sept 2026
WheatGrowth chamberRootMorphology / geometry measurementRoot system architectureStress response / tolerance
The Multiple Synthetic Derivatives (MSD) population is a unique hexaploid wheat resource that captures extensive genetic diversity from Aegilops tauschii and exhibits wide variation in agronomic traits. However, root system architecture (RSA), a key determinant of resource acquisition and stress adaptation, remains poorly characterized in this population. Here, we established a practical phenotyping framework for RSA analysis and evaluated MSD417 as a representative genotype. A two-dimensional cultivation platform enabling continuous imaging of seedling root growth under controlled conditions was established to quantify RSA traits and their responses to high temperatures. MSD417 was compared with its recurrent parent, Norin 61 (N61). Under controlled conditions, MSD417 displayed greater total root length, root system width, and convex hull area than N61, indicating enhanced early root vigor. This genotype also exhibited a wider seminal root angle, suggesting improved horizontal soil exploration while maintaining root depth. High-temperature treatment reduced overall root growth and minimized genotypic differences, indicating that temperature stress constrains RSA expression. Microscopic observations further revealed a lower height-to-width ratio of coleorhiza tissue of MSD417, suggesting restricted downward expansion. Collectively, this study establishes a practical framework for RSA phenotyping and demonstrates the potential of Aegilops tauschii-derived germplasm to enhance wheat root-related adaptive traits.
Why it matches plant phenotyping methods根系構造を連続画像化して定量する2次元表現型解析プラットフォームを構築し、RSA形質の測定に実質的に適用しているため、方法が中心的である。
abstractHere, we established a practical phenotyping framework for RSA analysis and evaluated MSD417 as a representative genotype.
Reproduction assets foundThe paper deposits its paper-specific root images (N61 and MSD417) and coleorhiza microscopic images in Zenodo with explicit DOIs. The R analysis scripts are only in Supplementary Document S1 with no public URL, so they do not qualify as a public code asset.Dataset · publicThe microscopic images of coleorhiza are deposited under https://doi.org/10.5281/zenodo.18091131.Open asset ↗Zenodo · 10.5281/zenodo.18091131pdf-page:14 lines:1-71Code / dataset availability confirmedEurope PMC · bioRxiv · Crossref · checked 5 Sept 2026
Specialized host-microbe interfaces are central to cellular interactions in plants. Intracellular structures such as haustoria formed by filamentous pathogens mediate nutrient exchange and effector delivery to host cells. Despite their biological importance, the lack of quantitative frameworks has largely confined the study of these interfaces to qualitative observations, limiting our ability to compare infection strategies, cellular responses, and spatial organization across cells and tissues. Here, we present HFinder , a deep learning-based framework for automated detection, segmentation, and quantitative analysis of plant-microbe interfaces in confocal images. Using an object-centric deep learning approach, HFinder enables robust identification of haustoria, microbial hyphae, and host organelles across diverse imaging conditions and pathosystems. We demonstrate that this framework supports quantitative analyses of subcellular processes at host-microbe interfaces, including effector secretion, perturbation of host cellular processes, and immune receptor accumulation at haustoria. HFinder provides a practical and scalable solution for the systematic digitalization of plant infection imaging data and establishes a general framework for quantitative studies of cellular dynamics at host-microbe contact zones.
Why it matches plant phenotyping methods植物と微生物の界面を共焦点画像から自動検出・分割し、ハウストリア等を定量解析する深層学習手法が中心であり、植物感染状態の画像ベース表現型解析に該当する。
abstractwe present HFinder , a deep learning-based framework for automated detection, segmentation, and quantitative analysis of plant-microbe interfaces in confocal images.
Reproduction assets foundThe paper's HFinder pre-trained models (trained phenotyping models/checkpoints) are explicitly deposited on Zenodo with a public DOI matching an allowed URL. The training image dataset is also stated to be publicly available on Zenodo, but no separate authors' URL for it is given in the supplied blocks, so only the preModel / weights · publicFor
convenience, HFinder is distributed with pre-trained models that can be applied directly to confocal image
analysis (available on Zenodo: https://doi.org/10.5281/zenodo.17091805)Open asset ↗Zenodo · 10.5281/zenodo.17091805pdf-page:5 lines:1-47Code / dataset availability confirmedEurope PMC · bioRxiv · checked 15 Sept 2026
The leaf blade and vasculature develop together within a shared morphological space. Despite shared molecular patterning pathways, it is unknown if developmental and evolutionary variation affect these tissues separately or together in a coordinated way. Grapevine leaves have a morphometric history and abundant data measuring the shape of the blade and vasculature together. Using a combination of topological data analysis and deep learning, we perform reciprocal semantic segmentation of leaf blade and vasculature. Each tissue contains sufficient information to predict the other. We hypothesize that this is due to a one-to-one relationship between blade and vein. Using thin plate splines to swap and warp different combinations of blade and vein shapes, we show that a set of leaves with a many-to-one relationship of blade and vein are distinguishable from true leaves. We also swap blade and vein across the developmental series and between species and show that only reversing the developmental series disrupts the relationship between blade and vasculature. We end by discussing the evolutionary and developmental implications that there is a unique, one-to-one mapping between blade and vein that allows each to be predicted from the other. Author summary Leaves are made of two closely connected parts: the flat blade that captures light and the network of veins that transports water, nutrients, and developmental signals. Although these tissues grow together and share common molecular patterning pathways, it has remained unclear whether a particular blade shape is uniquely linked to a specific vein pattern. In this study, we use grapevine leaves as a model system and combine mathematical shape analysis with deep learning to examine this relationship. We show that the shape of the blade alone can accurately predict the vein network, and that the vein network can likewise predict the blade. This finding suggests a near one-to-one relationship between these two tissues. To test this idea, we created artificial leaves in which blade and vein shapes were deliberately mismatched. Although these synthetic leaves appeared realistic at a global level, a neural network was able to distinguish them from real leaves based on subtle differences. We further show that this tight coupling is maintained by the developmental sequence of leaf growth rather than by species identity, revealing a conserved constraint linking leaf form and internal structure.
Why it matches plant phenotyping methods葉身と葉脈の形状を深層学習による相互セグメンテーションと形状解析で抽出・予測する方法が研究の中心であり、植物形態表現型の方法開発に該当する。
abstractUsing a combination of topological data analysis and deep learning, we perform reciprocal semantic segmentation of leaf blade and vasculature.
Reproduction assets foundThe Data Availability Statement explicitly lists three public Zenodo deposits containing data and code to reproduce the paper's analyses: reciprocal U-Net blade/vein prediction, the two-tower CNN 1:1 vein:blade relationship, and interspecies/intraspecies developmental series swaps. All URLs are in allowed_urls and the Code · public393
which the relationship between blade and vasculature is conserved or diversified across the
394
spectacular variety of leaf shapes remains to be seen.
395
Data Availability Statement
396
Data and code to reproduce this work can be found for the following analyses: Reciprocal
397
prediction of vein and blade from the other, https://zenodo.org/records/16920155; Two tower CNN
398
1:1 vein:blade relationship, https://zenodo.org/records/17014105; Interspecies and Intraspecies
399
developmental series swaps, https://zenodo.org/records/17013783
400
Conflict of Interest Statement
401
.
CC-BY-NC-ND 4.0 International license
available under a
(which was not certified by peer review) is the aOpen asset ↗zenodo · 16920155pdf-raw-page:22 lines:1-53Code · publicd across the
394
spectacular variety of leaf shapes remains to be seen.
395
Data Availability Statement
396
Data and code to reproduce this work can be found for the following analyses: Reciprocal
397
prediction of vein and blade from the other, https://zenodo.org/records/16920155; Two tower CNN
398
1:1 vein:blade relationship, https://zenodo.org/records/17014105; Interspecies and Intraspecies
399
developmental series swaps, https://zenodo.org/records/17013783
400
Conflict of Interest Statement
401
.
CC-BY-NC-ND 4.0 International license
available under a
(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuitOpen asset ↗zenodo · 17014105pdf-raw-page:22 lines:1-53Code · publicment
396
Data and code to reproduce this work can be found for the following analyses: Reciprocal
397
prediction of vein and blade from the other, https://zenodo.org/records/16920155; Two tower CNN
398
1:1 vein:blade relationship, https://zenodo.org/records/17014105; Interspecies and Intraspecies
399
developmental series swaps, https://zenodo.org/records/17013783
400
Conflict of Interest Statement
401
.
CC-BY-NC-ND 4.0 International license
available under a
(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made
The copyright holder for this preprint
this version posted January 29, 2026.
;
https:Open asset ↗zenodo · 17013783pdf-raw-page:22 lines:1-53Code / dataset availability confirmedEurope PMC · bioRxiv · checked 14 Sept 2026
Quinoa ( Chenopodium quinoa Willd.) is a genetically diverse Andean crop valued for its nutrition and adaptability to varied agro-climatic conditions with potential for cultivation in European and Mediterranean, particularly on marginal lands. Low temperatures during early sowing can impair germination, while delayed sowing increases the risk of poor maturation due to unfavorable autumn weather. To assess the adaptation of quinoa to low temperature conditions, that reflect cold stress, we evaluated germination and phenotypic variation in 60 accessions from highland and coastal ecotypes across three sowing dates in South-Western Germany: late winter (S1), early spring (S2), and spring (S3). Early sowing under low temperature conditions in S1 delayed seedling-emergence and reduced emergence percentages, yet these plants produced the highest average seed yield per plot (64 g) compared to S2 (46 g) and S3 (35 g). Highland accessions showed earlier seedling-emergence and with higher emergence percentages, while coastal types matured earlier and gave higher yields across sowing dates. A complementary laboratory experiment assessed germination under cold (4.4 °C) and control (18.3 °C) conditions, using both manual scoring and image analysis via a Mask R Convolutional Neural Network, to track seedling growth. This confirmed the beneficial germination performance of highland accessions under low temperature conditions, with strong agreement between manual and automated scoring. Our findings suggest that quinoa demonstrates resilience to cold stress with highland quinoa exhibiting superior germination traits, and early sowing, despite reduced emergence, can lead to higher yields. We conclude that combining favorable traits such as faster maturity and higher yield of coastal ecotypes with superior germination traits of highland accessions is a promising avenue for breeding improved quinoa varieties for cold climatic regions.
Why it matches plant phenotyping methodsMask R-CNNによる発芽・幼植物成長の画像解析を手動評価と比較し、強い一致を検証しており、植物表現型取得法の技術的検証を含む。
abstractA complementary laboratory experiment assessed germination under cold (4.4 °C) and control (18.3 °C) conditions, using both manual scoring and image analysis via a Mask R Convolutional Neural Network, to track seedling growth.
Reproduction assets foundThe paper states that all phenotypic data and R analysis scripts are available as supplementary material (publicly hosted with the bioRxiv preprint), while raw seed germination images are only available upon request. No separate repository or trained model checkpoint is named.Dataset · publicData availability: All phenotypic data and R scripts used for the analysis are available
as supplementary material.Open asset ↗pdf-page:1 lines:1-52Code / dataset availability confirmedEurope PMC · bioRxiv · OpenAlex · checked 15 Sept 2026
Lodging is a major contributor to decreased yield in tef, a staple cereal crop in Ethiopia. Semidwarf varieties have been developed with a goal to increase yield through reduced lodging, but studying lodging susceptibility currently requires a labor-intensive, imprecise, manual scoring method. Here we present workflows for analyzing tef stand height from UAS sensors across time to both predict lodging later in the season with early height and to measure the severity of lodging after a storm event. We compare 3D point clouds generated by photogrammetry from RGB images with those generated from LiDAR to estimate height, demonstrating that they produce similar results, despite differences in cost. Stand height and lodging can both be accurately measured with low-cost UAS, reducing the need for manual measurements and increasing precision and temporal resolution in plant breeding programs. Significance Statement Extreme weather or heavy grain can cause plant stems to bend, a process called lodging. Lodging significantly reduces crop yields globally, particularly in grain crops such as tef ( Eragrostis tef ). Semidwarf crops have previously been reported to be lodging-resistant, increasing crop yields. Here, we used uncrewed aerial systems (UAS) to measure plant growth, height, and lodging in gene edited semidwarf tef lines, and compared the results to ground-truth data. Using a UAS equipped with a red-green-blue (RGB) camera or LiDAR sensor, we measured plant height and lodging, and found that early-season height measurements could predict future lodging potential. The tools used were contributed to the open-source software PlantCV-Geospatial for community use. This work contributes to a broader understanding of genetic resistance to lodging, providing valuable insights for tef crop improvement and reduces the need for labor-intensive manual measurements.
Why it matches plant phenotyping methodsUASのRGB画像・LiDARから3D点群を生成し、植物の草高と倒伏を定量化・検証するワークフローが研究の中心であるため、植物フェノタイピング手法として含める。
abstractHere we present workflows for analyzing tef stand height from UAS sensors across time to both predict lodging later in the season with early height and to measure the severity of lodging after a storm event.
Reproduction assets foundThe paper states that code and data associated with the manuscript (UAS-based tef height/lodging phenotyping analyses) are publicly available in the authors' GitHub repository danforthcenter/teff-manuscript. The PlantCV-Geospatial package and D2S platform are general-purpose tools/platforms rather than paper-specific,.Code · publicInstitute Block Grant to K.M.M. and
470
N.F., the National Science Foundation (grant numbers 2120153 and 2346101 to N.F.),
471
the USDA NIFA AFRI (grant number 2022-67021-36467 to N.F.), and by the Bellwether
472
Foundation.
473
474
Data Availability
475
Code and data associated with this manuscript are available on GitHub
476
(https://github.com/danforthcenter/teff-manuscript).477
478
.
CC-BY 4.0 International license
available under a
(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made
The copyright holder for this preprint
this version posted January 7, 2026.
;
https://doi.org/10.64898/2026.01.0Open asset ↗danforthcenter/teff-manuscriptpdf-raw-page:13 lines:1-76Code / dataset availability confirmedEurope PMC · bioRxiv · checked 15 Sept 2026
FlowerAnnotation / quality controlObject detectionGrowth / development / phenology
ABSTRACT Herbarium specimens represent critical historical records of plant phenology, yet automating annotation of reproductive structures remains challenging given the diversity of floral morphologies, specimen age and quality, and image quality. Here, we present a machine learning pipeline that uses an ensemble modeling approach to detect flowers on herbarium specimens and deliver these data to the phenology research community. After testing multiple strategies for generating training data, we found in-house expert-curated annotations were essential for producing reliable results. Expert validation found relatively strong accuracy for detecting present floral structures, but still had moderately high false negative rates. Applying the ensemble to our filtered final image dataset of 22 million records resulted in 11.1 million records labeled with flowers present. However, only 2.9 million of these contained complete metadata necessary for downstream phenology research, highlighting the need for full label digitization efforts. Still, this dataset represents a large compilation of historical herbarium-derived phenology records available as a resource for the phenology community. We end by demonstrating how integrating these machine-labeled records into Phenobase, a publicly-available phenology database, expands taxonomic and temporal coverage for large-scale phenological analyses, and discuss remaining challenges and next steps.
Why it matches plant phenotyping methods植物標本画像から花の存在を自動検出し、精度検証と大規模な phenology データセット化を行う機械学習手法が研究の中心であるため、植物フェノタイピング手法として適格です。
abstractwe present a machine learning pipeline that uses an ensemble modeling approach to detect flowers on herbarium specimens and deliver these data to the phenology research community.
Reproduction assets foundThe paper's Data Availability Statement explicitly deposits the ensemble models, training/validation/test images, training data and final ensemble output on Zenodo, and the analysis code on GitHub; machine-labeled records are also served via the public Phenobase portal. All are paper-specific, public, and actionable.Dataset · publicors contributed to drafts and gave final
454
approval for publication.
455
456
Data Availability Statement
457
The ensemble data models and a corresponding JSON file with model metadata data are
458
housed on Zenodo (https://doi.org/10.5281/zenodo.17079402). Images used in training,
459
validation, and testing are located here: https://zenodo.org/records/17675089. Code used for
460
this project can be found on github (https://github.com/rafelafrance/phenobase/tree/v1.0.0).461
Training data and final ensemble output can be found on Zenodo
462
(https://doi.org/10.5281/zenodo.17675089).463
464
Supporting Information
465
Additional Supporting Information may be found online in the SupportinOpen asset ↗Zenodo · 17675089pdf-raw-page:19 lines:1-55Dataset · publictps://doi.org/10.5281/zenodo.17079402). Images used in training,
459
validation, and testing are located here: https://zenodo.org/records/17675089. Code used for
460
this project can be found on github (https://github.com/rafelafrance/phenobase/tree/v1.0.0).461
Training data and final ensemble output can be found on Zenodo
462
(https://doi.org/10.5281/zenodo.17675089).463
464
Supporting Information
465
Additional Supporting Information may be found online in the Supporting Information section at
466
the end of the article.
467
Appendix S1. List of difficult-to-annotate genera and families removed from training and
468
downstream data.
469
Appendix S2. Table S1. Validation results for held-oOpen asset ↗Zenodo · 10.5281/zenodo.17675089pdf-raw-page:19 lines:1-55Code · publiclity Statement
457
The ensemble data models and a corresponding JSON file with model metadata data are
458
housed on Zenodo (https://doi.org/10.5281/zenodo.17079402). Images used in training,
459
validation, and testing are located here: https://zenodo.org/records/17675089. Code used for
460
this project can be found on github (https://github.com/rafelafrance/phenobase/tree/v1.0.0).461
Training data and final ensemble output can be found on Zenodo
462
(https://doi.org/10.5281/zenodo.17675089).463
464
Supporting Information
465
Additional Supporting Information may be found online in the Supporting Information section at
466
the end of the article.
467
Appendix S1. List of difficult-to-annotaOpen asset ↗GitHub · rafelafrance/phenobasepdf-raw-page:19 lines:1-55Code / dataset availability confirmedbioRxiv · checked 13 Sept 2026
Above-ground vertical structure is a critical variable for ecosystem monitoring, carbon accounting, and land management. However, the high cost and limited coverage of airborne lidar hinder its widespread application. To address this, we developed NAIP-CHM, a 0.6-meter resolution canopy height and structure model (CHM) covering the contiguous United States, derived from National Agriculture Imagery Program (NAIP) aerial imagery. Unlike forestry-specific models that exclude human-made features, NAIP-CHM characterizes the full vertical structure of the landscape including vegetation, buildings, and infrastructure. We utilized a U-Net convolutional neural network with attention mechanisms and environmental conditioning, training and validating the model with a peer-reviewed, publicly available dataset of 22.8 million co-registered NAIP imagery and lidar-derived CHM pairs, with stratified sampling to ensure robustness in open-canopy ecosystems. The model achieved a pixel-wise root mean square error (RMSE) of 2.28 meters and an r2 of 0.87. Forested sites alone produced an r2 of 0.82 and RMSE of 3.82 meters. We provide the dataset, source code, and cloud-based tools to enable broad application without requiring specialized computational resources.
Why it matches plant phenotyping methods植生を含む景観の樹冠高・構造を航空画像から推定するモデルを開発し、公開データセットで検証している。植物キャノピーの明示的な構造形質推定が中心だが、建造物等も含むため植物以外の構造も対象とする点には留意が必要。
abstractwe developed NAIP-CHM, a 0.6-meter resolution canopy height and structure model (CHM) covering the contiguous United States, derived from National Agriculture Imagery Program (NAIP) aerial imagery.
Reproduction assets foundThe paper's NAIP-CHM canopy height model, its CONUS 0.6 m dataset, trained weights, and full training/inference code are all publicly released with explicit availability statements and author-hosted URLs (Rangeland Analysis Platform server, GitHub, Zenodo, Colab notebook, Earth Engine app).Dataset · publicFor bulk download, COGs and associated index files are available via HTTP from the Rangeland Analysis Platform server ( http://rangeland.ntsg.umt.edu/data/naip-chm/ ).Open asset ↗Rangeland Analysis Platform serverlines:76-83Model / weights · publicThe source code, trained model weights, validation data, and auxiliary datasets required to reproduce the results are permanently archived in a Zenodo repository 31 .Open asset ↗Zenodolines:89-134Code / dataset availability confirmedEurope PMC · bioRxiv · Crossref · checked 14 Sept 2026
Volume electron microscopy (vEM) provides nanometer-scale, three-dimensional imaging of cells, but applying it to plant systems remains challenging. Cell walls, large vacuoles, and tissue thickness complicate sample preparation and cryogenic imaging. Here we report a cryogenic vEM (cryo-vEM) workflow for unstained plant protoplasts that achieves volumetric imaging of whole vitrified sorghum stem protoplasts without chemical fixation, dehydration, resin embedding, or heavy-metal staining. The method integrates optimized protoplast isolation, plunge-freezing vitrification for native-state preservation, automated cryogenic focus ion beam scanning electron microscopy (cryo-FIB-SEM) slice-and-view acquisition, contrast enhancement and stack alignment, and AI-assisted human-in-the-loop 3D segmentation. Using sorghum stem protoplasts as a demonstration, the workflow captured large-volume frozen-hydrated protoplast ultrastructure, allowing visualization of major organelles, including the nucleus, mitochondria, vacuoles, ER/Golgi-like membranes, lipid bodies, and subcellular features consistent with nuclear-envelope pores. We further quantified organelle volumes and surface areas from the segmented 3D data, highlighting the potential for quantitative cellular ultrastructure analysis. This cryo-vEM workflow provides a platform for near-native structural studies of isolated plant protoplasts.
Why it matches plant phenotyping methods植物プロトプラストの三次元画像取得・セグメンテーション・オルガネラ形態量化を中核とする手法開発であり、植物の細胞形態形質を抽出するため。
abstractHere we report a cryogenic vEM (cryo-vEM) workflow for unstained plant protoplasts that achieves volumetric imaging of whole vitrified sorghum stem protoplasts
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 · public369 The codes are freely available at https://github.com/xzhang0123/vEMOpen asset ↗https://github.com/xzhang0123/vEM · xzhang0123/vEMpdf-page:10 lines:1-24Code / dataset availability confirmedEurope PMC · bioRxiv · checked 6 Sept 2026
Citrus farming plays an essential role in agriculture; however, diseases like canker, greening, black spot, and melanose significantly reduce yield and fruit quality. Efficient classification of citrus leaf diseases is important for crop health maintenance and optimal crop yield. Traditional methods for leaf disease detection are slow, labor-intensive, and often inaccurate, which highlights the need for automated solutions. This research presents a novel hybrid approach for identifying citrus diseases by combining a vision transformer with deep learning architectures. Using Bidirectional Encoder Representation from Image Transformers (BEIT) and MobileNetV2 as feature extractors, the proposed model captures distinctive features from images, which are then classified using Support Vector Machine (SVM). The dataset includes four different disease categories and a healthy class. Data augmentation techniques are applied to improve model robustness. The experimental findings demonstrate that CitriBEiTNet achieves a remarkable training accuracy of 99.82% and a testing accuracy of 99.57%, outperforming current leading techniques. This model provides an efficient, scalable, and economical approach for early disease identification, enabling farmers to take preventive measures and improve agricultural yields.
Why it matches plant phenotyping methods柑橘葉画像から病害状態を自動分類する深層学習手法の開発が研究の中心であり、植物の病害表現型を直接推定している。
abstractThis research presents a novel hybrid approach for identifying citrus diseases by combining a vision transformer with deep learning architectures.
Reproduction assets foundThe paper uses a public Kaggle citrus leaf image dataset (1,023 images across black spot, canker, greening, healthy) as its phenotyping input, with an explicit public URL. No author analysis code or trained model checkpoints are reported as publicly available.Dataset · publicThe Kaggle dataset is publicly available at:
https://www.kaggle.com/datasets/sourabh2001/citrus-leaves-dataset/data.Open asset ↗Kaggle · sourabh2001/citrus-leaves-datasetpdf-page:5 lines:1-61Code / dataset availability confirmedEurope PMC · bioRxiv · Crossref · checked 14 Sept 2026
ABSTRACT The development of remote sensing methods to estimate plant functional diversity is limited by mismatches between ecology and remote sensing sampling schemes, and the limited representativeness of local field campaigns. The Biodiversity Observing System Simulation Experiment (BOSSE) provides a modeling framework for benchmarking new methodologies. We used BOSSE to simulate 180 different synthetic “Scenes” encompassing a two-year-long time series of plant trait maps and imagery of hyperspectral reflectance factors, spectral indices, sun-induced chlorophyll fluorescence, land surface temperature, and estimates of plant traits (optical traits). We used these simulations to answer five fundamental, yet unsolved, questions: Q1. How should remote sensing characterize functional diversity in large surfaces (sites)? Diversity metric values saturate with the number of pixels involved, hampering comparisons between plant traits and remote sensing estimates in large areas. The average value of metrics computed over small samples should be used instead. Q2. Which sources of spectral information (or combinations thereof) can best capture plant functional diversity at the site scale? Accounting for background effects is the key. Optical traits (remote sensing estimates of plant traits) are the best estimators for plant functional diversity. Other variables succeed when filtered out of the soil pixels; their combination did not yield additional advantages. Q3. How should remote sensing estimates be validated/compared with plant functional diversity measurements? Leaf area index (LAI) is a better proxy of abundance than the pixel for Q Rao, but not for variance-based partitioning. It is more sensitive to sample size, but also more resistant to suboptimal spatial resolution. Q4. When (in the phenological year) can remote sensing best capture site-scale plant functional diversity? The estimation error decreased with LAI and stabilized at values above 1 m²/m². Q5. Which approaches and remote sensing variables are more resistant to the effects of suboptimal spatial resolution? Optical traits, fluorescence, and reflectance factors were the most robust variables. Still, field data resolution needs to be degraded to match the sensor’s resolution. We found a relative spatial resolution threshold of ∼30 % (where the pixel is around three times larger than the plants). Simulation frameworks like BOSSE enable testing methodologies beyond local contexts and address the current shortage of suitable global datasets, supporting the application and development of methods for assessing plant functional diversity with remote sensing. In the future, BOSSE could contribute to understanding observational results, refining and pre-testing new methodologies, and supporting the development of comparable experimental datasets.
Why it matches plant phenotyping methodsBOSSEを用いてリモートセンシングによる植物形質・機能多様性推定手法をシミュレーションベンチマークし、検証・比較する研究であり、植物フェノタイピング手法が中心である。
abstractThe Biodiversity Observing System Simulation Experiment (BOSSE) provides a modeling framework for benchmarking new methodologies.
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 · publicvariables, we used the “pyGNDiv” package (https://github.com/JavierPachecoLabrador/pyGNDiv-Open asset ↗JavierPachecoLabrador/pyGNDiv- · pyGNDivpdf-page:11 lines:1-60Code / dataset availability confirmedEurope PMC · bioRxiv · checked 6 Sept 2026
RGB / grayscaleLeafAnnotation / quality controlClassificationGrowth / development / phenology
ABSTRACT Plant phenology dictates many aspects of community function and ecosystem dynamics. Yet, global phenology data are still limited, especially in areas lacking monitoring programs. Here we present a new data resource, PhenoVision–Leaf, which extends a computer-vision pipeline utilizing iNaturalist digital image vouchers to produce global-scale leaf phenophase data for deciduous, woody genera. We first discuss our implementation of a new human annotation framework for leaf phenology on iNaturalist, aligning with phenophase definitions used by the larger phenology community. We then showcase the use of 165,988 crowdsourced annotated records to train a Vision Transformer model with a two-stage regime to maximize accuracy across single- and multi-image records. This approach extends Phenovision from scoring individual images to aggregating at the iNaturalist record level, better aligning with human annotation processes. Post-hoc validation showed high performance for detecting present green and colored leaves (>98% accuracy), and reasonable accuracy for breaking leaf buds (>87% accuracy). Applying PhenoVision–Leaf to over 26 million iNaturalist records yielded 5.6 million record-level phenology observations across 6,500 species and 57 families, filling geographic and taxonomic gaps. These data, now accessible through the Phenobase portal, establish a foundation for near real-time monitoring of leaf phenology, supporting global-scale synthesis analyses.
Why it matches plant phenotyping methods葉のフェノロジー状態を画像から推定するコンピュータビジョン手法と、注釈・学習・検証・大規模データ生成基盤が研究の中心であるため。
abstractwe present a new data resource, PhenoVision–Leaf, which extends a computer-vision pipeline utilizing iNaturalist digital image vouchers to produce global-scale leaf phenophase data
Reproduction assets foundThe paper's PhenoVision–Leaf record-level leaf phenology dataset (5.6M machine-labeled observations) is publicly available via the Phenobase portal, and the underlying iNaturalist images used for training and machine labeling are available through the iNaturalist open data repository on AWS. No author analysis code or Dataset · publicAll images associated with these records were
downloaded using iNaturalist’s open data repository on AWS
(https://registry.opendata.aws/inaturalist-open-data/).Open asset ↗pdf-page:4 lines:1-44Code / dataset availability confirmedEurope PMC · bioRxiv · Crossref · checked 15 Sept 2026
Understanding how plant populations respond to environmental variation through functional leaf traits remains challenging due to limitations of traditional phenotyping approaches. Hyperspectral reflectance offers a rapid, non-destructive and high-throughput method to capture functional trait variation and detect signatures of local adaptation across populations. We combined hyperspectral data, inverse modeling, and network analysis to investigate population-level variation in Streptanthus tortuosus. Using a common garden experiment with four geographically distinct populations, we applied partial least square discriminant analysis (PLS-DA) and ridge regression for population discrimination, inverse PROSPECT modeling to estimate leaf biochemical traits, and canonical correlation analysis to examine trait-climate relationships across historical (1900-1994) and recent (1995-2024) periods. We developed a spectral network approach treating wavelength correlations as biologically meaningful trait networks. Populations showed distinct, heritable spectral signatures with high classification accuracy. Significant population differences emerged in anthocyanins, carotenoids, chlorophyll, and water content. Trait-climate correlations shifted between time periods, consistent with historical climate adaptation. Network analysis revealed population-specific integration patterns, with more variable environments displaying greater spectral modularity. Hyperspectral signatures provide a high-throughput tool for detecting population-level adaptation and trait coordination. Our findings provide a framework to investigate how plant populations respond to climate change through evolved shifts in trait networks rather than isolated traits alone.
Why it matches plant phenotyping methodsハイパースペクトル計測と逆モデリングを用いて葉の機能形質を推定し、集団間比較・適応評価を行う手法が研究の中心であるため。
abstractHyperspectral reflectance offers a rapid, non-destructive and high-throughput method to capture functional trait variation
Reproduction assets foundThe paper's Data availability statement explicitly deposits raw hyperspectral data and source code in a public GitHub repository, which is an allowed URL.Code · publicRR, JL; Formal Analysis:
553
RR, JNM, TSM; Funding acquisition: JRG, JNM, TSM; Investigation: RR, JNM, TSM;
554
Visualization: RR; Writing – original draft: RR; Writing – review & editing: RR, BQ-C, JL, SA,
555
JRG, JNM, TSM
556
Data availability
557
Raw data and source code are available in the following Github repository.
558
https://github.com/rishavray/spectral-network
559
References
560
Albert R, Barabási A-L. 2002. Statistical mechanics of complex networks. Reviews of Modern
561
Physics 74: 47–97.
562
Anderson JT, DeMarche ML, Denney DA, Breckheimer I, Santangelo J, Wadgymar SM.
563
2025. Adaptation and gene flow are insufficient to rescue a montane plant under climate change.
564
ScieOpen asset ↗rishavray/spectral-networkpdf-raw-page:23 lines:1-60Code / dataset availability confirmedEurope PMC · bioRxiv · checked 15 Sept 2026
Significance Statement Early studies noting uneven spatial distribution of progeny genotypes after pollination support a hypothesis where differences in pollen tube growth rate can bias inheritance. We used computer vision and statistical analysis to show alleles reducing maize pollen fitness are likely to produce statistically significant increasing, decreasing, or curvilinear spatial patterns from the apex of the inflorescence to the base, suggesting that differential pollen tube growth is not the only mechanism at play. Summary Often, more pollen grains land on recipient flowers than there are ovules to fertilize. Consequently, the haploid male gametophyte engages in post-pollination competition, one way that pollen genotype can influence inheritance. The maize ( Zea mays subsp. mays L.) inflorescence (ear), with its elongated stigma and style structures (silks), has a conspicuous spatial heterogeneity, with longer silks at the base of the ear than those at the apex. To evaluate the hypothesis that alleles with reduced pollen fitness influence the spatial distribution of progeny genotypes along the ear, we developed an updated phenotyping platform that maps mutant Ds-GFP kernel phenotypes on the ear via an implementation of the Faster R-CNN machine vision model (EarVision.v2) and a statistical pipeline that evaluates the relationship between kernel position and transmission ratio (EarScape). In our dataset (1384 ears) representing 58 Ds-GFP alleles, none with Mendelian inheritance (0/48) showed any significant pollen-conditioned spatial trend. In contrast, 50% of alleles with a pollen-specific transmission defect (5/10) exhibited significant spatial effects. An insertion into a gene encoding a putative actin-binding protein, base-to-apex gradient1* ( bag1* ), conditions increased mutant transmission at the ear apex relative to the base. Surprisingly, mutant alleles of two other pollen-expressed genes can generate the opposite pattern, decreased mutant transmission toward the ear apex; and two mutant alleles of the sperm-cell attachment factor, gamete expressed2 ( gex2 ), can produce ears with transmission highest at both base and apex. We conclude that pollen fitness mutants have relatively common but heterogenous effects on the spatial distribution of progeny genotypes.
Why it matches plant phenotyping methodsトウモロコシ穂上のカーネル表現型を画像認識でマッピングし、空間的位置と遺伝子型伝達比を解析するプラットフォームおよび統計パイプラインを開発しており、表現型取得・抽出法が研究の中心である。
abstractwe developed an updated phenotyping platform that maps mutant Ds-GFP kernel phenotypes on the ear via an implementation of the Faster R-CNN machine vision model (EarVision.v2) and a statistical pipeline that evaluates the relationship between kernel position and transmission ratio (EarScape).
Reproduction assets foundThe paper's maize ear phenotyping assets are publicly available: the EarVision.v2 repo contains the training images with bounding-box annotations and the trained Faster R-CNN model, and the EarScannerUtilities repo contains the ear-scanning/projection code. The EarScape spatial-analysis repo (with coordinate .xml filesCode · publica license to display the preprint in perpetuity. It is made
available under a CC-BY 4.0 International license.
540 Varifocal Lens 1080P USB Camera with H.264 High DeYinition Sony IMX323 Webcam. The
541 code for scanning ears, generating projections, and uploading those into cloud storage was
542 also updated and is available at https://github.com/fowler-lab-osu/EarScannerUtilities.
543 The set of ear projections used for the training set included 409 examples from the
544 summer Yield seasons of 2018, 2019 and 2022, encompassing images generated from three
545 different digital cameras and two different versions of the MES. For this training set,
546 projections were manually annotated usingOpen asset ↗fowler-lab-osu/EarScannerUtilitiespdf-layout-page:20 lines:1-56Code / dataset availability confirmedEurope PMC · bioRxiv · checked 15 Sept 2026
Understanding how plants capture light and maintain their energy balance is crucial for predicting how ecosystems respond to environmental changes. By monitoring leaf inclination angle distributions (LIADs), we can gain insights into plant behaviour that directly influences ecosystem functioning. LIADs affect radiative transfer processes and reflectance signals, which are essential components of satellite-based vegetation monitoring. Despite their importance, scalable methods for continuously observing these dynamics across different plant species throughout day-night cycles are limited. We present AngleCam V2, a deep learning model that estimates LIADs from both RGB and near-infrared (NIR) night-vision imagery. We compiled a dataset of over 4,500 images across 200 globally distributed species to facilitate generalization across taxa. Moreover, we developed a method to simulate pseudo-NIR imagery from RGB imagery to enable an efficient training of a deep learning model for tracking LIADs across day and night. The model is based on a vision transformer architecture with mixed-modality training using the RGB and the synthetic NIR images. AngleCam V2 achieved substantial improvements in generalization compared to AngleCam V1 (R 2 = 0.62 vs 0.12 on the same holdout dataset). Phylogenetic analysis across 100 genera revealed no systematic taxonomic bias in prediction errors. Testing against leaf angle dynamics obtained from multitemporal terrestrial laser scanning demonstrated the reliable tracking of diurnal leaf movements (R 2 = 0.61-0.75) and the successful detection of water limitation-induced changes over a 14-day monitoring period. This method enables continuous monitoring of leaf angle dynamics using conventional cameras, enabling applications in ecosystem monitoring networks, plant stress detection, interpreting satellite vegetation signals, and citizen science platforms for global-scale understanding of plant structural responses.
Why it matches plant phenotyping methods葉の傾斜角分布という植物形質を画像から推定する深層学習手法を開発し、大規模データセット、既存モデル比較、レーザースキャンによる検証、水ストレス下での追跡評価まで実施しており、フェノタイピング手法が研究の中心です。
abstractWe present AngleCam V2, a deep learning model that estimates LIADs from both RGB and near-infrared (NIR) night-vision imagery.
Reproduction assets foundThe paper's Data Availability Statement explicitly provides public access to the authors' analysis code (Anonymous GitHub), the phenotyping image/trait dataset (Zenodo), and the pretrained AngleCam V2 model weights (Zenodo). All three are paper-specific, public, and actionable.Code · publicLK and TK conceived the ideas, designed the methodology, and led the analysis. TK, JP, RR, JF, LK,
26
and DL collected the data. LK and TK led the writing of the manuscript. All authors contributed
27
critically to the drafts and gave final approval for publication.
28
Data Availability Statement
29
The code is available here (https://anonymous.4open.science/r/AngleCamV2-2B38). The data
30
is available at (https://doi.org/10.5281/zenodo.17086253). The pretrained model is available
31
at (https://doi.org/10.5281/zenodo.17101166).32
Conflicts of Interest
33
All authors declare that they have no conflicts of interest.
34
2
.
CC-BY 4.0 International license
perpetuity. It is made available underOpen asset ↗anonymous.4open.science/r/AngleCamV2-2B38pdf-raw-page:2 lines:1-30Dataset · publicK, JP, RR, JF, LK,
26
and DL collected the data. LK and TK led the writing of the manuscript. All authors contributed
27
critically to the drafts and gave final approval for publication.
28
Data Availability Statement
29
The code is available here (https://anonymous.4open.science/r/AngleCamV2-2B38). The data
30
is available at (https://doi.org/10.5281/zenodo.17086253). The pretrained model is available
31
at (https://doi.org/10.5281/zenodo.17101166).32
Conflicts of Interest
33
All authors declare that they have no conflicts of interest.
34
2
.
CC-BY 4.0 International license
perpetuity. It is made available under a
preprint (which was not certified by peer review) is the author/funder, whoOpen asset ↗zenodo · 10.5281/zenodo.17086253pdf-raw-page:2 lines:1-30Model / weights · publicanuscript. All authors contributed
27
critically to the drafts and gave final approval for publication.
28
Data Availability Statement
29
The code is available here (https://anonymous.4open.science/r/AngleCamV2-2B38). The data
30
is available at (https://doi.org/10.5281/zenodo.17086253). The pretrained model is available
31
at (https://doi.org/10.5281/zenodo.17101166).32
Conflicts of Interest
33
All authors declare that they have no conflicts of interest.
34
2
.
CC-BY 4.0 International license
perpetuity. It is made available under a
preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in
The copyright holder for tOpen asset ↗zenodo · 10.5281/zenodo.17101166pdf-raw-page:2 lines:1-30Code / dataset availability confirmedEurope PMC · bioRxiv · checked 6 Sept 2026
Plant development and adaptation are highly dependent on cell morphology and growth. High turgor pressure in plants causes stress on the cell wall, followed by cell extension. In tip-growing cells, the localization of vesicles and cytoskeleton components has been well studied. However, there has been a lack of attention to the spatial profile of mechanical properties, specifically the cell wall elasticity. In this study, we introduce a new surface morphology-based method to measure the elasticity of the cell wall in tip-growing cells. Previous work is based on measurements from the wall meridional outline, a technique that cannot track the elastic deformation of the cell wall experimentally. Instead, we developed a way to infer the bulk modulus distribution from the cell surface by triangulating experimental marker points coming from fluorescent labeling. To justify the use of our protocol in tip-growing cells from the moss Physcomitrium patens , we replicated the experimental noise and moss morphology in simulated cells. In practice, we found that a larger triangulation improved robustness against noise, which agreed with our theoretical study. With multiple cell sampling, we determined that 10 cells were sufficient to recover the elasticity distribution with noise, but only when the elastic stretches were high enough. We then created a dimensionless map of inference error to verify a spatial change of P. patens bulk modulus within two folds. This technique will open the field to more comprehensive measurements of cell wall elasticity, providing a key step in understanding tip cell growth and morphogenesis. Author summary Tip-growing cells can be characterized by their fast growth concentrated at the cell’s apex. Their growth and morphogenesis are tightly regulated processes involving cell wall addition and rearrangement while the cell wall is under stress originating from the cell’s internal turgor pressure. We start by studying the cell wall’s elastic properties, one aspect of the cell growth process. We use a method of marker point tracking across the surface of the tip-growing cell to measure the wall’s elasticity profile. In this work, we present a parameter sensitivity study of this method on synthetic cells and report our results on experimental moss tip-growing cells. Our results suggest that this inference method can reliably measure a cell wall elasticity gradient under combined geometric and mechanical conditions that create elastic strains within 5% at the tip.
Why it matches plant phenotyping methodsコケの先端成長細胞における細胞壁弾性分布を、蛍光マーカーと表面形態から推定する新規測定法を開発し、シミュレーションおよび実細胞で検証しているため、植物フェノタイピング手法が中心である。
abstractIn this study, we introduce a new surface morphology-based method to measure the elasticity of the cell wall in tip-growing cells.
Reproduction assets foundThe authors' Data Availability statement explicitly deposits all relevant data and code, including code demonstrations, in a public GitHub repository (rholee-xu/surface-model), which contains the analysis code for the cell wall elasticity inference method.Code · publicAll relevant data and code, including code demonstrations, are available on the GitHub repository found here: https://github.com/rholee-xu/surface-modelOpen asset ↗rholee-xu/surface-modellines:45-63Code / dataset availability confirmedEurope PMC · bioRxiv · OpenAlex · checked 15 Sept 2026
Fruit growth has long been described using single- or double-sigmoid curves; however, these temporal models cannot fully capture the spatial heterogeneity that ultimately shapes a fruit. Here, we present a three-dimensional analysis pipeline that non-destructively tracks spatial fruit growth dynamics from field-collected imaginary. Surface landmarks were drawn, and video recordings were taken throughout development for three pome fruits, apple ( Malus × domestica ), Japanese pear ( Pyrus pyrifolia ) and European pear ( Pyrus communis ), and two drupe fruits, peach ( Prunus persica ) and Japanese apricot ( Prunus mume ), to track their motion. Using 3D Gaussian Splatting, we successfully reconstructed 3D models of the fruits, and the landmark displacement could be measured with high accuracy, with R 2 ≥ 0.98 when compared to manual recordings. We found a common spatial growth gradient in the longitudinal growth shared in the pomes and drupes of the Rosaceae; proximal (stem-end) regions exhibited more pronounced growth than the distal (stylar) end. An exception was found in European pear ‘Bartlett,’ which showed relatively vigorous growth in the distal region, explaining its distinct shape with expanded distal end. Transverse expansion varied far less than longitudinal expansion, with a possible association with initial fruit morphology. Inter-fruit growth variability peaked in the fastest-growing regions, particularly in the distal area of the European pear, highlighting the link between growth vigor and phenotypic variance. These results provide foundational insights into the developmental dynamics of both pome and drupe fruits of the Rosaceae family, contributing to the optimization of fruit size, shape, and uniformity.
Why it matches plant phenotyping methods3D Gaussian Splattingを用いて果実の3D再構成と空間的成長形質の非破壊計測パイプラインを開発し、手動記録との精度比較で検証しているため、フェノタイピング手法が中心である。
abstractHere, we present a three-dimensional analysis pipeline that non-destructively tracks spatial fruit growth dynamics
Reproduction assets foundThe paper's data availability statement deposits a subset of the generated 3DGS fruit reconstruction models (the paper's phenotyping outputs) on Figshare with a public DOI; additional data is request-only. No author analysis code is explicitly deposited.Dataset · publicFootnotes
Appendix A
Supplementary data to this article can be found online at https://doi.org/10.1016/j.plaphe.2026.100166 .
Appendix A.
Supplementary data
The following is the Supplementary data to this article:
Multimedia component 1
Multimedia component 1
Data availability
A subset of the generated 3D models is available at https://doi.org/10.6084/m9.figshare.30854579 , where the quality of the 3DGS reconstructions and the marking/measurement procedure can be examined. Additional data may be provided upon reasonable request to the corresponding author.
ReferencesOpen asset ↗figshare · 10.6084/m9.figshare.30854579lines:151-171Code / dataset availability confirmedbioRxiv · checked 15 Sept 2026
The accessibility of flying drones (Unoccupied Aerial Vehicles) presents scientists and managers with reproducible and cost-effective methods to monitor submerged aquatic vegetation. In particular, drone-borne topobathymetric LiDAR provides high-resolution (cm-scale), three-dimensional information about the geometry and structure of surveyed areas, allowing for quantification of vegetation volume in addition to bathymetry. For habitat-forming submerged and intertidal vegetation like seagrass, this information can advance research regarding the structure and patchiness of canopies in relation to biodiversity, blue carbon storage, and hydrodynamic processes. Here, we report how drone-borne LiDAR can be used to estimate the habitat volume of eelgrass (Zostera marina) within a sheltered bay in south-eastern Norway. After classifying LiDAR points using a Random Forest model, we created a Digital Terrain Model of the sea floor and a Digital Surface Model of the eelgrass canopy. From these models, we estimated eelgrass canopy volume to range between 862 and 1099 m3 across the small study area. From the volume, we estimated above-ground carbon storage in living eelgrass tissue to range between 96 and 122 kg. To our knowledge, this is the first study to utilise drone-borne LiDAR to quantify the volume and carbon-storage potential of a marine habitat-forming species like eelgrass, thereby demonstrating the potential of drone-borne LiDAR as an efficient tool to provide reproducible and high-resolution data for submerged aquatic habitats, including seagrass meadows.
Why it matches plant phenotyping methodsドローン搭載LiDARを用いて eelgrass のキャノピー体積という植物形態形質を推定する方法が研究の中心であり、分類、地形・表面モデル作成、再現可能な高解像度測定手法として記述されているため。
abstractHere, we report how drone-borne LiDAR can be used to estimate the habitat volume of eelgrass (Zostera marina) within a sheltered bay in south-eastern Norway.
Reproduction assets foundThe paper's R analysis code (point cloud cleaning, Random Forest classification, DTM/DSM/canopy height and biomass/carbon computations) is publicly available on the corresponding author's GitHub repository. The underlying LiDAR/field data are only available upon request, so no public data asset qualifies.Code · publicPre-print 15
Code for the present analysis is available at the corresponding author’s GitHub
585
(https://github.com/charles-patrick-lavin/NIVA-SeaBee-LiDAR), while the data
586
analysed are available upon request.
587
Acknowledgements
588
This work was funded by the Research Council of Norway and is a product of SeaBee
589
(Norwegian Infrastructure for drone- based research, mapping and monitoring in the
590
coastal zone, RCN project ID #296478). Additional funding was received frOpen asset ↗charles-patrick-lavin/NIVA-SeaBee-LiDARpdf-raw-page:15 lines:1-32Code / dataset availability confirmedEurope PMC · bioRxiv · checked 14 Sept 2026
Background The plant vacuole arises by orchestrated interplay of membrane trafficking, cytoskeletal rearrangements and a variety of signalling pathways. In the root, the characteristic large central vacuole develops by endomembrane reorganization occurring mainly in the transition zone. The vacuole’s bounding membrane - the tonoplast - can be visualized in vivo using fluorescent protein markers, allowing for quantitative analysis of confocal microscopy images. Tonoplast organization can thus serve as a sensitive indicator of changes to any of the processes involved in vacuole biogenesis. The Vacuolar Morphology Index (VMI) is widely accepted as a quantitative measure of vacuole structure. However, this metric has two drawbacks - it only reflects the size of the largest vacuolar compartment (missing therefore possible differences in the organization of smaller compartments), and its determination is labor intensive, limiting its use on large datasets. Results We developed an alternative metric for describing vacuole organization, named the Tonoplast Topology Index (TTI), which overcomes the above-mentioned shortcomings of the VMI. We compared the performance of our protocol with VMI on a simulated dataset and on real data. To validate the methods’ performance, we used it to confirm the previously reported differences in vacuole shape and size between Arabidopsis thaliana roots grown on the surface of an agar medium compared to those embedded inside the agar. Both VMI and TTI could efficiently detect the relatively subtle changes in vacuole organization depending on the position of the root in the agar, and provided correlated results. However, only TTI produced data with close to normal value distribution, simplifying subsequent statistical evaluation. Conclusions We present the protocol for TTI determination as a two-stage semi-automated procedure involving microscopic image analysis employing an ImageJ macro and subsequent processing of numeric data in the Jupyter Notebook environment, together with benchmarking image data. Since this implementation is freeware-based, platform-independent and (relatively) user-friendly, we hope it will find its use as a high throughput, added value alternative to the VMI metric.
Why it matches plant phenotyping methods植物の液胞構造を定量化する新規指標と半自動画像解析プロトコルを開発し、既存指標との比較・実データおよびシミュレーションによる検証、ベンチマークデータを提示しており、表現型取得・抽出法が中心である。
abstractWe developed an alternative metric for describing vacuole organization, named the Tonoplast Topology Index (TTI), which overcomes the above-mentioned shortcomings of the VMI.
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 software tool generated here are also available at https://github.com/GeorgeCaldarescu/TTI-Open asset ↗GeorgeCaldarescu/TTI-pdf-page:9 lines:1-52Code / dataset availability confirmedEurope PMC · bioRxiv · checked 15 Sept 2026
Chlorophyll breakdown is a central process during plant senescence or stress responses and leaf chlorophyll content is therefore a strong predictor of plant health. Chlorophyll quantification can be done in several ways, most of which are time-consuming or require specialized equipment. A simple alternative to these methods is the use of image-based chlorophyll estimation, which uses the color values in RGB images to calculate colorimetric visual indexes as a measure for the leaf chlorophyll content. Image-based chlorophyll measurement is non-destructive and, apart from a digital camera, requires no specialized equipment. Here, we developed the ImageJ plugin GreenLeafVI that facilitates high-throughput image analysis for measuring leaf chlorophyll content. Our plugin offers the option to white-balance images to decrease variation between images and has an optional background removal step. We show that this method can reliably quantify leaf chlorophyll content in a variety of plant species. In addition, we show that image-based chlorophyll quantification can replicate GWAS results based on traditional chlorophyll extraction methods, showing that this method is highly accurate.
Why it matches plant phenotyping methods葉のクロロフィル量を画像から推定するFIJIプラグインを開発し、複数植物種で信頼性とGWAS再現性を検証しており、植物フェノタイピング手法が中心である。
abstractHere, we developed the ImageJ plugin GreenLeafVI that facilitates high-throughput image analysis for measuring leaf chlorophyll content.
Reproduction assets foundThe paper's GreenLeafVI FIJI plugin (the authors' phenotyping analysis code) is publicly available on GitHub with explicit availability language. The underlying phenotype/trait datasets are only available upon request, so they do not qualify as public assets.Code · publicank BSc/MSc students Marion Larue, Karin Verkerk and Kim Roos for their help in phenotyping.
28
29
30 Data availability
31 The data that support the findings of this study are available from the corresponding author upon reasonable
32 request. The GreenLeafVI source code, documentation and further information is available at
33 https://github.com/jelmervanlieshout/GreenLeafVI.
9Open asset ↗jelmervanlieshout/GreenLeafVIpdf-layout-page:9 lines:1-45Code / dataset availability confirmedEurope PMC · bioRxiv · checked 13 Sept 2026
ArabidopsisRootMorphology / geometry measurementRoot system architecture
ABSTRACT Root system architecture (RSA) is central to plant adaptation and fitness, yet the design principles and regulatory mechanisms connecting RSA to environmental adaptation are not well understood. We developed Ariadne, a semi-automated software for quantifying cost-efficiency tradeoffs of RSA by mapping root networks onto a Pareto-optimality framework, which describes the balance between resource transport efficiency and construction cost. Applying Ariadne to Arabidopsis thaliana , we found that root architectures consistently assume Pareto-optimal forms across developmental stages, genotypes, and environmental conditions. Using the Discovery Engine, an engine that combines machine learning together with interpretability techniques, we found developmental stage, the hy5/chl1-5 genotype, and manganese availability as important determinants of the cost-efficiency tradeoff, with manganese exerting a unique influence not observed for other nutrients. These results reveal that RSA plasticity is genetically constrained to cost-efficiency optimal configurations and that developmental and environmental factors shift RSA on the pareto front, with manganese acting as a strong modulator of the transport efficiency and construction cost balance.
Why it matches plant phenotyping methodsRSAのコスト効率トレードオフを定量化する半自動ソフトウェアを開発し、植物形態形質の解析に適用しており、表現型取得・抽出手法が研究の中心である。
abstractWe developed Ariadne, a semi-automated software for quantifying cost-efficiency tradeoffs of RSA by mapping root networks onto a Pareto-optimality framework
Reproduction assets foundThe paper's authors developed the Ariadne software used for all RSA phenotyping and Pareto analysis in this study, and explicitly state it is publicly available on PyPI and provide a GitHub code availability URL. Both are paper-specific, public, actionable code assets. No public phenotype dataset deposit is stated; theCode · publicCode availability : https://github.com/Salk-Harnessing-Plants-Initiative/AriadneOpen asset ↗Salk-Harnessing-Plants-Initiative/Ariadnelines:235-276Code / dataset availability confirmedEurope PMC · bioRxiv · checked 14 Sept 2026
This work introduces WISER (whitening and successive least squares estimation refinement), an innovative and efficient method designed to enhance phenotype estimation by addressing population structure. WISER outperforms traditional methods such as least squares (LS) means and best linear unbiased prediction (BLUP) in phenotype estimation, offering a more accurate approach for omics-based selection and having the potential to improve association studies. Unlike existing approaches that correct for population structure, WISER provides a generalized framework applicable across diverse experimental setups, species, and omics datasets, including single nucleotide polymorphisms (SNPs), metabolomics, and near-infrared spectroscopy (NIRS) used as phenomic predictors. Central to WISER is the concept of whitening, a statistical transformation that removes correlations between variables and standardizes their variances. Within its framework, WISER extends classical methods that use eigen-information as fixed-effect covariates to correct for population structure, by relaxing their assumptions and implementing a true whitening matrix instead of a pseudo-whitening matrix. This approach corrects fixed effects (e.g., environmental effects) for the genetic covariance structure embedded within the experimental design, thereby minimizing confounding factors between fixed and genetic effects. To support its practical application, a user-friendly R package named wiser has been developed. The WISER method has been employed in analyses for genomic prediction and heritability estimation across four species and 33 traits using multiple datasets, including rice, maize, apple, and Scots pine. Results indicate that genomic predictive abilities based on WISER-estimated phenotypes consistently outperform the LS-means and BLUP approaches for phenotype estimation, regardless of the predictive model applied. This underscores WISER’s potential to advance omics analyses and related research fields by capturing stronger genetic signals.
Why it matches plant phenotyping methodsWISERは集団構造を補正して植物形質を推定する統計手法として開発・検証され、Rパッケージも提供されているため、形質取得・推定手法が中心である。
abstractThis work introduces WISER (whitening and successive least squares estimation refinement), an innovative and efficient method designed to enhance phenotype estimation by addressing population structure.
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 R package wiser can be easily installed from GitHub at https://github.com/ljacquin/wiser.Open asset ↗ljacquin/wiserpdf-page:4 lines:1-59Code / dataset availability confirmedEurope PMC · bioRxiv · checked 15 Sept 2026
Large-scale kinetic models of photosynthesis enable time-resolved predictions of traits related to this key process, and provide the means to identify factors limiting photosynthesis. However, their use is currently limited by the lack of efficient approaches to estimate the hundreds of genotype-specific kinetic parameters. Here, we present C4TUNE, an artificial neural network, which can efficiently predict parameters of a large-scale photosynthesis model from photosynthesis response curves. C4TUNE was trained on a biologically-relevant synthetic dataset comprising matched samples of parameters and response curves obtained using a C 4 photosynthesis kinetic model. To speed up the training of C4TUNE, we devised a surrogate neural network to predict photosynthesis response curves directly from the model parameters and environmental inputs. Given response curves as input, we showed that over 99% of the parameter vectors predicted by C4TUNE could be used directly in simulation of the kinetic model and resulted in excellent fits. Finally, we applied C4TUNE to predict parameters for a population of 68 maize genotypes across two seasons. The predicted genotype-specific parameters allowed pinpointing factors that limit photosynthetic efficiency, validated using simulations. Therefore, the use of C4TUNE presents a fast and precise approach for parameter prediction based on minimal datasets.
Why it matches plant phenotyping methodsC4TUNEは光合成応答曲線から遺伝子型別の光合成パラメータを推定するニューラルネットワーク手法であり、植物生理形質の抽出が研究の中心です。
abstractHere, we present C4TUNE, an artificial neural network, which can efficiently predict parameters of a large-scale photosynthesis model from photosynthesis response curves.
Reproduction assets foundThe paper deposits its maize gas exchange phenotype measurements (Zenodo 15966533), the synthetic neural-network training dataset (Zenodo 15926601), and the C4TUNE analysis/training code with predicted genotype parameters (GitHub pwendering/C4TUNE), all with explicit availability statements and public URLs.Dataset · publicwere tuned as described above (“Surrogate
648
model”). The final model was trained for 30 epochs with a batch size of 8.
649
The neural networks were implemented using Python 3.10.14 using the PyTorch library version
650
2.5.1 42
.
651
Data availability
652
The gas exchange measurements for maize genotypes are available at
653
https://doi.org/10.5281/zenodo.15966533. Part of these data has been used in another study
654
linking photosynthesis-related traits and hyperspectral reflectance data 43
. The generated
655
artificial data set for neural network training is available at
656
https://doi.org/10.5281/zenodo.15926601.657
.
CC-BY-NC-ND 4.0 International license
made available under a
(whOpen asset ↗zenodo · 10.5281/zenodo.15966533pdf-raw-page:21 lines:1-94Code · public22
Code availability
658
Custom code for the generation of the artificial dataset as well as code for neural model
659
definition and training are available at https://github.com/pwendering/C4TUNE. This
660
repository also contains the predicted parameters for the maize genotypes.
661
References
662
1. Zhu, X. G., Long, S. P. & Ort, D. R. Improving photosynthetic efficiency for greater
663
yield. Annu. Rev. Plant Biol. 61, 235–261 (2010).
664
2. Croce, R. et al. Perspectives on improving photosynthesis to increase crop yOpen asset ↗github · pwendering/C4TUNEpdf-raw-page:22 lines:1-69Code / dataset availability confirmedEurope PMC · bioRxiv · checked 15 Sept 2026
Hyperspectral reflectance provides rapid and precise phenotyping of plants in a non-destructive manner both in field and well-controlled settings. The resulting data have been used to devise machine learning (ML) models for paired measurements of different traits in diverse plants and crops. Yet, despite advances in using of hyperspectral data to reliably predict crop traits of interest, there are pressing issues concerning the training of ML models, the aggregation of data from crop field trials, and the generalizability of the models in different prediction settings. We collected hyperspectral reflectance data along with 25 anatomical, gas exchange, and chlorophyll fluorescence traits from 320 recombinant inbred lines of a maize Multi-Parent Advanced Generation Inter-Cross population grown across three consecutive seasons. We use these data to systematically: (1) compare the performance of representative ML models for different traits, including slow fluorescence kinetics whose predictability by hyperspectral data has not yet been investigated, (2) evaluate the ML model performance in prediction scenarios concerning unseen genotypes, unseen seasons, and the combination thereof, (3) investigate the effects of data aggregation of ML model performance. These problems are addressed in a rigorous nested cross-validation setting that provides a template for adequate assessment of performance of ML models for diverse crop traits considering the particularities of the experimental design.
Why it matches plant phenotyping methodsトウモロコシのハイパースペクトル反射データによる形質推定について、複数の機械学習モデル、未知遺伝子型・季節への汎化性能、データ統合の影響を系統的かつネスト化交差検証で評価しており、フェノタイピング手法の検証が中心である。
abstractWe use these data to systematically: (1) compare the performance of representative ML models for different traits, including slow fluorescence kinetics whose predictability by hyperspectral data has not yet been investigated, (2) evaluate the ML model performance in prediction scenarios concerning unseen genotypes, unseen seasons, and the combination thereof, (3) investigate the effects of data aggregation of ML model performance.
Reproduction assets foundThe paper's data availability statement explicitly provides all code and raw data (hyperspectral reflectance and trait measurements) for reproducibility via the authors' public GitHub repository.Code · publicidge, Cambridge, UK
9
†
These authors contributed equally.
10
*
Corresponding authors.
11
12
Email address:
13
rudan.xu@uni-potsdam.de
14
jfergu@essex.ac.uk
15
jk417@cam.ac.uk
16
nikoloski@mpimp-golm.mpg.de
17
18
Data availability statement
19
All code and raw data to ensure reproducibility of the results can be accessed at:
20
https://github.com/Rudan-X/HyperspectralML
21
22
Funding statement:
23
J.F. was supported by the European Union’s Horizon 2020 research and innovation program
24
grant 862201 (to J.K. and Z.N.). R.X. was supported by the International Max Planck Research
25
School "Molecular Plant Science" between the Max Planck Institute of Molecular Plant
26
Physiology and the UniveOpen asset ↗Rudan-X/HyperspectralMLpdf-raw-page:1 lines:1-71Code / dataset availability confirmedEurope PMC · bioRxiv · checked 6 Sept 2026
Summary Live imaging data analysis often requires an objective, local, and accurate way of quantification of cell dynamics. In the research field of polarized tip-growth, the cell fluctuations and/or fluctuations in tip position and growth direction hampers automated analyses of huge amounts of imaging sequences. The fluctuated nature in data makes it unclear how cell shape and growth are linked to intracellular events that could be the actual driving force of cell growth. To overcome these difficulties, we developed a powerful and user-friendly tool called KymoTip with an available format. In this software, novel functions such as coordinate normalization, tip-bottom detection, and signal kymograph were implemented. We confirmed that not only plasma membrane-labeled fluorescent images, but also images such as bright-field and cortical microtubule markers —so long as the cell contours can be identified— are amenable to KymoTip. Furthermore, by combining markers for cell contours with those that visualize intracellular structures, it becomes possible to quantitatively analyze various intracellular events, such as nuclear migration and calcium wave, in conjunction with cellular growth dynamics. Since KymoTip can be handled by non-specialist, it is expected to promote understanding of what happens at the sub- and cellular level with high throughput outcomes. Significance statement Faced with fluctuations in cell coordinates and cell tip positions, position correction of live imaging data and accurate detection of tip position are key challenges in plant developmental biology. We solved them with a powerful and user-friendly tool, KymoTip, that can realize cell position correction, cell tip detection with cell centerline, and quantification of intracellular events.
Why it matches plant phenotyping methods植物細胞のライブイメージから細胞形状・先端位置・成長動態を定量化する解析ソフトウェアを開発しており、植物フェノタイピング手法が中心です。
abstractwe developed a powerful and user-friendly tool called KymoTip
Reproduction assets foundThe paper's authors explicitly state that the KymoTip analysis code is publicly available on GitHub at https://github.com/blues0910/KymoTip, which is an allowed URL. This is the authors' own computational tool implementing the paper's tip-growth phenotyping analysis (segmentation, coordinate normalization, tip-bottom, Code · publicThe code for KymoTip is available on GitHub: https://github.com/blues0910/KymoTip.Open asset ↗blues0910/KymoTippdf-page:8 lines:1-44Code / dataset availability confirmedbioRxiv · checked 15 Sept 2026
The global carbon cycle depends heavily on the carbon sequestration rates of aquatic ecosystems. Sinking of phytoplankton is a rapid mediator of carbon sequestration, because phytoplankton are globally abundant photoautotrophs that grow rapidly. Pico- and nano-phytoplankton sinking velocities vary depending on their growth state, viability, clumping, and distribution in the water column. We introduced high throughput fluorescence microscopy of well-plates, to measure sinking velocities of three diatom strains, and three cyanobacteria strains, with cell radii spanning an order of magnitude, all grown under three different light levels. Cultures were measured for sinking velocities repeatedly across their growth trajectories. Tracking multiple fluorescence wavebands allowed us to simultaneously determine sinking velocities for living vs. dead cells. Sinking velocities varied strongly across growth light levels, and across growth stages. These monoclonal cultures furthermore show distinct sub-populations of slow- and fast-sinking cells. Our results departed widely from simple Stokes Law estimates of sinking based upon radii and mass density of cells. Complex, heterogeneous phytoplankton communities likely show more complicated sinking patterns than are currently expressed in biogeochemical ocean models. Our well-plate microscopy approach using parallel imaging of many samples generates high-throughput measures of cell sinking at population- or community-scales, to in turn improve modelling of carbon export to deeper layers.
Why it matches plant phenotyping methods植物プランクトンの沈降速度を高スループット蛍光顕微鏡で測定する手法を導入し、生活状態や集団スケールの生理・機能形質を定量化しているため、測定法が研究の中心です。
abstractWe introduced high throughput fluorescence microscopy of well-plates, to measure sinking velocities of three diatom strains, and three cyanobacteria strains
Reproduction assets foundThe paper's sinking-velocity analysis code is explicitly stated to be openly available on the authors' GitHub repository. The raw phenotype data is promised for Dryad only upon acceptance, so it is not yet publicly actionable.Code · publicfunctional groups of cyanobacteria, diatoms strains with 156
diameter less than 10µm and diatoms strains with diameter larger than 10µm based on 157
growth light, viability state (living vs. dead and dying) and slow vs. fast sinking 158
velocity clustering groups. 159
The code used to analyse the data is public available at 160
https://github.com/maxberthold/PhytoplanktonSinkVelocities. 161
Sinking according to Stokes’ law 162
Sinking velocities of spherical objects falling under the case of Reynolds numbers 163
smaller than 1 can be described by Stokes’ law. Several studies have used Stokes law or 164
a modified version of Stokes’ law to estimate sinking velocities of plankton and marine 16Open asset ↗maxberthold/PhytoplanktonSinkVelocitiespdf-raw-page:8 lines:1-44Code / dataset availability confirmedbioRxiv · Europe PMC · checked 13 Sept 2026
Polyploidy (whole-genome multiplication) is a common yet under-surveyed property of tissues across multicellular organisms. Polyploidy plays a critical role during tissue development, following acute stress, and during disease progression. Common methods to reveal polyploidy involve either destroying tissue architecture by cell isolation or by tedious identification of individual nuclei in intact tissue. Therefore, there is a critical need for rapid and high-throughput ploidy quantification using images of nuclei in intact tissues. Here, we present iSPy (Inferring Spatial Ploidy), a new unsupervised learning pipeline that is designed to create a spatial map of nuclear ploidy across a tissue of interest. We demonstrate the use of iSPy in Arabidopsis, Drosophila, and human tissue. iSPy can be adapted for a variety of tissue preparations, including whole mount and sectioned. This high-throughput pipeline will facilitate rapid and sensitive identification of nuclear ploidy in diverse biological contexts and organisms.
Why it matches plant phenotyping methodsArabidopsisを含む組織の核倍数性を画像から空間的に推定する新規計算パイプラインを開発しており、植物の状態計測手法が研究の中心である。
abstractwe present iSPy (Inferring Spatial Ploidy), a new unsupervised learning pipeline that is designed to create a spatial map of nuclear ploidy across a tissue of interest.
Reproduction assets foundThe paper's Data Availability Statement explicitly points to a public OSF data repository (containing the paper's imaging/phenotyping data) and a public GitLab repository for the iSPy analysis code, both with authors' URLs.Dataset · publicAll data are available in the main text, in the supplementary materials , and are publicly available in our OSF data repository https://osf.io/um7r3/ .Open asset ↗OSF · um7r3lines:234-294Code / dataset availability confirmedEurope PMC · bioRxiv · checked 15 Sept 2026
Woody plant encroachment (WPE)—a phenomenon similar to species invasion—is shifting many grasslands and savannas into shrub and evergreen-dominated ecosystems. Tracking WPE is difficult because shrubs and small trees are much smaller than the coarse resolution of common remote sensing platforms (> 10 m 2 ) and the impassibility of encroaching woody thickets slows ground-based approaches. Many agencies have been investing in fine resolution ( 90%), with the NEON-based models a few percent more accurate than NAIP. A model using both inputs had the highest accuracy. However, the accuracies of NAIP and NEON models differed for woody vegetation: compared to NEON, NAIP accuracy was, 82-93% compared to 94-98% for shrubs, 72-92% compared to 93-98% for deciduous trees, and 52-78% compared to 83-86% for evergreen trees (specifically Juniperus virginiana ). NEON-based models relied on canopy height (LiDAR) to make classifications, whereas the several bands of light make similar contributions to accuracy in the NAIP models. Finally, we found that both machine learning approaches had similar accuracy, but random forests ran substantially faster. We conclude that with large training datasets, publicly available aerial imagery and similar products (e.g., UAVs, micro-satellites) can produce fine-scale, high-accuracy remote sensing of WPE in this region with low up-front costs.
Why it matches plant phenotyping methods航空画像・LiDARと機械学習を用いて低木・樹木の植生状態を高解像度で推定し、NAIPとNEONおよび手法間の精度を比較しており、植物状態の取得・推定法が研究の中心である。
abstractTracking WPE is difficult because shrubs and small trees are much smaller than the coarse resolution of common remote sensing platforms
Reproduction assets foundThe authors deposited their paper-specific training/classification dataset (ground-truthed and computer-drawn vegetation polygons for Konza Prairie) publicly on EDI. The analysis code is only 'private-for-peer review' on Figshare, so it does not qualify as a public asset. NEON and NAIP imagery are generic third-party平台Dataset · public, U.S.A.
10
11
12
13
Open research statement:
14
Data sets utilized for this research are as follows:
15
Noble, B. and Z. Ratajczak. 2022. WPE01 Assessing the value added of NEON for using
16
machine learning to quantify vegetation mosaics and woody plant encroachment at
17
Konza Prairie ver 1. Environmental Data Initiative.
18
https://doi.org/10.6073/pasta/a7b40e41080460bb1123dcc7b6d4d942 (Accessed 2022-12-
19
08). https://portal.edirepository.org/nis/mapbrowse?scope=knb-lter-20
knz&identifier=167&revision=1
21
(which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission.
The copyright holder for this preprint
this version posted FeOpen asset ↗Environmental Data Initiative · 10.6073/pasta/a7b40e41080460bb1123dcc7b6d4d942pdf-raw-page:1 lines:1-47Code / dataset availability confirmedEurope PMC · bioRxiv · checked 14 Sept 2026
This work introduces WISER (whitening and successive least squares estimation refinement), an innovative and efficient method designed to enhance phenotype estimation by addressing population structure. WISER outperforms traditional methods such as least squares (LS) means and best linear unbiased prediction (BLUP) in phenotype estimation, offering a more accurate approach for omics-based selection and association studies. Unlike existing approaches which correct for population structure, WISER offers a generalized framework that can be applied across diverse experimental setups, species, and omics datasets, such as single nucleotide polymorphisms (SNPs), near-infrared spectroscopy (NIRS), and metabolomics. Within its framework, WISER extends classical methods that use eigen-information as fixed-effect covariates to correct for population structure, by relaxing their assumptions and implementing a true whitening matrix instead of a pseudo-whitening matrix. This approach corrects fixed effects (e.g., environmental effects) for the genetic covariance structure embedded within the experimental design, thereby removing confounding factors between fixed and genetic effects. To support its practical application, a user-friendly R package named wiser has been developed. The WISER method has been employed in analyses for genomic prediction and heritability estimation across four species and 33 traits using multiple datasets, including rice, maize, apple, and Scots pine. Results indicate that genomic predictive abilities based on WISER-estimated phenotypes consistently outperform the LS-means and BLUP approaches for phenotype estimation, regardless of the predictive model applied. This underscores WISER’s potential to advance omics analyses and related research fields by capturing stronger genetic signals.
Why it matches plant phenotyping methodsWISERは集団構造を補正して表現型を推定する計算手法として開発され、複数作物・多数形質で検証されている。Rパッケージも提供され、表現型推定が研究の中心である。
abstractThis work introduces WISER (whitening and successive least squares estimation refinement), an innovative and efficient method designed to enhance phenotype estimation by addressing population structure.
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 R package wiser can be easily installed from GitHub at https://github.com/ljacquin/wiser.Open asset ↗ljacquin/wiserpdf-page:4 lines:1-59Code / dataset availability confirmedbioRxiv · checked 15 Sept 2026
Leaf osmotic potential at full turgor ({pi}0) has been used frequently to indicate turgor loss point of plant leaves. However, even a rapid measurement of{pi} 0 using osmometry is time-consuming, if numerous leaf samples need to be measured. Because of this, researchers tend to use a small sample size to determine{pi} 0 and relate it to indices of crop performance. Yet the statistical and agronomic significance of using a small sample size of{pi} 0 to indicate crop performance is not known. We address this question using field measurements and statistical resampling. Six mature leaf samples were collected at the peak bloom stage from each of the 54 cotton plots in Texas, USA in 2024. The{pi} 0 of the collected leaves were measured using an osmometer. Seed cotton yields from the field plots were measured near the end of cotton season. To test the effect of sample size on strength of the linear relation between{pi} 0 and cotton yield, 1-6 resamples of{pi} 0 were randomly drawn with replacement from the original 6 measurements per plot for the 54 plots. The resampled data of{pi} 0 were then used as independent variable to predict cotton yield. We found that, considering the labor and cost, sampling 3 or 6 leaves per plot may not make a significant difference for the linear regression between{pi} 0 and cotton yield.
Why it matches plant phenotyping methods葉の浸透ポテンシャル測定におけるサンプル数の妥当性と、収量との関係に対する影響を再サンプリングで評価しており、測定プロトコルの技術的検証が中心である。
titleDoes sample size of leaf osmotic potential affect its relationship with cotton yield?
Reproduction assets foundThe paper's field-measured leaf osmotic potential and seed cotton yield dataset, plus the authors' resampling/regression computer code, are explicitly deposited publicly on Zenodo (record 14635663), as stated in the Data availability section.Dataset · publicect 9574-
2, is appreciated. We thank Jose Teran and Joe Gonzalez,
Farm Manager and Farm Foreman, respectively, at Uvalde
Research Center, and collaborating farmer Rick Kruger for
time/efforts invested in crop management.
Data availability
The data and computer code for reproduc-
ing the results of this paper are available from
https://zenodo.org/records/14635663.Bibliography
1. Megan K. Bartlett, Ya Zhang, Christine Scoffoni, Shanwen Sun, Rico Ardy, Kunfang Cao,
and Lawren Sack. Rapid determination of comparative drought tolerance traits: using an
osmometer to predict turgor loss point. Methods in Ecology and Evolution, 3:880–888, 2012.
2. Y. N. S. Cheung, M. T. Tyree, and J. Dainty. WOpen asset ↗Zenodo · 14635663pdf-raw-page:3 lines:1-85Code / dataset availability confirmedbioRxiv · checked 13 Sept 2026
Timelapse microscopy has recently been employed to study the metabolism and physiology of cyanobacteria at the single-cell level. However, the identification of individual cells in brightfield images remains a significant challenge. Traditional intensity-based segmentation algorithms perform poorly when identifying individual cells in dense colonies due to a lack of contrast between neighboring cells. Here, we describe a newly developed software package called Cypose which uses machine learning (ML) models to solve two specific tasks: segmentation of individual cyanobacterial cells, and classification of cellular phenotypes. The segmentation models are based on the Cellpose framework, while classification is performed using a convolutional neural network named Cyclass. To our knowledge, these are the first developed ML-based models for cyanobacteria segmentation and classification. When compared to other methods, our segmentation models showed improved performance and were able to segment cells with varied morphological phenotypes, as well as differentiate between live and lysed cells. We also found that our models were robust to imaging artifacts, such as dust and cell debris. Additionally, the classification model was able to identify different cellular phenotypes using only images as input. Together, these models improve cell segmentation accuracy and enable high-throughput analysis of dense cyanobacterial colonies and filamentous cyanobacteria.
Why it matches plant phenotyping methodsシアノバクテリア細胞の画像セグメンテーションと細胞表現型分類を行うソフトウェアおよび機械学習手法の開発が中心であり、植物細胞の形態・生存状態を抽出するフェノタイピング手法に該当する。
abstractHere, we describe a newly developed software package called Cypose which uses machine learning (ML) models to solve two specific tasks: segmentation of individual cyanobacterial cells, and classification of cellular phenotypes.
Reproduction assets foundThe paper's segmentation/classification models and analysis code are publicly available in the authors' GitHub repository (cameronlab/cypose). The microscopy training datasets are not public and are available only upon request.Code · publicAll code and trained models can be downloaded from https://github.com/cameronlab/cypose .Open asset ↗cameronlab/cyposelines:298-383Code / dataset availability confirmedbioRxiv · Europe PMC · checked 15 Sept 2026
Field / plotLaboratory / benchtopX-ray / CTRootWhole plant / canopy / plot / field
Soil compaction and escalating global drought increase soil strength and stiffness. It remains unclear which plant root biomechanical mechanisms/traits enable growth in these harsh conditions. Here, we combine synchrotron X-ray computed tomography with spatially resolved X-ray diffraction to characterize the biomechanics of a replica root-soil system. We map the strain field around the root tip, finding strong agreement with finite element simulations, thereby demonstrating a promising new in-vivo measurement protocol.
Why it matches plant phenotyping methods根周辺のひずみ場という根の力学的形質を、X線CT・回折と有限要素解析で測定・検証する新規プロトコルが研究の中心である。
abstractWe map the strain field around the root tip, finding strong agreement with finite element simulations, thereby demonstrating a promising new in-vivo measurement protocol.
Reproduction assets foundThe paper deposits its X-ray diffraction and X-ray imaging (XCT) measurements in the Southampton Pure repository (DOI 10.5258/SOTON/D3309.274) and its processing scripts in a companion deposit (DOI 10.5258/SOTON/D3309.276), both with explicit availability statements and public URLs.Dataset · public∇uT ), F(σ′) > 0,x ∈ Ω
σ′ = Cep
: (∇u+∇uT ), F(σ′) = 0,x ∈ Ω
u·ê1 = 0, x ∈ ΓAxis
u = 0, x ∈ ΓC,Top
u = [0,wstep]T , x ∈ Γbot ∪Γout
n̂·∇u = 0, x ∈ Γtop ∪ΓC,tip
n̂·σ = ppen, x ∈ (Γtop ∩Ω∩Ωc)∪(ΓC,tip ∩Ω∩Ωc)
. (29)
Data Records
273
All X-ray diffraction and X-ray imaging data used in this study can be found in the Pure repository: https://doi.org/10.5258/SOTON/D3309.274
Code availability
275
All scripts used to process the data can be found in the Pure repository: https://doi.org/10.5258/SOTON/D3309.276
References
277
1. Lee, H. et al. Ipcc, 2023: Climate change 2023: Synthesis report, summary for policymakers. contribution of working
278
groups i, ii and iii to the sixth assessment report ofOpen asset ↗Pure repository · 10.5258/SOTON/D3309.274pdf-raw-page:9 lines:1-96Code · publicu = 0, x ∈ Γtop ∪ΓC,tip
n̂·σ = ppen, x ∈ (Γtop ∩Ω∩Ωc)∪(ΓC,tip ∩Ω∩Ωc)
. (29)
Data Records
273
All X-ray diffraction and X-ray imaging data used in this study can be found in the Pure repository: https://doi.org/10.5258/SOTON/D3309.274
Code availability
275
All scripts used to process the data can be found in the Pure repository: https://doi.org/10.5258/SOTON/D3309.276
References
277
1. Lee, H. et al. Ipcc, 2023: Climate change 2023: Synthesis report, summary for policymakers. contribution of working
278
groups i, ii and iii to the sixth assessment report of the intergovernmental panel on climate change [core writing team, h.
279
lee and j. romero (eds.)]. ipcc, geneva, switzerland. (2023).
2Open asset ↗Pure repository · 10.5258/SOTON/D3309.276pdf-raw-page:9 lines:1-96Code / dataset availability confirmedbioRxiv · Europe PMC · checked 7 Sept 2026
Pollen function is critical for successful plant reproduction and crop productivity and it is important to develop accessible methods to quantitatively analyze pollen performance to enhance reproductive resilience. Here we introduce TubeTracker as a method to quantify key parameters of pollen performance such as, time to pollen grain germination, pollen tube tip velocity and pollen tube survival. TubeTracker integrates manual and automatic image processing routines and the graphical user interface allows the user to interact with the software to make manual corrections of automated steps. TubeTracker does not depend on training data sets required to implement machine learning approaches and thus can be immediately implemented using readily available imaging systems. Furthermore, TubeTracker is an excellent tool to produce the pollen performance data sets necessary to take advantage of emerging AI-based methods to fully automate analysis. We tested TubeTracker and found it to be accurate in measuring pollen tube germination and pollen tube tip elongation across multiple cultivars of tomato. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=114 SRC="FIGDIR/small/624782v2_ufig1.gif" ALT="Figure 1"> View larger version (33K): org.highwire.dtl.DTLVardef@1fc2a63org.highwire.dtl.DTLVardef@42f3a2org.highwire.dtl.DTLVardef@18911d6org.highwire.dtl.DTLVardef@1f236f0_HPS_FORMAT_FIGEXP M_FIG Graphical Abstract Graphical user interface of TubeTracker showing all supported functionalities. C_FIG
Why it matches plant phenotyping methods植物の花粉管画像から発芽時間、伸長速度、生存性などの表現型を抽出するソフトウェア手法を開発し、複数トマト品種で精度検証しているため。
abstractHere we introduce TubeTracker as a method to quantify key parameters of pollen performance such as, time to pollen grain germination, pollen tube tip velocity and pollen tube survival.
Reproduction assets foundThe paper's authors publicly released TubeTracker, the Python software used to perform all automated pollen germination, elongation, and survival phenotyping measurements in this study, on GitHub with explicit availability language and a video sample for training.Code · publicWe further encourage users to independently
improve upon our tool and have provided the complete python code at
https://github.com/souonkap/TubeTracker, along with installation instructions and a video
sample for training purposes.Open asset ↗souonkap/TubeTrackerpdf-page:22 lines:1-44Code / dataset availability confirmedbioRxiv · Crossref · checked 13 Sept 2026
Deep learning models have shown significant potential for plant pest and disease (PPD) diagnosis; however, their real-world effectiveness is often limited by variability between datasets, where models trained on one dataset perform poorly on others collected under different conditions. In this study, I evaluated the cross-dataset generalization of widely used deep learning architectures, including ResNet, EfficientNet, Inception, and MobileNet, across multiple tomato pest and disease datasets. As expected, models trained and tested on the same dataset achieved high performance. However, substantial performance degradation occurred when these models were tested on different datasets, highlighting the challenges posed by dataset variability. This trend was consistent across all evaluated architectures, indicating that changing the model architecture alone is insufficient to address these issues. The findings emphasize the need for more diverse and representative datasets to better capture variability in agricultural data and enhance the practical deployment of deep learning models for PPD diagnosis.
Why it matches plant phenotyping methodsトマトの病害・害虫状態を対象に、複数データセット間で深層学習診断モデルの汎化性能を比較評価しており、植物状態の推定手法の技術検証が中心である。
abstractIn this study, I evaluated the cross-dataset generalization of widely used deep learning architectures, including ResNet, EfficientNet, Inception, and MobileNet, across multiple tomato pest and disease datasets.
Reproduction assets foundThe paper's cross-dataset evaluation uses three public tomato pest/disease image datasets (PlantVillage, Tomato-Village, Tomato Leaf Disease), each with an explicit public URL in the data availability statement. No author analysis code or trained models are deposited.Dataset · publicThe PlantVillage dataset is available at https://github.com/spMohanty/PlantVillage-Dataset.Open asset ↗spMohanty/PlantVillage-Datasetpdf-page:8 lines:1-31Dataset · publicThe
Tomato-Village dataset is accessible at https://github.com/mamta-joshi-gehlot/Tomato-Village.Open asset ↗mamta-joshi-gehlot/Tomato-Villagepdf-page:8 lines:1-31Code / dataset availability confirmedbioRxiv · checked 14 Sept 2026
Seed color is a complex phenotype linked to both the impact of grains on human health and consumer acceptance of new crop varieties. Today seed color is often quantified via either qualitative human assessment or biochemical assays for specific colored metabolites. Imaging-based approaches have the potential to be more quantitative than human scoring while lower cost than biochemical assays. We assessed the feasibility of employing image analysis tools trained on rice (Oryza sativa) or wheat (Triticum aestivum) seeds to quantify seed color in sorghum (Sorghum bicolor ) using a dataset of > 1,500 images. Quantitative measurements of seed color from images were substantially more consistent across biological replicates than human assessment. Genome-wide association studies conducted using color phenotypes for 682 sorghum genotypes identified more signals near known seed color genes in sorghum with stronger support than manually scored seed color for the same experiment. Previously unreported genomic intervals linked to variation in seed color in our study co-localized with a gene encoding an enzyme in the biosynthetic pathway leading to anthocyanins, tannins, and phlobaphenes - colored metabolites in sorghum seeds - and with the sorghum ortholog of a transcription factor shown to regulate several enzymes in the same pathway in rice. The cross-species transferability of image analysis tools, without the retraining, may aid efforts to develop higher value and health-promoting crop varieties in sorghum and other specialty and orphan grain crops.
Why it matches plant phenotyping methods画像解析モデルを用いたソルガム種子色の定量化を中心に、手作業評価との再現性比較と遺伝解析による妥当性評価を行っているため、植物表現型計測手法として採用。
abstractWe assessed the feasibility of employing image analysis tools trained on rice (Oryza sativa) or wheat (Triticum aestivum) seeds to quantify seed color in sorghum (Sorghum bicolor ) using a dataset of > 1,500 images.
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 · public12 using codes available in https://github.com/NikeeShrestha/SorghumSeedSegmentation.Open asset ↗NikeeShrestha/SorghumSeedSegmentationpdf-page:6 lines:1-60Code / dataset availability confirmedEurope PMC · bioRxiv · checked 15 Sept 2026
SUMMARY There is an increasing demand to boost photosynthesis in rice to increase yield potential. Chloroplasts are the site of photosynthesis, and increasing the number and size of these organelles in the in leaf is a potential route to elevate leaf-level photosynthetic activity. Notably, bundle sheath cells do not make a significant contribution to overall carbon fixation in rice and thus various attempts are being made to increase chloroplast content in this cell type. In this study we developed and applied a deep learning tool named Chloro-Count to demonstrate that loss of OsHAP3H function in rice increases chloroplast occupancy in bundle sheath cells by 50%. Although limited to a single season, when grown in the field Oshap3H mutants exhibited increased numbers of tillers and panicles as compared to controls or gain of function mutants. The implementation of Chloro-Count enabled precise quantification of chloroplasts in loss- and gain-of-function OsHAP3H mutants and facilitated a comparison between 2D and 3D quantification methods. In wild-type rice, as the dimensions of bundle sheath cells increase, the volume of individual chloroplasts also increases. However, the larger the chloroplasts the fewer there are per bundle sheath cell. This observation revealed that a mechanism operates in bundle sheath cells to restrict chloroplast occupancy as cell dimensions increase. That mechanism is unperturbed in Oshap3H mutants. The use of Chloro-Count also revealed that 2D quantification, upon which most previous studies have relied, is compromised by the positioning of chloroplasts within the cell. Chloro-Count is therefore a valuable tool for accurate and high-throughput quantification of chloroplasts that has enabled the robust characterization of OsHAP3H effects on chloroplast biogenesis in rice. Whereas previous studies have increased chloroplast occupancy in bundle sheath cells by increasing the size of individual chloroplasts, loss of OsHAP3H function leads to an increase in chloroplast numbers.
Why it matches plant phenotyping methodsChloro-Countという深層学習ツールを開発し、葉肉細胞内の葉緑体数・占有率を高精度かつハイスループットに定量する手法が研究の中心であるため。
abstractwe developed and applied a deep learning tool named Chloro-Count
Reproduction assets foundThe paper's Chloro-Count deep learning tool (Mask R-CNN segmentation of chloroplasts and bundle sheath cells) is the authors' own analysis code, explicitly stated to be publicly available on GitHub. No public image/phenotype dataset deposit is stated; training images and Table S1 raw data are not linked to a public URLCode · publicn validated, they are mapped to
566 individual organelles/cells for volumetric analysis. An overview of the system for detecting and
567 measuring volumes of chloroplasts is presented in Figure 3A. The process for detecting and
568 measuring bundle sheaths follows an analogous workflow. The Chloro-Count code is available on
569 https://github.com/pedropgusmao/chloro-count.
570
571 Data collection and pre-processing
572 A total of 327 slices from 39 different cells were used during the training of both image segmentation
573 networks. Images from 29 cells were used for training, five for validation and five for testing. A total of
574 3,790 segments of chloroplasts were used for training, 287Open asset ↗pedropgusmao/chloro-countpdf-layout-page:16 lines:1-47Code / dataset availability confirmedEurope PMC · bioRxiv · checked 15 Sept 2026
Summary StomaVision is an automated tool designed for high-throughput detection and measurement of stomatal traits, such as stomatal number, pore size, and closure rate. It provides insights into plant responses to environmental cues, streamlining the analysis of micrographs from field-grown plants across various species, including monocots and dicots. Enhanced by a novel collection method that utilizes video recording, StomaVision increases the number of captured images for robust statistical analysis. Accessible via an intuitive web interface at and available for local use in a containerized environment at , this tool ensures long-term usability by minimizing the impact of software updates and maintaining functionality with minimal setup requirements. The application of StomaVision has provided significant physiological insights, such as variations in stomatal density, opening rates, and total pore area under heat stress. These traits correlate with critical physiological processes, including gas exchange, carbon assimilation, and water use efficiency, demonstrating the tool’s utility in advancing our understanding of plant physiology. The ability of StomaVision to identify differences in responses to varying durations of heat treatment highlights its value in plant science research. Plain language summary StomaVision is a tool that automatically counts and measures tiny openings on plant leaves, helping us learn how plants deal with their surroundings. It is easy to use and works well with various plant species. This tool helps scientists see how plants change under stress, making plant research easier and more accurate.
Why it matches plant phenotyping methods気孔数、孔サイズ、閉鎖率などの植物形質を画像から自動抽出するツールの開発・提供が研究の中心であり、植物フェノタイピング手法に該当する。
abstractStomaVision is an automated tool designed for high-throughput detection and measurement of stomatal traits, such as stomatal number, pore size, and closure rate.
Reproduction assets foundThe authors publicly release their StomaVision source code, trained YOLOv7-seg model, and all labeled stomata images on GitHub, plus a public Streamlit web portal for stomatal trait analysis. Cited datasets (Dryad/LeafNet, Cuticle Database) and generic libraries (VDP, Detectron2, Ultralytics, Label Studio) are prior/thCode · publicl for advancing our understanding of stomatal behavior,
841
particularly in an era in which plant resilience and adaptation are of paramount
842
concern.
843
844
845
Data Availability
846
The source code, trained model, user installation and training guideline, and all the
847
labeled images of leaf stomata are available at
848
https://github.com/YaoChengLab/StomaVision. The web portal of extracting stomatal
849
traits is available at https://stomavision.streamlit.app/.850
851
852
Author Contributions
853
TLW, PYC, XD, PLC, and YCL conceived the research. TLW, JYO, PXZ, YLW, RHW,
854
TCH, CYL, and YCL conducted the field and growth chamber experiments. TLW,
855
JYO, PXZ, YLW, and RHW produceOpen asset ↗YaoChengLab/StomaVisionpdf-raw-page:27 lines:1-65Code / dataset availability confirmedEurope PMC · bioRxiv · checked 15 Sept 2026
ABSTRACT Members of the Fusarium oxysporum species complex are pathogens of sugar beet causing Fusarium yellows. Fusarium yellows can reduce plant stand, yield, and extractable sugar. Improving host plant resistance against Fusarium -induced diseases, like Fusarium yellows, represents an important long-term breeding target in sugar beet breeding programs. Current methods for rating Fusarium yellows disease severity rely on an ordinal scale, which limits precision for intermediate phenotypes. In this study, we aimed to improve the accuracy and precision of rating Fusarium yellows by developing a standard area diagram (SAD). Two SAD versions were created using images of sugar beets infected with Fusarium oxysporum strain F19. Each version was tested using inexperienced raters. Comparing both the pilot and improved version showed no statistical differences in Lin’s Concordance Correlation Coefficient (LCC) values to assess accuracy and precision between the two versions (Cb = 0.99 for both versions, ρ c = 0.97 and 0.96 for version 1 and 2, respectively). In addition, five naïve Bayesian machine learning models which used pixel classification to determine disease score, were tested for congruency to human estimates in version 2. Root mean square error was lowest compared to the “true” values for the unweighted model and a model where necrotic tissue was given a 2x weight (12.4 and 12.6, respectively). The creation of this standard area diagram enables breeding programs to make consistent, accurate disease ratings regardless of personnel’s’ previous experience with Fusarium yellows. Additionally, more iterations of pixel quantification equations may overcome accuracy issues for rating Fusarium yellows.
Why it matches plant phenotyping methodsフザリウム萎黄病の植物症状を対象に、標準面積図と画像ピクセル分類による病害重症度評価法を開発・検証しており、植物フェノタイピング手法が中心である。
abstractIn this study, we aimed to improve the accuracy and precision of rating Fusarium yellows by developing a standard area diagram (SAD).
Reproduction assets foundThe paper's data availability statement explicitly deposits the authors' scripts, plant images, and excel sheets (including the RGB classifier training data) on a public GitHub repository, which is paper-specific and actionable.Code · publiceen 0-20%.
277
278
Acknowledgements
279
The authors would like to acknowledge the raters’ participation in this study. Funding
280
provided by USDA-ARS CRIS projects 3012-21220-011-000-D and 5050-21220-017-000-D.
281
Data availability statement
282
Scripts, images and excel sheets are available on the following Github page:
283
https://github.com/oetodd/Fusarium_standard_area_diagram_2024
284
285
and is also made available for use under a CC0 license.
was not certified by peer review) is the author/funder. This article is a US Government work. It is not subject to copyright under 17 USC 105
The copyright holder for this preprint (which
this version posted April 28, 2024.
;
https://doi.org/10Open asset ↗https://github.com/oetodd/Fusarium_standard_area_diagram_2024pdf-raw-page:13 lines:1-50Code / dataset availability confirmedbioRxiv · checked 15 Sept 2026
Variation in Drosophila compound eye size is studied across research fields, from evolutionary biology to biomedical studies, requiring the collection of large datasets to ensure robust statistical analyses. To address this, we present EyeHex, a tool for automatic segmentation of fruit fly compound eyes from brightfield and scanning electron microscopy (SEM) images. EyeHex features two integrated modules: the first utilizes machine learning to generate probability maps of the eye and ommatidia locations, while the second, a hard-coded module, leverages the hexagonal organization of the compound eye to map individual ommatidia. This iterative segmentation process, which adds one ommatidium at a time based on registered neighbors, ensures robustness to local perturbations. EyeHex also includes an analysis tool that calculates key metrics of the eye, such as ommatidia count and diameter distribution across the eye. With minimal user input for training and application, EyeHex achieves exceptional accuracy (>99.6% compared to manual counts on SEM images) and adapts to different fly strains, species, and image types. EyeHex offers a cost-effective, rapid, and flexible pipeline for extracting detailed statistical data on Drosophila compound eye variation, making it a valuable resource for high-throughput studies.
Why it matches plant phenotyping methods昆虫（ショウジョウバエ）の眼を対象としており植物ではないため、植物フェノタイピング文献の対象外です。
abstractautomatic segmentation of fruit fly compound eyes from brightfield and scanning electron microscopy (SEM) images
Reproduction assets foundThe paper's EyeHex MATLAB toolbox (with manual and sample images) and the post-segmentation analysis code are publicly available on GitHub, as stated in the Declarations. The segmentation results/analysis supplement is only a PDF supplement; the toolbox and analysis code are the paper-specific public assets.Code · publict of abbreviations
A-P: Anterior-Posterior
CT: Micro Computed Tomography
SEM: Scanning Electron Microscopy
GUI: Graphical User Interface
Declarations
Ethics approval and consent to participate
Not applicable
Consent for publication
Not applicable
Availability of data and Supplementary materials
EyeHex toolbox is available from https://github.com/huytran216/EyeHex-toolbox.
The analysis code following EyeHex segmentation for all eyes in the dataset can be
downloaded from https://github.com/huytran216/EyeHex_analysis.
Segmentation results and analysis for Hikone-AS (26 eyes) and Canton-SBH (12 eyes):
Supplementary_file.pdf
Competing interests
The authors declare that they have no competing intOpen asset ↗huytran216/EyeHex-toolboxpdf-layout-page:22 lines:1-53Code · publictions
Ethics approval and consent to participate
Not applicable
Consent for publication
Not applicable
Availability of data and Supplementary materials
EyeHex toolbox is available from https://github.com/huytran216/EyeHex-toolbox.
The analysis code following EyeHex segmentation for all eyes in the dataset can be
downloaded from https://github.com/huytran216/EyeHex_analysis.
Segmentation results and analysis for Hikone-AS (26 eyes) and Canton-SBH (12 eyes):
Supplementary_file.pdf
Competing interests
The authors declare that they have no competing interests.
Author’s contributions
AR collected the data (sample preparation and imaging), HT created EyeHex toolbox
and analyzed the data, AR and HTOpen asset ↗huytran216/EyeHex_analysispdf-layout-page:22 lines:1-53Code / dataset availability confirmedEurope PMC · bioRxiv · checked 14 Sept 2026
ArabidopsisMesh / voxelMicroscopyFlowerSegmentationGrowth / time-series analysisTrackingGrowth / development / phenology
Background Arabidopsis thaliana sepals are excellent models for analyzing growth of entire organs due to their relatively small size, which can be captured at a cellular resolution under a confocal microscope [1]. To investigate how growth of different tissue layers generates unique organ morphologies, it is necessary to live-image deep into the tissue. However, imaging deep cell layers of the sepal is practically challenging, as it is hindered by the presence of extracellular air spaces between mesophyll cells, among other factors which causes optical aberrations. Image processing is also difficult due to the low signal-to-noise ratio of the deeper tissue layers, an issue mainly associated with live imaging datasets. Addressing some of these challenges, we provide an optimized methodology for live imaging sepals and subsequent image processing. This helps us track the growth of individual cells on the outer and inner epidermal layers, which are the key drivers of sepal morphogenesis. Results For live imaging sepals across all tissue layers at early stages of development, we found that the use of a bright fluorescent membrane marker, coupled with increased laser intensity and an enhanced Z-resolution produces high-quality images suitable for downstream image processing. Our optimized parameters allowed us to image the bottommost cell layer of the sepal (inner epidermal layer) without compromising viability. We used a ‘voxel removal’ technique to visualize the inner epidermal layer in MorphoGraphX [2, 3] image processing software. Finally, we describe the process of optimizing the parameters for creating a 2.5D mesh surface for the inner epidermis. This allowed segmentation and parent tracking of individual cells through multiple time points, despite the weak signal of the inner epidermal cells. Conclusion We provide a robust pipeline for imaging and analyzing growth across inner and outer epidermal layers during early sepal development. Our approach can potentially be employed for analyzing growth of other internal cell layers of the sepals as well. For each of the steps, approaches, and parameters we used, we have provided in-depth explanations to help researchers understand the rationale and replicate our pipeline.
Why it matches plant phenotyping methodsライブイメージング、画像処理、細胞セグメンテーションと追跡を統合した、萼片の成長・形態解析パイプラインの最適化が中心であり、植物表現型取得法に該当する。
abstractwe provide an optimized methodology for live imaging sepals and subsequent image processing
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。Dataset · publicThe images of the WT flowers, as well as the final edited images can be accessed at https://doi.org/10.17605/OSF.IO/UMW9B . The images shown in this manuscript correspond to WT replicate 2.Open asset ↗OSF.IO/UMW9Blines:137-166Code / dataset availability confirmedEurope PMC · bioRxiv · checked 15 Sept 2026
MaizeLiDAR / point cloudLeafStem / branchWhole plant / canopy / plot / fieldMorphology / geometry measurementLeaf traitsWater status / transpiration
Advanced smartphone technology now integrates sophisticated sensors, increasing access to high-precision data acquisition. This study tested the hypothesis that the iPhone 13-Pro camera, with LiDAR technology, can accurately estimate maize leaf surface area (Zea mays). 3D point cloud models enabled non-destructive data collection, and four methods for canopy area extraction were evaluated in relation to plant transpiration rates. Results showed a strong correlation (R 2 =0.92, RMSE=49.78) between manually scanned and iPhone-estimated plant surface areas. Additionally, the stem-to-plant surface area ratio was found to be 12.3% (R 2 =0.9, RMSE=28.42). Using this ratio to predict canopy area showed a significant correlation (R 2 =0.83) with actual canopy measurements. The iPhone’s surface area measurement tool offers an advantage by scanning the entire plant surface, unlike traditional leaf area index measurements, which often cannot penetrate the canopy. Moreover, real-size surface measurement of the canopy correlated strongly (R 2 =0.83) with whole canopy transpiration rates measured gravimetrically. This study introduces a novel method for analyzing 3D plant traits using a portable, affordable, and accurate tool, which has the potential to enhance plant breeding and agricultural practices. 0. How to Use This Template The template details the sections that can be used in a manuscript. Note that each section has a corresponding style, which can be found in the “Styles” menu of Word. Sections that are not mandatory are listed as such. The section titles given are for articles. Review papers and other article types have a more flexible structure. Remove this paragraph and start section numbering with 1. For any questions, please contact the editorial office of the journal or support@mdpi.com .
Why it matches plant phenotyping methodsiPhoneのLiDARと3D点群を用いてトウモロコシの葉・植物表面積を推定する手法を開発・検証しており、植物形質の取得が研究の中心である。
abstractThis study tested the hypothesis that the iPhone 13-Pro camera, with LiDAR technology, can accurately estimate maize leaf surface area (Zea mays).
Reproduction assets foundThe paper states that all statistical code and data files for the maize 3D leaf phenotyping analysis are publicly available in the authors' GitHub repository.Code · publicconducted using the “scipy” package’s “f_oneway”
12 function [18]. The Python packages “pandas” [19] and “numpy” [20] were used to arrange the
13 data before plotting. The Python packages “matplotlib”, “seaborn” [21] were used for data
14 visualization. All statistical code and data files needed are available to download
15 at https://github.com/gavrielbs/3D_Corn_Phenotype.
16
17 PlantArray System by Plant-DiTech LTD
18 PlantArray is a high-throughput, multi-sensor physiological phenotyping gravimetric
19 platform. This plant phenotyping system performs quick plant screening based on precise
20 physiology traits measurements that are great indicators for yield potential with proven high
21 cOpen asset ↗gavrielbs/3D_Corn_Phenotypepdf-layout-page:4 lines:1-44Code / dataset availability confirmedbioRxiv · checked 15 Sept 2026
FlowerClassificationGrowth / time-series analysisGrowth / development / phenology
O_LIPhenology -- the timing of recurring life history events--is strongly linked to climate. Shifts in phenology have important implications for trophic interactions, ecosystem functioning and community ecology. However, data on plant phenology can be time consuming to collect and current records are biased across space and taxonomy. C_LIO_LIHere, we explore the performance of Convolutional Neural Networks (CNN) for classifying flowering phenology on a very large and taxonomically diverse dataset of citizen science images. We analyse >1.8 million iNaturalist records for plants listed in the National Botanical Gardens within South Africa, a country famed for its floristic diversity ([~]21,000 species) but poorly represented in phenological databases. C_LIO_LIWe were able to correctly classify images with >90% accuracy. Using metadata associated with each image, we then reconstructed the timing of peak flower production and length of the flowering season for the 6,986 species with >5 iNaturalist records. C_LIO_LIOur analysis illustrates how machine learning tools can leverage the vast wealth of citizen science biodiversity data to describe large-scale phenological dynamics. We suggest such approaches may be particularly valuable where data on plant phenology is currently lacking. C_LI
Why it matches plant phenotyping methods植物画像にCNNを適用して開花フェノロジーを分類し、開花時期と開花期間を推定する手法が研究の中心であるため、植物フェノタイピング手法研究に該当します。
abstractHere, we explore the performance of Convolutional Neural Networks (CNN) for classifying flowering phenology on a very large and taxonomically diverse dataset of citizen science images.
Reproduction assets foundThe authors publicly release all data and R code needed to recreate the analyses on GitHub (ML-Phenology-Code), and the phenotyping input data are iNaturalist research-grade observation images (1,807,310 images) downloaded via the iNaturalist GBIF DarwinCore Archive, both publicly accessible.Code · publicl images; R.D.S., N.B., and T.J.D. constructed and built
398
the models. R.D.S. analysed the data; R.D.S. and T.J.D. interpreted results; R.D.S. and T.J.D.
399
wrote the manuscript with significant input from N.B. and M.vdB.
400
Data availability
401
All data and R code needed to recreate analyses are available on GitHub at
402
https://github.com/rossdstewart/ML-Phenology-Code and at doi: xx
403
404
.
CC-BY-NC-ND 4.0 International license
perpetuity. It is made available under a
preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in
The copyright holder for this
this version posted December 23, 2023.
;
https://doi.Open asset ↗rossdstewart/ML-Phenology-Codepdf-raw-page:14 lines:1-47Code / dataset availability confirmedEurope PMC · bioRxiv · checked 7 Sept 2026
ABSTRACT Background The prediction of desirable traits in wheat from imaging data is an area of growing interest thanks to the increasing accessibility of remote sensing technology. However, as the amount of data generated continues to grow, it is important that the most appropriate models are used to make sense of this information. Here, the performance of neural network models in predicting grain asparagine content is assessed against the performance of other models. Results Neural networks had greater accuracies than partial least squares regression models and gaussian naïve Bayes models for prediction of grain asparagine content, yield, genotype, and fertiliser treatment. Genotype was also more accurately predicted from seed data than from canopy data. Conclusion Using wheat canopy spectral data and combinations of wheat seed morphology and spectral data, neural networks can provide improved accuracies over other models for the prediction of agronomically important traits.
Why it matches plant phenotyping methods画像・スペクトルデータから穀粒成分や収量などの植物形質を予測するニューラルネットワークを他手法と比較評価しており、形質推定法の性能検証が中心である。
abstractHere, the performance of neural network models in predicting grain asparagine content is assessed against the performance of other models.
Reproduction assets foundThe preprint states that the data and code used in this study (neural network/PLSR/GNB modelling of wheat canopy spectral and seed imaging data) are publicly available in the author's GitHub repository, which matches an allowed URL.Code · publicData and code used in this study are available at: https://github.com/JosephOddy/wheat-Open asset ↗JosephOddy/wheat-pdf-page:7 lines:1-50Code / dataset availability confirmedEurope PMC · bioRxiv · checked 7 Sept 2026
Functional plant ecology aims to understand how functional traits govern the distribution of species along environmental gradients, the assembly of communities, and ecosystem functions and services. The rapid rise of functional plant ecology has been fostered by the mobilization and integration of global trait datasets, but significant knowledge gaps remain about the functional traits of the ∼380,000 vascular plant species worldwide. The acquisition of urgently needed information through field campaigns remains challenging, time-consuming and costly. An alternative and so far largely untapped resource for trait information is represented by texts in books, research articles and on the internet which can be mobilized by modern machine learning techniques. Here, we propose a natural language processing (NLP) pipeline that automatically extracts trait information from an unstructured textual description of a species and provides a confidence score. To achieve this, we employ textual classification models for categorical traits and question answering models for numerical traits. We demonstrate the proposed pipeline on five categorical traits (growth form, life cycle, epiphytism, climbing habit and life form), and three numerical traits (plant height, leaf length, and leaf width). We evaluate the performance of our new NLP pipeline by comparing results obtained using different alternative modeling approaches ranging from a simple keyword search to large language models, on two extensive databases, each containing more than 50,000 species descriptions. The final optimized pipeline utilized a transformer architecture to obtain a mean precision of 90.8% (range 81.6-97%) and a mean recall of 88.6% (77.4-97%) on the categorical traits, which is an average increase of 21.4% in precision and 57.4% in recall compared to a standard approach using regular expressions. The question answering model for numerical traits obtained a normalized mean absolute error of 10.3% averaged across all traits. The NLP pipeline we propose has the potential to facilitate the digitalization and extraction of large amounts of plant functional trait information residing in scattered textual descriptions. Additionally, our study adds to an emerging body of NLP applications in an ecological context, opening up new opportunities for further research at the intersection of these fields.
Why it matches plant phenotyping methods植物機能形質を非構造化テキストから自動抽出するNLPパイプラインを開発し、複数モデルと大規模データベースで性能評価しており、形質取得・推定手法が研究の中心である。
abstractHere, we propose a natural language processing (NLP) pipeline that automatically extracts trait information from an unstructured textual description of a species and provides a confidence score.
Reproduction assets foundThe paper explicitly states that the code to train, evaluate, and use the NLP trait-extraction models is publicly available in the authors' GitHub repository. The POWO, Wikipedia, and GIFT data sources are third-party databases/cited resources rather than paper-specific deposits, so only the code repository qualifies.Code · public1 The code to train, evaluate and use the models is available at
https://github.com/ViktorDomazetoski/NLP-Plant-TraitsOpen asset ↗ViktorDomazetoski/NLP-Plant-Traitspdf-page:5 lines:1-50Code / dataset availability confirmedEurope PMC · bioRxiv · checked 7 Sept 2026
Field / plotLeafStomata / guard-cell complexPhysiological trait estimation2D/3D reconstructionPhotosynthesis / fluorescenceStomatal traitsWater status / transpiration
ABSTRACT Premise of the study The adaptive significance of stomata on both upper and lower leaf surfaces, called amphistomy, is unresolved. A widespread association between amphistomy and open, sunny habitats suggests the adaptive benefit of amphistomy may be greatest in these contexts, but this hypothesis has not been tested experimentally. Understanding why amphistomy evolves can inform its potential as a target for crop improvement and paleoenvironment reconstruction. Methods We developed a new method to quantify “amphistomy advantage”, AA, as the log-ratio of photosynthesis in an amphistomatous leaf to that of the same leaf but with gas exchange blocked through the upper (adaxial) surface, which we term “pseudohypostomy”. We used humidity to modulate stomatal conductance and thus compare photosynthetic rates at the same total stomatal conductance. We estimated AA and related physiological and anatomical traits in 12 populations, six coastal (open, sunny) and six montane (closed, shaded), of the indigenous Hawaiian species ‘ilima ( Sida fallax ). Key results Coastal ‘ilima leaves benefit 4.04 times more from amphistomy compared to their montane counterparts. Our evidence was equivocal with respect to two hypotheses – that coastal leaves benefit more because 1) they are thicker and therefore have lower CO 2 conductance through the internal airspace, and 2) that they benefit more because they have similar conductance on each surface, as opposed to most of the conductance being on the lower (abaxial) surface. Conclusions This is the first direct experimental evidence that amphistomy per se increases photosynthesis, consistent with the hypothesis that parallel pathways through upper and lower mesophyll increase the supply of CO 2 to chloroplasts. The prevalence of amphistomatous leaves in open, sunny habitats can partially be explained the increased benefit of amphistomy in ‘sun’ leaves, but the mechanistic basis of this observation is an area for future research.
Why it matches plant phenotyping methods葉の両面気孔性が光合成に与える効果を定量化する新しい生理的測定法を開発し、複数集団で比較検証しており、表現型取得が研究の中心である。
abstractWe developed a new method to quantify “amphistomy advantage”, AA, as the log-ratio of photosynthesis in an amphistomatous leaf to that of the same leaf but with gas exchange blocked through the upper (adaxial) surface, which we term “pseudohypostomy”.
Reproduction assets foundThe paper's Data Availability Statement explicitly provides a public GitHub repository containing the custom analysis scripts for this study's amphistomy advantage measurements. Raw data are only promised for future Dryad deposit (not yet available), so only the code asset qualifies.Code · publicCustom scripts are available on a GitHub repository (https://github.com/cdmuir/stomata-ilima) and will
be archived on Zenodo with a DOI and stable URL upon publication.Open asset ↗cdmuir/stomata-ilimapdf-page:16 lines:1-52Code / dataset availability confirmedEurope PMC · bioRxiv · checked 15 Sept 2026
We present a sensitive method for non-contact detection of chloroplast movements in leaves and other photosynthetic tissues, based on changes in the magnitude of cross-polarized reflectance. We examined changes in bidirectional red light reflectance during irradiation with blue light, known to trigger chloroplast relocations. Experiments on the model plant Arabidopsis thaliana , wild-type, and several mutants with disrupted chloroplast movements showed that the chloroplast avoidance response, induced by high blue light, led to a substantial increase in diffuse reflectance of unpolarized red light. The effects of the accumulation response in low blue light were the opposite. The specular reflectance of the leaf was unaffected by the chloroplast positioning. To further improve the specificity of the detection, we examined the effects of chloroplast relocations on the leaf reflectance of a linearly polarized incident beam. The greatest relative change associated with chloroplast movements was observed when the planes of polarization of the incident and detected beams were perpendicular. Further experiments revealed that the chloroplast positioning affected the magnitude of depolarization of light by the leaf. We applied the developed approach to examine chloroplast relocations in four angiosperm species collected in the field. The method allowed us to detect the chloroplast avoidance response in the green stems of bilberry, a sample not amenable to transmittance-based detection. Despite the importance of chloroplast movements for the optimization of photosynthetic efficiency and biomass production, high throughput reflectance-based methods are not routinely used for their detection. This method opens the possibility of non-invasive, non-contact detection of chloroplast relocations in a manner insensitive to the orientation of the leaf.
Why it matches plant phenotyping methods葉のクロロプラスト移動という植物状態を、偏光反射によって非接触・非侵襲的に検出する手法を開発し、複数種で適用しているため、植物フェノタイピング手法が研究の中心である。
abstractWe present a sensitive method for non-contact detection of chloroplast movements in leaves and other photosynthetic tissues, based on changes in the magnitude of cross-polarized reflectance.
Reproduction assets foundThe paper's Data availability statement openly deposits the paper's own reflectance/transmittance phenotype recordings (Arabidopsis WT/mutants and wild plants) on FigShare, and provides authors' public code: BeamJ (Java control software for the phenotyping setup) and openRayTracer (Mathematica ray-tracing package used,Dataset · publiced on the manuscript.
Conflict of interest
The authors declare no conflict of interest.
Funding
This study was supported by the National Science Centre Poland within the MINIATURA 4
project to P.H., number 2020/04/X/NZ4/01256.
Data availability
The data that support the findings of this study are openly available in FigShare at
https://doi.org/10.6084/m9.figshare.21082654 (reflectance and transmittance recordings for
Arabidopsis WT and mutants) and https://doi.org/10.6084/m9.figshare.24424843 (wild
plants). Java source code for the software is publicly available via GitHub at
https://github.com/pawelHerm/beamJ/tree/master/BeamJ. Wolfram Mathematica package for
ray tracing is available at httOpen asset ↗FigShare · 10.6084/m9.figshare.21082654pdf-raw-page:14 lines:1-47Dataset · publicthe National Science Centre Poland within the MINIATURA 4
project to P.H., number 2020/04/X/NZ4/01256.
Data availability
The data that support the findings of this study are openly available in FigShare at
https://doi.org/10.6084/m9.figshare.21082654 (reflectance and transmittance recordings for
Arabidopsis WT and mutants) and https://doi.org/10.6084/m9.figshare.24424843 (wild
plants). Java source code for the software is publicly available via GitHub at
https://github.com/pawelHerm/beamJ/tree/master/BeamJ. Wolfram Mathematica package for
ray tracing is available at https://github.com/plantPhotobiologyLab/openRayTracer.References
Banaś, A. K., Aggarwal, C., Łabuz, J., Sztatelman, O., Gabryś,Open asset ↗FigShare · 10.6084/m9.figshare.24424843pdf-raw-page:14 lines:1-47Code · publicthat support the findings of this study are openly available in FigShare at
https://doi.org/10.6084/m9.figshare.21082654 (reflectance and transmittance recordings for
Arabidopsis WT and mutants) and https://doi.org/10.6084/m9.figshare.24424843 (wild
plants). Java source code for the software is publicly available via GitHub at
https://github.com/pawelHerm/beamJ/tree/master/BeamJ. Wolfram Mathematica package for
ray tracing is available at https://github.com/plantPhotobiologyLab/openRayTracer.References
Banaś, A. K., Aggarwal, C., Łabuz, J., Sztatelman, O., Gabryś, H. (2012). Blue light
signalling in chloroplast movements. Journal of Experimental Botany, 63(4), 1559–
1574.
Baránková, B., LazáOpen asset ↗GitHub · pawelHerm/beamJpdf-raw-page:14 lines:1-47Code · publicuorescence. The filtered light was focused on a
photodetector (amplified silicon photodiode, PDA100A2, Thorlabs) with a plano-convex lens
(LA1074-A, Thorlabs). The angular size of the clear aperture of the collecting lens with
respect to the sample center was 0.019 steradian (calculated using our ray-tracing
Mathematica package https://github.com/plantPhotobiologyLab/openRayTracer). To control
the observation angle, the detector was mounted at the RBB300A/M rotation board
(Thorlabs). The experiments were performed with two angular positions of the polarizer: its
transmission axis was either parallel (transmits P) or perpendicular (transmits S component)
to the plane of incidence. The LEDs suOpen asset ↗GitHub · plantPhotobiologyLab/openRayTracerpdf-raw-page:6 lines:1-45Code / dataset availability confirmedbioRxiv · checked 14 Sept 2026
The evolutionary histories of species have been shaped by genomic, environmental, and morphological variation. Understanding the interactions among these sources of variation is critical to infer accurately the biogeographic history of lineages. Here, using the geographically widely distributed plum genus (Prunus, Rosaceae) as a model, we investigate how changes in genomic and environmental variation drove the diversification of this group, and we quantify the morphological features that facilitated or resulted from diversification. We sequenced 587 nuclear loci and complete chloroplast genomes from 99 species representing all major lineages in Prunus, with a special focus on the understudied tropical racemose group. The environmental variation in extant species was quantified by synthesizing bioclimatic variables into principal components of environmental variation using thousands of georeferenced herbarium specimens. We used machine learning algorithms to classify and measure morphological variation present in thousands of digitized herbarium sheet images. Our phylogenomic and biogeographic analyses revealed that ancient hybridization and/or allopolyploidy spurred the initial rapid diversification of the genus in the early Eocene, with subsequent diversification in the north temperate zone, neotropics, and paleotropics. This diversification involved successful transitions between tropical and temperate biomes, an exceedingly rare event in woody plant lineages, accompanied by morphological changes in leaf and reproductive morphology. The machine learning approach detected morphological variation associated with ancient hybridization and quantified the breadth of morphospace occupied by major lineages within the genus. The paleotropical lineages of Prunus have diversified steadily since the late Eocene/early Oligocene, while the neotropical lineages diversified much later. Critically, both the tropical and temperate lineages have continued to diversify. We conclude that the genomic rearrangements created by reticulation deep in the phylogeny of Prunus may explain why this group has been more successful than other groups with tropical origins that currently persist only in either tropical or temperate regions, but not both.
Why it matches plant phenotyping methods機械学習を用いて標本画像から植物の形態変異を分類・測定し、葉および生殖形態の表現型を定量化しており、形態計測ワークフローが研究の中心的要素である。
abstractWe used machine learning algorithms to classify and measure morphological variation present in thousands of digitized herbarium sheet images.
Reproduction assets foundThe paper's herbarium image data and supplementary material are deposited in Dryad; trained Prunus ML classifiers are on Hugging Face; author Jupyter notebooks for the ML workflow are on GitHub. All are paper-specific, public, and actionable.Dataset · public1126 Data Availability
1127
1128 Data available from the Dryad Digital Repository: DOI: 10.5061/dryad.x95x69pwr; Reviewer
1129 URL: http://datadryad.org/share/L-QUcxrgnpTr6l0Td3MxdfJDCUT1iRbPmT4SzA2LwMA.
1130
1131 Supplementary material, image data, and DNA sequence matrices are available from the Dryad
1132 Digital Repository. Raw sequence data were submitted to NCBI GenBank (SUB13638423).
1133 Machine learning models are hosted on Hugging Face with temporary URLs
1134 (https://huggingface.co/richiehodel/Prunus_lineage_classiOpen asset ↗Dryad Digital Repository · 10.5061/dryad.x95x69pwrpdf-layout-page:54 lines:1-31Code / dataset availability confirmedbioRxiv · Europe PMC · checked 15 Sept 2026
PremiseLeaf epidermal cell morphology is closely tied to plants evolutionary histories and growth environments, and is therefore of interest to many plant biologists. However, cell measurement can be time-consuming and restrictive with current methods. CuticleTrace is a suite of FIJI and R-based functions that streamlines and automates the segmentation and measurement of epidermal pavement cells across a wide range of cell morphologies and image qualities. Methods and ResultsWe evaluated CuticleTrace-generated measurements against those from alternate automated methods and expert and undergraduate hand-tracings across a taxonomically diverse 50-image dataset of variable image qualities. We observed [~]93% statistical agreement between CuticleTrace and expert hand-traced measurements, outperforming alternate methods. ConclusionsCuticleTrace is broadly applicable, modular, and customizable, and integrates data visualization and cell shape measurement with image segmentation, lowering the barrier to high-throughput studies of epidermal morphology by vastly decreasing the labor investment required to generate high-quality cell shape datasets.
Why it matches plant phenotyping methods葉の表皮細胞形態を画像からセグメンテーション・測定するソフトウェアを開発し、代替手法および専門家の手トレースと比較検証しており、植物表現型取得法が研究の中心です。
abstractCuticleTrace is a suite of FIJI and R-based functions that streamlines and automates the segmentation and measurement of epidermal pavement cells across a wide range of cell morphologies and image qualities.
Reproduction assets foundThe authors publicly release the CuticleTrace FIJI macros and R filtering notebook used for the paper's epidermal cell phenotyping analysis on GitHub, with explicit availability language and URL.Code · publicSB and SWP supervised and
291
directed the research.
292
DATA AVAILABILITY
293
All generated and analyzed data from this study are included in the published article and its
294
Supporting Information (Fig. S2). The code for the FIJI macros as well as the R notebook for
295
filtering cells is available in the GitHub repository: (https://github.com/benjlloyd/CuticleTrace).296
REFERENCES
297
Aono, A. H., J. S. Nagai, G. da S. M. Dickel, R. C. Marinho, P. E. A. M. de Oliveira, J. P. Papa,
298
and F. A. Faria. 2021. A stomata classification and detection system in microscope
299
images of maize cultivars. PLOS ONE 16: e0258679.
300
Barclay, R., J. Mcelwain, D. Dilcher, and B. Sageman. 2007. The COpen asset ↗benjlloyd/CuticleTracepdf-raw-page:13 lines:1-61Code / dataset availability confirmedbioRxiv · checked 14 Sept 2026
O_LIAccurate and real-time monitoring of grapevine freezing tolerance is crucial for the sustainability of the grape industry in cool climate viticultural regions. However, on-site data is limited. Current prediction models underperform under diverse climate conditions, which limits the large-scale deployment of these methods. C_LIO_LIWe combined grapevine freezing tolerance data from multiple regions in North America and generated a predictive model based on hourly temperature-derived features and cultivar features using AutoGluon, an automatic machine learning engine. Feature importance was quantified by AutoGluon and SHAP value. The final model was evaluated and compared with previous models for its performance under different climate conditions. C_LIO_LIThe final model achieved an overall 1.36 {degrees}C root-mean-square error during model testing and outperformed two previous models using three test cultivars at all testing regions. Two feature importance quantification methods identified five shared essential features. Detailed analysis of the features indicates that the model might have adequately extracted some biological mechanisms during training. C_LIO_LIThe final model, named NYUS.2, was deployed along with two previous models as an R shiny-based application in the 2022-2023 dormancy season, enabling large-scale and real-time simulation of grapevine freezing tolerance in North America for the first time. C_LI
Why it matches plant phenotyping methodsブドウの凍結耐性という植物状態を大規模・リアルタイムに推定する自動機械学習モデルを開発し、既存モデルとの性能比較と実運用展開まで行っており、表現型推定手法が中心です。
abstractWe combined grapevine freezing tolerance data from multiple regions in North America and generated a predictive model based on hourly temperature-derived features and cultivar features using AutoGluon, an automatic machine learning engine.
Reproduction assets foundThe authors publicly released the original LT50 training data and the source code for feature extraction, model training, and deployment in a GitHub repository explicitly stated in the Data availability section. The ACIS URL is a generic external climate data service, not a paper-specific asset.Code · publiclly yielding with a more generalizable model to help understand the biology of grapevine
526
freezing tolerance and quantify the threat of freezing under climate change.
527
5. Data availability
528
All the original training data and source code for feature extraction, modeling training and model
529
deployment are available at https://github.com/imbaterry11/NYUS.2
530
6. Acknowledgements
531
The authors would like to thank Lynn Mills (WA), Beth Ann Workmaster (WI), Katherine
532
Benedict (NS), Alexander Campbell and Jessee Tinslay (QC), Don Smith and Meredith Persico
533
(PA) and Hanna Martins, Felex Pike, and Bill Wilsey (NY) for their help in LT50 data collection.
534
This work was parOpen asset ↗https://github.com/imbaterry11/NYUS.2 · NYUS.2pdf-raw-page:25 lines:1-64Code / dataset availability confirmedEurope PMC · bioRxiv · checked 15 Sept 2026
MicroscopyCell / cellular structureFlowerTissue2D/3D reconstructionGrowth / development / phenology
Floral spurs are invaginations borne by perianth organs (petals and/or sepals) that have evolved repeatedly in various angiosperm clades. They typically store nectar and can limit the access of pollinators to this reward, resulting in pollination specialization that can lead to speciation in both pollinator and plant lineages. Despite the ecological and evolutionary importance of nectar spurs, the cellular mechanisms involved during spur development have only been described in detail in a handful of species, primarily with respect to epidermal cells. These studies show that the mechanisms involved are taxon-specific. Using confocal microscopy and automated 3D image analysis, we studied spur morphogenesis in Staphisagria picta (Ranunculaceae) and showed that the process is marked by an early phase of dominant cell proliferation, followed by a phase of anisotropic (directional) cell expansion. The comparison with Aquilegia , another taxon of Ranunculaceae with spurred petals, revealed that the convergence in form between the spurs of both taxa is obtained by partially similar developmental processes. The analytical pipeline designed here is an efficient method to visualize in 3D each cell of a developing organ, paving the way for future comparative studies of organ morphogenesis in multicellular eukaryotes. Highlight A new method of 3D analysis of plant tissues at the cellular level revealed that spur morphogenesis in Staphisagria picta is marked by an early phase of dominant cell proliferation, followed by a phase of anisotropic cell expansion. Floral spur development is analysed for the first time quantitatively, taking into account all tissues composing the organ, namely epidermis and parenchyma.
Why it matches plant phenotyping methods共焦点顕微鏡と自動3D画像解析による発生器官の細胞形態・増殖・異方的伸長の定量化手法が研究の中心であり、植物器官の表現型取得・解析に該当する。
abstractUsing confocal microscopy and automated 3D image analysis, we studied spur morphogenesis in Staphisagria picta (Ranunculaceae)
Reproduction assets foundThe paper's 3D segmentation/visualization pipeline (PlantSeg + MorphoLibJ + homemade Python scripts) is the paper-specific computational analysis, and the authors explicitly state the automation and visualization code is publicly available on GitHub. No separate public phenotype dataset or image deposit is stated; dataCode · public”.
235 Cell outliers, i.e. the 5% largest and smallest cells in terms of volume, were filtered out. To
236 visualize the interior of the petals, we relied on the opacity of the dots or on virtual sections.
237 The code that allowed the automation of the segmentations and the visualization of the data is
238 available on github [https://github.com/paulinedlpch/morphogenesis].Open asset ↗paulinedlpch/morphogenesispdf-layout-page:6 lines:1-57Code / dataset availability confirmedEurope PMC · bioRxiv · checked 7 Sept 2026
RadishRiceX-ray / CTRootObject detection2D/3D reconstructionGrowth / time-series analysisVisualization / data managementRoot system architecture
Crop genetic engineering for better root systems can offer practical solutions for food security and carbon sequestration; however, soil layers prevent direct visualization. Here, we demonstrate an original device with a distributed fiber-optic sensor for fully automated, real-time monitoring of underground root development. We demonstrate that spatially encoding an optical fiber with a flexible and durable polymer film in a spiral pattern can significantly enhance sensor detection. After signal processing, the resulting device can detect the penetration of a submillimeter-diameter object in the soil, indicating more than a magnitude higher spatiotemporal resolution than previously reported with underground monitoring techniques. We also developed computational models to visualize the roots of root crops and monocotyledons, and then applied them to radish and rice to compare the results with those of X-ray computed tomography. The device’s groundbreaking sensitivity and spatiotemporal resolution enable seamless and laborless phenotyping of root systems that are otherwise invisible underground.
Why it matches plant phenotyping methods地下根系を対象とする分布型光ファイバーセンサーと計算モデルを開発し、根系フェノタイピングへの適用・比較検証まで行うことが中心であるため。
abstractwe demonstrate an original device with a distributed fiber-optic sensor for fully automated, real-time monitoring of underground root development
Reproduction assets foundThe paper's custom MATLAB code for virtual root reconstruction from fiber-optic strain data is explicitly stated to be publicly available on the authors' GitHub repository (Fiber-RADGET). No separate public phenotype dataset deposit is mentioned; the supplementary movie is not a qualifying dataset URL.Code · publicThe
custom code for the virtual root reconstruction in MATLAB
(MathWorks, Massachusetts, USA) is available at
https://github.com/mtei1/Fiber-RADGET.git.Open asset ↗mtei1/Fiber-RADGETpdf-page:12 lines:1-24Code / dataset availability confirmedEurope PMC · bioRxiv · checked 15 Sept 2026
A bstract The actin cytoskeleton is essential in eukaryotes, not least in the plant kingdom where it plays key roles in cell expansion, cell division, environmental responses and pathogen defence. Yet, the precise structure-function relationships of properties of the actin network in plants are still to be unravelled, including details of how the network configuration depends upon cell type, tissue type and developmental stage. Part of the problem lies in the difficulty of extracting high-quality, three-dimensional, quantitative measures of actin network features from microscopy data. To address this problem, we have developed DRAGoN, a novel image analysis algorithm that can automatically extract the actin network across a range of cell types, providing seventeen different quantitative measures that describe the network at a local level. Using this algorithm, we then studied a number of cases in Arabidopsis thaliana , including several different tissues, a variety of actin-affected mutants, and cells responding to powdery mildew. In many cases we found statistically-significant differences in actin network properties. In addition to these results, our algorithm is designed to be easily adaptable to other tissues, mutants and plants, and so will be a valuable asset for the study and future biological engineering of the actin cytoskeleton in globally-important crops.
Why it matches plant phenotyping methods植物の顕微鏡画像からアクチンネットワークの構造特性を自動抽出する画像解析手法を開発しており、植物状態の定量的表現型取得が中心である。
abstractwe have developed DRAGoN, a novel image analysis algorithm that can automatically extract the actin network across a range of cell types, providing seventeen different quantitative measures that describe the network at a local level.
Reproduction assets foundThe paper's DRAGoN actin-network extraction algorithm (authors' analysis code) is explicitly stated to be freely available and open source on GitHub. No public phenotype dataset or image deposit is described in the supplied blocks.Code · publicery small amount. A much larger data set or perhaps an
artificial stimulation of the immune response (e.g. a microneedle assay[80]) may help in discerning these changes in
more detail.
To facilitate further development or optimisation for particular data sets, we have made the DRAGoN software freely
available and open source at https://github.com/JordanHembrow5/DRAGoN. The flexibility and non-specificity of this
tool is one of its main advantages and should enable it to be useful in a range of organisms, mutants, tissues, cell types
and environments. A number of key parameters (particularly those for the filtering and skeletonisation steps) can be
adjusted to best fit a given image modalityOpen asset ↗JordanHembrow5/DRAGoNpdf-layout-page:16 lines:1-48Code / dataset availability confirmedbioRxiv · Europe PMC · Crossref · checked 14 Sept 2026
Field / plotRootWhole plant / canopy / plot / fieldMorphology / geometry measurementGrowth / time-series analysisGrowth / development / phenologyRoot system architecture
1Roots have a central role in plant resource capture and are the interface between the plant and the soil that affect multiple ecosystem processes. Field pennycress (Thlaspi arvense L.) is a diploid annual cover crop species that has potential utility for reducing soil erosion and nutrient losses; and has rich seeds (30-35% oil) amenable to biofuel production and as a protein animal feed. The objective of this research was to (1) precisely characterize root system architecture and development, (2) understand adaptive responses of pennycress roots to nitrate nutrition, (3) and determine genotypic variance available in root development and nitrate plasticity. Using a root imaging and analysis pipeline, 4D pennycress root system architecture was characterized under four nitrate regimes (from zero to high nitrate concentration) across four time points (days 5, 9, 13, and 17 after sowing). Significant nitrate condition response and genotype interactions were identified for many root traits with greatest impact on lateral root traits. In trace nitrate conditions a greater lateral root count, length, interbranch density, and a steeper lateral root angle was observed compared to high nitrate conditions. Genotype-by-nitrate condition interaction was observed for root width, width:depth ratio, mean lateral root length, and lateral root density. These results illustrate root trait variance available in pennycress accessions that could be useful targets for breeding of improved nitrate responsive cover crops for greater productivity, resilience, and ecosystem service.
Why it matches plant phenotyping methods根系画像・解析パイプラインを用いた4D根系構造の取得と多数の根形質の抽出が研究の中心であり、植物フェノタイピング手法の実質的な応用に該当する。
abstractUsing a root imaging and analysis pipeline, 4D pennycress root system architecture was characterized under four nitrate regimes
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 · public151 codes including the packages needed are available https://doi.org/10.5281/zenodo.7536940. A total of 44Open asset ↗zenodo · 10.5281/zenodo.7536940pdf-page:5 lines:1-52Code / dataset availability confirmedEurope PMC · bioRxiv · checked 15 Sept 2026
Live imaging is the gold standard for determining how cellular development gives rise to organs. However, tracking all individual cells across whole organs over large developmental time windows is extremely challenging. In this work, we provide a comparably simple method for confocal live imaging of Arabidopsis thaliana first leaves across early development. Our imaging method works for both wild-type leaves and the complex curved leaves of the jaw-1D mutant. We find that dissecting the cotyledons, affixing a coverslip above the samples and mounting samples with perfluorodecalin yields optimal imaging series for robust cellular and organ level analysis. We provide details of our complementary image processing steps in MorphGraphX software for segmenting cells, tracking the cell lineages, and measuring a suite of cellular growth properties. We also provide MorphoGraphX image processing scripts that we developed to automate analysis of segmented images and data presentation. Our imaging techniques and processing steps combine into a robust imaging pipeline. With this pipeline we are able to examine important nuances in the cellular growth and differentiation of jaw-D versus WT leaves that have not been demonstrated before. Our pipeline is a practical starting place for researchers new to live imaging plant leaves, but also to anyone interested in improving the throughput and reliability of their live imaging process.
Why it matches plant phenotyping methods葉全体の共焦点ライブイメージング、細胞セグメンテーション・系譜追跡・成長特性測定を統合した実用的な表現型解析パイプラインの開発であり、方法が研究の中心です。
abstractIn this work, we provide a comparably simple method for confocal live imaging of Arabidopsis thaliana first leaves across early development.
Reproduction assets foundThe paper's phenotyping analysis code is publicly available in authors' GitHub repositories: MorphoGraphX processing/quantification scripts (iterative_growth_and_measures.py, multi_resize.py, batch_tiff.py) in roeder_lab_projects/mgx_scripts, ImageJ scripts, and R analysis/figure scripts in live_img_paper and jawdPaperCode · publice heat map representations of the
data with standardized parameters across time point comparisons and replicates (Video 4).
Data analysis
All data processing, analysis and plotting was performed in RStudio (2020; 2021). Scripts used
to process the data and create figures are enclosed as Supplemental Information and available at
https://github.com/kateharline/live_img_paper, https://github.com/kateharline/roeder_lab_proj-ects/tree/master/imagej_scripts and https://github.com/kateharline/jawd-paper.Data availability
Imaging data will be deposited XXXX.
Funding
Kate Harline was supported by NSF Graduate Research Fellowship (DGE-1650441). This work
was funded by NSF MCB-2203275 (AHKR), The SchwaOpen asset ↗kateharline/live_img_paperpdf-raw-page:20 lines:1-63Code · publicndardized parameters across time point comparisons and replicates (Video 4).
Data analysis
All data processing, analysis and plotting was performed in RStudio (2020; 2021). Scripts used
to process the data and create figures are enclosed as Supplemental Information and available at
https://github.com/kateharline/live_img_paper, https://github.com/kateharline/roeder_lab_proj-ects/tree/master/imagej_scripts and https://github.com/kateharline/jawd-paper.Data availability
Imaging data will be deposited XXXX.
Funding
Kate Harline was supported by NSF Graduate Research Fellowship (DGE-1650441). This work
was funded by NSF MCB-2203275 (AHKR), The Schwartz Research Fund Award (AHKR), and
the NationaOpen asset ↗kateharline/roeder_lab_proj-ectspdf-raw-page:20 lines:1-63Code · publicnalysis
All data processing, analysis and plotting was performed in RStudio (2020; 2021). Scripts used
to process the data and create figures are enclosed as Supplemental Information and available at
https://github.com/kateharline/live_img_paper, https://github.com/kateharline/roeder_lab_proj-ects/tree/master/imagej_scripts and https://github.com/kateharline/jawd-paper.Data availability
Imaging data will be deposited XXXX.
Funding
Kate Harline was supported by NSF Graduate Research Fellowship (DGE-1650441). This work
was funded by NSF MCB-2203275 (AHKR), The Schwartz Research Fund Award (AHKR), and
the National Institute Of General Medical Sciences of the National Institutes of Health under
Open asset ↗kateharline/jawd-paperpdf-raw-page:20 lines:1-63Code / dataset availability confirmedEurope PMC · bioRxiv · checked 15 Sept 2026
The generation of realistic plant and animal images from marker information could be a main contribution of artificial intelligence to genetics and breeding. Since morphological traits are highly variable and highly heritable, this must be possible. However, a suitable algorithm has not been proposed yet. This paper is a proof of concept demonstrating the feasibility of this proposal using ‘decoders’, a class of deep learning architecture. We apply it to Cucurbitaceae, perhaps the family harboring the largest variability in fruit shape in the plant kingdom, and to tomato, a species with high morphological diversity also. We generate Cucurbitaceae shapes assuming a hypothetical, but plausible, evolutive path along observed fruit shapes of C. melo . In tomato, we used 353 images from 129 crosses between 25 maternal and 7 paternal lines for which genotype data were available. In both instances, a simple decoder was able to recover expected shapes with large accuracy. For the tomato pedigree, we also show that the algorithm can be trained to generate offspring images from their parents’ shapes, bypassing genotype information. Data and code are available at https://github.com/miguelperezenciso/dna2image .
Why it matches plant phenotyping methodsDNA配列や親の形状から植物果実形状画像を生成する深層学習手法の概念実証であり、植物形態の取得・推定が研究の中心です。
titleComputer generation of fruit shapes from DNA sequence
Reproduction assets foundThe paper's cucurbit shape phenotyping inputs and analysis code are publicly available in the authors' dna2image GitHub repository, explicitly cited in the methods and data availability statement.Dataset · publichways. One pathway would be wild gourd (akin to pumpkin shape) scallop acorn; a
134
second pathway would be wild gourd marrow straightneck zucchini cocozelle
135
(Figure 1B). See also Figure 17 in (Paris 1989). We extracted contours from the
136
‘contours.png’ file, based in (Paris 1989) and available in GitHub
137
(https://github.com/miguelperezenciso/dna2image/blob/main/images/contours.png), using
138
OpenCV library (Bradski 2000). Contours were centered and 500 pseudo-landmarks were
139
obtained with the algorithm in Zingaretti et al. (2021). Next, contours were aligned with a
140
generalized procrustes algorithm implemented in python package ‘procrustes’ (Meng et al.
141
2022Open asset ↗https://github.com/miguelperezenciso/dna2image · contours.pngpdf-raw-page:5 lines:1-76Code · publicy, we have shown that very simple networks can be successfully trained in small
322
datasets to accurately predict fruit images. Although much work remains to be done, this
323
research opens new possibilities in the area of prediction of complex traits.
324
325
Data availability statement
326
All data and code are available at https://github.com/miguelperezenciso/dna2image.327
328
.
CC-BY 4.0 International license
available under a
(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made
The copyright holder for this preprint
this version posted September 22, 2022.
;
https://doi.org/10.1101/2022.09.19.Open asset ↗https://github.com/miguelperezenciso/dna2image.327 · dna2image.327pdf-raw-page:10 lines:1-73Code / dataset availability confirmedbioRxiv · checked 15 Sept 2026
Field / plotWhole plant / canopy / plot / fieldCounting
O_LIThe estimation of population size and its variation across space and time largely relies on counts of individuals, generally carried out within spatial units such as quadrats or sites. Missing individuals during counting (i.e. imperfect detection) results in biased estimates of population size and trends. Imperfect detection has been shown to be the rule in animal studies, and most studies now correct for this bias by estimating detection probability. Yet this correction remains exceptional in plant studies, suggesting that most plant ecologists implicitly assume that all individuals are always detected. C_LIO_LITo assess if this assumption is valid, we conducted a field experiment to estimate individual detection probability in plant counts conducted in 1x1 m quadrats. We selected 30 herbaceous plant species along a gradient of conspicuousness at 24 sites along a gradient of habitat closure, and asked groups of observers to count individuals in 10 quadrats using three counting methods requiring progressively increasing times to complete (quick count, unlimited count and cell count). In total, 158 participants took part in the experiment, allowing an analysis of the results of 5,024 counts. C_LIO_LIOver all field sessions, no observer succeeded in detecting all the individuals in the 10 quadrats. The mean detection rate was 0.44 (ranging from 0.11 to 0.82) for the quick count, 0.59 for the unlimited count (range 0.18-0.87) and 0.74 for the cell count (range 0.46-0.94). C_LIO_LIDetection probability increased with the conspicuousness of the target species and decreased with the density of individuals and habitat closure. The observers experience in botany had little effect on detection probability, whereas detection was strongly affected by the time observers spent counting. Yet although the more time-consuming methods increased detection probability, none achieved perfect detection, nor did they reduce the effect on detection probability of the variables we measured. C_LIO_LISynthesis. Our results show that detection is imperfect and highly heterogeneous when counting plants. To avoid biased estimates when assessing the size, temporal or spatial trends of plant populations, plant ecologists should use methods that estimate the detection probability of individuals rather than relying on raw counts. C_LI
Why it matches plant phenotyping methods植物個体数の計数における検出確率を、複数の計数法・多数の観察者・反復データで評価しており、植物個体群サイズの測定法の妥当性検証が研究の中心です。
abstractwe conducted a field experiment to estimate individual detection probability in plant counts conducted in 1x1 m quadrats.
Reproduction assets foundThe paper's data availability statement explicitly deposits all data (5,024 plant counts from 300 quadrats) and analysis code in a public GitHub repository, which is listed in allowed_urls.Code · publicanised and conducted the field sessions; J.P. analysed the data and led the writing of the
503
manuscript. All authors critically contributed to the drafts and gave their final approval for
504
publication.
505
506
Data availability statement
507
All data and code used for this work are available from the GitHub repository:
508
https://github.com/JanPerret/Individual_detection_in_plant_counts
509
510
.
CC-BY-NC-ND 4.0 International license
available under a
(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made
The copyright holder for this preprint
this version posted December 21, 2022.
;
https://doiOpen asset ↗JanPerret/Individual_detection_in_plant_countspdf-raw-page:31 lines:1-41Code / dataset availability confirmedbioRxiv · checked 8 Sept 2026
Background information to give context to the studyCanopy gaps are the most evident manifestation of how disturbances disrupt forest landscapes. The size distribution and return frequency of gaps, and subsequent recovery processes, determine whether the old-growth state can be reached. The aim or research questionWe used remote sensing metrics to compare the disturbance regime of four Amazon regions based on the size distribution of gaps, their dynamics and geometric characteristics. A brief summary of the methodology usedWe assessed gap dynamics at four sites in the central, central eastern, southeastern, and northeastern regions of the Brazilian Amazon using repeated airborne laser scanning surveys. We developed a novel analysis to quantify four possible stages of gap dynamics: formation, expansion, persisting and recovering. For that, we overlapped layers of gap locations from two consecutive airborne laser scanning surveys. Key results with some significance measuresThe gap fraction in our study sites varied between 1.26% to 7.84%. All the sites have similar proportion of gaps among size classes. What notably changed between sites was not the gap size-distribution, but the relative importance of stages of gap dynamics. Growing and persisting rates were greatest in the site with the stronger seasonal variation in climate, lower annual precipitation, higher mean wind speed and higher solar radiation. The conclusions, which address the main aimsThe concept of stability reflects the tendency of a system to quickly return to a position of equilibrium when disturbed. We showed that gap dynamics varied among sites, with one example of low recovery rate contrasted to three other sites with faster recovery. Our results support that such as assessing the size distribution of gaps, investigating their return frequency and severity is crucial for understanding forest dynamics at the landscape and regional scales.
Why it matches plant phenotyping methods航空レーザースキャンの反復データを用いて森林キャノピーギャップの動態を定量化する新規解析法を開発しており、植物キャノピー状態の抽出が研究の中心である。
abstractWe developed a novel analysis to quantify four possible stages of gap dynamics: formation, expansion, persisting and recovering.
Reproduction assets foundThe paper's core phenotyping input — repeated airborne laser scanning data for the four Amazon study sites (Ducke, Tapajos, Tanguro, Jari) from the Sustainable Landscapes Brazil project — is explicitly stated to be freely available at the EMBRAPA Paisagens Lidar webgis portal. No author analysis code, scripts, or gap-dDataset · publicThe Sustainable Landscape Brazil Project has repeatedly surveyed Amazonian sites
with airborne laser scanning. This data set gave us a unique opportunity to assess gap
dynamics across Amazonia (data freely available at:
https://www.paisagenslidar.cnptia.embrapa.br/webgis/).Open asset ↗pdf-page:4 lines:1-46Code / dataset availability confirmedbioRxiv · Europe PMC · Crossref · checked 15 Sept 2026
Quantifying healthy and degraded inner tissues in plants is of great interest in agronomy, for example, to assess plant health and quality and monitor physiological traits or diseases. However, detecting functional and degraded plant tissues in-vivo without harming the plant is extremely challenging. New solutions are needed in ligneous and perennial species, for which the sustainability of plantations is crucial. To tackle this challenge, we developed a novel approach based on multimodal 3D imaging and Artificial Intelligence (AI)-based image processing that allowed a noninvasive diagnosis of inner tissues in living plants. The method was successfully applied to the grapevine (Vitis vinifera L.) in vineyards where sustainability was threatened by trunk diseases, while the sanitary status of vines cannot be ascertained without injuring the plants. By combining MRI and X-ray CT 3D imaging with an automatic voxel classification, we could discriminate intact, degraded, and white rot tissues with a mean global accuracy of over 91%. Each imaging modality contribution to tissue detection was evaluated, and we identified quantitative structural and physiological markers characterizing wood degradation steps. The combined study of inner tissue distribution versus external foliar symptom history demonstrated that white rot and intact tissue contents are key measurements in evaluating vines sanitary status. We finally proposed a model for an accurate trunk disease diagnosis in grapevine. This work opens new routes for precision agriculture and in-situ monitoring of wood quality and plant health across plant species.
Why it matches plant phenotyping methodsブドウ樹内部組織と病害状態を、MRI・X線CT・自動ボクセル分類によって非破壊的に定量する手法を開発・評価しており、植物表現型取得が研究の中心である。
abstractwe developed a novel approach based on multimodal 3D imaging and Artificial Intelligence (AI)-based image processing that allowed a noninvasive diagnosis of inner tissues in living plants
Reproduction assets foundThe paper's imaging datasets (MRI, X-ray CT, photographic volumes, annotations) are only available 'upon reasonable request', but the authors' extended Trainable Segmentation plugin used for the machine-learning voxel classification is explicitly open-source on GitHub.Code · publicFernandez et al. 24
DATA AND CODE AVAILABILITY
The datasets generated and analyzed during the current study are available from the corresponding
author upon reasonable request.
The extension of the Trainable Segmentation plugin is open-source, and available as a fork of Trainable
Segmentation on GitHub: https://github.com/Rocsg/Trainable_Segmentation/tree/Hyperweka.
.
CC-BY-NC-ND 4.0 International license
perpetuity. It is made available under a
preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in
The copyright holder for this
this version posted February 3, 2023.
;
https://doi.org/10.1101/2022.06.09.495457
doOpen asset ↗Rocsg/Trainable_Segmentation · Hyperwekapdf-raw-page:24 lines:1-16Code / dataset availability confirmedEurope PMC · bioRxiv · checked 15 Sept 2026
Accurate prediction of the phenotypic outcomes produced by different combinations of genotypes, environments, and management interventions remains a key goal in biology with direct applications to agriculture, research, and conservation. The past decades have seen an expansion of new methods applied towards this goal. Here we predict maize yield using deep neural networks, compare the efficacy of two model development methods, and contextualize model performance using linear models, which are the conventional method for this task, and machine learning models We examine the usefulness of incorporating interactions between disparate data types. We find a deep learning model with interactions has the best average performance. Optimizing submodules for each datatype improved model performance relative to optimizing the whole model for all data types at once. Examining the effect of interactions in the best performing model revealed that including interactions altered the model’s sensitivity to weather and management features, including a reduction of the importance scores for timepoints expected to have limited physiological basis for influencing yield – those at the extreme end of the season, nearly 200 days post planting. Based on these results, deep learning provides a promising avenue for phenotypic prediction of complex traits in complex environments and a potential mechanism to better understand the influence of environmental and genetic factors.
Why it matches plant phenotyping methods遺伝子型・環境・管理データからトウモロコシ収量という植物形質を予測する深層学習手法を開発・比較しており、表現型推定が研究の中心である。
abstractHere we predict maize yield using deep neural networks, compare the efficacy of two model development methods, and contextualize model performance using linear models, which are the conventional method for this task, and machine learning models
Reproduction assets foundThe paper uses publicly available Genomes to Fields (G2F) maize phenotype/weather/soil data (2014-2019) and provides authors' custom Python processing/analysis scripts on two public Bitbucket repositories. A Zenodo deposit (10.5281/zenodo.6916775) with PCA eigenvectors is mentioned but its URL is not among the allowed,Dataset · publicrformance, while avoiding
160
overfitting the model to any location
161
Materials and Methods
162
Data Preparation
163
We used data from the Genomes to Fields (G2F) initiative for years 2014-2019
164
(McFarland et al. 2020), focusing on the sites within the continental United States. Each year’s
165
data are publicly available (https://www.genomes2fields.org/resources/), including weather and
166
soil data for field sites, genomic data, management schedules (e.g., application of fertilizer,
167
herbicides, irrigation) and yield (in addition to other phenotypic variables). We augmented this
168
through additional genomic and weather data. Weather data retrieved from Daymet (Thornton et
169
alOpen asset ↗Genomes to Fieldspdf-raw-page:7 lines:1-53Code · publicthe eigenvectors
400
resulting from the principal components analysis are provided to enable transformation of
401
provided genomes. Additional weather measurements were retrieved from Daymet (Thornton et
402
al. 2020). Custom python scripts for downloading, aggregating and processing these data are
403
available on bitbucket (https://bitbucket.org/washjake/maizemodel and
404
https://bitbucket.org/daniel_kick/maizemodel/ ) in the notebooks directory (files with the prefix
405
0.0 to 0.5).
406
407
and is also made available for use under a CC0 license.
was not certified by peer review) is the author/funder. This article is a US Government work. It is not subject to copyright under 17 USC 105Open asset ↗washjake/maizemodelpdf-raw-page:17 lines:1-46Code · publicscripts were used to
173
aggregate and standardize terminology across years. Rather than itemizing each operation, we
174
restrict ourselves to those which are likely to be of interest to those working with similar data
175
sets. The scripts used are available through Bitbucket
176
(https://bitbucket.org/washjake/maizemodel and https://bitbucket.org/daniel_kick/maizemodel/ ).
177
Scripts were written in Python (Van Rossum and Drake 2009 p. 3) and rely on scientific and
178
common general libraries (Seabold and Perktold 2010; Pedregosa et al. 2011; fuzzywuzzy
179
2017; Virtanen et al. 2020; team 2020; Harris et al. 2020; Da Costa-Luis et al. 2022) along with
180
plotting libraries for exploraOpen asset ↗daniel_kick/maizemodelpdf-raw-page:8 lines:1-57Code / dataset availability confirmedEurope PMC · bioRxiv · checked 15 Sept 2026
Background Cyst nematodes are one of the major groups of plant-parasitic nematode, responsible for considerable crop losses worldwide. Improving genetic resources, and therefore resistant cultivars, is an ongoing focus of many pest management strategies. One of the major bottlenecks in identifying the plant genes that impact the infection, and thus the yield, is phenotyping. The current available screening method is slow, has unidimensional quantification of infection limiting the range of scorable parameters, and does not account for phenotypic variation of the host. The ever-evolving field of computer vision may be the solution for both the above-mentioned issues. To utilise these tools, a specialised imaging platform is required to take consistent images of nematode infection in quick succession. Results Here, we describe an open-source, easy to adopt, imaging hardware and trait analysis software method based on a pre-existing nematode infection screening method in axenic culture. A cost-effective, easy-to-build and -use, 3D-printed imaging device was developed to acquire images of the root system of Arabidopsis thaliana infected with the cyst nematode Heterodera schachtii , replacing costly microscopy equipment. Coupling the output of this device to simple analysis scripts allowed the measurement of some key traits such as nematode number and size from collected images, in a semi-automated manner. Additionally, we used this combined solution to quantify an additional trait, root area before infection, and showed both the confounding relationship of this trait on nematode infection and a method to account for it. Conclusion Taken together, this manuscript provides a low-cost and open-source method for nematode phenotyping that includes the biologically relevant nematode size as a scorable parameter, and a method to account for phenotypic variation of the host. Together these tools highlight great potential in aiding our understanding of nematode parasitism.
Why it matches plant phenotyping methods植物寄生性線虫感染の画像取得・解析を自動化する低コストの装置とソフトウェアを開発し、線虫数・サイズおよび根面積を測定する手法が中心であるため。
abstractHere, we describe an open-source, easy to adopt, imaging hardware and trait analysis software method
Reproduction assets foundThe paper's ImageJ analysis scripts (root surface area, colored-agar variant, leaf surface count) and a custom Python color-normalization script are explicitly deposited in the authors' public GitHub repository (OlafKranse/A_low_cost_imaging_tower), directly reproducing this paper's phenotyping analysis. No phenotype/тCode · publici.org/10.1101/2022.07.14.500020; this version posted July 15, 2022. The copyright holder for this preprint
(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is
made available under a CC-BY 4.0 International license.
described in the script
(https://github.com/OlafKranse/A_low_cost_imaging_tower/blob/main/Imaging and
Analyses/automated_root_surface_area.ijm). A slightly adjusted script was used for plates
containing dye (https://github.com/OlafKranse/A_low_cost_imaging_tower/blob/main/Imaging and
Analyses/automated_root_surface_area_colored_agar.ijm). The root surface area for all the images
in the folderOpen asset ↗OlafKranse/A_low_cost_imaging_towerpdf-layout-page:6 lines:1-37Code · publicing and quantifiable traits
Automatic counting was performed on images taken as described above. Depending on the
treatment a different script was used to calculate the number and size of females. Before isolation,
the colour histogram for all images was normalised to the first image in the dataset using a custom
python script (https://github.com/OlafKranse/A_low_cost_imaging_tower/tree/main/Imaging and
Analyses/Normalise colour). The images were then processed in ImageJ for two different nematode
life stages: i) tanned cyst nematodes; ii) female nematodes.Open asset ↗OlafKranse/A_low_cost_imaging_towerpdf-layout-page:6 lines:1-37Code / dataset availability confirmedEurope PMC · bioRxiv · checked 15 Sept 2026
Summary Plant ecologists use functional traits to describe how plants respond to and influence their environment. Reflectance spectroscopy can provide rapid, non-destructive estimates of leaf traits, but it remains unclear whether general trait-spectra models can yield accurate estimates across functional groups and ecosystems. We measured leaf spectra and 22 structural and chemical traits for nearly 2000 samples from 104 species. These samples span a large share of known trait variation and represent several functional groups and ecosystems. We used partial least-squares regression (PLSR) to build empirical models for estimating traits from spectra. Within the dataset, our PLSR models predicted traits like leaf mass per area (LMA) and leaf dry matter content (LDMC) with high accuracy ( R 2 >0.85; %RMSE<10). Models for most chemical traits, including pigments, carbon fractions, and major nutrients, showed intermediate accuracy ( R 2 =0.55-0.85; %RMSE=12.7-19.1). Micronutrients such as Cu and Fe showed the poorest accuracy. In validation on external datasets, models for traits like LMA and LDMC performed relatively well, while carbon fractions showed steep declines in accuracy. We provide models that produce fast, reliable estimates of several widely used functional traits from leaf reflectance spectra. Our results reinforce the potential uses of spectroscopy in monitoring plant function around the world.
Why it matches plant phenotyping methods葉の反射スペクトルから構造・化学的形質を推定する分光センシングとPLSRモデルを構築し、外部データで検証しており、植物形質取得法が研究の中心です。
abstractReflectance spectroscopy can provide rapid, non-destructive estimates of leaf traits
Reproduction assets foundThe paper's fresh-leaf spectral data are publicly available via the CABO data portal, and the authors' analysis scripts are on GitHub. EcoSIS/EcoSML uploads are promised only upon publication and are not yet actionable.Dataset · publicected and curated the spectral and trait data. SK analyzed the data,
597 interpreted the results, and wrote the first draft with substantial contributions from EL. All authors
598 contributed to further revisions of the paper.
599
600 Data availability
601 All fresh-leaf spectral data are available through the CABO data portal (https://data.caboscience.org/leaf).
602 Upon publication, we will also upload all spectral data, as well as metadata and trait data, to theOpen asset ↗pdf-layout-page:38 lines:1-58Code · publicnder a CC-BY 4.0 International license.
603 Ecological Spectral Information System (EcoSIS, https://ecosis.org/), and upload models to the
604 Ecological Spectral Model Library (EcoSML, https://ecosml.org/). At that stage, we will update this
605 section accordingly. Analysis scripts are available as a repository on GitHub
606 (https://github.com/ShanKothari/CABO-trait-models).Open asset ↗ShanKothari/CABO-trait-modelspdf-layout-page:39 lines:1-14Code / dataset availability confirmedbioRxiv · checked 8 Sept 2026
X-ray fluorescence spectroscopy (XRF) is a powerful technique for the in vivo assessment of plant tissues. However, the potential X-ray exposure damages might affect the structure and elemental composition of living plant tissues leading to artefacts in the recorded data. Herein, we exposed soybean (Glycine max (L.) Merrill) leaves to several X-ray doses through a polychromatic benchtop microprobe X-ray fluorescence spectrometer, modulating the photon flux by adjusting either the beam size, focus, or exposure time. The structure, ultrastructure and physiological responses of the irradiated plant tissues were investigated through light and transmission electron microscopy (TEM). Depending on the dose, the X-ray exposure induced decreased K and X-ray scattering intensities, and increased Ca, P, and Mn signals on soybean leaves. Anatomical analysis indicated necrosis of the epidermal and mesophyll cells on the irradiated spots, where TEM images revealed the collapse of cytoplasm and cell-wall breaking. Furthermore, the histochemical analysis detected the production of reactive oxygen species, as well as inhibition of chlorophyll autofluorescence in these areas. Under certain X-ray exposure conditions, e.g., high photon flux and exposure time, XRF measurements may affect the soybean leaves structures, elemental composition, and cellular ultrastructure, and induce programmed cell death. These results shed light on the characterization of the radiation damage, and thus, help to assess the X-ray radiation limits and strategies for in vivo for XRF analysis. HighlightBy exposing soybean leaves to several X-ray doses, we show that the characteristic X-ray induced elemental changes stem from plants physiological signalling or responses rather than only sample dehydration.
Why it matches plant phenotyping methods植物組織のin vivo XRF測定における放射線損傷と測定アーティファクトを評価し、適用限界と測定条件を検証する研究であり、フェノタイピング手法の技術的妥当性が中心です。
abstractX-ray fluorescence spectroscopy (XRF) is a powerful technique for the in vivo assessment of plant tissues.
Reproduction assets foundThe paper's DATA AVAILABILITY section states the raw data (XRF spectra/maps and imaging measurements) are fully available on Figshare at the authors' public DOI, which matches an allowed URL.Dataset · publicThe raw data herein presented is fully available at Figshare
repository: https://doi.org/10.6084/m9.figshare.1858438Open asset ↗Figshare · 10.6084/m9.figshare.1858438pdf-page:6 lines:1-93Code / dataset availability confirmedEurope PMC · bioRxiv · checked 15 Sept 2026
Biological systems are the sum of their dynamic 3-dimensional (3D) parts. Therefore, it is critical to study biological structures in 3D and at high resolutions to gain insights into their physiological functions. Electron microscopy of metal replicas of unroofed cells and isolated organelles has been a key technique to visualize intracellular structures at nanometer resolution. However, many of these protocols require specialized equipment and personnel to complete them. Here we present novel accessible protocols to analyze biological structures in unroofed cells and biochemically isolated organelles in 3D and at nanometer resolutions, focusing on Arabidopsis clathrin-coated vesicles (CCVs) - an essential trafficking organelle lacking detailed structural characterization due to their low preservation in classical electron microscopy techniques. First, we establish a protocol to visualize CCVs in unroofed cells using scanning-transmission electron microscopy (STEM) tomography, providing sufficient resolution to define the clathrin coat arrangements. Critically, the samples are prepared directly on electron microscopy grids, removing the requirement to use extremely corrosive acids, thereby enabling the use of this protocol in any electron microscopy lab. Secondly, we demonstrate this standardized sample preparation allows the direct comparison of isolated CCV samples with those visualized in cells. Finally, to facilitate the high-throughput and robust screening of metal replicated samples, we provide a deep learning analysis workflow to screen the ‘pseudo 3D’ morphology of CCVs imaged with 2D modalities. Overall, we present accessible ways to examine the 3D structure of biological samples and provide novel insights into the structure of plant CCVs.
Why it matches plant phenotyping methods植物細胞内オルガネラの3D形態を取得・解析する電子顕微鏡プロトコルと深層学習ワークフローが研究の中心であり、植物CCV形態の技術的スクリーニング手法を提供している。
abstractHere we present novel accessible protocols to analyze biological structures in unroofed cells and biochemically isolated organelles in 3D and at nanometer resolutions, focusing on Arabidopsis clathrin-coated vesicles (CCVs)
Reproduction assets foundThe paper's Data Availability statement explicitly deposits example data (SEM replica images, training image pairs) and the analysis code (Cellpose-based CCV segmentation workflow) generated in this study at a public Zenodo DOI, making it a paper-specific, publicly actionable asset. The temography.com URLs are vendor/mCode · publicand round; LF, large and
354
flat) using an area threshold of 8500 nm2
(a CCV diameter of 105 nm) and a 3D value of 1.52 (the
355
average of the 3 smallest CCVs in control conditions determined to be spherical by the experimenter).
356
Data Availability
357
Example data and the code generated in this study is available at:
358
https://doi.org/10.5281/zenodo.6563819
359
Acknowledgements
360
This research was supported by the Scientific Service Units of Institute of Science and Technology
361
Austria (ISTA) through resources provided by the Electron Microscopy Facility, Lab Support Facility and
362
the Imaging and Optics Facility. A.J. is supported by funding from the Austrian Science FundOpen asset ↗zenodo · 10.5281/zenodo.6563819pdf-raw-page:12 lines:1-46Code / dataset availability confirmedbioRxiv · checked 15 Sept 2026
O_LIAnnual rings from 30 year old vines in a California rootstock trial were measured to determine the effects of 15 different rootstocks on Chardonnay and Cabernet Sauvignon scions. Viticultural traits measuring vegetative growth, yield, berry quality, and nutrient uptake were collected at the beginning and end of the lifetime of the vineyard. C_LIO_LIX-ray Computed Tomography (CT) was used to measure ring widths in 103 vines. Ring width was modeled as a function of ring number using a negative exponential model. Early and late wood ring widths, cambium width, and scion trunk radius were correlated with 27 traits. C_LIO_LIModeling of annual ring width shows that scions alter the width of the first rings but that rootstocks alter the decay thereafter, consistently shortening ring width throughout the lifetime of the vine. The ratio of yield to vegetative growth, juice pH, photosynthetic assimilation and transpiration rates, and stomatal conductance are correlated with scion trunk radius. C_LIO_LIRootstocks modulate secondary growth over years, altering hydraulic conductance, physiology, and agronomic traits. Rootstocks act in similar but distinct ways from climate to modulate ring width, which borrowing techniques from dendrochronology, can be used to monitor both genetic and environmental effects in woody perennial crop species. C_LI
Why it matches plant phenotyping methodsX線CTによる年輪幅・形成層幅・幹半径の測定が研究の主要な表現型取得手段であり、樹体の二次成長を遺伝的・環境的影響のモニタリングに用いる方法として扱われている。
abstractX-ray Computed Tomography (CT) was used to measure ring widths in 103 vines.
Reproduction assets foundThe paper deposits its X-ray CT cross-section images with landmarks (the phenotyping inputs for ring-width measurement) on Dryad, and all data plus analysis code in a public GitHub repository/Jupyter notebook. Both are paper-specific, publicly available, and actionable.Dataset · publicBMG, IK, MRM, ELM, AWS, ALD, SS, and DHC analyzed data. ZM and DHC
510 coordinated research, data analysis, and manuscript writing. DHC wrote a first draft of the
511 manuscript which all authors read, commented on, and edited.
512
513 Data Availability
514
515 X-ray CT cross-sections with landmarks are deposited on Dryad:
516 http://dx.doi.org/10.5061/dryad.gqnk98sqf. All data and code to reproduce results are posted on
517 the Github repository https://github.com/DanChitwood/grapevine_rings.
518
519 Supporting Information Table S1: Numbers of measured samples for each trait, for each
520 scion, for each year.
521
522 Table 1: Rootstock parentage
523
Rootstock Parentage
775 Paulsen V. berlaOpen asset ↗Dryad · 10.5061/dryad.gqnk98sqfpdf-layout-page:13 lines:1-51Code · publict writing. DHC wrote a first draft of the
511 manuscript which all authors read, commented on, and edited.
512
513 Data Availability
514
515 X-ray CT cross-sections with landmarks are deposited on Dryad:
516 http://dx.doi.org/10.5061/dryad.gqnk98sqf. All data and code to reproduce results are posted on
517 the Github repository https://github.com/DanChitwood/grapevine_rings.
518
519 Supporting Information Table S1: Numbers of measured samples for each trait, for each
520 scion, for each year.
521
522 Table 1: Rootstock parentage
523
Rootstock Parentage
775 Paulsen V. berlandieri Rességuier 2 × V. rupestris du Lot
1103 Paulsen V. berlandieri Rességuier 2 × V. rupestris du Lot
3309 Couderc V. Open asset ↗GitHub · DanChitwood/grapevine_ringspdf-layout-page:13 lines:1-51Code / dataset availability confirmedbioRxiv · Europe PMC · checked 15 Sept 2026
BackgroundMethods to accurately quantify disease severity are fundamental to plant pathogen interaction studies. Commonly used methods include visual scoring of disease symptoms, tracking pathogen growth in planta over time, and various assays that detect plant defense responses. Several image-based methods for phenotyping of plant disease symptoms have also been developed. Each of these methods has different advantages and limitations which should be carefully considered when choosing an approach and interpreting the results. ResultsIn this paper, we developed two image analysis methods and tested their ability to quantify different aspects of disease lesions in the cassava-Xanthomonas pathosystem. The first method uses ImageJ, an open-source platform widely used in the biological sciences. The second method is a few-shot support vector machine learning tool that uses a classifier file trained with five representative infected leaf images for lesion recognition. Cassava leaves were syringe infiltrated with wildtype Xanthomonas, a Xanthomonas mutant with decreased virulence, and mock treatments. Digital images of infected leaves were captured overtime using a Raspberry Pi camera. The image analysis methods were analyzed and compared for the ability to segment the lesion from the background and accurately capture and measure differences between the treatment types. ConclusionsBoth image analysis methods presented in this paper allow for accurate segmentation of disease lesions from the non-infected plant. Specifically, at 4-, 6-, and 9-days post inoculation (DPI), both methods provided quantitative differences in disease symptoms between different treatment types. Thus, either method could be applied to extract information about disease severity. Strengths and weaknesses of each approach are discussed.
Why it matches plant phenotyping methodsカシャバの病斑を画像から分割・定量する2手法を開発し、処理間の病徴・病害重症度の測定性能を比較検証しており、植物表現型取得が中心である。
abstractIn this paper, we developed two image analysis methods and tested their ability to quantify different aspects of disease lesions in the cassava-Xanthomonas pathosystem.
Reproduction assets foundThe paper deposits its phenotype measurement datasets and custom R analysis scripts on figshare, and documents the machine learning phenotyping workflow (PhenotyperCV) with a public GitHub wiki URL containing the workflow and software download instructions.Dataset · public9
● CSV: Comma separated plain text file
410
Declarations:
411
Ethics approval and consent to participate: Not applicable
412
Consent for publication: Not applicable
413
Availability of data and materials:
414
The datasets and custom R scripts generated and/or analyzed in this study are
415
available in the figshare repository, https://figshare.com/s/0148e5e4fc7f220ac4c3
416
Competing interests: The authors declare that they have no competing interests
417
Funding:
418
National Science Foundation GRFP DGE-2139839 and DGE-1745038 (KE)
419
Bill and Melinda Gates Foundation OPP1125410 (RBS)
420
Authors' contributions
421
.
CC-BY-NC-ND 4.0 International license
available under a
was not certifieOpen asset ↗figsharepdf-raw-page:19 lines:1-45Code · publicned leaf image was converted to a binary mask and referred to as the
365
“labeled image”. The machine learning image analysis tool is part of PhenotyperCV, a
366
C++11 header-only library designed for image-based plant phenotyping. The machine
367
learning workflow and software download instructions are available on GitHub
368
(https://github.com/jberry47/ddpsc_phenotypercv/wiki/Machine-Learning-Workflow).369
All steps of the machine learning workflow were run on the Mac terminal command line.
370
The labeled leaf mask image and original combined leaf graphic were used to create a
371
support vector machine learning classifier or YAML file. Individual images of inoculated
372
cassava leaves Open asset ↗github · jberry47/ddpsc_phenotypercvpdf-raw-page:17 lines:1-55Code / dataset availability confirmedbioRxiv · Europe PMC · checked 8 Sept 2026
O_LIMore than ever, ecologists seek to employ herbarium collections to estimate plant functional traits from the past and across biomes. However, many trait measurements are destructive, which may preclude their use on valuable specimens. Researchers increasingly use reflectance spectroscopy to estimate traits from fresh or ground leaves, and to delimit or identify taxa. Here, we extend this body of work to non-destructive measurements on pressed, intact leaves, like those in herbarium collections. C_LIO_LIUsing 618 samples from 68 species, we used partial least-squares regression to build models linking pressed-leaf reflectance spectra to a broad suite of traits, including leaf mass per area (LMA), leaf dry matter content (LDMC), equivalent water thickness, carbon fractions, pigments, and twelve elements. We compared these models to those trained on fresh- or ground-leaf spectra of the same samples. C_LIO_LIOur pressed-leaf models were best at estimating LMA (R2 = 0.932; %RMSE = 6.56), C (R2 = 0.855; %RMSE = 9.03), and cellulose (R2 = 0.803; %RMSE = 12.2), followed by water-related traits, certain nutrients (Ca, Mg, N, and P), other carbon fractions, and pigments (all R2 = 0.514-0.790; %RMSE = 12.8-19.6). Remaining elements were predicted poorly (R2 20). For most chemical traits, pressed-leaf models performed better than fresh-leaf models, but worse than ground-leaf models. Pressed-leaf models were worse than fresh-leaf models for estimating LMA and LDMC, but better than ground-leaf models for LMA. Finally, in a subset of samples, we used partial least-squares discriminant analysis to classify specimens among 10 species with near-perfect accuracy (>97%) from pressed- and ground-leaf spectra, and slightly lower accuracy (>93%) from fresh-leaf spectra. C_LIO_LIThese results show that applying spectroscopy to pressed leaves is a promising way to estimate leaf functional traits and identify species without destructive analysis. Pressed-leaf spectra might combine advantages of fresh and ground leaves: like fresh leaves, they retain some of the spectral expression of leaf structure; but like ground leaves, they circumvent the masking effect of water absorption. Our study has far-reaching implications for capturing the wide range of functional and taxonomic information in the worlds preserved plant collections. C_LI
Why it matches plant phenotyping methods押葉の反射分光から葉の機能形質を非破壊推定する手法を開発・比較検証しており、植物形質取得が研究の中心である。
abstractwe used partial least-squares regression to build models linking pressed-leaf reflectance spectra to a broad suite of traits
Reproduction assets foundThe paper's fresh-leaf spectral dataset is publicly available via the CABO data portal. Pressed/ground spectra, trait data, and PLSR/PLS-DA models are only promised 'upon publication' to EcoSIS and EcoSML, so those require contacting the authors; no author analysis code with a public URL is stated.Dataset · publicAll fresh-leaf spectral data are available through the CABO data portal (https://data.caboscience.org/leaf).Open asset ↗CABO data portalpdf-page:24 lines:1-32Code / dataset availability confirmedbioRxiv · checked 13 Sept 2026
O_LICitrus come in diverse sizes and shapes, and play a key role in world culture and economy. Citrus oil glands in particular contain essential oils which include plant secondary metabolites associated with flavor and aroma. Capturing and analyzing nuanced information behind the citrus fruit shape and its oil gland distribution provides a morphology-driven path to further our insight into phenotype-genotype interactions. C_LIO_LIWe investigated the shape of citrus fruit of 51 accessions based on 3D X-ray CT scan reconstructions. Accessions include all three ancestral citrus species, accessions from related genera, and several interspecific hybrids. We digitally separate and compare the size of fruit endocarp, mesocarp, exocarp, and oil gland tissue. Based on the centers of the oil glands, overall fruit shape is approximated with an ellipsoid. Possible oil gland distributions on this ellipsoid surface are explored using directional statistics. C_LIO_LIThere is a strong allometry along fruit tissues; that is, we observe a strong linear relationship between the volume of any pair of major tissues. This suggests that the relative growth of fruit tissues with respect to each other follows a power law. We also observe that on average, glands distance themselves from their nearest neighbor following a square root relationship, which suggests normal diffusion dynamics at play. C_LIO_LIThe observed allometry and square root models point to the existence of biophysical developmental constraints that govern novel relationships between fruit dimensions from both evolutionary and breeding perspectives. Understanding these biophysical interactions prompt an exciting research path on fruit development and breeding. C_LI Societal Impact StatementCitrus are intrinsically connected to human health and culture, including preventing human diseases like scurvy, and inspiring sacred rituals. Citrus fruits come in a stunning number of different sizes and shapes, ranging from small clementines to oversized pummelos, and fruits display a vast diversity of flavors and aromas. These qualities are key in both traditional and modern medicine and the production of cleaning and perfume products. By quantifying and modeling overall fruit shape and oil gland distribution, we can gain further insight into citrus development and the impacts of domestication and improvement on multiple characteristics of the fruit.
Why it matches plant phenotyping methods3D X線CT再構成とデジタル分離により、柑橘果実の形状・組織体積・油腺分布を定量化しモデル化しており、植物表現型の取得・解析が研究の中心です。
abstractCapturing and analyzing nuanced information behind the citrus fruit shape and its oil gland distribution provides a morphology-driven path to further our insight into phenotype-genotype interactions.
Reproduction assets foundThe paper explicitly deposits its processed citrus X-ray CT 3D reconstructions, segmented tissues, oil gland point clouds, and ellipsoidal approximations in Dryad, and its full image-processing and analysis code on GitHub. Both are paper-specific, public, and actionable.Code · public357 All our code is available at the https://github.com/amezqui3/vitaminC_morphology repos-Open asset ↗GitHub · amezqui3/vitaminC_morphologypdf-page:19 lines:1-48Code / dataset availability confirmedbioRxiv · OpenAlex · Europe PMC · checked 8 Sept 2026
The first draft of the Arabidopsis genome was released more than 20 years ago and despite intensive molecular research, more than 30% of Arabidopsis genes remained uncharacterized or without an assigned function. This is in part due to gene redundancy within gene families or the essential nature of genes, where their deletion results in lethality (i.e., the dark genome). High-throughput plant phenotyping (HTPP) offers an automated and unbiased approach to characterize subtle or transient phenotypes resulting from gene redundancy or inducible gene silencing; however, commercial HTPP platforms remain unaffordable. Here we describe the design and implementation of OPEN leaf, an open-source HTPP system with cloud connectivity and remote bilateral communication to facilitate data collection, sharing and processing. OPEN leaf, coupled with the SMART imaging processing package was able to consistently document and quantify dynamic morphological changes over time at the whole rosette level and also at leaf-specific resolution when plants experienced changes in nutrient availability. The modular design of OPEN leaf allows for additional sensor integration. Notably, our data demonstrate that VIS sensors remain underutilized and can be used in high-throughput screens to identify characterize previously unidentified phenotypes in a leaf-specific manner. Significance StatementMany bottlenecks exist in high-throughput phenotyping involving computing power for processing and a lack of focus on abiotic stresses that has prevented an advancement in phenotyping on par with genotyping. Therefore, we create an automated HTP system that performs nutrient studies on Arabidopsis thaliana with cloud-based image processing that quantifies plant traits at a whole and leaf-level.
Why it matches plant phenotyping methodsOPEN leafは、葉単位の形態形質を画像から自動取得・定量するオープンソース高スループット表現型解析システムの設計・実装が中心であり、明確に収載対象です。
abstractHere we describe the design and implementation of OPEN leaf, an open-source HTPP system with cloud connectivity and remote bilateral communication to facilitate data collection, sharing and processing.
Reproduction assets foundThe paper's image-analysis pipeline (SMART) used for rosette and leaf-specific phenotyping is explicitly released as public source code on GitHub and as a prepackaged Docker container. Phenotype data tables are only in supplementary material without a public URL, and other code repos (OPEN Controller, OPEN-leaf-cloud)'Code · public209 available as source code on GitHub (https://github.com/Computational-Open asset ↗pdf-page:8 lines:1-41Code / dataset availability confirmedEurope PMC · bioRxiv · checked 8 Sept 2026
Field / plotRootWhole plant / canopy / plot / fieldRoot system architecture
Root phenotyping describes methods for measuring root properties, or traits. While root phenotyping can be challenging, it is advancing quickly. In order for the field to move forward, it is essential to understand the current state and challenges of root phenotyping, as well as the pressing needs of the root biology community. In this letter, we present and discuss the results of a survey that was created and disseminated by members of the Graduate Student and Postdoc Ambassador Program at the 11th symposium of the International Society of Root Research. This survey aimed to (1) provide an overview of the objectives, biological models and methodological approaches used in root phenotyping studies, and (2) identify the main limitations currently faced by plant scientists with regard to root phenotyping. Our survey highlighted that (1) monocotyledonous crops dominate the root phenotyping landscape, (2) root phenotyping is mainly used to quantify morphological and architectural root traits, (3) 2D root scanning/imaging is the most widely used root phenotyping technique, (4) time-consuming tasks are an important barrier to root phenotyping, (5) there is a need for standardised, high-throughput methods to sample and phenotype roots, particularly under field conditions, and to improve our understanding of trait-function relationships.
Why it matches plant phenotyping methods根系フェノタイピングの手法、利用状況、限界、標準化ニーズを調査・整理したレビュー的研究であり、フェノタイピング方法論が中心です。
abstractRoot phenotyping describes methods for measuring root properties, or traits.
Reproduction assets foundThe paper's Data availability statement explicitly deposits the survey raw data and R analysis code on Zenodo (DOI 10.5281/zenodo.5901959), a public, paper-specific, actionable asset. The Nottingham Hidden Half maize image URL is only a credited Figure 1 image source, not a paper-specific dataset, and is not listed asaCode · publicRaw data and R code are available on Zenodo at https://doi.org/10.5281/zenodo.5901959.Open asset ↗Zenodo · 10.5281/zenodo.5901959pdf-page:10 lines:1-39Code / dataset availability confirmedbioRxiv · Europe PMC · Crossref · checked 15 Sept 2026
Near surface cameras, such as those in the PhenoCam network, are a common source of ground truth data in modelling and remote sensing studies. Despite having locations across numerous agricultural sites, few studies have used near surface cameras to track the unique phenology of croplands. Due to management activities, crops do not have a natural vegetation cycle which many phenological extraction methods are based on. For example, a field may experience abrupt changes due to harvesting and tillage throughout the year. A single camera can also record several different plants due to crop rotations, fallow fields, and cover crops. Current methods to estimate phenology metrics from image time series compress all image information into a relative greenness metric, which discards a large amount of contextual information. This can include the type of crop present, whether snow or water is present on the field, the crop phenology, or whether a field lacking green plants consists of bare soil, fully senesced plants, or plant residue. Here we developed a modelling workflow to create a daily time series of crop type and phenology, while also accounting for other factors such as obstructed images and snow covered fields. We used a mainstream deep learning image classification model, VGG16. Deep learning classification models do not have a temporal component, so to account for temporal correlation among images our workflow incorporates a hidden markov model in the post-processing. The initial image classification model had out of sample F1 scores of 0.83-0.85, which improved to 0.86-0.91 after all post-processing steps. The resulting time series show the progression of crops from emergence to harvest, and can serve as a daily, local scale dataset of field states and phenological stages for agricultural research.
Why it matches plant phenotyping methods近接カメラ画像から作物種とフェノロジーを日次推定する深層学習・HMMワークフローを開発しており、植物の状態・生育段階の抽出が研究の中心である。
abstractHere we developed a modelling workflow to create a daily time series of crop type and phenology, while also accounting for other factors such as obstructed images and snow covered fields.
Reproduction assets foundThe authors state that all analysis code, data, and final model predictions are publicly available in two Zenodo repositories (DOIs 10.5281/zenodo.5618316 and 10.5281/zenodo.5579797), directly reproducing this paper's PhenoCam crop phenology classification and HMM post-processing analysis.Code · public.4 [36], NumPy v.1.20.2 [37], and Pomegranate v0.14.5 [38] in
the Python programming language v3.7 [39]. In the R language v4.1 [40] we used the
zoo v1.8.0 [41], tidyverse v1.3.1 [42], and ggplot2 v3.3.5 [43] packages. All code for the
analysis, as well as the final model predictions, are available in a Zenodo repository ([44],
https://doi.org/10.5281/zenodo.5618316).3. Results
The overall F1 score, a summary statistic which incorporates recall and precision,
was 0.90-0.92 for the training data across the three categories of Dominant Cover, Crop
Type, and Crop Status (Figure 2). The overall F1 score for validation data, which was not
used in the model fitting, was 0.83-0.85 for the three cOpen asset ↗Zenodo · 10.5281/zenodo.5618316pdf-raw-page:7 lines:1-64Code · publicll authors have read and agreed to the published version of the
manuscript.
Funding: D.B. was supported by the United States Department of Agriculture, CRIS:3050-11210-009-
00D
Data Availability Statement: All code and data to reproduce this analysis, as well as the final model
predictions, are available in a Zenodo repository (https://doi.org/10.5281/zenodo.5579797).USC 105 and is also made available for use under a CC0 license.
(which was not certified by peer review) is the author/funder. This article is a US Government work. It is not subject to copyright under 17
The copyright holder for this preprint
this version posted January 5, 2022.
;
https://doi.org/10.1101/2021.10.20.465168
doiOpen asset ↗Zenodo · 10.5281/zenodo.5579797pdf-raw-page:16 lines:1-60Code / dataset availability confirmedEurope PMC · bioRxiv · checked 9 Sept 2026
Convolutional neural networks (CNNs) and deep learning are powerful and robust tools for ecological applications. CNNs can perform very well in various tasks, especially for visual tasks and image data. Image segmentation (the classification of all pixels in images) is one such task and can for example be used to assess forest vertical and horizontal structure. While such methods have been suggested, widespread adoption in ecological research has been slow, likely due to technical difficulties in implementation of CNNs and lack of toolboxes for ecologists. Here, we present R package imageseg which implements a workflow for general-purpose image segmentation using CNNs and the U-Net architecture in R. The workflow covers data (pre)processing, model training, and predictions. We illustrate the utility of the package with two models for forest structural metrics: tree canopy density and understory vegetation density. We trained the models using large and diverse training data sets from a variety of forest types and biomes, consisting of 3288 canopy images (both canopy cover and hemispherical canopy closure photographs) and 1468 understory vegetation images. Overall classification accuracy of the models was high with a Dice score of 0.91 for the canopy model and 0.89 for the understory vegetation model (assessed with 821 and 367 images, respectively), indicating robustness to variation in input images and good generalization strength across forest types and biomes. The package and its workflow allow simple yet powerful assessments of forest structural metrics using pre-trained models. Furthermore, the package facilitates custom image segmentation with multiple classes and based on color or grayscale images, e.g. in cell biology or for medical images. Our package is free, open source, and available from CRAN. It will enable easier and faster implementation of deep learning-based image segmentation within R for ecological applications and beyond.
Why it matches plant phenotyping methods森林の樹冠密度・下層植生密度という植物群落の構造形質を画像分割で推定するRパッケージと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。
abstractHere, we present R package imageseg which implements a workflow for general-purpose image segmentation using CNNs and the U-Net architecture in R.
Reproduction assets foundThe paper's imageseg R package (source code, pre-trained canopy/understory models, and training/testing image data) is publicly available on CRAN and GitHub, with explicit availability statements.Code · publicSource
code and the development version are available from GitHub
(https://github.com/EcoDynIZW/imageseg). Links to the pre-trained models, classification
examples and data used for model training and testing are available from:
https://github.com/EcoDynIZW/imageseg.Open asset ↗EcoDynIZW/imagesegpdf-page:9 lines:1-46Model / weights · publicLinks to the pre-trained models, classification
examples and data used for model training and testing are available from:
https://github.com/EcoDynIZW/imageseg.Open asset ↗EcoDynIZW/imagesegpdf-page:9 lines:1-46Code / dataset availability confirmedbioRxiv · Europe PMC · Crossref · checked 8 Sept 2026
BackgroundThe three-dimensional nuclear arrangement of chromatin impacts many cellular processes operating at the DNA level in animal and plant systems. Chromatin organization is a dynamic process that can be affected by biotic and abiotic stresses. Three-dimensional imaging technology allows to follow these dynamic changes, but only a few semi-automated processing methods currently exist for quantitative analysis of the 3D chromatin organization. ResultsWe present an automated method, Nuclear Object DetectionJ (NODeJ), developed as an imageJ plugin. This program segments and analyzes high intensity domains in nuclei from 3D images. NODeJ performs a Laplacian convolution on the mask of a nucleus to enhance the contrast of intra-nuclear objects and allows their detection. We reanalyzed public datasets and determined that NODeJ is able to accurately identify heterochromatin domains from a diverse set of Arabidopsis thaliana nuclei stained with DAPI or Hoechst. NODeJ is also able to detect signals in nuclei from DNA FISH experiments, allowing for the analysis of specific targets of interest. Conclusion and availabilityNODeJ allows for efficient automated analysis of subnuclear structures by avoiding the semi-automated steps, resulting in reduced processing time and analytical bias. NODeJ is written in Java and provided as an ImageJ plugin with a command line option to perform more high-throughput analyses. NODeJ can be downloaded from https://gitlab.com/axpoulet/image2danalysis/-/releases with source code, documentation and further information avaliable at https://gitlab.com/axpoulet/image2danalysis. The images used in this study are publicly available at https://www.brookes.ac.uk/indepth/images/ and https://doi.org/10.15454/1HSOIE.
Why it matches plant phenotyping methods植物核内構造を3D画像から自動抽出・解析するImageJプラグインを開発し、Arabidopsisデータセットで検証しているため、植物フェノタイピング手法が中心である。
abstractWe present an automated method, Nuclear Object DetectionJ (NODeJ), developed as an imageJ plugin.
Reproduction assets foundThe paper's authors publicly released NODeJ (source code and releases on GitLab) and the 3D nuclear images used for validation are publicly available via the INDEPTH image site and a data repository DOI.Code · publicNODeJ can be downloaded from
https://gitlab.com/axpoulet/image2danalysis/-/releases with source
code, documentation and further information avaliable at
https://gitlab.com/axpoulet/image2danalysis.Open asset ↗axpoulet/image2danalysispdf-page:1 lines:1-65Dataset · publicThe
images used in this report are available in these links: https://www.brookes.ac.uk/indepth/images/ and
https://doi.org/10.15454/1HSOIE.Open asset ↗10.15454/1HSOIEpdf-page:6 lines:1-75Dataset · publicThe images used in this
study are publicly available at
https://www.brookes.ac.uk/indepth/images/ and
https://doi.org/10.15454/1HSOIE.Open asset ↗pdf-page:1 lines:1-65Code / dataset availability confirmedbioRxiv · Europe PMC · OpenAlex · Crossref · checked 15 Sept 2026
Manual phenotyping of tomato plants is time consuming and labor intensive. Due to the lack of low-cost and open-access 3D phenotyping tools, the dynamic 3D growth of tomato plants during all growth stages has not been fully explored. In this study, based on the 3D structural data points generated by employing structures from motion algorithms on multiple-view images, we proposed a dynamic 3D phenotyping pipeline, 4DPhenoMVS, to calculate and analyze 14 phenotypic traits of tomato plants covering the whole life cycle. The results showed that the R2 values between the phenotypic traits and the manual measurements stem length, plant height, and internode length were more than 0.8. In addition, to investigate the environmental influence on tomato plant growth and yield in the greenhouse, eight tomato plants were chosen and phenotyped during 7 growth stages according to different light intensities, temperatures, and humidities. The results showed that stronger light intensity and moderate temperature and humidity contribute to a higher growth rate and higher yield. In conclusion, we developed a low-cost and open-access 3D phenotyping pipeline for tomato plants, which will benefit tomato breeding, cultivation research, and functional genomics in the future. HighlightsBased on the 3D structural data points generated by employing structures from motion algorithms on multiple-view images, we developed a low-cost and open-access 3D phenotyping tool for tomato plants during all growth stages.
Why it matches plant phenotyping methods低コストの多視点画像・3D再構成によるトマト表現型抽出パイプラインを開発し、複数形質を手測定と検証しており、方法が研究の中心である。
abstractwe proposed a dynamic 3D phenotyping pipeline, 4DPhenoMVS, to calculate and analyze 14 phenotypic traits of tomato plants covering the whole life cycle.
Reproduction assets foundThe paper's Data Availability statement provides a public URL for downloading all phenotypic data and multiview tomato images used in the 4DPhenoMVS pipeline. Source code is referenced only via Supplementary Note S1 with no authors' public URL in the supplied text, so it is not included as an actionable asset.Dataset · publicng Agricultural University and
478
Shenzhen Institute of agricultural genomics (SZYJY2021005, SZYJY2021007). We
479
thanked Harvest-Code Technology (Nanjing) Ltd. provided the materials and
480
experimental resources.
481
482
Data Availability
483
All the phenotypic data and images can be viewed and downloaded via the link
484
(http://plantphenomics.hzau.edu.cn/download_checkiflogin_en.action).485
486
References
487
Aguilar MA, Pozo JL, Aguilar FJ, Sanchez-Hermosilla J, Negreiros J. 2008. 3d
488
Surface Modelling of Tomato Plants Using Close-Range Photogrammetry. Archives
489
of Photogrammetry, Remote Sensing and Spatial 37, B5, 139-144.
490
An N, Welch SM, Markelz RJC, Baker RL, Palmer CM, Open asset ↗plantphenomics.hzau.edu.cnpdf-raw-page:24 lines:1-78Code / dataset availability confirmedEurope PMC · bioRxiv · checked 15 Sept 2026
Summary Mitochondria form highly dynamic populations in the cells of plants (and all eukaryotes). The characteristics of this collective behaviour, and how it is influenced by nuclear features, remain to be fully elucidated. Here, we use a recently-developed quantitative approach to reveal and analyse the physical and collective “social” dynamics of mitochondria in an Arabidopsis msh1 mutant where organelle DNA maintenance machinery is compromised. We use a newly-created line combining the msh1 mutant with mitochondrially-targeted GFP, and characterise mitochondrial dynamics with a combination of single-cell timelapse microscopy, computational tracking and network analysis. The collective physical behaviour of msh1 mitochondria is altered from wildtype in several ways: mitochondria become less evenly spread, and networks of inter-mitochondrial encounters become more connected with greater potential efficiency for inter-organelle exchange. We find that these changes are similar to those observed in friendly , where mitochondrial dynamics are altered by a physical perturbation, suggesting that this shift to higher connectivity may reflect a general response to mitochondrial challenges.
Why it matches plant phenotyping methods単なる生物学的測定ではなく、タイムラプス顕微鏡、計算追跡、ネットワーク解析を組み合わせて植物細胞内ミトコンドリアの動態状態を定量化する手法の実質的な適用である。
abstractwe use a recently-developed quantitative approach to reveal and analyse the physical and collective “social” dynamics of mitochondria
Reproduction assets foundThe paper explicitly states that all analysis code and data are available on the authors' GitHub repository, and a supplementary time-lapse microscopy video (phenotyping input) is hosted publicly. The Arabidopsis msh1 seed stock (N3372) used for the phenotyping is also publicly available from the NASC stock centre.Code · public14
average number of shortest paths crossing each node in the network. The mean connected
414
component number is the average number of disconnected subgraphs within the network.
415
416
Accession numbers
417
All analysis code and data is available from Github at
418
https://github.com/StochasticBiology/plant-mito-dynamics
419
420
Acknowledgments
421
422
J.M.C. is supported by the BBSRC and University of Birmingham via the MIBTP doctoral
423
training scheme (grant number BB/M01116X/1). This project has received funding from the
424
European Research Council (ERC) under the European Union’s Horizon 2020 research and
425
innovation programme (grantOpen asset ↗StochasticBiology/plant-mito-dynamicspdf-raw-page:14 lines:1-67Code / dataset availability confirmedEurope PMC · bioRxiv · checked 15 Sept 2026
Background 3D imaging, such as X-ray CT and MRI, has been widely deployed to study plant root structures. Many computational tools exist to extract coarse-grained features from 3D root images, such as total volume, root number and total root length. However, methods that can accurately and efficiently compute fine-grained root traits, such as root number and geometry at each hierarchy level, are still lacking. These traits would allow biologists to gain deeper insights into the root system architecture (RSA). Results We present TopoRoot, a high-throughput computational method that computes fine-grained architectural traits from 3D X-ray CT images of field-excavated maize root crowns. These traits include the number, length, thickness, angle, tortuosity, and number of children for the roots at each level of the hierarchy. TopoRoot combines state-of-the-art algorithms in computer graphics, such as topological simplification and geometric skeletonization, with customized heuristics for robustly obtaining the branching structure and hierarchical information. TopoRoot is validated on both real and simulated root images, and in both cases it was shown to improve the accuracy of traits over existing methods. We also demonstrate TopoRoot in differentiating a maize root mutant from its wild type segregant using fine-grained traits. TopoRoot runs within a few minutes on a desktop workstation for volumes at the resolution range of 400^3, without need for human intervention. Conclusions TopoRoot improves the state-of-the-art methods in obtaining more accurate and comprehensive fine-grained traits of maize roots from 3D CT images. The automation and efficiency makes TopoRoot suitable for batch processing on a large number of root images. Our method is thus useful for phenomic studies aimed at finding the genetic basis behind root system architecture and the subsequent development of more productive crops.
Why it matches plant phenotyping methodsX線CT画像からトウモロコシ根系の階層的形態形質を抽出する計算手法を開発し、実画像・シミュレーション画像で検証しているため、植物フェノタイピング手法が中心である。
abstractWe present TopoRoot, a high-throughput computational method that computes fine-grained architectural traits from 3D X-ray CT images of field-excavated maize root crowns.
Reproduction assets foundThe paper's TopoRoot phenotyping software (C++ pipeline computing root hierarchy and fine-grained traits from X-ray CT volumes) and the datasets generated/analysed in the study (including the test dataset) are publicly released on the authors' GitHub repository.Code · publicduce a
697
probability density field (e.g., deep learning). Since TopoRoot requires a gray-scale intensity
698
volume with three thresholds (shape, kernel and neighborhood), a binary segmentation will first
699
need to be converted into a Euclidean distance field.
700
Software availability
701
TopoRoot is available for free at: https://github.com/danzeng8/TopoRoot
702
.
CC-BY 4.0 International license
available under a
(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made
The copyright holder for this preprint
this version posted August 28, 2021.
;
https://doi.org/10.1101/2021.08.24.457522
doi:
bOpen asset ↗danzeng8/TopoRootpdf-raw-page:37 lines:1-53Dataset · public39
CT: Computed Tomography
723
Declarations
724
Ethics approval and consent to participate
725
Not applicable
726
Consent for publication
727
Not applicable
728
Availability of data and materials
729
The datasets generated and analysed during the current study are available in the TopoRoot
730
Github repository: https://github.com/danzeng8/TopoRoot
731
Competing interests
732
The authors declare that they have no competing interests.
733
Funding
734
This material is based upon work supported by the National Science Foundation under award
735
numbers DBI-1759836, DBI-1759807, DBI-1759796, EF-1971728, CCF-1907612, CCF-
736
2106672, and IOS-1638507. DZ is funded in part by aOpen asset ↗danzeng8/TopoRootpdf-raw-page:39 lines:1-45Code / dataset availability confirmedbioRxiv · Europe PMC · OpenAlex · Crossref · checked 8 Sept 2026
Selection for yield at high planting density has reshaped the leaf canopy of maize, improving photosynthetic productivity in high density settings. Further optimization of canopy architecture may be possible. However, measuring leaf angles, the widely studied component trait of leaf canopy architecture, by hand is a labor and time intensive process. Here, we use multiple, calibrated, 2D images to reconstruct the 3D geometry of individual sorghum plants using a voxel carving based algorithm. Automatic skeletonization and segmentation of these 3D geometries enable quantification of the angle of each leaf for each plant. The resulting measurements are both heritable and correlated with manually collected leaf angles. This automated and scaleable reconstruction approach was employed to measure leaf-by-leaf angles for a population of 366 sorghum plants at multiple time points, resulting in 971 successful reconstructions and 3,376 leaf angle measurements from individual leaves. A genome wide association study conducted using aggregated leaf angle data identified a known large effect leaf angle gene, several previously identified leaf angle QTL from a sorghum NAM population, and novel signals. Genome wide association studies conducted separately for three individual sorghum leaves identified a number of the same signals, a previously unreported signal shared across multiple leaves, and signals near the sorghum orthologs of two maize genes known to influence leaf angle. Automated measurement of individual leaves and mapping variants associated with leaf angle reduce the barriers to engineering ideal canopy architectures in sorghum and other grain crops.
Why it matches plant phenotyping methods複数の較正2D画像から3D植物形状を再構成し、葉ごとの葉角度を自動抽出する手法が研究の中心であり、遺伝性・手動測定との相関による検証も行っている。
abstractwe use multiple, calibrated, 2D images to reconstruct the 3D geometry of individual sorghum plants using a voxel carving based algorithm.
Reproduction assets foundThe paper's Data and Code availability statement provides three paper-specific public assets: the voxel carving/skeletonization code (GitHub cropsinsilico/SorghumVoxelCarving), the raw sorghum images analyzed (Zenodo deposit 10.5281/zenodo.4426620), and the phenotypic data, GWAS result files, and figure code (GitHub mtCode · publicThe code for reconstruction and skeletonization is hosted on GitHub: https://github.com/cropsinsilico/
SorghumVoxelCarving.Open asset ↗pdf-page:9 lines:1-59Code · publicPhenotypic data, GWAS result files and code for main figures are located on GitHub:
https://github.com/mtross2/Sorghum-3D-ReconstructionOpen asset ↗mtross2/Sorghum-3D-Reconstructionpdf-page:9 lines:1-59Code / dataset availability confirmedbioRxiv · Europe PMC · checked 15 Sept 2026
O_LIProper collection and preparation of empirical data still represent one of the most important, but also expensive steps in ecological and evolutionary/systematic research. Modern machine learning approaches, however, have the potential to automate a variety of tasks, which until recently could only be performed manually. Unfortunately, the application of such methods by researchers outside the field is hampered by technical difficulties, some of which, we believe, can be avoided. C_LIO_LIHere, we present GinJinn2, a user-friendly toolbox for deep learning-based object detection and instance segmentation on image data. Besides providing a convenient command-line interface to existing software libraries, it comprises several additional tools for data handling, pre- and postprocessing, and building advanced analysis pipelines. C_LIO_LIWe demonstrate the application of GinJinn2 for biological purposes using four exemplary analyses, namely the evaluation of seed mixtures, detection of insects on glue traps, segmentation of stomata, and extraction of leaf silhouettes from herbarium specimens. C_LIO_LIGinJinn2 will enable users with a primary background in biology to apply deep learning-based methods for object detection and segmentation in order to automate feature extraction from image data. C_LI
Why it matches plant phenotyping methods植物画像から種子、気孔、葉形状などを抽出する深層学習ツールを開発・提示しており、植物表現型取得のためのソフトウェアが中心である。
abstractwe present GinJinn2, a user-friendly toolbox for deep learning-based object detection and instance segmentation on image data.
Reproduction assets foundThe paper's GinJinn2 source code and manual are explicitly stated to be freely available on the authors' GitHub repository. The annotated Seeds, Yellow-sticky-traps, Leucanthemum, and stomata annotation datasets are only promised via GfBio 'will be supplied as soon as available', so they are not yet actionable public; Code · public, and wrote the manuscript. Both authors approved the final version of the
379 manuscript. We further note that UL and TO contributed equally to this work. The
380 order of their names in the author list was decided by coin toss.
381
382 Data availability
383 GinJinn2’s source code and manual are freely available at GitHub
384 (https://github.com/AGOberprieler/GinJinn2). The annotated Seeds, Yellow-sticky-
385 traps and Leucanthemum datasets are hosted by the German Federation for
386 Biological Data (GfBio; Link A, Link B, Link C; will be supplied as soon as available).
387 The images used for the Stomata analysis are hosted by the Cuticle Database
388 (Barclay et al., 2012), a Python scripOpen asset ↗https://github.com/AGOberprieler/GinJinn2pdf-raw-page:15 lines:1-35Code / dataset availability confirmedbioRxiv · Europe PMC · OpenAlex · Crossref · checked 15 Sept 2026
The here-on presented SimpleForest is written in C++ and published under GPL v3. As input data SimpleForest utilizes forestry scenes recorded as terrestrial laser scan clouds. SimpleForest provides a fully automated pipeline to model the ground as a digital terrain model, then segment the vegetation and finally build quantitative structure models of trees (QSMs) consisting of up to thousands of topologically ordered cylinders. These QSMs allow us to calculate traditional forestry metrics such as diameter at breast height, but also volume and other structural metrics that are hard to measure in the field. Our volume evaluation on three data sets with destructive volumes show high prediction qualities with concordance correlation coefficient CCC [Formula] of 0.91 (0.87), 0.94 (0.92) and 0.97 (0.93) for each data set respectively. We combine two common assumptions in plant modeling "The sum of cross sectional areas after a branch junction equals the one before the branch junction" (Pipe Model Theory) and "Twigs are self-similar" (West, Brown and Enquist model). As even sized twigs correspond to even sized cross sectional areas for twigs we define the Reverse Pipe Radius Branchorder (RPRB) as the square root of the number of supported twigs. The prediction model radius = B0 * RPRB relies only on correct topological information and can be used to detect and correct overestimated cylinders. In QSM building the necessity to handle overestimated cylinders is well known. The RPRB correction performs better with a CCC [Formula] of 0.97 (0.93) than former published ones 0.80 (0.88) and 0.86 (0.85) in our validation. We encourage forest ecologists to analyze output parameters such as the GrowthVolume published in earlier works, but also other parameters such as the GrowthLength, VesselVolume and RPRB which we define in this manuscript. Upload statementSelf-uploaded pre-print for peer-review submitted manuscript. The manuscript was submitted on 26th of July 2021 to Plos Computational Biology: I, Jan Hackenberg uploaded this manuscript because the automated journal upload was rejected for the following reason: Thank you for considering posting your manuscript "SimpleForest - a comprehensive tool for 3d reconstruction of tree from forest plot point clouds." as a preprint. Your manuscript does not meet bioRxivs criteria and therefore we will not be sending it for posting as a preprint. For more information about our checks, see link. We have noted that it contains material that is potentially subject to copyright. In particular, screenshot in Figure 1. Preprints posted to bioRxiv following submission to PLOS journals are done so under the CC BY license. To avoid a potential breach of the copyright that applies to the material listed above, we are unable to make the manuscript publicly available. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=62 SRC="FIGDIR/small/454344v1_fig1.gif" ALT="Figure 1"> View larger version (11K): org.highwire.dtl.DTLVardef@1094b17org.highwire.dtl.DTLVardef@120d7a3org.highwire.dtl.DTLVardef@12d2fbcorg.highwire.dtl.DTLVardef@1990a4d_HPS_FORMAT_FIGEXP M_FIG O_FLOATNOFig 1.C_FLOATNO Submission system screenshot. C_FIG Please note that this decision does not affect the editorial process at PLOS Computational Biology. Your manuscript is being separately assessed with regards to sending for peer review. From section Abstract on, the pdf you see is same as submitted one.
Why it matches plant phenotyping methods森林プロットの点群から樹木を3D再構成し、DBH・体積などの植物構造形質を定量化するソフトウェアと自動解析パイプラインを開発・検証しており、フェノタイピング手法が中心である。
abstractSimpleForest provides a fully automated pipeline to model the ground as a digital terrain model, then segment the vegetation and finally build quantitative structure models of trees (QSMs) consisting of up to thousands of topologically ordered cylinders.
Reproduction assets foundThe paper explicitly publishes its TLS point cloud datasets (5 datasets with harvested ground-truth volumes), SimpleForest processing/QSM scripts, R validation scripts, combined results table, and GPL v3 source code in a public Zenodo repository (10.5281/zenodo.5131717) and GitLab repository, all directly reproducing QCode · publicipts SimpleForest scripts to process S1 Dataset. 75
• Erythrophleum fordii denoising scripts: 76
https://zenodo.org/record/5131717/files/hackenbergErythrophleumDenoisingScripts.zip 77
• Pinus massoniana denoising scripts: 78
https://zenodo.org/record/5131717/files/hackenbergPinusDenoisingScripts.zip 79
• QSM modeling script: 80
https://zenodo.org/record/5131717/files/hackenbergQsm.xsct2 81
S2 Processing scripts SimpleForest scripts to process S2 Dataset. 82
• Denoising scripts: 83
https://zenodo.org/record/5131717/files/deTanagoDenoisingScriptsDenoisedClouds.zip 84
• Poisson reconstruction buttress script: 85
https://zenodo.org/record/5131717/files/deTanagoButtressPoisson.xsct2 86
• QSM modeOpen asset ↗zenodopdf-raw-page:4 lines:1-48Code · publicipt: 109
https://zenodo.org/record/5131717/files/wythamAnalysis.R 110
S5 Validation scripts SimpleForest scripts to validate results of S1 Processing scripts, S2 Processing 111
scripts, S3 Processing scripts. 112
• Combined results data table: 113
https://zenodo.org/record/5131717/files/tableAll.csv 114
• Volume validation: 115
https://zenodo.org/record/5131717/files/ValidationScriptAll.R 116
1.2 Software 117
S1 Software Software code repository. 118
• Under the GPL version 3 license: 119
https://gitlab.com/SimpleForest/computree/-/blob/master/pluginSimpleForest/GPL_v3_template 120
• we provide source code with compilation instructions for the here presented SimpleForestv5.3.1 plugin publishOpen asset ↗zenodopdf-raw-page:5 lines:1-46Dataset · publicripts to validate results of S4 Processing scripts. 108
• Statistical plotting script: 109
https://zenodo.org/record/5131717/files/wythamAnalysis.R 110
S5 Validation scripts SimpleForest scripts to validate results of S1 Processing scripts, S2 Processing 111
scripts, S3 Processing scripts. 112
• Combined results data table: 113
https://zenodo.org/record/5131717/files/tableAll.csv 114
• Volume validation: 115
https://zenodo.org/record/5131717/files/ValidationScriptAll.R 116
1.2 Software 117
S1 Software Software code repository. 118
• Under the GPL version 3 license: 119
https://gitlab.com/SimpleForest/computree/-/blob/master/pluginSimpleForest/GPL_v3_template 120
• we provide source code withOpen asset ↗zenodopdf-raw-page:5 lines:1-46Code · publiconScriptAll.R 116
1.2 Software 117
S1 Software Software code repository. 118
• Under the GPL version 3 license: 119
https://gitlab.com/SimpleForest/computree/-/blob/master/pluginSimpleForest/GPL_v3_template 120
• we provide source code with compilation instructions for the here presented SimpleForestv5.3.1 plugin published: 121
https://gitlab.com/SimpleForest/computree/-/commits/v5.3.1. 122
• Inside a subfolder this repository contains a Win10 compiled executable : 123
https://gitlab.com/SimpleForest/computree/-/tree/master/bin. 124
• Persistent 5.1.3: 125
https://doi.org/10.5281/zenodo.5138255 126
5/25
.
CC-BY-NC 4.0 International license
available under a
was not certified by peer review) Open asset ↗gitlab · SimpleForest/computreepdf-raw-page:5 lines:1-46Code / dataset availability confirmedbioRxiv · Europe PMC · checked 15 Sept 2026
Plants respond to the surrounding environment in countless ways. One of these responses is their ability to sense and orient their root growth toward the gravity vector. Root gravitropism is studied in many laboratories as a hallmark of auxin-related phenotypes. However, manual analysis of images and microscopy data is known to be subjected to human bias. This is particularly the case for manual measurements of root bending as the selection lines to calculate the angle are set subjectively. Therefore, it is essential to develop and use automated or semi-automated image analysis to produce reproducible and unbiased data. Moreover, the increasing usage of vertical-stage microscopy in plant root biology yields gravitropic experiments with an unprecedented spatiotemporal resolution. To this day, there is no available solution to measure root bending angle over time for vertical-stage microscopy. To address these problems, we developed ACORBA (Automatic Calculation Of Root Bending Angles), a fully automated software to measure root bending angle over time from vertical-stage microscope and flatbed scanner images. Moreover, the software can be used semi-automated for camera, mobile phone or stereomicroscope images. ACORBA represents a flexible approach based on both traditional image processing and deep machine learning segmentation to measure root angle progression over time. By its automated nature, the workflow is limiting human interactions and has high reproducibility. ACORBA will support the plant biologist community by reducing time and labor and by producing quality results from various kinds of inputs. Significance statementACORBA is implementing an automated and semi-automated workflow to quantify root bending and waving angles from images acquired with a microscope, a scanner, a stereomicroscope or a camera. It will support the plant biology community by reducing time and labor and by producing trustworthy and reproducible quantitative data.
Why it matches plant phenotyping methods根の屈曲角度を画像から自動抽出するソフトウェアとワークフローの開発が研究の中心であり、植物形態表現型の定量手法に該当する。
abstractwe developed ACORBA (Automatic Calculation Of Root Bending Angles), a fully automated software to measure root bending angle over time from vertical-stage microscope and flatbed scanner images.
Reproduction assets foundThe paper explicitly releases the ACORBA software (source code, trained models, annotated training libraries, notebooks, user manual) on SourceForge and the raw microscopy/scanner image stacks used for the root-angle measurements on Zenodo (DOI 10.5281/zenodo.5105719). Both are paper-specific, public, and actionable.Code · publicand
online Python image analysis and machine learning tutorials.
Availability of data and materials
The latest versions of ACORBA software training annotated libraries, source code, examples,
image pre-processing scripts, deep machine learning model training Jupyter notebooks and user
manual maintained by NBCS are available at https://sourceforge.net/projects/acorba/. The raw
microscopy and scanner stacks used in this paper are available at ZENODO
(https://doi.org/10.5281/zenodo.5105719). The analyzed results are supplemented (Supplemental
data).
Competing interests
The authors declare that they have no competing interests.
Funding
This work was supported by the European Research Council (GOpen asset ↗sourceforge.net/projects/acorbapdf-raw-page:17 lines:1-45Dataset · publicACORBA software training annotated libraries, source code, examples,
image pre-processing scripts, deep machine learning model training Jupyter notebooks and user
manual maintained by NBCS are available at https://sourceforge.net/projects/acorba/. The raw
microscopy and scanner stacks used in this paper are available at ZENODO
(https://doi.org/10.5281/zenodo.5105719). The analyzed results are supplemented (Supplemental
data).
Competing interests
The authors declare that they have no competing interests.
Funding
This work was supported by the European Research Council (Grant No. 803048), Charles
University Primus (Grant No. PRIMUS/19/SCI/09).
Author contributions
NBCS and MF conceived the pOpen asset ↗ZENODO · 10.5281/zenodo.5105719pdf-raw-page:17 lines:1-45Code / dataset availability confirmedbioRxiv · Europe PMC · checked 15 Sept 2026
Soil fungi establish mutualistic interactions with the roots of most vascular land plants. Arbuscular mycorrhizal (AM) fungi are among the most extensively characterised mycobionts to date. Current approaches to quantifying the extent of root colonisation and the abundance of hyphal structures in mutant roots rely on staining and human scoring involving simple, yet repetitive tasks prone to variations between experimenters. We developed AMFinder which allows for automatic computer vision-based identification and quantification of AM fungal colonisation and intraradical hyphal structures on ink-stained root images using convolutional neural networks. AMFinder delivered high-confidence predictions on image datasets of roots of multiple plant hosts (Nicotiana benthamiana, Medicago truncatula, Lotus japonicus, Oryza sativa) and captured the altered colonisation in ram1-1, str and smax1 mutants. A streamlined protocol for sample preparation and imaging allowed us to quantify mycobionts from the genera Rhizophagus, Claroideoglomus, Rhizoglomus and Funneliformis via flatbed scanning or digital microscopy including dynamic increases in colonisation in whole root systems over time. AMFinder adapts to a wide array of experimental conditions. It enables accurate, reproducible analyses of plant root systems and will support better documentation of AM fungal colonisation analyses. AMFinder can be accessed here: https://github.com/SchornacklabSLCU/amfinder.git
Why it matches plant phenotyping methods植物根の菌根菌感染状態を画像から自動識別・定量する手法とソフトウェアを開発しており、表現型取得・抽出が研究の中心です。
abstractWe developed AMFinder which allows for automatic computer vision-based identification and quantification of AM fungal colonisation and intraradical hyphal structures on ink-stained root images using convolutional neural networks.
Reproduction assets foundThe paper's AMFinder analysis software (amf/amfbrowser) and pre-trained CNN models are publicly available on the authors' GitHub repository under the MIT license. The training image datasets are not public and must be requested from the authors.Code · publicmanuscript. All authors
have read and approved the manuscript.
Data Availability
AMFinder is released under the terms of the open-source MIT license
(https://opensource.org/licenses/MIT) allowing unrestricted usage. Source code, pre-trained models
and detailed installation instructions are available on AMFinder GitHub webpage
(https://github.com/SchornacklabSLCU/amfinder.git). Training datasets are available upon request.
References
Abadi M, Agarwal A, Barham P, Brevdo E, Chen Z, Citro C, Corrado GS, Davis A, Dean J,
Devin M, et al. 2016. TensorFlow: Large-Scale Machine Learning on Heterogeneous Distributed
Systems. arXiv.
Bally J, Jung H, Mortimer C, Naim F, Philips JG, Hellens R, BombarelyOpen asset ↗SchornacklabSLCU/amfinderpdf-raw-page:21 lines:1-72Code / dataset availability confirmedEurope PMC · bioRxiv · checked 15 Sept 2026
This article describes a new methodology for detailed mapping of the lignification capacity of plant cell walls that we have called “REPRISAL” for REP orter R atiometrics I ntegrating S egmentation for A nalyzing L ignification. REPRISAL consists of the combination of three separate approaches. In the first approach, H*, G* and S* monolignol chemical reporters, corresponding to p -coumaryl alcohol, coniferyl alcohol and sinapyl alcohol, are used to label the growing lignin polymer in a fluorescent triple labelling strategy based on the sequential use of 3 main bioorthogonal chemical reactions. In the second step, an automatic parametric and/or artificial intelligence (AI) segmentation algorithm is developed that assigns fluorescent image pixels to 3 distinct cell wall zones corresponding to cell corners (CC), compound middle lamella (CML) and secondary cell walls (SCW). The last step corresponds to the exploitation of a ratiometric approach enabling statistical analyses of differences in monolignol reporter distribution (ratiometric method 1) and proportions (ratiometric method 2) within the different cell wall zones. In order to demonstrate the potential of REPRISAL for investigating lignin formation we firstly describe its use to map developmentally-related changes in the lignification capacity of WT Arabidopsis interfascicular fiber cells. We then show how it can be used to reveal subtle phenotypical differences in lignification by analyzing the Arabidopsis prx64 peroxidase mutant and provide further evidence for the implication of the AtPRX64 protein in floral stem lignification. Finally, we demonstrate the general applicability of REPRISAL by using it to map lignification capacity in poplar, flax and maize.
Why it matches plant phenotyping methodsREPRISALという蛍光画像・自動セグメンテーション・比率解析を統合した、細胞壁リグニン形成状態の植物フェノタイピング手法を開発し、複数種・変異体で適用している。
abstractThis article describes a new methodology for detailed mapping of the lignification capacity of plant cell walls that we have called “REPRISAL”
Reproduction assets foundThe authors publicly deposited their Fiji/ImageJ segmentation plugin (GUI, parametric macro, WEKA classifier and training data) plus representative confocal sample images in a Zenodo repository, explicitly referenced in the methods and supplementary data as containing the paper's lignification ratiometric analysis toolDataset · publicThe binary mask of each region was applied to each fluorescence channel and
569
fluorescence mean values were extracted for the 9 newly-created images. A recapitulative
570
montage image was then created to quickly estimate segmentation quality. The imageJ macro
571
and sample images are available in the Zenodo repository,
572
http://doi.org/10.5281/zenodo.4809980.573
574
AI Segmentation
575
The Machine learning approach is based on the “Waikato Environment for Knowledge
576
Analysis” (WEKA) implemented in ImageJ (Witten et al., 2016). We first defined a
577
classification based on four categories: i) secondary cell wall, ii) cell corners, iii) compound
578
middle lamella and iv) backgroOpen asset ↗zenodo · 10.5281/zenodo.4809980pdf-raw-page:21 lines:1-63Code / dataset availability confirmedbioRxiv · Europe PMC · checked 8 Sept 2026
Land surface phenology (LSP) enables global scale tracking of ecosystem processes, but its utility is limited in drylands due to low vegetation cover and resulting low annual amplitudes of vegetation indices (VIs). Due to the importance of drylands for biodiversity, food security, and the carbon cycle it is necessary to understand limitations in measuring dryland dynamics. Here, using simulated data and multi-temporal unmanned aerial vehicle (UAV) imagery of a desert shrubland, we explore the feasibility of detecting LSP with respect to fractional vegetation cover, plant functional types, VI uncertainty, and two different detection algorithms. Using simulated data we found that plants with distinct VI signals, such as deciduous shrubs, can require up to 60% fractional cover to consistently detect LSP. Evergreen plants, with lower seasonal VI amplitude, require considerably higher cover and can have undetectable phenology even with 100% vegetation cover. Our evaluation of two algorithms showed that neither performed the best in all cases. Even with adequate cover, biases in phenological metrics can still exceed 20 days, and can never be 100% accurate due to VI uncertainty from shadows, sensor view angle, and atmospheric interference. We showed how high-resolution UAV imagery enables LSP studies in drylands, and highlighted important scale effects driven by within canopy VI variation. With high-resolution imagery the open canopies of drylands are beneficial as they allow for straightforward identification of individual plants, enabling the tracking of phenology at the individual level. Drylands thus have the potential to become an exemplary environment for future LSP research.
Why it matches plant phenotyping methodsUAV画像とシミュレーションを用いて、乾燥地の植物フェノロジー検出アルゴリズムの実現可能性・精度・バイアスを評価しており、植物状態の測定法が中心です。
abstractusing simulated data and multi-temporal unmanned aerial vehicle (UAV) imagery of a desert shrubland, we explore the feasibility of detecting LSP
Reproduction assets foundThe paper's Data Availability statement explicitly deposits all simulated VI data, simulated UAV pixel time series, and reproduction code in a public Zenodo repository (DOI 10.5281/zenodo.4777207), which directly reproduces this paper's phenotyping analysis.Code · publicAll simulated VI data, simulated pixel time series from UAV imagery, and code for reproducing this
analysis, is available in the Zenodo data repository (https://doi.org/10.5281/zenodo.4777207).Open asset ↗Zenodo · 10.5281/zenodo.4777207pdf-page:14 lines:1-51Code / dataset availability confirmedEurope PMC · bioRxiv · checked 9 Sept 2026
ABSTRACT Over 800 million people across the tropics rely on cassava as a major source of calories. While the root dry matter content (RDMC) of this starchy root crop is important for both producers and consumers, characterization of RDMC by traditional methods is time-consuming and laborious for breeding programs. Alternate phenotyping methods have been proposed but lack the accuracy, cost, or speed ultimately needed for cassava breeding programs. For this reason, we investigated the use of a low-cost, handheld NIR spectrometer for field-based RDMC prediction in cassava. Oven-dried measurements of RDMC were paired with 21,044 scans of roots of 376 diverse clones from 10 field trials in Nigeria and grouped into training and test sets based on cross-validation schemes relevant to plant breeding programs. Mean partial least squares regression model performance ranged from R 2 p = 0.62 - 0.89 for within-trial predictions, which is within the range achieved with laboratory-grade spectrometers in previous studies. Relative to other factors, model performance was highly impacted by the inclusion of samples from the same environment in both the training and test sets. Random forest variable importance analysis of root spectra revealed increased importance in a region previously identified as predictive of water content in plants (~950 - 990 nm). With appropriate model calibration, the tested spectrometer will allow for field-based collection of spectral data with a smartphone for accurate RDMC prediction and potentially other quality traits, a step that could be easily integrated into existing harvesting workflows of cassava breeding programs. CORE IDEAS A low-cost, handheld near-infrared spectrometer was tested for phenotyping of cassava roots Plant breeding-relevant cross-validation schemes were used for predictions High prediction accuracies were achieved for cassava root dry matter content A spectral region predictive of plant water content was identified as important
Why it matches plant phenotyping methodsカッサバ根の乾物含量という植物形質を対象に、低コスト携帯型NIR分光計と予測モデルを開発・検証しており、フェノタイピング手法が研究の中心である。
abstractwe investigated the use of a low-cost, handheld NIR spectrometer for field-based RDMC prediction in cassava.
Reproduction assets foundThe paper's raw RDMC and NIR spectral data (21,044 SCiO scans paired with oven-dry RDMC from 10 Nigerian field trials) are publicly deposited on Cyverse under the GoreLab shared directory, as stated in the Data Availability section. The analysis R code on GitHub (GoreLab/CassavaNIRS) is paper-specific but its URL is anDataset · publicRaw RDMC and spectral data are available for download on Cyverse atOpen asset ↗Cyversepdf-page:20 lines:1-54Code / dataset availability confirmedbioRxiv · Europe PMC · Crossref · checked 8 Sept 2026
Progresses in agronomy rely on accurate measurement of the experimentations conducted to improve the yield component. Measurement of the plant density is required for a number of applications since it drives part of the crop fate. The standard manual measurements in the field could be efficiently replaced by high-throughput techniques based on high-spatial resolution images taken from UAVs. This study compares several automated detection of individual plants in the images from which the plant density can be estimated. It is based on a large dataset of high resolution Red/Green/Blue (RGB) images acquired from Unmanned Aerial Vehicules (UAVs) during several years and experiments over maize, sugar beet and sunflower crops at early stages. A total of 16247 plants have been labelled interactively on the images. Performances of handcrafted method (HC) were compared to those of deep learning (DL). The HC method consists in segmenting the image into green and background pixels, identifying rows, then objects corresponding to plants thanks to knowledge of the sowing pattern as prior information. The DL method is based on the Faster Region with Convolutional Neural Network (Faster RCNN) model trained over 2/3 of the images selected to represent a good balance between plant development stage and sessions. One model is trained for each crop. Results show that simple DL methods generally outperforms simple HC, particularly for maize and sunflower crops. A significant level of variability of plant detection performances is observed between the several experiments. This was explained by the variability of image acquisition conditions including illumination, plant development stage, background complexity and weed infestation. The image quality determines part of the performances for HC methods which makes the segmentation step more difficult. Performances of DL methods are limited mainly by the presence of weeds. A hybrid method (HY) was proposed to eliminate weeds between the rows using the rules developed for the HC method. HY improves slightly DL performances in the case of high weed infestation. When few images corresponding to the conditions of the testing dataset were complementing the training dataset for DL, a drastic increase of performances for all the crops is observed, with relative RMSE below 5% for the estimation of the plant density.
Why it matches plant phenotyping methodsUAV画像から個体を検出・計数し、作物密度を推定する画像解析手法を比較・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。
abstractThis study compares several automated detection of individual plants in the images from which the plant density can be estimated.
Reproduction assets foundThe paper's authors explicitly state that the deep-learning model architecture and data augmentation details are given in their public code repository on GitHub, which is an authors' public URL implementing the paper's plant detection/counting analysis.Code · public258 architectural details are given in the code (https://github.com/EtienneDavid/plants-counting-detection)Open asset ↗EtienneDavid/plants-counting-detectionpdf-page:9 lines:1-52Code / dataset availability confirmedEurope PMC · bioRxiv · checked 9 Sept 2026
Laboratory / benchtopRootMorphology / geometry measurementSegmentationRoot system architecture
Roots are central to the function of natural and agricultural ecosystems by driving plant acquisition of soil resources and influencing the carbon cycle. Root characteristics like length, diameter, and volume are critical to measure to understand plant and soil functions. RhizoVision Explorer is an open-source software designed to enable researchers interested in roots by providing an easy-to-use interface, fast image processing, and reliable measurements. The default broken roots mode is intended for roots sampled from pots or soil cores, washed, and typically scanned on a flatbed scanner, and provides measurements like length, diameter, and volume. The optional whole root mode for complete root systems or root crowns provides additional measurements such as angles, root depth, and convex hull. Both modes support providing measurements grouped by defined diameter ranges, the inclusion of multiple regions of interest, and batch analysis. RhizoVision Explorer was successfully validated against ground truth data using a novel copper wire image set. In comparison, the current reference software, the commercial WinRhizo™, drastically underestimated volume when wires of different diameters were in the same image. Additionally, measurements were compared with WinRhizo™ and IJ_Rhizo using a simulated root image set, showing general agreement in software measurements, except for root volume. Finally, scanned root image sets acquired in different labs for the crop, herbaceous, and tree species were used to compare results from RhizoVision Explorer with WinRhizo™. The two software showed general agreement, except that WinRhizo™ substantially underestimated root volume relative to RhizoVision Explorer. In the current context of rapidly growing interest in root science, RhizoVision Explorer intends to become a reference software, improve the overall accuracy and replicability of root trait measurements, and provide a foundation for collaborative improvement and reliable access to all. Abstract Figure
Why it matches plant phenotyping methods根画像から長さ・直径・体積などの植物形質を抽出するオープンソースソフトウェアの開発と、基準データおよび既存ソフトウェアとの技術検証が中心である。
abstractRhizoVision Explorer is an open-source software designed to enable researchers interested in roots by providing an easy-to-use interface, fast image processing, and reliable measurements.
Reproduction assets foundThe paper's copper wire validation image set is publicly deposited on Zenodo, and the authors' software binaries (Zenodo) and cvutil code library (GitHub) are explicitly released with public URLs. The simulated root image set (Zenodo 1159845) is cited prior work (Rose and Lobet 2018), not a paper-specific asset, and isDataset · publicThe copper wire image set used here is available in a public repository
and can be downloaded at http://doi.org/10.5281/zenodo.4677546 (Dhakal et al. 2021a).Open asset ↗zenodo · 10.5281/zenodo.4677546pdf-page:12 lines:1-49Code / dataset availability confirmedEurope PMC · bioRxiv · checked 15 Sept 2026
Field / plotRaman / spectroscopyLeafWhole plant / canopy / plot / fieldObject detectionCalibration / preprocessingGrowth / development / phenologyLeaf traitsWater status / transpiration
The measurement of leaf optical properties (LOP) using reflectance and scattering properties of light allows a continuous, time-resolved, and rapid characterization of many species traits including water status, chemical composition, and leaf structure. Variation in trait values expressed by individuals result from a combination of biological and environmental variations. Such species trait variations are increasingly recognized as drivers and responses of biodiversity and ecosystem properties. However, little has been done to comprehensively characterize or monitor such variation using leaf reflectance, where emphasis is more often on species average values. Furthermore, although a variety of platforms and protocols exist for the estimation of leaf reflectance, there is neither a standard method, nor a best practise of treating measurement uncertainty which has yet been collectively adopted. In this study, we investigate what level of uncertainty can be accepted when measuring leaf reflectance while ensuring the detection of species trait variation at several levels: within individuals, over time, between individuals, and between populations. As a study species, we use an economically and ecologically important dominant European tree species, namely Fagus sylvatica . We first use fabrics as standard material to quantify the measurement uncertainties associated with leaf clip (0.0001 to 0.4 reflectance units) and integrating sphere measurements (0.0001 to 0.01 reflectance units) via error propagation. We then quantify spectrally resolved variation in reflectance from F. sylvatica leaves. We show that the measurement uncertainty associated with leaf reflectance, estimated using a field spectroradiometer with attached leaf clip, represents on average a small portion of the spectral variation within a single individual sampled over time (2.7 ± 1.7%), or between individuals (1.5 ± 1.3% or 3.4 ± 1.7%, respectively) in a set of monitored F. sylvatica trees located in Swiss and French forests. In all forests, the spectral variation between individuals exceeded the spectral variation of a single individual measured within one week. However, measurements of variation within an individual at different canopy positions over time indicate that sampling design (e.g., standardized sampling, and sample size) strongly impacts our ability to measure between-individual variation. We suggest best practice approaches towards a standardized protocol to allow for rigorous quantification of species trait variation using leaf reflectance. Highlights We partition biological variation from measurement uncertainty for leaf spectra. Measurement uncertainty represents ca. 3% of spectral variation among beech trees. Biological variation within an individual increases by 80% as leaves mature. Maxima of uncertainty correspond to maxima of biological variation (water content). We recommend procedures to quantify biological variation in spectral measurements.
Why it matches plant phenotyping methods葉の反射分光測定における不確実性を定量化し、植物形質変異の検出能力と標準化プロトコルを評価する研究であり、フェノタイピング手法が中心です。
abstractWe first use fabrics as standard material to quantify the measurement uncertainties associated with leaf clip (0.0001 to 0.4 reflectance units) and integrating sphere measurements (0.0001 to 0.01 reflectance units) via error propagation.
Reproduction assets foundThe paper deposits its analysis scripts and source data in Dryad and its FieldSpec leaf/fabric reflectance spectra in the SPECCHIO spectral database, both publicly accessible with explicit identifiers.Dataset · publicumption. The number of
390
replicates is indicated for each dataset. Data processing and statistical analyses were all
391
performed in Matlab R2020a. Normality tests, t-tests and ANOVAs were performed on
392
individual wavelengths; see Results and figure captions for details. Scripts and source data
393
are available in Dryad (https://doi.org/10.5061/dryad.gtht76hkx). FieldSpec
394
spectroradiometer data are also deposited in SPECCHIO
395
(http://sc22.geo.uzh.ch:8080/SPECCHIO_Web_Interface/search, Hueni et al., 2020) and can
396
be found with the identifiers ‘Field spectroscopy Fabrics’ (dataset A), ‘Field spectroscopy F.
397
Sylvatica individual’ (dataset B), ‘Field spectroscopy F. sylvatOpen asset ↗Dryad · 10.5061/dryad.gtht76hkxpdf-raw-page:17 lines:1-49Dataset · publicall
391
performed in Matlab R2020a. Normality tests, t-tests and ANOVAs were performed on
392
individual wavelengths; see Results and figure captions for details. Scripts and source data
393
are available in Dryad (https://doi.org/10.5061/dryad.gtht76hkx). FieldSpec
394
spectroradiometer data are also deposited in SPECCHIO
395
(http://sc22.geo.uzh.ch:8080/SPECCHIO_Web_Interface/search, Hueni et al., 2020) and can
396
be found with the identifiers ‘Field spectroscopy Fabrics’ (dataset A), ‘Field spectroscopy F.
397
Sylvatica individual’ (dataset B), ‘Field spectroscopy F. sylvatica La Massane’ (dataset C),
398
‘Field spectroscopy F. sylvatica SwissForest’ (dataset D). Visualization and descriOpen asset ↗SPECCHIOpdf-raw-page:17 lines:1-49Code / dataset availability confirmedEurope PMC · bioRxiv · checked 15 Sept 2026
ABSTRACT The root system is critical for the survival of nearly all land plants and a key target for improving abiotic stress tolerance, nutrient accumulation, and yield in crop species. Although many methods of root phenotyping exist, within field studies one of the most popular methods is the extraction and measurement of the upper portion of the root system, known as the root crown, followed by trait quantification based on manual measurements or 2D imaging. However, 2D techniques are inherently limited by the information available from single points of view. Here, we used X-ray computed tomography to generate highly accurate 3D models of maize root crowns and created computational pipelines capable of measuring 71 features from each sample. This approach improves estimates of the genetic contribution to root system architecture, and is refined enough to detect various changes in global root system architecture over developmental time as well as more subtle changes in root distributions as a result of environmental differences. We demonstrate that root pulling force, a high-throughput method of root extraction that provides an estimate of root biomass, is associated with multiple 3D traits from our pipeline. Our combined methodology can therefore be used to calibrate and interpret root pulling force measurements across a range of experimental contexts, or scaled up as a stand-alone approach in large genetic studies of root system architecture.
Why it matches plant phenotyping methodsトウモロコシ根系を対象に、X線CTによる3Dモデル化と計算パイプラインで71形質を抽出し、根引抜き力との較正・解釈まで行う、中心的な表現型計測手法研究である。
abstractHere, we used X-ray computed tomography to generate highly accurate 3D models of maize root crowns and created computational pipelines capable of measuring 71 features from each sample.
Reproduction assets foundThe paper states that the authors' scripts for X-ray CT image processing and root feature extraction (batch-segmentation, batch-skeleton) are publicly available in the Topp-Roots-Lab GitHub repository. Raw phenotype data is said to be in Supplemental File 1, but no public URL for it is provided in the supplied blocks.Code · publicestimated by taking the 2D projection of the 3D volume, then
185 calculated using a similar approach to that described in Grift et al., 2011. DensityS features are
186 computationally similar to plant compactness traits described in Yang et al., 2014. Scripts used
187 for image processing and feature extraction are available at https://github.com/Topp-Roots-Lab/
188
189 Statistical Analysis
190
191 All downstream (i.e. post feature extraction) analysis was performed in the R statistical
192 computing environment. Initially, principal component analysis using all 71 3D roots traits was
193 used to identify large outliers, leading to the removal of 2 samples in the G2F 2017 data and 3
19Open asset ↗Topp-Roots-Labpdf-layout-page:5 lines:1-56Code / dataset availability confirmedbioRxiv · checked 8 Sept 2026
Field / plotLiDAR / point cloudRGB / grayscaleWhole plant / canopy / plot / fieldObject detection
Remote sensing can transform the speed, scale, and cost of biodiversity and forestry surveys. Data acquisition currently outpaces the ability to identify individual organisms in high resolution imagery. We outline an approach for identifying tree-crowns in RGB imagery while using a semi-supervised deep learning detection network. Individual crown delineation has been a long-standing challenge in remote sensing and available algorithms produce mixed results. We show that deep learning models can leverage existing Light Detection and Ranging (LIDAR)-based unsupervised delineation to generate trees that are used for training an initial RGB crown detection model. Despite limitations in the original unsupervised detection approach, this noisy training data may contain information from which the neural network can learn initial tree features. We then refine the initial model using a small number of higher-quality hand-annotated RGB images. We validate our proposed approach while using an open-canopy site in the National Ecological Observation Network. Our results show that a model using 434,551 self-generated trees with the addition of 2848 hand-annotated trees yields accurate predictions in natural landscapes. Using an intersection-over-union threshold of 0.5, the full model had an average tree crown recall of 0.69, with a precision of 0.61 for the visually-annotated data. The model had an average tree detection rate of 0.82 for the field collected stems. The addition of a small number of hand-annotated trees improved the performance over the initial self-supervised model. This semi-supervised deep learning approach demonstrates that remote sensing can overcome a lack of labeled training data by generating noisy data for initial training using unsupervised methods and retraining the resulting models with high quality labeled data.
Why it matches plant phenotyping methodsRGB画像から個体の樹冠を検出・ delineateする深層学習手法の開発と検証が中心であり、樹冠という植物形態形質を直接推定している。
abstractWe outline an approach for identifying tree-crowns in RGB imagery while using a semi-supervised deep learning detection network.
Reproduction assets foundThe paper uses publicly available NEON airborne LIDAR, RGB orthomosaic, and woody vegetation structure data from the San Joaquin Experimental Range site, and provides its authors' analysis code publicly on GitHub (weecology/DeepLidar, archived on Zenodo). Both are paper-specific, public, and actionable.Code · publicAll code for this project is available on GitHub
(https://github.com/weecology/DeepLidar) and archived on Zenodo [18].Open asset ↗weecology/DeepLidarpdf-page:4 lines:1-119Code / dataset availability confirmedbioRxiv · checked 15 Sept 2026
FlowerGrowth / time-series analysisGrowth / development / phenology
Phenology, the study of the timing of cyclical life history events and seasonal changes, is a fundamental aspect of how individual species, communities, and ecosystems will respond to climate change. Both biotic and abiotic phenological patterns are changing rapidly in response to changing seasonal temperatures and other climate-related drivers, and the consequences of these shifts for individual species and entire ecosystems are largely unknown. Landscape-scale simulations can address some of these needs for better predictions by demonstrating how phenology measures can vary with spatial and temporal grain of observations, and how phenological responses can vary with landscape heterogeneity and climate drivers. To explicitly examine the spatial and temporal scale-dependence of multiple phenology measures, we constructed simulated landscapes populated by virtual plant species with realistic phenologies and environmental sensitivities. This enabled us to examine phenology measures and environmental sensitivities along a continuum of spatial and temporal grains, while also controlling other aspects of sampling design. By relating measures of phenology calculated at a given spatiotemporal grain to average environmental conditions at that same grain size, we are able to determine observed environmental sensitivities for multiple phenological metrics at that spatial and temporal scale. We demonstrate that different phenological events change distinctly and predictably with spatial and temporal measurement scale, opening the way to incorporating scaling laws into predictions. Using plant flowering as our example, we identify that the timing of the beginnings or ends of an event (e.g., First Flower date, Last Flower date), can be especially sensitive to the spatial and temporal grain (or resolution) of observations. Our work provides an initial assessment of the role of observation scale in landscape phenology, and a general approach for incorporating scale-dependence into predictions of a variety of phenological time series.
Why it matches plant phenotyping methods植物の開花フェノロジー指標を空間・時間スケール別に算出・評価するシミュレーション手法を中心に扱い、観測スケール依存性を組み込む一般的アプローチを提示している。
abstractTo explicitly examine the spatial and temporal scale-dependence of multiple phenology measures, we constructed simulated landscapes populated by virtual plant species with realistic phenologies and environmental sensitivities.
Reproduction assets foundThe paper's simulation and analysis code is explicitly deposited on GitHub with an authors' public URL; no separate phenotype dataset deposit is stated (field datasets are cited prior work).Code · publicHarte and Newman 2014).For each phenology measure, we determined scaling effects
141
by comparing the phenology measures computed at a given scale to the measures taken at the
142
finest spatial and temporal scale available: 2m grain size and daily sampling. Code for the
143
simulation and detailed methods can be found at:
144
https://github.com/ibreckhe/phenoscaling_sims
145
146
Our simulation approach also allowed us to examine the scale-dependence of observed
147
environmental sensitivities. The phenology of the virtual species respond to two aspects of the
148
environment: the timing of seasonal snowpack disappearance (snow disappearance day, SDD)
149
and the accumulation of air temperatOpen asset ↗ibreckhe/phenoscaling_simspdf-raw-page:6 lines:1-61Code / dataset availability confirmedbioRxiv · checked 15 Sept 2026
Root hydraulic properties play a central role in the global water cycle, agricultural systems productivity, and ecosystem survival as they impact the global canopy water supply. However, the available experimental methods to quantify root hydraulic conductivities, such as the root pressure probing, are particularly challenging and their applicability on thin roots and small root segments is limited. There is a gap in methods enabling easy estimations of root hydraulic conductivities across a diversity of root types and at high resolution along root axes. In this case study, we analysed Zea mays (maize) plants of the var. B73 that were grown in pots for 14 days. Root cross-section data were used to extract anatomical measurements. We used the Generator of Root Anatomy in R (GRANAR) model to generate root anatomical networks from anatomical features. Then we used the Model of Explicit Cross-section Hydraulic Anatomy (MECHA) to compute an estimation of the root axial and radial hydraulic conductivities (kx and kr, respectively), based on the generated anatomical networks and cell hydraulic properties from the literature. The root hydraulic conductivity maps obtained from the root cross-sections suggest significant functional variations along and between different root types. Predicted variations of kr along the root axis were strongly dependent on the maturation stage of hydrophobic barriers. The same was also true for the maturation rates of the metaxylem. The different anatomical features, as well as their evolution along the root type add significant variation to the kr estimation in between root type and along the root axe. Under the prism of root types, anatomy, and hydrophobic barriers, our results highlight the diversity of root radial and axial hydraulic conductivities, which may be veiled under low-resolution measurements of the root system hydraulic conductivity. While predictions of our root hydraulic maps match the range and trend of measurements reported in the literature, future studies could focus on the quantitative validation of hydraulic maps. From now on, a novel method, which turns root cross-section images into hydraulic maps will offer an inexpensive and easily applicable investigation tool for root hydraulics, in parallel to root pressure probing experiments. One-Sentence summaryThe use of cross-section images and modelling tools to generate a map the axial and radial hydraulic conductivity along different root types for the maize cultivar B73.
Why it matches plant phenotyping methods根の断面画像から解剖学的形質を抽出し、モデルで軸方向・半径方向の根 hydraulic conductivity を推定する手法が研究の中心であるため、植物表現型計測手法として収録する。
abstractWe used the Generator of Root Anatomy in R (GRANAR) model to generate root anatomical networks from anatomical features. Then we used the Model of Explicit Cross-section Hydraulic Anatomy (MECHA) to compute an estimation of the root axial and radial hydraulic conductivities (kx and kr, respectively)
Reproduction assets foundThe paper provides two public, paper-specific assets: the GRANAR-MECHA coupling workflow (Jupyter/R repository with Zenodo DOI) and the B73_HydraulicMap repository containing the Rmarkdown script used to compute the root hydraulic maps plus all input and output data of the study.Code · publicection can be visualized through different figures that show the
186 proportion of the water fluxes in each compartiment (apoplastic and symplastic fluxes).
The whole script that was used to compute the root hydraulic maps from the root anatomical
188 measurement is presented as a Rmarkdown script stored in a GitHub repository
(https://github.com/granar/B73_HydraulicMap doi: 10.5281/zenodo.4320861). In the same
190 repository are stored all input and output data of this study.
192
10 of 24
.
CC-BY 4.0 International license
perpetuity. It is made available under a
preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint Open asset ↗granar/B73_HydraulicMap · 10.5281/zenodo.4320861pdf-raw-page:10 lines:1-20Code / dataset availability confirmedbioRxiv · checked 8 Sept 2026
Morphometrics has been applied in several fields of science including botany. Plant leaves are been one of the most important organs in the identification of plants due to its high variability across different plant groups. The differences between and within plant species reflect variations in genotypes, development, evolution, and environment. While traditional morphometrics has contributed tremendously to reducing the problems that come with the identification of plants and delimitation of species based on morphology, technological advancements have led to the creation of deep learning digital solutions that made it easy to study leaves and detect more characters to complement already existing leaf datasets. In this study, we demonstrate the use of MorphoLeaf in generating morphometric dataset from 140 leaf specimens from seven Cucurbitaceae species via scanning of leaves, extracting landmarks, data extraction, landmarks data quantification, and reparametrization and normalization of leaf contours. PCA analysis revealed that blade area, blade perimeter, tooth area, tooth perimeter, height of (each position of the) tooth from tip, and the height of each (position of the) tooth from base are important and informative landmarks that contribute to the variation within the species studied. Our results demonstrate that MorphoLeaf can quantitatively track diversity in leaf specimens, and it can be applied to functionally integrate morphometrics and shape visualization in the digital identification of plants. The success of digital morphometrics in leaf outline analysis presents researchers with opportunities to apply and carry out more accurate image-based researches in diverse areas including, but not limited to, plant development, evolution, and phenotyping.
Why it matches plant phenotyping methodsMorphoLeafによる葉画像のスキャン、ランドマーク抽出、形態計測データ化を中心に、葉の形状形質を定量化するソフトウェア／ワークフローを実証しているため。
abstractwe demonstrate the use of MorphoLeaf in generating morphometric dataset from 140 leaf specimens from seven Cucurbitaceae species via scanning of leaves, extracting landmarks, data extraction, landmarks data quantification, and reparametrization and normalization of leaf contours.
Reproduction assets foundThe preprint states its supplementary data (the Cucurbitaceae leaf morphometric dataset from MorphoLeaf analysis) is available online in the authors' GitHub repository, matching an allowed URL.Dataset · public411 The Data for this article is available online at: https://github.com/osooluwatobia/cucurbitaceae-Open asset ↗cucurbitaceae-pdf-page:19 lines:1-46Code / dataset availability confirmedbioRxiv · checked 14 Sept 2026
Broad scale remote sensing promises to build forest inventories at unprecedented scales. A crucial step in this process is designing individual tree segmentation algorithms to associate pixels into delineated tree crowns. While dozens of tree delineation algorithms have been proposed, their performance is typically not compared based on standard data or evaluation metrics, making it difficult to understand which algorithms perform best under what circumstances. There is a need for an open evaluation benchmark to minimize differences in reported results due to data quality, forest type and evaluation metrics, and to support evaluation of algorithms across a broad range of forest types. Combining RGB, LiDAR and hyperspectral sensor data from the National Ecological Observatory Networks Airborne Observation Platform with multiple types of evaluation data, we created a novel benchmark dataset to assess individual tree delineation methods. This benchmark dataset includes an R package to standardize evaluation metrics and simplify comparisons between methods. The benchmark dataset contains over 6,000 image-annotated crowns, 424 field-annotated crowns, and 3,777 overstory stem points from a wide range of forest types. In addition, we include over 10,000 training crowns for optional use. We discuss the different evaluation sources and assess the accuracy of the image-annotated crowns by comparing annotations among multiple annotators as well as to overlapping field-annotated crowns. We provide an example submission and score for an open-source baseline for future methods.
Why it matches plant phenotyping methods個体樹冠の画像ベース delineation を評価する標準ベンチマークデータセットと評価用Rパッケージを構築しており、植物形態の抽出・比較手法が中心である。
abstractwe created a novel benchmark dataset to assess individual tree delineation methods.
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 · public375 developed an R package ( https://github.com/weecology/NeonTreeEvaluation_package) forOpen asset ↗weecology/NeonTreeEvaluation_packagepdf-page:21 lines:1-71Code / dataset availability confirmedEurope PMC · bioRxiv · checked 14 Sept 2026
ABSTRACT Automatizing phenotype measurement is needed to increase plant breeding efficiency. Morphological traits are relevant in many fruit breeding programs, as appearance influences consumer preference. Often, these traits are manually or semi-automatically obtained. Yet, fruit morphology evaluation can be boosted by resorting to fully automatized procedures and digital images provide a cost-effective opportunity for this purpose. Here, we present an automatized pipeline for comprehensive phenomic and genetic analysis of morphology traits extracted from internal and external strawberry images. The pipeline segments, classifies and labels the images, extracts conformation features, including linear (area, perimeter, height, width, circularity, shape descriptor, ratio between height and width) and multivariate (Fourier Elliptical components and Generalized Procrustes) statistics. Internal color patterns are obtained using an autoencoder to smooth out the image. In addition, we develop a variational autoencoder to automatically detect the most likely number of underlying shapes. Bayesian modeling is employed to estimate both additive and dominant effects for all traits. As expected, conformational traits are clearly heritable. Interestingly, dominance variance is higher than the additive component for most of the traits. Overall, we show that fruit shape and color can be quickly and automatically evaluated and is moderately heritable. Although we study the strawberry species, the algorithm can be applied to other fruits, as shown in the GitHub repository https://github.com/lauzingaretti/DeepAFS .
Why it matches plant phenotyping methodsイチゴ果実の画像から形態・色彩形質を自動抽出するパイプラインの開発が研究の中心であり、遺伝解析はその応用である。
abstractHere, we present an automatized pipeline for comprehensive phenomic and genetic analysis of morphology traits extracted from internal and external strawberry images.
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 · publicFigure 2. Data analysis workflow (available at https://github.com/lauzingaretti/DeepAFS ). The input are all the
segmented internal and external fruit images.Open asset ↗lauzingaretti/DeepAFSpdf-page:5 lines:1-53Code / dataset availability confirmedEurope PMC · bioRxiv · checked 9 Sept 2026
Summary Soil is a heterogenous reservoir of essential elements needed for plant growth and development. Plants have evolved mechanisms to balance their nutritional needs based on availability of nutrients. This has led to genetically-based variation in the elemental composition ‘ionome’, of plants, both within and between species. We explore this natural variation using a panel of wild-collected, geographically widespread Arabidopsis thaliana accessions from the 1001 Genomes Project including over 1,135 accessions, and the 19 parental accessions of the Multi-parent Advanced Generation Inter-Cross (MAGIC) panel, all with full-genome sequences available. We present an experimental design pipeline for high-throughput ionomic screenings and analyses with improved normalisation procedures to account for errors and variability in conditions often encountered in large-scale, high-throughput data collection. We report quantification of the complete leaf and seed ionome of the entire collection using this pipeline and a digital tool-IonExplorer to interact with the dataset. We describe the pattern of natural ionomic variation across the A. thaliana species and identify several accessions with extreme ionomic profiles. It forms a valuable resource for exploratory QTL, GWA studies to identify genes underlying natural variation in leaf and seed ionome and genetic adaptation of plants to soil conditions.
Why it matches plant phenotyping methods大規模植物イオノーム取得のための高スループット実験・正規化パイプラインとデジタルツールを開発し、再利用可能なデータ資源として提示しているため、測定法が中心的です。
abstractWe present an experimental design pipeline for high-throughput ionomic screenings and analyses with improved normalisation procedures to account for errors and variability in conditions often encountered in large-scale, high-throughput data collection.
Reproduction assets foundThe paper's species-wide leaf and seed ionomic dataset for 1,135 A. thaliana accessions is made publicly accessible through the authors' interactive web tool Ion Explorer, which allows filtering, analysis, and download of the complete dataset as .csv files. This is a paper-specific, public, actionable asset. No author-Dataset · publicwe present an interactive web-based tool made available online: Ion Explorer
https://ffionexplorer.nottingham.ac.uk/ionmap/. Ion Explorer allows interactive visualisation,
analysis and comparison of the two large datasets.Open asset ↗Ion Explorerpdf-page:11 lines:1-44Code / dataset availability confirmedbioRxiv · Europe PMC · checked 15 Sept 2026
Plant thermal tolerance is a crucial research area as the climate warms and extreme weather events become more frequent. Leaves exposed to temperature extremes have inhibited photosynthesis and will accumulate damage to photosystem II (PSII) if tolerance thresholds are exceeded. Temperature-dependent changes in basal chlorophyll fluorescence (T-F0) can be used to identify the critical temperature at which PSII is inhibited. We developed and tested a high-throughput method for measuring the critical temperatures for PSII at low (CTMIN) and high (CTMAX) temperatures using a Maxi-Imaging fluorimeter and a thermoelectric Peltier plate heating/cooling system. We examined how experimental conditions: wet vs dry surfaces for leaves and heating/cooling rate, affect CTMIN and CTMAX across four species. CTMAX estimates were not different whether measured on wet or dry surfaces, but leaves were apparently less cold tolerant when on wet surfaces. Heating/cooling rate had a strong effect on both CTMAX and CTMIN that was species-specific. We discuss potential mechanisms for these results and recommend settings for researchers to use when measuring T-F0. The approach that we demonstrated here allows the high-throughput measurement of a valuable ecophysiological parameter that estimates the critical temperature thresholds of leaf photosynthetic performance in response to thermal extremes.
Why it matches plant phenotyping methods葉の熱耐性・PSII機能の臨界温度を高スループットに測定する蛍光イメージング手法を開発・検証しており、表現型取得法が研究の中心である。
abstractWe developed and tested a high-throughput method for measuring the critical temperatures for PSII at low (CTMIN) and high (CTMAX) temperatures using a Maxi-Imaging fluorimeter and a thermoelectric Peltier plate heating/cooling system.
Reproduction assets foundThe paper provides authors' public R code and example files for extracting Tcrit values from T-F0 chlorophyll fluorescence curves, hosted on the authors' GitHub repository. The paper also states phenotype data are openly available in figshare (10.6084/m9.figshare.12545093), but no figshare URL is present in the allowedCode · publican leaf temperature estimated from
227 two thermocouples attached to leaves on the plate and relative F0 values using the segmented R
228 package (Muggeo 2017) using the R Environment for Statistical Computing (R Core Team
229 2020). We provide example files and example R code for extracting Tcrit values from T-F0
230 curves at https://github.com/pieterarnold/Tcrit-extraction.
231
232 Surface wetness experiment: effect of wet vs dry surfaces for leaves on CTMIN and CTMAX
233 Most experiments that measure T-F0 have measured leaf samples with all excess surface
234 moisture removed, on a dry surface. However, maintaining water content of detached leaves by
235 providing a wet surface where leaOpen asset ↗pieterarnold/Tcrit-extractionpdf-layout-page:8 lines:1-46Code / dataset availability confirmedbioRxiv · checked 15 Sept 2026
LettuceAerial / UAVFlowerSegmentationGrowth / time-series analysisGrowth / development / phenology
Flower opening and closure are traits of reproductive importance in all angiosperms because they determine the success of self- and cross-pollination. The temporal nature of this phenotype rendered it a difficult target for genetic studies. Cultivated and wild lettuce, Lactuca spp., have composite inflorescences comprised of multiple florets that open only once. Different accessions were observed to flower at different times of day. An F6 recombinant inbred line population (RIL) had been derived from accessions of L. serriola x L. sativa that originated from different environments and differed markedly for daily floral opening time. This population was used to map the genetic determinants of this trait; the floral opening time of 236 RILs was scored over a seven-hour period using time-course image series obtained by drone-based remote phenotyping on two occasions, one week apart. Floral pixels were identified from the images using a support vector machine (SVM) machine learning algorithm with an accuracy above 99%. A Bayesian inference method was developed to extract the peak floral opening time for individual genotypes from the time-stamped image data. Two independent QTLs, qDFO2.1 (Daily Floral Opening 2.1) and qDFO8.1, were discovered. Together, they explained more than 30% of the phenotypic variation in floral opening time. Candidate genes with non-synonymous polymorphisms in coding sequences were identified within the QTLs. This study demonstrates the power of combining remote imaging, machine learning, Bayesian statistics, and genome-wide marker data for studying the genetics of recalcitrant phenotypes such as floral opening time. One sentence summaryMachine learning and Bayesian analyses of drone-mediated remote phenotyping data revealed two genetic loci regulating differential daily flowering time in lettuce (Lactuca spp.).
Why it matches plant phenotyping methodsドローン画像、機械学習、ベイズ推定を用いた花開花時刻の表現型取得・抽出が研究の中心であり、方法の精度も評価されている。
abstractthe floral opening time of 236 RILs was scored over a seven-hour period using time-course image series obtained by drone-based remote phenotyping
Reproduction assets foundThe paper's Data Availability statement provides two paper-specific public assets: authors' analysis scripts (machine learning and Bayesian inference) on GitHub, and the GPS-anchored drone aerial image data on HydroShare. Both are directly used for this paper's phenotyping and analysis.Code · public51 2015-51181-24283 to RWM.
452
453 Data Availability
454 GBS data of the RILs and WGS data of the parents are available on the NCBI SRA database under
455 BioProjects PRJNA642889, PRJNA510128, and PRJNA478460, respectively. Scripts used in the
456 study for machine learning and Bayesian inference are available on GitHub at
457 https://www.github.com/rkbhan/FloralOpening. GPS-anchored aerial image data are available on
458 HydroShare at https://www.hydroshare.org/resource/1c5855dbeb3c49a8b5779300550e08f1/.
20Open asset ↗rkbhan/FloralOpeningpdf-layout-page:20 lines:1-57Dataset · publicavailable on the NCBI SRA database under
455 BioProjects PRJNA642889, PRJNA510128, and PRJNA478460, respectively. Scripts used in the
456 study for machine learning and Bayesian inference are available on GitHub at
457 https://www.github.com/rkbhan/FloralOpening. GPS-anchored aerial image data are available on
458 HydroShare at https://www.hydroshare.org/resource/1c5855dbeb3c49a8b5779300550e08f1/.
20Open asset ↗pdf-layout-page:20 lines:1-57Code / dataset availability confirmedbioRxiv · checked 15 Sept 2026
Recent years have witnessed a convergence of data and methods that allow us to approximate the shape, size, and functional attributes of biological organisms. This is not only limited to traditional model species: given the ability to culture and visualize a specific organism, we can capture both its structural and functional attributes. We present a quantitative model for the colonial diatom Bacillaria paradoxa, an organism that presents a number of unique attributes in terms of form and function. To acquire a digital model of B. paradoxa, we extract a series of quantitative parameters from microscopy videos from both primary and secondary sources. These data are then analyzed using a variety of techniques, including two rival deep learning approaches. We provide an overview of neural networks for non-specialists as well as present a series of analysis on Bacillaria phenotype data. The application of deep learning networks allows for two analytical purposes. Application of the DeepLabv3 pre-trained model extracts phenotypic parameters describing the shape of cells constituting Bacillaria colonies. Application of a semantic model trained on nematode embryogenesis data (OpenDevoCell) provides a means to analyze masked images of potential intracellular features. We also advance the analysis of Bacillaria colony movement dynamics by using templating techniques and biomechanical analysis to better understand the movement of individual cells relative to an entire colony. The broader implications of these results are presented, with an eye towards future applications to both hypothesis-driven studies and theoretical advancements in understanding the dynamic morphology of Bacillaria.
Why it matches plant phenotyping methods珪藻の顕微鏡動画から形態・細胞内特徴・群体運動を抽出する画像処理および深層学習手法が研究の中心であり、植物表現型解析手法の開発に該当する。
abstractTo acquire a digital model of B. paradoxa, we extract a series of quantitative parameters from microscopy videos from both primary and secondary sources.
Reproduction assets foundThe paper's Bacillaria phenotyping data and analysis assets are publicly available: raw data, processed numeric/image data, and code in the authors' Digital-Bacillaria GitHub repository; skeleton-creation scripts in the Image-Skeletons subrepository; the OpenDevoCell segmentation platform (GitHub and web app); and a GfDataset · publicstrains contained in our primary data (videos) have been harvested from the Neckar
river in Germany (49°04'41.8"N 9°09'17.9"E). Samples were collected on September 14, 2019.
The average size of each cell (filament) is approximately 81µm.
Data Availability
Select unprocessed (raw) data are available at our Github repository
(https://github.com/devoworm/Digital-Bacillaria), processed numeric and image data (numeric
tables and skeletonized images), and select video files are available on the Open Science
Framework (DOI 10.17605/OSF.IO/AR8C3).
17Open asset ↗devoworm/Digital-Bacillariapdf-layout-page:17 lines:1-49Code · publicckground
color (select the background by color) to RGB value 0,0,0. To create a thick skeleton from a thin
skeleton, select the thin skeleton by color and then select the border function. The border width
should be set to 4, hard border, and filled with RGB value 0,217,0. The pseudo-code for GIMP
script-fu is located on Github (https://github.com/devoworm/Digital-Bacillaria/tree/master/Image-Skeletons).Image Tracking for Movement. We also employ image tracking for the primary microscopy
data. The tracking of a partial image (template) of a diatom can be used under certain conditions
to obtain its trajectory. In particular, a movement of the diatoms in a plane perpendicular to the
optical axiOpen asset ↗devoworm/Digital-Bacillariapdf-raw-page:10 lines:1-44Code · publicn-source software with a web interface called OpenDevoCell (based on DeepLearning 4J).
DeepLabv3 (Google, MountainView, California, USA) is a package for TensorFlow, and Deep
Learning 4J (Eclipse Foundation, Ottawa, Canada), a Java-based library that works with
TensorFlow. OpenDevoCell is open-source software located on Github
(https://github.com/devoworm/GSOC-2019/tree/master/OpenDevoCell) and as a web-based
application (https://open-devo-cell.herokuapp.com).11
.
CC-BY 4.0 International license
available under a
was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made
The copyright holder for this preprint (wOpen asset ↗devoworm/GSOC-2019pdf-raw-page:11 lines:1-35Code / dataset availability confirmedbioRxiv · checked 15 Sept 2026
The shapes of grapevine leaves have been critical to correctly identify economically important varieties throughout history. The correspondence of homologous features in nearly all grapevine species and varieties has enabled advanced morphometric approaches to mathematically classify leaf shape. These approaches either model leaves through the measurement of numerous vein lengths and angles or measure a finite number of corresponding landmarks and use Procrustean approaches to superimpose points and perform statistical analyses. Hand illustrations, too, play an important role in grapevine identification, as details omitted using the above methods can be visualized. Here, I use a saturating number of pseudo-landmarks to capture intricate, local features in grapevine leaves: the curvature of veins and the shapes of serrations. Using these points, averaged leaf shapes for 60 varieties of wine and table grapes are calculated that preserve features. A pairwise Procrustes distance matrix of the overall morphological similarity of each variety to the other classifies leaves into two main groups--deeply lobed and more entire--that correspond to the measurements of sinus depth by Pierre Galet. Using the system of Galet, pseudo-landmarks are converted into relative distance and angle measurements. Both Galet-inspired and Procrustean methods allow increased accuracy in predicting variety compared to a finite number of landmarks. Using Procrustean pseudo-landmarks captures grapevine leaf shape at the same level of detail as drawings and provides a quantitative method to arrive at mean leaf shapes representing varieties that can be used within a predictive statistical framework.
Why it matches plant phenotyping methodsブドウ葉の形状を擬似ランドマークとプロクルステス解析で定量化し、品種識別・平均葉形状推定に用いる手法の開発が中心である。
abstractHere, I use a saturating number of pseudo-landmarks to capture intricate, local features in grapevine leaves: the curvature of veins and the shapes of serrations.
Reproduction assets foundThe paper explicitly links public GitHub repositories and a Dryad DOI containing its leaf photographs, hand-traced landmark/pseudo-landmark raw data, visual-check outputs, interpolation code and outputs, and Procrustes analysis code and outputs — all paper-specific, public, and actionable.Dataset · public) the photo ID of the leaf indicating the vineyard position of
206 the vine it was collected from, 2) an enumerating value 1 through 4 specifying which of four
207 leaves for the variety the data corresponds to, and 3) which vector the data file represents.
208 These files, the raw data, are available at the following link:
209 https://github.com/DanChitwood/grapevine_ampelometry/tree/master/0_visual_check/ampel
210 ometry_data. Tracing all data for a single leaf took approximately 15 minutes. Because the data
211 was traced by hand, it was important to visually verify its accuracy. Analyses in Python were
212 undertaken using NumPy (Oliphant, 2006), pandas (McKinney, 2010), and Matplotlib (Open asset ↗DanChitwood/grapevine_ampelometrypdf-layout-page:5 lines:1-54Code · publicthe data
211 was traced by hand, it was important to visually verify its accuracy. Analyses in Python were
212 undertaken using NumPy (Oliphant, 2006), pandas (McKinney, 2010), and Matplotlib (Hunter,
213 2007) to plot the data on the actual photo. The code for plotting vectors onto the original photo
214 can be found here:
215 https://github.com/DanChitwood/grapevine_ampelometry/blob/master/0_visual_check/ampel
216 ometry_visual_check.ipynb. The visual checks for each of the 240 leaves analyzed in this study
217 can be found here:
218 https://github.com/DanChitwood/grapevine_ampelometry/tree/master/0_visual_check/outpu
219 t_visual_check
220
5Open asset ↗DanChitwood/grapevine_ampelometrypdf-layout-page:5 lines:1-54Code · publicith assigned numbers of points to every vector, interpolation was
240 used to calculate equidistant pseudo-landmarks. A function was created using the scipy
241 (Virtanen et al., 2020) interp1d function to interpolate the correct number of equidistance
242 points for each vector. The code used to interpolate points is here:
243 https://github.com/DanChitwood/grapevine_ampelometry/blob/master/1_interpolation/ampe
244 lometry_interpolation.ipynb. The interpolated points can be found here:
245 https://github.com/DanChitwood/grapevine_ampelometry/blob/master/1_interpolation/outp
246 ut_interpolated_points.txt
247
248 With corresponding points between all leaves, a Procrustes analysis could be peOpen asset ↗DanChitwood/grapevine_ampelometrypdf-layout-page:6 lines:1-54Code · publicesults saved as a pairwise distance matrix. The hclust() function in R using the
259 “mcquitty” method was used to hierarchically cluster varieties based on the pairwise distance
260 matrix and overall morphological similarity. The code for performing a Procrustes analysis for
261 each variety and outputs can be found here:
262 https://github.com/DanChitwood/grapevine_ampelometry/tree/master/2_procrustes_by_vari
263 ety
264
6Open asset ↗DanChitwood/grapevine_ampelometrypdf-layout-page:6 lines:1-54Code · publicll Procrustes mean
265
shape, super-imposed Procrustes coordinates for all leaves, and eigenvalues and eigenleaves
266
from a PCA. The superimposed Procrustes coordinates of all leaves and the mean shape were
267
plotted together. The code for the Procrustes analysis for all 240 leaves and the outputs can be
268
found here:
269
https://github.com/DanChitwood/grapevine_ampelometry/tree/master/3_overall_procrustes
270
271
Data analysis
272
273
To calculate allometry for each line segment, distances between all points were converted to
274
cm using the pixel to cm scale measured for each leaf. The lm() function in R was used to model
275
the natural log of the distance from each point to the neOpen asset ↗DanChitwood/grapevine_ampelometrypdf-raw-page:7 lines:1-92Code / dataset availability confirmedEurope PMC · OpenAlex · bioRxiv · checked 14 Sept 2026
Parasitic plants infect other plants by forming haustoria, specialized multicellular organs consisting of several cell types each of which has unique morphological features and physiological roles associated with parasitism. Understanding the spatial organization of cell types is, therefore, of great importance in elucidating the functions of haustoria. Here, we report a three-dimensional (3-D) reconstruction of haustoria from two Orobanchaceae species, the obligate parasite Striga hermonthica infecting rice and the facultative parasite Phtheirospermum japonicum infecting Arabidopsis . Our images reveal the spatial arrangements of multiple cell types inside haustoria and their interaction with host roots. The 3-D internal structures of haustoria highlight differences between the two parasites, particularly at the xylem connection site with the host. Our study provides structural insights into how organs interact between hosts and parasitic plants. One-sentence summary Three-dimensional image reconstruction was used to visualize the spatial organization of cell types in the haustoria of parasitic plants with special reference to their interaction with host roots.
Why it matches plant phenotyping methods寄生植物のハウストリア内部構造を3次元画像再構成で可視化することが研究の中心であり、植物器官の空間形態を抽出・比較している。
abstractHere, we report a three-dimensional (3-D) reconstruction of haustoria from two Orobanchaceae species
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 tools are available at https://github.com/yk-szk/ssrvtools.Open asset ↗yk-szk/ssrvtoolspdf-page:12 lines:1-46Code / dataset availability confirmedbioRxiv · checked 15 Sept 2026
Museum specimens are the main source of information on organisms’ morphological features. Although access to this information was commonly limited to researchers able to visit collections, it is now becoming freely available thanks to the digitization of museum specimens. With these images, we will be able to collectively build large-scale morphological datasets, but these will only be useful if the limits to this approach are well-known. To establish these limits, we used two-dimensional images of plant specimens to test the precision and accuracy of image-based data and analyses.To test measurement precision and accuracy, we compared leaf measurements taken from specimens and images of the same specimens. Then we used legacy morphometric datasets to establish differences in the quality of datasets and multivariate analyses between specimens and images. To do so, we compared the multivariate space based on original legacy data to spaces built with datasets simulating image-based data.We found that trait measurements made from images are as precise as those obtained directly from specimens, but as traits diminish in size, the accuracy drops as well. This decrease in accuracy, however, has a very low impact on dataset and analysis quality. The main problem with image-based datasets comes from missing observations due to image resolution or organ overlapping. Missing data lowers the accuracy of datasets and multivariate analyses. Although the effect is not strong, this decrease in accuracy suggests caution is needed when designing morphological research that will rely on digitized specimens.As highlighted by images of plant specimens, 2D images are reliable measurement sources, even though resolution issues lower accuracy for small traits. At the same time, the impossibility of observing particular traits affects the quality of image-based datasets and, thus, of derived analyses. Despite these issues, gathering phenotypic data from two-dimensional images is valid and may support large-scale studies on the morphology and evolution of a wide diversity of organisms.Competing Interest StatementThe authors have declared no competing interest.View Full Text
Why it matches plant phenotyping methods植物標本画像から形態形質を測定する画像ベース手法の精度・正確性を、実標本および既存データと比較検証しており、フェノタイピング手法が中心です。
abstractTo establish these limits, we used two-dimensional images of plant specimens to test the precision and accuracy of image-based data and analyses.
Reproduction assets foundThe paper's leaf measurement dataset (specimen and image measurements) is publicly deposited on MorphoBank project 3764, with an explicit availability statement and URL. The authors also state all data and code are on GitHub/Zenodo (DOI 10.5281/zenodo.3924506), but no matching URL is present in the allowed list, so no码Dataset · publicavoided sampling
79
size issues, as they are well represented in the Rio de Janeiro Botanical Garden herbarium (RB; acronym
80
according to Thiers, 2020, continuously updated. See the supplementary material for information on
81
measurements, sampling, and vouchers. Metric data is also available from MorphoBank project 3764
82
http://morphobank.org/permalink/?P3764). We used a digital caliper to measure specimens and
83
the FSI Viewer v. 5.6.6 software (available from RB’s website; http://jabot.jbrj.gov.br) to measure
84
images of the same leaves from the same specimens. All measurements, both from images and speci-
85
mens, were made twice to allow the following analyses.
86
First, we teOpen asset ↗MorphoBank · project 3764pdf-raw-page:4 lines:1-79Code / dataset availability confirmedbioRxiv · Europe PMC · OpenAlex · checked 14 Sept 2026
Field / plotPhotogrammetry / SfM / MVSLiDAR / point cloudStem / branchMorphology / geometry measurement2D/3D reconstructionArchitecture / morphology / geometry
Quantitative assessment of the effects of forest management on tree size and shape has been challenging as there has been a lack of methodologies for characterizing differences and possible changes comprehensively in space and time. Terrestrial laser scanning (TLS) and photogrammetric point clouds provide three-dimensional (3D) information on tree stem reconstructions required for characterizing differences between stem shapes and growth allocation. This data set includes 3D reconstructions of stems of Scots pine (Pinus sylvestris L.) trees from sample plots with different thinning treatments. The thinning treatments include two intensities of thinning, three thinning types as well as control (i.e. no thinning treatment since the establishment). The data set can be used in developing point clouds processing algorithms for single tree stem reconstruction and for investigating variation in stem size and shape of Scots pine trees. Additionally, it offers possibilities in characterizing the effects of various thinning treatments on stem size and shape of Scots pine trees from boreal forests. Data setZenodo https://zenodo.org/record/3701271 Data set licenseAttribution 4.0 International (CC BY 4.0)
Why it matches plant phenotyping methodsTLSと写真測量による樹幹の3D再構成データセットであり、樹幹サイズ・形状という植物形質の抽出アルゴリズム開発と評価に直接利用できるため、方法中心のデータセットとして含める。
abstractTerrestrial laser scanning (TLS) and photogrammetric point clouds provide three-dimensional (3D) information on tree stem reconstructions required for characterizing differences between stem shapes and growth allocation.
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。Dataset · publicThis data set includes three packed zip files that can be downloaded from
https://zenodo.org/record/3701271. The zip files include text files of stem points of each tree within
the sample plots from the three test sites.Open asset ↗Zenodopdf-page:4 lines:1-36Code / dataset availability confirmedbioRxiv · OpenAlex · Europe PMC · checked 15 Sept 2026
Microplot extraction (MPE) is a necessary image-processing step in unmanned aerial vehicle (UAV)-based research on breeding fields. At present, it is manually using ArcGIS, QGIS or other GIS-based software, but achieving the desired accuracy is time-consuming. We therefore developed an intuitive, easy-to-use semi-automatic program for MPE called Easy MPE to enable researchers and others to access reliable plot data UAV images of whole fields under variable field conditions. The program uses four major steps: (1). Binary segmentation, (2). Microplot extraction, (3). Production of *.shp files to enable further file manipulation, and (4). Projection of individual microplots generated from the orthomosaic back onto the raw aerial UAV images to preserve the image quality. Crop rows were successfully identified in all trial fields. The performance of proposed method was evaluated by calculating the intersection-over-union (IOU) ratio between microplots determined manually and by Easy MPE: The average IOU ({+/-}SD) of all trials was 91% ({+/-}3).
Why it matches plant phenotyping methodsUAV画像から育種圃場のマイクロプロットを半自動抽出する画像処理手法を開発し、手動結果とのIoUで性能検証しており、植物表現型取得ワークフローが中心です。
abstractWe therefore developed an intuitive, easy-to-use semi-automatic program for MPE called Easy MPE
Reproduction assets foundThe paper's Easy MPE microplot-extraction Python code is publicly available in the authors' GitHub repository, linked in the Data Availability section.Code · publicData Availability: Submission of a manuscript to Plant Phenomics implies that the data is
440
freely available upon request or has deposited to a open database, like NCBI. If data are in an
441
archive, include the accession number or a placeholder for it. Also include any materials that
442
must be obtained through an MTA.
443
https://github.com/oceam/EasyMPE
444
445
References
446
[1] D. Tilman, C. Balzer, J. Hill, B.L. Befort, Global food demand and the sustainable
447
intensification of agriculture, Proceedings of the National Academy of Sciences Dec
448
2011, 108 (50) 20260-20264; https://doi.org/10.1073/pnas.1116437108
449
[2] J.L. Araus, J.E. Cairns, Field high-throughput phenOpen asset ↗oceam/EasyMPEpdf-raw-page:16 lines:1-74Code / dataset availability confirmedEurope PMC · bioRxiv · checked 15 Sept 2026
Background Seed analysis is currently a bottleneck in phenotypic analysis of seeds. Measurements are slow and imprecise with potential for bias to be introduced when gathered manually. New acquisition tools were requested to improve phenotyping efficacy with an emphasis on obtaining colour information. Results A portable imaging system (BELT) supported by image acquisition and analysis software (phenoSEED) was created for small-seed optical analysis. Lentil ( Lens culinaris L.) phenotyping was used as the primary test case. Seeds were loaded into the system and all seeds in a sample were automatically and individually imaged to acquire top and side views as they passed through an imaging chamber. A Python analysis script applied a colour calibration and extracted quantifiable traits of seed colour, size and shape. Extraction of lentil seed coat patterning was implemented to further describe the seed coat. The use of this device was forecasted to eliminate operator biases, increase the rate of acquisition of traits, and capture qualitative information about traits that have been historically analyzed by eye. Conclusions Increased precision and higher rates of data acquisition compared to traditional techniques will help breeders to develop more productive cultivars. The system presented is available as an open-source project for academic and non-commercial use.
Why it matches plant phenotyping methods種子の色・サイズ・形状・種皮模様を自動取得・抽出する撮像システムと解析ソフトウェアの開発が中心であり、植物フェノタイピング手法に該当する。
abstractA portable imaging system (BELT) supported by image acquisition and analysis software (phenoSEED) was created for small-seed optical analysis.
Reproduction assets foundThe paper's phenoSEED image-analysis script (used for seed shape, size, colour, and clustering phenotyping) is explicitly stated to be publicly available on the authors' GitLab repository. The BELT image datasets themselves are only available on request, so they do not qualify as public assets.Code · publicde and hard-
ware plans available for academic and non-commercial
use. It is hoped that this will support the collection
of more easily cross-comparable data and encourage
other research groups to contribute to further de-
velopment of the project. At the time of publica-
tion, a version of the processing script is available at
https://gitlab.com/usask-speclab/phenoseed. Further
information on a comprehensive hardware and soft-
ware bundle will be made available as it is packaged
for distribution.
Methods
BELT System Design and Description
BELT (Figure 1) was designed around a 150 mm wide
conveyor with at white, low-gloss belt (Mini-Mover
Conveyors, Volcano CA) mounted on an audio-visual
carOpen asset ↗usask-speclab/phenoseedpdf-raw-page:8 lines:1-99Code / dataset availability confirmedbioRxiv · OpenAlex · Europe PMC · checked 15 Sept 2026
Association mapping studies have enabled researchers to identify candidate loci for many important environmental resistance factors, including agronomically relevant resistance traits in plants. However, traditional genome-by-environment studies such as these require a phenotyping pipeline which is capable of accurately and consistently measuring stress responses, typically in an automated high-throughput context using image processing. In this work, we present Latent Space Phenotyping (LSP), a novel phenotyping method which is able to automatically detect and quantify response to treatment directly from images. Using two synthetically generated image datasets, we first show that LSP is able to successfully recover the simulated QTL in both simple and complex synthetic imagery. We then demonstrate an example application of an interspecific cross of the model C4 grass Setaria. We propose LSP as an alternative to traditional image analysis methods for phenotyping, enabling association mapping studies without the need for engineering complex image processing pipelines.
Why it matches plant phenotyping methods画像から処理応答を自動検出・定量する新規フェノタイピング手法を提案し、合成データで検証した方法開発研究。
abstractIn this work, we present Latent Space Phenotyping (LSP), a novel phenotyping method which is able to automatically detect and quantify response to treatment directly from images.
Reproduction assets foundThe paper provides two paper-specific public assets: the LSP-Lab implementation of the Latent Space Phenotyping method on GitHub, and a figshare deposit containing the full datasets and utility scripts needed to reproduce the paper's results and figures. Setaria and sorghum image datasets are third-party (Baxter group)Dataset · publicFunding
This research was funded by a Canada First Research Excellence Fund grant from the
Natural Sciences and Engineering Research Council of Canada.
Data Availability
Full datasets and utility scripts needed for reproducing the results and figures presented in
Section 3 can be found at https://figshare.com/s/f710381c04c01e2ba319. The data
for the Setaria RIL experiment and the sorghum experiment are available from the sources
referenced by the authors of these datasets [8, 34].
References
[1] Virtual laboratory. http://www.algorithmicbotany.org/virtual_laboratory/.Accessed: 2017-08-01.
[2] Georgios Arvanitidis, Lars Kai Hansen, and Søren Hauberg. LatenOpen asset ↗figshare · f710381c04c01e2ba319pdf-raw-page:17 lines:1-44Code / dataset availability confirmedbioRxiv · Europe PMC · OpenAlex · checked 9 Sept 2026
Plant height (PH) data collected at high temporal resolutions can give insight into important growth parameters useful for identifying elite material in plant breeding programs and developing management guidelines in production settings. However, in order to increase the temporal resolution of PH data collection, more robust, rapid and low-cost methods are needed to evaluate field plots than those currently available. Due to their low cost and high functionality, unmanned aerial vehicles (UAVs) can be an efficient means for collecting height at various stages throughout development. We have developed a procedure for utilizing structure from motion algorithms to collect PH from RGB drone imagery and have used this platform to characterize a yield trial consisting of 24 maize hybrids planted in replicate under two dates and three planting densities in St Paul, MN in the summer of 2018. The field was imaged weekly after planting using a DJI Phantom 4 Advanced drone to extract PH and hand measurements were collected following aerial imaging of the field. In this work, we test the error in UAV PH measurements and compare it to the error obtained within manually acquired PH measurements. We also propose a method for improving the correspondence of manual and UAV measured height and evaluate the utility of using UAV obtained PH data for assessing growth of maize genotypes and for estimating end-season height.
Why it matches plant phenotyping methodsUAV画像とSfMを用いたトウモロコシ草丈抽出法の開発、手測定との誤差比較・検証、育種試験への実質的な適用が中心である。
abstractWe have developed a procedure for utilizing structure from motion algorithms to collect PH from RGB drone imagery
Reproduction assets foundThe paper explicitly states that the authors' custom MATLAB image-analysis and trait-extraction scripts for UAV-derived plant height are publicly available in a GitHub repository. No phenotype dataset or imagery deposit is stated in the supplied blocks.Code · publicThe scripts and processes used to perform the image analyses and trait extract are available at
https://github.com/SBTirado/UAV_PH.git.Open asset ↗SBTirado/UAV_PHpdf-page:6 lines:1-52Code / dataset availability confirmedbioRxiv · checked 9 Sept 2026
The aerial epidermis of plants plays a major role in their environment interactions, and the development of its cellular components -trichomes, stomata and pavement cells- is still not fully understood. We have performed a detailed screen of the leaf epidermis of two generations of the well-established Solanum pennellii ac. LA716 x Solanum lycopersicum cv. M82 introgression line (IL) population using a combination of scanning electron microscopy techniques. Quantification of the trichome and stomatal densities in the ILs revealed 18 genomic regions with a low trichome density and 4 ILs with a high stomatal density. We also found ILs with abnormal proportions of different trichome types and aberrant trichome morphologies. This work has led to the identification of new, unexplored genomic regions with roles in trichome and stomatal formation and provides an important dataset for further studies on tomato epidermal development that is publically available to the research community.
Why it matches plant phenotyping methods走査電子顕微鏡を用いた葉表皮の画像取得と、毛状突起・気孔密度および形態の定量が研究の中心であり、再利用可能な表現型データセットも提供しているため。
abstractWe have performed a detailed screen of the leaf epidermis of two generations of the well-established Solanum pennellii ac. LA716 x Solanum lycopersicum cv. M82 introgression line (IL) population using a combination of scanning electron microscopy techniques.
Reproduction assets foundThe paper states that all SEM micrographs used for the trichome/stomatal phenotyping screen are publicly available in the BioStudies database under accession S-BSST262. This is a paper-specific public asset (the SEM images underlying the phenotyping measurements). No author analysis code was deposited.Dataset · public321 study are available in the BioStudies database (http://www.ebi.ac.uk/biostudies) (McEntyre et al.,Open asset ↗BioStudiespdf-page:10 lines:1-44Code / dataset availability confirmedEurope PMC · bioRxiv · checked 14 Sept 2026
Hyperspectral remote sensing holds the potential to detect and quantify crop diseases in a rapid and non-invasive manner. Such tools could greatly benefit resistance breeding, but their adoption is hampered by i) a lack of specificity to disease-related effects and ii) insufficient robustness to variation in reflectance caused by genotypic diversity and varying environmental conditions, which are fundamental elements of resistance breeding. We hypothesized that relying exclusively on temporal changes in canopy reflectance during pathogenesis may allow to specifically detect and quantify crop diseases whilst minimizing the confounding effects of genotype and environment. To test this hypothesis, we collected time-resolved canopy hyperspectral reflectance data for 18 diverse genotypes on infected and disease-free plots and engineered spectral-temporal features representing this hypothesis. Our results confirm the lack of specificity and robustness of disease assessments based on reflectance spectra at individual time points. We show that changes in spectral reflectance over time are indicative of the presence and severity of septoria tritici blotch (STB) infections. Furthermore, the proposed time-integrated approach facilitated the delineation of disease from physiological senescence, which is pivotal for efficient selection of STB-resistant material under field conditions. A validation of models based on spectral-temporal features on a diverse panel of >300 wheat genotypes offered evidence for the robustness of the proposed method. This study demonstrates the potential of time-resolved canopy reflectance measurements for robust assessments of foliar diseases in the context of resistance breeding.
Why it matches plant phenotyping methods時系列ハイパースペクトル反射からコムギ葉病害の存在・重症度を推定する特徴量と手法を開発し、多様な遺伝子型で検証しており、植物表現型取得が研究の中心である。
abstractwe collected time-resolved canopy hyperspectral reflectance data for 18 diverse genotypes on infected and disease-free plots and engineered spectral-temporal features representing this hypothesis.
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2件を確認しました。Code · publicThe developed python script with
detailed annotations can be retrieved from github (https://github.com/and-jonas/stb_placl).Open asset ↗and-jonas/stb_placlpdf-page:8 lines:1-43Code · publicThe analysis was
implemented using a custom-developed R-Script (https://github.com/and-jonas/rfe).Open asset ↗and-jonas/rfepdf-page:13 lines:1-41Code / dataset availability confirmedEurope PMC · bioRxiv · Crossref · checked 14 Sept 2026
Functional ecology has increasingly focused on describing ecological communities based on their traits (measurable features affecting individuals fitness and performance). Analyzing trait distributions within and among forests could significantly improve understanding of community composition and ecosystem function. Historically, data on trait distributions are generated by (1) collecting a small number of leaves from a small number of trees, which suffers from limited sampling but produces information at the fundamental ecological unit (the individual); or (2) using remote sensing images to infer traits, producing information continuously across large regions, but as plots (containing multiple trees of different species) or pixels, not individuals. Remote sensing methods that identify individual trees and estimate their traits would provide the benefits of both approaches, producing continuous large-scale data linked to biological individuals. We used data from the National Ecological Observatory Network (NEON) to develop a method to scale up functional traits from 160 trees to the millions of trees within the spatial extent of two NEON sites. The pipeline consists of three stages: 1) image segmentation, to identify individual trees and estimate structural traits; 2) ensemble of models to infer leaf mass area (LMA), nitrogen, carbon, and phosphorus content using hyperspectral signatures, and DBH from allometry; and 3) predictions for segmented crowns for the full remote sensing footprint at the NEON sites. The R 2 values on held out test data ranged from 0.41 to 0.75 on held out test data. The ensemble approach performed better than single partial least squares models. Carbon performed poorly compared to other traits (R 2 of 0.41). The crown segmentation step contributed the most uncertainty in the pipeline, due to over-segmentation. The pipeline produced good estimates of DBH (R 2 of 0.62 on held out data). Trait predictions for crowns performed significantly better than comparable predictions on pixels, resulting in improvement of R 2 on test data of between to 0.26. We used the pipeline to produce individual level trait data for ∼5 million individual crowns, covering a total extent of ∼360 km 2 . This large dataset allows testing ecological questions on landscape scales, revealing that foliar traits are correlated with structural traits and environmental conditions.
Why it matches plant phenotyping methods個体樹木の構造・葉機能形質をリモートセンシング画像から推定するパイプラインを開発・評価しており、形質取得手法が研究の中心である。
abstractRemote sensing methods that identify individual trees and estimate their traits would provide the benefits of both approaches, producing continuous large-scale data linked to biological individuals.
BackgroundOne of the main features of plant cells is their strong plasticity, and their propensity to regenerate an organism from a single cell. Plant protoplasts are basic plant cells units in which the pecto-cellulosic cell wall has been removed, but the plasma membrane is intact. One of the main features of plant cells is their strong plasticity, which in some species, can be very close from what is defined as cell totipotency. Methods and differentiation protocols used in plant physiology and plant biology usually involve macroscopic vessels and containers that make difficult, for example, to follow the fate of the same protoplast all along its full development cycle, but also to perform continuous studies of the influence of various gradients in this context. These limits have hampered the precise study of regeneration processes. ResultsHerein, we present the design of a comprehensive, physiologically relevant, easy-to-use and low-cost microfluidic and microscopic setup for the monitoring of Physcomitrella patens (P. patens) growth and development on a long-term basis. The experimental solution we developed is made of two parts (i) a microfluidic chip composed of a single layer of about a hundred flow-through microfluidic traps for the immobilization of protoplasts, and (ii) a low-cost, light-controlled, custom-made microscope allowing the continuous recording of the moss development in physiological conditions. We validated the experimental setup with three proofs of concepts: (i) the kinetic monitoring of first division steps and cell wall regeneration, (ii) the influence of the photoperiod on growth of the protonemata, and (iii) finally the induction of leafy buds using a phytohormone, cytokinin. ConclusionsWe developed the design of a comprehensive, physiologically relevant, easy-to-use and low-cost experimental setup for the study of P. patens development in a microfluidic environment. This setup allows imaging of P. patens development at high resolution and over long time periods.
Why it matches plant phenotyping methods植物の発生・成長を長期間画像モニタリングするマイクロ流体チップとカスタム顕微鏡を開発しており、表現型取得系が研究の中心である。
abstractwe present the design of a comprehensive, physiologically relevant, easy-to-use and low-cost microfluidic and microscopic setup for the monitoring of Physcomitrella patens (P. patens) growth and development on a long-term basis.
Reproduction assets foundThe paper's availability statement points to a public GitHub repository (FattaccioliLab/PlantsOnChip) containing the authors' microfluidic chip design files, microscope control Matlab scripts, Micromanager configuration, Arduino connection map, and bill of materials used for the plant protoplast phenotyping/imaging. NoCode · publicfile of the 35 mm Petri dish adapter to the SM1 threading of the xy manual stage
• Matlab programming script of the microscope and Micromanager configuration file
• Connection map of the Arduino Due board
• Bill of materials of the custom-made microscope (references, manufacturers, suppliers, prices)
Documents are available on https://github.com/FattaccioliLab/PlantsOnChip
Supplementary movies.
• Division of a protoplast and cell wall regeneration kinetics
• Chloronemata growth under continuous illumination
Competing interests. No financial competing interests are to be declared.
Funding. This work has received support of “Institut Pierre-Gilles de Gennes” (Laboratoire
d’excellence : ANR-10-Open asset ↗FattaccioliLab/PlantsOnChippdf-raw-page:11 lines:1-29Code / dataset availability confirmedbioRxiv · Europe PMC · checked 15 Sept 2026
Root system architecture (RSA) is critical for plant growth, which is influenced by several edaphic, environmental, genetic and biotic factors including beneficial and pathogenic microbes. Studying root architecture and the dynamic changes that occur during a plants lifespan, especially for perennial crops growing over multiple growing seasons, is still a challenge because of the nature of their growing environment in soil. We describe the utility of an imaging platform called RhizoVision Crown to study RSA of alfalfa, a perennial forage crop affected by Phymatotrichopsis Root Rot (PRR) disease. Phymatotrichopsis omnivora is the causal agent of PRR disease that reduces alfalfa stand longevity. During the lifetime of the stand, PRR disease rings enlarge and the field can be categorized into three zones based upon plant status: asymptomatic, disease front and survivor. To study root architectural changes associated with PRR, a four-year old 25.6-hectare alfalfa stand infested with PRR was selected at the Red River Farm, Burneyville, OK during October 2017. Line transect sampling was conducted from four actively growing PRR disease rings. At each disease ring, six line transects were positioned spanning 15 m on either side of the disease front with one alfalfa root sampled at every 3 m interval. Each alfalfa root was imaged with the RhizoVision Crown platform using a backlight and a high-resolution monochrome CMOS camera enabling preservation of the natural root architectural integrity. The platforms image analysis software, RhizoVision Analyzer, automatically segmented images, skeletonized, and extracted a suite of features. Data indicated that the survivor plants compensated for damage or loss to the taproot through the development of more lateral and crown roots, and that a suite of multivariate features could be used to automatically classify roots as from survivor or asymptomatic zones. Root growth is a dynamic process adapting to ever changing interactions among various phytobiome components, by utilizing a low-cost, efficient and high-throughput Rhizo-Vision Crown platform we showed quantification of these changes occurring in a mature perennial forage crop.
Why it matches plant phenotyping methodsRhizoVision CrownとRhizoVision Analyzerによる根系形態の画像取得・自動解析が研究の中心であり、根系構造特徴の抽出と分類を実施しているため、植物フェノタイピング手法として含める。
abstractWe describe the utility of an imaging platform called RhizoVision Crown to study RSA of alfalfa
Reproduction assets foundThe paper's Data Availability section explicitly deposits the root crown images and R statistical analysis code on Zenodo (doi 10.5281/zenodo.2172832), a paper-specific public asset containing the phenotyping images and analysis code.Dataset · publicical analysis code generated from this study are available on
382
Zenodo.
383
York, Larry M., Young, Carolyn A., Mattupalli, Chakradhar, & Seethepalli, Anand. (2018). Images
384
and statistical analysis of alfalfa root crowns from inside and outside disease rings caused by
385
cotton root rot (Version 1.0.0) [Data set]. Zenodo. http://doi.org/10.5281/zenodo.2172832
386
387
ACKNOWLEDGEMENTS. We thank the Noble Research Institute, LLC for funding this project.
388
389
LITERATURE CITED.
390
Arias, M. M. D., Leandro, L. F., and Munkvold, G. P. 2013. Aggressiveness of Fusarium species and
391
impact of root infection on growth and yield of soybeans. Phytopathology 103:822-832.
392
Arif, M., FlOpen asset ↗Zenodo · 10.5281/zenodo.2172832pdf-raw-page:18 lines:1-49Code / dataset availability confirmedOpenAlex · Europe PMC · bioRxiv · Crossref · checked 10 Sept 2026
Abstract The rapid improvement of phenotyping capability, accuracy, and throughput have greatly increased the volume and diversity of phenomics data. A remaining challenge is an efficient way to identify phenotypic patterns to improve our understanding of the quantitative variation of complex phenotypes, and to attribute gene functions. To address this challenge, we developed a new algorithm to identify emerging phenomena from large-scale temporal plant phenotyping experiments. An emerging phenomenon is defined as a group of genotypes who exhibit a coherent phenotype pattern during a relatively short time. Emerging phenomena are highly transient and diverse, and are dependent in complex ways on both environmental conditions and development. Identifying emerging phenomena may help biologists to examine potential relationships among phenotypes and genotypes in a genetically diverse population and to associate such relationships with the change of environments or development. We present an emerging phenomenon identification tool called Temporal Emerging Phenomenon Finder (TEP-Finder). Using large-scale longitudinal phenomics data as input, TEP-Finder first encodes the complicated phenotypic patterns into a dynamic phenotype network. Then, emerging phenomena in different temporal scales are identified from dynamic phenotype network using a maximal clique based approach. Meanwhile, a directed acyclic network of emerging phenomena is composed to model the relationships among the emerging phenomena. The experiment that compares TEP-Finder with two state-of-art algorithms shows that the emerging phenomena identified by TEP-Finder are more functionally specific, robust, and biologically significant. The source code, manual, and sample data of TEP-Finder are all available at: http://phenomics.uky.edu/TEP-Finder/ .
Why it matches plant phenotyping methods植物の長期フェノタイピングデータから時間的な表現型パターンを抽出する新規アルゴリズムとツールを開発し、既存手法と比較検証しているため、方法が中心である。
abstractwe developed a new algorithm to identify emerging phenomena from large-scale temporal plant phenotyping experiments.
Reproduction assets foundThe paper states that the TEP-Finder source code, manual, and sample data (used for the plant photosynthesis phenotyping analysis) are publicly available at the authors' site http://phenomics.uky.edu/TEP-Finder/, which is an allowed URL.Code · publicThe
source code, manual, and sample data of TEP-Finder are all
available at: http://phenomics.uky.edu/TEP-Finder/.Open asset ↗phenomics.uky.edu/TEP-Finderpdf-page:1 lines:1-67Code / dataset availability confirmedOpenAlex · Europe PMC · bioRxiv · Crossref · checked 15 Sept 2026
Abstract Background: High-quality plant phenotyping and climate data lay the foundation of phenotypic analysis as well as genotype-by-environment interactions, which is important biological evidence not only to understand the dynamics between crop performance, genotypes, and environmental factors, but also for agronomists and farmers to monitor crops in fluctuating agricultural conditions. With the rise of Internet of Things technologies in recent years, many IoT-based remote sensing devices have been applied to phenotyping and crop monitoring that generate big plant-environment datasets every day; however, it is still technically challenging to calibrate, annotate, and aggregate big data effectively, especially when they were generated in multiple locations, and often at different scales. Findings: CropSurveyor is a PHP and SQL based server platform, which provides automated data collation, storage, device and experiment management through IoT-based sensors and distributed plant phenotyping workstations. It provides a two-component solution for monitoring biological experiments and networked devices, with interfaces specifically designed for distributed IoT devices and centralised data servers. Data transfer is performed automatically though an HTTP accessible RESTful API installed on both device-side and server-side of the CropSurveyor system, which synchronise daily representative crop growth images for quick and visual-based crop assessment, as well as detailed microclimate readings for GxE studies. CropSurveyor also supports the comparison of historical and ongoing crop performance whilst different experiments are being conducted. Conclusions: As an open-source experiment and data management system, CropSurveyor can be used to maintain and collate important crop performance and microclimate datasets captured by IoT sensors and distributed phenotyping installations. It provides near real-time environmental and crop growth monitoring in addition to historical and current data comparison through a single cloud-ready server system. Accessible both locally in the field through smart devices and remotely in an office using a PC, CropSurveyor has been used in wheat field experiments for prebreeding since 2016 and has the potential to enable scalable crop management and IoT-style agricultural practices in the near future.
Why it matches plant phenotyping methods分散型植物フェノタイピング設備とIoTセンサーのデータ収集・管理・比較を中核とするオープンソース基盤であり、植物生育画像や微気候データを用いた再利用可能なワークフローを提供している。
abstractCropSurveyor is a PHP and SQL based server platform, which provides automated data collation, storage, device and experiment management through IoT-based sensors and distributed plant phenotyping workstations.
Reproduction assets foundThe authors explicitly state that the CropSurveyor system code and the datasets supporting the paper's results (wheat phenotyping image/sensor data) are openly available on their public GitHub releases page, with a BSD-3-Clause license.Code · publicpanded to a larger and multi-site
397 level, which can then truly help inform decision in crop research and agricultural practices at the
398 national level, across a country’s arable land.
399
400 Availability and requirements
401 Project name: CropSurveyor for wheat prebreeding in Designing Future Wheat
402 Project home page: https://github.com/Crop-Phenomics-Group/cropsurveyor/releases
403 Operating system(s): Platform independent
404 Programming language: Python, PHP, JavaScript, SQL
405 Requirements: Apache (or other PHP5+) server, MySQL (or other SQL) server, a recent version of
406 Chrome, Firefox, or Safari
407 License: BSD-3-Clause available at: https://opensource.org/licenses/BSD-3Open asset ↗Crop-Phenomics-Group/cropsurveyorpdf-layout-page:16 lines:1-73Code / dataset availability confirmedEurope PMC · bioRxiv · checked 15 Sept 2026
Cell / cellular structureRootTissueMorphology / geometry measurementObject detectionRoot system architecture
The aboveground plant efficiency has improved significantly in recent years, and the improvement has led to a steady increase in global food production. The improvement of belowground plant efficiency has the potential to further increase food production. However, the belowground plant roots are harder to study, due to inherent challenges presented by root phenotyping. Several tools for identifying root anatomical features in root cross-section images have been proposed. However, the existing tools are not fully automated and require significant human effort to produce accurate results. To address this limitation, we propose a fully automated approach, called Deep Learning for Root Anatomy (DL-RootAnatomy), for identifying anatomical traits in root cross-section images. Using the Faster Region-based Convolutional Neural Network (Faster R-CNN), the DL-RootAnatomy models detect objects such as root, stele and late metaxylem, and predict rectangular bounding boxes around such objects. Subsequently, the bounding boxes are used to estimate the root diameter, stele diameter, and late metaxylem number and average diameter. Experimental evaluation using standard object detection metrics, such as intersection-over-union and mean average precision, has shown that our models can accurately detect the root, stele and late metaxylem objects. Furthermore, the results have shown that the measurements estimated based on predicted bounding boxes have very small root mean square error when compared with the corresponding ground truth values, suggesting that DL-RootAnatomy can be used to accurately detect anatomical features. Finally, a comparison with existing approaches, which involve some degree of human interaction, has shown that the proposed approach is more accurate than existing approaches on a subset of our data. A webserver for performing root anatomy using our deep learning pre-trained models is available at https://rootanatomy.org, together with a link to a GitHub repository that contains code that can be used to re-train or fine-tune our network with other types of root-cross section images. The labeled images used for training and evaluating our models are also available from the GitHub repository.
Why it matches plant phenotyping methods根横断面画像から根径・中心柱径・後期後生木部の数と平均径を自動推定する深層学習手法を開発し、既存手法との比較および精度検証を行っており、植物フェノタイピング手法が研究の中心である。
abstractwe propose a fully automated approach, called Deep Learning for Root Anatomy (DL-RootAnatomy), for identifying anatomical traits in root cross-section images.
Reproduction assets foundThe authors publicly release the labeled rice root cross-section image dataset (with ground truth measurements), the source code, and the pre-trained Faster R-CNN models via a GitHub repository linked from the paper's Data Availability Statement and webserver description.Dataset · publicthe preliminary version. CW
815 designed and developed the webserver. All authors read and approved the
816 final manuscript.
817 Funding
818 Contribution No. 19-072-J from Kansas Agriculture Experiment Station.
819 Data Availability Statement
820 The image datasets used in this study can be found in a GitHub repository
821 at https://github.com/cwang16/Root-Anatomy-Using-Faster-RCNN.
822 Acknowledgments
823 An earlier version of this manuscript has been released as a Pre-Print at
824 https://www.biorxiv.org/content/10.1101/442244v2.article-info [65].
825 References
826 [1] J. L. Araus, G. A. Slafer, C. Royo, M. D. Serret, Breeding for yield
827 potential and stress adaptation in cereals, CrOpen asset ↗https://github.com/cwang16/Root-Anatomy-Using-Faster-RCNNpdf-layout-page:50 lines:1-47Code / dataset availability confirmedbioRxiv · Crossref · checked 15 Sept 2026
Food production in conventional agriculture faces numerous challenges such as reducing waste, meeting demand, maintaining flavor, and providing nutrition. Contained environments under artificial climate control, or cyber-agriculture, could in principle be used to meet many of these challenges. Through such environments, phenotypic expression of the plant---mass, edible yield, flavor, and nutrients---can be actuated through a "climate recipe," where light, water, nutrients, temperature, and other climate and ecological variables are optimized to achieve a desired result. This paper describes a method for doing this optimization for the desired result of flavor by combining cyber-agriculture, metabolomic phenotype (chemotype) measurements, and machine learning. In a pilot experiment, (1) environmental conditions, i.e. photoperiod and ultraviolet (UV) light (known to affect production of flavor-active molecules in edible plants) were applied under different regimes to basil plants (Ocimum basilicum) growing inside a hydroponic farm with an open-source design; (2) flavor-active volatile molecules were measured in each plant using gas chromatography-mass spectrometry (GC-MS); and (3) symbolic regression was used to construct a surrogate model of this chemistry from the input environmental variables, and this model was used to discover new combinations of photoperiod and UV light to increase this chemistry. These new combinations, or climate recipes, were then implemented in the hydroponic farm, and several of them resulted in a marked increase in volatiles over control. The process also led to two important insights: it demonstrated a "dilution effect", i.e. a negative correlation between weight and desirable chemical species, and it discovered the surprising effect that a 24-hour photoperiod of photosynthetic-active radiation, the equivalent of all-day light, induces the most flavor molecule production in basil. In this manner, surrogate optimization through machine learning can be used to discover effective recipes for cyber-agriculture that would be difficult and time-consuming to find using hand-designed experiments.
Why it matches plant phenotyping methods植物の代謝表現型（風味関連揮発性物質）をGC-MSで測定し、機械学習による代理モデルで環境条件から表現型を予測・最適化するワークフローが研究の中心である。
abstractThis paper describes a method for doing this optimization for the desired result of flavor by combining cyber-agriculture, metabolomic phenotype (chemotype) measurements, and machine learning.
Reproduction assets foundThe paper's Data availability statement points to a public GitHub repository containing the underlying GC-MS chemotype/phenotype data and experimental results used for surrogate modeling.Dataset · publict on analysis instrumentation and Babak Hodjat and Hormoz Shahrzad for
514 modeling and optimization insights and comments on the manuscript.
515
516 Data availability
517 The data underlying the results presented in this study are freely available on the Open
518 Agriculture Initiative's public Github repository located at
519 https://github.com/OpenAgInitiative/flavor-data
520
521 Author contributions
522 AJJ and EM contributed to the conceptualization, analysis, methodology development,
523 investigation, visualization, and preparing the original draft of the manuscript. AJ ran the
524 biological and GC-MS experiments and EM ran the computational experiments. JdlP supervised
525 and contrOpen asset ↗OpenAgInitiative/flavor-datapdf-layout-page:24 lines:1-52Code / dataset availability confirmedEurope PMC · bioRxiv · checked 15 Sept 2026
ABSTRACT Hyperspectral reflectance phenotyping and genomic selection are two emerging technologies that have the potential to increase plant breeding efficiency by improving prediction accuracy for grain yield. Hyperspectral cameras quantify canopy reflectance across a wide range of wavelengths that are associated with numerous biophysical and biochemical processes in plants. Genomic selection models utilize genome-wide marker or pedigree information to predict the genetic values of breeding lines. In this study, we propose a multi-kernel GBLUP approach to genomic selection that uses genomic marker-, pedigree-, and hyperspectral reflectance-derived relationship matrices to model the genetic main effects and genotype × environment ( G × E ) interactions across environments within a bread wheat ( Triticum aestivum L.) breeding program. We utilized an airplane equipped with a hyperspectral camera to phenotype five differentially managed treatments of the yield trials conducted by the Bread Wheat Improvement Program, International Maize and Wheat Improvement Center (CIMMYT) at Ciudad Obregón, México over four breeding cycles. We observed that single-kernel models using hyperspectral reflectance-derived relationship matrices performed similarly or superior to marker-and pedigree-based genomic selection models when predicting within and across environments. Multi-kernel models combining marker/pedigree information with hyperspectral reflectance phentoypes had the highest prediction accuracies; however, improvements in accuracy over marker-and pedigree-based models were marginal when correcting for days to heading. Our results demonstrates the potential of hyperspectral imaging in predicting grain yield within a multi-environment context, it also supports further studies on integration of hyperspectral reflectance phenotyping in breeding programs.
Why it matches plant phenotyping methods航空機搭載ハイパースペクトル画像によるキャノピー反射率フェノタイピングを用い、穀粒収量予測への有効性と予測精度を評価しており、表現型取得法の応用が中心的です。
abstractHyperspectral cameras quantify canopy reflectance across a wide range of wavelengths that are associated with numerous biophysical and biochemical processes in plants.
Reproduction assets foundThe authors state that all phenotypic and genotypic data needed to reproduce the study's hyperspectral-reflectance genomic prediction results are publicly deposited on the CIMMYT Dataverse under handle hdl:11529/10548109. This is a paper-specific, publicly actionable phenotype dataset asset. No author analysis code or Dataset · publicbioRxiv preprint doi: https://doi.org/10.1101/389825; this version posted November 27, 2018. The copyright holder for this preprint (which was
not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available
under a CC-BY-NC-ND 4.0 International license.
1 All phenotypic and genotypic data required tOpen asset ↗pdf-layout-page:19 lines:1-60Code / dataset availability confirmedbioRxiv · Europe PMC · checked 10 Sept 2026
Root systems are branched networks that develop from simple growth properties of their individual roots. Yet a mature maize root system has many thousands of roots that each interact with soil structures, water and nutrient patches, and microbial ecologies in the micro-environments surrounding each root tip. Although the plasticity of root growth to these and other environmental factors is well known, how the many local processes contribute over time to global features of root system architecture is hardly understood. We employ an automated 3D root imaging pipeline to capture the growth of maize roots every four hours throughout seven days of seedling development. We model the contrasting architectures of two maize inbred genotypes and their hybrid to derive key parameters that distinguish complex growth patterns as a function of time. The statistical characteristics of local root growth defined the global system properties despite a large range of trait values. \"Computational dissection\" of a single root from each root system identified differences in the size of the root branching zone and lateral branching densities, but not radial patterns, that drove the contrasting root architectures from seedling to maturity. X-ray imaging of mature field-grown root crowns showed that seedling growth trajectories persisted throughout development and could predict eventual architectures, suggesting a strong genetic basis. The work connects individual and systemwide scales of root growth dynamics, providing the means for a function-valued approach to understanding the genetic and genetic x environment conditioning of root growth that will enable breeding for enhanced root traits.\n\nSIGNIFICANCE STATEMENTWhen and where roots grow determines their ability to capture short-lived and patchy water and nutrient resources to support the aboveground organs of the plant. Roots have no known long-distance external sensing mechanisms, but form branched networks that blindly explore the soil and respond to encountered local stimuli. How global architectures form from the many thousands of these local responses, and how they are controlled genetically are major open questions. Here we quantify differences in local root growth patterns of two inbred genotypes of maize that control contrasting systemwide properties. Measurements at the seedling stage were highly correlated with the complex architectures of mature root systems, paving the way for the development of crops with greater resource uptake capacity.
Why it matches plant phenotyping methods自動3D根画像パイプラインで根系成長を4時間ごとに取得・解析し、局所成長パラメータから根系構造形質を推定しており、フェノタイピング手法が研究の中心である。
abstractWe employ an automated 3D root imaging pipeline to capture the growth of maize roots every four hours throughout seven days of seedling development.
Reproduction assets foundThe paper explicitly states that the custom R code used to extract and analyze dynamic root traits from the 4D time-series phenotyping data is publicly available on the authors' GitHub (Topp-Roots-Lab/timeseries_analysis). This is a paper-specific, publicly actionable analysis code asset. No public phenotype dataset orCode · publicpaired it with DynamicRoots
software (46), and custom R code (available on Github: https://github.com/Topp-Roots-Open asset ↗Topp-Roots-pdf-page:5 lines:1-27Code / dataset availability confirmedbioRxiv · Europe PMC · checked 15 Sept 2026
Stomata control gas exchanges between the plant and the atmosphere. How natural variation in stomata size and density contributes to resolve trade-offs between carbon uptake and water-loss in response to local climatic variation is not yet understood. We developed an automated confocal microscopy approach to characterize natural genetic variation in stomatal patterning in 330 fully-sequenced Arabidopsis thaliana accessions collected throughout the European range of the species. We compared this to variation in water-use efficiency, measured as carbon isotope discrimination ({delta}13C). We detect substantial genetic variation for stomata size and density segregating within Arabidopsis thaliana. A positive correlation between stomata size and {delta}13C further suggests that this variation has consequences on water-use efficiency. Genome-wide association analyses indicate a complex genetic architecture underlying not only variation in stomata patterning but also to its co-variation with carbon uptake parameters. Yet, we report two novel QTL affecting {delta}13C independently of stomata patterning. This suggests that, in A. thaliana, both morphological and physiological variants contribute to genetic variance in water-use efficiency. Patterns of regional differentiation and co-variation with climatic parameters indicate that natural selection has contributed to shape some of this variation, especially in Southern Sweden, where water availability is more limited in spring relative to summer. These conditions are expected to favor the evolution of drought avoidance mechanisms over drought escape strategies.
Why it matches plant phenotyping methods自動共焦点顕微鏡法を開発し、330系統で気孔サイズ・密度という植物形質を大規模に測定しており、表現型取得法が研究の中心です。
abstractWe developed an automated confocal microscopy approach to characterize natural genetic variation in stomatal patterning in 330 fully-sequenced Arabidopsis thaliana accessions collected throughout the European range of the species.
Reproduction assets foundThe data accessibility statement lists public, paper-specific assets: phenotypic (stomata/δ13C) data to be deposited in AraPheno with a public URL, and the authors' GWAS and MTMM analysis scripts on GitHub. Raw images and image-analysis scripts are only available upon request (Dryad deposit pending acceptance), so theyCode · public1001genomes.org, (Seren et
920
al., 2017) and stored in a Dryad repository upon acceptance. Additionally, we provide an R
921
Markdown file, which contains all figures (except GWAS and MTMM) and the
922
corresponding R code used to create the figures and statistics in the supplemental material.
923
GWAS scripts are available at https://github.com/arthurkorte/GWAS. MTMM scripts are
924
available at https://github.com/Gregor-Mendel-Institute/mtmm.925
Genomic data used is publicly available in the 1001 genomes database (Alonso-Blanco et al.,
926
2016)
927
928
Author contributions
929
JdM, AK, and HD conceived the study. HD conducted the experiment and produced
930
phenotypic data for stomaOpen asset ↗arthurkorte/GWASpdf-raw-page:35 lines:1-46Code · publicory upon acceptance. Additionally, we provide an R
921
Markdown file, which contains all figures (except GWAS and MTMM) and the
922
corresponding R code used to create the figures and statistics in the supplemental material.
923
GWAS scripts are available at https://github.com/arthurkorte/GWAS. MTMM scripts are
924
available at https://github.com/Gregor-Mendel-Institute/mtmm.925
Genomic data used is publicly available in the 1001 genomes database (Alonso-Blanco et al.,
926
2016)
927
928
Author contributions
929
JdM, AK, and HD conceived the study. HD conducted the experiment and produced
930
phenotypic data for stomata traits. TM and AW were responsible for 13
C measurements. GM
931
provideOpen asset ↗Gregor-Mendel-Institute/mtmm.925pdf-raw-page:35 lines:1-46Code / dataset availability confirmedbioRxiv · Europe PMC · checked 14 Sept 2026
O_LIStomata fulfill an important physiological role and are often phenotyped by researchers in many fields. Currently, no fully automated method exists to perform this task. Researchers typically rely on manual counts of stomata, which is an error-prone method and difficult to reproduce.\nC_LIO_LIWe introduce StomataCounter, an automated stomata counting system using a deep convolutional neural network to identify pores in a variety of different microscopic images. We used a human-in-the-loop approach to train and refine a neural network on a large variety of microscopic images, which helps us achieve robust detection among a number of datasets.\nC_LIO_LIOur network achieves 98.1% identification accuracy on Ginkgo SEM micrographs, and 94.2% transfer accuracy when tested on untrained species.\nC_LIO_LITo facilitate adoption of the method, we make a web tool available under http://www.stomata.science/\nC_LI
Why it matches plant phenotyping methods気孔という植物形質の画像ベース自動同定・計数法を開発し、異なる画像・種で精度検証した研究であり、方法自体が中心です。
abstractWe introduce StomataCounter, an automated stomata counting system using a deep convolutional neural network to identify pores in a variety of different microscopic images.
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 · public125 codes for network training, as well as the webserver are available at http://stomata.science/source. To useOpen asset ↗pdf-page:5 lines:1-57Code / dataset availability confirmedbioRxiv · Europe PMC · Crossref · checked 14 Sept 2026
ArabidopsisLaboratory / benchtopRootMorphology / geometry measurementObject detectionTrackingRoot system architecture
Root analysis is essential for both academic and agricultural research. Despite the great advances in root phenotyping and imaging however, calculating root length is still performed manually and involves considerable amounts of labor and time. To overcome these limitations, we have developed MyROOT, a novel software for the semi-automatic quantification of root growth of seedlings growing directly in agar plates. Our method automatically determines the scale from the image of the plate, and subsequently measures the root length of the individual plants. To this aim, MyROOT combines a bottom-up root tracking approach with a hypocotyl detection algorithm. At the same time as providing accurate root measurements, MyROOT also significantly minimizes the user intervention required during the process. Using Arabidopsis, we tested MyROOT with seedlings from different growth stages. Upon comparing the data obtained using this software with that of manual root measurements, we found that there are no significant differences (t-test, p-value < 0.05). Thus, MyROOT will be of great aid to the plant science community by permitting high-throughput root length measurements while saving on both labor and time.
Why it matches plant phenotyping methods根長という植物形質の半自動画像計測ソフトウェアを開発し、手動測定との比較で検証しており、フェノタイピング手法が研究の中心です。
abstractwe have developed MyROOT, a novel software for the semi-automatic quantification of root growth of seedlings growing directly in agar plates.
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 · publicMyROOT software is available at https://www.cragenomica.es/research-Open asset ↗pdf-page:3 lines:1-45Code / dataset availability confirmedbioRxiv · Europe PMC · checked 15 Sept 2026
BackgroundRoot crown phenotyping has linked root properties to shoot mass, nutrient uptake, and yield in the field, which increases the understanding of soil resource acquisition and presents opportunities for breeding. The original methods using manual measurements have been largely supplanted by image-based approaches. However, most image-based systems have been limited to one or two perspectives and rely on segmentation from grayscale images. An efficient high-throughput root crown phenotyping system is introduced that takes images from five perspectives simultaneously, constituting the Multi-Perspective Imaging Platform (M-PIP). A segmentation procedure using the Expectation-Maximization Gaussian Mixture Model (EM-GMM) algorithm was developed to distinguish plant root pixels from background pixels in color images and using hardware acceleration (CPU and GPU). Phenes were extracted using MatLab scripts. Placement of excavated root crowns for image acquisition was standardized and is ergonomic. The M-PIP was tested on 24 soybean [Glycine max (L.) Merr.] cultivars released between 1930 and 2005.\n\nResultsRelative to previous reports of imaging throughput, this system provides greater throughput with sustained rates of 1.66 root crowns min-1. The EM-GMM segmentation algorithm with hardware acceleration was able to segment images in 10 s, faster than previous methods, and the output images were consistently better connected with less loss of fine detail. Image-based phenes had similar heritabilities as manual measures with the greatest effect sizes observed for Maximum Radius and Fine Radius Frequency. Correlations were also noted, especially among the manual Complexity score and phenes such as number of roots and Total Root Length. Averaging phenes across perspectives generally increased heritability, and no single perspective consistently performed better than others. Angle-based phenes, Fineness Index, Maximum Width, Holes, Solidity and Width-to-Depth Ratio were the most sensitive to perspective with decreased correlations among perspectives.\n\nConclusionThe substantial heritabilities measured for many phenes suggest that they are potentially useful for breeding. Multiple perspectives together often produced the greatest heritabilities, and no single perspective consistently performed better than others. Thus, as illustrated here for soybean, multiple perspectives may be beneficial for root crown phenotyping systems. This system can contribute to breeding efforts that incorporate under-utilized root phenotypes to increase food security and sustainability.
Why it matches plant phenotyping methods根冠形質を高スループットに取得する多視点画像プラットフォーム、画像セグメンテーション、形質抽出を開発・評価しており、植物フェノタイピング手法が研究の中心です。
abstractAn efficient high-throughput root crown phenotyping system is introduced that takes images from five perspectives simultaneously, constituting the Multi-Perspective Imaging Platform (M-PIP).
Reproduction assets foundThe paper's EM-GMM segmentation and MATLAB phene-extraction software for the M-PIP root crown phenotyping platform is publicly available on GitHub with a Zenodo DOI. Raw images and segmented masks are only available upon request, so they do not qualify as public assets.Code · publicnder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under
a CC-BY-NC 4.0 International license.
559 Availability of data and materials
560 Raw images and/or segmented masks are available upon request. Software code is available on github
561 (DOI: 10.5281/zenodo.1213805 | website: https://github.com/GatorSense/MPIP).
562 Competing Interests
563 The authors declare no competing interests.
564 Restrictions or Required Licenses
565 No restrictions on this research are known under local or national laws.
566 Funding
567 The authors gratefully acknowledge partial funding for the research from the United Soybean Board to
568 FBF.
569 Authors' cOpen asset ↗GatorSense/MPIP · 10.5281/zenodo.1213805pdf-layout-page:38 lines:1-44Code / dataset availability confirmedbioRxiv · Europe PMC · checked 15 Sept 2026
Traditional phenotyping of rice tillers is time consuming and labor intensive and lags behind the rapid development of rice functional genomics. Thus, dynamic phenotyping of rice tiller traits at a high spatial resolution and high-throughput for large-scale rice accessions is urgently needed. In this study, we developed a high-throughput micro-CT-RGB (HCR) imaging system to non-destructively extract 730 traits from 234 rice accessions at 9 time points. We used these traits to predict the grain yield in the early growth stage, and 30% of the grain yield variance was explained by 2 tiller traits in the early growth stage. A total of 402 significantly associated loci were identified by GWAS, and dynamic and static genetic components were found across the nine time points. A major locus associated with tiller angle was detected at nine time points, which contained a major gene TAC1. Significant variants associated with tiller angle were enriched in the 3'-UTR of TAC1. Three haplotypes for the gene were found and tiller angles of rice accessions containing haplotype H3 were much smaller. Further, we found two loci contained associations with both vigor-related HCR traits and yield. The superior alleles would be beneficial for breeding of high yield and dense planting.\n\nHighlightCombining high-throughput micro-CT-RGB phenotyping facility and genome-wide association study to dissect the genetic architecture of rice tiller development by using the indica subpopulation.
Why it matches plant phenotyping methods高スループットのマイクロCT-RGB画像システムを開発し、イネの形態形質を多数・時系列で抽出することが研究の中心であるため。GWASはその応用にあたる。
abstractwe developed a high-throughput micro-CT-RGB (HCR) imaging system to non-destructively extract 730 traits from 234 rice accessions at 9 time points.
Reproduction assets foundThe paper's own micro-CT-RGB phenotyping outputs (CT images, side-view RGB images, and extracted phenotypic traits for 234 rice accessions at 9 time points) are explicitly stated to be publicly viewable and downloadable from the authors' Huazhong Agricultural University plant phenotyping database. RiceVarMap is an exteDataset · public276
277
Phenotyping database extracted by HCR at 9 time points
278
During the tillering stage, 234 rice plants were automatically measured by HCR at 9
279
different development time points (once every 3 d, starting from 41 ~ 67 d after
280
sowing). All the phenotypic data and images can be viewed and downloaded via the
281
link http://plantphenomics.hzau.edu.cn/checkiflogin_en.action and then following
282
these steps: (1) select ‘rice’; (2) select ‘2015-tiller’ in the year section; (3) select one
283
of the accession IDs in the ID section and then press ‘search images’; (4) 9 CT images
284
and 9 side-view color images can be viewed and downloaded; (5) a similar process
285
can be used to viOpen asset ↗plantphenomics.hzau.edu.cnpdf-raw-page:10 lines:1-76Code / dataset availability confirmedbioRxiv · Europe PMC · OpenAlex · Crossref · checked 14 Sept 2026
MaizeGrowth / time-series analysisGrowth / development / phenology
Recent advances in automated plant phenotyping have enabled the collection time series measurements from the same plants of a wide range of traits over different developmental time scales. The availability of time series phenotypic datasets has increased interest in statistical approaches for comparing patterns of change between different plant genotypes and different treatment conditions. Two widely used methods of modeling growth over time are point-wise analysis of variance (ANOVA) and parametric sigmoidal curve fitting. Point-wise ANOVA yields discontinuous growth curves, which do not reflect the true dynamics of growth patterns in plants. In contrast, fitting a parametric model to a time series of observations does capture the trend of growth, however these models require assumptions regarding the true pattern of plant growth. Depending on the species, treatment regime, and subset of the plant lifecycle sampled this assumptions will not always hold true. Here we introduce a different approach - functional ANOVA - which yields continuous growth curves without requiring assumptions regarding patterns of plant growth. We compare and validate this approach using data from an experiment measuring growth of two maize (Zea mays ssp. mays) genotypes under two water availability treatments over a 21-day period. Functional ANOVA enables a nonparametric estimation of the dynamics of changes in plant traits over time without assumptions regarding curve shape. In addition to estimating smooth curves of trait values over time, functional ANOVA also estimates the the derivatives of these curves - e.g. growth rates - simultaneously. Using two different subsampling strategies, we demonstrate that this functional ANOVA method enables the comparison of growth curves between plants phenotyped on non-overlapping days with little reduction in estimation accuracy. This means functional ANOVA based approaches can allow larger numbers of samples and biological replicates to be scored in a single experiment given fixed amounts of phenotyping infrastructure and personnel.
Why it matches plant phenotyping methods植物の時系列表現型データから成長曲線と成長率を推定・比較する機能的ANOVAを導入し、実データで検証しているため、表現型解析手法が中心である。
abstractHere we introduce a different approach - functional ANOVA - which yields continuous growth curves without requiring assumptions regarding patterns of plant growth.
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。Dataset · publicThe raw image data employed in this study is hosted at CyVerse under doi 10.7946/P22K7V.Open asset ↗CyVerse · 10.7946/P22K7Vpdf-page:9 lines:1-40Code / dataset availability confirmedbioRxiv · Europe PMC · OpenAlex · checked 15 Sept 2026
In recent years, there has been an increasing interest in image-based plant phenotyping, applying state-of-the-art machine learning approaches to tackle challenging problems, such as leaf segmentation (a multi-instance problem) and counting. Most of these algorithms need labelled data to learn a model for the task at hand. Despite the recent release of a few plant phenotyping datasets, large annotated plant image datasets for the purpose of training deep learning algorithms are lacking. One common approach to alleviate the lack of training data is dataset augmentation. Herein, we propose an alternative solution to dataset augmentation for plant phenotyping, creating artificial images of plants using generative neural networks. We propose the Arabidopsis Rosette Image Generator (through) Adversarial Network: a deep convolutional network that is able to generate synthetic rosette-shaped plants, inspired by DC-GAN (a recent adversarial network model using convolutional layers). Specifically, we trained the network using A1, A2, and A4 of the CVPPP 2017 LCC dataset, containing Arabidopsis Thaliana plants. We show that our model is able to generate realistic 128 x 128 colour images of plants. We train our network conditioning on leaf count, such that it is possible to generate plants with a given number of leaves suitable, among others, for training regression based models. We propose a new Ax dataset of artificial plants images, obtained by our ARIGAN. We evaluate this new dataset using a state-of-the-art leaf counting algorithm, showing that the testing error is reduced when Ax is used as part of the training data.
Why it matches plant phenotyping methods植物フェノタイピング用の合成画像生成手法を開発し、葉数条件付き生成とデータセットの評価を行っており、表現型データ取得・解析基盤が研究の中心です。
abstractWe propose a new Ax dataset of artificial plants images, obtained by our ARIGAN.
Reproduction assets foundThe paper's authors publicly released their synthetic Ax dataset of 57 GAN-generated Arabidopsis plant images, with an explicit download URL stated in the paper. This is a paper-specific, publicly available asset directly tied to this paper's phenotyping analysis. No code or trained model availability is stated.Dataset · publicBatch normalization: Accelerating
Evaluation metrics of our experiments are reported in Ta- deep network training by reducing internal covariate shift.
In F. Bach and D. Blei, editors, Proceedings of the 32nd In-
ble 1. Our synthetic dataset Ax is available to download at
ternational Conference on Machine Learning, volume 37 of
http://www.valeriogiuffrida.academy/ax. Proceedings of Machine Learning Research, pages 448–456,
Lille, France, 07–09 Jul 2015. PMLR.
Acknowledgements [14] Y. LeCunn. The MNIST database of handwritten digits,
http://yann.lecun.com/exdb/mnist/.
This work was supported by The Alan Turing Institute un- [15] A. L. Maas, A. Y. Hannun, and A. Y. Ng. Rectifier non-
der theOpen asset ↗pdf-layout-page:7 lines:1-56Code / dataset availability confirmedbioRxiv · Europe PMC · OpenAlex · Crossref · checked 10 Sept 2026
O_LIPremise of the study: Image-based phenomics is a powerful approach to capture and quantify plant diversity. However, commercial platforms that make consistent image acquisition easy are often cost-prohibitive. To make high-throughput phenotyping methods more accessible, low-cost microcomputers and cameras can be used to acquire plant image data.\nC_LIO_LIMethods and Results: We used low-cost Raspberry Pi computers and cameras to manage and capture plant image data. Detailed here are three different applications of Raspberry Pi controlled imaging platforms for seed and shoot imaging. Images obtained from each platform were suitable for extracting quantifiable plant traits (shape, area, height, color) en masse using open-source image processing software such as PlantCV.\nC_LIO_LIConclusion: This protocol describes three low-cost platforms for image acquisition that are useful for quantifying plant diversity. When coupled with open-source image processing tools, these imaging platforms provide viable low-cost solutions for incorporating high-throughput phenomics into a wide range of research programs.\nC_LI
Why it matches plant phenotyping methods低コストの画像取得プラットフォームを開発・記述し、植物形質の定量化への適用性を示すことが中心である。
abstractTo make high-throughput phenotyping methods more accessible, low-cost microcomputers and cameras can be used to acquire plant image data.
Reproduction assets foundThe paper's PlantCV image-analysis scripts for its Raspberry Pi phenotyping examples are publicly available in the authors' danforthcenter/apps-phenotyping repository. The outreach and gphoto URLs are not paper-specific analysis assets.Code · publicAn example image has been p rocessed with PlantCV (Fahlgren et al., 2015), and
the analysis script is available at https://github.com/danforthcenter/apps-phenotypingOpen asset ↗danforthcenter/apps-phenotypingpdf-page:8 lines:1-52Code / dataset availability confirmedbioRxiv · Europe PMC · checked 14 Sept 2026
How complex developmental-genetic networks are translated into organs with specific 3D shapes remains an open question. This question is particularly challenging because the elaboration of specific shapes is in essence a question of mechanics. In plants, this means how the genetic circuitry affects the cell wall. The mechanical properties of the wall and their spatial variation are the key factors controlling morphogenesis in plants. However, these properties are difficult to measure and investigating their relation to genetic regulation is particularly challenging. To measure spatial variation of mechanical properties, one must determine the deformation of a tissue in response to a known force with cellular resolution. Here we present an automated confocal micro-extensometer (ACME), which greatly expands the scope of existing methods for measuring mechanical properties. Unlike classical extensometers, ACME is mounted on a confocal microscope and utilizes confocal images to compute the deformation of the tissue directly from biological markers, thus providing cellular scale information and improved accuracy. ACME is suitable for measuring the mechanical responses in live tissue. As a proof of concept we demonstrate that the plant hormone gibberellic acid induces a spatial gradient in mechanical properties along the length of the Arabidopsis hypocotyl.\n\nTerms
Why it matches plant phenotyping methods植物組織の力学的性質を細胞解像度で定量する自動共焦点マイクロ伸展計を開発し、画像から変形を抽出する方法を中心に実証しているため。
abstractHere we present an automated confocal micro-extensometer (ACME), which greatly expands the scope of existing methods for measuring mechanical properties.
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 positioners are controlled by a SmarAct MCS3D (SmarAct GmbH) controller (Figure 1C,
label 15) accompanied by its software library, which in turn is controlled by custom-made software
(available here: https://github.com/ACME-Robinson/InstallPackage)Open asset ↗ACME-Robinson/InstallPackagepdf-page:14 lines:1-54Code / dataset availability confirmedbioRxiv · Europe PMC · OpenAlex · Crossref · checked 15 Sept 2026
Automated phenotyping technologies are capable of providing continuous and precise measurements of traits that are key to todays crop research, breeding and agronomic practices. In additional to monitoring developmental changes, high-frequency and high-precision phenotypic analysis can enable both accurate delineation of the genotype-to-phenotype pathway and the identification of genetic variation influencing environmental adaptation and yield potential. Here, we present an automated and scalable field phenotyping platform called CropQuant, designed for easy and cost-effective deployment in different environments. To manage infield experiments and crop-climate data collection, we have also developed a web-based control system called CropMonitor to provide a unified graphical user interface (GUI) to enable realtime interactions between users and their experiments. Furthermore, we established a high-throughput trait analysis pipeline for phenotypic analyses so that lightweight machine-learning modelling can be executed on CropQuant workstations to study the dynamic interactions between genotypes (G), phenotypes (P), and environmental factors (E). We have used these technologies since 2015 and reported results generated in 2015 and 2016 field experiments, including developmental profiles of five wheat genotypes, performance-related traits analyses, and new biological insights emerged from the application of the CropQuant platform.
Why it matches plant phenotyping methods作物の形質取得を目的とした自動・スケーラブルな圃場フェノタイピング基盤、制御システム、ハイスループット形質解析パイプラインを開発・適用しており、方法論が研究の中心である。
abstractHere, we present an automated and scalable field phenotyping platform called CropQuant, designed for easy and cost-effective deployment in different environments.
Reproduction assets foundThe authors publicly distribute the CropQuant source code, SD card image, and high-definition field phenotyping movies via a Google Drive folder, with explicit availability statements. The picamera documentation link is a generic third-party library and is excluded.Code · publica link to the Creative Commons license, and indicate
601
if changes were made. Unless otherwise stated The Creative Commons Public Domain
602
Dedication waiver applies to the data and results made available in this paper.
603
604
Source code
605
Source code is freely available for academic usage, which can be downloaded at
606
https://drive.google.com/drive/folders/0B17ZL8AzLo8wNFJUVS1lOFkzb3M?usp=s
607
haring (an online Github repository is being prepared and will be updated in bioRxiv
608
as soon as possible)
609
610
.
CC-BY-NC-ND 4.0 International license
available under a
was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in Open asset ↗pdf-raw-page:13 lines:1-75Code / dataset availability confirmedEurope PMC · OpenAlex · bioRxiv · checked 15 Sept 2026
Background Plants demonstrate dynamic growth phenotypes that are determined by genetic and environmental factors. Phenotypic analysis of growth features over time is a key approach to understand how plants interact with environmental change as well as respond to different treatments. Although the importance of measuring dynamic growth traits is widely recognised, available open software tools are limited in terms of batch processing of image datasets, multiple trait analysis, software usability and cross-referencing results between experiments, making automated phenotypic analysis problematic. Results Here, we present Leaf-GP (Growth Phenotypes), an easy-to-use and open software application that can be executed on different platforms. To facilitate diverse scientific user communities, we provide three versions of the software, including a graphic user interface (GUI) for personal computer (PC) users, a command-line interface for high-performance computer (HPC) users, and an interactive Jupyter Notebook (also known as the iPython Notebook) for computational biologists and computer scientists. The software is capable of extracting multiple growth traits automatically from large image datasets. We have utilised it in Arabidopsis thaliana and wheat ( Triticum aestivum ) growth studies at the Norwich Research Park (NRP, UK). By quantifying growth phenotypes over time, we are able to identify diverse plant growth patterns based on a variety of key growth-related phenotypes under varied experimental conditions. As Leaf-GP has been evaluated with noisy image series acquired by different imaging devices and still produced reliable biologically relevant outputs, we believe that our automated analysis workflow and customised computer vision based feature extraction algorithms can facilitate a broader plant research community for their growth and development studies. Furthermore, because we implemented Leaf-GP based on open Python-based computer vision, image analysis and machine learning libraries, our software can not only contribute to biological research, but also exhibit how to utilise existing open numeric and scientific libraries (including Scikit-image, OpenCV, SciPy and Scikit-learn) to build sound plant phenomics analytic solutions, efficiently and effectively. Conclusions Leaf-GP is a comprehensive software application that provides three approaches to quantify multiple growth phenotypes from large image series. We demonstrate its usefulness and high accuracy based on two biological applications: (1) the quantification of growth traits for Arabidopsis genotypes under two temperature conditions; and (2) measuring wheat growth in the glasshouse over time. The software is easy-to-use and cross-platform, which can be executed on Mac OS, Windows and high-performance computing clusters (HPC), with open Python-based scientific libraries preinstalled. We share our modulated source code and executables (.exe for Windows; .app for Mac) together with this paper to serve the plant research community. The software, source code and experimental results are freely available at https://github.com/Crop-Phenomics-Group/Leaf-GP/releases .
Why it matches plant phenotyping methods植物の画像から複数の成長形質を自動抽出するソフトウェアと解析ワークフローが研究の中心であり、異なる画像装置での評価も行っているため。
abstractwe present Leaf-GP (Growth Phenotypes), an easy-to-use and open software application
Reproduction assets foundThe paper's authors publicly released the Leaf-GP phenotyping software (source code and executables) together with the 4.3 GB raw image datasets and experimental results used in the Arabidopsis and wheat growth studies, via their GitHub releases repository.Code · publich Park, Norwich UK
646
2
John Innes Centre, Norwich Research Park, Norwich UK
647
3
University of East Anglia, Norwich Research Park, Norwich UK
648
649
Availability of data and materials
650
All the 4.3 GB image datasets as well as The Leaf-GP software package and source code are freely
651
available from our online repository https://github.com/Crop-Phenomics-Group/Leaf-GP/releases.652
653
Open Access
654
The software is distributed under the terms of the Creative Commons Attribution 4.0 International
655
License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and
656
reproduction in any medium, provided you give appropriate credit to the originOpen asset ↗https://github.com/Crop-Phenomics-Group/Leaf-GP/releasespdf-raw-page:14 lines:1-97Code / dataset availability confirmedbioRxiv · Europe PMC · OpenAlex · checked 10 Sept 2026
Maize (Zea mays ssp. mays) is one of three crops, along with rice and wheat, responsible for more than 1/2 of all calories consumed around the world. Increasing the yield and stress tolerance of these crops is essential to meet the growing need for food. The cost and speed of plant phenotyping is currently the largest constraint on plant breeding efforts. Datasets linking new types of high throughput phenotyping data collected from plants to the performance of the same genotypes under agronomic conditions across a wide range of environments are essential for developing new statistical approaches and computer vision based tools. A set of maize inbreds - primarily recently off patent lines - were phenotyped using a high throughput platform at University of Nebraska-Lincoln. These lines have been previously subjected to high density genotyping, and scored for a core set of 13 phenotypes in field trials across 13 North American states in two years by the Genomes to Fields consortium. A total of 485 GB of image data including RGB, hyperspectral, fluorescence and thermal infrared photos has been released. Correlations between image-based measurements and manual measurements demonstrated the feasibility of quantifying variation in plant architecture using image data. However, naive approaches to measuring traits such as biomass can introduce nonrandom measurement errors confounded with genotype variation. Analysis of hyperspectral image data demonstrated unique signatures from stem tissue. Integrating heritable phenotypes from high-throughput phenotyping data with field data from different environments can reveal previously unknown factors influencing yield plasticity.
Why it matches plant phenotyping methods高速画像・ハイパースペクトル等を用いた植物表現型データセットの構築と、画像測定値を手動測定と比較する技術的検証が中心であるため、収載する。
abstractA total of 485 GB of image data including RGB, hyperspectral, fluorescence and thermal infrared photos has been released.
Reproduction assets foundThe paper releases ~485 GB of maize phenotyping image data (RGB, hyperspectral, fluorescence, thermal) publicly at plantvision.unl.edu/dataset, and the authors' validation/analysis source code is posted on GitHub (https://github.com/shanwai1234/Maize Phenotype Map). Both are paper-specific, public, and actionable.Dataset · publicA subset of the RGB images within
this dataset were previously analyzed in18 , and were made available for download from http://plantvision.unl.edu/dataset under
the terms of the Toronto Agreement.Open asset ↗pdf-page:6 lines:1-51Code · publicSource codes for all validation analysis are posted online (https://github.com/shanwai1234/Maize Phenotype Map).Open asset ↗shanwai1234/Maizepdf-page:6 lines:1-51Code / dataset availability confirmedEurope PMC · bioRxiv · OpenAlex · Crossref · checked 15 Sept 2026
ABSTRACT Sorghum ( Sorghum bicolor (L.) Moench) is a rapidly growing, high-biomass crop prized for abiotic stress tolerance. However, measuring genotype-by-environment (G × E) interactions remains a progress bottleneck. Here we describe strategies for identifying shape, color and ionomic indicators of plant nitrogen use efficiency. We subjected a panel of 30 genetically diverse sorghum genotypes to a spectrum of nitrogen deprivation and measured responses using high-throughput phenotyping technology followed by ionomic profiling. Responses were quantified using shape (16 measurable outputs), color (hue and intensity) and ionome (18 elements). We measured the speed at which specific genotypes respond to environmental conditions, both in terms of biomass and color changes, and identified individual genotypes that perform most favorably. With this analysis we present a novel approach to quantifying color-based stress indicators over time. Additionally, ionomic profiling was conducted as an independent, low cost and high throughput option for characterizing G × E, identifying the elements most affected by either genotype or treatment and suggesting signaling that occurs in response to the environment. This entire dataset and associated scripts are made available through an open access, user-friendly, web-based interface. In summary, this work provides analysis tools for visualizing and quantifying plant abiotic stress responses over time. These methods can be deployed as a time-efficient method of dissecting the genetic mechanisms used by sorghum to respond to the environment to accelerate crop improvement.
Why it matches plant phenotyping methods高スループット画像計測による形状・色・バイオマス応答の定量化と、経時的なストレス指標の解析手法が研究の中心であり、データセットと解析スクリプトも提供している。
abstractmeasured responses using high-throughput phenotyping technology followed by ionomic profiling
Reproduction assets foundThe authors publicly release the raw phenotyping data and figure-generating analysis scripts for this sorghum nitrogen-stress study via the PlantCV Danforth Center data-sets page, which is an allowed URL.Dataset · public29 concentrations in the two lower nitrogen treatment groups significantly affects shape but
130 not color. To further explore the effect that our experimental treatments had on the
131 measured shape characteristics and color for each individual genotype, an interactive
132 version of the generated data is available here:
133 (http://plantcv.danforthcenter.org/pages/data-sets/sorghum_abiotic_stress.html).
134 Many factors contribute to the ability of plants to utilize nutrients and presumably,
135 much of this is genetically explained. Correspondingly, genotype was a highly significant
136 variable (p-value = 0.003 when measuring area) within this dataset. To investigate how
137 much nitrogOpen asset ↗pdf-layout-page:5 lines:1-40Code / dataset availability confirmedEurope PMC · bioRxiv · Crossref · checked 14 Sept 2026
Detailed and realistic tree form generators have numerous applications in ecology and forestry. Here, we present an algorithm for generating morphological tree “clones” based on the detailed reconstruction of the laser scanning data, statistical measure of similarity, and a plant growth algorithm with simple stochastic rules. The algorithm is designed to produce tree forms, i.e. morphological clones, similar as a whole (coarse-grain scale), but varying in minute details of organization (fine-grain scale). We present a general procedure for obtaining these morphological clones. Although we opted for certain choices in our algorithm, its various parts may vary depending on the application. Namely, we have shown that specific multi-purpose procedural stochastic growth model can be algorithmically adjusted to produce the morphological clones replicated from the target experimentally measured tree. For this, we have developed a statistical measure of similarity (structural distance) between any given pair of trees, which allows for the comprehensive comparing of the tree morphologies in question by means of empirical distributions describing geometrical and topological features of a tree. Our algorithm can be used in variety of applications and contexts for exploration of the morphological potential of the growth models, arising in all sectors of plant science research. Summary Statement We present an algorithmic framework, based on the Bayesian inference, for generating morphological tree clones using a combination of stochastic growth models and experimentally derived tree structures.
Why it matches plant phenotyping methodsレーザースキャンによる樹木形態の再構成と形態類似度の計算、形態クローン生成アルゴリズムが研究の中心であり、植物形態を抽出・生成する方法論研究である。
abstractHere, we present an algorithm for generating morphological tree “clones” based on the detailed reconstruction of the laser scanning data, statistical measure of similarity, and a plant growth algorithm with simple stochastic rules.
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 Bayes-Forest toolbox is freely available at http://math.tut.fi/inversegroup/app/bayesforest/v1/.Open asset ↗Bayes-Forest toolboxpdf-page:15 lines:1-44Code / dataset availability confirmedbioRxiv · Europe PMC · Crossref · checked 11 Sept 2026
Dissecting the genetic basis of complex traits is aided by frequent and non-destructive measurements. Advances in range imaging technologies enable the rapid acquisition of three-dimensional (3D) data from an imaged scene. A depth camera was used to acquire images of Sorghum bicolor, an important grain, forage, and bioenergy crop, at multiple developmental timepoints from a greenhouse-grown recombinant inbred line population. A semi-automated software pipeline was developed and used to generate segmented, 3D plant reconstructions from the images. Automated measurements made from 3D plant reconstructions identified quantitative trait loci (QTL) for standard measures of shoot architecture such as shoot height, leaf angle and leaf length, and for novel composite traits such as shoot compactness. The phenotypic variability associated with some of the QTL displayed differences in temporal prevalence; for example, alleles closely linked with the sorghum Dwarf3 gene, an auxin transporter and pleiotropic regulator of both leaf inclination angle and shoot height, influence leaf angle prior to an effect on shoot height. Furthermore, variability in composite phenotypes that measure overall shoot architecture, such as shoot compactness, is regulated by loci underlying component phenotypes like leaf angle. As such, depth imaging is an economical and rapid method to acquire shoot architecture phenotypes in agriculturally important plants like sorghum to study the genetic basis of complex traits.
Why it matches plant phenotyping methods深度画像から3D植物再構成と形質自動抽出を行う半自動パイプラインを開発し、ソルガムのシュート構造形質を取得・評価しており、表現型取得法が研究の中心です。
abstractA semi-automated software pipeline was developed and used to generate segmented, 3D plant reconstructions from the images.
Reproduction assets foundThe paper explicitly deposits its authors' image acquisition/processing and QTL mapping code on GitHub, and its per-plant depth images, RGB images, and segmented meshes on the Dryad repository. Both are paper-specific, public, and actionable.Code · publicThe C++, Bash, and Python code written for image acquisition and processing, the R code written for
QTL mapping, the genotype and phenotype data, and the full multiple-QTL models for each phenotype
by timepoint combination can be found on GitHub at
https://github.com/MulletLab/SorghumReconstructionAndPhenotyping.Open asset ↗MulletLab/SorghumReconstructionAndPhenotypingpdf-page:8 lines:1-43