Abstract Visual assessments of growing forest nursery plants are time-consuming and often result in a lack of information at a physiological level. There exists a need for health screening in nurseries, that is fast and efficient, to improve overall health monitoring and nursery productivity. Rapid handheld sensors such as rapid thermal devices, leaf porometers and moisture meters, can provide regular information at a physiological level, that can improve the understanding of the impact of stress on young plant cuttings and their decline in health over time. This paper evaluates the utility and reliability of contemporary sensor technologies, to operationally monitor stress phases in juvenile forest plant cuttings during progressive moisture (dry-down) conditions. Furthermore, to assess whether thermal sensors could be used as an indicator, in conjunction with other variables such as soil water content or stomatal conductance, is needed operationally for fast screening during limited planting windows. Near Infra-Red Analysis (NiRA) data was collected to understand detailed plant functions at a finer reflectance level. A relationship was found where the increase in thermal signals reflects a depletion of water content, resulting in an eventual decline in stomatal conductance and, ultimately, plant mortality. Several algorithms were used in a preliminary test, using RapidMiner software, to discriminate between the four phases of plant health decline using physiological variables and NiRA data. Both Gradient Boosting Trees (GBT) and Deep Learning (DL) showed the best performances, achieving favourable accuracies of 96.8% and 91.2% without NiRA data, 84.6% and 88.2% with NiRA data, with shorter training times. Using thermal technology weighted amongst the highest of the best performing variables using GBT, the utility and accuracy showed good discrimination between the stages of plant decline and is encouraged for future research in this field.
Why it matches plant phenotyping methods植物のストレス段階を熱センサー、ポロメータ、含水率計、NiRAおよび機械学習で測定・識別する方法の有用性と信頼性を評価しており、表現型取得・判定手法が中心である。
abstractThis paper evaluates the utility and reliability of contemporary sensor technologies, to operationally monitor stress phases in juvenile forest plant cuttings during progressive moisture (dry-down) conditions.
Stomatal traits are key microscopic phenotypes for evaluating plant physiology, stress responses, and crop breeding potential. However, in vivo high-magnification microscopy often suffers from a shallow depth of field, causing noticeable defocus blur across different spatial locations and making it difficult to capture clear and complete stomatal structures in a single image. Multi-focus image fusion offers a practical solution, yet existing methods typically rely on supervised training, paired data, or hand-crafted rules, limiting their use in real agricultural microscopy scenarios. In this study, we propose an unsupervised multi-focus fusion framework for reconstructing fully focused stomatal microscopic images. The method integrates two-dimensional feature extraction with three-dimensional cross-focal-plane modeling to capture both spatial details and complementary information across focal planes. A max-response-guided spatial gating module is introduced to enhance focused regions while suppressing defocused responses. Additionally, dual sharpness priors based on perceptual features and wavelet high-frequency information enable pixel-wise pseudo-supervised learning without requiring all-in-focus ground-truth images. The model also predicts a probabilistic focal-plane volume for interpretable all-in-focus reconstruction. Experiments on a maize multi-focus image dataset demonstrate that the proposed method achieves superior or competitive performance across multiple fusion metrics, with entropy (EN), edge information preservation ( Q AB∕F ), Chen-Blum contrast metric ( Q CB ), and visual information fidelity for fusion (VIFF) reaching 7.43, 0.21, 0.41, and 1.01, respectively. Ablation studies confirm the effectiveness of the 3D modeling, spatial gating, and dual-prior sharpness supervision. More importantly, when the fused images serve as input to a YOLO-based stomatal instance segmentation model, the proposed method yields the best segmentation accuracy, with mAP50 and mAP50-95 reaching 0.9937 and 0.9121, respectively. Phenotypic measurements derived from the segmentation masks show high consistency with manual annotations, with the highest coefficient of determination R 2 = 0.97 achieved for stomatal count. These results indicate that the framework can act as an effective front-end module for automated microscopic stomatal phenotyping in agriculture.
Why it matches plant phenotyping methods植物の気孔表現型を対象に、マルチフォーカス画像融合、セグメンテーション、形質測定までを中核的に開発・検証しているため。
abstractwe propose an unsupervised multi-focus fusion framework for reconstructing fully focused stomatal microscopic images.
Reproduction assets foundThe authors state their data and code are publicly available on GitHub, covering the multi-focus stomatal microscopy dataset and the UMF-stomata fusion/phenotyping code.Code · publicOur data and code are available at: https://github.com/Longer-S/UMF-Stomata.Open asset ↗Longer-S/UMF-Stomatahtml-lines:640-655Dataset · publicOur data and code are available at: https://github.com/Longer-S/UMF-Stomata.Open asset ↗Longer-S/UMF-Stomatahtml-lines:683-756Plant phenotyping relevance match · UnverifiedOpenAlex · Europe PMC · bioRxiv · checked 5 Sept 2026
Abstract Background Leaves maintain hydraulic homeostasis during photosynthesis through the coordinated action of stomata, which regulate gas exchange and transpiration, and veins, which supply water to the leaf lamina. While functional links between stomatal and vascular traits are known in dicots, their potential genetic coordination in C4 crops remains poorly understood. We investigated the genetic architecture of these traits in maize using a Multi-parent Advanced Generation Inter-Cross (MAGIC) population and a low-cost, high-throughput phenotyping platform integrating leaf clearing, digital microscopy, artificial intelligence, and image analysis Results We phenotyped 285 recombinant inbred lines and the MAGIC founder lines, generating 8,072 images from 2,026 leaf samples taken from seedlings grown in controlled conditions. A YOLOv8-based model automatically detected stomata, while a custom and efficient image-processing pipeline quantified vein traits and stomatal spatial distribution patterns along cell bundles. This enabled simultaneous characterization of stomatal density, size, and distribution together with vein density, thickness, and bundle-associated spatial patterning. Substantial phenotypic variation was observed among genotypes, with strong correlations between abaxial and adaxial traits but no significant correlations between stomatal and vein traits. QTL mapping identified 37 genomic regions associated with stomatal and vein traits, including loci containing known developmental regulators such as stomatal density and distribution1 and stomagen1 , as well as novel loci controlling stomatal spatial patterns, divergence between leaf surfaces and veins traits. Conclusions These results support independent genetic control of stomata and veins and decoupled contribution to water-use efficiency, providing a novel genetic framework to independently optimize leaf hydraulic capacity and gas exchange in target environments.
Why it matches plant phenotyping methods葉の気孔・葉脈形質を自動画像解析で同時定量する高スループット表現型解析プラットフォームが研究の中心であり、形質抽出手法も具体的に記述されている。
abstractusing a Multi-parent Advanced Generation Inter-Cross (MAGIC) population and a low-cost, high-throughput phenotyping platform integrating leaf clearing, digital microscopy, artificial intelligence, and image analysis
Phenotyping stomatal traits and their developmental plasticity is time-consuming but holds potential to improve water use efficiency and photosynthesis for designing stress-tolerant crops under climate change. Here, we develop a robust, high-throughput pipeline for phenotyping 14 stomatal traits in winter wheat related to size, variation, maximum conductance, and spatial patterning. We (1) analyze over 25,000 images from 60 wheat cultivars grown in growth chamber, greenhouse, and field conditions; (2) investigate the impact of light, temperature, and reduced water and nitrogen supply on stomatal traits and their developmental plasticity across adaxial and abaxial surfaces; and (3) evaluate genetic diversity and breeding progress of stomatal traits. Stomatal traits were highly broad-sense heritable, were largely plastic in response to environmental conditions, and showed genotype-specific responses. Stomatal traits of third leaves under controlled environments with stable light and temperature conditions reliably captured the genetic variance of flag leaves under field conditions. Our data suggests that the upper leaf surface contributed more to transpiration and cooling through consistently higher stomatal density, area, and maximum conductance, while the lower surface facilitated CO₂ diffusion via systematic proper patterning and spacing. Breeding maintains the genetic diversity of stomatal traits, and our pipeline facilitates breeders to target them to enhance water use efficiency in high-yielding modern cultivars.
Why it matches plant phenotyping methods高スループットで14種類の気孔形質を抽出するパイプラインを開発しており、植物フェノタイピング手法が研究の中心である。
abstractwe develop a robust, high-throughput pipeline for phenotyping 14 stomatal traits in winter wheat related to size, variation, maximum conductance, and spatial patterning.
Reproduction assets foundThe paper's Data and code availability section states that all data are publicly available in a Zenodo repository and that the stomatal identification and trait quantification code is in the authors' public GitLab repository. Both URLs appear verbatim in the supplied blocks and match allowed_urls. The Zenodo DOI in theCode · publicThe code for all the programs in this paper, including the stomatal identification and trait quantification, can be found in our GitLab repository, https://scm.cms.hu-berlin.de/intensive-plant-food-systems-public/2026-mabrouk-stomatal-phenotyping .Open asset ↗intensive-plant-food-systems-public/2026-mabrouk-stomatal-phenotypinglines:197-215Code / dataset availability confirmedOpenAlex · Europe PMC · checked 5 Sept 2026
Abstract Background Stomata and pavement cells are fundamental components of the leaf epidermis, jointly regulating gas exchange, water loss, and leaf surface expansion. Stomata size, aperture, density, and pavement cell morphology are critical parameters for assessing plant transpiration efficiency, epidermal growth dynamics, and adaptive responses to environmental constraints. Despite their biological importance, quantifying stomatal and pavement-cell traits remains seldom not generalized, simple, and fast enough . Manual or semi-automated approaches limit large-scale phenotyping and restrict the integration of epidermal morphology into crop-improvement pipelines aimed at developing climate-resilient varieties with optimised stomatal patterning. To address such limitations, we developed Stomatalia , a deep learning-based platform designed to automate and standardise the quantification of stomatal and pavement cell traits. The algorithm was trained on epidermal images of cultivated and wild potato and tomato genotypes grown under optimal and abiotic-stress conditions. Stomatalia automatically detects stomata and pavement cells and extracts a broad range of morphological and integrative epidermal parameters, enabling high-throughput phenotyping within a unified workflow. Results Prior to platform development, we optimised a rapid, minimally-destructive leaf-printing protocol that generates negative impressions of the leaf surface within 40–100 s. Transparent positive prints were subsequently produced and imaged under a light microscope at scale settings ranging from 20 to 200 μm. The resulting images are analysed using Stomatalia’s user-friendly web-based interface, which runs an instance-segmentation deep learning algorithm to detect, count, and calculate stomatal and pavement cell parameters. The platform outputs structured files containing raw measurements, derived integrative traits, and associated metadata, facilitating downstream statistical and physiological analyses. Algorithm evaluation on independent datasets demonstrated high performance within the validated dicot imaging domain, with F 1 -scores ranging from 0.86 to 0.94 depending on image scale, species, and resolution, and high segmentation overlap for both stomata and pavement cells. The generality of stomatal detection was also tested on spring onion, chickpea, balsam poplar, and wheat in cross-species feasibility tests, although performance was more variable in monocots, and pavement-cell segmentation remained species- and architecture-dependent. Benchmarking against another publicly available app further showed that, under the tested web interface settings and image types, Stomatalia exhibited closer agreement with manual counts and substantially faster processing times. The practical performance of Stomatalia was further tested in a proof-of-concept trial with potato plants subjected to optimal irrigation and a long, gradual drought. The platform reliably quantified epidermal traits despite variations in leaf morphology and image quality, supporting the integrated interpretation of stomatal and pavement-cell responses under stress. Conclusions We developed Stomatalia as a robust, user-friendly deep learning platform for automated, high-throughput analysis of bright-field leaf epidermal images across varying magnifications and resolutions. Stomatalia facilitates rapid, reproducible, and coordinated phenotyping of stomatal and pavement cells by integrating methodological standardisation, computational automation, and multi-trait extraction in a single analytical workflow. Its strongest current application is the analysis of high-quality dicot leaf-print images, particularly in species and imaging conditions similar to those used for model training and validation. Cross-species and benchmark analyses further define its current scope: stomatal detection can be transferred to some additional epidermal architectures, whereas robust pavement-cell segmentation in monocots or highly divergent species will require further annotation and model retraining. Within these defined boundaries, Stomatalia provides a flexible and extensible framework for studying stomatal and pavement cell morphology and environmental plasticity, while also supporting broader efforts to dissect and optimise plant responses to abiotic stress.
Why it matches plant phenotyping methods気孔・舗装細胞の形態形質を画像から自動抽出する深層学習プラットフォームを開発し、独立データで性能評価・比較検証しているため、植物フェノタイピング手法が中心である。
abstractwe developed Stomatalia , a deep learning-based platform designed to automate and standardise the quantification of stomatal and pavement cell traits.
Reproduction assets foundThe authors publicly deposited the paper's test image datasets (raw/input leaf-print images, detection outputs, manual ground-truth counts, exported datasets) and the model file on Figshare, and provide a public Google Colab demo for running the Stomatalia algorithm. Both are paper-specific, public, and actionable.Dataset · publicThe test datasets and model file used in this work are available through the following link:
https://doi.org/10.6084/m9.figshare.32532672. The test_sets.zip archive contains the test image datasets
(cross-species and benchmark analysis), including raw/input images, detection output images, manual
ground-truth counts and exported datasets.Open asset ↗Figshare · 10.6084/m9.figshare.32532672pdf-page:25 lines:1-75Plant phenotyping relevance match · UnverifiedEurope PMC · checked 5 Sept 2026
Stomata of plants track the immediate demand for carbon dioxide for photosynthesis while limiting transpirational water loss. Solitary stomatal patterns are common, yet some land plants develop noncontiguous stomatal clustering, where 2 or more stomata occur in groups and overlay a single air cavity. Clustering improves stomatal efficiency, reduces plant water use, and increases resilience to environment stress. How cluster development and physiology interact and integrate with the environment are open questions. Here we used TESSERA, a deep-learning platform for stomatal detection with an interactive interface for data review. Tracking Begonia stomatal clustering patterns across various Begonias, we have uncovered correlations for stomatal clustering traits. The stomatal parameter data were applied to identify genetic loci involved in Begonia stomatal development using quantitative trait locus analysis. Combined with differential gene expression to refine the candidate list, our analysis reveals known and potential new Begonia candidates in stomatal development. As a test of this knowledge, we cloned Begonia SPEECHLESS (BegSPCH), a loci identified in this screen and an established development-related gene in Arabidopsis. Unexpectedly, Arabidopsis spch-3 mutants transformed to express BegSPCH developed stomatal clusters unlike the mutant plants expressing AtSPCH. Thus, various molecular and environmental factors likely overlay transcriptional regulation in stomatal development.
Why it matches plant phenotyping methodsTESSERAによる気孔検出プラットフォームを用いて気孔クラスタリング形質を抽出し、複数のBegoniaで解析しているため、植物表現型取得・解析手法が研究の中心的要素です。
abstractHere we used TESSERA, a deep-learning platform for stomatal detection with an interactive interface for data review.
Published1 Jul 2026Microscopy and microanalysis : the official journal of Microscopy Society of America, Microbeam Analysis Society, Microscopical Society of CanadaCited by 0 · OpenAlex ↗
Fuzhou represents a critical center for tea genetic diversity, yet the micromorphological basis for differentiating its local landraces remains poorly understood. Scanning electron microscopy (SEM) was employed to investigate the foliar micromorphology of 28 tea landraces from Fuzhou and to characterize structural differences among them. Adaxial epidermal wax ornamentation, stomatal architecture, and nonglandular trichome patterns provided important taxonomic characters for germplasm classification. Our analysis reveals that stomata are consistently paracytic and randomly oriented on the abaxial surface. However, their dimensions exhibit high phenotypic plasticity, with mean areas ranging from 421.92 to 822.26 µm2. Leaf surface ornamentation showed high phenotypic variability, with three identifiable types: straight, wrinkled, and undulated. The length, width, and type of nonglandular trichomes varied among the landraces, with values of nonglandular trichome length ranging from 269.99 to 632.31 µm and diameter from 9.72 to 14.62 μm. The nonglandular trichome ornamentation was categorized as smooth, long-stripe, and short-stick. The study demonstrated that SEM-based analysis of foliar micromorphological traits provides a valuable tool for tea germplasm identification and cultivar improvement. Specifically, the combination of adaxial epidermal wax ornamentation and nonglandular trichome surface ornamentation provides stable and reliable diagnostic micromorphological markers for accurate differentiation and identification of Fuzhou tea landraces, filling a critical micromorphological gap in the systematic study of local tea germplasm.
Why it matches plant phenotyping methodsSEM画像に基づく葉の微細形態形質の取得・分類を中心に、茶遺伝資源の識別へ応用しており、単なる生物学的測定ではなく植物フェノタイピング手法として中心的です。
abstractScanning electron microscopy (SEM) was employed to investigate the foliar micromorphology of 28 tea landraces from Fuzhou and to characterize structural differences among them.
Low-cost RGB imaging is accessible for phenotyping, but color varies with devices and illumination. We tested whether RGB-derived indices from a standardized smartphone setup can proxy cotton (Gossypium hirsutum L.) leaf traits at the early seedling stage. Leaves (n=80) from three growth-chamber experiments were imaged in a closed light-tent with an in-frame gray/white/black card, then corrected in Adobe Photoshop. Mean leaf RGB values (manual ROIs) were used to compute 15 RGB/CIELAB indices, which were screened against SPAD, specific leaf area (SLA), vein density, water content (WC), stomatal density, and stomatal size using Pearson r and second-order regression (adj. R², NRMSE). The strongest relationships were for SLA (h_ab; adj. R²=0.666), vein density (TGI; adj. R²=0.610), and SPAD (G; adj. R²=0.558). WC was moderately associated with c_ab (adj. R²=0.344), while stomatal traits were weakly explained, consistent with scale limits of top-down mean-color metrics. Standardized consumer RGB imaging can therefore support rapid first-pass screening of pigment- and structure-related leaf traits.
Why it matches plant phenotyping methods標準化スマートフォンRGB撮像と色補正・指数計算を用いて葉形質を推定し、SPAD、SLA、葉脈密度などとの関係を定量評価しているため、画像フェノタイピング手法の検証が中心である。
abstractWe tested whether RGB-derived indices from a standardized smartphone setup can proxy cotton (Gossypium hirsutum L.) leaf traits at the early seedling stage.
Introduction Stomata are vital gatekeepers of plants that regulate the fundamental trade-off between carbon gain and water loss. Precise, high-throughput identification of stomatal traits is therefore essential for assessing plant stress tolerance and water-use efficiency. However, conventional bounding box detection struggles to accurately localize densely distributed and arbitrarily oriented stomata. Methods This study proposes DFA-YOLO, an enhanced YOLOv11-OBB (Oriented Bounding Box) model for orientation-aware maize stomatal localization and preliminary OBB-derived trait extraction. Based on a maize stomatal dataset expanded from 1,053 to 3,597 microscopic images, DFA-YOLO integrates three task-specific components: (1) a cross-dataset MGD distillation strategy that transfers structural priors from single-stomata images to dense multi-stomata scenes; (2) a fixed-threshold Focaler-CIoU localization-loss reweighting strategy (u stomata = 0.95, d stomata = 0.00) to emphasize hard positive samples during OBB regression; and (3) a C3k2_AssemFormer feature-aggregation module that combines convolutional local feature extraction with linear attention-based context aggregation. Results DFA-YOLO achieved 94.1% mAP50, 84.8% mAP75, 74.1% mAP50-95, and 90.0% recall, with higher recall, mAP50, mAP75, and mAP50-95 than the YOLOv11-OBB baseline under the same OBB evaluation protocol. When deployed on an automated platform, the system processed images at 44.9 FPS and supported stomatal localization, density estimation, and preliminary orientation-aware size description. Discussion Under the tested maize microscopic imaging workflow, DFA-YOLO enables rapid extraction of detection-oriented stomatal traits and provides a prototype tool for high-throughput maize stomatal phenotyping.
Why it matches plant phenotyping methodsトウモロコシ気孔の画像検出・配向局在化と形質抽出を目的とするYOLOベース手法を開発し、データセット、精度比較、自動化プラットフォームでの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ある。
abstractThis study proposes DFA-YOLO, an enhanced YOLOv11-OBB (Oriented Bounding Box) model for orientation-aware maize stomatal localization and preliminary OBB-derived trait extraction.
Stomatal trait analysis is essential for optimizing crop photosynthesis and transpiration, yet deep learning studies have focused mainly on monocotyledons, leaving dicotyledonous crops such as melon (Cucumis melo L.) understudied. To bridge this gap, we established a dedicated melon stomatal dataset comprising 5,708 training images, 1,631 validation images, and 815 test images. On this basis, we developed an improved Mask R-CNN framework using Vision Transformer (ViT) as the backbone. Specifically, standard Multi-Head Attention (MHA) was replaced with Dynamically Composable Multi-Head Attention (DCMHA), which enhances information exchange across attention heads and alleviates the low-rank limitation of conventional attention. In addition, a modified effective Squeeze-and-Excitation (eSE) module was incorporated into the Feature Pyramid Network (FPN) to strengthen channel dependency modeling and multi-scale feature representation. On the melon dataset, the proposed model achieved a mean average precision (mAP) of 72.40 ± 0.09%, with AP50 and AP75 of 91.93 ± 0.14% and 84.59 ± 0.19%, respectively. Repeated-run statistical analyses showed that eSE significantly and consistently improved the main detection metrics across backbones, whereas DCMHA provided a more moderate gain within the ViT-based setting, with clearer support for AP50 than for mAP or AP75 under the baseline FPN setting. Overall, the combined configuration remained among the top-performing models for stomatal instance segmentation. Ellipse fitting further enabled automated quantification of stomatal length, width, count, area, and circumference, showing strong agreement with manual measurements (Pearson r = 0.978). The model also showed preliminary transferability to cucumber, watermelon, pumpkin, and loofah, with an average species-specific R² of 0.86, although each species was evaluated on a limited sample set.
Why it matches plant phenotyping methodsメロンの気孔形質を画像から自動抽出するデータセット、改良Mask R-CNN、インスタンスセグメンテーション、楕円フィッティングを開発・検証しており、植物フェノタイピング手法が研究の中心である。
abstractwe established a dedicated melon stomatal dataset comprising 5,708 training images, 1,631 validation images, and 815 test images.
Reproduction assets foundThe paper's authors explicitly state that the source code for model training and inference (including the key modules: DCMHA, eSE-enhanced FPN, Mask R-CNN/ViT pipeline) is publicly available at a GitHub repository, which matches an allowed URL. The melon stomatal image dataset (8,154 images) is described in detail but,Code · publicCode Availability
The source code for model training and inference, including the implementation of the
key modules, is publicly available at:
https://github.com/huangyao110/qk_maskrcnn_trsv2.gitOpen asset ↗huangyao110/qk_maskrcnn_trsv2pdf-page:22 lines:1-322Plant phenotyping relevance match · UnverifiedEurope PMC · Crossref · checked 5 Sept 2026
Abstract Real-time detection of plant stress and timely delivery of protective biomolecules are essential for improving crop resilience under adverse environmental conditions. However, conventional plant monitoring systems typically rely on ambient measurements and passive treatment strategies that fail to enable targeted plant recovery based on their localized physiological conditions. As a result, current approaches largely operate as open-loop systems, where sensing and intervention are not directly integrated, limiting the ability to respond dynamically to plant stress. This study presents an integrated plant healthcare platform that bridges this gap by combining leaf-level humidity sensing with stimulus-responsive delivery of the phytohormone salicylic acid (SA) to enable a closed-loop plant care system. The objective of this work was to develop a platform capable of monitoring transpiration driven humidity changes at the leaf surface and enabling controlled hormone delivery based on plant physiological responses. A temperature responsive hydrogel encapsulating SA was synthesized to achieve sustained biomolecule release while minimizing initial burst release. Salicylic acid release kinetics were evaluated using multiple mathematical models, with the Korsmeyer–Peppas model providing the best fit (R² = 0.9978), indicating that SA release was governed primarily by polymer relaxation and degradation mechanisms. Leaf-level relative humidity was continuously monitored on the abaxial surface under different treatment conditions. Plants treated with the hydrogel-based SA delivery system showed improved drought tolerance, with localized relative humidity increasing from approximately 20–30% in stressed plants to 60–70% after treatment, while untreated stressed plants did not show any noticeable recovery. This improvement was further supported by measurements of stomatal aperture, which showed a mean opening of 1.932 micrometers in treated plants, compared to 0.396 micrometers in untreated plants. SA treated seeds also demonstrated accelerated germination within 14 days. These findings demonstrate the potential of integrating plant wearable sensors with stimulus responsive biomaterials to establish closed-loop plant healthcare systems that couple physiological sensing with adaptive intervention.
Why it matches plant phenotyping methods葉面湿度を連続測定して植物の生理状態（蒸散・ストレス回復）を推定するセンサーと、応答型処置を統合した植物フェノタイピング／ケア基盤の開発が中心である。
abstractThis study presents an integrated plant healthcare platform that bridges this gap by combining leaf-level humidity sensing with stimulus-responsive delivery of the phytohormone salicylic acid (SA) to enable a closed-loop plant care system.
Artificial intelligence applied to plant phenotyping is crucial for consistent results, as stomata classification under stress impacts physiology, water use efficiency, and productivity. Manual analysis is laborious and error-prone, limiting the efficiency and accuracy of evaluations. In this context, this study developed a soybean-specific dataset from water deficit (WD) and well-watered (WW) plants, training YOLOv8 model for automated detection and classification of open vs. closed stomata. Soybean plants were grown under water deficit and well-watered conditions, generating significant variations in stomatal structural opening and associated gas exchange traits. To capture stomata variations, epidermal printing techniques were employed, with images obtained by microscopy. The dataset was labeled using the intelligent polygon tool of the Roboflow application, with 269 images of the adaxial and abaxial surfaces of leaves annotated in two categories: open and closed stomata. The images underwent geometric transformations to facilitate model training. The results demonstrated that the YOLOV8 neural network achieved precision recall and mAP greater than 90%, highlighting its effectiveness in detecting and classifying stomata. By integrating automated classification of aperture states (open and closed) with a defined physiological stress context in soybean, this work establishes a dataset specifically designed for functional analysis. This approach extends the applicability of deep learning toward stress-oriented plant physiology studies, offering a robust tool for evaluating crop adaptation under climate change scenarios.
Why it matches plant phenotyping methodsヨロウ豆の気孔開閉状態を画像から自動検出・分類するYOLOv8手法とデータセットを開発・評価しており、植物表現型取得が研究の中心である。
abstractthis study developed a soybean-specific dataset from water deficit (WD) and well-watered (WW) plants, training YOLOv8 model for automated detection and classification of open vs. closed stomata.
Brown spot disease, caused by Corynespora cassiicola , poses a major threat to kiwifruit production, leading to severe defoliation, nutrient loss, and significant economic damage. This study assessed 25 kiwifruit germplasm accessions and found 64% exhibited resistance, including three highly resistant (HR) cultivars; most commercial and wild germplasm accessions were moderately resistant (MR) or highly susceptible (HS). Three cultivars representing different resistance levels - HR 'Longshan' (LS), MR 'Jinyan' (JY), and HS 'Hongyang' (HY) - were selected for mechanistic analysis. Resistant types showed stronger structural defenses: 64.55% lower stomatal density, 60.05% more trichome branching, and 52.28% higher epicuticular wax content than susceptible ones. These traits delayed appressorium formation by 12 hours and hindered penetration peg development. After infection, resistant plants activated rapid immune responses-ROS burst, hypersensitive reaction, and extensive lignin deposition. In LS, four defense enzyme activities rose 12-48 hours earlier than in HY and reached 1.12-1.57 times higher levels. Six defense-related genes were significantly up-regulated within 48 hours post-inoculation (hpi). Stepwise regression of 22 variables identified five key predictors of resistance: stomatal density, lesion diameter at 120 hpi (cm), average Phenylalanine Ammonia-Lyase gene expression (0, 4 and 8 hpi), peroxidase enzyme average activity (36, 48 and 72 hpi), and H 2 O 2 accumulation average area (12, 24 and 36 hpi). A model based on these achieved 94.24% accuracy (R² = 0.95) in field validation, offering a reliable tool for evaluating kiwifruit resistance.
Why it matches plant phenotyping methodsキウイフルーツの病害抵抗性を推定する予測モデルを開発し、圃場で検証しており、植物の病害状態を評価する手法が中心的です。
titlepredictive model for estimating resistance levels
Achieving high-throughput and precise phenotypic quantification and imaging modalities of stomatal and epidermal cells across diverse species remains a primary bottleneck in elucidating the mechanisms of stomatal dynamics, epidermal patterning, and environmental adaptation of plants. Here, we developed EpiReasoner, an artificial intelligence framework comprising a vision module, EpiVision, and a knowledge-based reasoning module, EpiBrain, for the quantitative phenotypic analysis and domain-specific knowledge reasoning of stomatal complexes and pavement cells in plants. Operating across bright-field, scanning electron microscopy, and differential interference contrast modalities, EpiVision achieves precise instance segmentation in various monocotyledonous, dicotyledonous, and fern species. Its performance significantly surpasses current state-of-the-art models. Moreover, we defined 23 quantitative indices describing stomatal cell morphology and spatial distribution. For domain-specific tasks such as phenotype prediction, genotype deduction, and molecular mechanism reasoning, EpiBrain demonstrates a human preference rate significantly higher than that of general-purpose large language models, including GPT-5 and Claude Sonnet 4. The application of EpiReasoner to phenotypic data of stomatal density derived from a tomato natural population of 170 accessions successfully identified a major quantitative trait locus on chromosome 8. The candidate gene, SKP1-interaction partner 19L ( SKIP19L ), encoding an F-box family protein, exhibited severe allele frequency drift during tomato domestication, which is highly consistent with the adaptive trend of reduced stomatal density under artificial selection. EpiReasoner provides a novel paradigm that unifies visual phenomics and knowledge-driven reasoning for the biology of stomata and pavement cells, thereby significantly accelerating scientific discovery in plant science.
Why it matches plant phenotyping methods植物の気孔・表皮細胞を対象に、画像解析と知識推論を統合したフェノタイピング手法を開発しており、形態・空間分布の定量化が中心的な貢献である。
abstractwe developed EpiReasoner, an artificial intelligence framework comprising a vision module, EpiVision, and a knowledge-based reasoning module, EpiBrain, for the quantitative phenotypic analysis and domain-specific knowledge reasoning of stomatal complexes and pavement cells in plants.
Efficient gas and water exchange between plants and their environment largely depends on the number and distribution of stomata, cellular valves in leaf epidermis. Core genetic regulators of stomatal cell identity and pattern along with asymmetric stem-cell like divisions in stomatal precursors are hypothesized to customize stomatal production for optimal leaf performance. How these regulators work in concert and how division dynamics are modified and adjusted in different environments, however, are poorly understood. Here, we leveraged the variation in stomatal patterning in Arabidopsis thaliana accessions from diverse environments to define developmental rules and constraints in the stomatal lineage. The accessions subtle and quantitative variation enables us to identify which cellular parameters are flexible, revealing how developmental plasticity generates phenotypic plasticity. By developing live-cell imaging tools to track cellular behaviors during leaf growth under varying environmental conditions in these accessions, we could decompose stomatal density variation into its developmental origins. Variation in final stomatal numbers is driven by differences in the relative contributions of stomatal initiation, cell size-based fate thresholds, general proliferative capacity, and coordination between sister and neighbor cell behaviors. Overall, diverse accessions converge toward two lineage regimes: one dominated by autonomous decisions with loose cell-cell coordination, the other by extensive cell-cell coordination. Challenging accessions with environmental fluctuations revealed regime-specific flexibility, with plasticity primarily mediated by a single division-related parameter. Our results show how cellular parameters integrate into alternative developmental strategies that shape environmental responsiveness.
Why it matches plant phenotyping methods葉の成長中の細胞挙動を追跡するライブセルイメージングツールを開発し、気孔密度の発生的起源を定量化しており、植物フェノタイピング手法が研究の中心である。
abstractBy developing live-cell imaging tools to track cellular behaviors during leaf growth under varying environmental conditions in these accessions, we could decompose stomatal density variation into its developmental origins.
Manual phenotyping of photosynthesis-related traits in rice is labor-intensive and limits the scale and temporal resolution of genetic analysis under field conditions. Here, we integrated unmanned aerial vehicle (UAV)-based high-throughput phenotyping (HTP) with genome-wide association studies (GWAS) to dissect the diversity and genetic architecture of photosynthesis-related traits in a large indica rice diversity panel (>300 accessions) evaluated across three dry seasons. A total of 45 traits, including UAV-derived NDVI, canopy height, and canopy temperature, together with leaf gas-exchange, stomatal, anatomical, and agronomic traits, were quantified. UAV-derived traits captured temporal growth and senescence dynamics and showed strong and consistent correlations with leaf photosynthetic rate, stomatal conductance, flowering time, biomass, and grain yield. GWAS identified multiple QTLs for photosynthetic and HTP traits, including a cross-year stable transpiration-rate QTL (qTRMMOL-2-2) and a photosynthetic-rate QTL (qPHOTO-1-2). Haplotype analyses revealed that the wall-associated receptor-like kinase gene OsWAK6 and the potassium transporter gene OsHAK1 were strongly associated with variation in photosynthetic rate and transpiration, respectively. Several elite accessions with consistently high photosynthetic performance carried superior haplotypes at multiple qPHOTO loci, suggesting their potential value for breeding. Together, our results demonstrate that UAV-based HTP provides reliable field-scale proxies for physiological performance, and that integrating HTP with GWAS can enable the identification of genetic targets for improving photosynthesis, water use, and yield potential in rice. • Forty-five traits, including HTP, photosynthesis, and leaf morphology, were measured across three dry seasons in diverse Indica rice. • GWAS identified genes linked to photosynthesis and stomatal density, aiding in breeding resilient, high-yield rice. • UAV-based HTP data effectively tracked plant growth and senescence, correlating with photosynthetic rate. • GWAS co-localization revealed shared QTLs, suggesting multi-trait regulation by common genes.
Why it matches plant phenotyping methodsUAVベースのHTPによる植物形質取得と生理性能の推定が研究の中心であり、45形質を大規模・反復的に測定し、信頼性や他の生理形質との相関も評価している。
abstractwe integrated unmanned aerial vehicle (UAV)-based high-throughput phenotyping (HTP) with genome-wide association studies (GWAS)
Transmission electron microscopy (TEM) and energy-dispersive X-ray spectroscopy (EDS), with complementary light and scanning electron microscopies observations, are employed to reveal novel information regarding the foliar cuticle fine-structure of Araucaria grandifolia (Araucariaceae). Well-preserved foliar compressions of this taxon were collected from the Punta del Barco Formation (Baqueró Group, Aptian, Patagonia, Argentina). TEM sections revealed six types of cell cuticles: two representing the ordinary epidermal cells (OEC) of the upper and lower cuticle, and four related to the stomatal apparatus and associated cells: subsidiary and guard cell cuticles, and inner and outer associated OEC cuticles. Cuticles comprise either a granular A2 layer (cuticle proper) and a spongy-fibrilous B1 layer (cuticular layer), or solely a B1 spongy layer, which is similar to that of Nothopehuen brevis and Brachyphyllum garciarum , two Cretaceous Araucariaceae from Patagonia. The statistical evaluation of quantitative measurements revealed the relationships and hierarchies between cell cuticle types and ultrastructural layers, revealing for the first time the precise identity of Araucariaceae cuticles. TEM-EDS revealed a significant presence of phosphorus (P) and chlorine (Cl), highlighting the potential taxonomic and paleoenvironmental relevance of the P/Cl ratio. Additionally, the six cell cuticle types found in A. grandifolia are shown in a dichotomous key, and a cuticle three-dimensional reconstruction is provided. Finally, the paleoenvironment conditions under which the A. grandifolia plant lived during the Aptian in Patagonia are also inferred.
Why it matches plant phenotyping methods葉のクチクラ微細構造・元素組成という植物器官形質を、TEM、EDS、各種顕微鏡、定量解析、3D再構成で体系的に取得・解析しており、観察が分類・古生態の補助的な routine 測定に留まらず、方法に基づく形質記載の中心となっている。
abstractTransmission electron microscopy (TEM) and energy-dispersive X-ray spectroscopy (EDS), with complementary light and scanning electron microscopies observations, are employed to reveal novel information regarding the foliar cuticle fine-structure of Araucaria grandifolia (Araucariaceae).
Field / plotStem / branchStomata / guard-cell complexPhysiological trait estimationGrowth / time-series analysisStomatal traitsStress response / toleranceWater status / transpiration
Droughts have emerged as the primary driver of forest disturbances across Europe in the 21st century, significantly impacting both tree growth dynamics and mortality rates. Tree species are differently affected under drought, and these differences are related to species-specific plant hydraulic traits that govern water storage, hydraulic conductivity, and stomatal regulation. However, quantifying variability in these hydraulic traits across sites, species, and time remains challenging, as site measurements have historically rarely been comprehensive enough to assess the evolution of plant hydraulic behavior under drought stress. New continuous, high temporal resolution observational plant hydraulic data paired with process-based plant hydraulic modelling opens an opportunity to address this gap, by providing a framework to test and quantify theories based on first principles across species and sites.In this study, we apply the terrestrial biosphere model QUINCY, augmented by a recently developed plant hydraulic architecture module, across three eddy covariance sites in Germany covering broadleaved forest species (Aplern, Hainich, and Hartheim). The model is parameterized for three common temperate tree species present at the aforementioned sites. We constrain QUINCY across these species and sites using 30-minute resolution stem water potential measurements collected during the summer and autumn of 2023. Our results show that two groups of model parameters explain most of the simulated plant water potentials: parameters controlling plant water uptake from soil (plant ability to extract water from soil and the root distribution), and parameters regulating stomatal sensitivity to pre-dawn leaf water potential. Across species, we find ash to be more drought resistant than beech and hornbeam, as it closes its stomata earlier than other species under similar levels of drought stress, and it is characterised by a higher hydraulic capacitance per unit stem volume. Our study demonstrates how integrating the new generation of in situ plant hydraulic observations into vegetation models can facilitate the quantification of species-specific hydraulic parameters, effectively reducing uncertainty in, and providing robust constraints on, modelled responses to drought.
Why it matches plant phenotyping methods植物の水理状態・水理形質を連続観測と拡張モデルで定量化する手法の適用が研究の中心であり、単なる生物学的実験のルーチン測定ではない。
abstractOur study demonstrates how integrating the new generation of in situ plant hydraulic observations into vegetation models can facilitate the quantification of species-specific hydraulic parameters
Abstract Biological processes involve complex hierarchies where composite traits result from multiple component traits. However, holistically understanding of how sets of component traits interact to underpin genotype‐to‐phenotype relationships is generally lacking. Stomatal density (SD) is a tractable model system for exploring how high‐throughput phenotyping (HTP) data could be exploited by a new spatial analysis approach to better understand a developmentally and functionally important trait. SD is a composite trait, resulting from various components related to cell identity and size, which are themselves governed by a series of spatio‐developmental processes. Data from 192 recombinant inbred lines of maize [ Zea mays (L.)] were analyzed by a new stomatal patterning phenotype (SPP) to (1) describe the average spatial probability distribution of the nearest neighboring stomata; (2) derive a core set of component traits related to cell size, cell packing, and positional probabilities; (3) build a structural equation model of component traits underlying SD; and (4) identify stomatal patterning quantitative trait loci (QTL). The core set of SPP‐derived traits explained 74% of the variation in SD. Analyzing SPP component traits allowed some loci previously identified as generic SD QTL to be recognized as specific to lateral versus longitudinal elements of stomatal patterning. Therefore, this study highlights how novel insights can be gained by decomposing a composite trait (e.g., SD) into a set of component traits that were present in HTP data but not previously exploited.
Why it matches plant phenotyping methods新しいstomatal patterning phenotype（SPP）による空間解析・形質分解手法が中心で、HTPデータから気孔パターンの構成形質を抽出・解析している。
abstractexploited by a new spatial analysis approach
Plant phenotyping relevance match · UnverifiedCrossref · Europe PMC · checked 15 Sept 2026
ABSTRACT Sustainable agriculture urgently requires innovative, pesticide‐free strategies to mitigate herbivory and safeguard food security. Ultraviolet‐B (UV‐B) irradiation, with tunable intensity and cost‐effectiveness, has emerged as a promising non‐chemical method to enhance plant resistance, yet its underlying mechanisms remain elusive. Here, using tea plant ( Camellia sinensis ) and its major pest Ectropis obliqua as a model, we developed a multimodal framework that integrates AI‐enhanced electronic nose technology for real‐time volatile profiling with in situ hyperspectral stimulated Raman scattering (SRS) microscopy to characterize defense responses under precisely controlled UV‐B treatments. This approach identified herbivore‐induced volatiles—hexanal, (Z)‐3‐hexenol, octanal, and (Z)‐3‐hexenyl acetate—optimally induced at 1.2 kJ·m −2 UV‐B and linked to insect deterrence. SRS imaging further revealed elevated jasmonic acid derivatives and L‐phenylalanine, coupled with reduced protein levels and altered stomatal dynamics, all correlating with enhanced resistance. Transcriptomic and molecular analyses confirmed transcriptional regulation of these pathways. By bridging volatile detection, metabolic imaging, and molecular validation, this study pioneers a multimodal strategy that provides mechanistic insights into UV‐B–mediated plant defense and highlights the potential of multimodal methodologies as powerful tools for developing sustainable, pesticide‐free pest management solutions in precision agriculture.
Why it matches plant phenotyping methodsAI強化電子鼻とハイパースペクトルSRS顕微鏡を統合した植物防御応答のリアルタイム・多モーダル計測フレームワークが研究の中心であり、揮発性物質、代謝物、気孔動態などの植物状態を抽出している。
abstractwe developed a multimodal framework that integrates AI‐enhanced electronic nose technology for real‐time volatile profiling with in situ hyperspectral stimulated Raman scattering (SRS) microscopy to characterize defense responses
Abstract Field experiments are complex to interpret due to interactions between genotypes, environment, plant development and cultivation practices. This complexity challenges the accurate phenotyping of individual plant traits over the season. Here, we quantified the primary sources of seasonal variation in stomatal conductance (gs) across 15 grapevine cultivar–rootstock combinations within a large-scale phenotyping platform, comprising over 6000 observations. Environment-related traits and date of measurement accounted for up to 76% of the variance, potentially obscuring cultivar–rootstock effects. Therefore, we integrated machine learning, spatiotemporal normalization of the gs response, and the use of mixed models to disentangle the influences of environmental factors, plant material and crop performance related traits. After spatio-temporal normalization, cultivar and cultivar–rootstock interactions explained over 25% of the variation in gs, and Grenache exhibited the most conservative water-use behavior resulting in high water-use efficiency. Specific rootstock–scion combinations also exhibited smaller, but still significant, differences in gs and water-use efficiency, highlighting the specificity arising from the interaction within each rootstock–scion combination. The high variability in gs indicates that accurate quantification of rootstock–scion contributions to key traits in field studies is complex and requires accounting for spatial heterogeneity driven by the environment.
Why it matches plant phenotyping methods大規模な圃場フェノタイピングで測定した気孔コンダクタンスを対象に、機械学習、時空間正規化、混合モデルを統合して環境変動と遺伝的要因を分離する手法が中心である。
abstractwe integrated machine learning, spatiotemporal normalization of the gs response, and the use of mixed models to disentangle the influences of environmental factors, plant material and crop performance related traits.
High-throughput, low-cost phenotyping remains a critical bottleneck in wheat breeding, genetics, and crop management. This is particularly evident in the measurement of complex yield components (i.e., spike and spikelet counts), disease and grain-quality traits related to Fusarium Head Blight (FHB) and Fusarium-Damaged Kernels (FDK), and microscale physiological traits such as density and size of stomata and aperture. We introduce WheatAI (wheatai.net), an AI-powered web application designed to bridge the gap between advanced computer vision, AI and deep learning models, and high-throughput phenotyping (HTP) and practical agricultural applications. WheatAI v1.0 provides an accessible, browser-based interface that supports multiscale data ingestion from smartphones, Unmanned Aerial Vehicles (UAVs), and portable microscopes. The core functionalities of the platform include plot- and field-scale assessment via UAV- and smartphone-based wheat spike detection and counting, as well as smartphone-based spikelet counting. Additionally, it offers grain quality assessment through FDK ratio estimation and kernel morphometric measurements, such as length, width, and area, derived from smartphone images of kernel samples. For leaf-level analysis, WheatAI provides microscale phenotyping through automated stomatal counting, size, and aperture measurement from digital microscopy images. The system supports both single-image and bulk processing via a guided upload-and-run workflow. This platform is designed to reduce labor costs and rater subjectivity while accelerating field-to-lab decision cycles. By providing standardized, image-based outputs, WheatAI enables breeders, agronomists, and producers to implement high-throughput selection and precision scouting at scale.
Why it matches plant phenotyping methodsWheatAIは、画像から収量構成要素、病害関連形質、穀粒形態、気孔形質を抽出する高スループット植物フェノタイピング基盤そのものであり、方法・ソフトウェアの開発が中心です。
abstractWe introduce WheatAI (wheatai.net), an AI-powered web application designed to bridge the gap between advanced computer vision, AI and deep learning models, and high-throughput phenotyping (HTP) and practical agricultural applications.
Chemical imaging at high spatiotemporal resolution is crucial for advancing plant sciences and biotechnology. We demonstrate optical nanosensors for subcellular imaging of signaling molecules (H 2 O 2 ) and lipid corona formation in plant tissues at high spatial ( 2 O 2 waves (100 μM) from plant mesophyll to stomata and pavement cells. Ca 2+ induced higher endogenous H 2 O 2 in mesophyll cells, whereas organelle electron transport chain disruptors and salt stress generated similar H 2 O 2 across all leaf cell types. Furthermore, the nanosensor quenching kinetics in photosynthetic mesophyll (0.018 s -1 ) and epidermal (0.004 s -1 ) cells enabled the detection of plant lipid corona formation. Optical nanosensors elucidate spatiotemporal dynamics of plant signaling molecules and advance our understanding of biocorona formation.
Why it matches plant phenotyping methods植物組織内のシグナル分子を高時空間分解能で可視化する光学ナノセンサーの開発・実証が研究の中心であり、植物の生理状態を測定する方法論に該当する。
abstractWe demonstrate optical nanosensors for subcellular imaging of signaling molecules (H 2 O 2 ) and lipid corona formation in plant tissues
ABSTRACT Stomata are microscopic pores that play a vital role in transpiration and gaseous exchange from leaf surfaces in plants. The stomatal density and size directly influence photosynthesis and hydrodynamics capacity. Conventional approaches for counting and determining stomatal density is labour-intensive and lack scalability. Although there are several AI-based stomata finder tools that were published in the last decade, existing models were trained on model plants like wheat, barley and Arabidopsis . Stomata in such model plants are generally elliptical, but applying a universal model to all plant species is not feasible due to their diverse morphological characteristics. Previous studies have suggested using the stomatal index to quantify the ratio between epidermal cells and total stomatal count. However, this approach can be difficult to apply consistently, as epidermal cell shape and size vary across plant species. Instead, we propose measuring stomatal density based on the number of stomata per total imaged pixel area in the captured images. In this study, a comparison between YOLOv12 and RF-DETR models were made for real-time stomata detection in normal and difficult-to-image and out-of-focus occluded images. The in-house training dataset consisted of images of 300 rice,100 barley and 50 sugarcane leaves that were captured against a dark background. YOLOv12 outperformed RF-DETR with higher mAP50:95 score. The models were trained with image augmentation for 300 epochs and YOLOv12 achieved a peak mean average precision of 98.5% and exceled at detecting stomata across abaxial and adaxial surfaces of leaves of both monocot and dicot plants. StomaQuant has also been shown to be effective for both epidermal peel and ethanol decolorised samples. Thus, StomaQuant can be used to effectively and efficiently estimate the stomatal density and size in a wide range of host plant species.
Why it matches plant phenotyping methods気孔の検出・密度・サイズ推定を目的とする深層学習画像解析手法を開発し、複数モデルおよび困難画像で性能比較・検証しており、植物表現型取得が研究の中心である。
titleStomaQuant: Deep Learning-Based Quantification for Stomatal Trait Assessment
Stomatal conductance (gs) is indicative of plant carbon dioxide uptake via photosynthesis and water loss via transpiration, making it a crucial plant biophysical trait. Direct measurement of gs is labor-intensive and usually not scalable to large fields. Using manual measurements to estimate parameters of gs models is even more labor-intensive and prone to sampling errors. This study aimed to develop an automated pipeline for gs measurement and model calibration using thermal imagery data, which not only disentangles the impacts of genotype-specific stomatal traits and environmental conditions but also enables the prediction of gs in new environments. The methodology involved using simulated thermal imagery data generated from a 3D biophysical model to train a machine learning model that could be applied to real thermal images to predict stomatal model parameters and gs itself. The method was evaluated by comparing predictions against manual gs measurements, all of which were not part of the model training process, as the model was trained against only simulated images. When compared against manual gs measurements using a porometer, the prediction R2 was 0.7, which is likely comparable to the accuracy of the manual porometer-based gs measurements (relative to a leaf gas exchange system). The developed pipeline enables high-throughput gs model parameter calibration and gs estimation.
Why it matches plant phenotyping methods熱画像と機械学習を用いて植物の気孔コンダクタンスを推定・モデル較正するパイプラインを開発し、手動測定と比較検証しており、植物フェノタイプ取得法が研究の中心である。
abstractThis study aimed to develop an automated pipeline for gs measurement and model calibration using thermal imagery data
Stomatal pores, formed by guard cells, govern the critical trade-off between carbon assimilation and water loss in plants. Their dynamic responses to environmental stresses, such as stomatal oscillations and drought “stress memory” (hysteresis), have lacked a unified mechanistic explanation. While abscisic acid (ABA) is believed to play key roles in water stress responses, no model has linked its core regulatory kinetics to these complex stomatal behaviors. Here, we introduce a coupled hydropassive-hydroactive (HP-HA) model that integrates leaf hydraulics with the biokinetics of guard cell-autonomous ABA regulation and plasma membrane-mediated osmoregulation. We demonstrate that this framework predicts accurate, genotype-specific stomatal regulation across wildtype, ABA-insensitive mutant ( ost1-3 ), and ABA-synthesis mutant ( aao3-2 ) in Arabidopsis thaliana ( At ) and that non-linear feedbacks in ABA autoregulation can drive both stomatal oscillations and hysteresis. This work unifies genetic, signaling, and membrane processes with leaf-scale physiological dynamics, providing a new predictive foundation for understanding and modulating plant management of water use and water stress.
Why it matches plant phenotyping methods葉の水理とABA制御を統合した予測モデルを開発し、遺伝子型別の気孔コンダクタンス制御を検証しており、植物生理表現型の取得・予測手法が中心である。
abstractHere, we introduce a coupled hydropassive-hydroactive (HP-HA) model that integrates leaf hydraulics with the biokinetics of guard cell-autonomous ABA regulation and plasma membrane-mediated osmoregulation.
Reproduction assets foundThe paper's Code Availability section explicitly archives all MATLAB code used to generate the study's stomatal conductance modeling results in a Zenodo repository (DOI 10.5281/zenodo.17888362) and on GitHub (desai-sahil/sys-bio-gs), both listed as allowed URLs. This is author analysis code directly reproducing the hydCode · publicn analysis are provided in SI sections S5. Comprehensive tables listing all model parameters,
362
their sources, and the methodology for parameter fitting are provided in SI section S7.
363
364
Code Availability
365
All MATLAB code used to generate the results in this study is permanently archived in a Zenodo
366
repository at: https://doi.org/10.5281/zenodo.17888362. The most current version of the code is also
367
available on GitHub at: https://github.com/desai-sahil/sys-bio-gs.git. Refer to SI section S7.C for details
368
on steps to run the code to reproduce the results in main text.
369
370
371
Acknowledgements
372
We thank F. E. Rockwell, V. Bacheva, S. Sen, I. Gabay, E. Wu, J. BeldiOpen asset ↗Zenodo · 10.5281/zenodo.17888362pdf-raw-page:9 lines:1-74Code · publicnd the methodology for parameter fitting are provided in SI section S7.
363
364
Code Availability
365
All MATLAB code used to generate the results in this study is permanently archived in a Zenodo
366
repository at: https://doi.org/10.5281/zenodo.17888362. The most current version of the code is also
367
available on GitHub at: https://github.com/desai-sahil/sys-bio-gs.git. Refer to SI section S7.C for details
368
on steps to run the code to reproduce the results in main text.
369
370
371
Acknowledgements
372
We thank F. E. Rockwell, V. Bacheva, S. Sen, I. Gabay, E. Wu, J. Belding, and P. Jain for insightful
373
discussions. This work was supported by the Center for Research on Programmable Open asset ↗GitHub · desai-sahil/sys-bio-gspdf-raw-page:9 lines:1-74Plant phenotyping relevance match · UnverifiedbioRxiv · checked 15 Sept 2026
Stomata are pores in the leaf epidermis that regulate the trade-off between CO2 uptake for photosynthesis and water vapor loss to the atmosphere. Stomatal patterning therefore influences water use efficiency and is a target for engineering to avoid drought stress. However, there is limited understanding of how internal leaf anatomy is coordinated with stomatal development, in part due to the technical challenges of assessing three-dimensional anatomy with sufficient resolution. C4 grasses are understudied, and this is a significant knowledge gap given their file-like stomatal distribution and unique mesophyll organization. In this study, wild-type sorghum and a low-stomatal density transgenic line expressing a synthetic Epidermal Patterning Factor (EPFsyn) were studied. High-resolution microCT was paired with machine learning to characterize three-dimensional traits of mesophyll, epidermis, and airspace, which together determine gias. Sorghum internal leaf airspace is an arrangement of large sub-stomatal airspaces with thin air passageways. Adaxial and abaxial surfaces differed in stomatal patterning relative to mesophyll structures, sub-stomatal crypts and airspace CO2 conductance (gias). Surprisingly, adaxial stomata were consistently located above rather than between vascular bundles. Unexpectedly, gias was not significantly different in wild-type versus EPFsyn. EPFsyn plants had larger crypts and shifts in internal leaf anatomy, indicating a potential compensation mechanism for predicted impacts of reduced stomatal density on gias. These findings provide a new understanding of the interplay between leaf surface specific anatomy and internal structural patterning of the mesophyll in a C4 species, and provides knowledge relevant to engineering water use efficiency in crop species.
Why it matches plant phenotyping methods高解像度microCTと機械学習を組み合わせ、葉の三次元形態・気腔などの植物形質を抽出する手法が研究の主要部分であり、単なる生物学的測定ではない。
abstractHigh-resolution microCT was paired with machine learning to characterize three-dimensional traits of mesophyll, epidermis, and airspace
Faba bean ( Vicia faba L.) is a key protein crop, but its cultivation and yield stability are hindered by a number of environmental stresses. Stomata regulate gas exchange between the plant and atmosphere, playing a central role in photosynthesis and mediating plant responses to a wide range of environmental stressors. This study aimed to investigate variations in photosynthetic regulation in faba bean, and to examine leaf temperature and the response to short-term acute ozone (O₃) exposure as proxies for stomatal function. Here, we used a high-throughput plant phenotyping (HTPP) platform to screen 196 faba bean genotypes for photosynthetic and stomatal function under controlled conditions. A subset of extreme genotypes, identified based on relative leaf tempreture from the initial screening, was exposed to a 450 ppb O₃ treatment. Our results revealed strong positive relationship between photosynthetic efficiency and relative leaf temperature. A three-fold difference in relative leaf temperature was observed among genotypes. The O₃ treatment caused signicantly less damage in genotypes with higher leaf temperature compared to those with lower leaf temperature (p < 0.001). By combining a HTPP platform with elevated O₃ stress treatment, we identified faba bean genotypes with contrasting stomatal responses to the O₃ exposure. Our results advance understanding of the regulation mechanisms of photosynthetic light reactions and the role of stomatal function in modulating faba bean responses to environmental stressors. • High-throughput phenotyping reveals large variation in leaf temperature among faba bean genotypes. • Leaf temperature strongly affects photosynthetic regulation in faba bean. • The tested genotypes with higher leaf temperatures display increased ozone tolerance.
Why it matches plant phenotyping methodsHTPPプラットフォームを用いて196遺伝子型の葉温度、光合成、気孔機能を高スループット測定し、表現型に基づく選抜とオゾン応答評価を行っており、フェノタイピング手法の応用が研究の主要部分です。
abstractHere, we used a high-throughput plant phenotyping (HTPP) platform to screen 196 faba bean genotypes for photosynthetic and stomatal function under controlled conditions.
Field / plotStomata / guard-cell complexPhysiological trait estimationCalibration / preprocessingStomatal traitsWater status / transpiration
The development of commercially available porometers has allowed for higher throughput measurement of stomatal conductance, but a body of evidence has suggested a persistent positive bias in their measurements relative to "reference" measurements from instrumentation based on infra-red gas analysis. We compiled a data set comprised of 25 angiosperm species, across a range of field conditions and found that the LI-COR LI-600, an open flow-through porometer, produced an exponentially increasing bias relative to the LI-COR LI-6800 infra-red gas analyser-based instrument in response to increasing stomatal conductance and decreasing relative humidity. This bias was minimal at lower stomatal conductance (below roughly 0.25 mol m -2 s -1 ), but was pronounced for larger values. We hypothesised that this bias is the result of the assumption of a constant air temperature throughout the flow stream used by the instrument software to estimate stomatal conductance from raw sensor measurements. We relaxed this assumption, and applied psychrometrics to augment the typical gas exchange equations with an additional energy balance constraint to solve for the temperature change throughout the air flow stream. We found that including this temperature difference corrects the computed transpiration and stomatal conductance values, and brings the porometer measurement into agreement with that of the infra-red gas analysis-based system. Software is provided to apply the correction to LI-600 output files. For future instrument design iterations, explicit measurement of temperature variation in the flow stream provides a potential opportunity for improvement in measurement accuracy at high stomatal conductance.
Why it matches plant phenotyping methods気孔コンダクタンス測定器の系統誤差を検証し、物理モデルと補正ソフトウェアで植物生理形質の測定精度を改善する方法研究である。
abstractThe development of commercially available porometers has allowed for higher throughput measurement of stomatal conductance, but a body of evidence has suggested a persistent positive bias in their measurements relative to "reference" measurements from instrumentation based on infra-red gas analysis.
In nearly all plants, pores on the leaf surface called stomata are essential for photosynthesis and gas exchange. The shape and distribution of stomata on the leaf varies widely between plants and is directly connected to photosynthetic efficiency. However, our understanding of the factors, both genetic and environmental, that exert subtle but significant effects on stomatal morphology is limited by the time required to manually annotate stomata in large imaging datasets. Here, we present a lightweight and efficient tool, QuickSpotter, for semi-automated stomatal annotation from fluorescence images. First, we establish QuickSpotters ability to automatically and accurately annotate mature stomata across developmental time. We also introduce an optional, speedy proofreading utility, StomEdit, that allows the researcher to quickly validate and correct machine-generated annotations. We use QuickSpotter and StomEdit to quantify how stomatal morphology evolves at the population level during cotyledon development and demonstrate how the programs can be used to extract subtle differences in stomatal development following pharmacological treatments. Finally, we describe PairCaller, a pair-calling classifier that accompanies QuickSpotter and can be used to identify stomatal clusters, a physiologically relevant and widely studied developmental phenotype. Taken together, our suite of programs facilitates quantitative analyses of stomatal development at scale, enabling high-throughput analyses of leaf phenotypes under varied conditions.
Why it matches plant phenotyping methods蛍光画像から気孔形態・分布を半自動抽出するソフトウェア群を開発し、精度検証と植物表現型への適用を行っており、フェノタイピング手法が研究の中心である。
abstractwe present a lightweight and efficient tool, QuickSpotter, for semi-automated stomatal annotation from fluorescence images.
Stomata regulate gas and water exchange in plants and are crucial for plant productivity and survival, making their trait analysis essential for advancing plant biology research. While current machine learning methods enable automated stomatal trait extraction, existing approaches face significant limitations that require extensive manual labeling for training and additional human annotation when applied to new species. This study presents an automated system for extracting stomatal traits from Pisum sativum (pea) leaves that addresses these challenges through generative artificial intelligence. Our pipeline integrates imaging, detection, segmentation, and synthetic data generation processes. A nail polish impression technique was employed to prepare leaf microscopic images, followed by the application of deep learning networks to identify and segment stomata in these images. By including generative AI-produced synthetic data, our system achieves high segmentation accuracy across species, reducing manual relabeling requirements. This approach enables seamless cross-species model adaptation for many cases, alleviating the annotation bottleneck that often limits machine learning applications in plant biology. Our results demonstrate the pipeline's effectiveness for automated stomatal trait extraction and highlight generative AI's transformative potential in advancing stomatal detection methodologies, offering a scalable solution for broad-scale comparative stomatal analysis.
Why it matches plant phenotyping methods気孔形質を画像取得・検出・セグメンテーションで抽出する手法と、生成AIによる合成データを用いた種間適応を中心に開発しているため、植物フェノタイピング手法に該当する。
abstractThis study presents an automated system for extracting stomatal traits from Pisum sativum (pea) leaves that addresses these challenges through generative artificial intelligence.
Plant phenotyping relevance match · UnverifiedOpenAlex · Europe PMC · checked 6 Sept 2026
Stomata are vital for controlling gas exchange and water vapor release, which significantly affect photosynthesis and transpiration. Characterizing stomatal traits such as size, density, and distribution is essential for adaptation to the environment. While microscopy is widely used for this purpose, manual analysis is labor-intensive and time-consuming that limit large scale studies. To overcome this, we introduce an automated, high-throughput method that leverages YOLOv8, an advanced deep learning model, for more accurate and efficient stomatal trait measurement. Our approach provides a comprehensive analysis of stomatal morphology by examining both stomatal pores and guard cells. A key finding is the introduction of stomatal angles as a novel phenotyping trait, which can offer deeper insights into stomatal function. We developed a model using a carefully annotated dataset that accurately segments and analyzes stomatal guard cells from high-resolution images. Additionally, our study introduces a new opening ratio metric, calculated from the areas of the guard cells and the stomatal pore, providing a valuable morphological descriptor for future physiological research. This scalable system significantly enhances the precision and efficiency of large-scale plant phenotyping, offering a new tool to advance research in plant physiology.
Why it matches plant phenotyping methods深層学習による気孔画像解析と、気孔形態・角度・開口率の自動推定手法を開発しており、植物フェノタイピング手法が研究の中心である。
abstractwe introduce an automated, high-throughput method that leverages YOLOv8, an advanced deep learning model, for more accurate and efficient stomatal trait measurement.
Canola is an important economic and agronomic crop globally, but its yield is under threat due to climate change. Stomata are a key breeding target because of their importance in carbon capture and water use efficiency. However, screening for elite stomatal traits could be laborious and time-consuming. We developed a new toolkit called Stomatal Comprehensive Automated Neural Network or SCAN that combines the use of high-resolution portable digital microscopy with machine learning to automate stomatal trait phenotyping in canola. We show that SCAN can rapidly measure stomatal density, size, and pore area in canola at 97-99% accuracy, and capture real-time stomatal pore status that strongly correlated with leaf porometer measurement in canola. Here we use SCAN to investigate how leaf stomatal traits vary through a canopy in different ecotypes of canola grown in the field and glasshouse conditions. SCAN revealed that stomatal density in canola decreases in more expanded leaves with the abaxial surface having up to 40% more stomata that are 2× more open than the adaxial surface. SCAN also showed that patterns of stomatal traits in canola vary between leaf position in the canopy and change with environment in an ecotype-dependent manner.
Why it matches plant phenotyping methods葉の気孔形質を自動取得する画像・機械学習ツールを開発し、精度検証と既存測定との相関評価を行っており、植物表現型取得法が研究の中心である。
abstractWe developed a new toolkit called Stomatal Comprehensive Automated Neural Network or SCAN that combines the use of high-resolution portable digital microscopy with machine learning to automate stomatal trait phenotyping in canola.
Reproduction assets foundThe paper publicly releases its authors' analysis code, trained model weights, and training image datasets for the SCAN stomatal phenotyping pipeline via two GitHub repositories and two Roboflow datasets, with explicit availability statements in the Data availability section. Raw phenotype measurements are in a journalCode · publicThe full details of the weights, hyperparameters, training scripts, and datasets of the models can be found at https://github.com/William-Yao0993/FD_detection .Open asset ↗William-Yao0993/FD_detectionlines:45-53Dataset · publicThe microscopy images used to train the SCAN model are available as two public datasets: https://app.roboflow.com/danila-lab/fd-project-1 and https://app.roboflow.com/danila-lab/pore-segmentation/ .Open asset ↗danila-lab/fd-project-1lines:233-272Dataset · publicThe microscopy images used to train the SCAN model are available as two public datasets: https://app.roboflow.com/danila-lab/fd-project-1 and https://app.roboflow.com/danila-lab/pore-segmentation/ .Open asset ↗danila-lab/pore-segmentationlines:233-272Plant phenotyping relevance match · UnverifiedCrossref · checked 14 Sept 2026
Estimating stomatal conductance poses significant challenges in plant stress research, since traditional measurement methods interact physically with leaves, thereby altering their position and microclimate. To overcome this problem, we developed a contactless approach that combines 3D modeling, thermal imaging, and a modified energy balance equation to estimate stomatal conductance remotely and accurately. We evaluated this method by comparing model estimated total plant transpiration with gravimetric measurements. The estimates provided by our approach corresponded favorably with measurements across different environmental conditions, including non-stressed and short-term salinity stress scenarios. This method effectively tracks stomatal responses to rapid osmotic stress, offering a reliable tool for remote assessment of plant physiological dynamics.
Why it matches plant phenotyping methods3D画像・熱画像・エネルギー収支モデルを組み合わせ、植物の気孔コンダクタンスを非接触推定する手法の開発と検証が中心である。
abstractwe developed a contactless approach that combines 3D modeling, thermal imaging, and a modified energy balance equation to estimate stomatal conductance remotely and accurately.
Plant phenotyping relevance match · UnverifiedOpenAlex · Europe PMC · checked 6 Sept 2026
Stomata on the leaves of wheat serve as important gateways for gas exchange with the external environment. Their morphological characteristics, such as size and density, are closely related to physiological processes like photosynthesis and transpiration. However, due to the limitations of existing analysis methods, the efficiency of analyzing and mining stomatal phenotypes and their associated genes still requires improvement. To enhance the accuracy and efficiency of stomatal phenotype traits analysis and to uncover the related key genes, this study selected 210 wheat varieties. A novel semantic segmentation model based on transformer for wheat stomata, called Wheat Stoma Former (WSF), was proposed. This model enables fully automated and highly efficient stomatal mask extraction and accurately analyzes phenotypic traits such as the length, width, area, and number of stomata on both the adaxial (Ad) and abaxial (Ab) surfaces of wheat leaves based on the mask images. The model evaluation results indicate that coefficients of determination (R2) between the predicted values and the actual measurements for stomatal length, width, area, and number were 0.88, 0.86, 0.81, and 0.93, respectively, demonstrating the model’s high precision and effectiveness in stomatal phenotypic trait analysis. The phenotypic data were combined with sequencing data from the wheat 660 K SNP chip and subjected to a genome-wide association study (GWAS) to analyze the genetic basis of stomatal traits, including length, width, and number, on both adaxial and abaxial surfaces. A total of 36 SNP peak loci significantly associated with stomatal traits were identified. Through candidate gene identification and functional analysis, two genes—TraesCS2B02G178000 (on chromosome 2B, related to stomatal number on the abaxial surface) and TraesCS6A02G290600 (on chromosome 6A, related to stomatal length on the adaxial surface)—were found to be associated with stomatal traits involved in regulating stomatal movement and closure, respectively. In conclusion, our WSF model demonstrates valuable advances in accurate and efficient stomatal phenotyping for locating genes related to stomatal traits in wheat and provides breeders with accurate phenotypic data for the selection and breeding of water-efficient wheat varieties.
Why it matches plant phenotyping methods小麦気孔の画像から形態・密度を自動抽出するTransformer分割モデルを開発し、実測値との精度検証も行っているため、植物フェノタイピング手法が中心である。
abstractA novel semantic segmentation model based on transformer for wheat stomata, called Wheat Stoma Former (WSF), was proposed.
Stomata play a crucial role in plant immune responses, with their morphological characteristics closely linked to disease resistance. Accurate detection and analysis of stomatal phenotypic parameters are essential for soybean disease resistance research and variety breeding. However, traditional stoma detection methods are challenged by complex backgrounds and leaf vein structures in soybean images. To address these issues, we proposed a Soybean Stoma-YOLO (You Only Look Once) model (SS-YOLO) by incorporating large separable kernel attention (LSKA) in the Spatial Pyramid Pooling-Fast (SPPF) module of YOLOv8 and Deformable Large Kernel Attention (DLKA) in the Neck part. These architectural modifications enhanced YOLOV8's ability to extract multi-scale and irregular stomatal features, thus improving detection accuracy. Experimental results showed that SS-YOLO achieved a detection accuracy of 98.7%. SS-YOLO can effectively extract the stomatal features (e.g., length, width, area, and orientation) and calculate related indices (e.g., density, area ratio, variance, and distribution). Across different soybean rust disease stages, the variety Dandou21 (DD21) exhibited less variation in length, width, area, and orientation compared with Fudou9 (FD9) and Huaixian5 (HX5). Furthermore, DD21 demonstrated greater uniformity in stomatal distribution (SEve: 1.02-1.08) and a stable stomatal area ratio (0.06-0.09). The analysis results indicate that DD21 maintained stable stomatal morphology with rust disease resistance. This study demonstrates that SS-YOLO significantly improved stoma detection and provided valuable insights into the relationship between stomatal characteristics and soybean disease resistance, offering a novel approach for breeding and plant disease resistance research.
Why it matches plant phenotyping methods大豆葉画像から気孔を検出し、形態・分布などの植物形質を抽出するYOLO手法を開発しており、表現型取得が研究の中心である。
abstractwe proposed a Soybean Stoma-YOLO (You Only Look Once) model (SS-YOLO)
Terahertz (THz) time-domain spectroscopy allows the detection of temporal changes of plant water content in vivo and non-destructively, for example over the course of the day or at the onset of drought stress. By studying a wildtype and a genetically modified variant of Arabidopsis thaliana, we observed significant differences in their dehydration dynamics. For a better understanding of the underlying processes, we modelled this behaviour with a simple rate equation model, compared the results with the experimental data and correlated our model with the biological regulatory mechanisms. In particular, under drought stress, we found an almost three times ([Formula: see text]) higher maximal stomatal opening in the mutant than in the wildtype. Over the course of the day, the degree of stomatal opening shows an exponential decrease with a half-life [Formula: see text] of [Formula: see text]2.6 h in the wildtype and [Formula: see text]0.8 h in the mutant.
Why it matches plant phenotyping methodsTHz分光法による植物体内水分量と気孔開閉 dynamics の非破壊・経時的測定が研究の中心であり、水分状態・生理形質を定量化するフェノタイピング手法としてモデル検証も行っている。
abstractTerahertz (THz) time-domain spectroscopy allows the detection of temporal changes of plant water content in vivo and non-destructively
Sustainable agriculture urgently requires innovative, pesticide-free strategies to mitigate herbivory and safeguard food security. Ultraviolet-B (UV-B) irradiation, with tunable intensity and cost-effectiveness, has emerged as a promising non-chemical method to enhance plant resistance, yet its underlying mechanisms remain elusive. Here, using tea plant ( Camellia sinensis ) and its major pest Ectropis obliqua as a model, we developed a multimodal framework that integrates AI-enhanced electronic nose technology for real-time volatile profiling with in situ hyperspectral stimulated Raman scattering (SRS) microscopy to characterize defense responses under precisely controlled UV-B treatments. This approach identified herbivore-induced volatiles—hexanal, (Z)-3-hexenol, octanal, and (Z)-3-hexenyl acetate—optimally induced at 1.2 kJ·m -2 UV-B and linked to insect deterrence. SRS imaging further revealed elevated jasmonic acid derivatives and L-phenylalanine, coupled with reduced protein levels and altered stomatal dynamics, all correlating with enhanced resistance. Transcriptomic and molecular analyses confirmed transcriptional regulation of these pathways. By bridging volatile detection, metabolic imaging, and molecular validation, this study pioneers a multimodal strategy that provides mechanistic insights into UV-B–mediated plant defense and highlights the potential of multimodal methodologies as powerful tools for developing sustainable, pesticide-free pest management solutions in precision agriculture.
Why it matches plant phenotyping methodsAI強化電子鼻とSRS顕微鏡を統合した植物防御応答の取得・解析フレームワークが研究の中心であり、揮発性物質、代謝、気孔動態などの植物状態を測定しているため。
abstractwe developed a multimodal framework that integrates AI-enhanced electronic nose technology for real-time volatile profiling with in situ hyperspectral stimulated Raman scattering (SRS) microscopy to characterize defense responses
Summary Thermal imaging is a key plant phenotyping and monitoring technique but faces major bottlenecks in accurately and efficiently inferring stomatal conductance (gsw) from leaf temperature. The conductance index (Ig) was previously proposed to estimate gsw from thermography by linking temperature differences between real and artificial leaves (ALs) based on the leaf energy balance. However, Ig is highly sensitive to environmental fluctuations, hampering interpretation and reducing reproducibility. We developed a simple and novel correction factor (named DynG) for Ig that accounts for environmental fluctuations when scaling Ig to gsw. This was achieved by capturing temperature variations in a set of ALs with a range of known constant pore conductances. This approach provided the Ig–conductance relationship, using ALs as a reference, to infer gsw of real leaves from their measured Ig. In fluctuating environments, gsw estimated using DynG showed greater accuracy and stability than gsw calculated from Ig alone, and was in good agreement with gsw determined using lysimetric and gas exchange methods. DynG's power was further showcased in distinguishing gsw of Arabidopsis genotypes differing in stomatal traits (Col‐0, epf1epf2, and EPF2OE). We conclude that Ig corrected with DynG can reliably estimate gsw in fluctuating environments without complex modeling, opening new avenues for gsw phenotyping and monitoring.
Why it matches plant phenotyping methods熱画像から気孔コンダクタンスを推定する補正係数を開発し、変動環境下で既存法と比較検証した、植物フェノタイピング手法の中心的研究である。
abstractWe developed a simple and novel correction factor (named DynG) for Ig that accounts for environmental fluctuations when scaling Ig to gsw.
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 · publicRelated codes are available on GitHub ( https://github.com/jiayu0903/dynamic‐conductance‐index.git ).Open asset ↗https://github.com/jiayu0903/dynamic‐conductance‐index.gitlines:805-819Plant phenotyping relevance match · UnverifiedEurope PMC · checked 6 Sept 2026
Stomata, the gatekeepers of leaf gas exchange, regulate carbon dioxide uptake and water loss, functions increasingly critical as crops face more frequent, intense heat and drought. Under dry conditions, stomatal conductance ( g s ) typically decreases, limiting carbon assimilation and yield. Heat stress, in contrast, elicits variable g S responses: sometimes increasing to facilitate transpirational cooling, while at other times decreasing, especially when combined with drought. Heat and drought also induce complex, context-dependent shifts in stomatal anatomy. Smaller, denser stomata improve drought resilience in some cases, while reduced density confers greater tolerance in others. The optimal stomatal ideotype remains unknown, and different or even opposing traits may confer resilience dependent on the environmental scenario. Substantial genotypic variation in g s and stomatal anatomy, high heritability and co-localized quantitative trait loci for stomatal traits and yield highlight their untapped potential as breeding targets for climate-resilient crops. However, stomatal traits remain largely absent from breeding pipelines due to challenges of phenotyping at scale. This is changing rapidly. Advances in deep learning, porometry, digital microscopy, and remote sensing now enable high-throughput measurement of stomatal physiology and anatomy. Next-generation breeding technologies including clustered regularly interspaced short palindromic repeats (CRISPR), multi-omics approaches, and artificial intelligence-driven ideotype selection models could revolutionize breeding, allowing precise engineering of stomatal traits for resilience to environmental stress. The time has come to move beyond characterizing stomatal traits and start actively incorporating them into breeding strategies. By leveraging these technologies, stomatal traits can become high value targets, unlocking their potential to enhance crop performance in a hotter, drier future.
Why it matches plant phenotyping methods気孔の生理・解剖形質を大規模に取得する深層学習、ポロメトリー、デジタル顕微鏡、リモートセンシング技術を扱うレビューであり、植物フェノタイピング手法が実質的な主題に含まれる。
abstractAdvances in deep learning, porometry, digital microscopy, and remote sensing now enable high-throughput measurement of stomatal physiology and anatomy.
Fruit shape and stomatal distribution influence fruit water status and postharvest quality in southern highbush blueberry ( Vaccinium corymbosum hybrids). This study developed scalable phenotyping methods to quantify fruit morphology and stomatal traits in cultivars Colossus and Optimus across four developmental stages. Fruit volume was estimated using geometric models validated against three-dimensional (3D) scans, with the spheroid model offering the best compromise between accuracy and efficiency ( R 2 = 0.96). StoManager1 software was validated for automated stomatal phenotyping, showing strong concordance with manual counts ( R 2 = 0.96). Results revealed cultivar-specific differences in shape development and stomatal distribution. As fruits matured, both cultivars exhibited increases in volume and surface area with decreasing sphericity. Stomata were localized primarily to distal regions of the fruit, particularly the calyx, suggesting heterogeneous water loss pathways across the fruit surface. These findings establish a framework for integrating morphometric and anatomical traits into high-throughput phenotyping pipelines and future studies on fruit water relations.
Why it matches plant phenotyping methods果実形態と気孔形質を定量化するスケーラブルな表現型解析法を開発し、3Dスキャンおよび手動計数で検証しており、方法開発・検証が研究の中心である。
abstractThis study developed scalable phenotyping methods to quantify fruit morphology and stomatal traits
Implementing advanced approaches such as marker-assisted selection into classic breeding programs is critical for increasing genetic gain and meeting the population’s ever-growing food demand. Genome-wide association studies (GWAS) is a well-known method for detecting genetic markers related to various morphological and physiological traits. However, the ability to collect phenotypic data in large panels often limits the feasibility of genetic studies. This study aimed to assess the potential of UAV-borne thermal and hyperspectral imaging for estimating key wheat traits and identifying their genetic architecture. A diversity panel (300 genotypes) was characterized under well-watered and terminal-drought conditions in a rainout shelter facility. Stomatal conductance, leaf area index, and total chlorophyll content were estimated across two growing seasons. A support vector machine model that integrates canopy spectral reflectance and temperature emittance from UAV-borne imagery reduced the root mean square error of stomatal conductance estimation by 28% compared to using canopy reflectance alone. The models were further used to estimate the traits in the entire panel and to detect genomic markers associated with them and their dynamics throughout the season. Altogether, 16 genetic markers associated with alleles conferring these traits were detected, and the most promising markers were validated during an additional growing season. In the validation experiment, both the spectral estimation models and the allelic effect of the markers were consistent with the previous season. This study introduces, for the first time, the use of stomatal conductance estimation based on combining UAV hyperspectral and thermal imagery for genomic mapping. Implementing this integrated approach could promote the development of new climate-resilience wheat varieties to ensure food security worldwide by screening for stomatal conductance, which is not practical with manual measurements.
Why it matches plant phenotyping methodsUAV熱・ハイパースペクトル画像を統合し、気孔コンダクタンス等の植物形質を推定するモデルを開発・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。
abstractThis study aimed to assess the potential of UAV-borne thermal and hyperspectral imaging for estimating key wheat traits and identifying their genetic architecture.
Stomatal morphology plays a critical role in regulating plant gas exchange influencing water use efficiency and ecological adaptability. While traditional methods for analyzing stomatal traits rely on labor-intensive manual measurements, machine learning (ML) tools offer a promising alternative. In this study, we evaluate the suitability of a U-Net-based interactive ML software with corrective annotation for stomatal morphology phenotyping. The approach enables non-ML experts to efficiently segment stomatal structures across diverse datasets, including images from different plant species, magnifications, and imprint methods. We trained a single model based on images from five datasets and tested its performance on unseen data, achieving high accuracy for stomatal density (R 2 = 0.98) and size (R 2 = 0.90). Thresholding approaches applied to the U-Net segmentations further improved accuracy, particularly for density measurements. Despite significant variability between datasets, our findings demonstrate the feasibility of training a single segmentation model to analyze diverse stomatal data sets. Validation approaches showed that a semi-automatic approach involving correcting segmentations was five times faster than manual annotation while maintaining comparable accuracy. Our results also illustrate that ML metrics, such as the F1 score, correlate with accuracy in the statistical analysis of trait measurements with improvements diminishing after 2:30 h model training. The final model achieved high precision, allowing the detection of highly significant biological differences in stomatal morphology within plant, between genotypes and across growing environments. This study highlights interactive ML with corrective annotation as a robust and accessible tool for accelerating phenotyping in plant sciences, reducing technical barriers and promoting high-throughput analysis.
Why it matches plant phenotyping methods気孔形態を対象としたU-Net分割と対話的補正による表現型取得手法を開発・検証しており、精度、速度、汎用性を評価しているため。
abstractwe evaluate the suitability of a U-Net-based interactive ML software with corrective annotation for stomatal morphology phenotyping.
Reproduction assets foundThe paper's stomata image datasets (Datasets 1 and 3), validation and training sets, and all intermediate CNN segmentation models are explicitly deposited publicly on Zenodo (DOI 10.5281/zenodo.15316123). The Colab notebook link is for the generic RootPainter tool, not a paper-specific asset.Dataset · publicComplete Dataset 1 and Dataset 3, the validation set and the training dataset, and all intermediate CNN models are publicly available ( https://doi.org/ https://doi.org/10.5281/zenodo.15316123 ).Open asset ↗Zenodo · 10.5281/zenodo.15316123lines:235-281Plant phenotyping relevance match · UnverifiedEurope PMC · checked 6 Sept 2026
Field / plotStomata / guard-cell complexMorphology / geometry measurementStomatal traits
Stomata are ancient anatomical structures on leaves that regulate the exchange of water vapor, oxygen, and carbon dioxide between plants and the atmosphere. Acting as valve-like gateways between internal tissues and the external environment, stomata may function as locally interacting networks. Theoretical and experimental evidence suggests that local interactions among neighboring stomata influence their function and spatial arrangement. From this perspective, analyzing stomatal distributions as networks may yield novel insights into observed spatial patterns and their generative mechanisms. We hypothesize that variability in stomatal arrangements arises from shared underlying rules, with observed diversity reflecting an epiphenomenon. To test this, we employed a multi-species, multi-site common garden approach to assess potential convergences in stomatal distribution. A network-based framework enabled us to reduce individual-level variability and analyze stomatal patterns as interacting systems. Our results show that, across species and environments, stomatal spatial configurations consistently align with a null model linking minimum spanning tree (MST) length to stomatal density. Although a variety of patterns were present, over-dispersed arrangements predominated. These findings suggest that physical constraints during stomatal development impose strong limits on the range of viable spatial configurations that can evolve.
Why it matches plant phenotyping methods気孔分布という植物形態形質をネットワーク解析で定量化し、種・環境間で比較する手法の適用が研究の中心である。
abstractA network-based framework enabled us to reduce individual-level variability and analyze stomatal patterns as interacting systems.
L.), a vital global food crop, relies on its stomatal structure for regulating photosynthesis and responding to drought. Conventional manual stomatal detection methods are inefficient, subjective, and inadequate for high-throughput plant phenotyping research. To address this, we curated a dataset of over 1500 maize leaf epidermal stomata images and developed a novel lightweight detection model, StomaYOLO, tailored for small stomatal targets and subtle features in microscopic images. Leveraging the YOLOv11 framework, StomaYOLO integrates the Small Object Detection layer P2, the dynamic convolution module, and exploits large-scale epidermal cell features to enhance stomatal recognition through auxiliary training. Our model achieved a remarkable 91.8% mean average precision (mAP) and 98.5% precision, surpassing numerous mainstream detection models while maintaining computational efficiency. Ablation and comparative analyses demonstrated that the Small Object Detection layer, dynamic convolutional module, multi-task training, and knowledge distillation strategies substantially enhanced detection performance. Integrating all four strategies yielded a nearly 9% mAP improvement over the baseline model, with computational complexity under 8.4 GFLOPS. Our findings underscore the superior detection capabilities of StomaYOLO compared to existing methods, offering a cost-effective solution that is suitable for practical implementation. This study presents a valuable tool for maize stomatal phenotyping, supporting crop breeding and smart agriculture advancements.
Why it matches plant phenotyping methodsトウモロコシの気孔を画像から検出するモデルとデータセットを開発・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。
abstractwe curated a dataset of over 1500 maize leaf epidermal stomata images and developed a novel lightweight detection model, StomaYOLO
Reproduction assets foundThe paper's analysis code (StomaYOLO detector) is openly available on GitHub with an authors' URL; the phenotype image dataset itself is only available on request from the corresponding author.Code · publicThe code that support the findings of this study are openly available in GitHub at https://github.com/yangziqi2003/StomaYOLO (accessed on 5 May 2025).Open asset ↗https://github.com/yangziqi2003/StomaYOLO · StomaYOLOlines:426-440Plant phenotyping relevance match · UnverifiedEurope PMC · checked 14 Sept 2026
Monitoring sucrose transport in plants is essential for understanding plant physiology and improving agricultural practices, yet effective sensors for continuous and real-time in-vivo monitoring are lacking. In this study, we developed a plant-insertable sucrose sensor capable of real-time sucrose concentration monitoring and demonstrated its application as a useful tool for plant research by monitoring the sugar-translocating path from leaves to the lower portion of plants through the stem in living plants. The biosensor consists of a bilirubin oxidase-based biocathode and a needle-type bioanode integrating glucose oxidase, invertase, and mutarotase, with the two electrodes separated by an agarose gel for ionic connection. The sensor exhibits a sensitivity of 6.22 μA mM -1 cm -2 , a limit of detection of 100 μM, a detection range up to 60 mM, and a response time of 90 s at 100 μM sucrose. Additionally, the sensor retained 86 % of its initial signal after 72 h of continuous measurement. Day-night monitoring from the biosensor inserted in strawberry guava (Psidium cattleianum) showed higher sucrose transport activity at night, following well the redistribution of photosynthetically produced sugars. In addition, by monitoring the forced translocation of sucrose dissolved in the stable isotopically labeled water, we demonstrated that a young seedling of Japanese cedar known as Sugi (Cryptomeria japonica) can absorb and transport both water and sucrose through light-dependently opened stomata, which is the recently revealed path for liquid uptake by higher plants. These findings highlight the potential of our sensor for studying dynamic plant processes and its applicability in real-time monitoring of sugar transport under diverse environmental conditions.
Why it matches plant phenotyping methods植物体内のスクロース濃度・輸送をリアルタイム測定する挿入型バイオセンサーを開発し、性能評価と生体植物での実証を行った研究であり、植物生理状態の取得方法が中心です。
abstractwe developed a plant-insertable sucrose sensor capable of real-time sucrose concentration monitoring
With a growing world population and mounting pressure on natural resources, the need for efficient, sustainable food production is becoming increasingly urgent. A promising route towards improving agricultural productivity is to expand the use of sensors to monitor plants and their environment with high accuracy and temporal resolution. Data generated by such sensors is useful for optimizing irrigation, nutrition and illumination in the context of autonomous greenhouses, while allowing mitigation of plant stress due to pests, diseases and extreme climate conditions. We simultaneously tested ten different types of sensors for monitoring early signs of drought stress in mature, high-wire tomato plants grown in rockwool. Sensors ranged from high-density climate sensors to novel sensors for monitoring plant-specific parameters like acoustic emissions, stomatal dynamics, sap flow and stem diameter. Withholding water for two days led to a quick and complete depletion of water in the rockwool slabs, and strongly affected whole-plant transpiration, resulting in strong changes in: acoustic emissions, stomatal pore area, stomatal conductance, and stem diameter, all of which were found to be significant indicators of early drought stress. This work demonstrates the usefulness of these sensors in a greenhouse environment and provides a comparison between measured parameters in magnitude and onset time, aimed at the early detection of drought stress. Our study aims to facilitate sensor selection and implementation in precision agriculture.
Why it matches plant phenotyping methods複数の植物センサーを比較し、トマトの早期乾燥ストレス指標を測定・評価することが研究の中心であり、センサー選択と実装に再利用可能な技術的知見を提供している。
abstractWe simultaneously tested ten different types of sensors for monitoring early signs of drought stress in mature, high-wire tomato plants grown in rockwool.
ABSTRACT Stomatal anatomy and physiology define CO 2 availability for photosynthesis and regulate plant water use. Despite being key drivers of yield and dynamic responsiveness to abiotic stresses, conventional measurement techniques of stomatal traits are laborious and slow, limiting adoption in plant breeding. Advances in instrumentation and data analyses present an opportunity to screen stomatal traits at scales relevant to plant breeding. We present a high‐throughput robust field‐based phenotyping approach, FieldDino, for screening stomatal physiology and anatomy. The method allows measurements to be collected in p @0.5 of 97.1% for stomatal detection. When validated in large field trials of 200 wheat genotypes under two irrigation treatments, FieldDino captured wide diversity in stomatal traits. FieldDino enables stomatal data collection and analysis at unprecedented scales in the field. This will advance research on stomatal biology and accelerate the incorporation of stomatal traits into plant breeding programs for resilience to abiotic stress.
Why it matches plant phenotyping methodsFieldDinoは、圃場で気孔の生理・解剖形質を高スループットに取得するフェノタイピング手法として開発され、大規模圃場試験で検証されているため含める。
abstractWe present a high‐throughput robust field‐based phenotyping approach, FieldDino, for screening stomatal physiology and anatomy.
Reproduction assets foundThe paper's Data Availability Statement explicitly points to a public GitHub repository containing the 3D-printed leaf clip STL files, the Python stomatal annotation/measurement script, and the FieldDino app, plus a public Roboflow dataset hosting the training/validation stomatal image set used to train the YOLOv8-M模型.Code · publicrse.roboflow.com/narrabri-plant-physiology-hclvi/fielddino-training-set-200x . As outlined, all files for the 3D printed leaf clip, the Python script for stomatal annotation and the files and instructions for installing and using the FieldDino App are provided in a public GitHub repository which guides users through each step – https://github.com/williamtsalter/FieldDinoMicroscopy . Validation datasets for the method are available on Roboflow – https://universe.roboflow.com/narrabri-plant-physiology-hclvi/fielddino-training-set-200x .
References
Baloch , M. J.
,
J.
Dunwell
,
K.
DrN
, et al. 2013 . “
Morpho‐Physiological Characterization of Spring Wheat Genotypes Under Drought Stress
.” InterOpen asset ↗williamtsalter/FieldDinoMicroscopylines:337-498Dataset · publicResearch Infrastructure Strategy (NCRIS). Open access publishing facilitated by The University of Sydney, as part of the Wiley ‐ The University of Sydney agreement via the Council of Australian University Librarians.
Data Availability Statement
The data that support the findings of this study are openly available in Roboflow at https://universe.roboflow.com/narrabri-plant-physiology-hclvi/fielddino-training-set-200x . As outlined, all files for the 3D printed leaf clip, the Python script for stomatal annotation and the files and instructions for installing and using the FieldDino App are provided in a public GitHub repository which guides users through each step – https://github.com/williamtOpen asset ↗narrabri-plant-physiology-hclvi/fielddino-training-set-200xlines:337-498Plant phenotyping relevance match · UnverifiedOpenAlex · Europe PMC · bioRxiv · checked 15 Sept 2026
Abstract Biological processes involve complex hierarchies where composite traits result from multiple component traits. However, holistically understanding of how sets of component traits interact to underpin genotype-to-phenotype relationships is generally lacking. Stomatal density (SD) is a tractable model system for exploring how high-throughput phenotyping (HTP) data could be exploited by a new spatial analysis approach to better understand a developmentally and functionally important trait. SD is a composite trait, resulting from various components related to cell identity and size, which are themselves governed by a series of spatio-developmental processes. Data from 180 recombinant inbred lines of maize ( Zea mays (L.)) were analyzed by a new Stomatal Patterning Phenotype (SPP) to: (1) describe the average spatial probability distribution of the nearest neighboring stomata; (2) derive a core set of component traits related to cell size, cell packing and positional probabilities; (3) build a structural equation model of component traits underlying SD; and (4) identify stomatal patterning quantitative trait loci (QTL). The core set of SPP-derived traits explained 74% of the variation in SD. Analyzing SPP component traits allowed some loci previously identified as generic SD QTL to be recognized as specific to lateral versus longitudinal elements of stomatal patterning. Therefore, this study highlights how novel insights can be gained by decomposing a composite trait (e.g. SD) into a set of component traits that were present in HTP data but not previously exploited.
Why it matches plant phenotyping methods新しい空間解析手法（SPP）により、気孔密度を細胞サイズ・配置・位置確率などの表現型構成要素へ分解し、HTPデータから形質を抽出・解析しているため、植物フェノタイピング手法が研究の中心である。
abstracthigh-throughput phenotyping (HTP) data could be exploited by a new spatial analysis approach
Abstract Botrytis cinerea is a filamentous fungus that infects over 200 species of crops causing grey mold disease with devastating losses to agriculture worldwide. The heavy reliance on synthetic fungicides in the strawberry industry has led to the emergence of fungicide resistance in B. cinerea . Therefore, understanding the fundamental biology of B. cinerea is the first step in the search for novel antifungals. Although B. cinerea is one of the most serious pathogens of strawberry ( Fragaria x ananassa ), few protocols have been specifically developed to study this pathosystem. Consequently, early development of pathogen penetration in strawberry is poorly understood. Here we developed assays using detached strawberry leaves, fruit and petals to study B. cinerea infection. These assays allow comparison of treatment effect on the same fruit, and facilitate the screening of fungicides or biocontrol agents. Through real-time PCR, chlorophyll fluorescence analysis, scanning electron and confocal microscopy, we quantified the lesion and fungal biomass of B. cinerea in the early stages of infection in fruit and petals, and demonstrated that B. cinerea penetrates through stomata of strawberry achenes, revealing a previously unrecognized infection route in this host. These data provide a deeper understanding of the B. cinerea -strawberry interaction and will serve as a foundation for future studies seeking novel antifungal treatments against B. cinerea .
Why it matches plant phenotyping methodsイチゴの病徴（病斑）と感染状態を定量するアッセイを開発し、顕微鏡・蛍光解析などによる表現型取得が研究の中心である。
abstractHere we developed assays using detached strawberry leaves, fruit and petals to study B. cinerea infection.
Carbon dots (CDs) have emerged as promising nanomaterials for bioimaging and stress monitoring due to their unique optical and functional properties. CDs were synthesized using citric acid and o -phenylenediamine via microwave-assisted heating, named as CP-CDs. High-resolution transmission electron microscopy observed an average particle size of 3.65 ± 0.40 nm with graphitic cores. Raman spectroscopy and Fourier transform infrared spectroscopy confirmed diverse functional groups. The CDs exhibited excitation-dependent fluorescence with a peak emission at 432 nm, a high quantum yield of 54.91%, and a fluorescence lifetime of 9.50 ± 0.15 ns, making them highly suitable for bioimaging. Confocal microscopy demonstrated tissue-specific localization in lettuce plant cells. In stem cells, CP-CDs predominantly targeted mitochondria, confirmed by a colocalization with Mito-Tracker Red. In contrast, leaf cells showed selective accumulation at the stomatal openings. Under salt- and heat-induced stress, stem cells exhibited an increase in mitochondrial fluorescence, indicating stress-responsive interactions, whereas leaf cells maintained consistent stomatal localization. Further, enhanced fluorescence from chloroplasts under stress conditions suggested synergistic effects with chlorophyll. Also, stress conditions caused CP-CDs to accumulate at the cell boundaries in stem cells, highlighting their sensitivity to stress-induced changes. These findings demonstrate the optical properties, tissue-specific uptake, and organelle-level localization of CP-CDs, underlining their potential for bioimaging, stress detection, and targeted delivery systems in plants.
Why it matches plant phenotyping methods植物細胞のストレス応答を蛍光ナノプローブと共焦点イメージングで検出する手法の開発・実証が中心であり、単なる生物学的測定ではない。
titleExploring Carbon Dot as a Fluorescent Nanoprobe for Imaging of Plant Cells under Salt/Heat-Induced Stress Conditions.
Effective water management is crucial for ensuring the healthy growth and high yield of crops, and it relies on accurate monitoring of plant water status. As a core indicator of plant gas exchange capacity, stomatal conductance (Gs) directly determines the efficiency of photosynthesis and transpiration, significantly impacting crop growth and yield formation. Therefore, timely and accurate prediction of stomatal conductance is essential for optimizing water management strategies and improving crop yield and quality. However, stomatal conductance is influenced by a variety of environmental factors and plant physiological traits, making its variability complex and dynamic. These challenges result in difficulties in selecting key features, insufficient prediction accuracy, and a lack of transparency in model decision-making processes. To address these issues, this study proposes a novel approach that combines a random forest (RF) feature selection method with a Tree-structured Parzen Estimator (TPE)-optimized light gradient boosting machine (LightGBM) model (TPE-LightGBM). This approach leverages UAV-based hyperspectral, thermal infrared imagery, and meteorological data to improve predictive performance. Additionally, SHAP (SHapley Additive exPlanations) analysis is incorporated to offer insights into the model’s decision-making process by revealing feature dependencies. In the feature selection process, we compared four common methods, including mutual information (MI), successive projection algorithm (SPA), recursive feature elimination (RFE), and least absolute shrinkage and selection operator (LASSO) to ensure the significance and effectiveness of the selected features. To comprehensively evaluate model performance, we also compared five predictive models: ridge regression (RR), partial least squares regression (PLSR), random forest regression (RFR), random search-optimized LightGBM (Random-LightGBM), and grid search-optimized LightGBM (Grid-LightGBM). The experimental results revealed that the combination of RF and TPE-optimized LightGBM significantly outperformed all other models, achieving the highest prediction accuracy. The optimal number of features was determined to be N = 15, with a coefficient of determination (R²) of 0.862, a root mean square error (RMSE) of 0.037, and a mean absolute error (MAE) of 0.029. Through SHAP analysis, the study not only identifies key influencing factors such as photosynthetically active radiation (PAR), canopy temperature (CT), and red-edge spectral bands, but also sheds light on how these factors interact with each other to influence stomatal conductance. The proposed model provides an innovative approach to effectively predicting stomatal conductance, enabling agricultural managers to better understand and regulate chili pepper’s water status, thereby promoting healthy chili pepper growth and efficient resource management.
Why it matches plant phenotyping methodsUAVのハイパースペクトル・熱赤外画像からチリ pepper の気孔コンダクタンスという生理形質を推定するモデルを開発・比較・解釈しており、表現型取得・推定手法が研究の中心です。
abstractThis approach leverages UAV-based hyperspectral, thermal infrared imagery, and meteorological data to improve predictive performance.
Lettuce, a vegetable rich in nutritional and medicinal value, is commonly analyzed as a modern industrial crop through macroscopic factors such as temperature, humidity, and light, but its microscopic characteristics are still underexplored. At the microscopic scale, stomatal characteristics are the most indicative of lettuce growth status and serve as crucial pathways for plant gas exchange and carbon-water cycle regulation. Therefore, stomatal research is an important area in crop breeding and stress analysis, and stomatal feature detection is a key step in this field. Current traditional methods for stomatal feature measurement are inefficient, imprecise, and labor-intensive. This study proposes a method for stomatal feature extraction of lettuce leaves based on an improved U-net network to improve measurement efficiency and accuracy. To this end, a dual symmetric path structure was designed, incorporating two independent decoding paths to separately extract global contextual information and local detail features, effectively integrating multi-scale information during the decoding phase via feature concatenation and convolutional fusion modules. To mitigate edge information loss caused by repeated down-sampling in the U-Net network, a hybrid dilated convolution module was incorporated into the encoding phase, with overlapping pooling replacing standard pooling to enhance the network's precision in recognizing small objects. Furthermore, the network incorporates a CBAM attention mechanism module to strengthen its capacity for extracting effective features. To optimize network performance, the NAdam optimization function was employed to speed up convergence and minimize computational resource consumption. The MFe (Measurement Feature) visualization and interaction system developed using OpenCV enables precise measurement of stomatal major and minor axes, area, and density for lettuce leaves. Experimental results indicate that the improved U-Net network achieved enhancements of 3.31 %, 6.55 %, and 4.08 % in IoU, PA, and MPA metrics, respectively. This confirms the effectiveness of the network modifications, offering a valuable reference for microscopic studies of plant stomata.
Why it matches plant phenotyping methodsレタス葉の気孔を画像から自動抽出し、面積・軸長・密度を測定する改良U-Net手法と測定システムを開発・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ある。
abstractThis study proposes a method for stomatal feature extraction of lettuce leaves based on an improved U-net network to improve measurement efficiency and accuracy.
Cacao is a globally significant crop, but its production is severely threatened by diseases, particularly Black Pod Rot (BPR) caused by Phytophthora spp. Understanding plant-pathogen interactions, especially stomatal responses, is crucial for disease management. Machine learning offers a powerful, yet largely untapped, approach to analyze and interpret complex plant responses in plant biology and pathology, particularly in the context of plant-pathogen interactions. This study explores the use of machine learning to analyze and interpret complex stomatal responses in cacao leaves during pathogen interactions. We investigated the impact of the black pod rot pathogen (Phytophthora megakarya) and a non-pathogenic fungus (Rhizoctonia solani) on stomatal aperture in two cacao genotypes (SCA6 and Pound7) under varying light conditions. Image analysis revealed diverse stomatal responses, including no change, opening, and closure, that were influenced by the interplay of genotype, pathogen isolate, and light conditions. Notably, SCA6 exhibited stomatal opening in response to P. megakarya specifically under a 12-hour light/dark cycle, suggesting a light-dependent activation of pathogen virulence factors. In contrast, Pound7 displayed stomatal closure in response to both P. megakarya and R. solani, indicating the potential recognition of conserved Pathogen-Associated Molecular Patterns (PAMPs) and a broader defense response. To further analyze these interactions, we employed machine learning techniques to predict stomatal area size. Our analysis identified key morphological features, with size-related traits being the strongest predictors. Shape-related traits also played a significant role when size-related traits were excluded from the prediction. This study demonstrates the power of combining image analysis and machine learning for discerning subtle, multivariate traits in stomatal dynamics during plant-pathogen interactions, paving the way for future applications in high-throughput disease phenotyping and the development of resistant crop varieties.
Why it matches plant phenotyping methodsカカオ葉の気孔開度を画像解析で測定し、機械学習で気孔面積を予測する手法が研究の中心であり、病原体応答の表現型解析および将来の高スループット病害フェノタイピングへの応用を実証している。
abstractThis study explores the use of machine learning to analyze and interpret complex stomatal responses in cacao leaves during pathogen interactions.
High lipid producing (HLP) tobacco (Nicotiana tabacum) is a potential biofuel crop that produces an excess of 30% dry weight as lipid bodies in the form of triacylglycerol. While using HLP tobacco as a sustainable fuel source is promising, it has not yet been tested for its tolerance to warmer environments that are expected in the near future as a result of climate change. We found that HLP tobacco had reduced stomatal conductance, which results in increased leaf temperatures up to 1.5°C higher under control and high temperature (38°C day/28°C night) conditions, reduced transpiration, and reduced CO 2 assimilation. We hypothesize this reduction in stomatal conductance is due to the presence of excessive, large lipid droplets in HLP guard cells imaged using confocal microscopy. High temperatures also significantly reduced total fatty acid levels by 55% in HLP plants; thus, additional engineering may be needed to maintain high titers of leaf oil under future climate conditions. High-throughput image analysis techniques using open-source image analysis platform PlantCV for thermal image analysis (plant temperature), stomata microscopy image analysis (stomatal conductance), and fluorescence image analysis (photosynthetic efficiency) were developed and applied in this study. A corresponding set of PlantCV tutorials are provided to enable similar studies focused on phenotyping future crops under adverse conditions.
Why it matches plant phenotyping methodsPlantCVを用いた熱画像・気孔顕微鏡画像・蛍光画像の高スループット解析手法を開発・適用し、植物温度、気孔関連指標、光合成効率を推定しているため、表現型取得手法が中心的です。
abstractHigh-throughput image analysis techniques using open-source image analysis platform PlantCV for thermal image analysis (plant temperature), stomata microscopy image analysis (stomatal conductance), and fluorescence image analysis (photosynthetic efficiency) were developed and applied in this study.
Reproduction assets foundThe paper's raw phenotyping image data (thermal, fluorescence, stomata, confocal microscopy) are deposited on Zenodo, and the authors' PlantCV analysis workflows and R scripts are on GitHub, including three PlantCV tutorials for thermal, stomata, and photosynthesis analysis.Dataset · publicaxial side of the leaf rather than a cross section. While small lipid droplets were present in the WT stomatal guard cells and epidermis, large lipid droplets were present in the HLP guard cells under both control and after 7 days of treatment (representative control images in Figure 8A–D , complete dataset available on Zenodo, https://zenodo.org/records/10711864 ). In addition, while HLP oil appeared to form spherical droplets, it did not “line” the stomatal opening as in WT (Figure 8C,D ).
Figure 8
High lipid producing (HLP) had excessive oil droplets in stomatal guard cells.
Representative confocal microscopy images, shown as focused Z‐stack, of tobacco leaf tissue fixed in paraformaOpen asset ↗Zenodolines:115-123Code · publicmated marginal means (LSMEANS) to determine which sample types were significantly different from others. Means are reported in text with standard error. Plots were made using ggplot2 package (v.3.5.0) in R. Jupyter notebooks associated with PlantCV analyses and R scripts associated with this manuscript are available on Github ( https://github.com/danforthcenter/tobacco‐heat‐paper ).
AUTHOR CONTRIBUTIONS
DKA, MAG, PDB, BSJ and KMM designed experiments. KMM and BSJ performed experiments and data analysis. KJC designed and aided KMM in confocal and brightfield microscopy experiments and advised TEM experiments. JW performed TEM experiments, and KG‐O and SK performed data analysis of TEM images.Open asset ↗GitHublines:171-182Code · publictification was used to isolate only individual plants in each mask. Then, the mask was applied to the registered thermal image to calculate the average plant temperature, as well as a histogram of pixel temperatures for each plant. A PlantCV workflow was used to analyze the images in parallel. A tutorial is available on GitHub: https://github.com/danforthcenter/plantcv‐tutorial‐thermal?tab=readme‐ov‐file (Acosta‐Gamboa et al., 2024 ). Scripts for this project are available at https://github.com/danforthcenter/tobacco‐heat‐paper . Raw image data are available on Zenodo, https://zenodo.org/records/10711864 .
Stomatal aperture measurements
To measure stomatal number and aperture, leaf impressioOpen asset ↗GitHublines:142-146Code · publicpackage was then used to calculate the number of stomata and the area of the aperture. A limitation of this method is that it does not provide the width and length of stomata, or measurements of the guard cells themselves; instead, it provides the aperture area (a result of length and width). A tutorial is available on GitHub: https://github.com/danforthcenter/plantcv‐stomata‐tutorial‐pcv4 (Murphy, 2024 ). Scripts for this project are available at https://github.com/danforthcenter/tobacco‐heat‐paper . Raw image data are available on Zenodo, https://zenodo.org/records/10711864 .
Photosynthesis and gas exchangeOpen asset ↗GitHublines:142-146Code · publicPlantCV (Gehan et al., 2017 ) using the photosynthesis package; the chlorophyll fluorescence image was used to mask the image for only plant pixels, and average F
v / F
m , F q ′ / F m ′ , NPQ, chlorophyll index, and anthocyanin index were calculated as an average per plant at each timepoint. A tutorial is available on GitHub: https://github.com/danforthcenter/plantcv‐tutorial‐photosynthesis?tab=readme‐ov‐file (Schuhl et al., 2024 ). Scripts for this project are available at https://github.com/danforthcenter/tobacco‐heat‐paper . Raw image data are available on Zenodo, https://zenodo.org/records/10711864 .
Microscopy imaging of lipids
Leaf samples analyzed for lipid content were taken from thOpen asset ↗GitHublines:156-164Plant phenotyping relevance match · UnverifiedEurope PMC · checked 6 Sept 2026
Main conclusion Incompatible chestnut grafts exhibited a notably reduced stomatal conductance, mirroring the trend observed for leaf chlorophyll content. Woody tissues at the graft interface of these combinations showed a significantly higher total phenolic content, especially in the internal layers. In recent years, significant efforts have been made to study the mechanisms of graft incompatibility in horticultural species, though research on minor species like chestnut remains limited. This study investigated the physiological and chemical dynamics in various chestnut grafts, aiming to develop a method for the early detection of graft incompatibility. The total phenolic content (TPC) and specific phenolic markers were analyzed at two phenological stages, callusing (CAL) and end of the vegetative cycle (EVC), using spectrophotometric and chromatographic techniques. These analyses were performed on three sections comprising the graft. Stomatal conductance (G sw ) and leaf chlorophyll content were assessed during the growing season as support tools, being non-destructive useful indicators of plant water status. Significant differences in the physiological traits among compatible and incompatible grafting combinations were evident and remained stable throughout the season. Compatible combinations consistently displayed greater leaf chlorophyll content and higher stomatal conductance, highlighting their superior physiological performance. TPC increased significantly from the CAL to EVC stage across all experimental grafting combinations and in all three analyzed sections. Greater phenol accumulation was observed at the graft union of incompatible combinations, particularly in the inner woody tissues. The phytochemical fingerprint revealed castalagin as the dominant compound, with significant increases in benzoic acids, catechins, and tannins during the growing season. However, the role of gallic acid and catechin as markers of graft incompatibility remains uncertain. The multidisciplinary approach provided valuable insights into the issue of graft incompatibility.
Why it matches plant phenotyping methods胸枯ぎ接ぎの不親和性という植物状態を、化学・生理指標で早期検出する方法の開発と比較評価が明示されており、単なる routine 測定ではない。
abstractThis study investigated the physiological and chemical dynamics in various chestnut grafts, aiming to develop a method for the early detection of graft incompatibility.
Stomata are vital for CO 2 and water vapor exchange, with guard cells' aperture and ultrastructure highly responsive to environmental cues. However, traditional methods for studying guard cell ultrastructure, which rely on chemical fixation and embedding, often distort cell morphology and compromise membrane integrity. In contrast, plunge-freezing in liquid ethane rapidly preserves cells in a near-native vitreous state for cryogenic electron microscopy. Using this approach, we applied Cryo-Focused Ion Beam-Scanning Electron Microscopy (cryo-FIB-SEM) to study the guard cell ultrastructure of Vicia faba, a higher plant model chosen for its sensitivity to external factors and ease of epidermis isolation, advancing beyond previous cryo-FIB-SEM applications in lower plant algae. The results firstly introduced cryo-FIB-SEM volume imaging, enabling subcellular ultrastructure visualization of higher plants like V. faba in a vitrified, unaltered state. 3D models of organelles such as stromules, chloroplast protrusions, chloroplasts, starch granules, mitochondria, and vacuoles were reconstructed from cryo-FIB-SEM volumetric data, with their surface area and volume initially determined using manual segmentation. Future studies using this near-native volume imaging technique hold promise for investigating how environmental factors like drought or salinity influence stomatal behavior and the morphology of guard cells and their organelles.
Why it matches plant phenotyping methods高等植物のガードセルを対象に、cryo-FIB-SEMによる近天然状態の3D画像取得とオルガネラ形態の再構築・定量を主要な技術貢献として扱っているため、植物フェノタイピング手法研究に該当する。
abstractThe results firstly introduced cryo-FIB-SEM volume imaging, enabling subcellular ultrastructure visualization of higher plants like V. faba in a vitrified, unaltered state.
Plant phenotyping relevance match · UnverifiedOpenAlex · Europe PMC · checked 6 Sept 2026
The stomatal phenotype is a crucial microscopic characteristic of the leaf surface, and modulating the stomata of maize leaves can enhance photosynthetic carbon assimilation and water use efficiency, thereby playing a vital role in maize yield formation. The evolving imaging and image processing technologies offer effective tools for precise analysis of stomatal phenotypes. This study employed Jingnongke 728 and its parental inbred to capture stomatal images from various leaf positions and abaxial surfaces during key reproductive stages using rapid scanning electron microscopy. We uesd a target detection and image segmentation approach based on YOLOv5s and Unet to efficiently obtain 11 phenotypic traits encompassing stomatal count, shape, and distribution. Manual validation revealed high detection accuracies for stomatal density, width, and length, with R2 values of 0.92, 0.97, and 0.95, respectively. Phenotypic analyses indicated a significant positive correlation between stomatal density and the percentage of guard cells and pore area (r=0.36), and a negative correlation with stomatal area and subsidiary cell area (r=-0.34 and -0.46). Additionally, stomatal traits exhibited notable variations with reproductive stages and leaf layers. Specifically, at the monocot scale, stomatal density increased from 74.35 to 87.19 Counts/mm2 from lower to upper leaf layers. Concurrently, the stomatal shape shifted from sub-circular (stomatal roundness = 0.64) to narrow and elongated (stomatal roundness = 0.63). Throughout the growth cycle, stomatal density remained stable during vegetative growth, decreased during reproductive growth with smaller size and narrower shape, and continued to decline while increasing in size and tending towards a rounded shape during senescence. Remarkably, hybrid 728 differed notably from its parents in stomatal phenotype, particularly during senescence. Moreover, the stomatal density of the hybrids showed negative super parental heterosis (heterosis rate = -0.09), whereas stomatal dimensions exhibited positive super parental heterosis, generally resembling the parent MC01. This investigation unveils the dynamic variations in maize stomatal phenotypes, bolstering genetic analyses and targeted improvements in maize, and presenting a novel technological instrument for plant phenotype studies.
Why it matches plant phenotyping methodsYOLOv5sとUnetによる画像取得・分割手法で、トウモロコシ気孔の11形質を抽出し、手動検証で精度を評価しており、植物表現型取得法が中心である。
abstractWe uesd a target detection and image segmentation approach based on YOLOv5s and Unet to efficiently obtain 11 phenotypic traits encompassing stomatal count, shape, and distribution.
Abstract Stomata regulate gas exchange between plants and the atmosphere, but analysing their morphology is challenging due to anatomical variability and artefacts during image acquisition. Deep learning (DL) can address these challenges but often requires large and diverse datasets, which are costly and error prone to produce. Generative adversarial networks (GANs) offer a solution by generating artificial data via unsupervised learning. However, GANs often suffer from problems including mode collapse, vanishing gradients, and network failure, particularly with small datasets. Here, we present StomaGAN, a deep convolutional GAN (DCGAN) with tailored modifications to address common GAN issues. We collected 559 stomatal impressions of field, or faba bean (Vicia faba) consisting of ~3000 stoma, 80% of which were used to train StomaGAN. Evaluation metrics, including generator and discriminator loss progression and a mean Fréchet Inception Distance (FID) score of 61.4 across eight experimental runs confirm successful training. To validate StomaGAN, we generated artificial images to train a deep convolutional neural network (DCNN) based on the DeepLabV3 framework for stomata detection from real, unseen images. The DCNN achieved a mean Interception over Union (IoU) of 0.95 on artificial training images and 0.91 on real, unseen, images across varying magnifications. Our results demonstrate that StomaGAN effectively generates high-quality synthetic datasets, enabling reliable stomatal detection and enhancing phenotypic analysis. This approach reduces the need for extensive manual data collection and simplifies complex morphological assessments.
Why it matches plant phenotyping methods植物の気孔画像解析と形態評価のためのGANおよび検出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研究の中心である。
abstractHere, we present StomaGAN, a deep convolutional GAN (DCGAN) with tailored modifications to address common GAN issues.
Microscopic analyses of cytoskeleton organization are crucial for understanding various cellular activities, including cell proliferation and environmental responses in plants. Traditionally, assessments of cytoskeleton dynamics have been qualitative, relying on microscopy-assisted visual inspection. However, the transition to quantitative digital microscopy has introduced new technical challenges, with segmentation of cytoskeleton structures proving particularly demanding. In this study, we examined the utility of a deep learning-based segmentation method for accurate quantitative evaluation of cytoskeleton organization using confocal microscopic images of the cortical microtubules in tobacco BY-2 cells. The results showed that, although conventional methods sufficed for measurement of cytoskeleton angles and parallelness, the deep learning-based method significantly improved the accuracy of density measurements. To assess the versatility of the method, we extended our analysis to physiologically significant models in the context of changes in cytoskeleton density, namely Arabidopsis thaliana guard cells and zygotes. The deep learning-based method successfully improved the accuracy of cytoskeleton density measurements for quantitative evaluations of physiological changes in both stomatal movement in guard cells and intracellular polarization in elongating zygotes, confirming its utility in these applications. The results demonstrate the effectiveness of deep learning-based segmentation in providing precise and high-throughput measurements of cytoskeleton density, and has the potential to automate and expedite analyses of large-scale image datasets.
Why it matches plant phenotyping methods植物細胞の画像から細胞骨格密度を定量抽出する深層学習セグメンテーション法が研究の中心であり、精度評価と複数の植物細胞モデルへの適用も行っている。
abstractwe examined the utility of a deep learning-based segmentation method for accurate quantitative evaluation of cytoskeleton organization using confocal microscopic images
Plant phenotyping relevance match · UnverifiedCrossref · Europe PMC · checked 6 Sept 2026
This study conducts a rigorous comparative analysis between two cutting-edge instance segmentation methods, Mask R-CNN and YOLOv8, focusing on stomata pore analysis. A novel dataset specifically tailored for stomata pore instance segmentation, named PhenomicsStomata, was introduced. This dataset posed challenges such as low resolution and image imperfections, prompting the application of advanced preprocessing techniques, including image enhancement using the Lucy-Richardson Algorithm. The models underwent comprehensive evaluation, considering accuracy, precision, and recall as key parameters. Notably, YOLOv8 demonstrated superior performance over Mask R-CNN, particularly in accurately calculating stomata pore dimensions. Beyond this comparative study, the implications of our findings extend across diverse biological research, providing a robust foundation for advancing our understanding of plant physiology. Furthermore, the preprocessing enhancements offer valuable insights for refining image analysis techniques, showcasing the potential for broader applications in scientific domains. This research marks a significant stride in unraveling the complexities of plant structures, offering both theoretical insights and practical applications in scientific research.
Why it matches plant phenotyping methods気孔孔の画像セグメンテーション手法を比較・評価し、専用データセットを導入して気孔寸法を推定しているため、植物フェノタイピング手法が中心です。
abstractThis study conducts a rigorous comparative analysis between two cutting-edge instance segmentation methods, Mask R-CNN and YOLOv8, focusing on stomata pore analysis.
SoybeanTomatoWatermelonField / plotFruitStomata / guard-cell complexPhysiological trait estimationGrowth / development / phenologyStomatal traitsWater status / transpiration
The combination of flexible electronics and plant science has generated various plant-wearable sensors, yet challenges persist in their applications in real-world agriculture, particularly in high-throughput settings. Overcoming the trade-off between sensing sensitivity and range, adapting them to a wide range of crop types, and bridging the gap between sensor measurements and biological understandings remain the primary obstacles. Here we introduce PlantRing, an innovative, nano-flexible sensing system designed to address the aforementioned challenges. PlantRing employs bio-sourced carbonized silk georgette as the strain sensing material, offering exceptional resolution (tensile deformation: < 100 μm), stretchability (tensile strain up to 100 %), and remarkable durability (season long), exceeding existing plant strain sensors. PlantRing effectively monitors plant growth and water status, by measuring organ circumference dynamics, performing reliably under harsh conditions and being adaptable to a wide range of plants. Applying PlantRing to study fruit cracking in tomato and watermelon reveals novel hydraulic mechanism, characterized by genotype-specific excess sap flow within the plant to fruiting branches. Its high-throughput application enabled large-scale quantification of stomatal sensitivity to soil drought, a traditionally difficult-to-phenotype trait, facilitating drought tolerant germplasm selection. Combing PlantRing with soybean mutant led to the discovery of a potential novel function of the GmLNK2 circadian clock gene in stomatal regulation. More practically, integrating PlantRing into feedback irrigation achieves simultaneous water conservation and quality improvement, signifying a paradigm shift from experience- or environment-based to plant-based feedback control. Collectively, PlantRing represents a groundbreaking tool ready to revolutionize botanical studies, agriculture, and forestry.
Why it matches plant phenotyping methodsPlantRingという高スループットの植物装着型センサーを開発し、器官周径、水状態、気孔感度などの植物形質を直接測定すること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 introduce PlantRing, an innovative, nano-flexible sensing system designed to address the aforementioned challenges.
Artificial intelligence and machine learning (AI/ML) can be used to automatically analyze large image datasets. One valuable application of this approach is estimation of plant trait data contained within images. Here we review 39 papers that describe the development and/or application of such models for estimation of stomatal traits from epidermal micrographs. In doing so, we hope to provide plant biologists with a foundational understanding of AI/ML and summarize the current capabilities and limitations of published tools. While most models show human-level performance for stomatal density (SD) quantification at superhuman speed, they are often likely to be limited in how broadly they can be applied across phenotypic diversity associated with genetic, environmental, or developmental variation. Other models can make predictions across greater phenotypic diversity and/or additional stomatal/epidermal traits, but require significantly greater time investment to generate ground-truth data. We discuss the challenges and opportunities presented by AI/ML-enabled computer vision analysis, and make recommendations for future work to advance accelerated stomatal phenotyping.
Why it matches plant phenotyping methodsAI/ML画像解析による気孔形質推定を扱うレビューであり、植物フェノタイピング手法の開発・応用、性能と限界の評価が中心です。
abstractHere we review 39 papers that describe the development and/or application of such models for estimation of stomatal traits from epidermal micrographs.
Plant physiology and metabolism rely on the function of stomata, structures on the surface of above-ground organs that facilitate the exchange of gases with the atmosphere. The morphology of the guard cells and corresponding pore that make up the stomata, as well as the density (number per unit area), are critical in determining overall gas exchange capacity. These characteristics can be quantified visually from images captured using microscopy, traditionally relying on time-consuming manual analysis. However, deep learning (DL) models provide a promising route to increase the throughput and accuracy of plant phenotyping tasks, including stomatal analysis. Here we review the published literature on the application of DL for stomatal analysis. We discuss the variation in pipelines used, from data acquisition, pre-processing, DL architecture, and output evaluation to post-processing. We introduce the most common network structures, the plant species that have been studied, and the measurements that have been performed. Through this review, we hope to promote the use of DL methods for plant phenotyping tasks and highlight future requirements to optimize uptake, predominantly focusing on the sharing of datasets and generalization of models as well as the caveats associated with utilizing image data to infer physiological function.
Why it matches plant phenotyping methods気孔画像から形態・密度などの植物形質を推定する深層学習手法を体系的にレビューしており、フェノタイピング手法が中心である。
abstractHere we review the published literature on the application of DL for stomatal analysis.
The epidermal leaf patterns of plants exhibit remarkable diversity in cell shapes, sizes, and arrangements, driven by environmental interactions that lead to significant adaptive changes even among closely related species. The Solanaceae family, known for its high diversity of adaptive epidermal structures, has traditionally been studied using qualitative phenotypic descriptions. To advance this, we developed a workflow combining multi-scale computer vision, image processing, and data analysis to extract digital descriptors for leaf epidermal cell morphology. Applied to nine wild potato species, this workflow quantified key morphological parameters, identifying descriptors for trichomes, stomata, and pavement cells, and revealing interdependencies among these traits. Principal component analysis (PCA) highlighted two main axes, accounting for 45% and 21% of variance, corresponding to features such as guard cell shape, trichome length, stomatal density, and trichome density. These axes aligned well with the historical and geographical origins of the species, separating southern from Central American species, and forming distinct clusters for monophyletic groups. This workflow thus establishes a quantitative foundation for investigating leaf epidermal cell morphology within phylogenetic and geographic contexts.
Why it matches plant phenotyping methods葉表皮細胞の形態形質を画像から抽出・定量するコンピュータビジョン／画像処理ワークフローの開発と適用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収載する。
abstractwe developed a workflow combining multi-scale computer vision, image processing, and data analysis to extract digital descriptors for leaf epidermal cell morphology.
Reproduction assets foundThe paper's quantitative phenotyping measurements (trichome types and morphometric parameters of leaf epidermal cells for nine wild potato species) are publicly available as Supplementary Tables S1 and S2 at the MDPI supplementary URL. Microscopy images are not publicly deposited and are available only upon request; noSupplement · publicObjects of the Institute of Cytology and Genetics SB RAS.
Abbreviations
The following abbreviations are used in this manuscript:
LSM
Laser scanning microscopy
PI
Propidium iodide
DAPI
4′,6-diamidino-2-phenylindole
PCA
Principal component analysis
Supplementary Materials
The following supporting information can be downloaded at https://www.mdpi.com/article/10.3390/plants13213084/s1 , Table S1: Trichome types for the studied wild potato species; Table S2: Morphometric parameters for leaf epidermal cells of the studied wild potato species, including Area, Length, Width, Elongation, Circularity, Rectangularity, Perimeter, Convex Hull Area, Convex Hull Perimeter, and Convex Hull Coverage.
Author Open asset ↗lines:139-176Code / dataset availability confirmedEurope PMC · checked 7 Sept 2026
Recent research has shown that optimizing photosynthetic and stomatal traits holds promise for improved crop performance. However, standard phenotyping tools such as gas exchange systems have limited throughput. In this work, a novel approach based on a bespoke gas exchange chamber allowing combined measurement of the quantum yield of PSII (Fq'/Fm'), with an estimation of stomatal conductance via thermal imaging was used to phenotype a range of bread wheat (Triticum aestivum L.) genotypes. Using the dual-imaging methods and traditional approaches, we found broad and significant variation in key traits, including photosynthetic CO2 uptake at saturating light and ambient CO2 concentration (Asat), photosynthetic CO2 uptake at saturating light and elevated CO2 concentration (Amax), the maximum velocity of Rubisco for carboxylation (Vcmax), time for stomatal opening (Ki), and leaf evaporative cooling. Anatomical analysis revealed significant variation in flag leaf adaxial stomatal density. Associations between traits highlighted significant relationships between leaf evaporative cooling, leaf stomatal conductance, and Fq'/Fm', highlighting the importance of stomatal conductance and stomatal rapidity in maintaining optimal leaf temperature for photosynthesis in wheat. Additionally, gsmin and gsmax were positively associated, indicating that potential combinations of preferable traits (i.e. inherently high gsmax, low Ki, and maintained leaf evaporative cooling) are present in wheat. This work highlights the effectiveness of thermal imaging in screening dynamic gs in a panel of wheat genotypes. The wide phenotypic variation observed suggested the presence of exploitable genetic variability in bread wheat for dynamic stomatal conductance traits and photosynthetic capacity for targeted optimization within future breeding programmes.
Why it matches plant phenotyping methods特注ガス交換チャンバーと熱画像を組み合わせた動的な気孔コンダクタンス・光合成形質の取得手法を開発し、複数のコムギ遺伝子型で実証しているため、植物フェノタイピング手法が中心です。
abstracta novel approach based on a bespoke gas exchange chamber allowing combined measurement of the quantum yield of PSII (Fq'/Fm'), with an estimation of stomatal conductance via thermal imaging was used to phenotype a range of bread wheat (Triticum aestivum L.) genotypes.
Reproduction assets foundThe paper's Data Availability statement points to a public Dryad repository containing the raw phenotyping data (photosynthesis and stomatal kinetics measurements) for this study, matching an allowed URL. Supplementary datasets S1–S2 are calculation spreadsheets but no standalone public URL is given for them beyond theDataset · publicRaw data can be accessed from the Dryad Digital Repository ( Faralli et al. , 2024 ) ( https://doi.org/10.5061/dryad.79cnp5j4d ).Open asset ↗Dryad Digital Repository · 10.5061/dryad.79cnp5j4dlines:117-171Plant phenotyping relevance match · UnverifiedOpenAlex · Europe PMC · Crossref · bioRxiv · checked 15 Sept 2026
Abstract Stomatal anatomy and physiology define CO 2 availability for photosynthesis and regulate plant water use. Despite being key drivers of yield and dynamic responsiveness to abiotic stresses, conventional measurement techniques of stomatal traits are laborious and slow, limiting adoption in plant breeding. Advances in instrumentation and data analyses present an opportunity to screen stomatal traits at scales relevant to plant breeding. We present a high-throughput field-based phenotyping approach, FieldDino, for screening of stomatal physiology and anatomy. The method allows coupled measurements to be collected in <15 s and consists of: (1) stomatal conductance measurements using a handheld porometer; (2) in situ collection of epidermal images with a digital microscope, 3D-printed leaf clip and Python-based app; and (3) automated deep learning analysis of stomatal features. The YOLOv8-M model trained on images collected in the field achieved strong performance metrics with an mAP@0.5 of 97.1% for stomatal detection. Validation in large field trials of 200 wheat genotypes with two irrigation treatments captured wide diversity in stomatal traits. FieldDino enables stomatal data collection and analysis at unprecedented scales in the field. This will advance research on stomatal biology and accelerate the incorporation of stomatal traits into plant breeding programs for resilience to abiotic stress. Highlight Chaplin et al., have developed FieldDino which enables rapid, high-throughput phenotyping of stomatal traits, advancing plant breeding research by integrating streamlined in-field measurements with automated deep learning analysis.
Why it matches plant phenotyping methodsFieldDinoは、圃場での気孔生理・解剖形質の高速取得、画像収集、深層学習による自動抽出を統合した植物フェノタイピング手法であり、開発と大規模検証が研究の中心です。
abstractWe present a high-throughput field-based phenotyping approach, FieldDino, for screening of stomatal physiology and anatomy.
Urban green infrastructure (UGI) plays a vital role in mitigating climate change risks, including urban development-induced warming. The effective maintenance and monitoring of UGI are essential for detecting early signs of water stress and preventing potential fire hazards. Recent research shows that plants close their stomata under limited soil moisture availability, leading to an increase in leaf temperature. Multi-spectral cameras can detect thermal differentiation during periods of water stress and well-watered conditions. This paper examines the thermography of five characteristic green wall and green roof plant types (Pachysandra terminalis, Lonicera nit. Hohenheimer, Rubus tricolor, Liriope muscari Big Blue, and Hedera algeriensis Bellecour) under different levels of water stress compared to a well-watered reference group measured by thermal cameras. The experiment consists of a (1) pre-test experiment identifying the suitable number of days to create three different levels of water stress, and (2) the main experiment tested the suitability of thermal imaging with a drone to detect water stress in plants across three different dehydration stages. The thermal images were captured analyzed from three different types of green infrastructure. The method was suitable to detect temperature differences between plant types, between levels of water stress, and between GI types. The results show that leaf temperatures were approximately 1–3 °C warmer for water-stressed plants on the green walls, and around 3–6 °C warmer on the green roof compared to reference plants with differences among plant types. These insights are particularly relevant for UGI maintenance strategies and regulations, offering valuable information for sustainable urban planning.
Why it matches plant phenotyping methods熱画像・ドローンを用いて植物の水ストレスを検出する方法の適用性を実験的に評価しており、植物状態の取得・判定が研究の中心である。
abstractthe main experiment tested the suitability of thermal imaging with a drone to detect water stress in plants across three different dehydration stages.
Monitoring in-vivo stomatal conductance (gₛ) dynamics is essential for predicting crop water usage and yield sensitivity in response to climate change. Leaf and canopy spectroscopy offer a non-destructive method for gₛ monitoring; however, the underlying mechanisms connecting leaf spectra with stomatal anatomical and behavioral traits, and their subsequent impacts on gₛ, remain underexplored. In this study, we conducted a wheat field trial, collecting comprehensive measurements of stomatal anatomical (i.e., size, density) and behavioral (i.e., opening ratio, pore area) traits by a customized, high-resolution microscope, leaf spectra via a handheld spectroradiometer, and gₛvia a handheld AP4 Leaf Porometer across various genotypes, nitrogen treatments, growth stages, and diurnal environments. We observed substantial gₛ variability, with stomatal anatomical and behavioral traits jointly accounting for 79% of this variability. We further examined the relationship between leaf spectra and stomatal traits/conductance using a partial least square regression (PLSR) model and discovered that a single PLSR spectral model accurately predicted the variability of each of these traits and gₛ across our datasets. Furthermore, we demonstrated a strong correspondence between spectral variations resulting from gₛ and spectral alternation induced by stomatal anatomical and behavioral traits. By analyzing the diurnal association between spectral and gₛ variability, we revealed important biophysical mechanisms underlying relationships among spectra, stomatal anatomical and behavioral traits, and gₛ. Collectively, our findings highlight the potential of leaf spectroscopy in advancing crop physiology monitoring, contributing to enhanced food security and sustainability.
Why it matches plant phenotyping methods小麦の気孔形質と気孔コンダクタンスを分光計測・PLSRで非破壊推定する手法を中心に検証しており、植物フェノタイピング手法の開発・検証に該当する。
abstractLeaf and canopy spectroscopy offer a non-destructive method for gₛ monitoring
This manuscript describes a deep learning-based algorithm for inferring stomatal phenotypes from microscope images. Botanists spray compounds on leaf surfaces and observe their state under a microscope to study the effects of different compounds on stomatal opening and closing. We propose a stomatal orientation-based method that uses stomatal orientation to guide stomatal measurements. This method has three modules: a stomata detection module that locates the stoma region and orientation using deep learning-based object detection. A stoma segmentation module that segments the aperture, guard cell, and thick inner wall from the stoma ROI image. And a phenotype quantification module that calculates the phenotype parameters by analyzing the mask image. The experimental results show that the proposed method can resolve stomata with high accuracy (the average$R^{2}$of the previous method is 0.66, and the proposed method is 0.96). For the development of the community, we will release the algorithm and tool involved in this article in GitHub.
Why it matches plant phenotyping methods顕微鏡画像から気孔の検出・セグメンテーション・表現型パラメータ定量を行う深層学習手法とツールの開発が中心であり、植物表現型測定法に該当する。
abstractThis manuscript describes a deep learning-based algorithm for inferring stomatal phenotypes from microscope images.
Field / plotStomata / guard-cell complexClassificationObject detectionStomatal traits
Plants' leaf stomata are crucial for various scientific research, including identifying species, studying ecology, conserving ecosystems, improving agriculture, and advancing the field of deep learning. This dataset, containing 1083 images, encompasses 11 species from two distinct locations in Bangladesh: nine from the Sundarbans mangrove forest and two from the Ratargul Swamp Forest. It is a valuable tool for refining machine learning algorithms that specialize in detecting stomata and categorizing species accurately. Researchers can explore a deeper understanding of plant physiology, adaptation mechanisms, and environmental interactions by employing pattern recognition, deep learning, and feature extraction techniques. Additionally, this dataset could be a potential tool for enhancing research in macroscopic metamaterials, extending its impact beyond traditional biological studies into interdisciplinary fields of technology and material science.
Why it matches plant phenotyping methods気孔画像データセット自体が研究の中心で、画像から気孔を検出する再利用可能な表現型取得基盤を提供しているため。
abstractThis dataset, containing 1083 images, encompasses 11 species from two distinct locations in Bangladesh
Reproduction assets foundThis Data in Brief article deposits its own stomata image dataset (1083 images, 11 species) and stomatal trait measurements in Mendeley Data, with a direct public URL and DOI given in the Specifications Table.Dataset · publicSiedentopf Trinocular Compound Microscope (AmScope T340A) was used to image stomata with AmScope camera software.
Data source location
Country: BangladeshForest: Sundarbans Mangrove Forest, Ratargul Swamp Forest
Data accessibility
Repository name: Mendeley DataData identification number: 10.17632/4brcwhmvyk.4Direct URL to data: https://data.mendeley.com/datasets/4brcwhmvyk/4
Related research article
Dey, B., Ahmed, R., Ferdous, J., Haque, M.M.U., Khatun, R., Hasan, F.E., Uddin, S.N., 2023. Automated plant species identification from the stomata images using deep neural network: A study of selected mangrove and freshwater swamp forest tree species of Bangladesh. Ecol. Inform. 75, 102128.httpsOpen asset ↗Mendeley Data · 10.17632/4brcwhmvyk.4html-lines:1-37Plant phenotyping relevance match · UnverifiedEurope PMC · bioRxiv · checked 15 Sept 2026
Stomata are vital for CO2 and water vapor exchange, with guard cells’ aperture and ultrastructure highly responsive to environmental cues. However, traditional methods for studying guard cell ultrastructure, which rely on chemical fixation and embedding, often distort cell morphology and compromise membrane integrity, leaving no suitable methodology until now. In contrast, plunge-freezing in liquid ethane rapidly preserves cells in a near-native vitreous state for cryogenic electron microscopy. Using this approach, we applied Cryo-Focused Ion Beam-Scanning Electron Microscopy (cryo- FIB-SEM) to study the guard cell ultrastructure of Vicia faba , a higher plant model chosen for its sensitivity to external factors and ease of epidermis isolation, advancing beyond previous cryo-FIB-SEM applications in lower plant algae. The results firstly introduced cryo-FIB-SEM volume imaging, enabling subcellular ultrastructure visualization of higher plants like V. faba in a vitrified, unaltered state. 3D models of organelles such as stromules, chloroplast protrusions, chloroplasts, starch granules, mitochondria, and vacuoles were reconstructed from cryo-FIB-SEM volumetric data, with their surface area and volume initially determined using manual segmentation. Future studies using this near-native volume imaging technique hold promise for investigating how environmental factors like drought or salinity influence stomatal behavior and the morphology of guard cells and their organelles.
Why it matches plant phenotyping methods高等植物の細胞・オルガネラ形態を取得するcryo-FIB-SEM 3Dイメージング手法を導入し、体積データから表面積・体積を定量化しており、表現型取得法が研究の中心です。
abstractThe results firstly introduced cryo-FIB-SEM volume imaging, enabling subcellular ultrastructure visualization of higher plants like V. faba in a vitrified, unaltered state.
Drought is a principal limiting factor in the production of agricultural crops; however, quinoa possesses certain adaptive and tolerance factors that make it a potentially valuable crop under drought-stress conditions. Within this context, the objective of the present study was to evaluate morphological and physiological changes in ten quinoa genotypes under three irrigation treatments: normal irrigation, drought-stress followed by recovery irrigation, and terminal drought stress. The experiments were conducted at the UNSA Experimental Farm in Majes, Arequipa, Peru. A series of morphological, physiological, and remote measurements were taken, including plant height, dry biomass, leaf area, stomatal density, relative water content, selection indices, chlorophyll content via SPAD, multispectral imaging, and reflectance measurements via spectroradiometry. The results indicated that there were numerous changes under the conditions of terminal drought stress; the yield variables of total dry biomass, leaf area, and plant height were reduced by 69.86%, 62.69%, and 27.16%, respectively; however, under drought stress with recovery irrigation, these changes were less pronounced with a reduction of 21.10%, 27.43%, and 17.87%, respectively, indicating that some genotypes are adapted or tolerant of both water-limiting conditions (Accession 50, Salcedo INIA and Accession 49). Remote sensing tools such as drones and spectroradiometry generated reliable, rapid, and precise data for monitoring stress and phenotyping quinoa and the optimum timing for collecting these data and predicting yield impacts was from 79–89 days after sowing (NDRE and CREDG r Pearson 0.85).
Why it matches plant phenotyping methods乾燥ストレス実験ではあるが、ドローン、マルチスペクトル画像、分光反射を用いたキヌアの表現型取得とストレス・収量影響のモニタリングが明示され、手法の適時性と予測精度も評価されているため。
abstractRemote sensing tools such as drones and spectroradiometry generated reliable, rapid, and precise data for monitoring stress and phenotyping quinoa
concentrations, a trend already observed in multiple plant species. However, it is unclear whether such responses are based on genetic changes and evolutionary adaptation. Here we make use of extensive knowledge of 43 genes in the stomatal development pathway and newly generated genome information of 191 Arabidopsis thaliana historical herbarium specimens collected over 193 years to directly link genetic variation with climate change. While we find that the essential transcription factors SPCH, MUTE and FAMA, central to stomatal development, are under strong evolutionary constraints, several regulators of stomatal development show signs of local adaptation in contemporary samples from different geographic regions. We then develop a functional score based on known effects of gene knock-out on stomatal development that recovers a classic pattern of stomatal density decrease over the past centuries, suggesting a genetic component contributing to this change. This approach combining historical genomics with functional experimental knowledge could allow further investigations of how different, even in historical samples unmeasurable, cellular plant phenotypes may have already responded to climate change through adaptive evolution.
Why it matches plant phenotyping methods遺伝情報と既知の機能的効果を統合した機能スコアを開発し、歴史標本では直接測定できない気孔密度・発生表現型を推定する手法が研究の中心であるため。
abstractWe then develop a functional score based on known effects of gene knock-out on stomatal development that recovers a classic pattern of stomatal density decrease over the past centuries
Abstract Thermal remote sensing indicators of crop water status can help to optimize irrigation across time and space. The Crop Water Stress Index (CWSI), calculated from thermal data, has been widely used in irrigation management as it has a proven association with evapotranspiration ratios. However, different approaches can be used to calculate the CWSI. The aim of this study is to identify the most robust method for estimating the CWSI in a commercial Merlot vineyard using high-resolution thermal imaging from Unoccupied Aerial Systems (UAS). To that end, three different methods were used to estimate the CWSI: Jackson’s model (CWSIj), Wet Artificial Reference Surface (WARS) method (CWSIw), and the Bellvert approach (CWSIb). A simpler indicator calculated as the difference between canopy and air temperature (Tc–Ta) was the benchmark to beat. The water status of a vine cultivar with anisohydric behavior (Merlot) in a vineyard in central Spain was assessed for two years with different agroclimatic conditions. Canopy temperature (Tc) was obtained from UAS flights at 9:00 h and 12:00 h solar hour over eight days during the irrigation period (June–August), and from vines under five different irrigation treatments. Stem water potential (SWP), stomatal conductance (gs), and leaf temperature (TL) were recorded at the time of the flights and compared with the thermal indices (CWSIj, CWSIw, CWSIb) and the benchmark indicator (Tc–Ta). Results show that the simpler indicator of water stress, Tc–Ta, performed better at identifying varying levels of crop hydration than CWSIb or CWSIw at 12:00 h. Under conditions of extreme aridity, the latter indices were less accurate than the physically-based CWSIj at 12:00 h, which had the highest correlation with SWP (r = 0.84), followed by the benchmark index Tc–Ta (r = 0.70 at 12:00). Considering the current climatic trends towards aridification, the CWSIj emerges as a useful operational tool, with robust performance across days and times of day. These results are important for irrigation management and could contribute to improving water use efficiency in agriculture.
Why it matches plant phenotyping methodsUAS熱画像からブドウの水分状態を推定する複数の熱指標を比較・検証しており、植物生理状態の取得方法が研究の中心である。
abstractThe aim of this study is to identify the most robust method for estimating the CWSI in a commercial Merlot vineyard using high-resolution thermal imaging from Unoccupied Aerial Systems (UAS).
Field / plotStomata / guard-cell complexPhysiological trait estimationStomatal traitsWater status / transpiration
The Mediterranean region chronically experiences high levels of tropospheric ozone (O₃) that can affect the health of vegetation. However, limiting plant growing conditions, such as low soil moisture, may restrict the stomatal phytotoxic ozone dose (POD) absorbed by vegetation, modulating O₃ detrimental effects. Atmospheric chemistry transport models that estimate POD for O₃ risk assessment of effects on vegetation species, such as the European Monitoring and Evaluation Programme (EMEP), have adopted the soil moisture index (SMI) to consider the influence of soil moisture on POD. The objectives of this study were the parametrization and validation of the SMI effect on stomatal conductance (gₛ) for improving the POD estimation and O₃ risk assessment for different vegetation species under water-limiting growing conditions, using field data collected in Italy and Spain and a literature review. The modelled SMI from EMEP proved to be a good indicator of soil moisture dynamics across sites and years, although it showed a general tendency to overestimate soil moisture availability for plants, particularly in the driest seasons. New parametrizations derived for modelling SMI effects on gₛ under Mediterranean conditions proposed in this study stress the importance of using species-specific parameters for species showing contrasting water-saving strategies in contrast of the current approach of using a simple relation between SMI and gₛ for all the species. Furthermore, gₛ modelling parametrizations based on soil water potential (SWP) were found to be more suitable than SMI for local scale estimation of POD under water-limiting conditions. Further consideration of rooting depth and distribution will be required in the future to determine the soil depth at which the soil moisture should be measured in POD modelling, since these features represent one of the most important uncertainties affecting the estimation of POD that could not be addressed with the present database.
Why it matches plant phenotyping methods植物の気孔コンダクタンスを推定するモデルのパラメータ化・検証が研究の中心であり、植物の生理状態を定量化する方法論的貢献が明確である。
abstractThe objectives of this study were the parametrization and validation of the SMI effect on stomatal conductance (gₛ) for improving the POD estimation and O₃ risk assessment for different vegetation species under water-limiting growing conditions
Abstract Background Automating stomatal trait measurement has gained popularity because of their inherent importance for field phenotyping application as stomata are critical for both carbon capture and water use efficiency in plants. Such tool has been reported for rice, wheat, tomato, barley and oil palm. However, none exist yet for canola, which is an important economic and agronomic crop globally. Results We developed a new toolkit called Stomatal Comprehensive Automated Neural Network or SCAN by combining the use of high-resolution portable digital microscopy with machine learning based on You Only Look Once algorithm (YOLOv8). Digital micrographs of leaf surfaces enter the SCAN pipeline, which includes stomata detection, stomata segmentation and stomatal pore segmentation models, to measure stomatal density, stomatal size and stomatal pore area, respectively. In addition to SCAN’s ability to measure leaf stomatal traits in canola at 89 to 94% accuracy, we also showed that SCAN can be used to predict stomatal density even in species not included in the training set such as Arabidopsis, tobacco, rice, wheat, maize and proso millet. SCAN was designed for the biological science community with the premise that users are not required to possess advanced programming capabilities to manage dependency prerequisites, execute the models, and integrate the analysis. This was achieved by packaging the models into a desktop application system that can be accessed offline. Conclusion Overall, SCAN provides a non-destructive, real-time, portable, and high-throughput measurement of leaf stomatal traits in canola. The minimised hardware requirement and user-friendly desktop application system make SCAN suitable for field phenotyping application.
Why it matches plant phenotyping methodsカノーラ葉の気孔形質を画像と機械学習で自動抽出するツールを開発し、精度検証と他種での適用性評価を行っており、フェノタイピング手法が中心である。
abstractWe developed a new toolkit called Stomatal Comprehensive Automated Neural Network or SCAN by combining the use of high-resolution portable digital microscopy with machine learning based on You Only Look Once algorithm (YOLOv8).
Reproduction assets foundThe paper's authors publicly deposit the SCAN pipeline's model weights, hyperparameters, training scripts, and datasets in the FD_detection GitHub repository, and provide the SCAN application itself (with download and demonstration) in a second GitHub repository. Both are paper-specific, public, and actionable.Code · publicin Table S1.
123
124
The training tasks were carried out on an Ubuntu 20.04 Linux server at the Research School of Biology in
125
Australian National University, using two Nvidia A30 (24G) Graphic Processing Units (GPUs). The full
126
details of models’ weights, hyperparameters, training scripts and datasets can be found at
127
https://github.com/William-Yao0993/FD_detection.128
129
Model evaluation
130
131
Mean Average Precision (mAP, Fig. 3) and F1 score were used to assess model ability (Fig. 4). mAP is
132
calculated as the mean value of each class area under the precision-recall curve over thresholds, and the
133
F1 score is the harmonic mean of precision and recall. The formulas are deOpen asset ↗William-Yao0993/FD_detectionpdf-raw-page:4 lines:1-81Plant phenotyping relevance match · UnverifiedEurope PMC · bioRxiv · checked 15 Sept 2026
Microscopic analyses of cytoskeleton organization are crucial for understanding various cellular activities, including cell proliferation and environmental responses in plants. Traditionally, assessments of cytoskeleton dynamics have been qualitative, relying on microscopy-assisted visual inspection. However, the transition to quantitative digital microscopy has introduced new technical challenges, with segmentation of cytoskeleton structures proving particularly demanding. In this study, we examined the utility of a deep learning-based segmentation method for accurate quantitative evaluation of cytoskeleton organization using confocal microscopic images of the cortical microtubules in tobacco BY-2 cells. The results showed that, although conventional methods sufficed for measurement of cytoskeleton angles and parallelness, the deep learning-based method significantly improved the accuracy of density measurements. To assess the versatility of the method, we extended our analysis to physiologically significant models in the context of changes in cytoskeleton density, namely Arabidopsis thaliana guard cells and zygotes. The deep learning-based method successfully improved the accuracy of cytoskeleton density measurements for quantitative evaluations of physiological changes in both stomatal movement in guard cells and intracellular polarization in elongating zygotes, confirming its utility in these applications. The results demonstrate the effectiveness of deep learning-based segmentation in providing precise and high-throughput measurements of cytoskeleton density, and has the potential to automate and expedite analyses of large-scale image datasets.
Why it matches plant phenotyping methods植物細胞画像から細胞骨格密度を定量化する深層学習セグメンテーション法を開発・評価しており、植物状態の表現型抽出が研究の中心である。
abstractwe examined the utility of a deep learning-based segmentation method for accurate quantitative evaluation of cytoskeleton organization using confocal microscopic images
Plant stomatal phenotype traits play an important role in improving crop water use efficiency, stress resistance and yield. However, at present, the acquisition of phenotype traits mainly relies on manual measurement, which is time-consuming and laborious. In order to obtain high-throughput stomatal phenotype traits, we proposed a real-time recognition network SLPA-Net for stomata localization and phenotypic analysis. After locating and identifying stomatal density data, ellipse fitting is used to automatically obtain phenotype data such as apertures. Aiming at the problems of small stomata and high similarity to background, we introduced ECANet to improve the accuracy of stoma and aperture location. In order to effectively alleviate the unbalance problem in bounding box regression, we replaced the Loss function with a more effective Focal EIoU Loss. The experimental results show that SLPA-Net has excellent performance in the migration generalization and robustness of stomata and apertures detection and identification, as well as the correlation between stomata phenotype data obtained and artificial data.
Why it matches plant phenotyping methods気孔の位置・識別から密度や開度などの表現型を自動抽出するリアルタイム画像解析ネットワークを開発し、精度・頑健性・手動測定との相関を評価しており、表現型取得手法が中心である。
abstractwe proposed a real-time recognition network SLPA-Net for stomata localization and phenotypic analysis.
LeafStomata / guard-cell complexPhysiological trait estimationStomatal traitsWater status / transpiration
Stomatal conductance (gₛ) is a key leaf-level function controlling water, carbon, and energy exchange between vegetation and the surrounding environment. Conventionally, semi-empirical models have been used to model gₛ, but these models require re-parameterization as ecosystems undergo phenological changes over the growing season. In contrast, machine learning (ML) models offer a potential path to overcome this problem but are less interpretable than process-based models. This study explores ML as an approach to develop flexible and robust models of gₛ for a range of plant functional types (PFTs), including C3 crops, C3 grasses, shrubs, and tree species across different continents. An explainable machine-learning approach (eXML) was used here to provide novel interpretations and insights into the ML model formulations and relative predictor importance. We contrast the performance of three ML architectures: extreme gradient boosting, random forests, and neural networks. Models were developed and examined using many combinations of environmental and physiological predictors. The results demonstrated that ML models significantly outperform conventional semi-empirical models in predicting gₛ responses to the environment, while not requiring re-parameterization as is required in the semi-empirical paradigm. Particular focus is placed on models formulated around predictor sets that are: (a) relevant to gₛ estimation in modern terrestrial biophysical simulation models, and (b) composed of variables describing environmental and physiological drivers that can be remotely sensed non-invasively. “Generalized” models developed using data from all four PFTs demonstrated strong predictive performance using only three predictor variables, capturing 63–80 % of the variability in stomatal conductance across all ML architectures. Four predictor variables resulted in models capturing 79–83 % of gₛ variability, and models developed using all five predictor variables examined here were able to capture as much as 87 % of gₛ variability across all PFTs. Uncertainty in gₛ predictions was quantified using quantile regression. Shapley additive explanations was applied to unravel instance-based positive and negative contributions of environmental and physiological predictors to gₛ modeling, while illustrating that the models are consistent with the underlying ecophysiology. This work demonstrates the power of ML to introduce a new paradigm in the simulation of highly dynamic ecophysiological processes critical to environmental prediction.
Why it matches plant phenotyping methods機械学習モデルを開発・比較し、植物の生理形質である気孔コンダクタンスを推定・検証しているため、表現型取得・推定手法が研究の中心である。
abstractThis study explores ML as an approach to develop flexible and robust models of gₛ for a range of plant functional types (PFTs)
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:1-65Plant phenotyping relevance match · UnverifiedEurope PMC · checked 15 Sept 2026
Key message Innovatively, we consider stomatal detection as rotated object detection and provide an end-to-end, batch, rotated, real-time stomatal density and aperture size intelligent detection and identification system, RotatedeStomataNet. Stomata acts as a pathway for air and water vapor in the course of respiration, transpiration, and other gas metabolism, so the stomata phenotype is important for plant growth and development. Intelligent detection of high-throughput stoma is a key issue. Nevertheless, currently available methods usually suffer from detection errors or cumbersome operations when facing densely and unevenly arranged stomata. The proposed RotatedStomataNet innovatively regards stomata detection as rotated object detection, enabling an end-to-end, real-time, and intelligent phenotype analysis of stomata and apertures. The system is constructed based on the Arabidopsis and maize stomatal data sets acquired destructively, and the maize stomatal data set acquired in a non-destructive way, enabling the one-stop automatic collection of phenotypic, such as the location, density, length, and width of stomata and apertures without step-by-step operations. The accuracy of this system to acquire stomata and apertures has been well demonstrated in monocotyledon and dicotyledon, such as Arabidopsis, soybean, wheat, and maize. The experimental results that the prediction results of the method are consistent with those of manual labeling. The test sets, the system code, and their usage are also given ( https://github.com/AITAhenu/RotatedStomataNet ).
Why it matches plant phenotyping methods気孔の位置・密度・開口部サイズなどの植物表現型を自動取得する深層学習システムを開発し、複数作物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。
abstractprovide an end-to-end, batch, rotated, real-time stomatal density and aperture size intelligent detection and identification system
Automated guard cell detection and measurement are vital for understanding plant physiological performance and ecological functioning in global water and carbon cycles. Most current methods for measuring guard cells and stomata are laborious, time-consuming, prone to bias, and limited in scale. We developed StoManager1, a high-throughput tool utilizing geometrical, mathematical algorithms, and convolutional neural networks to automatically detect, count, and measure over 30 guard cell and stomatal metrics, including guard cell and stomatal area, length, width, stomatal aperture area/guard cell area, orientation, stomatal evenness, divergence, and aggregation index. Combined with leaf functional traits, some of these StoManager1-measured guard cell and stomatal metrics explained 90% and 82% of tree biomass and intrinsic water use efficiency (iWUE) variances in hardwoods, making them substantial factors in leaf physiology and tree growth. StoManager1 demonstrated exceptional precision and recall (mAP@0.5 over 0.96), effectively capturing diverse stomatal properties across over 100 species. StoManager1 facilitates the automation of measuring leaf stomatal and guard cells, enabling broader exploration of stomatal control in plant growth and adaptation to environmental stress and climate change. This has implications for global gross primary productivity (GPP) modeling and estimation, as integrating stomatal metrics can enhance predictions of plant growth and resource usage worldwide. Easily accessible open-source code and standalone Windows executable applications are available on a GitHub repository (https://github.com/JiaxinWang123/StoManager1) and Zenodo (https://doi.org/10.5281/zenodo.7686022).
Why it matches plant phenotyping methods植物の気孔・孔辺細胞形質を画像から自動検出・計測する高スループット手法とソフトウェアを開発・検証しており、植物フェノタイピング手法が研究の中心である。
abstractWe developed StoManager1, a high-throughput tool utilizing geometrical, mathematical algorithms, and convolutional neural networks to automatically detect, count, and measure over 30 guard cell and stomatal metrics
Stomata regulate plant gas exchange under changing environments, but observations of the dynamics of single stomata in planta are sparse. We developed a compact microscope system that can measure the kinetics of tens of stomata in planta simultaneously, with sub-minute time resolution. Dark field imaging with green light was used to create 3D stacks from which 2D surface projection were constructed to resolve stomatal apertures within the field of view. Stomatal dynamics of Chrysanthemum morifolium (Chrysanthemum) and Zea Mays (Maize) under dynamically changing light intensity were categorized, and a kinetic model was fitted to the data for quantitative comparison. In addition, we also resolved dynamics of the surface position of the leaf, related to dynamics of leaf thickness or bending. Maize stomata oscillated frequently between open and closed states under constant growth light and these oscillating stomata responded faster to changes in light than non-oscillating stomata at the same aperture. The slow closure of Chrysanthemum stomata reduced water use efficiency (WUE). Over 50% showed delayed or partial closure, leading to unnecessarily large apertures after reduced light. Stomata with larger apertures had more lag and similar closure speeds compared to those with smaller apertures and lag, further reducing WUE. In contrast, maize stomata with larger apertures closed faster, with no lag. In conclusion, our new system enables fine mapping of the heterogeneity of movement in neighboring stomata, providing new insights on the relations between stomatal dimensions, relative position and aperture changes under fluctuating light intensity.
Why it matches plant phenotyping methods植物体内の個々の気孔運動を高時間分解能で測定する顕微鏡・画像解析システムを開発し、定量比較に用いており、植物表現型取得法が研究の中心である。
abstractWe developed a compact microscope system that can measure the kinetics of tens of stomata in planta simultaneously, with sub-minute time resolution.
Abstract: Toxicity of emerging organic pollutants to photosystems of aquatic plants are still not well clarified. This study aimed to develop a novel ecotoxicological experimental protocol based on nanoscale electrochemical mapping of photosynthetic oxygen evolution of aquatic plants by SECM (scanning electrochemical microscopy). The protocol was also checked by confocal laser scanning microscopy (CLSM), the traditional Clark oxygen electrode method and chlorophyll fluorescence technique. The typical persistent organic pollutant 2,4-dichlorophenol (2,4-DCP) in water environment and the common aquatic Microsorium pteropus (M. pteropus) were chosen as the model organic pollutant and tested plant, respectively. It was found that the SECM method could well discriminated the responses of stoma micromorphology and spatial pattens of photosynthetic oxygen evolution on single stoma. The shape of stoma blurred with increasing 2,4-DCP concentration, which was in good agreement with the CLSM images. The dose-response curves and IC50 values obtained from the SECM data were verified by the data measured by the traditional Clark oxygen electrode method and chlorophyll fluorescence test. The IC50 value of single stoma oxygen emission of 24 h exposed plant leave, which was derived from the SECM current data (32535 μg L-1), was close to those calculated from the maximum photosynthetic efficiency (Fv/Fm) measured by chlorophyll fluorescence test (33963 μg L-1), and the Clark oxygen electrode method photosynthetic oxygen evolution rate (32375 μg L-1). The 72 h and 96 h 2,4-DCP exposure data further confirmed the reliability of the nanoscale stoma oxygen emission mapping methodology for ecotoxicological assessment. In this protocol, the procedures for how to collect effective electrochemical data and how to extract useful information from the single stoma oxygen emission pattern were well established. This study showed that SECM is a feasible and reliable ecotoxicological tool for evaluation of toxicity of organic pollutants to higher plants with unique nanoscale visualization advantage over the conventional methods.
Why it matches plant phenotyping methods植物の単一気孔における酸素放出と光合成応答を定量するSECM手法を開発し、他の測定法で検証した研究であり、植物表現型の取得方法が中心である。
abstractaimed to develop a novel ecotoxicological experimental protocol based on nanoscale electrochemical mapping of photosynthetic oxygen evolution of aquatic plants by SECM
Here, we show the possible correlation between the anatomical characteristics of epidermal cells of Arabidopsis thaliana with the stomata transient opening, which is commonly called the Wrong-Way Response (WWR). The WWR was induced by either reduced air humidity or leaf excision. Five genotypes of A. thaliana Col8, epf1epf2, lcd1-1, SALK069, and UBP, respectively, with anatomical differences in epidermal cells such as stomatal density, stomata size, size, and shape of the pavement cells were selected. These genotypes allowed us to investigate the mutual effects of stomata density and size on WWR. Scanning Electron Microscopy (SEM) was applied for image acquisition of the abaxial and adaxial surface of the leaves and the main features of the epidermal cells were extracted by one of the additions to the MiToBo plugin of ImageJ/Fiji called PaCeQuant. The stomatal conductance to water vapor (gs) was measured using the portable photosynthesis measurement system LICor-6800. Our linear models showed that the size of the stomata explained the rate of WWR induced by reduced air humidity, so genotypes with smaller stomata showed a smaller rate of the WWR. After leaf excision, however, there was no correlation between the size of the stomata and the rate of the WWR. Moreover, we found that after both, reduced air humidity and leaf excision, the size of the pavement cells on the abaxial surface is correlated to the rate of the WWR; genotypes with smaller pavement cells on the abaxial surface had a smaller rate of WWR.
Why it matches plant phenotyping methods葉表皮細胞の形態形質をSEM画像とPaCeQuantで抽出し、気孔応答との関連を解析しており、画像ベースの植物形質取得が実質的に含まれる。
abstractScanning Electron Microscopy (SEM) was applied for image acquisition of the abaxial and adaxial surface of the leaves and the main features of the epidermal cells were extracted by one of the additions to the MiToBo plugin of ImageJ/Fiji called PaCeQuant.
Premise The adaptive significance of amphistomy (stomata on both upper and lower leaf surfaces) 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 amphistomy informs its potential as a target for crop improvement and paleoenvironment reconstruction. Methods We developed a method to quantify "amphistomy advantage" ( AA $\text{AA}$ ) as the log-ratio of photosynthesis in an amphistomatous leaf to that of the same leaf but with gas exchange blocked through the upper surface (pseudohypostomy). Humidity modulated stomatal conductance and thus enabled comparing photosynthesis at the same total stomatal conductance. We estimated AA $\text{AA}$ and leaf traits in six coastal (open, sunny) and six montane (closed, shaded) populations of the indigenous Hawaiian species 'ilima (Sida fallax). Results Coastal 'ilima leaves benefit 4.04 times more from amphistomy than montane leaves. Evidence was equivocal with respect to two hypotheses: (1) that coastal leaves benefit more because they are thicker and 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 conductance being through the lower surface. Conclusions This is the first direct experimental evidence that amphistomy increases photosynthesis, consistent with the hypothesis that parallel pathways through upper and lower mesophyll increase CO 2 supply to chloroplasts. The prevalence of amphistomatous leaves in open, sunny habitats can partially be explained by the increased benefit of amphistomy in "sun" leaves, but the mechanistic basis remains uncertain.
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実験手法を開発し、複数集団で適用しているため、植物の生理形質取得法が中心である。
abstractWe developed a method to quantify "amphistomy advantage" ( AA $\text{AA}$ ) as the log-ratio of photosynthesis in an amphistomatous leaf to that of the same leaf but with gas exchange blocked through the upper surface (pseudohypostomy).
Reproduction assets foundThe paper's raw phenotyping data (stomatal traits, leaf thickness, gas exchange) are publicly deposited on Dryad, and the authors' custom analysis scripts are on GitHub with a Zenodo archive; both are paper-specific and directly actionable.Dataset · public7341. This is publication #213 from the School of Life
Sciences, University of Hawaiʻi at Mānoa.
DATA AVAILABILITY STATEMENT
Custom scripts are available on a GitHub repository
(https://github.com/cdmuir/stomata-ilima) and archived
on Zenodo: https://doi.org/10.5281/zenodo.10369114 (Muir,
2023). Raw data are deposited on Dryad: https://doi.org/10.5061/dryad.rxwdbrvfw (Triplett et al., 2024).
ORCID
Thomas N. Buckley http://orcid.org/0000-0001-7610-7136
Christopher D. Muir http://orcid.org/0000-0003-2555-3878
REFERENCES
Anonymous. 2022. Yellow ʻilima (Sida fallax). https://www.inaturalist.org/taxa/54995-Sida-fallax. iNaturalist.
Ball, J. T., I. E. Woodrow, and J. A. Berry. 1987. A model prediOpen asset ↗Dryad · 10.5061/dryad.rxwdbrvfwpdf-raw-page:9 lines:1-93Code · publicfor advice on leaf sectioning. Startup
funds were provided by the University of Hawaiʻi, NSF
Award 1929167 to C.D.M., and T.N.B. received NSF Award
2307341. This is publication #213 from the School of Life
Sciences, University of Hawaiʻi at Mānoa.
DATA AVAILABILITY STATEMENT
Custom scripts are available on a GitHub repository
(https://github.com/cdmuir/stomata-ilima) and archived
on Zenodo: https://doi.org/10.5281/zenodo.10369114 (Muir,
2023). Raw data are deposited on Dryad: https://doi.org/10.5061/dryad.rxwdbrvfw (Triplett et al., 2024).
ORCID
Thomas N. Buckley http://orcid.org/0000-0001-7610-7136
Christopher D. Muir http://orcid.org/0000-0003-2555-3878
REFERENCES
Anonymous. 2022. YellowOpen asset ↗GitHub · cdmuir/stomata-ilimapdf-raw-page:9 lines:1-93Plant phenotyping relevance match · UnverifiedEurope PMC · checked 15 Sept 2026
Stomata are microscopic pores found in the plant leaf epidermis. Regulation of stomatal aperture is pivotal not only for balancing carbon dioxide uptake for photosynthesis and transpirational water loss but also for restricting bacterial invasion. While plants close stomata upon recognition of microbes, pathogenic bacteria, such as Pseudomonas syringae pv. tomato DC3000 (Pto), reopen the closed stomata to gain access into the leaf interior. In conventional assays for assessing stomatal responses to bacterial invasion, leaf epidermal peels, leaf discs, or detached leaves are floated on bacterial suspension, and then stomata are observed under a microscope followed by manual measurement of stomatal aperture. However, these assays are cumbersome and may not reflect stomatal responses to natural bacterial invasion in a leaf attached to the plant. Recently, a portable imaging device was developed that can observe stomata by pinching a leaf without detaching it from the plant, together with a deep learning-based image analysis pipeline designed to automatically measure stomatal aperture from leaf images captured by the device. Here, building on these technical advances, a new method to assess stomatal responses to bacterial invasion in Arabidopsis thaliana is introduced. This method consists of three simple steps: spray inoculation of Pto mimicking natural infection processes, direct observation of stomata on a leaf of the Pto-inoculated plant using the portable imaging device, and automated measurement of stomatal aperture by the image analysis pipeline. This method was successfully used to demonstrate stomatal closure and reopening during Pto invasion under conditions that closely mimic the natural plant-bacteria interaction.
Why it matches plant phenotyping methods携帯型撮像装置と深層学習画像解析によって、感染植物の気孔開度を自動測定する方法が中心的に導入されているため。
abstracta portable imaging device was developed that can observe stomata by pinching a leaf without detaching it from the plant, together with a deep learning-based image analysis pipeline designed to automatically measure stomatal aperture
Leaf stomata facilitate the exchange of water and CO 2 during photosynthetic gas exchange. The shape, size, and density of leaf pores have not been constant over geologic time, and each morphological trait has potentially been impacted by changing environmental and climatic conditions, especially by changes in the concentration of atmospheric carbon dioxide. As such, stomatal parameters have been used in simple regressions to reconstruct ancient carbon dioxide, as well as incorporated into more complex gas-exchange models that also leverage plant carbon isotope ecology. Most of these proxy relationships are measured on chemically cleared leaves, although newer techniques such as creating stomatal impressions are being increasingly employed. Additionally, many of the proxy relationships use angiosperms with broad leaves, which have been increasingly abundant in the last 130 million years but are absent from the fossil record before this. We focus on the methodology to define stomatal parameters for paleo-CO 2 studies using two separate methodologies (one corrosive, one non-destructive) to prepare leaves on both scale- and broad-leaves collected from herbaria with known global atmospheric CO 2 levels. We find that the corrosive and non-corrosive methodologies give similar values for stomatal density, but that measurements of stomatal sizes, particularly guard cell width (GCW), for the two methodologies are not comparable. Using those measurements to reconstruct CO 2 via the gas exchange model, we found that reconstructed CO 2 based on stomatal impressions (due to inaccurate measurements in GCW) far exceeded measured CO 2 for modern plants. This bias was observed in both coniferous (scale-shaped) and angiosperm (broad) leaves. Thus, we advise that applications of gas exchange models use cleared leaves rather than impressions.
Why it matches plant phenotyping methods葉の気孔形態（密度・サイズ）を測定する2手法を比較・検証し、CO2推定への影響を評価しており、植物表現型取得法が研究の中心です。
abstractWe focus on the methodology to define stomatal parameters for paleo-CO 2 studies using two separate methodologies (one corrosive, one non-destructive) to prepare leaves on both scale- and broad-leaves collected from herbaria with known global atmospheric CO 2 levels.
Reproduction assets foundThe paper's stomatal phenotyping measurements (stomatal density, guard cell length/width, CO2 reconstructions) are publicly deposited as supplemental data tables on Mendeley Data (DOI 10.17632/gs6rn9tjxn.1), explicitly linked in the Supplementary Materials and Data Availability sections. No author analysis code or phenDataset · publicSupplemental Data Tables: All Measurements—Data are available at Mendeley Data: 10.17632/gs6rn9tjxn.1 (accessed on 13 November 2023).Open asset ↗Mendeley Data · 10.17632/gs6rn9tjxn.1lines:58-75Plant phenotyping relevance match · UnverifiedEurope PMC · checked 15 Sept 2026
The production of stomata, the epidermal pores of plants, is influenced by diverse environmental signals including high temperature. To assess its impact on stomatal formation, researchers need to grow plants in a carefully designed regime under controlled conditions and capture clear, microscopic views of the epidermis. Here, we describe a procedure to study the effect of high temperature on stomatal formation. This method can generate high-quality epidermal images of cotyledons, leaves, and hypocotyl of young Arabidopsis seedlings, which allow the determination of the pattern, density, and index of stomata on these tissues. Besides temperature, the protocol can serve as a general approach to examine stomatal phenotype and the effect of other external signals on stomatal formation.
Why it matches plant phenotyping methods若いシロイヌナズナの表皮画像を取得し、気孔のパターン・密度・指数を定量する手順が中心であり、気孔表現型の測定法として収載対象です。
abstractHere, we describe a procedure to study the effect of high temperature on stomatal formation.
One of the major plant stress level indicators is reactive oxygen species (ROS). They have been known to play a central role in regulating plant responses to various environmental stresses. This book chapter specifically covers abiotic stress induced by a drought hormone abscisic acid and biotic stress induced by Pseudomonas syringe DC3000 on single cell-type guard cells. We describe in detail the measurement of ROS production starting from sample preparation to data analysis by fluorescence intensity acquisition using ImageJ software. We discussed the problems faced while performing the experiment and addressed how to overcome them by providing specific guidelines to ensure high quality repeatable data.
Why it matches plant phenotyping methodsアラビドプシスの孔辺細胞におけるROSという生理状態を、蛍光画像取得とImageJ解析で測定する手順を中心に扱い、再現性確保の指針まで提示しているため、植物フェノタイピング手法として採用する。
abstractWe describe in detail the measurement of ROS production starting from sample preparation to data analysis by fluorescence intensity acquisition using ImageJ software.
The quantification of stomatal pore size has long been a fundamental approach to understand the physiological response of plants in the context of environmental adaptation. Automation of such methodologies not only alleviates human labor and bias but also realizes new experimental research methods through massive analysis. Here, we present an image analysis pipeline that automatically quantifies stomatal aperture of Arabidopsis thaliana leaves from bright-field microscopy images containing mesophyll tissue as noisy backgrounds. By combining a You Only Look Once X-based stomatal detection submodule and a U-Net-based pore segmentation submodule, we achieved a mean average precision with an intersection of union (IoU) threshold of 50% value of 0.875 (stomata detection performance) and an IoU of 0.745 (pore segmentation performance) against images of leaf discs taken with a bright-field microscope. Moreover, we designed a portable imaging device that allows easy acquisition of stomatal images from detached/undetached intact leaves on-site. We demonstrated that this device in combination with fine-tuned models of the pipeline we generated here provides robust measurements that can substitute for manual measurement of stomatal responses against pathogen inoculation. Utilization of our hardware and pipeline for automated stomatal aperture measurements is expected to accelerate research on stomatal biology of model dicots.
Why it matches plant phenotyping methods葉画像から気孔開度を自動抽出する画像解析パイプラインと携帯型撮像装置を開発・性能評価しており、植物表現型取得が中心である。
abstractwe present an image analysis pipeline that automatically quantifies stomatal aperture of Arabidopsis thaliana leaves
Reproduction assets foundThe paper's Data Availability section explicitly deposits the authors' ONNX model weights and pipeline code, plus masked/unmasked test images, on a public GitHub repository and Zenodo (DOI 10.5281/zenodo.7549843). These are paper-specific, publicly actionable assets for the stomatal aperture phenotyping pipeline. The YCode · publicPlant Cell Physiol. 00(00): 1–10 (2023) doi:https://doi.org/10.1093/pcp/pcad018
Supplementary Data
Supplementary data are available at PCP online.
Data Availability
The model weights and codes in ONNX format to execute
the Arabidopsis stomata quantification pipeline and mask
and unmasked test data are available at https://github.com/phytometrics/arabidopsis_leaf_stomata_quantification.Model weights and the test images are also available on Zen-
odo with the following DOI: https://doi.org/10.5281/zenodo.7549843. The software used to process image streams from the
portable device is under development at https://github.com/phytometrics/cvgui_linux.Funding
Grant-in-Aid for Transformative ResearcOpen asset ↗phytometrics/arabidopsis_leaf_stomata_quantificationpdf-raw-page:9 lines:1-84Dataset · publicData Availability
The model weights and codes in ONNX format to execute
the Arabidopsis stomata quantification pipeline and mask
and unmasked test data are available at https://github.com/phytometrics/arabidopsis_leaf_stomata_quantification.Model weights and the test images are also available on Zen-
odo with the following DOI: https://doi.org/10.5281/zenodo.7549843. The software used to process image streams from the
portable device is under development at https://github.com/phytometrics/cvgui_linux.Funding
Grant-in-Aid for Transformative Research Areas (21H05151 and
21H05149 to A.M. and 21H05152 to Y.T.), Grant-in-Aid for Sci-
entific Research (B) (19H02960 to A. M.), and Grant-in-Aid foOpen asset ↗10.5281/zenodo.7549843pdf-raw-page:9 lines:1-84Plant phenotyping relevance match · UnverifiedbioRxiv · Crossref · checked 7 Sept 2026
Leaf plastids harbor a plethora of biochemical reactions including photosynthesis, one of the most important metabolic pathways on earth. Scientists are eager to unveil the physiological processes within the organelle but also their interconnection with the rest of the plant cell. An increasingly important feature of this venture is to use experimental data in the design of metabolic models. A remaining obstacle has been the limited in situ volume information of plastids and other cell organelles. To fill this gap for chloroplasts, we established three microscopy protocols delivering in situ volumes based on: 1) chlorophyll fluorescence emerging from the thylakoid membrane, 2) a CFP marker embedded in the envelope, and 3) calculations from serial block-face scanning electron microscopy (SBFSEM). The obtained data were corroborated by comparing wild-type data with two mutant lines affected in the plastid division machinery known to produce small and large mesophyll chloroplasts, respectively. Furthermore, we also determined the volume of the much smaller guard cell plastids. Interestingly, their volume is not governed by the same components of the division machinery which defines mesophyll plastid size. Based on our three approaches the average volume of a mature Col-0 wild-type mesophyll chloroplasts is 93 {micro}m3. Wild-type guard cell plastids are approximately 18 {micro}m3. Lastly, our comparative analysis shows that the chlorophyll fluorescence analysis can accurately determine chloroplast volumes, providing an important tool to research groups without access to transgenic marker lines expressing genetically encoded fluorescence proteins or costly SBFSEM equipment. Significance statement -sentence summaryThis work describes and compares three different strategies to obtain accurate volumes of leaf plastids from Arabidopsis, the most widely used model plant. We hope our contribution will support quantitative metabolic flux modeling and spark other projects aimed at a more metric-driven plant cell biology.
Why it matches plant phenotyping methods葉緑体体積という植物器官・細胞形態形質を取得する3種類の顕微鏡プロトコルを確立・比較し、変異体で検証しているため、表現型取得法が研究の中心です。
abstractwe established three microscopy protocols delivering in situ volumes based on: 1) chlorophyll fluorescence emerging from the thylakoid membrane, 2) a CFP marker embedded in the envelope, and 3) calculations from serial block-face scanning electron microscopy (SBFSEM).
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:1-52Plant phenotyping relevance match · UnverifiedOpenAlex · Crossref · checked 15 Sept 2026
PlantCV is an open-source open-development image analysis software package for plant phenotyping written in Python that has been actively developed since 2014. A new version of PlantCV was recently released. Major goals of the version 4 release were to 1) simplify the process of developing workflows by reducing the amount of coding needed; 2) broadening the set of supported data types; and 3) introducing interactive annotation tools that can be used directly in PlantCV workflow notebooks. Here we highlight the use of point annotations that can be used to quickly collect sets of points for parameterization of functions such as regions of interest or the identification of landmark points. Another application of point annotations this for image annotation, which is a major bottleneck in plant phenomics. For example, we have used point annotations to analyze microscopy images aimed at measurement of quinoa salt bladders, the number and size of stomata, and scoring of pollen germination. These tasks have traditionally been low throughput and have required manual scoring, but our point annotation tools can be used along with traditional segmentation methods to semi-automatically detect and annotate images. The PlantCV point annotation tools also allow users to correct semi-automated detection results before classification (e.g., germinated vs non-germinated pollen) and extraction of size & color traits per object. Once images are annotated, results can be analyzed directly or potentially can be used as labeled data in supervised learning methods.
Why it matches plant phenotyping methodsPlantCVの画像解析ソフトウェアと対話的アノテーション機能を開発・紹介し、植物画像から器官数・サイズ・色などの形質を半自動抽出する方法が中心である。
abstractPlantCV is an open-source open-development image analysis software package for plant phenotyping written in Python
PlantCV is an open-source open-development image analysis software package for plant phenotyping written in Python that has been actively developed since 2014. A new version of PlantCV was recently released. Major goals of the version 4 release were to 1) simplify the process of developing workflows by reducing the amount of coding needed; 2) broadening the set of supported data types; and 3) introducing interactive annotation tools that can be used directly in PlantCV workflow notebooks. Here we highlight the use of point annotations that can be used to quickly collect sets of points for parameterization of functions such as regions of interest or the identification of landmark points. Another application of point annotations this for image annotation, which is a major bottleneck in plant phenomics. For example, we have used point annotations to analyze microscopy images aimed at measurement of quinoa salt bladders, the number and size of stomata, and scoring of pollen germination. These tasks have traditionally been low throughput and have required manual scoring, but our point annotation tools can be used along with traditional segmentation methods to semi-automatically detect and annotate images. The PlantCV point annotation tools also allow users to correct semi-automated detection results before classification (e.g., germinated vs non-germinated pollen) and extraction of size & color traits per object. Once images are annotated, results can be analyzed directly or potentially can be used as labeled data in supervised learning methods.
Why it matches plant phenotyping methodsPlantCVの画像解析ソフトウェアと点アノテーション機能の開発・適用を中心に扱い、植物器官の数・サイズ・色などの形質抽出を半自動化する方法論的研究である。
abstractPlantCV is an open-source open-development image analysis software package for plant phenotyping written in Python
Stomata play an essential role in regulating water and carbon dioxide levels in plant leaves, which is important for photosynthesis. Previous deep learning-based plant stomata detection methods are based on horizontal detection. The detection anchor boxes of deep learning model are horizontal, while the angle of stomata is randomized, so it is not possible to calculate stomata traits directly from the detection anchor boxes. Additional processing of image (e.g., rotating image) is required before detecting stomata and calculating stomata traits. This paper proposes a novel approach, named DeepRSD (deep learning-based rotating stomata detection), for detecting rotating stomata and calculating stomata basic traits at the same time. Simultaneously, the stomata conductance loss function is introduced in the DeepRSD model training, which improves the efficiency of stomata detection and conductance calculation. The experimental results demonstrate that the DeepRSD model reaches 94.3% recognition accuracy for stomata of maize leaf. The proposed method can help researchers conduct large-scale studies on stomata morphology, structure, and stomata conductance models.
Why it matches plant phenotyping methods植物葉の気孔を対象に、回転を考慮した検出と気孔形質・コンダクタンス算出を一体化する手法を開発しており、フェノタイピング手法が中心である。
abstractThis paper proposes a novel approach, named DeepRSD (deep learning-based rotating stomata detection), for detecting rotating stomata and calculating stomata basic traits at the same time.
Reproduction assets foundThe paper's DeepRSD stomata detection and conductance calculation code is explicitly stated to be publicly hosted on GitHub at the authors' repository URL. No public dataset of the 2,192 maize stomata images is mentioned.Code · publicThe code of anchor-free stomata detection and stomata conductance calculation has been hosted to GitHub and is available at https://github.com/sswangbo159357/Rotating-stomata-detection .Open asset ↗sswangbo159357/Rotating-stomata-detectionlines:288-533Plant phenotyping relevance match · UnverifiedEurope PMC · checked 15 Sept 2026
Main conclusion This study developed the reliable Mask R-CNN model to detect stomata in Lonicera caerulea. The obtained data could be utilized for evaluating some characters such as stomatal number and aperture area. The native distribution of haskap (Lonicera caerulea L.), a small-shrub species, extends through Northern Eurasia, Japan, and North America. Stomatal observation is important for plant research to evaluate the physiological status and to investigate the effect of ploidy levels on phenotypes. However, manual annotation of stomata using microscope software or ImageJ is time consuming. Therefore, an efficient method to phenotype stomata is needed. In this study, we used the Mask Regional Convolutional Neural Network (Mask R-CNN), a deep learning model, to analyze the stomata of haskap efficiently and accurately. We analyzed haskap plants (dwarf and giant phenotypes) with the same ploidy but different phenotypes, including leaf area, stomatal aperture area, stomatal density, and total number of stomata. The R-square value of the estimated stomatal aperture area was 0.92 and 0.93 for the dwarf and giant plants, respectively. The R-square value of the estimated stomatal number was 0.99 and 0.98 for the two phenotypes. The results showed that the measurements obtained using the models were as accurate as the manual measurements. Statistical analysis revealed that the stomatal density of the dwarf plants was higher than that of the giant plants, but the maximum stomatal aperture area, average stomatal aperture area, total number of stomata, and average leaf area were lower than those of the giant plants. A high-precision, rapid, and large-scale detection method was developed by training the Mask R-CNN model. This model can help save time and increase the volume of data.
Why it matches plant phenotyping methodsMask R-CNNによる気孔数・開口面積の画像ベース表現型計測法を開発し、手動測定との精度比較で検証しているため。
abstractTherefore, an efficient method to phenotype stomata is needed.
Plant stomata are essential channels for gas exchange between plants and the environment. The infrared gas-exchange system has greatly accelerated the studies of stomatal conductance (gₛ). Nevertheless, due to the lack of in-situ monitoring techniques, the behavior of stomata themselves remains poorly understood, especially in nocturnal environmental conditions. Here, a deep-learning-based stoma tracking pipeline (StomataTracker) was first proposed to continuously monitor stoma traits from unprecedentedly long-term, continuous, and non-destructive video data. Compared to the semi-automatic method (ImageJ), the open-source StomataTracker could greatly improve the extraction efficiency from 207 s to 1.47 s of stomatal traits, including stomatal area, perimeter, length, and width. The R² adjusted of the four stomatal traits ranged from 0.620 to 0.752. In addition, the rhythm of wheat stomata opening in a completely dark environment was first reported from long-term video data. The closed time of stoma at night was negatively correlated with stomatal traits, and the R ranged from −0.583 to −0.855. The heterogeneity of stomatal behavior also highlighted that smaller stomata have the rhythm pattern of longer closure time at night. Overall, our study provides a novel perspective for stomatal study, and it is conducive to accelerating the application of stomatal circadian rhythm in wheat breeding.
Why it matches plant phenotyping methods深層学習による気孔追跡パイプラインを開発し、動画から気孔形質を抽出・既存法と比較検証しているため、植物フェノタイピング手法が中心である。
abstractHere, a deep-learning-based stoma tracking pipeline (StomataTracker) was first proposed to continuously monitor stoma traits from unprecedentedly long-term, continuous, and non-destructive video data.
Stomata are the gatekeepers of plant water use and must quickly respond to changes in plant water status to ensure plant survival under fluctuating environmental conditions. The mechanism for their closure is highly sensitive to disturbances in leaf water status, which makes isolating their response to declining water content difficult to characterise and to compare responses among species. Using a small-scale non-destructive nuclear magnetic resonance spectrometer as a leaf water content sensor, we measure the stomatal response to rapid induction of water deficit in the leaves of nine species of eucalypt from contrasting climates. We found a strong linear correlation between relative water content at 50% stomatal conductance (RWC gs50 ) and mean annual temperature at the climate of origin of each species. We also show evidence for stomata to maintain control over water loss well below turgor loss point in species adapted to warmer climates and secondary increases in stomatal conductance despite declining water content. We propose that RWC gs50 is a promising trait to guide future investigations comparing stomatal responses to water deficit. It may provide a useful phenotyping trait to delineate tolerance and adaption to hot temperatures and high leaf-to-air vapour pressure deficits.
Why it matches plant phenotyping methods非侵襲NMRを葉の水分状態センサーとして用い、乾燥応答を定量する新しい表現型RWC gs50を提案しており、測定手法と再利用可能な形質が研究の中心です。
abstractUsing a small-scale non-destructive nuclear magnetic resonance spectrometer as a leaf water content sensor, we measure the stomatal response to rapid induction of water deficit
Abstract Stomata act as a pathway for air and water vapor during respiration, transpiration and other gas metabolism, so the stomata phenotype is important for plant growth and development. Intelligent detection of high throughput stoma is a key issue. However, current existing methods usually suffer from detection error or cumbersome operations when facing densely and unevenly arranged stomata. The proposed RotatedStomataNet innovatively regards stomata detection as rotated object detection, enabling an end-to-end, real-time and intelligent phenotype analysis of stomata and apertures. The system is constructed based on the Arabidopsis and maize stomatal data sets acquired in a destructive way, and the maize stomatal data set acquired in a nondestructive way, enabling one-stop automatic collection of phenotypic such as the location, density, length and width of stomata and apertures without step-by-step operations. The accuracy of this system to acquire stomata and apertures has been well demonstrated in monocotyledon and dicotyledon, such as Arabidopsis, soybean, wheat, and maize. And the experimental results showed that the prediction results of the method are consistent with those of manual labeled. The test sets, system code, and its usage are also given (https://github.com/AITAhenu/RotatedStomataNet).
Why it matches plant phenotyping methods気孔の検出と開度・密度・寸法などの表現型を自動抽出する画像解析手法を開発し、複数作物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。
abstractThe proposed RotatedStomataNet innovatively regards stomata detection as rotated object detection, enabling an end-to-end, real-time and intelligent phenotype analysis of stomata and apertures.
Reproduction assets foundThe paper explicitly states that the test sets, system code, and usage instructions are publicly available at the authors' GitHub repository (https://github.com/AITAhenu/RotatedStomataNet). This is a paper-specific asset: the RotatedStomataNet system code for rotated object detection of stomata and apertures, together,Code · publicThe test sets, system code, and its usage are also given (https://github.com/AITAhenu/RotatedStomataNet).Open asset ↗AITAhenu/RotatedStomataNetpdf-page:3 lines:1-49Plant phenotyping relevance match · UnverifiedCrossref · checked 14 Sept 2026
Abstract Changes in cytosolic calcium (Ca2+) concentration are among the earliest reactions to a multitude of stress cues. While a plethora of Ca2+-permeable channels may generate distinct Ca2+ signatures and contribute to response specificities, the mechanisms by which Ca2+ signatures are decoded are poorly understood. Here, we developed a genetically encoded Förster resonance energy transfer (FRET)-based reporter that visualizes the conformational changes in Ca2+-dependent protein kinases (CDPKs/CPKs). We focused on two CDPKs with distinct Ca2+-sensitivities, highly Ca2+-sensitive Arabidopsis (Arabidopsis thaliana) AtCPK21 and rather Ca2+-insensitive AtCPK23, to report conformational changes accompanying kinase activation. In tobacco (Nicotiana tabacum) pollen tubes, which naturally display coordinated spatial and temporal Ca2+ fluctuations, CPK21-FRET, but not CPK23-FRET, reported oscillatory emission ratio changes mirroring cytosolic Ca2+ changes, pointing to the isoform-specific Ca2+-sensitivity and reversibility of the conformational change. In Arabidopsis guard cells, CPK21-FRET-monitored conformational dynamics suggest that CPK21 serves as a decoder of signal-specific Ca2+ signatures in response to abscisic acid and the flagellin peptide flg22. Based on these data, CDPK-FRET is a powerful approach for tackling real-time live-cell Ca2+ decoding in a multitude of plant developmental and stress responses.
Why it matches plant phenotyping methods植物体内のCa2+依存的な生理状態をリアルタイム可視化するFRETレポーターを開発しており、植物表現型取得法が研究の中心である。
abstractHere, we developed a genetically encoded Förster resonance energy transfer (FRET)-based reporter that visualizes the conformational changes in Ca2+-dependent protein kinases (CDPKs/CPKs).
Stomatal opening in plant leaves is regulated through a balance of carbon and water exchange under different environmental conditions. Accurate estimation of stomatal regulation is crucial for understanding how plants respond to changing environmental conditions, particularly under climate change. A new generation of optimality-based modelling schemes determines instantaneous stomatal responses from a balance of trade-offs between carbon gains and hydraulic costs, but most such schemes do not account for biochemical acclimation in response to drought. Here, we compare the performance of seven instantaneous stomatal optimisation models with and without accounting for photosynthetic acclimation. Using experimental data from 38 plant species, we found that accounting for photosynthetic acclimation improves the prediction of carbon assimilation in a majority of the tested models. Non-stomatal mechanisms contributed significantly to the reduction of photosynthesis under drought conditions in all tested models. Drought effects on photosynthesis could not accurately be explained by the hydraulic impairment functions embedded in the stomatal models alone, indicating that photosynthetic acclimation must be considered to improve estimates of carbon assimilation during drought. Summary Statement Accounting for photosynthetic acclimation improves the predictions of carbon assimilation in all the stomatal optimization models evaluated. The influence of drought on photosynthesis cannot be fully explained by the hydraulic impairment function of the stomatal models alone.
Why it matches plant phenotyping methods7種類の気孔最適化モデルを比較評価し、光合成順化を組み込んだモデルの炭素同化予測性能を検証しているため、植物生理形質の計算的推定手法の技術検証が中心です。
abstractHere, we compare the performance of seven instantaneous stomatal optimisation models with and without accounting for photosynthetic acclimation.
Drought adaptation for water-limited environments relies on traits that optimize plant water budgets. Limited transpiration (LT) reduces water demand under high vapor pressure deficit (VPD) (i.e., dry air condition), conserving water for efficient use during the reproductive stage. Although studies in controlled environments report genetic variation for LT, confirming its replicability in field conditions is critical for developing water-resilient crops. Here we test the existence of genetic variation for LT in sorghum in field trials and whether canopy temperature (TC) is a surrogate method to discriminate this trait. We phenotyped transpiration response to VPD (TR-VPD) via stomatal conductance (gs), canopy temperature (TC) from fixed IRT sensors (TCirt), and unoccupied aerial system thermal imagery (TCimg) in 11 genotypes. Replicability among phenomic approaches for three genotypes revealed genetic variability for TR-VPD. Genotypes BTx2752 and SC979 carry the LT trait, while genotype DKS54-00 has the non-LT trait. TC can determine differences in TR-VPD. However, the broad sense heritability (H2) and correlations suggest that canopy architecture and stand count hampers TCirt and TCimg measurement. Unexpectedly, observations of gs and VPD showed non-linear patterns for genotypes with LT and non-LT traits. Our findings provide further insights into the genetics of plant water dynamics.
Why it matches plant phenotyping methods圃場での蒸散応答という植物生理形質を、固定式赤外線センサーとUAS熱画像で測定し、手法間の再現性と代理指標としての妥当性を評価しており、フェノタイピング手法が中心である。
abstractHere we test the existence of genetic variation for LT in sorghum in field trials and whether canopy temperature (TC) is a surrogate method to discriminate this trait.
MaizeLeafStomata / guard-cell complexTissueStomatal traitsWater status / transpiration
The water status of the living tissue in leaves between the xylem and stomata (outside xylem zone - OXZ) play a critical role for plant function and global mass and energy balance but has remained largely inaccessible. We resolve the local water relations of OXZ tissue using a nanogel reporter of water potential ({psi}), AquaDust, that enables an in-situ, non-destructive measurement of both{psi} of xylem and highly localized{psi} at the terminus of transpiration in the OXZ. Working in maize, these localized measurements reveal gradients in the OXZ that are several fold larger than those based on conventional methods, and values of{psi} in the mesophyll apoplast well below the macroscopic turgor loss potential. We find a strong loss of hydraulic conductance in both the bundle sheath and the mesophyll with decreasing xylem potential but not with evaporative demand. Our measurements suggest an active role played by the OXZ in regulating the transpiration path and our methods provide novel means to study this phenomenon.
Why it matches plant phenotyping methodsAquaDustを用いた葉内の局所的な水ポテンシャル測定法が研究の中心であり、植物の水状態・水理特性を定量化している。
abstractWe resolve the local water relations of OXZ tissue using a nanogel reporter of water potential ({psi}), AquaDust, that enables an in-situ, non-destructive measurement of both{psi} of xylem and highly localized{psi} at the terminus of transpiration in the OXZ.
Volumetric soil water content is commonly used for irrigation management in fruit trees. By integrating direct information on tree water status into measurements of soil water content, we can improve detection of water stress and irrigation scheduling. Thermal-based indicators can be an alternative to traditional measurements of midday stem water potential and stomatal conductance for irrigation management of pear trees ( Pyrus communis L.). These indicators are easy, quick, and cost-effective. The soil and tree water status of two cultivars of pear trees 'D'Anjou' and 'Bartlett' submitted to regulated deficit irrigation was measured regularly in a pear orchard in Rock Island, WA (USA) for two seasons, 2021 and 2022. These assessments were compared to the canopy temperature (Tc), the difference between the canopy and air temperature (Tc-Ta) and the crop water stress index (CWSI). Trees under deficit irrigation had lower midday stem water potential and stomatal conductance but higher Tc, Tc-Ta, and CWSI. Tc was not a robust method to assess tree water status since it was strongly related to air temperature (R = 0.99). However, Tc-Ta and CWSI were greater than 0°C or 0.5, respectively, and were less dependent on the environmental conditions when trees were under water deficits (midday stem water potential values -2 s -1 . Soil water content (SWC) was the first indicator in detecting the deficit irrigation applied, however, it was not as strongly related to the tree water status as the thermal-based indicators. Thus, the relation between the indicators studied with the stem water potential followed the order: CWSI > Tc-Ta > SWC = Tc. A multiple regression analysis is proposed that combines both soil water content and thermal-based indices to overcome limitations of individual use of each indicator.
Why it matches plant phenotyping methodsナシ樹の水分状態を熱画像由来の指標で推定し、土壌水分や茎水ポテンシャルと比較・検証することが中心であり、植物生理状態のフェノタイピング手法に該当する。
abstractThermal-based indicators can be an alternative to traditional measurements of midday stem water potential and stomatal conductance for irrigation management of pear trees
Shoot and leaf blight caused by Teratosphaeria destructans is one of the most devastating foliar diseases on Eucalyptus . Therefore, breeding for resistance to this disease is considered urgent. Differences in susceptibility to T. destructans have been observed in the field but a robust inoculation protocol has, until recently, been unavailable and a disease scoring method for precise phenotyping has not been established. A first objective of this study was to determine the optimal conidial concentration for T. destructans inoculations on a susceptible Eucalyptus host. This concentration was then used to determine differences in susceptibility of six genotypes of Eucalyptus grandis × E. urophylla to the pathogen by assessing the percentage of infected stomata using electron microscopy and the percentage of leaf area covered by lesions (PLACL) using image processing. In addition, we developed a disease susceptibility index (SI) of six categories ranging from highly resistant (SI = 0) to highly susceptible (SI = 1.5 to 2). The more resistant genotypes were moderately resistant, with an SI value of 0.49 to 0.54 and a PLACL of 6.5 to 9%. In contrast, the more susceptible genotype scored an SI of 1.52 and PLACL of 48%. Host susceptibility was also assessed relative to the sporulation of the pathogen. This showed that the percentage of sporulation was not significantly correlated with host resistance. Overall, the results provide the basis for rigorous screening and selection of resistant genotypes to the disease caused by T. destructans using artificial inoculation.
Why it matches plant phenotyping methodsユーカリの病害抵抗性を定量するため、画像処理による病斑面積測定と疾病感受性指数を開発し、人工接種によるスクリーニングへの適用基盤を示しており、表現型取得法が研究の中心である。
abstracta disease scoring method for precise phenotyping has not been established
Wearable plant sensors hold tremendous potential for smart agriculture. We report a lower leaf surface-attached multimodal wearable sensor for continuous monitoring of plant physiology by tracking both biochemical and biophysical signals of the plant and its microenvironment. Sensors for detecting volatile organic compounds (VOCs), temperature, and humidity are integrated into a single platform. The abaxial leaf attachment position is selected on the basis of the stomata density to improve the sensor signal strength. This versatile platform enables various stress monitoring applications, ranging from tracking plant water loss to early detection of plant pathogens. A machine learning model was also developed to analyze multichannel sensor data for quantitative detection of tomato spotted wilt virus as early as 4 days after inoculation. The model also evaluates different sensor combinations for early disease detection and predicts that minimally three sensors are required including the VOC sensors.
Why it matches plant phenotyping methods植物の生理状態・病害状態を連続取得するウェアラブル多モーダルセンサープラットフォームの開発が中心であり、機械学習による病害の定量検出も含むため。
abstractWe report a lower leaf surface-attached multimodal wearable sensor for continuous monitoring of plant physiology by tracking both biochemical and biophysical signals of the plant and its microenvironment.
Background Stomata are tiny pores on the leaf surface that are central to gas exchange. Stomatal number, size and aperture are key determinants of plant transpiration and photosynthesis, and variation in these traits can affect plant growth and productivity. Current methods to screen for stomatal phenotypes are tedious and not high throughput. This impedes research on stomatal biology and hinders efforts to develop resilient crops with optimised stomatal patterning. We have developed a rapid non-destructive method to phenotype stomatal traits in three crop species: wheat, rice and tomato. Results The method consists of two steps. The first is the non-destructive capture of images of the leaf surface from plants in their growing environment using a handheld microscope; a process that only takes a few seconds compared to minutes for other methods. The second is to analyse stomatal features using a machine learning model that automatically detects, counts and measures stomatal number, size and aperture. The accuracy of the machine learning model in detecting stomata ranged from 88 to 99%, depending on the species, with a high correlation between measures of number, size and aperture using the machine learning models and by measuring them manually. The rapid method was applied to quickly identify contrasting stomatal phenotypes. Conclusions We developed a method that combines rapid non-destructive imaging of leaf surfaces with automated image analysis. The method provides accurate data on stomatal features while significantly reducing time for data acquisition and analysis. It can be readily used to phenotype stomata in large populations in the field and in controlled environments.
Why it matches plant phenotyping methods葉面画像の取得と機械学習による気孔形質の自動抽出を中心に、精度検証も行った植物フェノタイピング手法の開発研究。
abstractWe have developed a rapid non-destructive method to phenotype stomatal traits in three crop species: wheat, rice and tomato.
Abstract Phytoliths constitute microscopic SiO 2 -rich biominerals formed in the cellular system of many living plants and are often preserved in soils, sediments and artefacts. Their analysis contributes significantly to the identification and study of botanical remains in (paleo)ecological and archaeological contexts. Traditional identification and classification of phytoliths rely on human experience, and as such, an emerging challenge is to automatically classify them to enhance data homogeneity among researchers worldwide and facilitate reliable comparisons. In the present study, a deep artificial neural network (NN) is implemented under the objective to detect and classify phytoliths, extracted from modern wheat ( Triticum spp.). The proposed methodology is able to recognise four phytolith morphotypes: (a) Stoma, (b) Rondel, (c) Papillate, and (d) Elongate dendritic. For the learning process, a dataset of phytolith photomicrographs was created and allocated to training, validation and testing data groups. Due to the limited size and low diversity of the dataset, an end-to-end encoder-decoder NN architecture is proposed, based on a pre-trained MobileNetV2, utilised for the encoder part and U-net, used for the segmentation stage. After the parameterisation, training and fine-tuning of the proposed architecture, it is capable to classify and localise the four classes of phytoliths in unknown images with high unbiased accuracy, exceeding 90%. The proposed methodology and corresponding dataset are quite promising for building up the capacity of phytolith classification within unfamiliar (geo)archaeological datasets, demonstrating remarkable potential towards automatic phytolith analysis.
Why it matches plant phenotyping methods植物由来の植物珪酸体を画像から検出・分類する深層学習手法とデータセットの開発が研究の中心であり、植物形態情報の取得・抽出に該当する。
abstracta deep artificial neural network (NN) is implemented under the objective to detect and classify phytoliths, extracted from modern wheat
Reproduction assets foundThe paper's phytolith photomicrograph dataset (annotated images of four morphotypes from modern wheat) is explicitly stated to be publicly available on Kaggle. Code and trained NNs are mentioned as contributions but no public repository URL is provided, so only the dataset qualifies.Dataset · publicData availability
The dataset of the current study is publicly available in the Kaggle platform: https://www.kaggle.com/datasets/georgepetrakis/phytolith-photomicrographs .Open asset ↗Kaggle · georgepetrakis/phytolith-photomicrographslines:130-152Plant phenotyping relevance match · UnverifiedbioRxiv · Crossref · checked 7 Sept 2026
Changes in cytosolic calcium concentration are among the earliest reactions to a multitude of stress cues. Whereas a plethora of calcium-permeable channels may generate distinct calcium signatures and contribute to response specificities, the mechanisms by which calcium signatures are decoded is poorly understood. Here we develop a genetically encoded, FRET-based reporter that visualizes the conformational change of calcium-dependent protein kinases (CDPKs/CPKs), preceding kinase activation, for calcium-dependent AtCPK21 and calcium-independent AtCPK23. In pollen tubes, naturally displaying a physiological calcium range, CPK21-FRET, but not CPK23-FRET, report activity oscillations with similar features to cytosolic calcium, suggesting an isoform-specific calcium dependency and reversibility of the conformational change. In guard cells CPK21-FRET identifies CPK21 as a decoder of signal-specific calcium signatures in response to ABA and flg22. Based on this data, CDPK-FRET stands as a novel approach for tackling real-time live-cell calcium decoding in a multitude of plant developmental and stress responses.
Why it matches plant phenotyping methods植物細胞内のカルシウム依存的キナーゼ構造変化をリアルタイム可視化するFRETレポーターを開発しており、植物の生理状態を測定する方法が中心である。
abstractHere we develop a genetically encoded, FRET-based reporter that visualizes the conformational change of calcium-dependent protein kinases (CDPKs/CPKs)
Wheat ( Triticum aestivum L.) is one of the most important crops as it provides 20% of calories and proteins to the human population. To overcome the increasing demand in wheat grain production, there is a need for a higher grain yield, and this can be achieved in particular through an increase in the grain weight. Moreover, grain shape is an important trait regarding the milling performance. Both the final grain weight and shape would benefit from a comprehensive knowledge of the morphological and anatomical determinism of wheat grain growth. Synchrotron-based phase-contrast X-ray microtomography (X-ray µCT) was used to study the 3D anatomy of the growing wheat grain during the first developmental stages. Coupled with 3D reconstruction, this method revealed changes in the grain shape and new cellular features. The study focused on a particular tissue, the pericarp, which has been hypothesized to be involved in the control of grain development. We showed considerable spatio-temporal diversity in cell shape and orientations, and in tissue porosity associated with stomata detection. These results highlight the growth-related features rarely studied in cereal grains, which may contribute significantly to the final grain weight and shape.
Why it matches plant phenotyping methodsシンクロトロンX線マイクロCTと3D再構成を中核に、発達中コムギ粒の3D形状・細胞形態・組織空隙を抽出しており、植物器官の形態表現型取得が中心である。
abstractSynchrotron-based phase-contrast X-ray microtomography (X-ray µCT) was used to study the 3D anatomy of the growing wheat grain during the first developmental stages.
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 development was integrated into the Imago software, which is
freely available at https://github.com/SciCompJ/Imago (accessed on 21 February 2023).Open asset ↗SciCompJ/Imagopdf-page:23 lines:1-59Code / dataset availability confirmedEurope PMC · checked 7 Sept 2026
Leaves grown at different light intensities exhibit considerable differences in physiology, morphology and anatomy. Because plant leaves develop over three dimensions, analyses of the leaf structure should account for differences in lengths, surfaces, as well as volumes. In this manuscript, we set out to disentangle the mesophyll surface area available for diffusion per leaf area ( S m,LA ) into underlying one-, two- and three-dimensional components. This allowed us to estimate the contribution of each component to S m,LA , a whole-leaf trait known to link structure and function. We introduce the novel concept of a 'stomatal vaporshed,' i.e. the intercellular airspace unit most closely connected to a single stoma, and use it to describe the stomata-to-diffusive-surface pathway. To illustrate our new theoretical framework, we grew two cultivars of Vitis vinifera L. under high and low light, imaged 3D leaf anatomy using microcomputed tomography (microCT) and measured leaf gas exchange. Leaves grown under high light were less porous and thicker. Our analysis showed that these two traits and the lower S m per mesophyll cell volume ( S m,Vcl ) in sun leaves could almost completely explain the difference in S m,LA . Further, the studied cultivars exhibited different responses in carbon assimilation per photosynthesizing cell volume ( A Vcl ). While Cabernet Sauvignon maintained A Vcl constant between sun and shade leaves, it was lower in Blaufränkisch sun leaves. This difference may be related to genotype-specific strategies in building the stomata-to-diffusive-surface pathway.
Why it matches plant phenotyping methods3D葉解剖をmicroCTで画像化し、葉の拡散面積関連形質を分解・推定する新しい理論枠組みを提示しており、表現型取得・解析法が研究の中心である。
abstractwe set out to disentangle the mesophyll surface area available for diffusion per leaf area ( S m,LA ) into underlying one-, two- and three-dimensional components.
Reproduction assets foundThe paper's Data Availability statement explicitly deposits all raw and segmented microCT imaging data plus extracted trait data on Zenodo, and the vaporshed-extraction analysis code in the public leaf-traits-microct GitHub repository. Both are paper-specific, public, and actionable.Dataset · publicAll imaging data (raw microCT scans and segmented scans) and data extracted from those images are available on Zenodo ( https://doi.org/10.5281/zenodo.5994663 ).Open asset ↗Zenodo · 10.5281/zenodo.5994663lines:219-265Plant phenotyping relevance match · UnverifiedEurope PMC · checked 14 Sept 2026
Stomata are the microscopic pores on plant leaves that open or close to regulate the flux of water from leaves. Guard cells of stomata are known to react to environmental conditions such as light and CO2 in order to optimize CO2 uptake and water loss. Stomatal anatomy (aperture, length, width, etc.) influences leaf-level physiology traits including conductance to water. Stomatal anatomy can be visualized in situ by microscopy, but the difficulty of regulating the atmospheric environment of a microscope stage means that the conditions under which imaging is done are rarely physiologically relevant. Alternatively, portable photosynthesis measuring instruments offer a non-destructive estimate of leaf gas exchange, including stomatal conductance, while the leaf experiences tightly controlled steady-state or dynamic environmental conditions. However, these measurements reflect stomatal characteristics in aggregate on a leaf area basis, which are heavily influenced by the mesophyll as well as epidermal structure and function. Observing the behavior of stomata by microscopy simultaneous to controlling the leaf environment and measuring gas exchange fluxes would allow advances in the understanding of leaf structure-function relationships. To reconcile the microscopic stomatal characteristics with leaf-level gas exchange we have combined laser scanning confocal microscopy and gas exchange instruments to simultaneously observe stomatal characteristics (e.g. stomatal aperture, pore depth, closing speed) and leaf-level traits like photosynthesis, transpiration, and stomatal conductance. Results are presented for the use of this approach on diverse plant species.
Why it matches plant phenotyping methods共焦点顕微鏡とガス交換計測を統合し、気孔形態・動態および光合成、蒸散、気孔コンダクタンスを同時取得する手法が研究の中心である。
abstractwe have combined laser scanning confocal microscopy and gas exchange instruments to simultaneously observe stomatal characteristics (e.g. stomatal aperture, pore depth, closing speed) and leaf-level traits like photosynthesis, transpiration, and stomatal conductance.
Plant stomata phenotypic traits can provide a basis for enhancing crop tolerance in adversity. Manually counting the number of stomata and measuring the height and width of stomata obviously cannot satisfy the high-throughput data. How to detect and recognize plant stomata quickly and accurately is the prerequisite and key for studying the physiological characteristics of stomata. In this research, we consider stomata recognition as a multi-object detection problem, and propose an end-to-end framework for intelligent detection and recognition of plant stomata based on feature weights transfer learning and YOLOv4 network. It is easy to operate and greatly facilitates the analysis of stomata phenotypic traits in high-throughput plant epidermal cell images. For different cultivars, multi-scales, rich background features, high density, and small stomata object images, the proposed method can precisely locate multiple stomata in microscope images and automatically give phenotypic traits of stomata. Users can also adjust the corresponding parameters to maximize the accuracy and scalability of automatic stomata detection and recognition. Experimental results on actual data provided by the National Maize Improvement Center show that the proposed method is superior to the existing methods in high stomata automatic detection and recognition accuracy, low training cost, strong generalization ability.
Why it matches plant phenotyping methods気孔の位置検出と形態形質の自動抽出を目的とする画像ベースの高スループット表現型解析手法を開発し、実データで性能評価しているため。
abstractautomatically give phenotypic traits of stomata.
Plants are constantly exposed to various environmental stresses, among which, microbial pathogens are one of the major threats. Studies have shown that the host actin cytoskeleton undergoes active rearrangement during the plant-microbe interaction. This actin remodeling is required for plant resistance to bacterial infection. In this chapter, we introduce a protocol routinely used in our laboratory to investigate actin dynamics in response to bacterial cues. We describe the bacterial inoculation methods, plant sample preparation, and imaging techniques used to monitor actin responses in different Arabidopsis cell types including epidermal cells from light-grown leaves and dark-grown hypocotyls, as well as guard cells. We further introduce a high-throughput image analysis method for quantifying cytoskeletal changes. This protocol has allowed us to dissect the host cell contribution to actin remodeling and identify actin-binding proteins as stimulus-response regulators of the cytoskeleton.
Why it matches plant phenotyping methods植物細胞のアクチン動態を画像化し、細胞骨格変化を定量する高スループット画像解析法と実験プロトコルが中心であり、植物の応答状態を取得・抽出する手法に該当する。
abstractWe further introduce a high-throughput image analysis method for quantifying cytoskeletal changes.
The quantification of stomatal pore size has long been a fundamental approach to understand the physiological response of plants in the context of environmental adaptation. Automation of such methodologies not only alleviates human labor and bias, but also realizes new experimental research methods through massive analysis. Here, we present an image analysis pipeline that automatically quantifies stomatal aperture of Arabidopsis thaliana leaves from brightfield microscopy images containing mesophyll tissue as noisy backgrounds. By combining a YOLOX-based stomatal detection submodule and a U-Net-based pore segmentation submodule, we achieved 0.875 mAP 50 (mean average precision; stomata detection performance) and 0.745 IoU (intersection of union; pore segmentation performance) against images of leaf discs taken with a brightfield microscope. Moreover, we designed a portable imaging device that allows easy acquisition of stomatal images from detached/undetached intact leaves on-site. We further combined this device with fine-tuned models of the pipeline we generated here and recapitulated manual measurement of stomatal responses against pathogen inoculation. Utilization of our hardware and pipeline for automated stomatal aperture measurements is expected to accelerate research on stomatal biology of model dicots.
Why it matches plant phenotyping methods葉画像から気孔開度を自動定量する画像解析パイプラインと携帯型撮像装置を開発し、性能評価・手動測定との再現性確認まで行っており、植物フェノタイピング手法が中心である。
abstractHere, we present an image analysis pipeline that automatically quantifies stomatal aperture of Arabidopsis thaliana leaves from brightfield microscopy images
High-oil tobacco varieties have been recently engineered to produce increased leaf oil content for future food and fuel needs. An engineered variety of Nicotiana tabacum produces ~30 percent of leaf dry weight in lipids in the form of triacylglycerol (TAG), a significant increase relative to the less than 1 percent storage oil normally found in wild-type leaves. This high-oil tobacco also accumulates oil bodies in stomatal guard cells. In order to understand the impact of oil on guard cell shape, aperture, and dynamics, we have co-opted computer vision tools in PlantCV to create an accurate, flexible, and high-throughput method for microscopy image analysis of stomata. To this end, leaf impressions are made with silicone putty; clear nail polish peels of the putty impressions are imaged using light microscopy. Binary thresholding followed by point-and-click regions of interest and morphology calculations provide stomatal counts, aperture, and other shape characteristics. Applying this method to high-oil tobacco demonstrated reduced stomatal aperture but the same number of stomata per unit leaf area, providing a mechanistic explanation of high-oil tobacco responses to high temperature and water deficit stresses.
Why it matches plant phenotyping methods植物の気孔形態・開度・密度を画像から抽出する高スループット手法の開発と適用が中心であり、単なる生物学的測定ではない。
abstractwe have co-opted computer vision tools in PlantCV to create an accurate, flexible, and high-throughput method for microscopy image analysis of stomata
Plant phenotyping relevance match · UnverifiedbioRxiv · Europe PMC · checked 15 Sept 2026
BackgroundStomata are tiny pores on the leaf surface that are central to gas exchange. Stomatal number, size and aperture are key determinants of plant transpiration and photosynthesis, and variation in these traits can affect plant growth and productivity. Current methods to screen for stomatal phenotypes are tedious and not high throughput. This impedes research on stomatal biology and hinders efforts to develop resilient crops with optimised stomatal patterning. We have developed a rapid non-destructive method to phenotype stomatal traits in four species: wheat, rice, tomato and Arabidopsis. ResultsThe method consists of two steps. The first is the non-destructive capture of images of the leaf surface from plants in their growing environment using a handheld microscope; a process which only takes a few seconds compared to minutes for other methods. The second is to analyse stomatal features using a machine learning model that automatically detects, counts and measures stomatal number, size and aperture. The accuracy of the machine learning model in detecting stomata ranged from 76% to 99%, depending on the species, with a high correlation between measures of number, size and aperture between measurements using the machine learning models and by measuring them manually. The rapid method was applied to quickly identify contrasting stomatal phenotypes. ConclusionsWe developed a method that combines rapid non-destructive imaging of leaf surfaces with automated image analysis. The method provides accurate data on stomatal features while significantly reducing time for data acquisition and analysis. It can be readily used to phenotype stomata in large populations in the field and in controlled environments.
Why it matches plant phenotyping methods気孔形質を対象に、携帯顕微鏡による非破壊画像取得と機械学習による自動検出・計測手法を開発し、手動測定との精度比較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 have developed a rapid non-destructive method to phenotype stomatal traits in four species: wheat, rice, tomato and Arabidopsis.
Organelles contribute to plant growth via their movements and interactions, which ensure efficient metabolic flow and help plants adapt to environmental stress. Live-cell imaging of the interactions of organelles has been performed in yeast, plant, and animal cells. However, high-throughput quantitative methods are needed to simultaneously analyze the interactions of many organelles in living plant cells. Here, we developed a semi-automatic high-throughput method to quantitatively evaluate the interactions between peroxisomes and chloroplasts using a distance transformation algorithm and high-resolution 3D fluorescent images taken by confocal laser scanning microscopy. Using this method, we measured the 3D distance between the center of peroxisome and chloroplast surface in Arabidopsis thaliana . We then compared the distances between these organelles in leaf mesophyll cells under light and dark conditions. This distance was shorter in the light than in the dark, which is in agreement with the findings of previous studies. We used our method to evaluate peroxisome-chloroplast (plastid) interactions in different cell types in the light and dark, including guard, stem, and root cells. Like in mesophyll cells, the distance between the peroxisome and chloroplast was shorter in the light in guard and stem cells, but not in root cells, suggesting that photosynthetic plastids (chloroplasts) play important roles in these interactions. When leaf mesophyll cells were incubated under high-intensity light, the frequency of shorter distances between peroxisomes and chloroplasts significantly increased. Our high-throughput, semi-automatic method represents a powerful tool for evaluating peroxisome-chloroplast interactions in different types of plant cells under various environmental conditions.
Why it matches plant phenotyping methods植物細胞内オルガネラ間距離という観測可能な植物状態を、3D蛍光画像と距離変換アルゴリズムで定量する高スループット手法を開発しており、方法が研究の中心である。
abstractHere, we developed a semi-automatic high-throughput method to quantitatively evaluate the interactions between peroxisomes and chloroplasts using a distance transformation algorithm and high-resolution 3D fluorescent images taken by confocal laser scanning microscopy.
Pores in the leaf epidermis called stomata allow plants to take up carbon dioxide for photosynthesis, but are also pathways for water vapor loss. New image acquisition and analysis methods are allowing high-throughput phenotyping of stomatal patterning, which can be applied to better understand the genetic basis of variation in certain species. However, it takes considerable data and effort to train the models and their ability to accurately detect epidermal structures is constrained by the training data. This issue of context dependency, the inability to perform effectively in novel contexts, is the main hurdle preventing widespread adoption of machine learning in high-throughput phenotyping of intraspecific, interspecific, and environmental variation. Here we show the limited ability of a Mask-RCNN tool trained and successfully applied to Zea mays, to analyze images from a closely related grass called Setaria viridis. We then demonstrate successful retraining of the tool to cope with the novel amounts of diversity presented by this new species. The stomatal complexes in optical tomography images of mature Setaria leaves were accurately identified by comparison to expert raters (R 2 = 0.84). This study highlights the challenge of context dependency for widespread application of machine learning tools for phenotyping plant traits, even in closely related species. At the same time, it also provides a new tool that can be applied to leverage Setaria as a model C4 species, and a roadmap for the translation of a machine learning tool to analyze stomatal patterning in diverse datasets of new plant species.
Why it matches plant phenotyping methodsSetariaの葉画像から気孔を自動検出する機械学習ツールの再訓練・適応と、専門家比較による精度検証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。
abstractNew image acquisition and analysis methods are allowing high-throughput phenotyping of stomatal patterning
Plant phenotyping relevance match · UnverifiedOpenAlex · Europe PMC · Crossref · checked 8 Sept 2026
MAIN CONCLUSION: The combination of image-based phenotyping with in-depth anatomical analysis allows for a thorough investigation of plant physiological plasticity in acclimation, which is driven by environmental conditions and mediated by anatomical traits. Understanding the ability of plants to respond to fluctuations in environmental conditions is critical to addressing climate change and unlocking the agricultural potential of crops both indoor and in the field. Recent studies have revealed that the degree of eco-physiological acclimation depends on leaf anatomical traits, which show stress-induced alterations during organogenesis. Indeed, it is still a matter of debate whether plant anatomy is the bottleneck for optimal plant physiology or vice versa. Here, we cultivated 'Salanova' lettuces in a phenotyping chamber under two different vapor pressure deficits (VPDs; low, high) and watering levels (well-watered, low-watered); then, plants underwent short-term changes in VPD. We aimed to combine high-throughput phenotyping with leaf anatomical analysis to evaluate their capability in detecting the early stress signals in lettuces and to highlight the different degrees of plants' eco-physiological acclimation to the change in VPD, as influenced by anatomical traits. The results demonstrate that well-watered plants under low VPD developed a morpho-anatomical structure in terms of mesophyll organization, stomatal and vein density, which more efficiently guided the acclimation to sudden changes in environmental conditions and which was not detected by image-based phenotyping alone. Therefore, we emphasized the need to complement high-throughput phenotyping with anatomical trait analysis to unveil crop acclimation mechanisms and predict possible physiological behaviors after sudden environmental fluctuations due to climate changes.
Why it matches plant phenotyping methods画像ベースの高スループット表現型解析を解剖学的形質と統合し、環境変化によるストレス・順化シグナルの検出能力を評価することが研究目的の中心であるため、方法適用研究として含める。
abstractThe combination of image-based phenotyping with in-depth anatomical analysis allows for a thorough investigation of plant physiological plasticity in acclimation
Abstract The stomata on the leaf surface are mainly responsible for the material exchange between the internal and external environments of the plant, a large number of methods have been proposed to automatically measure the distribution position and number of stomatal, but few methods could achieve both stomatal count and open/closed‐state judgment. Therefore, this study proposes an automatic detection method for leaf stomatal morphology analysis based on an attention mechanism and deep learning. In order to obtain more stomatal feature information and send it to the network for learning, the proposed method adds a coordinate attention (CA) mechanism to the YOLOV5 backbone part. At the same time, in order to avoid the overfitting of the model during the training process, the authors added the training trick of label smoothing. Finally, the detection ability of the proposed method for stomata is verified on the broad bean leaves stomata dataset. The experimental results show that our method achieves a detection accuracy of 0.934 and an mAP of 0.968. By comparing with other state‐of‐the‐art algorithms, the detection capability of our method has been significantly improved. The generalization of the model is verified on the wheat leaf stomatal dataset. The experimental results show that our method can achieve a detection accuracy of 0.894 and an mAP of 0.907.
Why it matches plant phenotyping methods植物葉の気孔形態を自動検出し、数と開閉状態を推定する画像解析法を開発・検証しており、植物フェノタイピング手法が研究の中心である。
abstractthis study proposes an automatic detection method for leaf stomatal morphology analysis based on an attention mechanism and deep learning.
Reproduction assets foundThe paper's broad bean/wheat leaf stomata microscopy image dataset (951 broad bean + 160 wheat images with YOLO-format annotations) is openly deposited on Zenodo per the data availability statement. No code or trained model deposit is explicitly stated.Dataset · publicOF INTEREST
problems, and its indicators are better than the six comparison The authors declare that there are no conflict of interests, we do
algorithms above. not have any possible conflicts of interest.
DATA AVAILABILITY STATEMENT
5 CONCLUSIONS The data that support the findings of this study are openly
available in zendo at https://doi.org/10.5281/zenodo.6302925.
In order to better detect and count the position, number, and
open/closed-status of stomata in plant leaves, we introduce a AUTHOR CONTRIBUTIONS
modified end-to-end target detection model YOLOv5 in this Xin Li: Conceptualization; Data curation; Formal analysis;
study. In order to improve the ability of YOLOv5s model to InvestiOpen asset ↗zenodo · 10.5281/zenodo.6302925pdf-layout-page:9 lines:1-55Plant phenotyping relevance match · UnverifiedbioRxiv · Europe PMC · checked 15 Sept 2026
Stomata and leaf veins play an essential role in transpiration and the movement of water throughout leaves. These traits are thus thought to play a key role in the adaptation of plants to drought and a better understanding of the genetic basis of their variation and coordination could inform efforts to improve drought tolerance. Here, we explore patterns of variation and covariation in leaf anatomical traits and analyze their genetic architecture via genome-wide association (GWA) analyses in cultivated sunflower (Helianthus annuus L.). Traits related to stomatal density and morphology as well as lower order veins were manually measured from digital images while the density of minor veins was estimated using a novel deep learning approach. Leaf, stomatal, and vein traits exhibited numerous significant correlations that generally followed expectations based on functional relationships. Correlated suites of traits could further be separated along three major principal component (PC) axes that were heavily influenced by variation in traits related to gas exchange, leaf hydraulics, and leaf construction. While there was limited evidence of colocalization when individual traits were subjected to GWA analyses, major multivariate PC axes that were most strongly influenced by several traits related to gas exchange or leaf construction did exhibit significant genomic associations. These results provide insight into the genetic basis of leaf trait covariation and showcase potential targets for future efforts aimed at modifying leaf anatomical traits in sunflower. Significance StatementUsing traditional and automated/high-throughput (using a novel deep learning approach) phenotyping methods we studied leaf anatomical variation in sunflower. Genome-wide association (GWA) analyses identified numerous genomic regions underlying individual trait variation and regions underlying major multivariate axes of phenotypic variation. These results illustrate the value of employing a multivariate approach to GWA analyses and shed light on the extent to which leaf trait (co-)variation can be genetically decoupled to explore novel phenotypic space.
Why it matches plant phenotyping methods葉の解剖形質を画像から取得し、特に小脈密度を新規深層学習手法で推定しており、植物形質抽出法の適用が明示された研究である。
abstractTraits related to stomatal density and morphology as well as lower order veins were manually measured from digital images while the density of minor veins was estimated using a novel deep learning approach.
Leaf water content (LWC) permanently fluctuates under variable transpiration rate and sap flow and influences e.g. stomatal responses and osmotic adjustment of plant cells. Continuous recordings of LWC are therefore central for the investigation of the regulatory networks stabilizing leaf hydration. Available measurement methods, however, either influence local hydration, interfere with the local leaf micro-environment or cannot easily be combined with other techniques. To overcome these limitations a non-invasive sensor was developed which uses light transmission in the NIR range for precise continuous recordings of LWC. For LWC measurements the transmission ratio of two NIR wavelengths was recorded using a leaf-specific calibration. Pulsed measurement beams enabled measurements under ambient light conditions. The contact-free sensor allows miniaturization and can be integrated into many different experimental settings. Example measurements of LWC during disturbances and recoveries of leaf water balance show the high precision and temporal resolution of the LWC sensor and demonstrate possible method combinations. Simultaneous measurements of LWC and transpiration allows to calculate petiole influx informing about the dynamic leaf water balance. With simultaneous measurements of stomatal apertures the relevant stomatal and hydraulic processes are covered, allowing insights into dynamic properties of the involved positive and negative feed-back loops.
Why it matches plant phenotyping methodsNIR透過を用いて葉の含水量を連続・非侵襲測定するセンサーを開発し、精度と時間分解能を実証しており、植物生理形質の取得法が研究の中心である。
abstracta non-invasive sensor was developed which uses light transmission in the NIR range for precise continuous recordings of LWC
Measuring plant-balance and -water dynamics is essential to gain better insight into plant health. In the Plantena research program (https://www.4tu.nl/plantenna/en/), new techniques have been developed for direct monitoring of plant traits. These include water status monitoring based on Ultrasound, magnetic resonance imaging (MRI) or radiofrequency (RF), volatile compound emission (“e-Nose”) and continuous stomatal aperture sensing (SAS). The SAS sensor enables real-time autonomous imaging of stomatal apertures inside the growth environment to asses dynamic behavior of individual stomata within the ensemble. For e-Nose, a new approach is being explored to utilize an electronic nose to smell insects for early detection of pests, thus safeguarding crop harvests while minimizing pesticide usage. RF sensing , Ultrasound and MRI are non-invasive techniques for real-time monitoring of internal plant parameters. RF is being investigated for monitoring of water and mineral content, Ultrasound technology enables the determination of internal plant parameters in a fast, non-contact, and non-destructive matter, thereby providing new ways for water monitoring, pest detection, and selective breeding. MRI enables monitoring of water content and flow in plants and offers the potential for non-invasive metabolite detection. Additionally, low-cost, autonomous sensor nodes are being developed for integration of novel and existing plant-sensors into a high density network (“internet of plants”). To this end, a smart and efficient power management scheme is being developed to adapt the sensor nodes to a wide range of environmental scenarios. A first demonstration of sensor innovations will be set up in spring 2022 in a commercial greenhouse environment. In this contribution we will present preliminary results of novel plant-sensors as well as the set-up of the Plantenna Demonstrator facility. Outlook: Results of the Plantenna Demonstrator will validate performance of the plant-sensor innovations in a real-life environment and by combining these with existing sensors will provide valuable datasets for assessing plant response to climate variability and stress conditions.
Why it matches plant phenotyping methods植物の水分状態、気孔開度、内部パラメータなどを測定する新規センサー群と統合プラットフォームの開発・実証が中心であり、植物表現型計測手法に該当する。
abstractnew techniques have been developed for direct monitoring of plant traits.
Nowadays, about 50% of the global yield loss is due to climate change. Increasing Vapor Pressure Deficit (VPD) and drought are among the principal environmental stressors, affecting stomatal regulation and reducing plant photosynthesis and biomass accumulation. Recent studies have revealed that the extent of plant acclimation is closely related to the anatomical traits of the leaves, which change with environmental conditions. It is not yet clear how the interaction between these environmental factors affects plant morpho-physiological development and plant capacity of acclimation under changing conditions. To fill this gap, in this study we used a high-throughput phenotyping facility (at the IPK-Gatersleben, Germany) to grow two lettuce cultivars ( Lactuca sativa L. var. capitata ) with green and red leaves under different VPDs (low and high) and watering regimes (well-watered, WW, and low watered, LW regimes). Two trials were performed: the first trial was conducted at a VPD of 0.7 kPa (low VPD) and the second at 1.4 kPa (high VPD), both with WW and WD conditions. After 12 days of cultivation in the phenotyping chamber, the environmental conditions were switched, and plants were kept for 5 days at the opposite VPD to evaluate their acclimation ability. RGB imaging was applied to track changes in morphological parameters, near-infrared camera (NIR) was used to estimate plant-water relationships, and FLUO made it possible to evaluate changes in photosystem II reflecting optimal/stressful conditions. At the end of the experimental trials, the leaf samples were characterized in terms of stomatal and mesophyll traits by light microscopy. A specific focus was dedicated to exploring how stomata regulation and water use efficiency affect carbon gain and biomass allocation in pre-acclimated lettuces to different environmental conditions (VPDs) and hence undergoing sudden changes in the VPD. To test the influence of the different independent factors: i) VPD, ii) cultivar (C), iii) water (W) on the dependent variables, a three-way analysis of variance (ANOVA) was performed. Additionally, correlation plots and the principal component analysis were performed to explore correlations between morpho-anatomical and phenotypic data points. The results showed that WW plants at low VPD developed a morpho-anatomical structure in terms of mesophyll organization, stomatal and vein density which more efficiently guided acclimation to sudden changes in the environmental conditions and which was not detected by image-based phenotyping alone. Therefore, we emphasized the need to complement high-throughput phenotyping with the analysis of anatomical traits to unravel the mechanisms of crop acclimation under sudden fluctuation in environmental conditions due to climate change. Such an approach can help improving knowledge on how stomatal regulation and carbon allocation affect productivity in warmer areas and drier climates, with high impact also for the design of cultivation protocols for sustainable indoor farming.
Why it matches plant phenotyping methods高スループット表現型解析施設を用い、RGB・NIR・蛍光画像から形態、水分関係、光化学系IIを追跡する手法を、解剖学的測定と組み合わせて実質的に適用している。
abstractwe used a high-throughput phenotyping facility (at the IPK-Gatersleben, Germany)
Plant phenotyping relevance match · UnverifiedbioRxiv · Europe PMC · checked 15 Sept 2026
Using microscopy to investigate stomatal behaviour is a common technique in plant physiology research. Manual inspection and measurement of stomatal features is a low throughput process in terms of time and human effort, which relies on expert knowledge to identify and measure stomata accurately. This process represents a significant bottleneck in research pipelines, adding significant researcher time to any project that requires it. To alleviate this, we introduce StomaAI (SAI): a reliable and user-friendly tool that measures stomata of the model plant Arabidopsis (dicot) and the crop plant barley (monocot grass) via the application of deep computer vision. We evaluated the reliability of predicted measurements: SAI is capable of producing measurements consistent with human experts and successfully reproduced conclusions of published datasets. Hence, SAI boosts the number of images that biologists can evaluate in a fraction of the time so is capable of obtaining more accurate and representative results.
Why it matches plant phenotyping methods気孔画像から形質を自動抽出する深層コンピュータビジョンツールを開発し、専門家測定および既報データとの一致性を検証しており、植物表現型取得法が研究の中心である。
abstractwe introduce StomaAI (SAI): a reliable and user-friendly tool that measures stomata of the model plant Arabidopsis (dicot) and the crop plant barley (monocot grass) via the application of deep computer vision.
Stomata, microscopic pores on leaf surfaces, regulate the uptake of carbon dioxide and the simultaneous loss of water vapor by leaves. New image acquisition and analysis methods are allowing high-throughput phenotyping of stomatal patterning, which in turn have been applied to better understand the genetic basis of variation in certain species. However, it takes considerable data and effort to train the models, and their ability to accurately detect epidermal structures is constrained to morphologies found within the training data. This issue of context dependency, the inability to perform effectively in novel contexts, is the main hurdle preventing widespread adoption of machine learning in high-throughput phenotyping of intraspecific, interspecific, and environmental variation. Here we show the limited ability of a Mask-RCNN tool, which was previously trained and successfully applied to Zea mays, to analyze images from a closely related grass, Setaria viridis. We then demonstrate successful retraining of the tool to cope with the novel diversity presented by this new species. The stomatal complexes in optical tomography images of mature Setaria leaves were accurately identified by comparison to expert raters (R2 = 0.84). This study highlights the challenge of context dependency for widespread application of machine learning tools for phenotyping plant traits, even in closely related species. At the same time, it also provides a new tool that can be applied to leverage Setaria as a model C4 species, while also providing a roadmap for translation of a machine learning to analyze stomatal patterning in new plant species.
Why it matches plant phenotyping methods植物の気孔画像を対象とする機械学習ツールの種間適用性を検証し、新種に対応する再訓練と専門家比較による精度評価を行っており、表現型取得・抽出手法が研究の中心です。
abstractHere we show the limited ability of a Mask-RCNN tool, which was previously trained and successfully applied to Zea mays, to analyze images from a closely related grass, Setaria viridis.
Plants live in highly dynamic surroundings and need to cope with constant environmental challenges. In order to do so, they developed quick reactions to stress that allow them to gain time while mounting a major response. This first line of defense includes the stomata, leaf epidermal pores in charge of regulating water loss and photosynthesis. Stomatal movements are controlled by the stress phytohormone abscisic acid (ABA), which induces fast closure of the stomata upon perception of stress conditions. By modulating plasma membrane ion channels, ABA leads to loss of water from the guard cells surrounding the stomatal pore and a consequent reduction of its aperture. Here, we provide a microscopy-based method to assess the plant's response to ABA through measurements of the stomatal aperture. This protocol describes a simple, quick, and unexpensive method to prepare high-quality impressions of leaves from Arabidopsis thaliana seedlings from long-lasting silicone-based casts, allowing detailed imaging and accurate determination of the aperture of stomatal pores.
Why it matches plant phenotyping methods葉のシリコーン印象と顕微鏡画像を用いて気孔開度という植物形態・生理形質を測定する方法を中心に提示しており、単なるABA処理実験ではなく、印象作製と画像測定プロトコルが主要な貢献である。
abstractHere, we provide a microscopy-based method to assess the plant's response to ABA through measurements of the stomatal aperture.
Stomatal observation and automatic stomatal detection are useful analyses of stomata for taxonomic, biological, physiological, and eco-physiological studies. We present a new clearing method for improved microscopic imaging of stomata in soybean followed by automated stomatal detection by deep learning. We tested eight clearing agent formulations based upon different ethanol and sodium hypochlorite (NaOCl) concentrations in order to improve the transparency in leaves. An optimal formulation-a 1:1 ( v / v ) mixture of 95% ethanol and NaOCl (6-14%)-produced better quality images of soybean stomata. Additionally, we evaluated fixatives and dehydrating agents and selected absolute ethanol for both fixation and dehydration. This is a good substitute for formaldehyde, which is more toxic to handle. Using imaging data from this clearing method, we developed an automatic stomatal detector using deep learning and improved a deep-learning algorithm that automatically analyzes stomata through an object detection model using YOLO. The YOLO deep-learning model successfully recognized stomata with high mAP (~0.99). A web-based interface is provided to apply the model of stomatal detection for any soybean data that makes use of the new clearing protocol.
Why it matches plant phenotyping methodsダイズ葉の気孔画像取得法と、気孔を自動検出・解析する深層学習手法を開発・評価しており、植物形質取得が研究の中心である。
abstractWe present a new clearing method for improved microscopic imaging of stomata in soybean followed by automated stomatal detection by deep learning.
Reproduction assets foundThe paper's soybean stomatal phenotype dataset (Table S2, manual vs. automatic stomatal density for 386 accessions) is publicly available via the MDPI supplement, and the trained YOLOv5 stomata-detection model is publicly served through the authors' web application. The 183-image training dataset and analysis code haveSupplement · publicnowledge the personnel from the Plant Genetics and Breeding lab at the Kyungpook National University for their time and work at the greenhouse.
Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Supplementary Materials
The following are available online at https://www.mdpi.com/article/10.3390/plants10122714/s1 , Table S1: The representative methods for detecting stomata in different species, Table S2: Comparison stomata density between counting by manual and develped program.
Click here for additional data file.
Author Contributions
Conceptualization, J.-D.L. and Y.J.K.; methodology, S.N.S.; formal analysis, H.P., S.H.COpen asset ↗lines:69-162Plant phenotyping relevance match · UnverifiedEurope PMC · checked 14 Sept 2026
Tea plants that have a large leaf area mainly suffer from heavy metal accumulation in the above-ground parts through foliar uptake. With the world rapid industrialization, this pollution in tea is considered a crucial challenge due to its potential health risks. The present study proposes an innovative approach based on visible and near-infrared (Vis-NIR) spectroscopy coupled with chemometrics for the characterization of tea chemical indicators under airborne lead stress, which can be performed fast and in situ. The effects of lead stress on chemical indicators and accumulation in leaves of the two tea varieties at different time intervals and levels of treatment were investigated. In addition, changes in cell structure and leaf stomata were monitored during foliar uptake of aerosol particles by transmission electron microscopy (TEM) and scanning electron microscopy (SEM). The spectral variation was able to classify the tea samples into the Pb treatment groups through the linear discriminant analysis (LDA) model. Two machine learning techniques, namely, partial least squares (PLS) and radial basis function neural network (RBFNN), were evaluated and compared for building the quantitative determination models. The RBFNN models combined with correlation-based feature selection (CFS) and PLS data compression methods were used to optimize the prediction performance. The results demonstrated that the PLS-RBFNN as a non-linear model outperformed the PLS model and provided the R-value of 0.944, 0.952, 0.881, 0.937, and 0.930 for prediction of MDA, starch, sucrose, fructose, glucose, respectively. It can be concluded that the proposed approach has strong application potential in monitoring the quality and safety of plants under airborne heavy metal stress.
Why it matches plant phenotyping methodsVis-NIR分光とケモメトリクスを用いて、Pbストレス下の茶葉の化学的状態を非破壊・迅速推定する手法を開発し、複数モデルの性能比較と予測評価を行っているため、植物状態の取得・推定が中心である。
abstractThe present study proposes an innovative approach based on visible and near-infrared (Vis-NIR) spectroscopy coupled with chemometrics for the characterization of tea chemical indicators under airborne lead stress, which can be performed fast and in situ.
Plant phenotyping relevance match · UnverifiedbioRxiv · Europe PMC · checked 8 Sept 2026
Stomata are dynamic structures that control the gaseous exchange of CO2 from the external to internal environment and water loss through transpiration. The density and morphology of stomata have important consequences in crop productivity and water use efficiency, both are integral considerations when breeding climate change resilient crops. The phenotyping of stomata is a slow manual process and provides a substantial bottleneck when characterising phenotypic and genetic variation for crop improvement. There are currently no open-source methods to automate stomatal counting. We used 380 human annotated micrographs of O. glaberrima and O. sativa at x20 and x40 objectives for testing and training. Training was completed using the transfer learning for deep neural networks method and R-CNN object detection model. At a x40 objective our method was able to accurately detect stomata (n = 540, r = 0.94, p<0.0001), with an overall similarity of 99% between human and automated counting methods. Our method can batch process large files of images. As proof of concept, characterised the stomatal density in a population of 155 O. glaberrima accessions, using 13,100 micrographs. Here, we present developed Stomata Detector; an open source, sophisticated piece of software for the plant science community that can accurately identify stomata in Oryza spp., and potentially other monocot species.
Why it matches plant phenotyping methodsイネの気孔を画像から自動検出・計数するオープンソース手法を開発し、手動計数との精度比較で検証しているため、植物表現型取得法が研究の中心です。
abstractThe phenotyping of stomata is a slow manual process and provides a substantial bottleneck when characterising phenotypic and genetic variation for crop improvement.
Plant phenotyping relevance match · UnverifiedOpenAlex · Europe PMC · checked 15 Sept 2026
Stomata are integral to plant performance, enabling the exchange of gases between the atmosphere and the plant. The anatomy of stomata influences conductance properties with the maximal conductance rate, gsmax, calculated from density and size. However, current calculations of stomatal dimensions are performed manually, which are time-consuming and error prone. Here, we show how automated morphometry from leaf impressions can predict a functional property: the anatomical gsmax. A deep learning network was derived to preserve stomatal morphometry via semantic segmentation. This forms part of an automated pipeline to measure stomata traits for the estimation of anatomical gsmax. The proposed pipeline achieves accuracy of 100% for the distinction (wheat vs. poplar) and detection of stomata in both datasets. The automated deep learning-based method gave estimates for gsmax within 3.8 and 1.9% of those values manually calculated from an expert for a wheat and poplar dataset, respectively. Semantic segmentation provides a rapid and repeatable method for the estimation of anatomical gsmax from microscopic images of leaf impressions. This advanced method provides a step toward reducing the bottleneck associated with plant phenotyping approaches and will provide a rapid method to assess gas fluxes in plants based on stomata morphometry.
Why it matches plant phenotyping methods葉面印象画像から気孔形態を自動抽出し、解剖学的gsmaxを推定する深層学習パイプラインの開発・精度検証が中心であり、植物表現型計測手法に該当する。
abstractA deep learning network was derived to preserve stomatal morphometry via semantic segmentation.
SorghumThermalStomata / guard-cell complexPhysiological trait estimationStomatal traitsWater status / transpiration
Stomata allow CO2 uptake by leaves for photosynthetic assimilation at the cost of water vapor loss to the atmosphere. The opening and closing of stomata in response to fluctuations in light intensity regulate CO2 and water fluxes and are essential for maintaining water-use efficiency (WUE). However, a little is known about the genetic basis for natural variation in stomatal movement, especially in C4 crops. This is partly because the stomatal response to a change in light intensity is difficult to measure at the scale required for association studies. Here, we used high-throughput thermal imaging to bypass the phenotyping bottleneck and assess 10 traits describing stomatal conductance (gs) before, during and after a stepwise decrease in light intensity for a diversity panel of 659 sorghum (Sorghum bicolor) accessions. Results from thermal imaging significantly correlated with photosynthetic gas exchange measurements. gs traits varied substantially across the population and were moderately heritable (h2 up to 0.72). An integrated genome-wide and transcriptome-wide association study identified candidate genes putatively driving variation in stomatal conductance traits. Of the 239 unique candidate genes identified with the greatest confidence, 77 were putative orthologs of Arabidopsis (Arabidopsis thaliana) genes related to functions implicated in WUE, including stomatal opening/closing (24 genes), stomatal/epidermal cell development (35 genes), leaf/vasculature development (12 genes), or chlorophyll metabolism/photosynthesis (8 genes). These findings demonstrate an approach to finding genotype-to-phenotype relationships for a challenging trait as well as candidate genes for further investigation of the genetic basis of WUE in a model C4 grass for bioenergy, food, and forage production.
Why it matches plant phenotyping methods高スループット熱画像で気孔コンダクタンスを測定し、ガス交換測定との相関で検証した植物フェノタイピング手法が研究の中心である。
abstractHere, we used high-throughput thermal imaging to bypass the phenotyping bottleneck and assess 10 traits describing stomatal conductance (gs) before, during and after a stepwise decrease in light intensity for a diversity panel of 659 sorghum (Sorghum bicolor) accessions.
Stomata, microscopic pores on leaf surfaces, regulate the uptake of carbon dioxide and the simultaneous loss of water vapor by leaves. New image acquisition and analysis methods are allowing high-throughput phenotyping of stomatal patterning, which in turn have been applied to better understand the genetic basis of variation in certain species. However, it takes considerable data and effort to train the models, and their ability to accurately detect epidermal structures is constrained to morphologies found within the training data. This issue of context dependency, the inability to perform effectively in novel contexts, is the main hurdle preventing widespread adoption of machine learning in high-throughput phenotyping of intraspecific, interspecific, and environmental variation. Here we show the limited ability of a Mask-RCNN tool, which was previously trained and successfully applied to Zea mays, to analyze images from a closely related grass, Setaria viridis. We then demonstrate successful retraining of the tool to cope with the novel diversity presented by this new species. The stomatal complexes in optical tomography images of mature Setaria leaves were accurately identified by comparison to expert raters (R2 = 0.84). This study highlights the challenge of context dependency for widespread application of machine learning tools for phenotyping plant traits, even in closely related species. At the same time, it also provides a new tool that can be applied to leverage Setaria as a model C4 species, while also providing a roadmap for translation of a machine learning to analyze stomatal patterning in new plant species.
Why it matches plant phenotyping methods植物の気孔形質を画像から自動抽出する機械学習ツールの種間適用性を評価し、Setaria向けに再訓練・検証した研究であり、フェノタイピング手法が中心である。
abstractNew image acquisition and analysis methods are allowing high-throughput phenotyping of stomatal patterning
To measure stomatal traits automatically and nondestructively, a new method for detecting stomata and extracting stomatal traits was proposed. Two portable microscopes with different resolutions (TipScope with a 40× lens attached to a smartphone and ProScope HR2 with a 400× lens) are used to acquire images of living stomata in maize leaves. FPN model was used to detect stomata in the TipScope images and measure the stomata number and stomatal density. Faster RCNN model was used to detect opening and closing stomata in the ProScope HR2 images, and the number of opening and closing stomata was measured. An improved CV model was used to segment pores of opening stomata, and a total of 6 pore traits were measured. Compared to manual measurements, the square of the correlation coefficient (R 2 ) of the 6 pore traits was higher than 0.85, and the mean absolute percentage error (MAPE) of these traits was 0.02%-6.34%. The dynamic stomata changes between wild-type B73 and mutant Zmfab1a were explored under drought and re-watering condition. The results showed that Zmfab1a had a higher resilience than B73 on leaf stomata. In addition, the proposed method was tested to measure the leaf stomatal traits of other nine species. In conclusion, a portable and low-cost stomata phenotyping method that could accurately and dynamically measure the characteristic parameters of living stomata was developed. An open-access and user-friendly web portal was also developed which has the potential to be used in the stomata phenotyping of large populations in the future.
Why it matches plant phenotyping methods生きた葉の気孔形質を画像取得・深層学習・セグメンテーションで自動抽出する手法を開発し、手動測定との比較検証とWebポータル提供まで行っており、植物フェノタイピング手法が中心である。
abstracta new method for detecting stomata and extracting stomatal traits was proposed.
Reproduction assets foundThe paper's Data Availability Statement deposits the trained stomata detection/segmentation models and all labelled leaf stomata images at a public Huazhong Agricultural University plant phenomics download portal, which directly supports this paper's phenotyping measurements. The analysis source codes are only 'availabDataset · publical document of web portal.
File S2 Technical document of EXE software.
Data Availability Statement
The operating procedure for stomatal trait extraction is shown in Video S1 . The detailed technical documentation is given in Note S1 . The trained model, user guideline and all the labelled images of leaf stomata are available at http://plantphenomics.hzau.edu.cn/download_checkiflogin_en.action . The source codes are available from the first/corresponding author. The web portal of extracting stomatal traits was available at http://x40833180q.zicp.vip .Open asset ↗plantphenomics.hzau.edu.cnlines:638-643Code / dataset availability confirmedEurope PMC · checked 15 Sept 2026
Stomata are adjustable pores on leaf surfaces that regulate the tradeoff of CO2 uptake with water vapor loss, thus having critical roles in controlling photosynthetic carbon gain and plant water use. The lack of easy, rapid methods for phenotyping epidermal cell traits have limited discoveries about the genetic basis of stomatal patterning. A high-throughput epidermal cell phenotyping pipeline is presented here and used for quantitative trait loci (QTL) mapping in field-grown maize (Zea mays). The locations and sizes of stomatal complexes and pavement cells on images acquired by an optical topometer from mature leaves were automatically determined. Computer estimated stomatal complex density (SCD; R2 = 0.97) and stomatal complex area (SCA; R2 = 0.71) were strongly correlated with human measurements. Leaf gas exchange traits were genetically correlated with the dimensions and proportions of stomatal complexes (rg = 0.39-0.71) but did not correlate with SCD. Heritability of epidermal traits was moderate to high (h2 = 0.42-0.82) across two field seasons. Thirty-six QTL were consistently identified for a given trait in both years. Twenty-four clusters of overlapping QTL for multiple traits were identified, with univariate versus multivariate single marker analysis providing evidence consistent with pleiotropy in multiple cases. Putative orthologs of genes known to regulate stomatal patterning in Arabidopsis (Arabidopsis thaliana) were located within some, but not all, of these regions. This study demonstrates how discovery of the genetic basis for stomatal patterning can be accelerated in maize, a C4 model species where these processes are poorly understood.
Why it matches plant phenotyping methods光学トポメータ画像と機械学習による葉表皮形質の自動・高速取得パイプラインが研究の中心であり、測定精度も人手測定と比較検証されているため。
abstractA high-throughput epidermal cell phenotyping pipeline is presented here and used for quantitative trait loci (QTL) mapping in field-grown maize (Zea mays).
Reproduction assets foundThe article explicitly deposits its optical topometry epidermal images (the paper's phenotyping input data) in the Illinois Data Bank with a public DOI. No author analysis code or trained model checkpoint is stated as publicly available; Mask R-CNN reference is a third-party library, and R packages are generic tools.Dataset · publicun with all SNPs included in the model. All the SNPs with a P -value smaller than 0.05 after the inclusion of all other retained SNPs were reported as putatively pleiotropic QTNs ( Supplemental Table S4 ).
Data availability
Data availabilityOptical tomography images from this article can be found in the Illinois Data Bank under https://doi.org/10.13012/B2IDB-8275554_V1 .
Supplemental data
The following materials are available in the online version of this article.
Supplemental Figure S1.
Examples of input images and the predictions of cell instances made for them across a range of epidermis morphology and image qualities.
Supplemental Figure S2.Open asset ↗Illinois Data Bank · 10.13012/B2IDB-8275554_V1lines:194-204Plant phenotyping relevance match · UnverifiedCrossref · Europe PMC · checked 9 Sept 2026
MaizeLiDAR / point cloudLeafStomata / guard-cell complexPhysiological trait estimationGrowth / development / phenologyStomatal traitsYield / yield components
In recent years, it is becoming clearer that plant growth and its yield are affected by sound with certain sounds, such as seedling of corn directing itself toward the sound source and its ability to distinguish stuttering of larvae from other sounds. However, methods investigating the effects of sound on plants either take a long time or are destructive. Here, we propose using laser biospeckle, a non-destructive and non-contact technique, to investigate the activities of an arugula plant for sounds of different frequencies, namely, 0 Hz or control, 100 Hz, 1 kHz, 10 kHz, including rock and classical music. Laser biospeckles are generated when scattered light from biological tissues interfere, and the intensities of such speckles change in time, and these changes reflect changes in the scattering structures within the biological tissue. A leaf was illuminated by light from a laser light of wavelength 635 nm, and the biospeckles were recorded as a movie by a CMOS camera for 20 sec at 15 frames per second (fps). The temporal correlation between the frames was characterized by a parameter called biospeckle activity (BA)under the exposure to different sound stimuli of classical and rock music and single-frequency sound stimuli for 1min. There was a clear difference in BA between the control and other frequencies with BA for 100 Hz being closer to control, while at higher frequencies, BA was much lower, indicating a dependence of the activity on the frequency. As BA is related to changes from both the surface as well as from the internal structures of the leaf, LSM (laser scanning microscope) observations conducted to confirm the change in the internal structure revealed more than 5% transient change in stomatal size following exposure to one minute to high frequency sound of 10kHz that reverted within ten minutes. Our results demonstrate the potential of laser biospeckle to speedily monitor in vivo response of plants to sound stimuli and thus could be a possible screening tool for selecting appropriate frequency sounds to enhance or delay the activity of plants. (337 words)
Why it matches plant phenotyping methodsレーザーバイオスペックルを用いて植物の生体活動を非破壊・迅速に測定する手法が研究の中心であり、音刺激への応答を評価する植物フェノタイピング手法として明確に記述されている。
abstractHere, we propose using laser biospeckle, a non-destructive and non-contact technique, to investigate the activities of an arugula plant for sounds of different frequencies
Plant phenotyping relevance match · UnverifiedCrossref · Europe PMC · checked 9 Sept 2026
Drought is one of the most devasting and frequent abiotic stresses in agriculture. While many morphological, biochemical and physiological indicators are being used to quantify plant drought responses, stomatal control, and hence the transpiration and photosynthesis regulation through it, is of particular importance in marking the plant capacity of balancing stress response and yield. Due to the difficulties in simultaneous, large-scale measurement of stomatal traits such as sensitivity and speed of stomatal closure under progressive soil drought, forward genetic mapping of these important behaviors has long been unavailable. The recent emerging phenomic technologies offer solutions to identify the water relations of whole plant and assay the stomatal regulation in a dynamic process at the population level. Here, we report high-throughput physiological phenotyping of water relations of 106 cowpea accessions under progressive drought stress, which, in combination of genome-wide association study (GWAS), enables genetic mapping of the complex, stomata-related drought responsive traits “critical soil water content” (θ cri ) and “slope of transpiration rate declining” (K Tr ). The 106 accessions showed large variations in θ cri and K Tr , indicating that they had broad spectrum of stomatal control in response to soil water deficit, which may confer them different levels of drought tolerance. Univariate GWAS identified six and fourteen significant SNPs associated with θ cri and K Tr , respectively. The detected SNPs distributed in nine chromosomes and accounted for 8.7–21% of the phenotypic variation, suggesting that both stomatal sensitivity to soil drought and the speed of stomatal closure to completion were controlled by multiple genes with moderate effects. Multivariate GWAS detected ten more significant SNPs in addition to confirming eight of the twenty SNPs as detected by univariate GWAS. Integrated, a final set of 30 significant SNPs associated with stomatal closure were reported. Taken together, our work, by combining phenomics and genetics, enables forward genetic mapping of the genetic architecture of stomatal traits related to drought tolerance, which not only provides a basis for molecular breeding of drought resistant cultivars of cowpea, but offers a new methodology to explore the genetic determinants of water budgeting in crops under stressful conditions in the phenomics era.
Why it matches plant phenotyping methods高スループット生理フェノタイピングにより、乾燥下の気孔関連形質を大規模・動的に測定し、再利用可能な測定ワークフローとしてGWASに適用しているため、フェノタイピング手法が中心的です。
abstractHere, we report high-throughput physiological phenotyping of water relations of 106 cowpea accessions under progressive drought stress
Plant stomata are essential structures (pores) that control the exchange of gases between plant leaves and the atmosphere, and also they influence plant adaptation to climate through photosynthesis and transpiration stream. Many works in literature aim for a better understanding of these structures and their role in the evolution process and the behavior of plants. Although stomata studies in dicots species have advanced considerably in the past years, even there is not much knowledge about the stomata of cereal grasses. Due to the high morphological variation of stomata traits intra- and inter-species, detecting and classifying stomata automatically becomes challenging. For this reason, in this work, we propose a new system for automatic stomata classification and detection in microscope images for maize cultivars based on transfer learning strategy of different deep convolution neural netwoks (DCNN). Our performed experiments show that our system achieves an approximated accuracy of 97.1% in identifying stomata regions using classifiers based on deep learning features, which figures out as a nearly perfect classification system. As the stomata are responsible for several plant functionalities, this work represents an important advance for maize research, providing an accurate system in replacing the current manual task of categorizing these pores on microscope images. Furthermore, this system can also be a reference for studies using images from different cereal grasses.
Why it matches plant phenotyping methodsトウモロコシ葉の気孔を顕微鏡画像から自動検出・分類する画像解析手法を開発し、精度評価しており、植物表現型取得が中心である。
abstractwe propose a new system for automatic stomata classification and detection in microscope images for maize cultivars based on transfer learning strategy of different deep convolution neural netwoks (DCNN).
Reproduction assets foundThe paper's Data Availability statement explicitly deposits all microscope images and analysis code in a public Zenodo record, directly reproducing this paper's maize stomata classification/detection experiments.Dataset · publicour findings can significantly benefit future research. As future work, we intend to develop a computational toolkit to support specialists in the biology area in their studies.
Supporting information
S1 File
(TXT)
Click here for additional data file.
Data Availability
All images and code are available from the ZENODO database. https://zenodo.org/record/3938047#.YE_l6v7Q85k .
Funding Statement
FAF received support of the Brazilian scientific funding agency CNPq through project #408919/2016-7 and São Paulo Research Foundation FAPESP grant #2018/23908-1. JPP received support of the Brazilian scientific funding agency CNPq through project #307066/2017-7. FAF received GPUs as donation from NVIDIOpen asset ↗ZENODO · zenodo.org/record/3938047lines:250-290Plant phenotyping relevance match · UnverifiedOpenAlex · checked 14 Sept 2026
Sanbon Chaka Gosa1, Bogale Abebe Gebeyo12, Ravitejas Patil1, Ramón Mencia1, Dani Zamir1, Menachem Moshelion1# 1 The R.H. Smith Institute of Plant Sciences and Genetics in Agriculture, The R.H. Smith Faculty of Agriculture, Food and Environment, The Hebrew University of Jerusalem, Rehovot, 76100 Israel 2 Current address: Department of Horticulture, College of Agriculture and Natural Resource, Dilla University, Dilla, Ethiopia #Corresponding Author Abstract Plant productivity in general and under stress, in particular, is a complex and comlitative trait largely influenced by environmental conditions. Among the most important traits are the whole-plant water-balance regulation mechanisms that dynamically change in order to maximize the metabolic activity of the plant. Due to the difficulty of high-throughput phenotyping of these physiological traits (e.g. transpiration, stomatal conductance, and photosynthesis), they are usually measured in static conditions or modeled based on only a few measuring points (low resolution). To overcome this challenge, we utilized a high-throughput gravimetric functional-phenotyping platform (PlantArray) along with a practical reverse phenotyping approach. We selected 30 tomato lines from multiple years of field yield data and functionally phenotyped them for their dynamic response curves using a variety of stress scenarios implemented using drought conditions (each plant received irrigation based on the amount of water it transpired). Our results show that resilient and tolerable traits, in the field, are associated with stomatal plastic conductance, i.e., maximum under well-irrigation, yet the rapid response to changes in environmental conditions (soil and atmospheric). The plastic traits of the idiotype lines were shown to increase water use efficiency (momentarily), thus maximizing yield in water deficit conditions. Based on manual characterizations of the idiotypes, it has been found that their abaxial surfaces have a greater density of stomata and a higher aperture during the early morning. Additionally, these lines showed rapid recovery after a drought. Our study concluded that reverse functional phenotyping can significantly reduce the pre-breading processes for yield-related traits. Keywords: Functional phenotyping, crops yield, —dynamic response, drought stress, stomatal conductance, reverse phenomics
Why it matches plant phenotyping methods高スループットの重力測定型機能的フェノタイピング基盤を用い、乾燥条件下の蒸散・気孔コンダクタンスなど動的生理形質を抽出することが研究の中心である。
abstractwe utilized a high-throughput gravimetric functional-phenotyping platform (PlantArray) along with a practical reverse phenotyping approach.
LeafStomata / guard-cell complexPhysiological trait estimationStomatal traitsWater status / transpiration
In leaf gas exchange measurements, cuticular conductance to water ( g cw ) is indistinguishable from and included in stomatal conductance to water vapor ( g sw ). Here we developed a simple technique to isolate g cw by directly measuring leaf intercellular CO 2 concentration ( C i(m) ) along with gas exchange during photosynthetic light induction. We derived stomatal conductance to CO 2 ( g sc(m) ) from the C i(m) independently of g sw . Plotting g sw against g sc(m) during the early induction phase within ~10 min, we found a highly linear relationship with a positive intercept. Assuming negligible cuticular CO 2 transport, complete stomatal closure occurs when g sc(m) =0. Then, we considered the residual g sw (i.e., intercept) as g cw . Indeed, these g cw estimates succeeded in correcting the calculation. Our technique, owing to its robustness and increased throughput, will allow for more rapid screening of crops, more reliable gas exchange analysis, and more accurate prediction of plant function under natural environmental conditions.
Why it matches plant phenotyping methods葉のガス交換からクチクラコンダクタンスを推定する新規かつ高スループットな生理表現型測定法を開発しており、方法自体が研究の中心です。
abstractHere we developed a simple technique to isolate g cw by directly measuring leaf intercellular CO 2 concentration ( C i(m) ) along with gas exchange during photosynthetic light induction.
Abstract AimsPrediction of whole-plant short-term water use efficiency (WUE s,P ) is essential to indicate plant performance and facilitates comparison across different temporal and spatial scales. Here, the isotope model for WUE s,P was scaled-up from the leaf to the whole-plant level.MethodsFor WUE s,P modelling, leaf gas exchange information, plant respiration and “unproductive” water loss were taken into account. Specifically, in shaping the expression of the WUE s,P , we emphasized the role of both stomatal ( g sw ) and mesophyll conductance ( g m ). ResultsThe verification showed that estimates of g sw from the coupled photosynthesis ( P n,L )- g sw model accounting for the effect of soil water stress slightly outperformed the model neglecting the soil water status effect, and the established coupled P n,L - g m model proved more effective in the estimation of g m than the previously proposed model. Introducing the two diffusion control functions into the whole-plant model, the developed model for WUE s,P effectively captured its response pattern to different CO 2 concentration ( C a ) and soil water content (SWC) conditions. ConclusionsOverall, this study confirmed that accurate estimation of WUE s,P requires an improved predictive accuracy of g sw and g m . These results have important implications for predicting how plants respond to climate change.
Why it matches plant phenotyping methods植物全体の水利用効率を推定するモデルを開発・検証しており、植物の生理状態を定量化する方法が研究の中心です。
abstractHere, the isotope model for WUE s,P was scaled-up from the leaf to the whole-plant level.
The stomatal index of the leaf is the ratio of the number of stomata to the total number of stomata and epidermal cells. Comparing with the stomatal density, the stomatal index is relatively constant in environmental conditions and the age of the leaf and, therefore, of diagnostic characteristics for a given genotype or species. Traditional assessment methods involve manual counting of the number of stomata and epidermal cells in microphotographs, which is labor-intensive and time-consuming. Although several automatic measurement algorithms of stomatal density have been proposed, no stomatal index pipelines are currently available. The main aim of this research is to develop an automated stomatal index measurement pipeline. The proposed method employed Faster regions with convolutional neural networks (R-CNN) and U-Net and image-processing techniques to count stomata and epidermal cells, and subsequently calculate the stomatal index. To improve the labeling speed, a semi-automatic strategy was employed for epidermal cell annotation in each micrograph. Benchmarking the pipeline on 1,000 microscopic images of leaf epidermis in the wheat dataset (Triticum aestivum L.), the average counting accuracies of 98.03 and 95.03% for stomata and epidermal cells, respectively, and the final measurement accuracy of the stomatal index of 95.35% was achieved. R2 values between automatic and manual measurement of stomata, epidermal cells, and stomatal index were 0.995, 0.983, and 0.895, respectively. The average running time (ART) for the entire pipeline could be as short as 0.32 s per microphotograph. The proposed pipeline also achieved a good transferability on the other families of the plant using transfer learning, with the mean counting accuracies of 94.36 and 91.13% for stomata and epidermal cells and the stomatal index accuracy of 89.38% in seven families of the plant. The pipeline is an automatic, rapid, and accurate tool for the stomatal index measurement, enabling high-throughput phenotyping, and facilitating further understanding of the stomatal and epidermal development for the plant physiology community. To the best of our knowledge, this is the first deep learning-based microphotograph analysis pipeline for stomatal index assessment.
Why it matches plant phenotyping methods葉の顕微鏡画像から気孔と表皮細胞を検出・計数し、気孔指数を自動推定する画像解析パイプラインの開発とベンチマーク検証が研究の中心である。
abstractThe main aim of this research is to develop an automated stomatal index measurement pipeline.
Reproduction assets foundThe authors explicitly state the stomatal index pipeline code is fully open-source on GitHub and the wheat microscopic image dataset is downloadable as a zip release from the same repository. Both are paper-specific, public, and directly actionable.Code · publicThe code is fully open-source for academic usage and can be downloaded at https://github.com/WeizhenLiuBioinform/stomatal_indexOpen asset ↗WeizhenLiuBioinform/stomatal_indexlines:361-376Dataset · publicThe wheat dataset is available for downloading at https://github.com/WeizhenLiuBioinform/stomatal_index/releases/download/wheat1.0/wheat_dataset.zipOpen asset ↗WeizhenLiuBioinform/stomatal_index · wheat1.0lines:361-376Supplement · publicSupplementary Table 2
Description of the cuticle dataset used for training and testing the stomatal index measurement model.Open asset ↗lines:651-703Code / 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:1-35Plant phenotyping relevance match · UnverifiedCrossref · checked 9 Sept 2026
Field / plotLeafStomata / guard-cell complexWhole plant / canopy / plot / fieldPhysiological trait estimationStomatal traitsWater status / transpiration
Evapotranspiration (ET) is a key moisture flux in both the urban stormwater management and the urban energy budgets. While there are established methods for estimating ET for agricultural crops, relatively little is known about ET rates associated with plants in urban Green Infrastructure settings. The aim of this study was to evaluate the feasibility of using porometry to estimate ET rates. Porometry provides an instantaneous measurement of leaf stomatal conductance. There are two challenges when estimating ET from porometry: converting from leaf stomatal conductance to leaf ET and scaling from leaf ET to canopy ET. Novel approaches to both challenges are proposed here. ET was measured from three commonly occurring urban plant species (Sedum spectabile, Bergenia cordifolia and Primula vulgaris) using a direct mass loss method. This data was used to evaluate the estimates made from porometry in a preliminary study (Sheffield, UK). The Porometry data captured expected trends in ET, with clear differences between the plant species and the reproducible decreasing rates of ET in response to reductions in soil moisture content.
Why it matches plant phenotyping methods植物の葉・キャノピー蒸発散を推定するポロメトリー手法を提案し、直接測定で評価しており、植物生理形質の取得・推定法が研究の中心である。
abstractThe aim of this study was to evaluate the feasibility of using porometry to estimate ET rates.
Plant phenotyping relevance match · UnverifiedOpenAlex · Crossref · Europe PMC · checked 14 Sept 2026
MaizeSorghumRGB / grayscaleLeafStomata / guard-cell complexClassificationCountingSegmentationStomatal traitsWater status / transpiration
Future food security in the face of climate change requires rapid, efficient, and flexible plant genetic improvement. For this, an integrated understanding of developmental and physiological mechanisms from DNA sequences (genotypes) to terminal traits (phenotypes) under different environmental conditions is indispensable. Stomatal traits influencing photosynthesis, gas exchange, and water use are crucial targets for plant improvement programs involving major crop C4 plants sorghum (Sorghum bicolor) and maize (Zea mays; Leakey et al., 2019). However, the ability to select optimal plant genotypes is still challenged by the pace at which the acquisition and processing of stomatal phenotypic data can be accomplished. Artificial intelligence (AI) is revolutionizing the way in which problems are approached and solved across a wide range of disciplines. Machine learning, an AI subfield, is increasingly being used in agriculture to classify plants, identify pests, predict weather conditions, and track yield, among several other applications (van Dijk et al., 2021). In this issue of Plant Physiology, Bheemanahalli et al. (2021), Ferguson et al. (2021), and Xie et al. (2021) introduce the use of AI-enabled high-throughput stomatal phenotyping platforms in combination with screening methods to identify specific genes and variations controlling stomatal-related traits in sorghum and maize. While considerable attempts have been made to address the bottlenecks associated with the phenotyping of stomatal traits through computer-aided image acquisition, previously developed methods suffered from issues of being time- and labor-intensive and of inaccurate stomata identification, classification, and quantification in C4 grass species (Furbank and Tester, 2011). By addressing those issues, the studies by the three groups present end-to-end pipelines that use a deep learning algorithm to automatically identify, classify, and quantify stomatal traits associated with plant water use efficiency (WUE) and drought tolerance (Figure 1A). By integrating this pipeline with genomic studies, the authors further report the underlying genetic architecture of stomatal traits (Figure 1C). Developing and integrating deep learning-based high-throughput-phenotyping with genomic studies identifies genetic regions for stomatal traits in C4 grasses. A, A phenotypic pipeline showing rapid image acquisition by optical topometry and image analysis by a deep learning algorithm (Mask R-CNN) provides a powerful tool for identifying optimal stomatal traits. B, Correlation between manually measured and computationally predicted stomatal complex area in sorghum. C, Association of phenotypic data with genetic variants. Adapted from Figure 1B inXie et al. (2021), Figure 3A inBheemanahalli et al. (2021), and Figure 5D inFerguson et al. (2021). Developing and integrating deep learning-based high-throughput-phenotyping with genomic studies identifies genetic regions for stomatal traits in C4 grasses. A, A phenotypic pipeline showing rapid image acquisition by optical topometry and image analysis by a deep learning algorithm (Mask R-CNN) provides a powerful tool for identifying optimal stomatal traits. B, Correlation between manually measured and computationally predicted stomatal complex area in sorghum. C, Association of phenotypic data with genetic variants. Adapted from Figure 1B inXie et al. (2021), Figure 3A inBheemanahalli et al. (2021), and Figure 5D inFerguson et al. (2021). Traditional stomatal phenotyping involves plant tissue collection and preparation for imaging, image data acquisition under microscope, and manual phenotyping of traits of interest. To relieve the phenotyping bottleneck, Ferguson et al. (2021) and Xie et al. (2021) used optical topometry, a rapid and nondestructive method for measuring surface characteristics at the nanometer scale, and acquired images of leaves to extract morphology-related stomata traits. The three-dimensional topographic layer of the raw images was first filtered to capture the points of interest and then flattened to two dimensions in grayscale with luminosity optimization and contrast enhancement. After preprocessing, the authors trained a convolutional neural network model (Mask R-CNN) for automatic detection and counting of stomatal traits, such as number, density, and area (He et al., 2017). A typical deep learning framework for phenotyping starts with feeding and preprocessing of raw images, followed by several layers of automatic feature extraction during training, and ends up with the trained and validated model that can identify, classify, quantify, and predict the phenotypic traits of interest (Singh et al., 2018). Mask R-CNN detects and localizes objects of interest, such as stomata, using bounding boxes and generates precise segmentation masks (Figure 1A). The algorithm then uses several convolutional layers to identify and classify object region and then to predict object type. To train the Mask R-CNN model, the authors first labeled the input images as stomata and/or pavement cells and randomly split the entire image set into a training set for model training and validation set for model validation. Stomatal traits predicted by the deep learning-based methods exhibited strong correlations with manual measurements across the three studies while dramatically reducing the required time and labor burden. For example, manual and computer-predicted measurements of stomatal complex area in sorghum leaves were significantly and positively correlated with each other (R2 > 0.96, Figure 1B). In maize, the computer-predicted means of stomatal complex density and pavement cell density showed high significant correlations with those of the manually obtained values (R2SCD = 0.974 and R2PD = 0.961, respectively). Bheemanahalli et al. (2021) showed a significant (P < 0.001) strong relationship between predicted and manual observations of abaxial and adaxial stomata density, suggesting the reliability and accuracy of automated deep learning-based methods. Next, the three sets of authors applied the deep learning-based pipelines to sets of maize and sorghum accessions and identified candidate genes with known and putative roles for key WUE traits. Xie et al. (2021) utilized QTL mapping in a biparental mapping population of maize and identified high-confidence QTLs that were putatively pleiotropic and correlated with stomatal patterning and leaf gas exchange traits consistently in 2 years. They found these QTLs overlap with genetic position harboring not only known stomatal developmental and patterning genes, such as putative maize orthologs of Arabidopsis thaliana (Arabidopsis) EPIDERMAL PATTERNING FACTOR 2, PANGLOSS1, and CYCLINA2;1, but also genes previously not linked to stomatal traits. A genome-wide association study (GWAS) on diverse grain sorghum accessions by Bheemanahalli et al. (2021) provided evidence for more than 71 genetic loci having significant association with stomatal traits, such as abaxial and adaxial stomatal density and stomatal complex area, and almost half as many overlapped with previously reported genomic regions. Further clarification of these regions revealed candidate putative genes including ATP-binding cassette transporter, BRASSINOSTEROID INSENSITIVE 1-associated receptor kinase 1, homeodomain-START transcription factor, and basic helix-loop-helix family transcription factor, putative orthologs of which are known to regulate leaf development, stomatal morphology, and stomatal lineage, respectively, in dicot and monocot models. To improve the efficiency of GWAS and boost confidence in the identification of candidate genes, Ferguson et al. (2021) determined the transcriptomes of sorghum leaf tissues using a transcriptome-wide association study (TWAS). They used Fisher’s combined test to integrate information from TWAS and GWAS and identified 394 unique candidate genes with high confidence of being associated with key stomatal and/or photosynthetic traits (Figure 1C). Included among these were two-thirds of genes that contained deleterious nonsynonymous/missense variants and had been previously identified as regulators of stomatal patterning and leaf development and anatomy in Arabidopsis. Examples include BETA-KETOSYL-CoA SYNTHASE 1, a cell wall EXPANSIN-TYPE PROTEIN 2, an ABA-sensitive MAP KINASE, PURPLE ACID PHOSPHATASE 10, and EPIDERMAL PATTERNING FACTOR 2. Overall, the work by Bheemanahalli et al. (2021), Ferguson et al. (2021), and Xie et al. (2021) extends our understanding of stomatal biology and opens up the potential to engineer stomatal traits to enhance WUE and drought resistance without compromising yield in crops. Although the deep convolutional neural network platform showed potential for rapid and efficient WUE phenotyping, many questions arise, such as whether this platform is generalizable to diverse physiological traits across different crops and environments. In the coming years, it will be interesting to see how high-throughput phenotyping tools accelerate the rate at which traits of interest are quantified and characterized, revealing gene-environment interaction modules of complex traits.
Why it matches plant phenotyping methods深層学習と光学トポメトリーによる気孔形態の高スループット取得・自動定量、および手動測定との技術検証を中心に扱う方法論的レビューである。
abstractintroduce the use of AI-enabled high-throughput stomatal phenotyping platforms
Strigolactones (SLs) are a class of plant hormones modulating developmental programs in response to endogenous and exogenous stimuli and mediating biotic interactions. However, a comprehensive view on the spatio-temporal pattern of SL signaling has not been established and tools for a systematic in planta analysis do not exist. Here, we present Strigo-D2, a genetically encoded ratiometric SL signaling sensor, allowing the examination of SL signaling distribution with cellular resolution and its rapid response to altered SL levels in intact plants. By monitoring the abundance of a truncated and fluorescently labeled SUPPRESSOR OF MAX2 1-LIKE 6 (SMXL6) protein, a proteolytic target of the SL signaling machinery, we show that all cell types investigated have the capacity to respond to changes in SL levels but with very different dynamics. In particular, SL signaling is pronounced in vascular cells but low in guard cells and the meristematic region of the root. We also show that other hormones leave Strigo-D2 activity unchanged indicating that initial SL signaling steps work in isolation from other hormonal signaling pathways. Specificity and spatio-temporal resolution of Strigo-D2 underline the value of the sensor for monitoring SL signaling in a broad range of biological contexts and with highly instructive analytical depth.
Why it matches plant phenotyping methods植物体内のストリゴラクトンシグナルを細胞解像度で時空間的に測定する遺伝子コード型バイオセンサーを開発し、センサーの特異性と有用性を実証しているため、植物フェノタイピング手法が中心である。
abstractHere, we present Strigo-D2, a genetically encoded ratiometric SL signaling sensor, allowing the examination of SL signaling distribution with cellular resolution and its rapid response to altered SL levels in intact plants.
Identifying and quantifying the number and size of stomata on leaf surfaces is useful for a wide range of plant ecophysiological studies, specifically those related to water-use efficiency of different plant species or agricultural crops. The time-consuming nature of manually counting and measuring stomata have limited the utility of manual methods for large-scale precision agriculture applications. A deep learning segmentation network was developed to automate the analysis of stomatal density and size and to distinguish between open and closed stomata using citrus trees grafted on different rootstocks as a model system. A novel method was developed utilizing the Mask-RCNN algorithm, which allows identification, quantification, and characterization of stomata from leaf epidermal peel microscopic images with an accuracy of up to 99%. Moreover, this method permits the differentiation of open and closed stomata with 98% precision and measurement of individual stomata size. In the citrus model system, significant differences in the size and density of stomata and diurnal regulation patterns were detected that were associated with the rootstock cultivar on which the trees were grafted. Nearly 9000 individual stomata were analyzed, which would have been impractical using manual methods. The novel automated method presented here is not only accurate, but also rapid and low-cost, and can be applied to a variety of crop and non-crop plant species.
Why it matches plant phenotyping methods柑橘葉の気孔密度・サイズ・開閉状態を画像から自動抽出するMask-RCNN手法を開発し、精度を検証しており、植物フェノタイピング手法が中心である。
abstractA deep learning segmentation network was developed to automate the analysis of stomatal density and size and to distinguish between open and closed stomata using citrus trees grafted on different rootstocks as a model system.
GreenhouseStomata / guard-cell complexPhysiological trait estimationStomatal traitsWater status / transpiration
The effect of considering cultivar differences in stomatal conductance (gₛ) on relative air humidity (RH)-related energy demand was addressed. We conducted six experiments in order to study the variation in evapotranspiration (ETc) of six pot rose cultivars, investigate the underlying processes and parameterise a gₛ-based ETc model. Several levels of crop ETc were realised by adjusting the growth environment. The commonly applied Ball–Woodrow–Berry gₛ-sub-model (BWB-model) in ETc models was validated under greenhouse conditions, and showed a close agreement between simulated and measured ETc. The validated model was incorporated into a greenhouse simulator. A scenario simulation study showed that selecting low-gₛ cultivars reduces energy demand (≤5.75%), depending on the RH set point. However, the BWB-model showed poor prediction quality at RH lower than 60% and a good fit at higher RH. Therefore, an attempt was made to improve model prediction: the in situ-obtained data were employed to adapt and extend either the BWB-model, or the Liu-extension with substrate water potential (Ψ; BWB-Liu-model). Both models were extended with stomatal density (Dₛ) or pore area. Although the modified BWB-Liu-model (considering Dₛ) allowed higher accuracy (R² = 0.59), as compared to the basic version (R² = 0.31), the typical lack of Ψ prediction in greenhouse models may be problematic for implementation into real-time climate control. The current study lays the basis for the development of cultivar specific cultivation strategies as well as improving the gₛ sub-model for dynamic climate conditions under low RH using model-based control systems.
Why it matches plant phenotyping methods栽培実験の単なる生理測定ではなく、気孔形質を組み込んだ蒸発散・気孔コンダクタンスモデルを検証・改良し、温室制御への適用可能性を評価しているため、方法開発・検証が中心である。
abstractThe commonly applied Ball–Woodrow–Berry gₛ-sub-model (BWB-model) in ETc models was validated under greenhouse conditions
Recent technical advances in the computer-vision domain have facilitated the development of various methods for achieving image-based quantification of stomata-related traits. However, the installation cost of such a system and the difficulties of operating it on-site have been hurdles for experimental biologists. Here, we present a platform that allows real-time stomata detection during microscopic observation. The proposed system consists of a deep neural network model-based stomata detector and an upright microscope connected to a USB camera and a graphics processing unit (GPU)-supported single-board computer. All the hardware components are commercially available at common electronic commerce stores at a reasonable price. Moreover, the machine-learning model is prepared based on freely available cloud services. This approach allows users to set up a phenotyping platform at low cost. As a proof of concept, we trained our model to detect dumbbell-shaped stomata from wheat leaf imprints. Using this platform, we collected a comprehensive range of stomatal phenotypes from wheat leaves. We confirmed notable differences in stomatal density ( SD ) between adaxial and abaxial surfaces and in stomatal size ( SS ) between wheat-related species of different ploidy. Utilizing such a platform is expected to accelerate research that involves all aspects of stomata phenotyping.
Why it matches plant phenotyping methods低コストの顕微鏡画像と深層学習による気孔検出・形質定量化プラットフォームの開発であり、植物フェノタイピング手法が研究の中心です。
abstractHere, we present a platform that allows real-time stomata detection during microscopic observation.
Reproduction assets foundThe paper's stomata-detection GUI, trained SSD model weights, and model-training workflow are publicly available in the authors' GitHub repository (onsite_stomata_platform) with an executable Colab training notebook. The raw phenotype/image datasets are only available on request per the Data Availability Statement.Code · publicDetailed codes and instructions to reproduce the regarding system as well as the stomata detection model is described in Google Colaboratory executable notebook 7 hosted at https://github.com/totti0223/onsite_stomata_platform .Open asset ↗totti0223/onsite_stomata_platformlines:155-166Code / dataset availability confirmedCrossref · checked 15 Sept 2026
Abstract Sorghum (Sorghum bicolor) is a model C4 crop made experimentally tractable by extensive genomic and genetic resources. Biomass sorghum is studied as a feedstock for biofuel and forage. Mechanistic modeling suggests that reducing stomatal conductance (gs) could improve sorghum intrinsic water use efficiency (iWUE) and biomass production. Phenotyping to discover genotype-to-phenotype associations remains a bottleneck in understanding the mechanistic basis for natural variation in gs and iWUE. This study addressed multiple methodological limitations. Optical tomography and a machine learning tool were combined to measure stomatal density (SD). This was combined with rapid measurements of leaf photosynthetic gas exchange and specific leaf area (SLA). These traits were the subject of genome-wide association study and transcriptome-wide association study across 869 field-grown biomass sorghum accessions. The ratio of intracellular to ambient CO2 was genetically correlated with SD, SLA, gs, and biomass production. Plasticity in SD and SLA was interrelated with each other and with productivity across wet and dry growing seasons. Moderate-to-high heritability of traits studied across the large mapping population validated associations between DNA sequence variation or RNA transcript abundance and trait variation. A total of 394 unique genes underpinning variation in WUE-related traits are described with higher confidence because they were identified in multiple independent tests. This list was enriched in genes whose Arabidopsis (Arabidopsis thaliana) putative orthologs have functions related to stomatal or leaf development and leaf gas exchange, as well as genes with nonsynonymous/missense variants. These advances in methodology and knowledge will facilitate improving C4 crop WUE.
Why it matches plant phenotyping methods光学トモグラフィーと機械学習ツールによる気孔密度測定を中心的な方法として開発・適用し、ガス交換等の表現型を大規模集団で評価しているため。
abstractThis study addressed multiple methodological limitations. Optical tomography and a machine learning tool were combined to measure stomatal density (SD).
Reproduction assets foundThe paper's optical tomography leaf images (the sensor inputs used for machine-learning stomatal density phenotyping) are publicly deposited in the Illinois Data Bank. Phenotypic trait data (Supplemental Table S12) are public but only via the article's supplemental material without a listed URL; RNA-seq (PRJNA522466) GDataset · publichttps://www.ncbi.nlm.nih.gov/bioproject/PRJNA522466/ .
Genotyping-by-sequencing data are available at: https://doi.org/10.5281/zenodo.5019227 . Phenotypic data are available as
part of the supplemental
material ( Supplemental Table
S12 ). Optical tomography images from this article can be found in the Illinois
Data Bank under: https://doi.org/10.13012/B2IDB-1411926_V1 .
Supplemental data
The following materials are available in the online version of this article.Open asset ↗10.13012/B2IDB-1411926_V1lines:985-1051Plant phenotyping relevance match · UnverifiedbioRxiv · Europe PMC · checked 8 Sept 2026
Stomata allow CO2 uptake by leaves for photosynthetic assimilation at the cost of water vapor loss to the atmosphere. The opening and closing of stomata in response to fluctuations in light intensity regulate CO2 and water fluxes and are essential to maintenance of water-use efficiency (WUE). However, little is known about the genetic basis for natural variation in stomatal movement, especially in C4 crops. This is partly because the stomatal response to a change in light intensity is difficult to measure at the scale required for association studies. High-throughput thermal imaging was used to bypass the phenotyping bottleneck and assess 10 traits describing stomatal conductance (gs) before, during and after a stepwise decrease in light intensity for a diversity panel of 659 sorghum accessions. Results from thermal imaging significantly correlated with photosynthetic gas-exchange measurements. gs traits varied substantially across the population and were moderately heritable (h2 up to 0.72). An integrated genome-wide and transcriptome-wide association study (GWAS/TWAS) identified candidate genes putatively driving variation in stomatal conductance traits. Of the 239 unique candidate genes identified with greatest confidence, 77 were orthologs of Arabidopsis genes related to functions implicated in WUE, including stomatal opening/closing (24 genes), stomatal/epidermal cell development (35 genes), leaf/vasculature development (12 genes), or chlorophyll metabolism/photosynthesis (8 genes). These findings demonstrate an approach to finding genotype-to-phenotype relationships for a challenging trait as well as candidate genes for further investigation of the genetic basis of WUE in a model C4 grass for bioenergy, food, and forage production. One sentence summaryRapid phenotyping of 659 accessions of Sorghum bicolor revealed heritable stomatal responses to a decrease in light. GWAS/TWAS was used to identify candidate genes influencing traits important to WUE.
Why it matches plant phenotyping methods熱画像を用いた高スループットな気孔コンダクタンス測定を開発・適用し、ガス交換測定との相関で検証しているため、植物表現型取得法が研究の中心である。
abstractHigh-throughput thermal imaging was used to bypass the phenotyping bottleneck and assess 10 traits describing stomatal conductance (gs) before, during and after a stepwise decrease in light intensity for a diversity panel of 659 sorghum accessions.
The arrangement patterns of stomata on the leaf surface influence water loss and CO 2 uptake via transportation and diffusion between stomata, the sites of photosynthesis, and vasculature. However, the quantification of such patterns remains unclear. Based on the distance between stomata, we developed three independent indices to quantify stomatal arrangement pattern (SAP). “Stomatal evenness” was used to quantify the regularity of the distribution of stomata based on a minimum spanning tree, “stomatal divergence” described the divergence in the distribution of stomata based on their distances from their center of gravity, and “stomatal aggregation” was used to quantitatively distinguish the SAP as clustered, random, or regularly distributed based on the nearest-neighbor distances. These three indices address the shortcoming of stomatal density that only describes “abundance” and may, collectively, have a better capacity to explore crop development, plant adaptation and evolution, and potentially ultimately enable a more accurate reconstruction of the palaeoclimate.
Why it matches plant phenotyping methods気孔配置パターンを定量化する新しい3指標を開発しており、植物形態形質の抽出法が研究の中心である。
abstractthe quantification of such patterns remains unclear. Based on the distance between stomata, we developed three independent indices to quantify stomatal arrangement pattern (SAP).
Reproduction assets foundThe authors state that an R script (stomata_arrange function) implementing their three stomatal arrangement pattern indices is available in the Supplementary Information, which is publicly accessible at the Frontiers supplementary material URL. No separate phenotype dataset deposit is identified; empirical photomicrogrCode · publicthe script (R statistical language) used to compute these three indices ( stomata_arrange function) is available in the Supplementary Information .Open asset ↗lines:298-332Plant phenotyping relevance match · UnverifiedEurope PMC · checked 9 Sept 2026
A non-destructive thermal imaging method was used to study the stomatal response of salt-treated Arabidopsis thaliana plants to excessive light. The plants were exposed to different levels of salt concentrations (0, 75, 150, and 220 mM NaCl). Time-dependent thermograms showed the changes in the temperature distribution over the lamina and provided new insights into the acute light-induced temporary response of Arabidopsis under short-term salinity. The initial response of plants, which was associated with stomatal aperture, revealed an exponential growth in temperature kinetics. Using a single-exponential function, we estimated the time constants of thermal courses of plants exposed to acute high light. The saline-induced impairment in stomatal movement caused the reduced stomatal conductance and transpiration rate. Limited transpiration of NaCl-treated plants resulted in an increased rosette temperature and decreased thermal time constants as compared to the controls. The net CO 2 assimilation rate decreased for plants exposed to 220 mM NaCl; in the case of 75 mM NaCl treatment, an increase was observed. A significant decline in the maximal quantum yield of photosystem II under excessive light was noticeable for the control and NaCl-treated plants. This study provides evidence that thermal imaging as a highly sensitive technique may be useful for analyzing the stomatal aperture and movement under dynamic environmental conditions.
Why it matches plant phenotyping methods熱画像を用いて葉温・温度 kinetics から気孔開閉や蒸散応答を定量化することが研究の中心であり、塩・高光条件下の生理フェノタイプ取得法として実質的に適用・評価している。
abstractA non-destructive thermal imaging method was used to study the stomatal response of salt-treated Arabidopsis thaliana plants to excessive light.
Abstract Stomatal density (SD) and stomatal complex area (SCA) are important traits that regulate gas exchange and abiotic stress response in plants. Despite sorghum (Sorghum bicolor) adaptation to arid conditions, the genetic potential of stomata-related traits remains unexplored due to challenges in available phenotyping methods. Hence, identifying loci that control stomatal traits is fundamental to designing strategies to breed sorghum with optimized stomatal regulation. We implemented both classical and deep learning methods to characterize genetic diversity in 311 grain sorghum accessions for stomatal traits at two different field environments. Nearly 12,000 images collected from abaxial (Ab) and adaxial (Ad) leaf surfaces revealed substantial variation in stomatal traits. Our study demonstrated significant accuracy between manual and deep learning methods in predicting SD and SCA. In sorghum, SD was 32%–39% greater on the Ab versus the Ad surface, while SCA on the Ab surface was 2%–5% smaller than on the Ad surface. Genome-Wide Association Study identified 71 genetic loci (38 were environment-specific) with significant genotype to phenotype associations for stomatal traits. Putative causal genes underlying the phenotypic variation were identified. Accessions with similar SCA but carrying contrasting haplotypes for SD were tested for stomatal conductance and carbon assimilation under field conditions. Our findings provide a foundation for further studies on the genetic and molecular mechanisms controlling stomata patterning and regulation in sorghum. An integrated physiological, deep learning, and genomic approach allowed us to unravel the genetic control of natural variation in stomata traits in sorghum, which can be applied to other plants.
Why it matches plant phenotyping methodsソルガム気孔形質の遺伝解析が主目的だが、古典的手法と深層学習による画像フェノタイピングを実装し、手動法との精度比較・検証を行っており、形質抽出法が実質的に中心的な役割を持つ。
abstractWe implemented both classical and deep learning methods to characterize genetic diversity in 311 grain sorghum accessions for stomatal traits at two different field environments.
Leaf stomata regulate the process of gas exchange between the plant and the atmosphere, therefore play an important role in plant growth and water use. Thermal infrared sensing of leaf surface temperature is proved to be an indirect but effective approach to estimate leaf stomatal conductance, and shows the potential to rapidly differentiate genotypes for water-use related traits. The objective of this study was to estimate leaf stomatal conductance from thermal IR images of crops and relevant environmental parameters. The experiment was conducted in the NU-Spidercam field phenotyping facility near Mead, NE. Leaf stomatal conductance was measured from soybean, sorghum, maize, and sunflower using a leaf porometer. Thermal IR images of the crop canopies were captured by a thermal IR camera and then processed to extract crop canopy temperature (Tc). In addition, weather variables including solar radiation, air temperature, relative humidity, and wind speed were extracted from a nearby weather station. Correlation analysis was implemented to explore the relationships between these variables. Multiple linear regression (MLR), random forest (RF), gradient boosting machine (GBM) were applied to model stomatal conductance from Tc and weather variables. The Pearson correlation coefficients between predicted and measured stomatal conductance were 0.495 for MLR, 0.591 for RF, and 0.878 for GBM when Tc was not used as an input variable. After adding Tc as input, Pearson correlation coefficients were improved to 0.584 for MLR, 0.593 for RF, and 0.896 for GBM. The mean absolute errors for the three models were 225, 237, and 129 mmol/(m2·s) when Tc was included as a model input. This research would lead to rapid assessment of leaf stomatal conductance and crop water status using thermal IR imaging.
Why it matches plant phenotyping methods熱赤外画像と環境変数から葉の気孔コンダクタンスを推定する手法を開発・評価しており、植物生理形質の取得が研究の中心です。
abstractThe objective of this study was to estimate leaf stomatal conductance from thermal IR images of crops and relevant environmental parameters.
Patchy stomata are a common and characteristic phenomenon in plants. Understanding and studying the regulation mechanism of patchy stomata are of great significance to further supplement and improve the stomatal theory. Currently, the common methods for stomatal behavior observation are based on static images, which makes it difficult to reflect dynamic changes of stomata. With the rapid development of portable microscopes and computer vision algorithms, it brings new chances for stomatal movement observation. In this study, a stomatal behavior observation system (SBOS) was proposed for real-time observation and automatic analysis of each single stoma in wheat leaf using object tracking and semantic segmentation methods. The SBOS includes two modules: the real-time observation module and the automatic analysis module. The real-time observation module can shoot videos of stomatal dynamic changes. In the automatic analysis module, object tracking locates every single stoma accurately to obtain stomatal pictures arranged in time-series; semantic segmentation can precisely quantify the stomatal opening area (SOA), with a mean pixel accuracy (MPA) of 0.8305 and a mean intersection over union (MIoU) of 0.5590 in the testing set. Moreover, we designed a graphical user interface (GUI) so that researchers could use this automatic analysis module smoothly. To verify the performance of the SBOS, the dynamic changes of stomata were observed and analyzed under chilling. Finally, we analyzed the correlation between gas exchange and SOA under drought stress, and the correlation coefficients between mean SOA and net photosynthetic rate (Pn), intercellular CO 2 concentration (Ci), stomatal conductance (Gs), and transpiration rate (Tr) are 0.93, 0.96, 0.96, and 0.97.
Why it matches plant phenotyping methods顕微鏡動画から個々の気孔を追跡し、セグメンテーションで気孔開口面積を定量化する観測・解析システムを開発しており、植物表現型取得が研究の中心です。
abstracta stomatal behavior observation system (SBOS) was proposed for real-time observation and automatic analysis of each single stoma in wheat leaf using object tracking and semantic segmentation 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 · public(2) The module is easy to install with the aid of an executable program (EXE) ( https://github.com/shem123456/Stomata-segmentation-with-GUI ).Open asset ↗https://github.com/shem123456/Stomata-segmentation-with-GUIlines:61-68Plant phenotyping relevance match · UnverifiedEurope PMC · checked 15 Sept 2026
Background The anthropogenic increase of atmospheric CO 2 concentration (c a ) is impacting carbon (C), water, and nitrogen (N) cycles in grassland and other terrestrial biomes. Plant canopy stomatal conductance is a key player in these coupled cycles: it is a physiological control of vegetation water use efficiency (the ratio of C gain by photosynthesis to water loss by transpiration), and it responds to photosynthetic activity, which is influenced by vegetation N status. It is unknown if the c a -increase and climate change over the last century have already affected canopy stomatal conductance and its links with C and N processes in grassland. Results Here, we assessed two independent proxies of (growing season-integrating canopy-scale) stomatal conductance changes over the last century: trends of δ 18 O in cellulose (δ 18 O cellulose ) in archived herbage from a wide range of grassland communities on the Park Grass Experiment at Rothamsted (U.K.) and changes of the ratio of yields to the CO 2 concentration gradient between the atmosphere and the leaf internal gas space (c a - c i ). The two proxies correlated closely (R 2 = 0.70), in agreement with the hypothesis. In addition, the sensitivity of δ 18 O cellulose changes to estimated stomatal conductance changes agreed broadly with published sensitivities across a range of contemporary field and controlled environment studies, further supporting the utility of δ 18 O cellulose changes for historical reconstruction of stomatal conductance changes at Park Grass. Trends of δ 18 O cellulose differed strongly between plots and indicated much greater reductions of stomatal conductance in grass-rich than dicot-rich communities. Reductions of stomatal conductance were connected with reductions of yield trends, nitrogen acquisition, and nitrogen nutrition index. Although all plots were nitrogen-limited or phosphorus- and nitrogen-co-limited to different degrees, long-term reductions of stomatal conductance were largely independent of fertilizer regimes and soil pH, except for nitrogen fertilizer supply which promoted the abundance of grasses. Conclusions Our data indicate that some types of temperate grassland may have attained saturation of C sink activity more than one century ago. Increasing N fertilizer supply may not be an effective climate change mitigation strategy in many grasslands, as it promotes the expansion of grasses at the disadvantage of the more CO 2 responsive forbs and N-fixing legumes.
Why it matches plant phenotyping methodsセルロースδ18Oと収量/ガス交換比を用いた葉面積規模の気孔コンダクタンス推定を相互検証し、歴史的再構築への有用性を評価しており、単なる生物学的測定以上に推定手法の検証が含まれる。
abstractHere, we assessed two independent proxies of (growing season-integrating canopy-scale) stomatal conductance changes over the last century
Plant pathogenic Ralstonia cause wilt diseases by colonizing xylem vessels and disrupting water transport. Due to the abundance of Ralstonia cells in vessels, the dogma is that bacterial biomass clogs vessels and reduces the flow of xylem sap. However, the physiological mechanism of xylem disruption during bacterial wilt disease is untested. Using a tomato and Ralstonia pseudosolanacearum GMI1000 model, we visualized and quantified the spatiotemporal dynamics of xylem disruption during bacterial wilt disease. First, we measured stomatal conductance of leaflets on mock-inoculated and wilt-symptomatic plants. Wilted leaflets had reduced stomatal conductance, as did turgid leaflets located on the same petiole as wilted leaflets. Next, we used X-ray microcomputed tomography (X-ray microCT) and light microscopy to differentiate between mechanisms of xylem disruption: blockage by bacterial biomass, blockage by vascular tyloses, or sap displacement by gas embolisms. We imaged stems on plants with intact roots and leaves to quantify embolized vessels. Embolized vessels were rare, but there was a slight trend of increased vessel embolisms in infected plants with low bacterial population sizes. To test the hypothesis that vessels are clogged during bacterial wilt, we imaged excised stems after allowing the sap to evaporate during a brief dehydration. Most xylem vessels in mock-infected plants emptied their contents after excision, but non-conductive clogged vessels were abundant in infected plants by 2 days post infection. At wilt onset when bacterial populations exceeded 5x108 cfu/g stem tissue, approximately half of the xylem vessels were clogged with electron-dense bacterial biomass. We found no evidence of tyloses in the X-ray microCT reconstructions or light microscopy on the preserved stems. Bacterial blockage of vessels appears to be the principal cause of vascular disruption during Ralstonia wilt.
Why it matches plant phenotyping methodsX線マイクロCTを用いて感染植物の木部塞栓・導管閉塞を可視化・定量し、病態に関わる植物の生理状態を測定している。病理学的機序の研究ではあるが、画像取得と定量が主要な技術的根拠である。
titleX-ray microcomputed tomography provides direct evidence that Ralstonia biomass clogs xylem vessels.
Background Stomata analysis using microscope imagery provides important insight into plant physiology, health and the surrounding environmental conditions. Plant scientists are now able to conduct automated high-throughput analysis of stomata in microscope data, however, existing detection methods are sensitive to the appearance of stomata in the training images, thereby limiting general applicability. In addition, existing methods only generate bounding-boxes around detected stomata, which require users to implement additional image processing steps to study stomata morphology. In this paper, we develop a fully automated, robust stomata detection algorithm which can also identify individual stomata boundaries regardless of the plant species, sample collection method, imaging technique and magnification level. Results The proposed solution consists of three stages. First, the input image is pre-processed to remove any colour space biases occurring from different sample collection and imaging techniques. Then, a Mask R-CNN is applied to estimate individual stomata boundaries. The feature pyramid network embedded in the Mask R-CNN is utilised to identify stomata at different scales. Finally, a statistical filter is implemented at the Mask R-CNN output to reduce the number of false positive generated by the network. The algorithm was tested using 16 datasets from 12 sources, containing over 60,000 stomata. For the first time in this domain, the proposed solution was tested against 7 microscope datasets never seen by the algorithm to show the generalisability of the solution. Results indicated that the proposed approach can detect stomata with a precision, recall, and F-score of 95.10%, 83.34%, and 88.61%, respectively. A separate test conducted by comparing estimated stomata boundary values with manually measured data showed that the proposed method has an IoU score of 0.70; a 7% improvement over the bounding-box approach. Conclusions The proposed method shows robust performance across multiple microscope image datasets of different quality and scale. This generalised stomata detection algorithm allows plant scientists to conduct stomata analysis whilst eliminating the need to re-label and re-train for each new dataset. The open-source code shared with this project can be directly deployed in Google Colab or any other Tensorflow environment.
Why it matches plant phenotyping methods植物の気孔を顕微鏡画像から自動検出・個別境界推定し、形態解析に利用する画像ベース表現型解析手法を開発・検証しているため。
abstractIn this paper, we develop a fully automated, robust stomata detection algorithm which can also identify individual stomata boundaries
Reproduction assets foundThe paper's authors publicly release their complete Mask R-CNN stomata segmentation analysis code at the Smart-Robotic-Viticulture/MaskStomata GitHub repository, with explicit availability statements in multiple blocks. The microscope image datasets themselves are only available from the corresponding author on requestCode · publicThe complete code for the project can be accessed at: https://github.com/Smart-Robotic-Viticulture/MaskStomata .Open asset ↗Smart-Robotic-Viticulture/MaskStomatalines:136-157Plant phenotyping relevance match · UnverifiedCrossref · Europe PMC · checked 13 Sept 2026
Summary Quantitative information on the spatiotemporal distribution of polarised proteins is central for understanding cell‐fate determination, yet collecting sufficient data for statistical analysis is difficult to accomplish with manual measurements. Here we present Polarity Measurement (P ome ), a semi‐automated pipeline for the quantification of cell polarity and demonstrate its application to a variety of developmental contexts. P ome analysis reveals that, during asymmetric cell divisions in the Arabidopsis thaliana stomatal lineage, polarity proteins BASL and BRXL2 are more asynchronous and less mutually dependent than previously thought. A similar analysis of the linearly arrayed stomatal lineage of Brachypodium distachyon revealed that the MAPKKK BdYDA1 is segregated and polarised following asymmetrical divisions. Our results demonstrate that P ome is a versatile tool, which by itself or combined with tissue‐level studies and advanced microscopy techniques can help to uncover new mechanisms of cell polarity.
Why it matches plant phenotyping methods植物細胞の極性を定量化する半自動パイプラインを開発・適用しており、植物の細胞状態を抽出する方法が研究の中心です。
abstractHere we present Polarity Measurement (P ome ), a semi‐automated pipeline for the quantification of cell polarity and demonstrate its application to a variety of developmental contexts.
Plant phenotyping relevance match · UnverifiedbioRxiv · Europe PMC · checked 8 Sept 2026
Research on stomata, i.e., morphological structures of plants, has increased in popularity in the last years. These structures (pores) are in charge of the interaction between the internal plant system and the environment, working on different processes such as photosynthesis and transpiration stream. Besides, a better understanding of the pore mechanism plays a significant role when exploring the evolution process, as well as the behavior of plants. Although the study of stomata in dicots species of plants has advanced considerably in the past years, there is little information about stomata of cereal grasses. Also, automated detection of these structures have been considered in the literature, but some gaps are still uncovered. This fact is motivated by high morphological variation of stomata and the presence of noise from the image acquisition step. In this work, we propose a new methodology for automatic stomata classification and a new detection system in microscope images for maize cultivars. We have achieved an approximated accuracy of 97.1% in the identification of stomata regions using classifiers based on deep learning features, which figures out as a nearly perfect classification system.
Why it matches plant phenotyping methodsトウモロコシの気孔を顕微鏡画像から自動分類・検出する画像ベースの植物表現型取得法を開発しており、方法が研究の中心である。
abstractwe propose a new methodology for automatic stomata classification and a new detection system in microscope images for maize cultivars.
Abstract Background: Stomata analysis using microscope imagery provides important insight into plant physiology, health and the surrounding environmental conditions. Plant scientists are now able to conduct automated high-throughput analysis of stomata in microscope data, however, existing detection methods are sensitive to the appearance of stomata in the training images, thereby limiting general applicability. In addition, existing methods only generate bounding-boxes around detected stomata, which require users to implement additional image processing steps to study stomata morphology. In this paper, we develop a fully automated, robust stomata detection algorithm which can also identify individual stomata boundaries regardless of the plant species, sample collection method, imaging technique and magnification level. Results: The proposed solution consists of three stages. First, the input image is pre-processed to remove any colour space biases occurring from different sample collection and imaging techniques. Then, a Mask R-CNN is applied to estimate individual stomata boundaries. The feature pyramid network embedded in the Mask R-CNN is utilised to identify stomata at different scales. Finally, a statistical filter is implemented at the Mask R-CNN output to reduce the number of false positive generated by the network. The algorithm was tested using 16 datasets from 12 sources, containing over 60,000 stomata. For the first time in this domain, the proposed solution was tested against 7 microscope datasets never seen by the algorithm to show the generalisability of the solution. Results indicated that the proposed approach can detect stomata with a precision, recall, and F-score of 95.10\%, 83.34\%, and 88.61\%, respectively. A separate test conducted by comparing estimated stomata boundary values with manually measured data showed that the proposed method has an IoU score of 0.70; a 7\% improvement over the bounding-box approach. Conclusions: The proposed method shows robust performance across multiple microscope image datasets of different quality and scale. This generalised stomata detection algorithm allows plant scientists to conduct stomata analysis whilst eliminating the need to re-label and re-train for each new dataset. The open-source code shared with this project can be directly deployed in Google Colab or any other Tensorflow environment.
Why it matches plant phenotyping methods気孔の画像から個体境界を自動抽出する汎用的な画像解析手法を開発し、多数のデータセットで汎化性能と手動測定との一致を検証しており、植物表現型取得が中心である。
abstractIn this paper, we develop a fully automated, robust stomata detection algorithm which can also identify individual stomata boundaries regardless of the plant species, sample collection method, imaging technique and magnification level.
Reproduction assets foundThe paper's complete analysis code (Mask R-CNN stomata segmentation pipeline, training setup, fine-tuning instructions) is publicly available on the authors' GitHub repository. The microscope image datasets themselves are only available on request from the corresponding author, so they do not qualify as public assets.Code · publicThe complete code for the project can be accessed at: :
https://github.com/Smart-Robotic-Viticulture/MaskStomata.Open asset ↗Smart-Robotic-Viticulture/MaskStomatapdf-page:19 lines:1-68Plant phenotyping relevance match · UnverifiedEurope PMC · checked 9 Sept 2026
Permanent wilting point (PWP) is generally used to ascertain plant resistance against abiotic drought stress and designated as the soil water content (θ) corresponding to soil suction (ψ) at 1500 kPa obtained from the soil water retention curve. Determination of PWP based on only pre-assumed ψ may not represent true wilting condition for soils with contrasting water retention abilities. In addition to ψ, there is a need to explore significance of additional plant parameters (i.e., stomatal conductance and photosynthetic status) in determining PWP. This study introduces a new framework for determining PWP by integrating plant leaf response and ψ during drought. Axonopus compressus were grown in two distinct textured soils (clayey loam and silty sand), after which drought was initiated till wilting. Thereafter, ψ and θ within the root zone were measured along with corresponding leaf stomatal conductance and photosynthetic status. It was found that coarse textured silty sand causes wilting at much lower ψ (≈ 300 kPa) than clayey loam (≈ 1600 kPa). Plant response to drought was dependent on the relative porosity and mineralogy of the soil, which governs the ease at which roots can grow, assimilate soil O 2 , and uptake water. For clay loam, the held water within the soil matrix does not facilitate easy root water uptake by relatively coarse root morphology. Contrastingly, fine root hair formation in silty sand facilitated higher plant water uptake and doubled the plant survival time.
Why it matches plant phenotyping methods植物の萎凋点を、土壌水分ポテンシャルだけでなく葉の気孔コンダクタンスと光合成状態を統合して判定する新しい枠組みを提案しており、植物状態の取得・評価法が研究の中心です。
abstractThis study introduces a new framework for determining PWP by integrating plant leaf response and ψ during drought.
The CO 2 and water vapor exchange between leaf and atmosphere are relevant for plant physiology. This process is done through the stomata. These structures are fundamental in the study of plants since their properties are linked to the evolutionary process of the plant, as well as its environmental and phytohormonal conditions. Stomatal detection is a complex task due to the noise and morphology of the microscopic images. Although in recent years segmentation algorithms have been developed that automate this process, they all use techniques that explore chromatic characteristics. This research explores a unique feature in plants, which corresponds to the stomatal spatial distribution within the leaf structure. Unlike segmentation techniques based on deep learning tools, we emphasize the search for an optimal threshold level, so that a high percentage of stomata can be detected, independent of the size and shape of the stomata. This last feature has not been reported in the literature, except for those results of geometric structure formation in the salt formation and other biological formations.
Why it matches plant phenotyping methods葉の顕微鏡画像から気孔を自動セグメンテーションする手法の開発が中心であり、植物の形態的形質取得に直接関わる。
titleAutomatic Stomatal Segmentation Based on Delaunay-Rayleigh Frequency Distance.
Reproduction assets foundThe paper's Supplementary Materials section explicitly states that the DRTB solution (authors' analysis code) is available online at https://github.com/mlacarrasco/drtb and that the images database (stomatal microscopy images used for phenotyping) is available at https://github.com/mlacarrasco/drtb/tree/main/database. Dataset · publicOur solution can be accessed online at https://github.com/mlacarrasco/drtb , and images database are available online at https://github.com/mlacarrasco/drtb/tree/main/database .Open asset ↗mlacarrasco/drtblines:61-134Plant phenotyping relevance match · UnverifiedEurope PMC · checked 15 Sept 2026
The phytohormones salicylic acid (SA), jasmonic acid (JA), and ethylene (ET) are central regulators of biotic and abiotic stress responses in Arabidopsis thaliana . Here, we generated modular fluorescent protein-based reporter lines termed COLORFUL-PR1pro, -VSP2pro, and -PDF1.2apro. These feature hormone-controlled nucleus-targeted transcriptional output sensors and the simultaneous constitutive expression of spectrally separated nuclear reference and plasma membrane-localized reporters. This set-up allowed the study of cell-type specific hormone activities, cellular viability and microbial invasion. Moreover, we developed a software-supported high-throughput confocal microscopy imaging protocol for output quantification to resolve the spatio-temporal dynamics of respective hormonal signaling activities at single-cell resolution. Proof-of-principle analyses in A. thaliana leaves revealed distinguished hormone sensitivities in mesophyll, epidermal pavement and stomatal guard cells, suggesting cell type-specific regulatory protein activities. In plant-microbe interaction studies, we found that virulent and avirulent Hyaloperonospora arabidopsidis ( Hpa ) isolates exhibit different invasion dynamics and induce spatio-temporally distinct hormonal activity signatures. On the cellular level, these hormone-controlled reporter signatures demarcate the nascent sites of Hpa entry and progression, and highlight initiation, transduction and local containment of immune signals.
Why it matches plant phenotyping methods単一細胞レベルの植物ホルモン活動を定量する蛍光レポーター系と、ソフトウェア対応の高スループット共焦点画像解析プロトコルを開発しており、植物状態の取得・抽出法が研究の中心である。
abstractHere, we generated modular fluorescent protein-based reporter lines termed COLORFUL-PR1pro, -VSP2pro, and -PDF1.2apro.
Sorghum is a model C4 crop made experimentally tractable by extensive genomic and genetic resources. Biomass sorghum is also studied as a feedstock for biofuel and forage. Mechanistic modelling suggests that reducing stomatal conductance (gs) could improve sorghum intrinsic water use efficiency (iWUE) and biomass production. Phenotyping for discovery of genotype to phenotype associations remain bottlenecks in efforts to understand the mechanistic basis for natural variation in gs and iWUE. This study addressed multiple methodological limitations. Optical tomography and a novel machine learning tool were combined to measure stomatal density (SD). This was combined with rapid measurements of leaf photosynthetic gas exchange and specific leaf area (SLA). These traits were then the subject of genome-wide association study (GWAS) and transcriptome-wide association study (TWAS) across 869 field-grown biomass sorghum accessions. SD was correlated with plant height and biomass production. Plasticity in SD and SLA were interrelated with each other, and productivity, across wet versus dry growing seasons. Moderate-to-high heritability of traits studied across the large mapping population supported identification of associations between DNA sequence variation, or RNA transcript abundance, and trait variation. 394 unique genes underpinning variation in WUE-related traits are described with higher confidence because they were identified in multiple independent tests. This list was enriched in genes whose orthologs in Arabidopsis have functions related to stomatal or leaf development and leaf gas exchange. These advances in methodology and knowledge will aid efforts to improve the WUE of C4 crops.
Why it matches plant phenotyping methods光学トモグラフィーと新規機械学習ツールによる気孔密度測定が研究の中心的な方法的貢献であり、大規模集団への実質的な適用も行っている。
abstractThis study addressed multiple methodological limitations. Optical tomography and a novel machine learning tool were combined to measure stomatal density (SD).
Stomata are pores in the epidermal tissue of leaf plants formed by specialised cells called guard cells, which regulate the stomatal opening. Stomata facilitate gas exchange, being pivotal in the regulation of processes such as photosynthesis and transpiration. The analysis of the number and behaviour of stomata is a task carried out by studying microscopic images, and that can serve, among other things, to better manage crops in agriculture or to better understand how plants fix CO₂ and lose water under different conditions. However, quantifying the number of stomata in an image traditionally has been a labor intensive and thus expensive process since an image might contain dozens of stomata. Several automatic stomata detection models have been developed and presented in the literature, but they fail to generalise to images from species different to those employed to train the model; and, in addition, they lack a simple interface to employ them. In this work, we tackle these problems by training a YOLO model. Such a model achieves a F1-score of 0.91 in images from the species employed for training it, and similar F1-score for datasets containing images of different species. Moreover, in order to facilitate the use of the model, we have developed LabelStoma, an open-source and simple-to-use graphical user interface that employs the YOLO model. In addition, this tool provides a simple method to adapt the YOLO model to the users’ images, and, therefore, customising the model to the users’ needs. Thanks to this work, the analysis of plant stomata of different species will be more reliable and comparable; and, the developed tools will help to advance our understanding of CO₂ and H₂O dynamics in plants, such as photosynthesis and transpiration, and ecosystems related processes, such as carbon and water cycles.
Why it matches plant phenotyping methods気孔を顕微鏡画像から自動検出・定量するYOLOモデルとGUIツールを開発し、異種間性能を検証しており、植物表現型取得法が中心である。
abstractIn this work, we tackle these problems by training a YOLO model.
Plant phenotyping relevance match · UnverifiedbioRxiv · Europe PMC · checked 9 Sept 2026
Stomata are adjustable pores on leaf surfaces that regulate the trade-off of CO2 uptake with water vapor loss, thus having critical roles in controlling photosynthetic carbon gain and plant water use. The lack of easy, rapid methods for phenotyping epidermal cell traits have limited the use of quantitative, forward and reverse genetics to discover the genetic basis of stomatal patterning. A new high-throughput epidermal cell phenotyping pipeline is presented here and used for quantitative trait loci (QTL) mapping in field-grown maize. The locations and sizes of stomatal complexes and pavement cells on images acquired by an optical topometer from mature leaves were automatically determined. Computer estimated stomatal complex density (SCD; R2 = 0.97) and stomatal complex area (SCA; R2 = 0.71) were strongly correlated with human measurements. Leaf gas exchange traits correlated with the dimensions and proportion of stomatal complexes but, unexpectedly, did not correlate with SCD. Genetic variation in epidermal traits were consistent across two field seasons. Out of 143 QTLs in total, 36 QTLs were consistently identified for a given trait in both years. 24 hotspots of overlapping QTLs for multiple traits were identified. Orthologs of genes known to regulate stomatal patterning in Arabidopsis were located within some, but not all, of these regions. This study demonstrates how discovery of the genetic basis for stomatal patterning can be accelerated in maize, a model for C4 species where these processes are poorly understood. One sentence summaryOptical topometry and machine learning tools were developed to assess epidermal cell patterning, and applied to analyze its genetic architecture alongside leaf photosynthetic gas exchange in maize.
Why it matches plant phenotyping methods光学トポメトリーと機械学習による葉表皮細胞形質の高速・高スループット取得法を開発し、ヒト測定との検証およびQTL解析への実質的応用を行っているため、植物フェノタイピング手法が中心である。
abstractA new high-throughput epidermal cell phenotyping pipeline is presented here and used for quantitative trait loci (QTL) mapping in field-grown maize.
Plant phenotyping relevance match · UnverifiedCrossref · Europe PMC · checked 15 Sept 2026
The rapidly growing demand for humidity sensing in various applications such as noninvasive epidermal sensing, water status tracking of plants, and environmental monitoring has triggered the development of high-performance humidity sensors. In particular, timely communication with plants to understand their physiological status may facilitate preventing negative influence of environmental stress and enhancing agricultural output. In addition, precise humidity sensing at bio-interface requires the sensor to be both flexible and stable. However, challenges still exist for the realization of efficient and large-scale production of flexible humidity sensors for bio-interface applications. Here, a convenient, effective, and robust method for massive production of flexible and wearable humidity sensor is proposed, using laser direct writing technology to produce laser-induced graphene interdigital electrode (LIG-IDE). Compared to previous methods, this strategy abandons the complicated and costly procedures for traditional IDE preparation. Using graphene oxide (GO) as the humidity-sensitive material, a flexible capacitive-type GO-based humidity sensor with low hysteresis, high sensitivity (3215.25 pF/% RH), and long-term stability (variation less than ± 1%) is obtained. These superior properties enable the sensor with multifunctional applications such as noncontact humidity sensing and human breath monitoring. In addition, this flexible humidity sensor can be directly attached onto the plant leaves for real-time and long-term tracking transpiration from the stomata, without causing any damage to plants, making it a promising candidate for next-generation electronics for intelligent agriculture.
Why it matches plant phenotyping methods植物葉に装着して蒸散をリアルタイム追跡する柔軟湿度センサーの製造・性能開発が論文の中心であり、植物生理状態の測定法に該当する。
abstractHere, a convenient, effective, and robust method for massive production of flexible and wearable humidity sensor is proposed, using laser direct writing technology to produce laser-induced graphene interdigital electrode (LIG-IDE).
Digital image processing is commonly used in plant health and growth analysis, aiming to improve research efficiency and repeatability. One focus is analysing the morphology of stomata, with the aim to better understand the regulation of gas exchange, its link to photosynthesis and water use and how they are influenced by climatic conditions. Despite the key role played by these cells, their microscopic analysis is largely manual, requiring intricate sample collection, laborious microscope application and the manual operation of a graphical user interface to identify and measure stomata. This research proposes a simple, end-to-end solution which enables automatic analysis of stomata by introducing key changes to imaging techniques, stomata detection as well as stomatal pore area calculation. An optimal procedure was developed for sample collection and imaging by investigating the suitability of using an automatic microscope slide scanner to image nail polish imprints. The use of the slide scanner allows the rapid collection of high-quality images from entire samples with minimal manual effort. A convolutional neural network was used to automatically detect stomata in the input image, achieving average precision, recall and F-score values of 0.79, 0.85, and 0.82 across four plant species. A novel binary segmentation and stomatal cross section analysis method is developed to estimate the pore boundary and calculate the associated area. The pore estimation algorithm correctly identifies stomata pores 73.72% of the time. Ultimately, this research presents a fast and simplified method of stomatal assay generation requiring minimal human intervention, enhancing the speed of acquiring plant health information.
Why it matches plant phenotyping methods気孔の画像取得、検出、孔面積推定を自動化するエンドツーエンドの植物表現型測定法を開発・評価しており、方法が研究の中心である。
abstractThis research proposes a simple, end-to-end solution which enables automatic analysis of stomata by introducing key changes to imaging techniques, stomata detection as well as stomatal pore area calculation.
Scanning electron microscopy (SEM) is widely used to investigate the surface morphology, and physiological state of plant leaves. Conventionally used methods for sample preparation are invasive, irreversible, require skill and expensive equipment, and are time and labor consuming. This study demonstrates a method to obtain in vivo surface information of plant leaves by imaging replicas with SEM that is rapid and non-invasive. Dental putty was applied to the leaves for 5 minutes and then removed. Replicas were then imaged with SEM and compared to fresh leaves, and leaves that were processed conventionally by chemical fixation, dehydration and critical point drying. The surface structure of leaves was well preserved on the replicas. The outline of epidermal as well as guard cells could be clearly distinguished enabling determination of stomatal density. Comparison of the dimensions of guard cells revealed that replicas did not differ from fresh leaves, while conventional sample preparation induced strong shrinkage (-40% in length and -38% in width) of the cells when compared to guard cells on fresh leaves. Tilting the replicas enabled clear measurement of stomatal aperture dimensions. Summing up, the major advantages of this method are that it is inexpensive, non-toxic, simple to apply, can be performed in the field, and that results on stomatal density and in vivo stomatal dimensions in 3D can be obtained in a few minutes.
Why it matches plant phenotyping methods植物葉の気孔密度・開口寸法を非侵襲的に取得するSEMレプリカ法を開発・比較検証しており、植物表現型取得法が中心である。
abstractThis study demonstrates a method to obtain in vivo surface information of plant leaves by imaging replicas with SEM that is rapid and non-invasive.
Stomata are microscopic pores on the plant epidermis that regulate the water content and CO2 levels in leaves. Thus, they play an important role in plant growth and development. Currently, most of the common methods for the measurement of pore anatomy parameters involve manual measurement or semi-automatic analysis technology, which makes it difficult to achieve high-throughput and automated processing. This paper presents a method for the automatic segmentation and parameter calculation of stomatal pores in microscope images of plant leaves based on deep convolutional neural networks. The proposed method uses a type of convolutional neural network model (Mask R-CNN (region-based convolutional neural network)) to obtain the contour coordinates of the pore regions in microscope images of leaves. The anatomy parameters of pores are then obtained by ellipse fitting technology, and the quantitative analysis of pore parameters is implemented. Stomatal microscope image datasets for black poplar leaves were obtained using a large depth-of-field microscope observation system, the VHX-2000, from Keyence Corporation. The images used in the training, validation, and test sets were taken randomly from the datasets (562, 188, and 188 images, respectively). After 10-fold cross validation, the 188 test images were found to contain an average of 2278 pores (pore widths smaller than 0.34 μm (1.65 pixels) were considered to be closed stomata), and an average of 2201 pores were detected by our network with a detection accuracy of 96.6%, and the intersection of union (IoU) of the pores was 0.82. The segmentation results of 2201 stomatal pores of black poplar leaves showed that the average measurement accuracies of the (a) pore length, (b) pore width, (c) area, (d) eccentricity, and (e) degree of stomatal opening, with a ratio of width-to-maximum length of a stomatal pore, were (a) 94.66%, (b) 93.54%, (c) 90.73%, (d) 99.09%, and (e) 92.95%, respectively. The proposed stomatal pore detection and measurement method based on the Mask R-CNN can automatically measure the anatomy parameters of pores in plants, thus helping researchers to obtain accurate stomatal pore information for leaves in an efficient and simple way.
Why it matches plant phenotyping methods葉の気孔画像から形態・開口状態を自動抽出・定量する画像解析手法を開発し、精度検証しており、植物フェノタイピング手法が研究の中心です。
abstractThis paper presents a method for the automatic segmentation and parameter calculation of stomatal pores in microscope images of plant leaves based on deep convolutional neural networks.
Reproduction assets foundThe paper's authors explicitly state that the complete project code for the Mask R-CNN-based stomatal pore detection and measurement method is publicly available on GitHub. The image datasets themselves are not stated as deposited by the authors (the generalization datasets are cited prior work, Stomatacounter [39]).Code · publicThe complete code for the project can be accessed at
https://github.com/lijunyu159/stomatal_pore_measurement-MaskRCNN (accessed on 15 July 2020).Open asset ↗lijunyu159/stomatal_pore_measurement-MaskRCNNpdf-page:9 lines:1-57Plant phenotyping relevance match · UnverifiedCrossref · checked 9 Sept 2026
Abstract The absorption of foliar fertilizer is a complex process and is poorly understood. The ability to visualize and quantify the pathway that elements take following their application to leaf surfaces is critical for understanding the science and for practical applications of foliar fertilizers. By the use of synchrotron-based X-ray fluorescence to analyze the in vivo localization of elements, our study aimed to investigate the penetration of foliar-applied Zn absorbed by apple ( Malus domestica Borkh.) leaves with different physiological surface properties, as well as the possible interactions between foliar Zn level and the mineral nutrient status of treated leaves. The results indicate that the absorption of foliar-applied Zn was largely dependent on plant leaf surface characteristics. High-resolution elemental maps revealed that the high binding capacity of the cell wall for Zn contributed to the observed limitation of Zn penetration across epidermal cells. Trichome density and stomatal aperture had opposite effects on Zn fertilizer penetration: a relatively high density of trichomes increased the hydrophobicity of leaves, whereas the presence of stomata facilitated foliar Zn penetration. Low levels of Zn promoted the accumulation of other mineral elements in treated leaves, and the complexation of Zn with phytic acid potentially occurred owing to exposure to high-Zn conditions. The present study provides direct visual evidence for the Zn penetration process across the leaf surface, which is important for the development of strategies for Zn biofortification in crop species.
Why it matches plant phenotyping methodsシンクロトロンX線蛍光による高解像度元素マッピングを用いて、リンゴ葉内のZn浸透経路と栄養状態を直接可視化・定量しており、植物の生理状態の取得法が研究の中心にある。
abstractThe ability to visualize and quantify the pathway that elements take following their application to leaf surfaces is critical
Plant leaf stomata are the gatekeepers of the atmosphere-plant interface and are essential building blocks of land surface models as they control transpiration and photosynthesis. Although more stomatal trait data are needed to significantly reduce the error in these model predictions, recording these traits is time-consuming, and no standardized protocol is currently available. Some attempts were made to automate stomatal detection from photomicrographs; however, these approaches have the disadvantage of using classic image processing or targeting a narrow taxonomic entity which makes these technologies less robust and generalizable to other plant species. We propose an easy-to-use and adaptable workflow from leaf to label. A methodology for automatic stomata detection was developed using deep neural networks according to the state of the art and its applicability demonstrated across the phylogeny of the angiosperms.We used a patch-based approach for training/tuning three different deep learning architectures. For training, we used 431 micrographs taken from leaf prints made according to the nail polish method from herbarium specimens of 19 species. The best-performing architecture was tested on 595 images of 16 additional species spread across the angiosperm phylogeny.The nail polish method was successfully applied in 78% of the species sampled here. The VGG19 architecture slightly outperformed the basic shallow and deep architectures, with a confidence threshold equal to 0.7 resulting in an optimal trade-off between precision and recall. Applying this threshold, the VGG19 architecture obtained an average F -score of 0.87, 0.89, and 0.67 on the training, validation, and unseen test set, respectively. The average accuracy was very high (94%) for computed stomatal counts on unseen images of species used for training.The leaf-to-label pipeline is an easy-to-use workflow for researchers of different areas of expertise interested in detecting stomata more efficiently. The described methodology was based on multiple species and well-established methods so that it can serve as a reference for future work.
Why it matches plant phenotyping methods葉の顕微画像から気孔を自動検出・計数する画像解析ワークフローを開発し、多様な植物種で性能検証しており、植物フェノタイピング手法が研究の中心です。
abstractA methodology for automatic stomata detection was developed using deep neural networks
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。Dataset · publicAll light microscope images used in this study are made freely accessible on Zenodo under the CC‐by license ( http://doi.org/10.5281/zenodo.3579227 ).Open asset ↗Zenodo · 10.5281/zenodo.3579227lines:186-229Dataset · publicThe example image set can also be downloaded here: http://doi.org/10.5281/zenodo.3902280 .Open asset ↗Zenodo · 10.5281/zenodo.3902280lines:186-229Plant phenotyping relevance match · UnverifiedOpenAlex · checked 15 Sept 2026
Plant pathogens and environmental stress can have a detrimental impact on forest health, biomass and growth. Early detection and identification of biotic and abiotic stresses, ideally before visible symptoms are present, are important for effective forest management practice and early control of pathogen, and to minimize damage. However, reliably detecting and identifying plant diseases and stress in the field are usually constrained by time and the large size of the area of interest. In this issue, Fallon et al. (2020) demonstrates how hyperspectral reflectance measurements can be used to detect fungal diseases and drought stress in two North American Oak species. The work of Fallon and co-authors exemplifies that fast and non-destructive high-throughput field phenotyping is not limited to crops and agricultural settings (Singh et al. 2016, Lowe et al. 2017), but is also promising to detect and classify the early onset of diseases and stress in forest tree species. Much focus has been on reflectance measurements in the visible, near-infrared (NIR) and short-wave infrared (SWIR), because changes in these wavelength areas are associated with several physiological and biochemical traits (Ge et al. 2019). Hyperspectral measurements are non-destructive, so repeated measurements can be taken of the same plant, and it takes only a few seconds to obtain multiple scans of a single leaf (Ge et al. 2019). In addition, multiple traits or properties can be modelled and estimated from a single leaf scan. All this greatly advances detection and monitoring of biological processes and stress responses at scales from single leaves to ecosystems, to assess for example, the relationship between water shortage and photosynthetic properties of crops (El-Hendawy et al. 2017), to identify species richness and biodiversity in prairie ecosystems (Wang et al. 2018), or for pre-visual detection of symptoms of pathogen infection (Zarco-Tejada et al. 2018). The physical basis for hyperspectral systems is the measurement of the leaf reflectance spectrum resulting from the interaction of the plant canopy with the incoming solar radiation (Mulla 2013). For plant and vegetation studies, the wavelength bands between 400–2500 nm are of particular interest (Curran 1989, Pauli et al. 2016). These bands provide information on the physiological status, structural properties and biochemical composition of plants (Figure 1). This includes the 400–700 nm or photosynthetically active region, where absorption of solar radiation by chlorophyll a, b and carotenoids determines the spectrum; the 700–1400 nm or NIR region, where healthy and non-stressed plants are highly reflective; and the 1400–2500 nm or SWIR region, which can be indicative of water content and the biochemical composition of leaves (Homolová et al. 2013). Spectral reflectance curve for healthy vegetation. Leaf pigment content and composition largely determine the spectrum in the visible region, e.g., absorption by chlorophylls at 430 and 460 nm and 640 and 660 nm cause the dip in spectral reflectance at these wavelengths bands. In the NIR and SWIR, protein content, water content and biochemical composition contribute to the shape of the curve, which then can be used as a diagnostic tool to identify vegetation type or infer plant physiological and health status (modified from after Curran (1989), Pauli et al. (2016)). In their paper, Fallon et al. (2020) identified several diagnostic wavelengths for the detection of oak wilt in asymptomatic leaves in the region of 820–1320 nm, and in the region of 780–2400 nm for the detection of oak wilt in symptomatic leaves caused by cell rupture and altered leaf cellular structure that are not correlated with drought stress and changes in leaf water potential. For the detailed wavelength information, see Fallon et al. (2020). The detection and classification of healthy versus stressed or diseased vegetation follows two approaches: the use of a combination of two or more key wavelengths in the spectrum, commonly known as vegetation indices, and the use of the entire reflectance spectrum. Numerous vegetation indices were developed for monitoring vegetation dynamics, stress and ecological conditions, including the widely used normalized difference vegetation index as a greenness index; the photochemical reflectance index and the chlorophyll/carotenoid index, both of which track carotenoid pigment dynamics (Ustin et al. 2009, Gamon et al. 2016, Wong et al. 2019, D’Odorico et al. 2020); or the plant water index for estimating plant water concentration (Peñuelas et al. 1997). These indices use only a narrow part of the spectra captured by hyperspectral sensors. An extensive body of literature has explored the usefulness of broader parts of the reflectance spectra, including the NIR and SWIR, showing that these regions can reveal chemistry and foliar traits in forest canopies related to photosynthesis, pigments, protein, nitrogen and lignin contents non-destructively (Curran 1989, Wessman et al. 1989, Ustin et al. 2009). In order to handle and analyse broader parts or entire spectra from hyperspectral remote sensing, machine learning algorithms such as partial least squares (PLS) regression (Haaland and Thomas 1988, Wold et al. 2001) have been used for studying leaf physiological and chemical traits in agricultural systems (Ge et al. 2019). In addition, partial least squares discriminant analysis (PLS-DA) is increasingly used in remote sensing-based classification of plant communities and species identification (Peerbhay et al. 2013, Wang et al. 2018). Beyond determining foliar traits and leaf chemical composition, there are considerable efforts underway to exploit hyperspectral reflectance for the early detection of symptoms of plant pathogens to be able to minimize the enormous losses to agricultural or damage to tree yields (Zarco-Tejada et al. 2018). Both pathogen infections and water stress have been shown to cause changes in physiological function such as a decrease in photosynthesis (Cotrozzi et al. 2017, Zarco-Tejada et al. 2018). In agricultural systems, diagnosing and differentiating among multiple pathogens as well as differentiating pathogens and nutrient deficiencies have been quite successful (Abdulridha et al. 2019). However, in natural mixed forest stands differentiating between stress responses that cause very similar symptoms such as wilting due to drought or fungal infection is far more complex and elusive. Nevertheless, Fallon et al. (2020) show how hyperspectral reflectance measurements can differentiate between different fungal diseases and drought stress in North American Oak species. They subjected seedlings of Quercus ellipsoidales and Quercus macrocarpa to drought or inoculated the seedlings with the oak wilt fungus (Bretziella fagacerarum) or the bur oak blight fungus (Tubakia iowensis). The authors then compared instantaneous photosynthesis and stomatal conductance and measured leaf and seedling canopy spectral reflectance to assess physiological changes and symptom appearance. Spectral models developed using PLS-DA were able to predict treatment effects from reflectance data. Most importantly, the spectral models were able to reliably diagnose fungal disease before visible symptoms were apparent. The specific spectral regions that provided the diagnostic information for early disease detection were all in the NIR and SWIR. These bands were significantly different in oak wilt-infected seedlings compared with other treatments, whereas in the visible spectral region oak wilt infection was indistinguishable from drought stress. Interestingly, the diagnostic wavelengths in the NIR and SWIR region are also associated with water content and leaf water potential, and hence the physiological measurements complementing the spectral reflectance data revealed a strong correlation with the mechanisms that contribute to differences between plant disease response and fungal infection. Fallon and co-authors argue that because oak wilt fungus causes cell rupture in the leaves and alters leaf cellular structure, water availability in the leaves is permanently altered and results in much greater reduction in stomatal conductance than in drought stressed plants. Nevertheless, the authors also observed that the accuracy of the spectral models to detect fungal infection differed between the two oak species, and argued this might be caused by differences in the progression of the fungal diseases and differences in the systemic responses of the two oak species to a fungal infection. Fallon et al. (2020) clearly demonstrate that spectral reflectance is a strong diagnostic tool that can reflect different physiological changes associated with stress and fungal diseases response mechanisms; however, the authors also demonstrate that the ability of spectral models as a diagnostic tool can vary from species to species. The results presented by Fallon’s study are solely based on experiments with seedlings, but eventually the goal is to scale this approach to larger trees. It will be interesting to see how accurately this approach will diagnose and differentiate fungal disease and drought stress in complex, whole canopies using versatile hyperspectral sensors on, i.e., drones, where canopy architecture, viewing geometries and solar angle pose additional technical challenges that need to be addressed. None declared.
Why it matches plant phenotyping methods植物の病害・乾燥ストレスを hyperspectral reflectance で診断する手法を中心に解説する方法論的レビューであり、植物フェノタイピング手法が主要内容である。
abstracthyperspectral reflectance measurements can be used to detect fungal diseases and drought stress in two North American Oak species
Proximal sensors in controlled environment agriculture (CEA) are used to monitor plant growth, yield, and water consumption with non-destructive technologies. Rapid and continuous monitoring of environmental and crop parameters may be used to develop mathematical models to predict crop response to microclimatic changes. Here, we applied the energy cascade model (MEC) on green- and red-leaf butterhead lettuce ( Lactuca sativa L. var. capitata ). We tooled up the model to describe the changing leaf functional efficiency during the growing period. We validated the model on an independent dataset with two different vapor pressure deficit (VPD) levels, corresponding to nominal (low VPD) and off-nominal (high VPD) conditions. Under low VPD, the modified model accurately predicted the transpiration rate (RMSE = 0.10 Lm -2 ), edible biomass (RMSE = 6.87 g m -2 ), net-photosynthesis (rBIAS = 34%), and stomatal conductance (rBIAS = 39%). Under high VPD, the model overestimated photosynthesis and stomatal conductance (rBIAS = 76-68%). This inconsistency is likely due to the empirical nature of the original model, which was designed for nominal conditions. Here, applications of the modified model are discussed, and possible improvements are suggested based on plant morpho-physiological changes occurring in sub-optimal scenarios.
Why it matches plant phenotyping methods植物の生理・成長形質を予測する数学モデルを改良し、独立データセットで検証しており、形質推定手法の検証が中心です。
abstractWe tooled up the model to describe the changing leaf functional efficiency during the growing period.
SunflowerTobaccoLeafStomata / guard-cell complexPhysiological trait estimationStomatal traitsWater status / transpiration
O_LIMinimum conductance (gw,min) in leaves is important for water relations in land plants. Yet, its regulation is unclear due to measurement constraints. C_LIO_LICuticle conductance to water vapor (gcw) was estimated from the difference between calculated and direct measurement of CO2 concentration in the leaf airspace (Ci) of amphi-stomatous tobacco and sunflower. We estimated gcw in a series of light and dark experiments, and partitioned gw,min into cuticle and stomatal components. Some leaves were detached to simulate severe drought through desiccation conditions where gw,min is generally determined. C_LIO_LIBetween light and dark experiments each gcw was in close agreement, and successfully corrected the discrepancies of calculations from direct measurements. In the dark, either stomatal or cuticle conductance dominated the gw,min, suggesting either of them can control the minimum water loss. In the detached leaves, gcw could not be estimated likely due to unsaturation in the leaf airspace, and gw,min was progressively underestimated. C_LIO_LIBesides cuticle, leaf water status is a potential pitfall of the standard gas exchange model. Our technique is useful to study the minimal gas exchange as well as to refine the model. C_LI
Why it matches plant phenotyping methods葉の最小コンダクタンスを分解・推定する測定技術を開発し、光・暗条件で検証しており、植物の生理形質取得が中心である。
abstractCuticle conductance to water vapor (gcw) was estimated from the difference between calculated and direct measurement of CO2 concentration in the leaf airspace (Ci)
O_LICuticle conductance (gcw) can bias calculations of intercellular CO2 concentration inside the leaf (Ci) when stomatal conductance (gsw) is small. C_LIO_LIWe examined how the light induction of photosynthesis impacts calculations by directly measuring Ci along with standard gas exchange in sunflower and tobacco leaves. C_LIO_LIWhen photosynthesis was induced from dark to saturating light (1200 mol m-2 s-1 PAR) the calculated Ci was significantly larger than measured Ci and the difference decreased as gsw increased. This difference could lead to over-estimation of rubisco deactivation by limited CO2 supply during early induction of photosynthesis. However, only small differences in Ci were observed during the induction from shade (50 mol m-2 s-1 PAR) because gsw was sufficiently large. The induction from dark also allowed robust estimations of gcw when combined with direct Ci measurements. These gcw estimates succeeded in correcting the calculation, suggesting that the cuticle was the major source of error. C_LIO_LIDespite a technical restriction to amphi-stomatous leaves, the presented technique has a potential to provide insights into the cuticle conductance on intact stomatous leaf surfaces. C_LI
Why it matches plant phenotyping methods光合成誘導中の直接Ci測定とガス交換を組み合わせ、葉面のクチクラコンダクタンスを推定・補正する技術が研究の中心であり、植物の生理形質を取得する方法として適格です。
abstractThe induction from dark also allowed robust estimations of gcw when combined with direct Ci measurements.
BarleyMicroscopyLeafStomata / guard-cell complexVisualization / data management
Background In situ analysis of biomarkers such as DNA, RNA and proteins are important for research and diagnostic purposes. At the RNA level, plant gene expression studies rely on qPCR, RNAseq and probe-based in situ hybridization (ISH). However, for ISH experiments poor stability of RNA and RNA based probes commonly results in poor detection or poor reproducibility. Recently, the development and availability of the RNAscope RNA-ISH method addressed these problems by novel signal amplification and background suppression. This method is capable of simultaneous detection of multiple target RNAs down to the single molecule level in individual cells, allowing researchers to study spatio-temporal patterning of gene expression. However, this method has not been optimized thus poorly utilized for plant specific gene expression studies which would allow for fluorescent multiplex detection. Here we provide a step-by-step method for sample collection and pretreatment optimization to perform the RNAscope assay in the leaf tissues of model monocot plant barley. We have shown the spatial distribution pattern of HvGAPDH and the low expressed disease resistance gene Rpg1 in leaf tissue sections of barley and discuss precautions that should be followed during image analysis. Results We have shown the ubiquitous HvGAPH and predominantly stomatal guard cell associated subsidiary cell expressed Rpg1 expression pattern in barley leaf sections and described the improve RNAscope methodology suitable for plant tissues using confocal laser microscope. By addressing the problems in the sample collection and incorporating additional sample backing steps we have significantly reduced the section detachment and experiment failure problems. Further, by reducing the time of protease treatment, we minimized the sample disintegration due to over digestion of barley tissues. Conclusions RNAscope multiplex fluorescent RNA-ISH detection is well described and adapted for animal tissue samples, however due to morphological and structural differences in the plant tissues the standard protocol is deficient and required optimization. Utilizing barley specific HvGAPDH and Rpg1 RNA probes we report an optimized method which can be used for RNAscope detection to determine the spatial expression and semi-quantification of target RNAs. This optimized method will be immensely useful in other plant species such as the widely utilized Arabidopsis.
Why it matches plant phenotyping methods植物組織における空間的遺伝子発現の蛍光RNA-ISH法を、サンプル前処理・切片保持・画像解析を含めて最適化した研究であり、植物の状態を取得する方法が中心である。
abstractHere we provide a step-by-step method for sample collection and pretreatment optimization to perform the RNAscope assay in the leaf tissues of model monocot plant barley.
Guard cell (GC) length proved to be efficient in sorting diploids from haploids in doubled haploid development in maize (Zea mays L). It compensates for the weakness of widely used R1‐nj marker approach that showed low reliability in haploids identification from tropical genotypes. Guard cell length differs between haploid and diploid plants, and these differences were evaluated in the progeny of three different induction crosses obtained using Krasnodar haploid inducer. Epidermal impressions of the abaxial surface of leaves removed from the second, third, and fourth nodes (from base to apex) were collected and measured using an optical microscope. This was also conducted on the flowering phenotype. Guard cell length varied according to germplasm source, leaf stage, and ploidy level. Mean GC length ranged from 23.67 to 33.82 μm in haploids, and from 36.1 to 41.25 μm in diploids. Based on these differences in GC length at any of the chosen leaf stages (second, third, or fourth), diploid and haploid maize plants were successfully classified. Classification efficiency was found to be more closely related to germplasm source than leaf stage. Comparing GC length according to phenotype (haploid or diploid), GC limits for classification as a diploid plant (threshold points) were estimated and ranged from 29.74 to 34.49 μm, depending on germplasm source. The highest false discovery rate was 2.93% and false negative rate was 15.06%, indicating that classification based on GC length was reliable.
Why it matches plant phenotyping methodsトウモロコシの気孔保護細胞長を光学顕微鏡で測定し、倍加半数体と二倍体を分類する方法の性能評価・閾値推定が研究の中心である。
abstractGuard cell (GC) length proved to be efficient in sorting diploids from haploids in doubled haploid development in maize
ArabidopsisBarleyChlorophyll fluorescenceMicroscopyLeafStomata / guard-cell complexTissueObject detectionVisualization / data managementStomatal traits
In situ analysis of biomarkers such as DNA, RNA and proteins are important for research and diagnostic purposes. At the RNA level, plant gene expression studies rely on qPCR, RNAseq and probe-based in situ hybridization (ISH). However, for ISH experiments poor stability of RNA and RNA based probes commonly results in poor detection or poor reproducibility. Recently, the development and availability of the RNAscope RNA-ISH method addressed these problems by novel signal amplification and background suppression. This method is capable of simultaneous detection of multiple target RNAs down to the single molecule level in individual cells, allowing researchers to study spatio-temporal patterning of gene expression. However, this method has not been optimized thus poorly utilized for plant specific gene expression studies which would allow for fluorescent multiplex detection. Here we provide a step-by-step method for sample collection and pretreatment optimization to perform the RNAscope assay in the leaf tissues of model monocot plant barley. We have shown the ubiquitous HvGAPH and predominantly stomatal guard cell expressed Rpg1 expression pattern in barley leaf sections and described the improve RNAcope methodology suitable for plant tissues using confocal laser microscope. By addressing the problems in the sample collection and incorporating additional sample backing steps we have significantly reduced the section detachment and experiment failure problems. Further, by reducing the time of protease treatment, we minimized the sample disintegration due to over digestion of barley tissues. Thus, we optimized the RNAscope detection method in plants to visualize the spatial expression and semi-quantification of target RNAs which can be employed in other plants such as the widely utilized model dicot plant Arabidopsis.
Why it matches plant phenotyping methods植物組織内のRNA発現を空間的に可視化・半定量するRNAscope法を、植物組織向けに最適化・実証した方法開発研究であり、表現型取得法が中心である。
abstractHere we provide a step-by-step method for sample collection and pretreatment optimization to perform the RNAscope assay in the leaf tissues of model monocot plant barley.
Leaf water content (LWC) is one of the physiological parameters most commonly used for describing crop growth status and productivity. Thus, rapid and non-destructive methods for the prediction of LWC are important. Here, a rapid and accurate LWC monitoring method using terahertz time-domain spectroscopy (THz-TDS) was tested on soybean. A high-precision mathematical model was developed to predict LWC based on the results of THz-TDS. Next, the model was used to study the effect of various levels of water -stress, different growth media, and leaf treatment with exogenous ABA. Reliable results showed that the correlation coefficient and root mean square error of the prediction set were 0.9153 and 0.0526, respectively. LWC gradually decreased over time under different moisture treatments, and even more slowly under conditions of normal water supply than under water stress. The trend followed by LWC under water deficit was dependent on the water-holding capacity of the growth medium. Soil had the best water-holding capacity, followed by the seedling matrix and the vermiculite matrix. Furthermore, the stomata of the adaxial and abaxial leaf surfaces closed due to water deficit, as determined by laser scanning confocal microscopy (LSCM). We found that stomatal opening in leaves significantly decreased under water stress to prevent excessive water loss, consistent with the observed reduction in leaf moisture content. Results indicated a rapid increase in LWC upon ABA treatment followed by a slower decrease. Similarly, changes in stomatal opening were observed using LSCM, consistent with the changes in leaf water content detected macroscopically. This study showed that THz radiation technology combined with modeling methods provides an effective, contact-free, safe, and non-destructive technique to measure water content in soybean leaves. This technique could facilitate further study of plant-water relations.
Why it matches plant phenotyping methodsTHz-TDSと数理モデルを用いてダイズ葉の含水量という植物生理形質を非破壊推定する方法を開発・検証しており、方法が研究の中心である。
abstracta rapid and accurate LWC monitoring method using terahertz time-domain spectroscopy (THz-TDS) was tested on soybean.
The micro-morphology of leaf epidermises is valuable for the study of leaf development and function, as well as the classification of plant species. There have been few studies comparing different preparation and imaging methods for visualizing the leaf epidermis. Here, four specimen preparation methods were used to investigate the leaf epidermis morphology of Arabidopsis , radish, cucumber, wheat, rice, and maize, under an inverted basic light microscope (LM), a laser scanning confocal microscope (LSCM), or a scanning electron microscope (SEM). Optical microscope specimens were obtained using either the direct isolation method or the chloral hydrate-based clearing method. SEM images were obtained using a standard stage for conventional dehydrated samples or a Coolstage for fresh tissue. Different parts of epidermis peels were well focused under the LM. Investigation of samples cleared by chloral hydrate is convenient and autofluorescence of cell walls can be detected in rice. The resolution of images of conventional SEM leaf samples was generally higher than the Coolstage images at the same magnification, whereas local collapse and shrinkage were observed in leaves with high water content when using the conventional method. However, stomatal apparatuses of Arabidopsis , cucumber, radish, and maize deformed and showed poor appearance when using the Coolstage. Moreover, we usually used glutaraldehyde as an SEM fixative when using t-butanol for freeze-drying, though methanol is considered a better fixative in recent studies. In addition, fresh samples were not stable on the Coolstage. Thus, we compared four different t-butanol freeze-drying methods and two Coolstage methods. The dimension and morphology of tissues were compared using the six different methods. The results indicate that methanol fixative obviously reduced shrinkage of SEM samples compared with glutaraldehyde and formaldehyde alcohol acetic acid (FAA) fixatives. The use of methanol and a graded series of steps improved the preservation of samples. Preparing samples with optimal cutting temperature compound and observing at -30°C helped to increase the stability of Coolstage samples. In summary, our results provide an overview of the shortcomings and merits of four different methods, and might provide some information about choosing an optimal method for visualizing epidermal morphology.
Why it matches plant phenotyping methods葉表皮形態の可視化について、複数の試料調製法・顕微鏡法を比較し、組織形態の保存性や画像品質を評価しており、植物形質取得法が研究の中心である。
abstractThere have been few studies comparing different preparation and imaging methods for visualizing the leaf epidermis.
Reproduction assets foundThe paper reports LM/LSCM/SEM imaging of leaf epidermises and shrinkage/stability measurements. No author analysis code, trained models, or external repository deposit is mentioned. The only paper-specific public asset is the article's Supplementary Material, which the authors state contains all data generated or analySupplement · publicgy Project of Henan Province (182102110234).
Conflict of Interest
The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
Supplementary Material
The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fpls.2020.00133/full#supplementary-material
Click here for additional data file.
Abbreviations
SEM, scanning electron microscope; LSCM, laser scanning confocal microscope; LM, light microscope; DIC, differential interference contrast; CPD, critical point drying; OCT, optimum cutting temperature.
References
Bailes E. J. GlovOpen asset ↗lines:293-368Plant phenotyping relevance match · UnverifiedEurope PMC · checked 15 Sept 2026
Stomata are key innovation in plants that drives the global carbon and water cycle. In the past few decades, many stomatal models have been developed for studying gas exchange, photosynthesis, and transpirational characteristics of plants, but they provide limited information on stomatal mechanisms at the molecular and cellular levels. Quantitative mathematical modeling offers an effective in silico approach to explore the link between microscopic transporter functioning and the macroscopic stomatal characteristics. As a first step, a dynamic system model based on the guard cell membrane transport system was developed and encoded in the OnGuard software. This software has already generated a wealth of testable predictions and outcomes sufficient to guide phenotypic and mutational studies. It has a user-friendly interface, which can be easily accessed by researchers to manipulate the key elements and parameters in the system for guard cell simulation in plants. To promote the adoption of this OnGuard application, here we outline a standard protocol that will enable users with experience in basic plant physiology, cell biology, and membrane transport to advance quickly in learning to use it.
Why it matches plant phenotyping methods植物の気孔生理をシミュレーションし、表現型・変異研究を支援するOnGuard2ソフトウェアの標準プロトコルを提示しており、植物表現型取得・解析のためのツール開発／適用が中心である。
abstractAs a first step, a dynamic system model based on the guard cell membrane transport system was developed and encoded in the OnGuard software.
The use of photosystem II (PSII) inhibitors allows simulating cascade of defense and damage responses, including the oxidative stress. In our study, PSII inhibiting herbicide metribuzin was applied to the leaf of the model plant species Chenopodium album. The temporally and spatially resolved cascade of defense responses was studied noninvasively at the leaf level by combining three imaging approaches: Raman spectroscopy as a principal method, corroborated by chlorophyll a fluorescence (ChlF) and infrared thermal imaging. ChlF imaging show time-dependent transport in acropetal direction through veins and increase of area affected by metribuzin and demonstrated the ability to distinguish between fast processes at the level of electron transport (1 − Vj) from slow processes at the level of non-photochemical energy dissipation (NPQ) or maximum efficiency of PSII photochemistry (Fv/Fm). The high-resolution resonance Raman images show zones of local increase of carotenoid signal 72 h after the herbicide application, surrounding the damaged tissue, which points to the activation of defense mechanisms. The shift in the carotenoid band indicates structural changes in carotenoids. Finally, the increase of leaf temperature in the region surrounding the spot of herbicide application and expanding in the direction to the leaf tip proves the metribuzin effect on slow stomata closure.
Why it matches plant phenotyping methods植物葉の薬剤応答を、Raman・蛍光・熱画像で非侵襲かつ時空間的に取得・評価する手法の適用が中心であり、単なる routine 測定を超える。
abstractThe temporally and spatially resolved cascade of defense responses was studied noninvasively at the leaf level by combining three imaging approaches: Raman spectroscopy as a principal method, corroborated by chlorophyll a fluorescence (ChlF) and infrared thermal imaging.
Plant phenotyping relevance match · UnverifiedCrossref · Europe PMC · checked 15 Sept 2026
SunflowerThermalLeafRootStomata / guard-cell complexPhysiological trait estimationGrowth / development / phenologyStomatal traitsPlant / canopy temperatureWater status / transpiration
Plant adaptation to biotic and abiotic stresses is governed by a variety of factors, among which the regulation of stomatal aperture in response to water deficit or pathogens plays a crucial role. Identifying small molecules that regulate stomatal movement can therefore contribute to understanding the physiological basis by which plants adapt to their environment. Large-scale screening approaches that have been used to identify regulators of stomatal movement have potential limitations: some rely heavily on the abscisic acid (ABA) hormone signaling pathway, therefore excluding ABA-independent mechanisms, while others rely on the observation of indirect, long-term physiological effects such as plant growth and development. The screening method presented here allows the large-scale treatment of plants with a library of chemicals coupled with a direct quantification of their transpiration by thermal imaging. Since evaporation of water through transpiration results in leaf surface cooling, thermal imaging provides a non-invasive approach to investigate changes in stomatal conductance over time. In this protocol, Helianthus annuus seedlings are grown hydroponically and then treated by root feeding, in which the primary root is cut and dipped into the chemical being tested. Thermal imaging followed by statistical analysis of cotyledonary temperature changes over time allows for the identification of bioactive molecules modulating stomatal aperture. Our proof-of-concept experiments demonstrate that a chemical can be carried from the cut root to the cotyledon of the sunflower seedling within 10 minutes. In addition, when plants are treated with ABA as a positive control, an increase in leaf surface temperature can be detected within minutes. Our method thus allows the efficient and rapid identification of novel molecules regulating stomatal aperture.
Why it matches plant phenotyping methods熱画像と統計解析を用いて葉温から蒸散・気孔開度を直接定量する大規模スクリーニング法が、研究の中心的な技術貢献として提示されている。
abstractThe screening method presented here allows the large-scale treatment of plants with a library of chemicals coupled with a direct quantification of their transpiration by thermal imaging.
This study investigated visible foliar ozone (O3) injury in three deciduous tree species with different growth patterns (indeterminate, Alnus glutinosa (L.) Gaertn.; intermediate, Sorbus aucuparia L.; and determinate, Vaccinium myrtillus L.) from May to August 2018. Ozone effects on the timing of injury onset and a plant injury index (PII) were investigated using two O3 indices, i.e., AOT40 (accumulative O3 exposure over 40 ppb during daylight hours) and PODY (phytotoxic O3 dose above a flux threshold of Y nmol m−2 s−1). A new parameterization for PODY estimation was developed for each species. Measurements were carried out in an O3 free-air controlled exposure (FACE) experiment with three levels of O3 treatment (ambient, AA; 1.5 × AA; and 2.0 × AA). Injury onset was found in May at 2.0 × AA in all three species and the timing of the onset was determined by the amount of stomatal O3 uptake. It required 4.0 mmol m−2 POD0 and 5.5 to 9.0 ppm·h AOT40. As a result, A. glutinosa with high stomatal conductance (gs) showed the earliest emergence of O3 visible injury among the three species. After the onset, O3 visible injury expanded to the plant level as confirmed by increased PII values. In A. glutinosa with indeterminate growth pattern, a new leaf formation alleviated the expansion of O3 visible injury at the plant level. V. myrtillus showed a dramatic increase of PII from June to July due to higher sensitivity to O3 in its flowering and fruiting stage. Ozone impacts on PII were better explained by the flux-based index, PODY, as compared with the exposure-based index, AOT40. The critical levels (CLs) corresponding to PII = 5 were 8.1 mmol m−2 POD7 in A. glutinosa, 22 mmol m−2 POD0 in S. aucuparia, and 5.8 mmol m−2 POD1 in V. myrtillus. The results highlight that the CLs for PII are species-specific. Establishing species-specific O3 flux-effect relationships should be key for a quantitative O3 risk assessment.
Why it matches plant phenotyping methods種別ごとのPODY推定パラメータを新規開発し、可視的な葉・植物体のオゾン傷害指数（PII）を定量化してフラックス指標との関係を評価しているため、植物表現型の取得・評価法が実質的に中心である。
abstractA new parameterization for PODY estimation was developed for each species.
Main conclusion The qPCR assay developed to differentiate haploid and diploid maize leaf samples was unsuccessful due to DNA content difference. Haploid cells are packed more closely together with less cellular expansion. Increased ploidy content (> 2 N) directly correlates with increased cell size in plants, but few studies have examined cell morphology in plants with reduced ploidy (i.e., haploids). To pioneer a scalable new ploidy test, we compared DNA content and cellular morphology of haploid and diploid maize leaves. The amount of genomic DNA recovered from standardized leaf-punch samples was equivalent between these two ploidy types, while both epidermal and mesophyll cell types were smaller in haploid plants. Pavement cells had a substantially smaller size than mesophyll cells, and this effect was more pronounced in the abaxial epidermis. Interveinal distance and guard cell size were significantly reduced in haploids, but the cell percentage comprising stomata did not change. These results confirm the direct correlation between ploidy content and cell size in plants, and suggest that reduced cell expansion predominantly explains DNA content equivalence between haploid and diploid samples, confounding efforts to develop a haploid detection method using DNA content.
Why it matches plant phenotyping methodsハプロイド／二倍体という植物状態の判別法を開発・評価し、DNA量と葉の細胞形態を測定して手法の失敗要因も検証しているため、方法開発・検証が中心です。
abstractThe qPCR assay developed to differentiate haploid and diploid maize leaf samples was unsuccessful due to DNA content difference.
Contamination of fresh cucumbers ( Cucumis sativus L.) with Escherichia coli O157:H7 can impact the health of consumers. Despite this, the pertinent mechanisms underlying E. coli O157:H7 colonization and internalization remain poorly documented. Herein we aimed to elucidate these mechanisms in cucumbers using an inverted fluorescence microscope and hyperspectral microscopy. We observed that E. coli O157:H7 primarily colonized around the stomata on cucumber epidermis without invading the internal tissues of intact cucumbers. Once the bacterial cells had infiltrated into the internal tissues, they colonized the cucumber placenta and vascular bundles (xylem vessels, in particular), and also migrated along the xylem vessels. Moreover, the movement rate of E. coli O157:H7 from the stalk to the flower bud was faster than that from the flower bud to the stalk. We then used hyperspectral microscope imaging to categorize the infiltrated and uninfiltrated areas with high accuracy using the spectral angle mapper (SAM) classification method, which confirmed the results obtained upon using the inverted fluorescence microscope. We believe that our results are pivotal for developing science-based food safety practices, interventions for controlling E. coli O157:H7 internalization, and new methods for detecting E. coli O157:H7-plant interactions.
Why it matches plant phenotyping methodsキュウリ組織への細菌侵入状態を、蛍光顕微鏡とハイパースペクトル画像解析で分類・検証しており、感染植物の状態取得手法が主要な貢献に含まれる。
abstractWe then used hyperspectral microscope imaging to categorize the infiltrated and uninfiltrated areas with high accuracy using the spectral angle mapper (SAM) classification method
Ion transporters are key players of cellular processes. The mechanistic properties of ion transporters have been well elucidated by biophysical methods. Meanwhile the understanding of their exact functions in the whole cell homeostasis is limited by the difficulty to monitor their activity in vivo . The development of biosensors to track subtle changes in intracellular parameters provides an invaluable key to tackle this challenging issue. Here, we adapted the use of a dual biosensor using guard cells as experimental model to visualize the impact on the cytosol of anion transport from intracellular compartments. To image the activity of AtCLCa, a vacuolar NO 3 - /H + exchanger regulating stomata aperture in Arabidopsis thaliana , we expressed a genetically encoded biosensor, ClopHensor allowing monitoring the dynamics of cytosolic anion concentration and pH. We first show that ClopHensor is not only a Cl - but also a NO 3 - sensor. We were then able to unravel and quantify the variations of NO 3 - and pH in the cytosol. Our data show that AtCLCa activity modifies cytosolic pH and NO 3 - , demonstrating that the transport activity of a vacuolar exchanger has a profound impact on cytosolic homeostasis. We propose that a major function of this endomembrane transporter is to adjust cytosolic conditions to cellular needs. This opens a novel perspective on the function of intracellular transporters of the CLC family in eukaryotes: not only controlling the intra organelle lumen but also actively modifying cytosolic conditions. Significance Intracellular transporters are key actors in cell biological processes. Their disruption causes major physiological defects. The role of intracellular ion transporters is usually seen through an “intra organelle” lens, meanwhile their potential action on cytosolic ion homeostasis is still a black box. The case of a plant CLC is used as a model to uncover the missing link between the regulation of conditions inside the vacuole and inside the cytosol. The development of an original live imaging workflow to simultaneously measure pH and anion dynamics in the cytosol reveals the role of an Arabidopsis thaliana CLC, AtCLCa, in the modification of cytosolic pH. Our data highlight an unsuspected function of endomembrane transporters in the regulation of cytosolic pH.
Why it matches plant phenotyping methods植物細胞内のpH・陰イオン動態を可視化・定量するライブイメージング法と遺伝子コード型バイオセンサーを開発・適用しており、植物の生理状態の取得が中心的です。
abstractThe development of biosensors to track subtle changes in intracellular parameters provides an invaluable key to tackle this challenging issue.
Stomata regulate important physiological processes in plants and are often phenotyped by researchers in diverse fields of plant biology. Currently, there are no user-friendly, fully automated methods to perform the task of identifying and counting stomata, and stomata density is generally estimated by manually counting stomata. We introduce StomataCounter, an automated stomata counting system using a deep convolutional neural network to identify stomata in a variety of different microscopic images. We use a human-in-the-loop approach to train and refine a neural network on a taxonomically diverse collection of microscopic images. Our network achieves 98.1% identification accuracy on Ginkgo scanning electron microscropy micrographs, and 94.2% transfer accuracy when tested on untrained species. To facilitate adoption of the method, we provide the method in a publicly available website at http://www.stomata.science/.
Why it matches plant phenotyping methods気孔の識別・計数という植物形質の取得を自動化するニューラルネットワークを開発し、異なる種で精度検証した研究であり、方法が中心的です。
abstractWe introduce StomataCounter, an automated stomata counting system using a deep convolutional neural network to identify stomata in a variety of different microscopic images.
Background The stomata of plants mainly regulate gas exchange and water dispersion between the interior and external environments of plants and play a major role in the plants' health. The existing methods of stomata segmentation and measurement are mostly for specialized plants. The purpose of this research is to develop a generic method for the fully automated segmentation and measurement of the living stomata of different plants. The proposed method utilizes level set theory and image processing technology and can outperform the existing stomata segmentation and measurement methods based on threshold and skeleton in terms of its versatility. Results The single stomata images of different plants were the input of the method and a level set based on the Chan-Vese model was used for stomatal segmentation. This allowed the morphological features of the stomata to be measured. Contrary to existing methods, the proposed segmentation method does not need any prior information about the stomata and is independent of the plant types. The segmentation results of 692 living stomata of black poplars show that the average measurement accuracies of the major and minor axes, area, eccentricity and opening degree are 95.68%, 95.53%, 93.04%, 99.46% and 94.32%, respectively. A segmentation test on dayflower ( Commelina benghalensis ) stomata data available in the literature was completed. The results show that the proposed method can effectively segment the stomata images (181 stomata) of dayflowers using bright-field microscopy. The fitted slope of the manually and automatically measured aperture is 0.993, and the R 2 value is 0.9828, which slightly outperforms the segmentation results that are given in the literature. Conclusions The proposed automated segmentation and measurement method for living stomata is superior to the existing methods based on the threshold and skeletonization in terms of versatility. The method does not need any prior information about the stomata. It is an unconstrained segmentation method, which can accurately segment and measure the stomata for different types of plants (woody or herbs). The method can automatically discriminate whether the pore region is independent or not and perform pore region extraction. In addition, the segmentation accuracy of the method is positively correlated with the stomata's opening degree.
Why it matches plant phenotyping methods植物の気孔画像から形態形質と開口度を自動抽出する汎用セグメンテーション・測定法を開発し、複数植物種で精度検証しており、表現型取得手法が研究の中心である。
abstractThe purpose of this research is to develop a generic method for the fully automated segmentation and measurement of the living stomata of different plants.
The 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:1-44Plant phenotyping relevance match · UnverifiedbioRxiv · checked 13 Sept 2026
Stomata, functionally specialized micrometer-sized pores on the epidermis of leaves (mainly on the lower epidermis), control the flow of gases and water between the interior of the plant and atmosphere. Real-time monitoring of stomatal dynamics can be used for predicting the plant hydraulics, photosensitivity, and gas exchanges effectively. To date, several techniques offer the direct or indirect measurement of stomatal dynamics, yet none offer real-time, long-term persistent measurement of multiple stomal apertures simultaneously of an intact leaf in a field under natural conditions. Here, we report a high-resolution portable microscope-based technique for in situ real-time field imaging and monitoring of stomata. Our technique is capable of analyzing and quantifying the multiple lower epidermis stomal pore dynamics simultaneously and does not require any physical or chemical manipulation of a leaf. An upward facing objective lens in our portable microscope allows the imaging of lower epidermis stomatal opening of a leaf while upper epidermis being exposed to the natural environment. Small depth of field (~ 1.3 m) of a high-magnifying objection lens assists in focusing the stomatal plane in highly non-planar tomato leaf having a high density of trichome (hair-like structures). For long-term monitoring, the leaf is fixed mechanically by a novel designed leaf holder providing freedom to expose the upper epidermis to the sunlight and lower epidermis to the wind simultaneously. In our study, a direct relation between the stomatal opening and the intensity of sunlight illuminating on the upper epidermis has been observed in real-time. In addition, real-time porosity of leaf (ratio between the areas of stomatal opening to the area of a leaf) and stomatal aspect ratio (ratio between the major axis and minor axis of stomatal opening) along with stomatal density have been quantified.
Why it matches plant phenotyping methods携帯型顕微鏡を用いて、野外で葉の気孔開度・密度・形状などの植物形質をリアルタイムに画像取得・定量する手法を開発しており、フェノタイピング手法が研究の中心である。
abstractHere, we report a high-resolution portable microscope-based technique for in situ real-time field imaging and monitoring of stomata.
Low radiation is one of the most important factors which limits potential yield in potato. Under humid conditions, the dominance of diffuse radiation not only imposes challenges for radiation use efficiency in crops but also limits the water status surveillance through non-invasive methods like infrared radiometry. This study was carried out in the humid desert of the Peruvian central coast with the aim to relate maximum stomatal conductance (gₛ_ₘₐₓ, an important water status indicator) with leaf and air temperature (dT) and crop water stress index (CWSI). In a potted trial, gₛ_ₘₐₓ vs. dT were compared along the day in well-irrigated (field capacity) and water restricted (half field capacity) plants. In an additional field experiment, CWSI was validated by testing two irrigation timing treatments with pre-established gₛ_ₘₐₓ threshold (0.15 [T1] and 0.50 [T2] mol H₂O m⁻² s⁻¹) against a control (frequently irrigated). An acute stomatal closure sensitivity was detected which drove a gₛ_ₘₐₓ fall (gₛ↓) near the solar noon. The intense stomatal closure caused a dT rise which showed positive higher values (> 2 °C) after gₛ↓. The significant yield reduction of T1 in relation to the control (− 38.2 ± 10.7%) highlighted that gₛ_ₘₐₓ values > 0.15 must be used to warrant a high potato yield. These findings support the use of CWSI values ≤ 0.3–0.4 as thresholds for an appropriate irrigation in potatoes with assessments taken at around 15:00 hours, time in which plants have accumulated enough radiation allowing an appropriate detection of thermal emission under humid conditions.
Why it matches plant phenotyping methods赤外放射計とCWSIによるジャガイモの水ストレス・水状態推定を比較検証し、灌漑判定の閾値まで評価しており、表現型取得手法が中心です。
abstractThese findings support the use of CWSI values ≤ 0.3–0.4 as thresholds for an appropriate irrigation in potatoes with assessments taken at around 15:00 hours, time in which plants have accumulated enough radiation allowing an appropriate detection of thermal emission under humid conditions.
The stomatal density (SD) can be a promising target to improve the leaf photosynthesis in soybeans (Glycine max (L.) Merr). In a conventional SD evaluation, the counting process of the stomata during a manual operation can be time-consuming. We aimed to develop a high-throughput technique for evaluating the SD and elucidating the variation in the SD among various soybean accessions. The central leaflet of the first trifoliolate was sampled, and microscopic images of the leaflet replica were obtained among 90 soybean accessions. The Single Shot MultiBox Detector, an algorithm for an object detection based on deep learning, was introduced to develop an automatic detector of the stomata in the image. The developed detector successfully recognized the stomata in the microscopic image with high-throughput. Using this technique, the value of R 2 reached 0.90 when the manually and automatically measured SDs were compared in the 150 images. This technique discovered a variation in SD from 93 ± 3 to 166 ± 4 mm -2 among the 90 accessions. Our detector can be a powerful tool for a SD evaluation with a large-scale population in crop species, accelerating the identification of useful alleles related to the SD in future breeding programs.
Why it matches plant phenotyping methods気孔密度という植物形質を画像から自動抽出する高スループット手法を開発し、手動測定との比較で技術検証しているため、方法論が中心です。
abstractWe aimed to develop a high-throughput technique for evaluating the SD
Plant phenotyping relevance match · UnverifiedCrossref · OpenAlex · Europe PMC · checked 15 Sept 2026
Improved genetic, genomic and statistical technologies have increased the capacity to enrich breeding populations for key alleles underpinning adaptation and continued genetic gain. In turn, directed genomic selection together with increased heritability will reduce genetic variance to narrow the genetic base in many crop breeding programs. Diverse genetic resources (GR), including wild and weedy relatives, landraces and reconstituted synthetics, have potential to contribute novel alleles for key traits. Targeted trait identification may also identify genetic diversity in addressing new challenges including the need for modified root architecture, greater nutrient-use efficiency, and adaptation to warmer air and soil temperatures forecast with climate change. Yet while core collections and other GR sources have historically been invaluable for major gene control of disease and subsoil constraints, the mining of genetically (and phenotypically) complex traits in GR remains a significant challenge owing to reduced fertility, limited seed quantities and poor adaptation through linkage drag with undesirable alleles. High-throughput field phenomics (HTFP) offers the opportunity to capture phenotypically complex variation underpinning adaptation in traditional phenotypic selection or statistics-based breeding programs. Targeted HTFP will permit the reliable phenotyping of greater numbers of GR-derived breeding lines using smaller plot sizes and at earlier stages of population development to reduce the duration of breeding cycles and the loss of potentially important alleles with linkage drag. Two key opportunities are highlighted for use of HTFP in selection among GR-derived wheat breeding lines for greater biomass and stomatal conductance through canopy temperature.
Why it matches plant phenotyping methods作物遺伝資源へのハイスループット圃場フェノミクスの活用を論じるレビューであり、植物形質の取得・選抜への方法論的応用が中心である。
titleReview: High-throughput phenotyping to enhance the use of crop genetic resources
In spite of the significant progress made in recent years, the use of thermography to derive biologically relevant traits remains a challenge under fluctuating conditions. The aim of this study was to rethink the current method to process thermograms and derive temporal responses of stomatal conductance (gsw) using dynamic energy balance equations. Time-series thermograms provided the basis for a spatial and temporal characterization of gsw responses in wheat (Triticum aestivum). A leaf replica with a known conductance was used to validate the approach and to test the ability of our model to be used with any material and under any environmental conditions. The results highlighted the importance of the co-ordinated stomatal responses that run parallel to the leaf blade despite their patchy distribution. The diversity and asymmetry of the temporal response of gsw observed after a step increase and step decrease in light intensity can be interpreted as a strategy to maximize photosynthesis per unit of water loss and avoid heat stress in response to light flecks in a natural environment. This study removes a major bottleneck for plant phenotyping platforms and will pave the way to further developments in our understanding of stomatal behaviour.
Why it matches plant phenotyping methods熱画像と動的エネルギー収支方程式から気孔コンダクタンスを推定する手法を開発し、既知コンダクタンスの葉レプリカで検証している。植物表現型取得法が中心である。
abstractThe aim of this study was to rethink the current method to process thermograms and derive temporal responses of stomatal conductance (gsw) using dynamic energy balance equations.
Correlations of leaf traits with environmental conditions are widely used for reconstruction of palaeoclimate and to analyse the evolution of land plants. Evaluation of climate-dependent leaf traits of fossil floras can potentially contribute to our understanding of long-term responses of vegetation to changing climate. In this contribution, basic aspects and methods of palaeoclimate reconstruction by fossil leaf morphology, such as leaf margin analysis and CLAMP, are presented and discussed with respect to recent results on functional leaf traits. Also addressed is the use of stomatal data (density and size) for obtaining palaeoatmospheric CO2 as well as the (possible) interference of CO2 with other abiotic environmental parameters, leading to “non-analogue climates” which cannot be found today. There is much evidence that CO2, as an essential factor for gas exchange and therefore palaeoecophysiology, acted as an important driver in land plant evolution. For instance, elevated CO2 levels of the past and present are assumed to affect leaf shape evolution, because stomatal conductance is negatively correlated with atmospheric CO2 thereby affecting leaf heat dissipation. This topic is addressed in detail as an exemplary case of the interference of multiple environmental parameters. Results of a gas exchange model with coupled heat transfer indicate that the effect of elevated CO2 on leaf temperature may be minor, at least when water supply is not limited. This example demonstrates that ecophysiological analyses of trait–climate relationships can contribute to identifying adaptive features of leaf architecture and to evaluate predictions into the future as well as into the past.
Why it matches plant phenotyping methods化石葉の形態・気孔形質を用いた古気候・古大気推定法を紹介・検討する方法論的レビューであり、植物形質の取得と解釈が中心です。
abstractbasic aspects and methods of palaeoclimate reconstruction by fossil leaf morphology, such as leaf margin analysis and CLAMP, are presented and discussed
Abstract Reliable discrimination of haploid (H), doubled haploid (DH) and crossing (C) plants in early growth stages could streamline DH production in maize. By detecting in early growth stages undesirable sterile H plants and undesirable heterozygous C plants, a large proportion of resources required for DH production could be saved. The goal of this study was to evaluate the effectiveness of early classification of plants in growth stages V3‐V4 as H, DH or C in the context of DH production using flow cytometry and stomata length. As the reference classification, we used a field score based on plant phenotype commonly applied in DH production and research. Our results show that identification of misclassified C seeds is possible because the overlap in distributions of stomata length between H&DH and C plants is small and the association between flow cytometry and the reference field score is high. In contrast, overlap between H and DH distributions is substantial. Consequently, the main application we see for these classification methods in early growth stages is the identification of C seedlings.
Why it matches plant phenotyping methodsトウモロコシの倍数性を早期分類するため、気孔長とフローサイトメトリーの有効性を参照分類と比較評価しており、表現型取得・分類法の検証が研究の中心である。
abstractThe goal of this study was to evaluate the effectiveness of early classification of plants in growth stages V3‐V4 as H, DH or C in the context of DH production using flow cytometry and stomata length.
Background Microscopic images are widely used in plant biology as an essential source of information on morphometric characteristics of the cells and the topological characteristics of cellular tissue pattern due to modern computer vision algorithms. High-resolution 3D confocal images allow extracting quantitative characteristics describing the cell structure of leaf epidermis. For some issues in the study of cereal leaves development, it is required to apply the staining techniques with fluorescent dyes and to scan rather large fragments consisting of several frames. We aimed to develop a tool for processing multi-frame multi-channel 3D images obtained from confocal laser scanning microscopy and taking into account the peculiarities of the cereal leaves staining. Results We elaborated an ImageJ-plugin LSM-W 2 that allows extracting data on Leaf Surface Morphology from Laser Scanning Microscopy images. The plugin is a crucial link in a workflow for obtaining data on structural properties of leaf epidermis and morphological properties of epidermal cells. It allows converting large lsm-files (laser scanning microscopy) into segmented 2D/3D images or tables with data on cells and/or nuclei sizes. In the article, we also represent some case studies showing the plugin application for solving biological tasks. Namely the plugin is applied in the following cases: defining parameters of jigsaw-puzzle pattern for maize leaf epidermal cells, analysis of the pavement cells morphological parameters for the mature wheat leaf grown under control and water deficit conditions, initiation of cell longitudinal rows, and detection of guard mother cells emergence at the initial stages of the stomatal morphogenesis in the growth zone of a wheat leaf. Conclusion The proposed plugin is efficient for high-throughput analysis of cellular architecture for cereal leaf epidermis. The workflow implies using inexpensive and rapid sample preparation and does not require the applying of transgenesis and reporter genetic structures expanding the range of species and varieties to study. Obtained characteristics of the cell structure and patterns further could act as a basis for the development and verification for spatial models of plant tissues formation mechanisms accounting for structural features of cereal leaves. Availability The implementation of this workflow is available as an ImageJ plugin distributed as a part of the Fiji project (FijiisjustImageJ: https://fiji.sc/ ). The plugin is freely available at https://imagej.net/LSM_Worker , https://github.com/JmanJ/LSM_Worker and http://pixie.bionet.nsc.ru/LSM_WORKER/ .
Why it matches plant phenotyping methodsレーザー走査型共焦点画像から葉表皮細胞の形態・サイズを抽出するImageJプラグインを開発しており、植物フェノタイピング手法が研究の中心である。
abstractWe aimed to develop a tool for processing multi-frame multi-channel 3D images obtained from confocal laser scanning microscopy and taking into account the peculiarities of the cereal leaves staining.
Expansion of gene families facilitates robustness and evolvability of biological processes but impedes functional genetic dissection of signalling pathways. To address this, quantitative analysis of single cell responses can help characterize the redundancy within gene families. We developed high-throughput quantitative imaging of stomatal closure, a response of plant guard cells, and performed a reverse genetic screen in a group of Arabidopsis mutants to five stimuli. Focussing on the intersection between guard cell signalling and the endomembrane system, we identified eight clusters based on the mutant stomatal responses. Mutants generally affected in stomatal closure were mostly in genes encoding SNARE and SCAMP membrane regulators. By contrast, mutants in RAB5 GTPase genes played specific roles in stomatal closure to microbial but not drought stress. Together with timed quantitative imaging of endosomes revealing sequential patterns in FLS2 trafficking, our imaging pipeline can resolve non-redundant functions of the RAB5 GTPase gene family. Finally, we provide a valuable image-based tool to dissect guard cell responses and outline a genetic framework of stomatal closure.
Why it matches plant phenotyping methods気孔閉鎖という植物状態を定量化する高スループット画像法と解析パイプラインの開発が中心であり、遺伝学的スクリーニングへの応用も含むため。
abstractWe developed high-throughput quantitative imaging of stomatal closure
The evolution of disease resistance in plants occurs within a framework of interacting phenotypes, balancing natural selection for life-history traits along a continuum of fast-growing and poorly defended, or slow-growing and well-defended lifestyles. Plant populations connected by gene flow are physiologically limited to evolving along a single axis of the spectrum of the growth-defense trade-off, and strong local selection can purge phenotypic variance from a population or species, making it difficult to detect variation linked to the trade-off. Hybridization between two species that have evolved different growth-defense trade-off optima can reveal trade-offs hidden in either species by introducing phenotypic and genetic variance. Here, I investigated the phenotypic and genetic basis for variation of disease resistance in a set of naturally formed hybrid poplars. The focal species of this dissertation were the balsam poplar (Populus balsamifera), black balsam poplar (P. trichocarpa), narrowleaf cottonwood (P. angustifolia), and eastern cottonwood (P. deltoides). Vegetative cuttings of samples were collected from natural populations and clonally replicated in a common garden. Ecophysiology and stomata traits, and the severity of poplar leaf rust disease (Melampsora medusae) were collected. To overcome the methodological bottleneck of manually phenotyping stomata density for thousands of cuticle micrographs, I developed a publicly available tool to automatically identify and count stomata. To identify stomata, a deep con- volutional neural network was trained on over 4,000 cuticle images of over 700 plant species. The neural network had an accuracy of 94.2% when applied to new cuticle images and phenotyped hundreds of micrographs in a matter of minutes. To understand how disease severity, stomata, and ecophysiology traits changed as a result of hybridization, statistical models were fit that included the expected proportion of the genome from either parental species in a hybrid. These models in- dicated that the ratio of stomata on the upper surface of the leaf to the total number of stomata was strongly linked to disease, was highly heritable, and wass sensitive to hybridization. I further investigated the genomic basis of stomata-linked disease variation by performing an association genetic analysis that explicitly incorporated admixture. Positive selection in genes involved in guard cell regulation, immune sys- tem negative regulation, detoxification, lipid biosynthesis, and cell wall homeostasis were identified. Together, my dissertation incorporated advances in image-based phenotyping with evolutionary theory, directed at understanding how disease frequency changes when hybridization alters the genomes of a population.
Why it matches plant phenotyping methods数千枚の葉表皮画像から気孔を自動検出・計数する画像ベース表現型解析ツールを開発し、新規画像で精度検証しているため、方法が中心的です。
abstractTo overcome the methodological bottleneck of manually phenotyping stomata density for thousands of cuticle micrographs, I developed a publicly available tool to automatically identify and count stomata.
This chapter gives examples of basic procedures of quantification of plant structures with use of image analysis, which are commonly employed to describe differences among experimental treatments or phenotypes of plant material. Tasks are demonstrated with the use of ImageJ, a widely used public domain Java image processing program. Principles of sampling design based on systematic uniform random sampling for quantitative studies of anatomical parameters are given to obtain their unbiased estimations and simplified "rules of thumb" are presented. The basic procedures mentioned in the text are: (1) sampling, (2) calibration, (3) manual length measurement, (4) leaf surface area measurement, (5) estimation of particle density demonstrated on an example of stomatal density, and (6) analysis of epidermal cell shape.
Why it matches plant phenotyping methods植物構造の画像解析による定量手順を体系的に説明する方法論的章であり、葉面積、気孔密度、細胞形状などの表現型取得が中心です。
abstractThis chapter gives examples of basic procedures of quantification of plant structures with use of image analysis
Leaf stomata are important structures used for exchanging matter between plants and the environment, and they are very sensitive to environmental changes. The method of efficiently extracting stomata, as well as measuring stomatal density and area, still lacks established techniques. This study focused on the leaves of Fraxinus pennsylvanica Marshall, Ailanthus altissima (Mill.) Swingle, and Sophora japonica (L.) Schott grown on different underlying surfaces and carried out an analysis of stomatal information using multiscale segmentation and classification recognition as well as microscopy images of leaf stomata via eCognition Developer 64 software (Munich, Germany). Using this method, we further analyzed the ecological significance of stomata. The results were as follows: (1) The best parameters of stomatal division and automatic extraction rules were scale parameter 120–125 + shape parameter 0.7 + compactness parameter 0.9 + brightness value 160–220 + red light band >95 + shape–density index 1.5–2.2; the accuracy of stomatal density and stomatal area using this method were 98.2% and 95.4%, respectively. (2) There was a very significant correlation among stomatal density, stomatal area, and stomatal shape index under different growing environments. When the stomatal density increased, the stomatal area lowered remarkably and the stomatal shape tended to be flat, suggesting that the plants had adopted some regulatory behavior at the stomatal level that might be an ecological trade-off strategy for plants to adapt to a particular growing environment. These findings provide a new approach and applicable parameters for stomata extraction, which can further calculate the stomatal density and stomatal area and deepen our understanding of the relationship between stomata and the environment. The study provides useful information for urban planners on the breeding and introduction of high-temperature-resistant urban plants.
Why it matches plant phenotyping methods葉の顕微鏡画像から気孔を自動抽出し、密度・面積を推定する画像解析手法の開発と精度検証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。
abstractThe method of efficiently extracting stomata, as well as measuring stomatal density and area, still lacks established techniques.
Abstract Leaf gas exchange is central to the analysis of photosynthetic processes and the development of more productive, water efficient and stress tolerant crops. This has led to a rapid expansion in the use of commercial plant photosynthesis systems which combine infra-red gas analysis and chlorophyll fluorescence (Chl-Flr) capabilities. The present review provides an introduction to the principles, common sources of error, basic measurements and protocols when using these plant photosynthesis systems. We summarise techniques to characterise the physiology of light harvesting, photosynthetic capacity and rates of respiration in the light and dark. The underlying concepts and calculation of mesophyll conductance of CO2 from the intercellular air-space to the carboxylation site within chloroplasts using leaf gas exchange and Chl-Flr are introduced. The analysis of stomatal kinetic responses is also presented, and its significance in terms of stomatal physiological control of photosynthesis that determines plant carbon and water efficiency in response to short-term variations in environmental conditions. These techniques can be utilised in the identification of the irrigation technique most suited to a particular crop, scheduling of water application in precision irrigation, and phenotyping of crops for growth under conditions of drought, temperature extremes, elevated [CO2] or exposure to pollutants.
Why it matches plant phenotyping methods植物のガス交換・クロロフィル蛍光測定と解析プロトコルを体系的に解説し、作物フェノタイピングへの応用を扱う方法論レビューである。
abstractThe present review provides an introduction to the principles, common sources of error, basic measurements and protocols when using these plant photosynthesis systems.
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 (δ 13 C). We detect substantial genetic variation for stomata size and density segregating within Arabidopsis thaliana. A positive correlation between stomata size and δ 13 C 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 stomatal patterning but also to its covariation with carbon uptake parameters. Yet, we report two novel QTL affecting δ 13 C independently of stomatal 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 covariation 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 favour the evolution of drought avoidance mechanisms over drought escape strategies.
Why it matches plant phenotyping methods自動化共焦点顕微鏡による気孔サイズ・密度の表現型取得法を開発し、330系統へ大規模適用しているため、植物表現型計測が中心である。
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 paper's Data Accessibility statement explicitly deposits raw confocal image data, image analysis scripts, and phenotypic data in a Dryad repository, uploads genotypic phenotype means to AraPheno, and provides authors' GWAS and MTMM analysis scripts on GitHub. All are paper-specific, public, and actionable.Dataset · publicRaw image data and image analysis scripts are stored in a Dryad repository ( https://doi.org/10.5061/dryad.n068q74 ). Phenotypic data are provided as supplemental material and included in the Dryad repository.Open asset ↗Dryad · 10.5061/dryad.n068q74lines:153-213Code · publicGWAS scripts are available at https://github.com/arthurkorte/GWAS .Open asset ↗GitHub · arthurkorte/GWASlines:153-213Code · publicMTMM scripts are available at https://github.com/Gregor-Mendel-Institute/mtmm .Open asset ↗GitHub · Gregor-Mendel-Institute/mtmmlines:153-213Plant phenotyping relevance match · UnverifiedOpenAlex · checked 13 Sept 2026
Every plant organ, from tuber to tepal, is formed by cells that divide along precisely placed cell plates. While much is known about the molecular biology behind cell plate formation (e.g., Gu et al., 2016), why cells divide where they do is much less clear. Dividing cells have much in common with soap bubbles, which assume rounded shapes with the minimum surface area to reduce the energy associated with surface tension (Besson and Dumais, 2011). Like soap bubbles, cells divide into equally sized daughter cells via a division plane with the smallest surface area possible. This century-old concept, soap-film minimization, might well describe the physics behind cell division plane specification, but plant cells don't always follow the rules. For example, plant cells divide asymmetrically during root development and in response to external cues such as wounding. In such instances, biological regulation kicks in to guide the plant's shape to best suit its needs. Pinpointing processes in which the choice of cell division plane deviates from the rules of soap film minimization would thus uncover processes under strong biological control, making them ripe for analysis. While soap film minimization makes sense conceptually, this theory has only recently been examined mathematically due to the complexity of the calculations required. In a pioneering study, Martinez et al. (2018) developed a 3D geometry-based model to generate local minimum areas of predicted divisions for any cell shape (soap-film minima). The authors gathered data for their model by taking confocal micrographs of maize (Zea mays) leaf epidermal cells and noted the locations of the preprophase band (PPB) in each cell, as PPB location accurately predicts the future division plane. After extracting the data, importing it into Surface Evolver software, and performing 3D mathematical modeling, a process known as gradient descent revealed the soap-film minima where cells would divide into two daughter cells of equal volume. In most cases, the predicted soap-film minima matched the locations of PPBs in maize cells. However, like bricks in a wall, cells in adjacent files appeared staggered. Instead of forming PPBs in the predicted regions, some cells were somehow influenced by their neighbors to shift PPB formation to avoid creating a four-way junction that may weaken the mechanical strength of the tissue. Time-lapse imaging successfully captured such events in vivo. This finding points to an active mechanism for avoiding the creation of four-way junctions that bypass cell geometry. Cells in young maize leaves tended to undergo more longitudinal divisions than those in older leaves. The model underpredicted such divisions, suggesting that developmental or mechanical forces also help orient the final division plane. Since periclinal divisions were predicted to occur ∼7% of the time but rarely occur in the epidermis, the authors looked at developing maize ligule cells. These cells expanded outwardly (in the Z-plane) prior to periclinal division (see figure), suggesting that directional cell expansion may ultimately drive periclinal division in developing ligules. Predicting division planes in maize ligules. Micrographs of developing ligule cells expressing PIP2-CFP (magenta) to identify cell outlines and TAN1-YFP (green) to observe division site location. The PPB and 3D reconstructions in both the XY (bottom left) and XZ plane (right) show the periclinal division plane. Bars = 10 µm. (Reprinted from Martinez et al. [2018], Figures 4B and 4C.) To test their model's generality, the authors analyzed other cell types, including Arabidopsis thaliana guard cells and nematode (Caenorhabditis elegans) embryonic cells. Following time-lapse imaging, the data were subjected to soap-film minimization, which accurately predicted division plane locations in most cases. Thus, this model could potentially be used to identify the correct placement of the division plane in cells of any shape and to highlight all-important deviations, making it an exciting tool for developmental biology.
Why it matches plant phenotyping methods細胞画像と3D幾何モデルを用いて植物細胞の分裂面を予測・評価する手法が中心であり、再利用可能な形態計測・計算ワークフローを扱っている。
abstractdeveloped a 3D geometry-based model to generate local minimum areas of predicted divisions for any cell shape (soap-film minima).
Contents Summary 693 I. Introduction 693 II. Comparison of various definitions and measurement techniques of minimum conductance 694 III. Cuticular conductance 695 IV. Contribution of stomata 696 V. Environmental and ecological variation in minimum conductance 696 VI. Use of minimum conductance in models 698 VII. Conclusions 703 Acknowledgements 703 References 703 Summary When the rate of photosynthesis is greatly diminished, such as during severe drought, extreme temperature or low light, it seems advantageous for plants to close stomata and completely halt water loss. However, water loss continues through the cuticle and incompletely closed stomata, together constituting the leaf minimum conductance ( g min ). In this review, we critically evaluate the sources of variation in g min , quantitatively compare various methods for its estimation, and illustrate the role of g min in models of leaf gas exchange. A literature compilation of g min as measured by the weight loss of detached leaves is presented, which shows much variation in this trait, which is not clearly related to species groups, climate of origin or leaf type. Much evidence points to the idea that g min is highly responsive to the growing conditions of the plant, including soil water availability, temperature and air humidity – as we further demonstrate with two case studies. We pay special attention to the role of the minimum conductance in the Ball–Berry model of stomatal conductance, and caution against the usual regression‐based method for its estimation. The synthesis presented here provides guidelines for the use of g min in ecosystem models, and points to clear research gaps for this drought tolerance trait.
Why it matches plant phenotyping methods葉の最小コンダクタンスという植物生理形質について、測定技術を比較評価し、モデル利用の指針を示すレビューであり、形質取得法が中心的です。
abstractA literature compilation of g min as measured by the weight loss of detached leaves is presented
TobaccoStomata / guard-cell complexStress / disease detectionGrowth / development / phenologyStomatal traitsStress response / toleranceWater status / transpiration
Plant growth and development are negatively affected by a wide range of external stresses, including water deficits. Especially, plants generally reduce the stomatal aperture to decrease transpiration levels upon drought stress. Advanced technologies, such as wireless communications, the Internet of things (IoT), and smart sensors have been applied to practical smart farming and indoor planting systems to monitor plants’ signals effectively. In this study, we develop a flexible polyimide (PI)-based sensor for real-time monitoring of water conditions in tobacco plants. The stoma response, by which a plant adjusts to drought stress to maintain homeostasis, can be confirmed through the examination of evaporated water. Using a flexible PI-based sensor, a plant’s response variation is translated into an electrical signal. The sensors are integrated with a Bluetooth (BLE) module and a processing module and show potential as smart real-time water sensors in smart farms.
Why it matches plant phenotyping methodsタバコの乾燥ストレスに伴う気孔応答・水分状態を電気信号として取得する柔軟センサーを開発しており、植物状態の取得法が研究の中心です。
abstractIn this study, we develop a flexible polyimide (PI)-based sensor for real-time monitoring of water conditions in tobacco plants.
Empirical models of plant drought responses rely on parameters that are difficult to specify a priori. We test a trait- and process-based model to predict environmental responses from an optimization of carbon gain vs hydraulic risk. We applied four drought treatments to aspen (Populus tremuloides) saplings in a research garden. First we tested the optimization algorithm by using predawn xylem pressure as an input. We then tested the full model which calculates root-zone water budget and xylem pressure hourly throughout the growing season. The optimization algorithm performed well when run from measured predawn pressures. The per cent mean absolute error (MAE) averaged 27.7% for midday xylem pressure, transpiration, net assimilation, leaf temperature, sapflow, diffusive conductance and soil-canopy hydraulic conductance. Average MAE was 31.2% for the same observations when the full model was run from irrigation and rain data. Saplings that died were projected to exceed 85% loss in soil-canopy hydraulic conductance, whereas surviving plants never reached this threshold. The model fit was equivalent to that of an empirical model, but with the advantage that all inputs are specific traits. Prediction is empowered because knowing these traits allows knowing the response to climatic stress.
Why it matches plant phenotyping methods植物の干ばつ応答を予測する最適化モデルを開発・検証し、複数の生理形質を定量的に予測しているため、方法が研究の中心です。
abstractWe test a trait- and process-based model to predict environmental responses from an optimization of carbon gain vs hydraulic risk.
Stomatal conductance is a critical regulating factor in plant water relations and responses to abiotic stress. Abscisic acid (ABA) is one of the plant hormones that regulates stomatal conductance and leaf transpiration. The presence of endogenous or exogenous ABA induces stomatal closure, which reduces leaf transpiration rates and increases tolerance to abiotic stress. In this study, visible near-infrared (Vis-NIR) spectroscopy, as well as proximal multispectral and thermal imaging were used to evaluate changes in stomatal conductance through exogenous ABA applications to apple trees. ABA was applied twice at 500 mg kg−1 in 2016, with five control and five ABA-treated trees in a three-year-old apple orchard. Proximal Vis-NIR spectral reflectance (350–2500 nm) data, and multispectral and thermal infrared images were acquired from control and treated trees after 1–3 days of exogenous ABA application to the trees. Ground reference stomatal conductance was also measured to compare the data with proximal sensing data. Partial least square regression (PLSR), linear support vector machines (SVM), and quadratic SVM algorithms were applied to classify the control and ABA-treated leaves, before and after feature selection using rank features technique and stepwise regression analysis. The average classification accuracy ranged between 80 and 85% at 3 days after treatment with the entire Vis-NIR spectra, while the accuracies ranged between 74 and 80% with five selected spectral bands. The ABA treatment effects could not be observed with crop water stress index extracted from thermal images, although the leaf temperature in ABA-treated trees were higher than the untreated control trees. Green normalized difference vegetation index extracted from multispectral images also did not show any differences between control and ABA-treated trees. Overall, results suggest that the hyperspectral Vis-NIR sensing was able to acquire spectral changes pertinent to the dynamic processes such as stomatal conductance, independent from non-responsive traditional vegetation indices that lacked responsive spectral bands.
Why it matches plant phenotyping methodsリンゴ樹の気孔コンダクタンスという植物生理形質を、近接ハイパースペクトル・マルチスペクトル・熱画像で推定し、地上基準値との比較および分類精度評価を行っており、センシング手法が研究の中心である。
abstractIn this study, visible near-infrared (Vis-NIR) spectroscopy, as well as proximal multispectral and thermal imaging were used to evaluate changes in stomatal conductance through exogenous ABA applications to apple trees.
In this protocol, we describe how to quantify starch in guard cells of Arabidopsis thaliana using the fluorophore propidium iodide and confocal laser scanning microscopy. This simple method enables monitoring, with unprecedented resolution, the dynamics of starch in guard cells.
Why it matches plant phenotyping methodsガード細胞内デンプンを蛍光標識と共焦点顕微鏡で定量する具体的な画像計測プロトコルが研究の中心であり、植物の生理状態を測定する方法に該当する。
abstractwe describe how to quantify starch in guard cells of Arabidopsis thaliana using the fluorophore propidium iodide and confocal laser scanning microscopy.
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:1-57Plant phenotyping relevance match · UnverifiedCrossref · checked 13 Sept 2026
Abstract. Elevated levels of tropospheric Ozone [O3] causes damage to terrestrial vegetation, affecting leaf stomatal functioning and reducing photosynthesis. Climatic impacts under future raised atmospheric Greenhouse Gas (GHG) concentrations will also impact on the Net Primary Productivity (NPP) of vegetation, which might for instance alter viability of some crops. Together, ozone damage and climate change may adjust the current ability of terrestrial vegetation to offset a significant fraction of carbon dioxide (CO2) emissions. Climate impacts on the land surface are well studied, but arguably large-scale modelling of raised surface level [O3] effects is less advanced. To date most models representing ozone damage use either [O3] concentration or, more recently, flux-uptake related reduction of stomatal opening, estimating suppressed land-atmosphere water and CO2 fluxes. However there is evidence that for some species, [O3] damage can also cause an inertial sluggishness of stomatal response to changing surface meteorological conditions. In some circumstances e.g. droughts, this loss of stomata control can cause them to be more open than without ozone interference. The extent of this effect may be dependent on magnitude and cumulated time of exposure to raised [O3], suggesting experiments to analyze this require operation over long timescales such as full growing seasons. To both aid model development and provide empiricists with a system on to which measurements can be mapped, we present a parameter-sparse framework specifically designed to capture sluggishness. This contains a single time-delay parameter τO3, characterising the timescale for stomata to catch up with the level of opening they would have with- out damage. The larger the value of this parameter, the more sluggish the modelled stomatal response. Through variation of τO3, we find it is possible to have qualitatively similar responses to factorial experiments with and without raised [O3], when comparing to measurement timeseries presented in the literature. This low-parameter approach lends itself to the inclusion of ozone-induced inertial effects being incorporated in the terrestrial vegetation component of Earth System Models (ESMs).
Why it matches plant phenotyping methods植物の気孔応答の遅延を表現・推定する低パラメータ理論モデルを開発しており、植物生理状態の取得・記述手法が研究の中心である。
abstractwe present a parameter-sparse framework specifically designed to capture sluggishness.
High-throughput phenotyping of plant traits is a powerful tool to further our understanding of plant growth and its underlying physiological, molecular, and genetic determinisms. This protocol describes the methodology of a standard phenotyping experiment in PHENOPSIS automated platform, which was engineered in INRA-LEPSE (https://www6.montpellier.inra.fr/lepse) and custom-made by Optimalog company. The seminal method was published by Granier et al. (2006). The platform is used to explore and test various ecophysiological hypotheses (Tisné et al. , 2010; Baerenfaller et al. , 2012; Vile et al. , 2012; Bac-Molenaar et al. , 2015; Rymaszewski et al. , 2017). Here, the focus concerns the preparation and management of experiments, as well as measurements of growth-related traits ( e.g ., projected rosette area, total leaf area and growth rate), water status-related traits ( e.g ., leaf dry matter content and relative water content), and plant architecture-related traits ( e.g ., stomatal density and index and lamina/petiole ratio). Briefly, a completely randomized (block) design is set up in the growth chamber. Next, the substrate is prepared, its initial water content is measured and pots are filled. Seeds are sown onto the soil surface and germinated prior to the experiment. After germination, soil watering and image (visible, infra-red, fluorescence) acquisition are planned by the user and performed by the automaton. Destructive measurements may be performed during the experiment. Data extraction from images and estimation of growth-related trait values involves semi-automated procedures and statistical processing.
Why it matches plant phenotyping methodsPHENOPSIS自動表現型解析プラットフォームの実験・画像取得・形質抽出手順を体系的に記述しており、植物表現型取得法が中心である。
abstractThis protocol describes the methodology of a standard phenotyping experiment in PHENOPSIS automated platform
Plant biologists often need to observe the growth behavior of their chosen species. To this end, the plants need constant environmental and stable light conditions, which are preferably variable in quantity and quality so that studies under different setups can be conducted. These requirements are met by climatic chambers featuring light emitting diodes (LED) lights, which can - in contrast to fluorescent lights - be set to different wavelengths. LEDs are energy conserving and emit virtually no heat even at light intensities, which often constitutes a problem with other light sources. The presented protocol provides a step-by-step guidance of how to program a climatic chamber equipped with variable LED lights as well as describing several approaches for in depth analysis of growth phenotypes. Depending on the experimental set-up various characteristics of the growing plants can be observed and analyzed. Here we describe how to determine fresh weight, leaf area, photosynthetic activity, and stomatal density. We demonstrate that in order to obtain reliable data and draw valid conclusions it is mandatory to use a sufficient number of individuals for statistical evaluation. Taking too few plants for this kind of analysis results in high statistical errors and consequently in less clear interpretations of the data.
Why it matches plant phenotyping methods可変LED付き環境チャンバーの設定と、植物の成長表現型を取得・解析する手順を中心としたプロトコルであり、単なる生物学的実験の測定ではない。
abstractThe presented protocol provides a step-by-step guidance of how to program a climatic chamber equipped with variable LED lights as well as describing several approaches for in depth analysis of growth phenotypes.
Abscisic acid (ABA) is a sesquiterpenoid (15-carbon) hormone that comprehensively regulates plant stress responses, development, and senescence. Stomata are epidermal pores on plant surface used for exchanging gases such as carbon dioxide, water vapor, and oxygen. One of the mechanisms that ABA regulates leaf senescence is to control stomatal movement and thus water loss during leaf senescence. Here we describe the procedure of measuring stomatal movement in response to ABA treatments, which will provide a useful protocol to investigate ABA signaling in leaf senescence.
Why it matches plant phenotyping methodsABA処理に対する気孔運動という植物の生理形質を測定する手順そのものを提示しており、測定プロトコルが中心である。
abstractHere we describe the procedure of measuring stomatal movement in response to ABA treatments, which will provide a useful protocol to investigate ABA signaling in leaf senescence.
Background Stomatal behavior in grapevines has been identified as a good indicator of the water stress level and overall health of the plant. Microscope images are often used to analyze stomatal behavior in plants. However, most of the current approaches involve manual measurement of stomatal features. The main aim of this research is to develop a fully automated stomata detection and pore measurement method for grapevines, taking microscope images as the input. The proposed approach, which employs machine learning and image processing techniques, can outperform available manual and semi-automatic methods used to identify and estimate stomatal morphological features. Results First, a cascade object detection learning algorithm is developed to correctly identify multiple stomata in a large microscopic image. Once the regions of interest which contain stomata are identified and extracted, a combination of image processing techniques are applied to estimate the pore dimensions of the stomata. The stomata detection approach was compared with an existing fully automated template matching technique and a semi-automatic maximum stable extremal regions approach, with the proposed method clearly surpassing the performance of the existing techniques with a precision of 91.68% and an F1-score of 0.85. Next, the morphological features of the detected stomata were measured. Contrary to existing approaches, the proposed image segmentation and skeletonization method allows us to estimate the pore dimensions even in cases where the stomatal pore boundary is only partially visible in the microscope image. A test conducted using 1267 images of stomata showed that the segmentation and skeletonization approach was able to correctly identify the stoma opening 86.27% of the time. Further comparisons made with manually traced stoma openings indicated that the proposed method is able to estimate stomata morphological features with accuracies of 89.03% for area, 94.06% for major axis length, 93.31% for minor axis length and 99.43% for eccentricity. Conclusions The proposed fully automated solution for stomata detection and measurement is able to produce results far superior to existing automatic and semi-automatic methods. This method not only produces a low number of false positives in the stomata detection stage, it can also accurately estimate the pore dimensions of partially incomplete stomata images. In addition, it can process thousands of stomata in minutes, eliminating the need for researchers to manually measure stomata, thereby accelerating the process of analysing plant health.
Why it matches plant phenotyping methodsブドウ葉の気孔検出と孔寸法測定を自動化する画像解析手法を開発し、既存法および手動測定と比較検証しているため、植物フェノタイピング手法が中心である。
abstractThe main aim of this research is to develop a fully automated stomata detection and pore measurement method for grapevines, taking microscope images as the input.
Stomatal function can be used effectively to monitor plant hydraulics, photosensitivity, and gas exchange. Current approaches to measure single stomatal aperture, such as mold casting or fluorometric techniques, do not allow real time or persistent monitoring of the stomatal function over timescales relevant for long term plant physiological processes, including vegetative growth and abiotic stress. Herein, we utilize a nanoparticle-based conducting ink that preserves stomatal function to print a highly stable, electrical conductometric sensor actuated by the stomata pore itself, repeatedly and reversibly for over 1 week. This stomatal electro-mechanical pore size sensor (SEMPSS) allows for real-time tracking of the latency of single stomatal opening and closing times in planta, which we show vary from 7.0 ± 0.5 to 25.0 ± 0.5 min for the former and from 53.0 ± 0.5 to 45.0 ± 0.5 min for the latter in Spathiphyllum wallisii. These values are shown to correlate with the soil water potential and the onset of the wilting response, in quantitative agreement with a dynamic mathematical model of stomatal function. A single stoma of Spathiphyllum wallisii is shown to distinguish between incident light intensities (up to 12 mW cm -2 ) with temporal latency slow as 7.0 ± 0.5 min. Over a seven day period, the latency in opening and closing times are stable throughout the plant diurnal cycle and increase gradually with the onset of drought. The monitoring of stomatal function over long term timescales at single stoma level will improve our understanding of plant physiological responses to environmental factors.
Why it matches plant phenotyping methods単一気孔の開閉遅延をリアルタイム・長期測定する電気機械センサーを開発し、植物の水分状態や萎凋との相関で実証しており、表現型取得法が研究の中心である。
abstractThis stomatal electro-mechanical pore size sensor (SEMPSS) allows for real-time tracking of the latency of single stomatal opening and closing times in planta
Guard cells dynamically adjust their shape in order to regulate photosynthetic gas exchange, respiration rates and defend against pathogen entry. Cell shape changes are determined by the interplay of cell wall material properties and turgor pressure. To investigate this relationship between turgor pressure, cell wall properties and cell shape, we focused on kidney-shaped stomata and developed a biomechanical model of a guard cell pair. Treating the cell wall as a composite of the pectin-rich cell wall matrix embedded with cellulose microfibrils, we show that strong, circumferentially oriented fibres are critical for opening. We find that the opening dynamics are dictated by the mechanical stress response of the cell wall matrix, and as the turgor rises, the pectinaceous matrix stiffens. We validate these predictions with stomatal opening experiments in selected Arabidopsis cell wall mutants. Thus, using a computational framework that combines a 3D biomechanical model with parameter optimization, we demonstrate how to exploit subtle shape changes to infer cell wall material properties. Our findings reveal that proper stomatal dynamics are built on two key properties of the cell wall, namely anisotropy in the form of hoop reinforcement and strain stiffening.
Why it matches plant phenotyping methods3D生体力学モデルとパラメータ最適化により、気孔の形状変化から細胞壁の物性を推定する方法を開発し、変異体実験で検証しており、表現型取得・推定が研究の中心である。
abstractusing a computational framework that combines a 3D biomechanical model with parameter optimization, we demonstrate how to exploit subtle shape changes to infer cell wall material properties
Reproduction assets foundThe paper's authors explicitly state that all simulation, data-processing, and graphing scripts for their guard cell biomechanical model are freely available in a public GitHub repository (stomasimulator), which is a paper-specific, publicly actionable code asset. No separate public phenotype dataset deposit is stated;Code · publicAll of the scripts used to run these simulations, process the data and generate the graphs are freely available at https://github.com/woolfeh/stomasimulator .Open asset ↗https://github.com/woolfeh/stomasimulator · stomasimulatorlines:410-476Plant phenotyping relevance match · UnverifiedEurope PMC · checked 10 Sept 2026
The number of stomata on leaves can be affected by intrinsic development programming and various environmental factors, in addition the control of stomatal apertures is extremely important for the plant stress response. In response to elevated temperatures, transpiration occurs through the stomatal apertures, allowing the leaf to cool through water evaporation. As such, monitoring of stomata behavior to elevated temperatures remains as an important area of research. The protocol allows analysis of stomatal aperture, morphology, and density through a non-destructive imprint of Arabidopsis thaliana leaf surface. Stomatal counts were performed and observed under a scanning electron microscope.
Why it matches plant phenotyping methodsシリコンポリマー印象法と走査電子顕微鏡を用いて、葉の気孔開度・形態・密度を非破壊測定する具体的な表現型取得プロトコルが中心である。
abstractThe protocol allows analysis of stomatal aperture, morphology, and density through a non-destructive imprint of Arabidopsis thaliana leaf surface.
Stomatal regulation is a key process in the physiology of Coffea arabica (C. arabica). Intrinsically linked to photosynthesis and water relations, it provides insights into the plant's adaptive capacity, survival and growth. The ability to rapidly quantify this parameter for C. arabica under different agroecological systems would be an indispensable tool. Using a Flir E6 MIR Camera, an index that is equivalent to stomatal conductance (I g ) was compared with stomatal conductance measurements (g s ) in a mature coffee plantation. In order to account for varying meteorological conditions between days, the methods were also compared under stable meteorological conditions in a laboratory and I g was also converted to absolute stomatal conductance values (g 1 ). In contrast to typical plant-thermography methods which measure indices once per day over an extended time period, we used high resolution hourly measurements over daily time series with 9 sun and 9 shade replicates. Eight daily time series showed a strong correlation between methods, while the remaining 10 were not significant. Including several other meteorological parameters in the calculation of g 1 did not contribute to any stronger correlation between methods. Total pooled data (combined daily series) resulted in a correlation of ρ=0.66 (P≤2.2e-16), indicating that our approach is particularly useful for situations where absolute values of stomatal conductance are not required, such as for comparative purposes, screening or trend analysis. We use the findings to advance the protocol for a more accurate methodology which may assist in quantifying advantageous microenvironment designs for coffee, considering the current and future climates of coffee growing regions.
Why it matches plant phenotyping methods熱画像による気孔コンダクタンス推定を実測値と比較・検証し、測定プロトコルを改良する研究であり、植物生理形質の取得法が中心である。
abstractUsing a Flir E6 MIR Camera, an index that is equivalent to stomatal conductance (I g ) was compared with stomatal conductance measurements (g s )
Abstract Thermometry and thermography are alternative methods used for measuring stomatal conductivity via transpirative cooling. However, the influence of mixed soil–plant information contained in thermal images compared to thermometric spot measurements on the measurement quality and relationships to agronomic traits remains unclear. To evaluate their respective influence, canopy temperature was measured simultaneously by two infrared thermometers (thermometry), which were oriented oblique to the plant canopy and mounted on a tractor, and a hand‐held, nadir oriented thermal camera (thermography) in irrigated and drought‐stressed spring barley cultivar trials in 2011. Canopy temperatures were separated from soil temperatures and extracted from the thermal images by matching thermal and RGB images. Thermometric measurements conducted at the beginning of shooting during a stable period of high radiation were more closely related to total plant biomass and straw yield at harvest than thermography under both irrigated and drought‐stressed conditions. Taking into account the results of this evaluation, thermometry was used for assessing the agronomic importance of stomatal sensitivity, the earliness of stomatal closure, of spring barley cultivars subjected to different water supply in 2013. In this year, 16 spring barley cultivars were grown under mild drought stress and rainfed conditions. A stomatal sensitivity index was derived relating canopy temperatures of the cultivars grown under rainfed and drought‐stressed conditions to each other. Under rainfed conditions, stomatal sensitivity was negatively related to grain protein yield with a coefficient of determination of R 2 = .43. Under increasing terminal drought stress, positive regression slopes of stomatal sensitivity to grain yield, biomass yield and culms/m 2 were observed with coefficients of determination amounting to R 2 = .22, .31 and .36, respectively. Stomatal sensitivity negatively impacts agricultural production under well‐watered conditions, but maintains productivity under conditions of terminal drought.
Why it matches plant phenotyping methods熱画像と赤外線温度計による作物キャノピー温度の測定・抽出法を比較評価し、測定品質と農業形質との関係を検証しており、表現型取得法が研究の中心である。
titleThermal phenotyping of stomatal sensitivity in spring barley
Estimation of stomatal aperture using low viscosity silicone-base impression material has the advantage of working with the whole leaf. The developmental stage and the environment strongly affect the stomatal aperture. Therefore, it is mandatory to have accurate estimations of the stomatal aperture of intact leaves under different situations. With this technique, it is possible to get the real picture at any moment. The outputs of the data include studies on cell area and morphology, epidermis cell and stomata lineages, among others. This protocol is useful for the accurate estimation of stomatal aperture in many samples of intact leaves in Arabidopsis thaliana .
Why it matches plant phenotyping methodsシリコーンゴム印象法により、無傷葉の気孔開度を正確に推定する実験プロトコルであり、植物生理形質の取得方法自体が中心である。
abstractEstimation of stomatal aperture using low viscosity silicone-base impression material has the advantage of working with the whole leaf.
Accurately predicting photosynthesis in response to water and nitrogen stress is the first step toward predicting crop growth, yield and many quality traits under fluctuating environmental conditions. While mechanistic models are capable of predicting photosynthesis under fluctuating environmental conditions, simplifying the parameterization procedure is important toward a wide range of model applications. In this study, the biochemical photosynthesis model of Farquhar, von Caemmerer and Berry (the FvCB model) and the stomatal conductance model of Ball, Woodrow and Berry which was revised by Leuning and Yin (the BWB-Leuning-Yin model) were parameterized for Lilium ( L. auratum × speciosum "Sorbonne") grown under different water and nitrogen conditions. Linear relationships were found between biochemical parameters of the FvCB model and leaf nitrogen content per unit leaf area ( N a ), and between mesophyll conductance and N a under different water and nitrogen conditions. By incorporating these N a -dependent linear relationships, the FvCB model was able to predict the net photosynthetic rate ( A n ) in response to all water and nitrogen conditions. In contrast, stomatal conductance ( g s ) can be accurately predicted if parameters in the BWB-Leuning-Yin model were adjusted specifically to water conditions; otherwise g s was underestimated by 9% under well-watered conditions and was overestimated by 13% under water-deficit conditions. However, the 13% overestimation of g s under water-deficit conditions led to only 9% overestimation of A n by the coupled FvCB and BWB-Leuning-Yin model whereas the 9% underestimation of g s under well-watered conditions affected little the prediction of A n . Our results indicate that to accurately predict A n and g s under different water and nitrogen conditions, only a few parameters in the BWB-Leuning-Yin model need to be adjusted according to water conditions whereas all other parameters are either conservative or can be adjusted according to their linear relationships with N a . Our study exemplifies a simplified procedure of parameterizing the coupled FvCB and g s model that is widely used for various modeling purposes.
Why it matches plant phenotyping methods水・窒素条件下の光合成速度と気孔コンダクタンスという植物生理形質を推定する結合モデルのパラメータ化と予測検証が研究の中心であり、単なる生物学的実験のルーチン測定ではない。
abstractthe biochemical photosynthesis model of Farquhar, von Caemmerer and Berry (the FvCB model) and the stomatal conductance model of Ball, Woodrow and Berry which was revised by Leuning and Yin (the BWB-Leuning-Yin model) were parameterized for Lilium
"And from the turf would leap a branching tree— Wonders unheard of; for, by Nature, each Slowly increases from its lawful seed…" —from Titus Lucretius Carus, "Substance is eternal" in On the Nature of Things, Book I (translated, in verse, by W. E. Leonard) In On the Nature of Things, Lucretius speculates on the necessity of plant development: Branching trees simply do not "leap" from the turf, rather, the branching patterns of shoots and roots develop over time, "slowly increas[sing] from [their] lawful seed" (Leonard, 2015). Over 2000 years ago, the essence of the plant phenotype was written in a poem; plants are four-dimensional beings, branching structures that emerge through time. This qualitative realization of the nature of plant phenotype is self-apparent, but quantitative models of plant morphology are less forthcoming. Plant morphology should be quantified comprehensively. Conventional analyses of phenotypic traits consider specific plant features and assess only a small proportion of overall morphological variation. There is a critical need for methods to quantify the complete morphology of a plant, including the growing branching structures of both the root and shoot. Plant morphology can be considered across scales; for example, the main trunks of a tree define its coarse architecture, but local branching patterns of twigs distributed throughout the tree also contribute to the overall morphology. The branching patterns of plants can be considered from the perspective of topology. Topology is a field of mathematics concerned with the connectedness, or contiguousness, of structures. Smaller branches split from larger branches, creating a hierarchy of connectedness appropriate for topological analysis. The ability to quantify and compare the total branching structures of plants across scales has implications for studies of plant genetics, development, evolution, and environmental response, all of which currently rely on traits that measure facets of overall plant morphology. Persistent homology, a mathematical method that captures topological features across scales, is well suited to quantify the growing branching architectures of plants. We begin by considering existing models and morphometric methods used to quantify and compare plant morphology before introducing persistent homology and its applications. Morphometrics, as the name implies, is concerned with the measurement of shape and size. Morphometric analyses can measure linear features as well as shapes and three-dimensional (3D) structures. Leaves, floral organs, seeds, and cell shapes are some plant structures amenable to shape analysis. Landmarks, homologous points found in every sample (Chitwood et al., 2016), or pseudo-landmarks, equidistant points placed between landmarks (Langlade et al., 2005), are a simple way to represent shape as a multicoordinate object. Shapes can also be viewed as waves that form a closed contour, to which a Fourier-based decomposition technique, elliptical Fourier descriptors (EFDs), can be applied (Kuhl and Giardina, 1982). Both landmarks and EFDs are multivariate representations of shapes that are descriptive. They can be used to identify the main sources of shape variance or distinguish different groups—such as species, organs, or developmental stages. Although leaves, flowers, seeds, cells, and other parts of plants can be described as shapes, the overall architecture of a plant is not a shape. Rather, plants—both the shoots and the roots—are branching structures. Many analyses quantifying branching patterns have been applied to plants previously. Leonardo da Vinci described relationships in the diameters and lengths of branch hierarchies in trees (Long, 1994), but this fails to capture branching architecture itself. The parameters underlying branch patterns, and their potential adaptive significance, have been modeled and can be quantified using fractal-based methods (Zeide and Pfeifer, 1991). Fractal methods, however, measure complexity and self-similarity rather than properties of topological spaces. L-systems (Lindenmayer systems) are recursive systems that expand strings of symbols into larger strings based on a set of rules (Prusinkiewicz and Hanan, 2013). The iterated results of these systems can produce intricate branching patterns, with self-similarity, reminiscent of diverse plant morphologies. However, L-systems are generative models and cannot descriptively measure topological properties. Although each powerful in their own way, morphometrics, fractal-based methods, branch hierarchies, L-systems, and generative models cannot comprehensively measure the topologies of branching architectures in plants. Persistent homology is a mathematical theory of topology that has much potential if applied to plants. "Homology" in persistent homology is not the same as in biology, that is, features that correspond between organisms based on descent from a common ancestor. Rather, mathematical homology refers to homology groups recording the connectedness of components. For example, H0 (zero order homology) describes path-connected components (that is, contiguous features). A solid cylinder and a branching structure are both a single, connected component. But the details of a branching structure can be revealed by studying how homology persists across the scales of a mathematical function (Edelsbrunner and Harer, 2008; Weinberger, 2011). For example, consider the height, as measured by the vertical distance to the ground, of any point on a tree. We might create a simple function of "height", and then traverse the structure of the tree, starting at the highest tips and proceeding to the trunk and the ground. As we traverse the tree and the function, starting at the highest points there would be many isolated branches that are not connected. As we proceed through the function, some branches merge. We can record the "birth" and "death" of homology group components (path-connected components, in this example branches) as a persistence barcode (Fig. 1A, B). The x-axis of the persistence barcode is the scale of the function ("height", in this case) and the y-axis distinct, connected components (in the jargon of topology, referred to as H0 bars). For example, the "birth" of an H0 bar is due to a new connected component, and the "death" of an H0 bar is because two components merged. When two components merge, the shortest "dies" and the longer "persists". Each bar in a persistence barcode therefore corresponds to a branch and records where a branch begins and ends with respect to the scales of a function. Persistence barcode of the topology of a grape cluster rachis. (A) Lower panel, surface voxels (like pixels, but 3D) of a grape rachis are colored by their geodesic distance (the curved distance along the rachis) to the base. The most distant to closest voxels are colored from red to blue. Panels, left to right, traverse the geodesic distance function, and portions of the rachis are colored gray as the function progresses. Upper panel, a persistence barcode, in which each bar corresponds to a branch. H0 (zero order homology, y-axis) branches are "born" and "die" along the distance function (x-axis). When two branches merge, the longest persists in the barcode. Vertical lines in the barcode indicate the corresponding position along the geodesic distance function indicated in the panels below, and the number of connected branches corresponds to the number of bars. (B) H0 persistence barcode for a simple branching structure, to demonstrate the relationship between connected components along the scale of a geodesic distance function and the "birth" and "death" of bars in the barcode. (C) Bottleneck distance is a robust metric to compare the overall distance between persistence barcodes. It can be used with traditional statistical techniques used in biology to quantify overall morphological differences between plant structures. Shown are three different branching structures that have been analyzed using principal component analysis (PCA) and the corresponding bottleneck distance between the structures. Persistence barcodes can be compared against each other as a pairwise distance matrix using a bottleneck distance method, providing a useful tool to compare the similarity of any branching structure to another. Briefly, bottleneck distance calculates the minimal cost to move a branch from one branching structure to resemble another based on permuting the persistence barcodes of two structures against each other. The bottleneck distance is a robust metric of similarity between two branching structures (Edelsbrunner and Harer, 2008) that can be used to perform principal component analysis, discriminant analysis, hierarchical clustering, or other statistical methods commonly used in biological studies (Fig. 1C). Persistence barcodes and bottleneck distances can be calculated using software packages written for a variety of programming languages, such as phom (Tausz, 2011), Dionysus (Morozov, 2012), Perseus (Nanda, 2012), PHAT (Bauer et al., 2014), Gudhi (Maria et al., 2014), TDA (Fasy et al., 2014), or javaPlex (Adams et al., 2014). The computational intensiveness of persistent homology methods depends on the complexity and size of the data being analyzed. It is useful to explain the application of persistent homology to plant morphology using actual examples from plants. Take for example, the branching architecture of the rachis of a grape cluster. If successive two-dimensional (2D) radiograms (Fig. 2A) created by the detection of X-rays that pass through (rather than being absorbed by) the rachis are taken at different angles, a tomographic 3D reconstruction can be computed (X-ray computed tomography [CT]) (Fig. 2B). Now, apply a geodesic distance function to every surface voxel (a voxel is a 3D pixel), measuring the voxel distance to the base. Geodesic distance is calculated as the shortest curved distance of each voxel to the rachis base; it is different from simple "vertical height to the ground" because it records the distance of any point in a structure to its base as if driving along the curves of the structure itself, as if it were a road, to the base (Fig. 1A). If we traverse the geodesic distance function, we start at the most distal termini of the rachis branches, farthest from the base. As we get closer to the base, these termini will fuse with each other, such that where there were two branches there is only one; or, as the distance function is traversed, a new branch will be detected. As the geodesic distance function is traversed across scales, the "birth" and "death" of the connected components (the branches) are recorded as bars in a persistence barcode. Each bar represents an individual connected component. The "birth" and "death" of each bar, in this instance, records the geodesic length of each branch. If two branches fuse, the longest persists, and ultimately only a single bar persists in the barcode. If many different rachises were measured similarly, a pairwise distance matrix between their persistence barcodes, quantifying the overall differences in their topological spaces, could be calculated using the bottleneck distance. X-ray computed tomography (CT) radiograms of a grape cluster. (A) 2D radiogram of a grape rachis; X-rays, absorbed or passing through the rachis, are detected to create a silhouette. (B) Radiograms taken at successive different angles can be used to create a 3D reconstruction of the rachis. Persistent homology is flexible enough to accommodate more than strict branching topologies. The overall morphology of shoots and roots, including lateral organs like leaves, also possess a topological space. The shoots and roots of a tomato seedling, for example, can be modeled as surface voxels from a 3D X-ray CT scan reconstruction. A number of distance functions can be calculated relative to soil level where the shoot and root meet. Height distance is a vertical straight line from any surface voxel to the soil (Fig. 3A). Geodesic distance, as explained earlier, is the shortest curved path along the seedling to the soil (Fig. 3B). Functions combining different distance functions can measure novel features. For example, the arccos(height distance/geodesic distance) of any voxel is an approximate measure of the angle relative to soil level, which is sensitive to organ bending and branch angles (Fig. 3C). From these two functions (the geodesic function and the height function), persistence barcodes, capturing the respective topological spaces, can be calculated and compared with each other (Fig. 3D, E). Each bar in the barcodes in Fig. 3D and E, for example, corresponds to a branch that arises across the scale of the respective function. Using a bottleneck distance method, a comparison of the topological distance between any two barcodes—any two shoots, any two roots, or a shoot and a root (Fig. 1C)—can quantify branching architecture and phenotypic variation. Persistent homology applied to the shoot and root architecture of a tomato seedling. (A–C) Colormaps of distance functions of surface voxels to soil level for shoots and roots of a seedling of Solanum lycopersicum cv. M82. (A) A height distance function, which is the vertical distance of each surface voxel to soil level. (B) A geodesic distance function, which is the shortest curved distance of any surface voxel along the surface of the plant to soil level. (C) An angle function, which is arccos(height/geodesic), resulting in the angle of each surface voxel relative to soil level. (D, E) Persistence barcodes for the geodesic distance functions of the (D) shoot and (E) root. Persistent homology opens new vistas into ways to capture the exquisite features of plants comprehensively. Persistent homology is an adaptable solution that can provide a common framework to interpret innumerable types of phenotypic data. Importantly, persistent homology can be used with any function that scales topological spaces of an object. That persistent homology can be used with functions tailored to specific questions lies at the heart of its versatility. Consider a set of points—for example, stomata on a leaf, locations of trees imaged by satellite across large swaths of land (Mander et al., 2017), or point cloud data from an agricultural field as measured by lidar (light detection and ranging). Apply to these point data a function increasing the radii of balls around the points and recording the number of connected components as a persistence barcode. As the points with larger and larger circumferences intersect with each other, they form connected components that have a "birth" and a "death". The resulting persistence barcode captures the unique topological patterning of the distances of the points to each other. Shapes, too, can be considered as a collection of 2D points. A density function, measuring the density of nearby pixels for any given pixel can be calculated. Then, thresholds of the spatial distribution and connectedness of different density levels results in a persistence barcode, effectively measuring shapes as a topological space (Li et al., 2017). The density function can be selected to be orientation invariant or robust to disparate shape features, excelling where traditional morphometric methods often fail. Textures can also be analyzed using a persistent homology approach, classifying grass pollen based on surface ornamentation, for example (Mander et al., 2013). Ultimately, any topological space in plant morphology manifests over time. If plant morphology is simplified to a branching structure, then plants are four-dimensional beings, topologies that grow through time. It is tempting to simply measure the topology of the plants we see before our eyes, on our timescale. Homology groups, though, can be applied in n-dimensional spaces, and the true branching forms of trees and roots across time can easily be described by persistent homology, as can static snapshots of their ephemeral forms. The versatility of persistent homology to describe diverse topological spaces across scales, and in any number of dimensions, promises to reveal previously unnoticed facets of the plant form and to perhaps bring us closer to their true underlying nature.
Why it matches plant phenotyping methods植物の分岐形態・根系およびシュート構造を、X線CTとpersistent homologyで定量化・比較する計算フェノタイピング手法を中心に解説しているため。
abstractThere is a critical need for methods to quantify the complete morphology of a plant, including the growing branching structures of both the root and shoot.
Stomatal regulation presumably evolved to optimize CO 2 for H 2 O exchange in response to changing conditions. If the optimization criterion can be readily measured or calculated, then stomatal responses can be efficiently modelled without recourse to empirical models or underlying mechanism. Previous efforts have been challenged by the lack of a transparent index for the cost of losing water. Yet it is accepted that stomata control water loss to avoid excessive loss of hydraulic conductance from cavitation and soil drying. Proximity to hydraulic failure and desiccation can represent the cost of water loss. If at any given instant, the stomatal aperture adjusts to maximize the instantaneous difference between photosynthetic gain and hydraulic cost, then a model can predict the trajectory of stomatal responses to changes in environment across time. Results of this optimization model are consistent with the widely used Ball-Berry-Leuning empirical model (r 2 > 0.99) across a wide range of vapour pressure deficits and ambient CO 2 concentrations for wet soil. The advantage of the optimization approach is the absence of empirical coefficients, applicability to dry as well as wet soil and prediction of plant hydraulic status along with gas exchange.
Why it matches plant phenotyping methods環境条件から気孔応答、植物の水理状態、ガス交換を推定する最適化モデルを開発・検証しており、生理的な植物状態の計算的推定が中心である。
abstractprediction of plant hydraulic status along with gas exchange
There are two major methodical approaches with which changes of status in stomatal pores are addressed: indirectly by measurement of leaf transpiration, and directly by measurement of stomatal apertures. Application of the former method requires special equipment, whereas microscopic images are utilized for the direct measurements. Due to obscure visualization of cell boundaries in intact leaves, a certain degree of invasive leaf manipulation is often required. Our aim was to develop a protocol based on the minimization of leaf manipulation and the reduction of analysis completion time, while still producing consistent results. We applied rhodamine 6G staining of Arabidopsis thaliana leaves for stomata visualization, which greatly simplifies the measurement of stomatal apertures. By using this staining protocol, we successfully conducted analyses of stomatal responses in Arabidopsis leaves to both closure and opening stimuli. We performed long-term monitoring of living stomata and were able to document the same leaf before and after treatment. Moreover, we developed a protocol for rapid-fixation of epidermal peels, which enables high throughput data analysis. The described method allows analysis of stomatal apertures with minimal leaf manipulation and usage of the same leaf for sequential measurements, and will facilitate the analysis of several lines in parallel.
Why it matches plant phenotyping methods気孔開度という植物形質を顕微鏡画像から測定するための染色・固定・高速解析プロトコルを開発しており、表現型取得法が研究の中心である。
abstractOur aim was to develop a protocol based on the minimization of leaf manipulation and the reduction of analysis completion time, while still producing consistent results.
In greenhouses, reducing water consumption by increasing water efficiency in order to fulfil the requirements of sustainability is a challenge. To reach this goal, we need to better understand the water demand of plants. Transpiration is the main mechanism involved in water transfer, which is controlled by stomatal resistance Rs. Predictive models can be used to assess this parameter. However, few models currently exist for greenhouse plants grown in pots. The aim of this work is to develop a model of Rs based on full factorial design (FFD), and to validate it for greenhouse plants at various growth stages.FFD is based on an optimisation process to establish a polynomial relationship between Rs and radiation, humidity, and temperature. To establish the parameters of the model, a set of experiments was conducted inside a 10-m2 growth chamber with New Guinea Impatiens grown in pots. Rs was measured with a porometer under nine climatic scenarios. Once the parameters were determined, the FFD model was validated against experimental data recorded from a greenhouse Impatiens crop, and compared with the Jarvis model. The slopes of the linear regression between measured Rs values and Rs values predicted from the FFD and Jarvis models varied within the range 0.89–1.12 for FFD and 0.45–0.54 for Jarvis.FFD was therefore able to correctly simulate Rs. Its main advantage was to only require few data for its calibration, contrary to the Jarvis model. In a next step, it will be used to predict transpiration rates.
Why it matches plant phenotyping methods植物の蒸散関連形質である気孔抵抗を予測するモデルを開発し、ポロメータ測定および既存モデルとの比較で検証しており、表現型取得・推定手法が研究の中心です。
abstractThe aim of this work is to develop a model of Rs based on full factorial design (FFD), and to validate it for greenhouse plants at various growth stages.
Plant phenotyping relevance match · UnverifiedOpenAlex · Europe PMC · checked 11 Sept 2026
Modern imaging technology provides new approaches to plant phenotyping for traits relevant to crop yield and resource efficiency. Our objective was to investigate water use strategies at early growth stages in durum wheat genetic resources using shoot imaging at the ScreenHouse phenotyping facility combined with physiological measurements. Twelve durum landraces from different pedoclimatic backgrounds were compared to three modern check cultivars in a greenhouse pot experiment under well-watered (75% plant available water, PAW) and drought (25% PAW) conditions. Transpiration rate was analyzed for the underlying main morphological (leaf area duration) and physiological (stomata conductance) factors. Combining both morphological and physiological regulation of transpiration, four distinct water use types were identified. Most landraces had high transpiration rates either due to extensive leaf area (area types) or both large leaf areas together with high stomata conductance (spender types). All modern cultivars were distinguished by high stomata conductance with comparatively compact canopies (conductance types). Only few landraces were water saver types with both small canopy and low stomata conductance. During early growth, genotypes with large leaf area had high dry-matter accumulation under both well-watered and drought conditions compared to genotypes with compact stature. However, high stomata conductance was the basis to achieve high dry matter per unit leaf area, indicating high assimilation capacity as a key for productivity in modern cultivars. We conclude that the identified water use strategies based on early growth shoot phenotyping combined with stomata conductance provide an appropriate framework for targeted selection of distinct pre-breeding material adapted to different types of water limited environments.
Why it matches plant phenotyping methodsシュート画像計測を用いた植物フェノタイピングを生理測定と組み合わせ、水利用戦略の形態・生理形質を抽出することが研究の中心であるため。
abstractusing shoot imaging at the ScreenHouse phenotyping facility combined with physiological measurements
Establishing the connection between genotype and phenotype is currently one of the most significant challenges facing modern plant biology. Although spectacular advances in next-generation DNA sequencing have allowed genomic data to become commonplace throughout biology, progress has been much slower in translating this discrete DNA base pair information into an accurate description of phenotypic variation. The limiting factor for quantifying phenotypes, particularly in the context of agricultural and native plant populations, has been the lack of phenotypic information of a scale, density, and accuracy comparable to DNA sequencing data. The extensive collection of phenotypic data for many physiological and developmental traits from many individuals remains onerous (Furbank and Tester, 2011). As a result, for large populations, there is often a focus on traits that are easy or inexpensive to measure, while more costly or difficult-to-score phenotypes are studied in only a few individuals. This is especially true for traits that are complex in nature, meaning that they have polygenic inheritance and varying responses to the environment. Complex traits are of primary interest not only because they represent the majority of important agronomic crop traits but also because they govern key biological processes that influence overall plant productivity and adaptability in plant populations (Lynch and Walsh, 1998). Success in deciphering these processes could translate into increased genetic gains in plant breeding, elucidation of the mechanisms impacting important ecophysiological traits, and improved crop management decisions to further maximize yield and quality. In light of these potential benefits, the aim of this Update article is to present the basic principles of phenomics, summarize the current state of field-based phenotyping, and highlight key challenges and limitations. In addition, the areas of data collection and management, environmental characterization, and crop growth models (CGMs) are presented as topics where further consideration is needed to capitalize on advancements in phenotyping technologies. Phenomics, or high-throughput phenotyping, which emerged in recent years in response to limited phenotyping capacity, is the use of sensor and imaging technologies that permits the rapid, low-cost measurement of many phenotypes across time and space with less labor; it can include laboratory, greenhouse, and field-based applications. In model plant species with small physical stature, such as Arabidopsis (Arabidopsis thaliana), large populations can be evaluated under controlled environmental conditions. However, the use of controlled environmental systems is not scalable for many areas of interest. Native species often need to be evaluated in their natural environment and over a broad geographic and climatic distribution, and agricultural crop trials must simultaneously evaluate thousands of potential cultivars. Furthermore, these controlled systems are unable to replicate the environmental variables of a field environment that influence complex traits such as grain yield or drought tolerance. Therefore, field-based, high-throughput phenotyping (FB-HTP) capacity is desperately needed to understand phenotypic variation relevant to a broad range of research areas such as food and nutritional security, anthropogenic effects on the environment, and ecological community interactions. Plants are intrinsically related to their environment, and observed phenotypes are a direct product of this interaction. Therefore, the ability to study and quantify phenotypes under real-world conditions is essential to the basic understanding, as well as improvement, of ecophysiological traits. Recent technological developments have enabled progress in plant phenotyping, but areas such as root phenotyping are still lacking the needed instrumentation in order to capitalize on these developments. Extraction of high-dimensional phenotype data from images is becoming more commonplace with advancements in image-processing software. This is leading to the discovery of novel phenotypes not identified previously but that are more related to underlying physiological processes. Developments in envirotyping and crop growth modeling can provide a useful framework for understanding plant development. The physical basis for most nondestructive, proximal sensing systems is the quantification of absorption, transmission, or reflectance characteristics of the electromagnetic radiation (EM) spectrum’s interaction with the plant canopy surface (Mulla, 2013; Araus and Cairns, 2014). The EM spectrum, specifically the wavelengths between 400 and 2,500 nm, can be broken down into three major parts that offer information about plant status, structural properties, and biochemical composition (Fig. 1). These three subregions are composed of (1) the photosynthetically active region (400–700 nm PAR), in which photosynthetic pigments, namely chlorophylls a and b, strongly absorb light; (2) the near-infrared region (700–1,400 nm), in which healthy plant tissue is highly reflective; and (3) the shortwave infrared region (1,400–2,500 nm), in which water and biomolecules contribute to reflectance characteristics (Jones and Vaughan, 2010; Homolová et al., 2013). In addition to these regions, thermal infrared, typically 8 to 13 μm when used for remote sensing, can provide information about canopy temperature (Jones, 2004). The variation present in these spectral traits give rise to ecological, species-, and genotype-specific phenotypes. Typical spectral reflectance curve for healthy vegetation. The unique spectral signature of vegetation in the wavelength range of 350 to 2,500 nm allows it to be differentiated from other types of land features. The shape of the reflectance spectrum is influenced by the chlorophyll content, health, water content, and biochemical composition of the vegetation (Curran, 1989; Jones and Vaughan, 2010), which then can be used to help identify the type of vegetation and diagnose its status. Thermal sensing of vegetation is valuable for the detection of drought stress and closure of stomata. There are five common types of sensors that are used to measure spectral variation, with differences among them in the specific targeted wavelengths. The inset image depicts the spectra underlying solar-induced chlorophyll fluorescence based on application of the Fraunhofer line discrimination principle using three spectral bands (FLD3). Measurements of chlorophyll fluorescence can be used to detect the early stages of biotic or abiotic stress before the appearance of visible symptoms. NIR, Near-infrared region. Robust sensors mounted on a field-deployable vehicle are imperative for FB-HTP. Although the aim of this review is not to summarize specific sensor technologies (for summary, see Jones and Vaughan, 2010; Sankaran et al., 2015), a brief list is provided for orientation. The most common types of canopy sensors include digital imaging via red-green-blue cameras; multispectral, including color-infrared modified digital cameras; hyperspectral; thermal; fluorescence; and three-dimensional (3D; time-of-flight and stereo cameras as well as light detection and ranging). The choice of vehicle for positioning sensors directly impacts the scale of research that can be as well as the sensor and (for review of and see et al., Sankaran et al., phenotyping large ecological and the only and et al., but small systems are a for areas et al., 2013). for field vehicle include and small in addition to 2010; et al., 2013; et al., et al., et al., reflectance can provide into overall plant as well as specific physiological processes. most and remote sensing have on using to overall plant (for see et al., et al., with vegetation the most well Although these can be they use less of spectra and lack the ability to give information on physiological processes. In canopy using or can canopy and composition by more of the research have that data can be to in species et al., et al., 2015), with specific including of canopy et al., and et al., from these biochemical can give further into phenotypes, including species and to native by the of species et al., 2014). However, spectral of are influenced by environmental and the biochemical composition of the plant et al., it to understand wavelengths across the spectrum translate into biological meaning and In the complex interaction of light with the canopy surface and the effects of light a significant have been to this with the by and but these canopy reflectance models the structural characteristics of into a and of is the and of the sensor which can into the of the spectral data must be for in the model et al., these the use of canopy reflectance data large provided by and systems has However, is needed for the study of phenotypes the recent technological the ability to use more sensors in with or permits the collection of canopy data with In addition, most of of data when provide more information sensors can sensor In one of the of et a light of canopy and spectral reflectance sensors to in that the of data from sensors more data from sensor sensor data used by et to phenotype for developmental et an sensors that are of of plant physical such as canopy and can surface images of systems have been used to study the of and its interaction with environmental canopy temperature has been used to the water of as with surface to conditions et al., et the use of to study the physiological processes underlying and drought responses in an under to the of canopy traits in response to field conditions one and over the The collection of phenotypic data enabled the detection of with with plant growth stages (Fig. the ability of to plant under and used a to identify growth characteristics among evaluated under varying The novel ability of to the of a phenotype the needed to focus on developmental in order to is a of many effects with time et al., the of and are further by as well as their interaction with the environment, it is not to this with time Therefore, the of genotype with to or in the of natural of plant growth environmental and the study of developmental stages or throughout the the growth of to be As a result, the genetic of can be with phenotype data by measurement of a such as throughout plant the of identified to be in the context of environmental conditions. Although its application in is still in its fluorescence which the light from chlorophyll a via chlorophyll can provide information about the photosynthetic under abiotic and biotic and Furthermore, in fluorescence often the of visible and it useful for the early detection of abiotic and biotic stress et al., and et al., The use of solar-induced chlorophyll based on the Fraunhofer line discrimination principle et al., using et al., for the early detection of in a et al., 2013). As an of this to ecological the of chlorophyll fluorescence to primary has enabled the of in and productivity in response to climatic et al., et al., Although fluorescence imaging is it still from the as other spectral imaging including or under field and unable to the underlying of the The plant as a with the environment and quantifying to biotic a of FB-HTP. The of early and accurate detection of plant and in the field have a The of many biotic including by and can be to detect is is and are this is to for the direct and of in the field et al., and and 2015), and these are et al., and et al., The early detection of plant to their remains the such as visible and infrared and imaging have been for their ability to detect biotic stress with of et al., 2010; and Although the of remains the detection with the and a for the detection of biotic stress in provide a early The of biotic stress is identify the plant phenotypic that are specifically response to biotic However, of abiotic and biotic these water content, thermal and in red-green-blue are among the major phenotypic by biotic and abiotic and can water the leading to to of drought stress and 2013). to identify phenotypic of biotic stress have this by using in with field to In addition to it be to use data from to plant on a ecological scale et al., 2014). These are because of the and of field but the for the and management of current and plant biotic and abiotic stress early that to phenotypic research of the differences between biotic and abiotic stress responses has potential for many are under controlled conditions with a and an ability to abiotic biotic in As an fluorescence used to or et al., et al., et al., et al., et the ability to detect early stages of of by the using fluorescence and thermal research controlled to understand these complex and their phenotypic with focus on in that early stages of can be to translate to early detection systems for plant and high-throughput root phenotyping in the field has been a valuable but for plant In many the and in the early are still state of the they are by modern especially digital images (Fig. et al., root et al., 2015), and et al., are common in which are studied in the but limited information to the root there remains a lack of a of root phenotypes and their genetic and environmental et al., the in of root as it time and sensors that or with The is that are composed of water and which are in the Furthermore, most have structural and other properties, such as water and over These significant for in digital data that can root and to the of are for a root that and by The less from which root can be in such as root of the from a of and information to the of these challenges for root phenotyping be in the there have been advances in sensor image and In the application of that use and field information for the of has for root to based on differences in their (for see et al., 2013). is used to and EM into the which with in of their the across the in a reflectance of the a can be et al., can a which can be used to images or models of root systems et al., 2014). There are to becoming an for field root phenotyping, especially for et al., the between ability and wavelengths can but can less and Furthermore, that have reflectance to such as root temperature and the of also are to influence extensive must be especially for in conditions with root systems have not especially for et al., et al., has not been to be of less which the majority of root for many et al., et al., currently be useful to provide a of and root in but be because many important in which to understand root water content, root variation, and with also of to of the has been used to water in a field et al., as well as root et al., but it has limited that and be to has potential to for by of interest et al., also has been to root in (for see et al., and for in et al., these are only et al., 2013). The of root et al., 2013; and a to direct root detection many between the have to be As and other ability to and quantify biological and processes from highly and data et al., et al., et al., on the limited of presented it that in remains in its with challenges facing the most factor to of is the lack of that offer a to a of biological As the most are by research of agricultural plant and which the of to that are These including large and are not to most the needed of of for these are specific to the research they are not to other field or research This a significant as it the extensive of these types of that is and needed by the it is to which type of be to the of the research that is to be must be easy to use and in the have a and have under field conditions. The to these is to for that are that the of are to contribute to the and of these needed of these because of the extensive research and in to their application of basic but they still have a major in that they are only to phenotype plant of this and there is still a need for that can capitalize on their to to data as well as scale phenotypes and the phenotypes. Although the of the and need to become and the most to that is the remains of is the of data that are and biological meaning be from these data to important become especially relevant with to the large of image data in the Although imaging technologies are to plant biology, they have been in other including and and have to and This is because imaging and sensor data the of information that not be by but is essential for understanding plant biology. This can be by et used high-throughput image to into drought stress an the to 400 phenotypic traits from but more they to traits of physiological that growth as well as traits, not with of this type highlight the potential for high-dimensional data to phenotypes that to further the basic understanding of plant biology. However, as data of this are a healthy must be between phenotypic data from with and traits can be of for biological There are still many that as to becoming an in the plant among these challenges are the of to this and to this information for into plant biology. these in three areas important to the of phenomics, and in are data management and of the environment in which plant phenotypes most application of these data for understanding the mechanisms plant growth and development. in is the ability to and phenotype data from using and The of phenotyping with can be into by and or information to information on ecological or The data and image-processing challenges for these are with in data and data trials of thousands of from a common while or of natural populations of with limited data collection In order to data collection and a of have been from to for and is an of an application to specifically the of plant where data collection is and 2014). In is an application to capitalize on and is used to identify and improved data management and data a and have been to this need and There also is a of to and data in the field the and of data et al., 2014). In order to be these on data collection using and This is to the of they are phenotype data are that they can range from populations to to specific and can be on or the data are in and scale, data and are that present significant challenges (for see et al., In order to be high-dimensional data must be in such a that data can be and to In addition, data collection must be a to to these is the of the information that environmental and et al., which types of be for a to it and a et al., be and used to measure phenotypes be a that et al., 2013). the to has phenotype data and has that not only to phenotypic data under field conditions but also the to and see and then and This information of traits of and for to measure traits, including when the data be that of the phenotypic data can be of where and by they to and maximize the potential to data across or the use of controlled and for and phenotypic is to data using these community in the that many data on them for data However, while mechanisms to based on are to phenotypic are et a of plant phenotypes for species as to by are to with that are in and increased in the of these three of the phenotype be as of the data plant phenotype data as could be used to and In summary, data is for data and plant phenotyping the The as the for plant phenotyping across many species and from of the research of this is to and that in the and of phenotypic data. The a useful to a on the of plant phenotyping and data Although it is well that in to the of the environment to the This understanding of or to in the environment. Furthermore, the effects of environmental such as temperature and water on are environmental conditions influence the and of and stages of and et al., to the of physiological traits, plant developmental and growth there is a need to a understanding of the of interactions. to these complex are the management in agricultural and natural that can have large effects on with their environment. this which is the of the environment in which are et al., to be The of conditions has become commonplace with the of and using data from However, the ability to quantify the environment, including and content, remains the of and electromagnetic which measure to water content, and have useful in and et al., et al., 2013; et al., These are not their and electromagnetic are typically it to the of the In addition, these are for in most natural environmental conditions throughout the of a a of sensors is the for environmental data et a low-cost that sensors for temperature and as well as temperature and the of these they could be throughout the a further the of the of to a could be to data collection in time et al., and sensors and envirotyping also could be in remote for of time with to the natural their in the have into useful for the of management and models are using or data on and are then used to of such as or the models have been used in crop and breeding, but this modeling of physiological processes (for summary, see et al., 2014). In the context of breeding, models have been used for to envirotyping the of environment variables using the model as an that the growth and to the phenotyping the of the or as phenotyping et al., et al., from of is to their of and growth variables with from and understanding of genetic in order to the and the that provide improved in et al., The of environment such as drought that then can be in of phenotypes in one or more trials on a of et al., et al., et al., 2014). this the models need to be of the of one or more with the typically data and an accurate The on model are for envirotyping for other of and use can be to drought or to et al., et al., types of of models are in ecological research to the and of or with a need for and data and and management are the major variables to in agricultural modeling of the environment in is typically more to in species distribution, and of which the and the specific influence on plant phenotyping can be to to of models in to plant These include (1) use of models to which traits to (2) of phenotypes using of plant (3) of plant growth from of data from plant and from plant phenotyping to the physiological basis of models (Fig. the most common use of models to phenotyping has been in with the have been with the especially the of with to the or of types of et al., models are to this as as their of and the for the are the of data types for modeling phenotypes. The the in which environment management and their interaction the phenotype and can be with genomic and crop models to phenotypes. and in with from provide the data needed to genotype-specific crop models that the of plant under environmental conditions. genomic which to genetic with crop models provide the ability to genetic effects and interactions. The of these has the potential to the accuracy of phenotypes. The most use of has been to of that in for of in current or et al., et al., the models used have been well for a range of these models are more and useful in plant phenotyping and the modeling has been by physiological and et that the of effects for root into a that root yield via increased to The and most of this that one of these major across populations and to be highly with in trials over genetic and This physiological understanding of a from model and to traits to measure et al., The current of application in is by the of et a can be between genomic and data to biological to the has been in and more phenomics, significant its and Although early to that be a valuable for the plant the in is and on a the it is that low-cost need to be that more have to the and can and evaluate them the context of their research only this on these to improvement, increased research into data management and to be that accurate can be between further in the of this As with the is often with and but these are healthy of a in areas of plant are to field-based research into a an where and to the of plant biology. progress in with the of data of as to data the These advancements in with phenotyping, a and envirotyping a field and can be evaluated in the context of environmental conditions. this the to developmental the of genetic effects in to the environment and in a not previously on this could these into their to further the understanding of plant and its with the environment, which a of the of in response to climatic conditions. to this be the to with to the of phenotypes in a of including in which have not been This not only in the of but also the of natural populations that could be from climatic variation and their interaction with other There is a need to evaluate the data by high-throughput phenotyping is true biological and that is the of it provide information or phenotypes be more useful by to become the focus of field-based research that can be the context of environmental conditions and The of and envirotyping with high-throughput phenotyping be to understanding the interaction between and their environment. This help the physiological mechanisms for observed phenotypes as well as the of phenotypes. and for on the article and for on field for not of many because of space field-based, high-throughput phenotyping electromagnetic radiation three-dimensional crop growth model
Why it matches plant phenotyping methods植物フェノミクスとフィールド高スループット表現型解析の原理、センサー・画像技術、データ処理、課題を中心に扱うレビューであり、方法論が主題。
abstractthe aim of this Update article is to present the basic principles of phenomics, summarize the current state of field-based phenotyping, and highlight key challenges and limitations.
An accurate estimation of stomatal resistance (rS) also under drought stress conditions is of pivotal importance for any process‐based prediction of transpiration and the energy budget of real crop canopies and quantification of drought stress. A new model for rS was developed and parameterized for winter wheat using data from field experiments accounting for the influences of net radiation (RNₑₜ), air temperature (TAᵢᵣ) and vapour pressure deficit of the atmosphere (VPD) interacting with an average water potential in the rooted soil (ψRₒₒₜₑdSₒᵢₗ). rS is simulated with a limiting factor approach as maximum of the metabolic (related to photosynthesis) and hydraulic (related to drought stress) acting influences assuming that, if drought stress occurs, it will dominate stomatal control: rS = max(rS(TAᵢᵣ), rS(RNₑₜ), rS(VPD, ψRₒₒₜₑdSₒᵢₗ)). This transitional approach is suited to reproduce measured daily time courses of rS with a varying accuracy for the single measurement dates but performed satisfactorily for the whole data set (r² = 0.63, RMSE = 59 s m⁻¹, EF = 0.60). This new semi‐empiric approach calculates rS directly from external environmental conditions. Therefore, it can be easily implemented in existing model frameworks as link between operational crop growth models that use the concept of radiation use efficiency instead of mechanistic photosynthesis modelling and soil–vegetation–atmosphere transport models.
Why it matches plant phenotyping methodsコムギの気孔抵抗という植物生理形質を環境変数から推定する新規モデルを開発し、実測時系列で検証しており、表現型取得・推定法が中心である。
abstractA new model for rS was developed and parameterized for winter wheat
Cell walls are likely to be essential determinants of the amazing strength and flexibility of the guard cells that surround each stomatal pore in plants, but surprisingly little is known about cell wall composition, organization, and dynamics in guard cells. Recent analyses of cell wall organization and stomatal function in the guard cells of Arabidopsis thaliana mutants with defects in cellulose and xyloglucan have allowed for the development of new hypotheses about the relative contributions of these components to guard cell function. Advanced image analysis methods can allow for the automated detection of key structures, such as microtubules (MTs) and Cellulose Synthesis Complexes (CSCs), in guard cells, to help determine their contributions to stomatal function. A major challenge in the mechanical modeling of dynamic biological structures, such as guard cell walls, is to connect nanoscale features (e.g., wall polymers and their molecular interactions) with cell-scale mechanics; this challenge can be addressed by applying multiscale computational modeling that spans multiple spatial scales and physical attributes for cell walls.
Why it matches plant phenotyping methods気孔細胞の構造・機能を対象に、画像解析による構造検出とマルチスケール機械モデルを統合する方法論が中心であり、単なる生物学的測定ではない。
abstractAdvanced image analysis methods can allow for the automated detection of key structures, such as microtubules (MTs) and Cellulose Synthesis Complexes (CSCs), in guard cells
This paper proposes a methodology for plant analysis and identification based on extracting texture features from microscopic images of leaf epidermis. All the experiments were carried out using 32 plant species with 309 epidermal samples captured by an optical microscope coupled to a digital camera. The results of the computational methods using texture features were compared to the conventional approach, where quantitative measurements of stomatal traits (density, length and width) were manually obtained. Epidermis image classification using texture has achieved a success rate of over 96%, while success rate was around 60% for quantitative measurements taken manually. Furthermore, we verified the robustness of our method accounting for natural phenotypic plasticity of stomata, analysing samples from the same species grown in different environments. Texture methods were robust even when considering phenotypic plasticity of stomatal traits with a decrease of 20% in the success rate, as quantitative measurements proved to be fully sensitive with a decrease of 77%. Results from the comparison between the computational approach and the conventional quantitative measurements lead us to discover how computational systems are advantageous and promising in terms of solving problems related to Botany, such as species identification.
Why it matches plant phenotyping methods葉表皮の顕微鏡画像からテクスチャ特徴を抽出し、従来の気孔形質測定と比較・頑健性評価を行う手法開発および検証であり、植物表現型取得が中心である。
abstractThis paper proposes a methodology for plant analysis and identification based on extracting texture features from microscopic images of leaf epidermis.
Abstract. We present a dynamic twin-cuvette system for quantifying the trace-gas exchange fluxes between plants and the atmosphere under controlled temperature, light, and humidity conditions. Compared with a single-cuvette system, the twin-cuvette system is insensitive to disturbing background effects such as wall deposition. In combination with a climate chamber, we can perform flux measurements under constant and controllable environmental conditions. With an Automatic Temperature Regulated Air Humidification System (ATRAHS), we are able to regulate the relative humidity inside both cuvettes between 40 and 90 % with a high precision of 0.3 %. Thus, we could demonstrate that for a cuvette system operated with a high flow rate (> 20 L min−1), a temperature-regulated humidification system such as ATRAHS is an accurate method for air humidification of the flushing air. Furthermore, the fully automatic progressive fill-up of ATRAHS based on a floating valve improved the performance of the entire measurement system and prevented data gaps. Two reactive gas species, ozone (O3) and peroxyacetyl nitrate (PAN), were used to demonstrate the quality and performance of the twin-cuvette system. O3 and PAN exchange with Quercus ilex was investigated over a 14 day measurement period under controlled climate chamber conditions. By using O3 mixing ratios between 32 and 105 ppb and PAN mixing ratios between 100 and 350 ppt, a linear dependency of the O3 flux as well as the PAN flux in relation to its ambient mixing ratio could be observed. At relative humidity (RH) of 40 %, the deposition velocity ratio of O3 and PAN was determined to be 0.45. At that humidity, the deposition of O3 to the plant leaves was found to be only controlled by the leaf stomata. For PAN, an additional resistance inhibited the uptake of PAN by the leaves. Furthermore, the formation of water films on the leaf surface of plants inside the chamber could be continuously tracked with our custom built leaf wetness sensors. Using this modified leaf wetness sensor measuring the electrical surface conductance on the leaves, an exponential relationship between the ambient humidity and the electrical surface conductance could be determined.
Why it matches plant phenotyping methods植物葉とのガス交換フラックスと葉面湿潤を定量する双キュベットおよびセンサー系の開発・性能実証が研究の中心であり、植物の生理状態を測定する方法論的研究である。
abstractWe present a dynamic twin-cuvette system for quantifying the trace-gas exchange fluxes between plants and the atmosphere under controlled temperature, light, and humidity conditions.