Plant phenotyping methods.

Monitoring phases of plant stress in juvenile commercial forest cuttings using contemporary nursery sensor technologies →

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.

AI-enabled simultaneous phenotyping of leaf vein and stomatal traits uncovers independent genetic control in maize →

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

Genetic markers of stomatal cluster development in Begoniaceae revealed through trait analysis assisted by interactive deep-learning. →

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.

Foliar Micromorphological Variability in Tea Landraces from Fuzhou: Insights from Scanning Electron Microscopy. →

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.

Evaluation of RGB-Derived Indices for Cotton Leaf Phenotyping under a Standardized Imaging Setup →

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.

DFA-YOLO: an enhanced YOLOv11-OBB and knowledge distillation-based maize stomata detection system. →

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.

From Sensing to Action: A Leaf Humidity–Triggered Closed Loop System for Precision Salicylic Acid Delivery to Mitigate Plant Stress →

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.

Automated phenotyping of soybean stomatal responses to water deficit using YOLOv8 →

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.

Defense mechanisms of kiwifruit against Corynespora cassiicola and predictive model for estimating resistance levels. →

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

EpiReasoner: An Integrated Artificial Intelligence Framework for Phenotype-to-Genotype Reasoning in Plant Epidermal Development →

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.

Stomatal setpoints and environmental responsiveness are sculpted by developmental trajectories →

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.

Technological advances in imaging and modelling of leaf structural traits: a review of heat stress in wheat →

Abiotic stresses such as heat waves significantly reduce wheat productivity by altering leaf anatomy and physiology, leading to reduced photosynthetic carbon assimilation and crop yield. Despite the advancement in various imaging technologies at the field, canopy, plant, tissue, cellular, and subcellular levels, phenotyping of imaging-based leaf structural traits (e.g. vein density, stomatal density, and stomatal aperture) for abiotic stresses is still time-consuming and expensive without the aid of artificial intelligence (AI) and machine learning (ML). This review consolidates current knowledge of wheat leaf structural and functional adaptations to heat stress and highlights key advancements in imaging technologies for studying these important phenotypic traits. Recent high-resolution, non-destructive imaging technologies, including confocal laser scanning microscopy, X-ray computed tomography, and optical coherence tomography, have enabled in vivo visualization of plants. Integrating these imaging techniques with AI/ML facilitates high-throughput phenotyping and the modelling of stress responses. We emphasize the potential for future research to leverage these technological advancements in imaging and AI, combining imaging data with physiological and multi-omics studies to deepen the understanding of plant heat tolerance mechanisms. Such multidisciplinary integration in leaf structure phenotyping will accelerate the development of resilient wheat varieties, offering critical insights for crop improvement in the face of climate change.

Why it matches plant phenotyping methods植物の葉構造・機能形質を対象とする画像計測技術とAI/MLによる表現型解析を中心に整理したレビューであり、植物フェノタイピング手法レビューに該当する。

abstractThis review consolidates current knowledge of wheat leaf structural and functional adaptations to heat stress and highlights key advancements in imaging technologies for studying these important phenotypic traits.

Non‐Invasive Estimation of Short‐Term Changes of Transpiration Using a Combination of 3D Imaging and Energy Balance Modelling →

Conventional approaches to measuring stomatal conductance (gs) and transpiration often rely on instruments that interfere with plant physiology. Porometers, for example, restrict natural leaf movement, apply pressure, and introduce dry airflow that can alter stomatal behaviour, thereby reducing the relevance of such measurements. Prior studies report discrepancies among devices attributable to such interferences (Toro et al. 2019). To minimise artefacts, transpiration should be estimated remotely without physical contact, which theoretically can be achieved via a thermal leaf energy-balance approach that infers gs from leaf temperature, radiative load, and boundary-layer terms. In this study, we combine 3D plant models, light interception models, and thermal imaging to solve the energy-balance equation of individual leaves, estimating transpiration entirely remotely. Approaches to estimate stomatal conductance based on the energy-balance equation were developed recently to aid phenotyping of plantss. Most methods either imposed rapid changes in air humidity to perturb transpiration and, consequently, leaf temperature (Driever et al. 2023), or relied on ‘dry’ and ‘wet’ reference surfaces (as in Leinonen et al. 2006) to compute stress indices (Vialet-Chabrand and Lawson 2020). These methods require reference materials to assess surface temperatures under maximum and zero transpiration, showing the effect of longwave radiation. However, reference-material methods were constrained by heterogeneity in light interception caused by variation in leaf angle and orientation, because reference surfaces could not reorient like real leaves (Zhang et al. 2025). In this study, we addressed this challenge by using thermal imaging and 3D photogrammetry to capture leaf temperature and geometry noninvasively, allowing parameter estimation for each leaf individually. Here, ρ is the density of air (kg m−3), cp is the specific heat capacity of air (J kg−1 K−1) and rHR is the parallel resistance to heat and radiative transfer on the leaf surface (s m−1), s is the slope of the curve relating saturating water vapour pressure to temperature (Pa °C−1). TL and TA are leaf and air temperatures (°C), respectively, δe is air vapour pressure deficit (Pa), γ is the psychrometric constant (Pa K−1) and rva is the boundary layer resistance to water vapour (s m−1) (Supporting Information S2: Equation S1). The net radiative energy Rn in the energy-balance term was obtained from the same 3D light interception model, which integrates measured direct and lateral scattered irradiance (W m−2) (Supplement Material and Methods, File S2). Stomatal conductance gs (m s−1) is the inverse of stomatal resistance rs (s m−1). To experimentally obtain a wide range of gs values, we grew eggplant (Solanum melongena L.) plants in hydroponic units in growth chambers under four sets of environmental conditions (Table 1). Thirty-day-old plants (4–5-leaf stage) were placed on balances (Supplementary Materials and Methods, File S2). Units were sealed with plastic film to minimise evaporation. Mass loss attributable to transpiration was logged automatically every 30 s. To induce short-term changes in stomatal conductance, we imposed an acute osmotic stress by delivering a saline NaCl solution with high electrical conductivity (60 mS cm−1) to the root zone, producing a steep drop in root osmotic potential. This created rapid physiological and morphological responses that altered incident irradiance at the leaves, leaf temperature, and consequently energy balance, stomatal conductance and transpiration. We chose this stressor for operational simplicity. Any perturbation that modifies transpiration dynamics and thus gas exchange could have served our purpose. The total transpiration of a leaf, Et (kg s−1), is the product of the total conductance to water vapour from the mesophyll to the atmosphere, gv (m s−1), calculated from the estimated stomatal resistance rs (s m−1) and the boundary layer conductance gva (m s−1), the difference between water vapour concentration inside the leaf Cvs (dimensionless), and in surrounding air Cva (dimensionless), the leaf area A (m2), and the density of water ρw (kg/m3) (Jones 1992). Estimated stomatal conductance was obtained from leaf energy balance calculation (Equation 1). Boundary-layer conductance was computed from measured wind speed and leaf dimensions (leaf area, length, width) extracted from structure-from-motion 3D reconstructions (Supporting Information S1: Equation S6; Grace et al. 1980). Transpiration was then calculated for each leaf at each thermal 3D imaging time point, and whole-plant transpiration for comparison with gravimetric logs was the sum of all per-leaf estimates. As a non-invasive approach, we evaluated the plausibility or our model derived stomatal conductance (Equation 2) indirectly by comparing calculated and measured whole plant transpiration. We emphasise that this is not a direct validation of gs. Rather, the close agreement between modelled and measured transpiration across the wide range of environmental treatments, both stressed and non-stressed, provides confidence that the inferred gs is realistic. RGB and thermal images acquired before, during, and after stress application enabled dynamic tracking of leaf position and temperature (Supplementary Material and Methods, File S2). As expected, osmotic stress application had immediate effects on morphology and physiology. While control leaves maintained an angle of around 110° throughout, osmotic shock induced immediate turgor loss and drooping in all environments except one (Figure 1A,B). Leaf angles recovered to pre-stress positions within 1 h, indicating adaptation to the osmotic shock and restoration of turgor. Only environment 4 (high light, low air temperature and low humidity) maintained turgor during stress. Angle shifts were most pronounced in older leaves, which drooped and reduced light interception; younger leaves better preserved structure and turgor (Supporting Information S1: Figure S2). These angle changes also altered incident irradiance at the leaf surface (Supporting Information S1: Figure S3). These morphological responses coincided with increases in leaf temperature, consistent with altered water fluxes and stomatal regulation after stress. Across environments, plants showed a uniform rise in leaf temperature following osmotic stress, regardless of initial temperature (Supporting Information S1: Figure S4). This response held across leaf ages, encompassing older (Figure 1C) and younger (Figure 1D) leaves. Stomatal conductance estimated with our method followed the same pattern, dropping rapidly after osmotic shock in both older (Figure 1E) and younger (Figure 1F) leaves (Supporting Information S1: Figure S5). We estimated no stomatal conductance recovery to pre-stress conditions over the time course of stress exposure. Model-estimated and gravimetrically measured transpiration showed identical time courses across all four environmental conditions (Figure 1G–J). Transpiration rates did not recover to the same extent as leaf turgor, indicating long-term effects of the osmotic shock. Across environments and time points, correlation between model estimated and measured whole-plant transpiration was high (Figure 1K). In this study, stomatal conductance (gs) is a model-derived quantity inferred from the same physically constrained framework and model (leaf temperature, boundary-layer conductance and vapour pressure deficit). Since we did not measure gs directly, we cannot validate gs directly. Instead, we used a non-invasive check via transpiration. Model predictions closely tracked measured transpiration across the four controlled environments. This agreement increases confidence that the inferred gs is realistic, while we acknowledge that transpiration agreement alone is not a rigorous validation and cannot fully rule out compensating errors. Our study demonstrated the potential of our approach to estimate transpiration accurately by combining 3D imaging and thermography with physiological modelling without the use of reference materials that imitate real leaves. This remote approach enables simultaneous assessment of morphological and physiological responses to stress, yielding a more integrated view on plant transpiration and gas exchange. In contrast to chamber and porometer measurements or IR methods requiring wet and dry references or calibration plates, our workflow is reference-free. Absorbed shortwave radiation is derived from measured irradiance and a 3D reconstruction of leaf geometry, with no external reference materials. Moreover, remote measurements avoid continuous pressure from clamp-on porometers, permitting long-term observation and capture of rapid stress responses without sustained damage or microclimate artifacts. Further, the approach is not limited by any clamp on sensors and as such enables multi-leaf tracking. Applied to crop canopies, this approach could improve understanding of canopy processes that influence productivity and enable remote estimation of canopy transpiration. Future research could further improve by replacing our strong saline solution stress by gradual soil drying to depict a more realistic and natural stress while testing the approach under long-term conditions. Recent studies indicate that, with rising atmospheric CO2 concentrations, breeding for reduced stomatal conductance could increases WUE without affecting photosynthetic capacity (Srivastava et al. 2024). As such, remote systems for high-throughput plant phenotyping (HTP) are required to scan vast quantities of plants. We see a potential use of our system for such purposes to quickly estimated whole plant and individual leaf transpiration, as initial image capturing is very fast. A large bottleneck in our work was 3D model generation speed and manual extraction of leaf parameters from these 3D models. Both could be streamlined with more automated software, possibly including neural network solutions. The authors have nothing to report. The authors declare no conflict of interest. The data that support the findings of this study are available from the corresponding author upon reasonable request. Please note: The publisher is not responsible for the content or functionality of any supporting information supplied by the authors. Any queries (other than missing content) should be directed to the corresponding author for the article.

Why it matches plant phenotyping methods3D画像、熱画像、光遮断モデル、エネルギーバランスモデルを統合し、葉ごとの蒸散と気孔コンダクタンスを非侵襲的に推定する手法を開発・評価しており、植物表現型取得が研究の中心である。

abstractIn this study, we combine 3D plant models, light interception models, and thermal imaging to solve the energy-balance equation of individual leaves, estimating transpiration entirely remotely.

Characterizing plant hydraulic behaviour under drought stress using vegetation modelling →

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

Simulation of citrus foliar gas exchange across diverse meteorological conditions: application of the optimal stomatal regulation method. →

Introduction The optimal stomatal regulation theory provides an eco-evolutionary framework for interpreting the trade-off between CO 2 uptake and water loss. This theory postulates that the marginal water cost of carbon gain ( λ=∂E/∂A ) remains approximately constant over short timescales, thereby offering a mechanistic basis for predicting stomatal behavior and gas exchange. Methods In this study, leaf-level meteorological variables and gas exchange parameters of orchard citrus were measured throughout the entire phenological period during 2021-2022. We developed a family of optimal stomatal conductance-based models (OSCMs), comprising six forms: Rubisco-limited forms (OSCvc and OSCvcd), RuBP-regeneration-limited forms (OSCvj and OSCvjd), and combined forms that dynamically select the prevailing biochemical limitation (OSC and OSCd). Results The key parameter λ was estimated daily and averaged over the entire phenological period. Using daily λ inputs, the three models produced stomatal conductance ( g s ) with accuracies ranked as OSCvjd (R 2 = 0.73) > OSCd (0.63) > OSCvcd (0.40). When a long-term constant λ was applied, model performance declined with accuracies ranked as OSCvj (0.66) > OSC (0.52) > OSCvc (0.38). Discussion The OSC model also produced intercellular CO 2 concentration ( c i ) and photosynthesis ( A ) reasonably well (R 2 = 0.78 and 0.48, respectively). Under moderate meteorological conditions (air temperature 30-40 °C and vapor pressure deficit 1-2 kPa), the OSC model showed its best performance with a mean absolute relative error of 35.2% for g s estimation. Overall, the OSCMs provided a mechanistic approach to simulate citrus leaf gas exchange requiring minimal species-specific traits and routine meteorological inputs. This modeling strategy supports rapid assessment of plant physiological status and estimation of foliar carbon-water fluxes in orchard management under subtropical climates.

Why it matches plant phenotyping methods柑橘葉のガス交換・気孔コンダクタンスを推定するモデル群を開発し、実測値との精度比較で検証しており、植物生理形質の取得・推定法が研究の中心である。

abstractWe developed a family of optimal stomatal conductance-based models (OSCMs), comprising six forms

Spatial analysis of cell patterning to aid genetic and phenotypic understanding of grass stomatal density: A case study in maize →

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

Multimodal Dissection of UV‐B–Induced Plant Defense Against Insect in Tea Plants →

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

Deep Learning-Based Classification of Water Stress in Maize Using Biospeckle Activity Maps →

Biospeckle imaging enables non-destructive observation of dynamic physiological activity in plant tissues; however, the relative sensitivity of different biospeckle activity maps to water stress and their implications for data-driven classification remain insufficiently understood. This study systematically evaluates multiple biospeckle activity mapping approaches for water stress analysis in maize (Zea mays L.) leaves and examines how their characteristics influence deep learning–based classification performance. Maize plants were subjected to three irrigation levels (0%, 50%, and 100%) over a 7-day experimental period. Stomatal conductance was measured as an independent physiological reference, and a microfluidic phantom experiment was conducted to verify the physical response behavior of the biospeckle imaging system. Temporal variations in biospeckle activity were statistically analyzed, followed by deep learning–based classification using representative two-dimensional convolutional neural network models. Statistical analysis revealed that biospeckle activity exhibited stress-dependent responses, with severe water stress (0%) being consistently distinguishable, whereas moderate and well-watered conditions (50% and 100%) showed partially overlapping patterns. These trends were consistent with stomatal conductance measurements. Deep learning models trained on different biospeckle activity maps achieved classification accuracies of up to 0.73 and macro-averaged F1 scores of 0.73, with notable differences in performance depending on the selected activity representation. These results suggest that while traditional statistical parameters show limited linearity, the proposed deep learning-based biospeckle analysis could serve as a useful tool for water stress classification. By capturing complex spatial-texture features, this study presents a potential data-driven approach for precision plant phenotyping.

Why it matches plant phenotyping methods植物の水ストレス状態を推定するバイオスペックル画像マッピングと深層学習分類を系統的に評価し、独立した生理指標およびファントム実験で検証しているため、フェノタイピング手法が中心です。

abstractThis study systematically evaluates multiple biospeckle activity mapping approaches for water stress analysis in maize (Zea mays L.) leaves and examines how their characteristics influence deep learning–based classification performance.

Disentangling within season sources of variation for field-level phenotyping of grapevine →

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.

Seasonal dynamics and sun/shade heterogeneity of leaf gas exchange and VOC emissions inside a tall temperate forest canopy →

Leaf gas exchange is the key driver of forest carbon uptake and directly determines forest carbon sink activity. Additionally, plants release a variety of biogenic volatile organic compounds (VOCs) acting as stress signals of trees. However, continuous hourly resolved measurements of leaf gas exchange and VOC emissions in tall tree canopies are challenging and remain scarce. To this end, we developed a sophisticated in-situ leaf gas exchange measurement system with 24 cuvettes deployed on mature Fagus sylvatica (n=3) and Pseudotsuga menziesii (n=3) individuals in a mixed temperate forest. We additionally measured sap flux density (Js), radial growth and tree water deficit (TWD) to gain a holistic picture of seasonal leaf and stem water and carbon flux dynamics during the summer of 2024. During midsummer, we found a gradual reduction of stomatal conductance (gs) and VOC emissions of sun, but not shade branchlets of P. menziesii in response to moderate atmospheric and edaphic drying. Decreased gs led to a downregulation of transpiration (E), Js, and carbon isotope discrimination accompanied by an increase in TWD and intrinsic water used efficiency. Leaf gas exchange of shade branchlets remained unaffected due to microclimatic buffering effects. Contrarily, sun leaves of F. sylvatica, profited from sunny midsummer conditions and increased leaf gas exchange, whereas shade leaves benefitted from more diffuse light during early summer exhibiting similar carbon assimilation, transpiration and VOC emissions as sun leaves. For both species we found a clear time lag of four to five hours between maximum leaf and stem water fluxes and a delay of up to 20 hours for the recovery of TWD, highlighting the role of stem water reserves. Pronounced seasonal and diurnal differences of leaf gas exchange, stem water fluxes and VOC emissions showed, that continuous data are essential to better understand variability of ecosystem flux dynamics.

Why it matches plant phenotyping methods樹木葉のガス交換を連続測定する24チャンバーのin situ測定システムを開発し、植物の生理形質・状態を取得する方法が研究の中心であるため。

abstractwe developed a sophisticated in-situ leaf gas exchange measurement system with 24 cuvettes deployed on mature Fagus sylvatica (n=3) and Pseudotsuga menziesii (n=3) individuals in a mixed temperate forest.

WheatAI v1.0: An AI-Powered High Throughput Wheat Phenotyping Platform →

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.

StomaQuant: Deep Learning-Based Quantification for Stomatal Trait Assessment →

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

Automated calibration of stomatal conductance models from thermal imagery by leveraging synthetic images generated from Helios 3D biophysical model simulations →

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

High-resolution microCT reveals relationships between stomata and interior leaf anatomy in Sorghum →

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

Linking stomatal function with photosynthetic light reactions and stress response in faba bean →

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.

A Psychrometric Temperature Correction for the Positive Bias Observed in Stomatal Conductance Measured by the Open Flow-Through LI-600 Porometer. →

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.

A user-friendly machine-learning program to quantify stomatal features from fluorescence images →

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.

A stomata imaging and segmentation pipeline incorporating generative AI to reduce dependency on manual groundtruthing. →

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.

Advanced phenotyping features utilizing deep learning techniques for automated analysis of stomatal guard cell orientation. →

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.

Estimating short-term changes of stomatal conductance using a combination of 3D imaging and energy balance modelling →

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.

WSF: A Transformer-Based Framework for Microphenotyping and Genetic Analyzing of Wheat Stomatal Traits. →

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.

Stoma Detection in Soybean Leaves and Rust Resistance Analysis. →

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)

The dynamics of stomatal closure of Arabidopsis thaliana determined by terahertz spectroscopy and a water transport model. →

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

Multimodal Dissection of UV-B--Induced Plant Defense →

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

Smarter stomata: emergent technologies unlocking yield potential in a changing climate. →

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.

Scalable Methods for Fruit Shape and Stomatal Phenotyping in Southern Highbush Blueberry →

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

Topological equivalence of stomata distribution patterns across vascular plants →

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.

A plant-insertable multi-enzyme biosensor for the real-time monitoring of stomatal sucrose uptake. →

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

Comparison of multiple plant sensors aimed at early detection of drought stress in the greenhouse →

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.

Historical plant collections provide unique insight into the long-term and in-situ physiological responses of pants to global environmental change →

Global environmental change has severe impacts on plants and ecosystems. Anthropogenic carbon emissions, for example, lead to elevated CO 2 (eCO 2 ) in the atmosphere which can stimulate photosynthesis (A n ) and stomatal conductance (g s ) with impacts for carbon, water and nutrient pools and fluxes in terrestrial ecosystems. In fact, approximately 25% of the annual anthropogenic CO 2 emissions are taken up and stored in the biosphere which slows down the growth of CO 2 in the atmosphere and dampens climate change. The stimulation of A n and g s by eCO 2 is thought to critically contribute to this carbon uptake. If eCO 2 will continue to stimulate A n and g s and ecosystem carbon uptake in the future is, however, unclear. This is because important questions regarding the effects of eCO 2 on A n and g s and how these effects are influenced by different plant species or different environmental agents such as nutrient and water availability are unresolved. Experiments are often too short-lived to resolve the complexity of interactions by which eCO 2 affects A n and g s in natural ecosystems. Also, monitoring programs are often not sufficiently long-term to capture in-situ responses of plants to rising atmospheric CO 2 . New and innovative tools are therefore needed to understand how eCO 2 and other global change drivers impact A n and g s in plants and to resolve with this a key uncertainty in the coupled carbon-climate system. The analysis of archived plant material, e.g. in herbarium collections, offers an exciting new opportunitiy to complement experiments and long-term monitoring programmes to reconstruct the long-term in-situ physiological responses of plants to environmental change. In my presentation, we will introduce a new approach that allows for the first time the quantitative reconstruction of A n and g s from the carbon isotope composition and nitrogen content per unit leaf area in archived plant material. I will show how we have applied this new approach to 3000 plant samples from the Herbaria Basel that have been collected across Switzerland from 1850 to today to infer for the long-term in-situ physiological responses of plants to global environmental change. Our data indicate a uniform 20% increase of A n between 1850 and today and a small, yet steady decline in g s . Most interestingly we found very little differences in these responses among different plant functional types or among plants originating from different habitats (wet - dry or nutrient poor - nutrient rich), suggesting a uniform in-situ physiological response of plants to eCO 2 . Our data contributes a new approach to assess the long-term physiological responses of plants to global environmental change and has important implications for modelling past and future carbon and water relations in terrestrial ecosystems.

Why it matches plant phenotyping methodsアーカイブ植物試料から炭素同位体組成と葉面積当たり窒素量を用いて光合成速度と気孔コンダクタンスを定量推定する新手法を開発し、大規模試料へ適用しているため、植物フェノタイピング手法が中心である。

abstractwe will introduce a new approach that allows for the first time the quantitative reconstruction of A n and g s from the carbon isotope composition and nitrogen content per unit leaf area in archived plant material.

Spatial analysis of cell patterning to aid genetic and phenotypic understanding of grass stomatal density: a case study in maize →

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

Analysis of Irrigation, Crop Growth and Physiological Information in Substrate Cultivation Using an Intelligent Weighing System →

The online dynamic collection of irrigation and plant physiological information is crucial for the precise irrigation management of nutrient solutions and efficient crop cultivation in vegetable soilless substrate cultivation facilities. In this study, an intelligent weighing system was installed in a tomato substrate cultivation greenhouse. The monitored values from the intelligent weighing system’s pressure-type module were used to calculate irrigation start–stop times, frequency, volume, drainage volume, drainage rate, evapotranspiration, evapotranspiration rate, and stomatal conductance. In contrast, the monitored values of the suspension-type weighing module were used to calculate the amount of weight change in the plants, which supported the dynamic and quantitative characterization of substrate cultivation irrigation and crop growth based on an intelligent weighing system. The results showed that the monitoring curves of pressure and flow sensors based on the pressure-type module could accurately identify the irrigation start time and number of irrigations and calculate the irrigation volume, drainage volume, and drainage rate. The calculated irrigation amount was closely aligned with that determined by an integrated-water–fertilizer automatic control system (R2 = 0.923; mean absolute error (MAE) = 0.105 mL; root-mean-square error (RMSE) = 0.132 mL). Furthermore, transpiration rate and leaf stomatal conductance were obtained through inversion, and the R2, MAE, and RMSE of the extinction coefficient correction model were 0.820, 0.014 mol·m−2·s−1, and 0.017 mol·m−2·s−1, respectively. Compared to traditional estimation methods, the MAE and RMSE decreased by 12.5% and 15.0%, respectively. The measured values of fruit picking and leaf stripping linearly fitted with the calculated values of the suspended weighing module, and R2, MAE, and RMSE were 0.958, 0.145 g, and 0.143 g, respectively. This indicated that data collection based on the suspension-type weighing module could allow for a dynamic analysis of plant weight changes and fruit yield. In summary, the intelligent weighing system could accurately analyze irrigation information and crop growth physiological indicators under the practical application conditions of facility vegetable substrate cultivation, providing technical support for the precise management of nutrient solutions.

Why it matches plant phenotyping methodsインテリジェント計量システムを用いて植物重量変化、蒸散、気孔コンダクタンス、果実収量などの形質を取得・推定し、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。

abstractFurthermore, transpiration rate and leaf stomatal conductance were obtained through inversion

PlantRing: A high-throughput wearable sensor system for decoding plant growth, water relations, and innovating irrigation →

The integration of flexible electronics with plant science has generated various plant-wearable sensors, yet challenges persist in their application to real-world agriculture, particularly in high-throughput settings. Overcoming the trade-off between sensing sensitivity and range, adapting sensors to a wide range of crop types, and bridging the gap between sensor measurements and biological understandings remain primary obstacles. Here, we introduce PlantRing, an innovative, nano-flexible sensing system designed to address these challenges. PlantRing employs bio-sourced carbonized silk georgette as the strain-sensing material, offering an exceptional detection limit (0.03%–0.17% strain, depending on sensor model), high stretchability (tensile strain up to 100%), and remarkable durability (season-long use). PlantRing effectively monitors plant growth and water status by measuring organ circumference dynamics, performing reliably under harsh conditions, and adapting to a wide range of plant species. Applying PlantRing to study fruit cracking in tomato and watermelon has revealed a novel hydraulic mechanism characterized by genotype-specific excess sap flow within the plant to fruiting branches. Its high-throughput application has enabled large-scale quantification of stomatal sensitivity to soil drought—a long-standing aspiration in plant biology—facilitating the selection of drought-tolerant germplasm. Combining PlantRing with a soybean mutant has led to the discovery of a potential novel function of the circadian clock gene GmLNK2 in stomatal regulation. More practically, integrating PlantRing into feedback irrigation achieves simultaneous water conservation and quality improvement, signifying a paradigm shift from reliance on experience or environmental cues to plant-based feedback control. Collectively, PlantRing represents a groundbreaking tool poised to revolutionize botanical studies, agriculture, and forestry.

Why it matches plant phenotyping methods植物器官周径を測定するウェアラブル高スループットセンサーを開発し、成長・水分状態・気孔感度などの表現型を定量化する方法が研究の中心である。

abstractHere, we introduce PlantRing, an innovative, nano-flexible sensing system designed to address these challenges.

Predicting stomatal conductance of chili peppers using TPE-optimized LightGBM and SHAP feature analysis based on UAVs’ hyperspectral, thermal infrared imagery, and meteorological data →

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.

Automatic stomatal phenotyping of lettuce leaves for plant factory: An improved U-network approach →

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.

Pathogen-specific stomatal responses in cacao leaves to Phytophthora megakarya and Rhizoctonia solani. →

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.

PlantRing: A high-throughput wearable sensor system for decoding plant growth, water relations, and innovating irrigation. →

The integration of flexible electronics with plant science has generated various plant-wearable sensors, yet challenges persist in their application to real-world agriculture, particularly in high-throughput settings. Overcoming the trade-off between sensing sensitivity and range, adapting sensors to a wide range of crop types, and bridging the gap between sensor measurements and biological understandings remain primary obstacles. Here, we introduce PlantRing, an innovative, nano-flexible sensing system designed to address these challenges. PlantRing employs bio-sourced carbonized silk georgette as the strain-sensing material, offering an exceptional detection limit (0.03%-0.17% strain, depending on sensor model), high stretchability (tensile strain up to 100%), and remarkable durability (season-long use). PlantRing effectively monitors plant growth and water status by measuring organ circumference dynamics, performing reliably under harsh conditions, and adapting to a wide range of plant species. Applying PlantRing to study fruit cracking in tomato and watermelon has revealed a novel hydraulic mechanism characterized by genotype-specific excess sap flow within the plant to fruiting branches. Its high-throughput application has enabled large-scale quantification of stomatal sensitivity to soil drought-a long-standing aspiration in plant biology-facilitating the selection of drought-tolerant germplasm. Combining PlantRing with a soybean mutant has led to the discovery of a potential novel function of the circadian clock gene GmLNK2 in stomatal regulation. More practically, integrating PlantRing into feedback irrigation achieves simultaneous water conservation and quality improvement, signifying a paradigm shift from reliance on experience or environmental cues to plant-based feedback control. Collectively, PlantRing represents a groundbreaking tool poised to revolutionize botanical studies, agriculture, and forestry.

Why it matches plant phenotyping methodsPlantRingは植物器官の周径変化を測定し、成長・水分状態・気孔感度などの表現型を高スループットに取得するウェアラブルセンサーシステムであり、センサー開発と実証が研究の中心です。

abstractHere, we introduce PlantRing, an innovative, nano-flexible sensing system designed to address these challenges.

Insights into chestnut (Castanea spp.) graft incompatibility through the monitoring of chemical and physiological parameters. →

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.

Early Detection of Water Stress in Kauri Seedlings Using Multitemporal Hyperspectral Indices and Inverted Plant Traits →

Global climate variability is projected to result in more frequent and severe droughts, which can have adverse effects on New Zealand’s endemic tree species such as the iconic kauri (Agathis australis). Several studies have investigated the physiological response of kauri to medium- and long-term water stress; however, no research has used hyperspectral technology for the early detection and characterization of water stress in this species. In this study, physiological (stomatal conductance (gs), assimilation rate (A), equivalent water thickness (EWT)) and leaf-level hyperspectral measurements were recorded over a ten-week period on 100 potted kauri seedlings subjected to control (well-watered) and drought treatments. In addition, plant functional traits (PTs) were retrieved from spectral reflectance data via inversion of the PROSPECT-D radiative transfer model. These data were used to (i) identify key PTs and narrow-band hyperspectral indices (NBHIs) associated with the expression of water stress and (ii) develop classification models based on single-date and multitemporal datasets for the early detection of water stress. A significant decline in soil water content and physiological responses (gs and A) occurred among the trees in the drought treatment in weeks 2 and 4, respectively. Although no significant treatment differences (p > 0.05) were observed in EWT across the whole duration of the experiment, lower mean values in the drought treatment were apparent from week 4 onwards. In contrast, several spectral bands and NBHIs exhibited significant differences the week after water was withheld. The number and category of significant NBHIs varied up to week 4, after which a substantial increase in the number of significant indices was observed until week 10. However, despite this increase, the single-date models did not show good model performance (F1 score > 0.70) until weeks 9 and 10. In contrast, when multitemporal datasets were used, the classification performance ranged from good to outstanding from weeks 2 to 10. This improvement was largely due to the enhanced temporal and feature representation in the multitemporal models. Among the input NBHIs, water indices emerged as the most important predictors, followed by photochemical indices. Furthermore, a comparison of inverted and measured EWT showed good correspondence (mean absolute percentage error (MAPE) = 8.49%, root mean squared error (RMSE) = 0.0026 g/cm2), highlighting the potential use of radiative transfer modelling for high-throughput drought monitoring. Future research is recommended to scale these measurements to the canopy level, which could prove valuable in detecting and characterizing drought stress at a larger scale.

Why it matches plant phenotyping methodsハイパースペクトル計測とPROSPECT-D逆解析による植物機能形質の推定、および水ストレス早期検出モデルの開発・評価が研究の中心である。

abstractleaf-level hyperspectral measurements were recorded over a ten-week period

Application of cryo-FIB-SEM for investigating ultrastructure in guard cells of higher plants →

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.

Analysis of stomatal characteristics of maize hybrids and their parental inbred lines during critical reproductive periods. →

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.

Navigating Challenges in Interpreting Plant Physiology Responses through Gas Exchange Results in Stressed Plants →

Review Navigating Challenges in Interpreting Plant Physiology Responses through Gas Exchange Results in Stressed Plants Diego A. Márquez *, Anna Gardner and Florian A. Busch School of Biosciences and Birmingham Institute of Forest Research, University of Birmingham, Edgbaston, Birmingham B15 2TT, UK * Correspondence: d.a.marquez@bham.ac.uk Received: 14 November 2024; Revised: 20 December 2024; Accepted: 27 December 2024; Published: 13 January 2025 Abstract: This paper explores the challenges that arise when performing and interpreting leaf gas exchange measurements in plants subjected to abiotic stress. It highlights how factors such as cuticular fluxes, stomatal closure, and common assumptions about gas exchange can lead to errors, especially under stress conditions. Key phenomena such as substomatal cavity unsaturation and stomatal patchiness during water stress are discussed in detail, as they significantly complicate the calculation of gas exchange parameters under stress. The paper also addresses the importance of other factors, including steady-state conditions, the differences between adaxial and abaxial surface responses, and boundary layer effects, all of which play critical roles in influencing the accuracy of measurements. Important physiological indicators—such as intrinsic water-use efficiency, minimum leaf conductance, substomatal CO2 concentration, and mesophyll conductance—are analysed in the context of how stress-induced discrepancies in data often result from measurement artefacts rather than true physiological differences. To address these challenges, the paper outlines practical approaches to improving measurement accuracy, offering insights on standardising experimental conditions and minimising errors. By recognising these issues, gaps in current knowledge are identified, providing a comprehensive overview of the challenges in interpreting leaf gas exchange data under stress conditions and suggesting areas for further study.

Why it matches plant phenotyping methods植物の葉ガス交換測定における誤差要因、解釈上の課題、精度改善と標準化を中心に扱う方法論レビューであり、植物生理状態の取得・評価法が中核である。

abstractThis paper explores the challenges that arise when performing and interpreting leaf gas exchange measurements in plants subjected to abiotic stress.

Integrating phenotyping and modelling approaches StomaGAN: improving image-based analysis of stomata through generative adversarial networks →

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.

Comparative analysis of stomatal pore instance segmentation: Mask R-CNN vs. YOLOv8 on Phenomics Stomatal dataset. →

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.

PlantRing: A high-throughput wearable sensor system for decoding plant growth, water relations and innovating irrigation →

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.

Detection of fusarium wilt-induced physiological impairment in strawberry plants using hyperspectral imaging and machine learning →

Strawberry (Fragraria x ananassa) is a crop affected by various soil-borne fungal pathogens with mostly non-specific foliar symptoms and often requiring laboratory isolation for correct diagnosis. Moreover, these nonspecific foliar symptoms, appreciated by the human eye, appear after some time following infection by the pathogen. Early detection of plant diseases is one of the primary objectives in agriculture because it may contribute to identifying more tolerant cultivars in breeding programs and optimise pesticide use in agricultural production with earlier applications in emerging disease foci. New technologies, such as remote sensing and machine learning (ML) algorithms, have arisen as potential tools to improve the ability to detect and classify different crop diseases. The combined use of hyperspectral imagery and ML algorithms were investigated to detect and classify the physiological stress caused by early infections of Fusarium wilt in strawberry plants. Six ML models, namely artificial neural network, decision tree, K-nearest neighbour, support vector machine, multinomial logistic regression and Naïve Bayes were developed to estimate physiological stress associated with Fusarium wilt disease. The results showed that stomatal conductance (gₛ) and photosynthesis (A) declined even without visual symptoms of the disease. Among the six ML models evaluated, the artificial neural network model showed the highest classification performance with an overall accuracy of 81%, regardless of the physiological parameter utilized for model training. Moreover, the artificial neural network accurately predicted the absolute values of both physiological parameters (gₛ and A) based on the complete spectral signature from visually healthy foliar tissue, achieving coefficients of determination of 84% and 81%, respectively. Consequently, ML models utilizing physiological response data and hyperspectral imaging exhibited remarkable robustness, facilitating the estimation of Fusarium wilt severity in strawberry plants even without visual symptoms.

Why it matches plant phenotyping methodsハイパースペクトル画像と機械学習を用いて、イチゴ植物の感染初期の生理的ストレスと萎凋病重症度を推定する手法が研究の中心であるため。

abstractThe combined use of hyperspectral imagery and ML algorithms were investigated to detect and classify the physiological stress caused by early infections of Fusarium wilt in strawberry plants.

Machine learning-enabled computer vision for plant phenotyping: a primer on AI/ML and a case study on stomatal patterning →

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.

Application of deep learning for the analysis of stomata: a review of current methods and future directions →

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.

FieldDino: High-throughput physio-morphological phenotyping of stomatal characteristics for plant breeding research →

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.

Thermographic Analysis of Green Wall and Green Roof Plant Types under Levels of Water Stress →

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.

Enhancing wheat crop physiology monitoring through spectroscopic analysis of stomatal conductance dynamics →

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

Smart: Stoma Measurement, Analysis, Report Tool for Microscope Image and Its Application in Plant Phenotyping →

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.

Application of cryo-FIB-SEM for investigating organelle ultrastructure in guard cells of higher plants →

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.

Century-long timelines of herbarium genomes predict plant stomatal response to climate change →

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

Thermal imaging from UAS for estimating crop water status in a Merlot vineyard in semi-arid conditions →

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).

Validation and parametrization of the soil moisture index for stomatal conductance modelling and flux-based ozone risk assessment of Mediterranean plant species →

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

High Throughput Image-Based Phenotyping for Determining Morphological and Physiological Responses to Single and Combined Stresses in Potato. →

High throughput image-based phenotyping is a powerful tool to non-invasively determine the development and performance of plants under specific conditions over time. By using multiple imaging sensors, many traits of interest can be assessed, including plant biomass, photosynthetic efficiency, canopy temperature, and leaf reflectance indices. Plants are frequently exposed to multiple stresses under field conditions where severe heat waves, flooding, and drought events seriously threaten crop productivity. When stresses coincide, resulting effects on plants can be distinct due to synergistic or antagonistic interactions. To elucidate how potato plants respond to single and combined stresses that resemble naturally occurring stress scenarios, five different treatments were imposed on a selected potato cultivar (Solanum tuberosum L., cv. Lady Rosetta) at the onset of tuberization, i.e. control, drought, heat, waterlogging, and combinations of heat, drought, and waterlogging stresses. Our analysis shows that waterlogging stress had the most detrimental effect on plant performance, leading to fast and drastic physiological responses related to stomatal closure, including a reduction in the quantum yield and efficiency of photosystem II and an increase in canopy temperature and water index. Under heat and combined stress treatments, the relative growth rate was reduced in the early phase of stress. Under drought and combined stresses, plant volume and photosynthetic performance dropped with an increased temperature and stomata closure in the late phase of stress. The combination of optimized stress treatment under defined environmental conditions together with selected phenotyping protocols allowed to reveal the dynamics of morphological and physiological responses to single and combined stresses. Here, a useful tool is presented for plant researchers looking to identify plant traits indicative of resilience to several climate change-related stresses.

Why it matches plant phenotyping methods高スループット画像・複数センサーによる形態・生理形質の取得と、最適化したストレス処理およびフェノタイピングプロトコルが研究の中心的手法として記述されているため、実質的なフェノタイピング手法の適用に該当する。

titleHigh Throughput Image-Based Phenotyping for Determining Morphological and Physiological Responses to Single and Combined Stresses in Potato

SLPA-Net: A Real-Time Recognition Network for Intelligent Stomata Localization and Phenotypic Analysis. →

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.

Explainable machine learning for predicting stomatal conductance across multiple plant functional types →

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)

Hyperspectral imaging reveals small-scale water gradients in apple leaves due to minimal cuticle perforation by Venturia inaequalis conidiophores. →

Effects of Venturia inaequalis on water relations of apple leaves were studied under controlled conditions without limitation of water supply to elucidate their impact on the non-haustorial biotrophy of this pathogen. Leaf water relations, namely leaf water content and transpiration, were spatially resolved by hyperspectral imaging and thermography; non-imaging techniques-gravimetry, a pressure chamber, and porometry-were used for calibration and validation. Reduced stomatal transpiration 3-4 d after inoculation coincided with a transient increase of water potential. Perforation of the plant cuticle by protruding conidiophores subsequently increased cuticular transpiration even before visible symptoms occurred. With sufficient water supply, cuticular transpiration remained at elevated levels for several weeks. Infections did not affect the leaf water content before scab lesions became visible. Only hyperspectral imaging was suitable to demonstrate that a decreased leaf water content was strictly limited to sites of emerging conidiophores and that cuticle porosity increased with sporulation. Microscopy confirmed marginal cuticle injury; although perforated, it tightly surrounded the base of conidiophores throughout sporulation and restricted water loss. The role of sustained redirection of water flow to the pathogen's hyphae in the subcuticular space above epidermal cells, to facilitate the acquisition and uptake of nutrients by V. inaequalis, is discussed.

Why it matches plant phenotyping methodsリンゴ葉の水分含量・蒸散をハイパースペクトル画像と熱画像で空間定量し、非画像手法で校正・検証している。病原体研究ではあるが、感染葉の生理状態を取得する画像計測法が実質的に中心である。

abstractLeaf water relations, namely leaf water content and transpiration, were spatially resolved by hyperspectral imaging and thermography; non-imaging techniques-gravimetry, a pressure chamber, and porometry-were used for calibration and validation.

RotatedStomataNet: a deep rotated object detection network for directional stomata phenotype analysis. →

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

Measuring stomatal and guard cell metrics for plant physiology and growth using StoManager1 →

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

Viewing stomata in action: Autonomous in planta imaging of individual stomatal movement links morphology and kinetics →

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.

Ecotoxicity of 2,4-dichlorophenol to Microsorium pteropus by High Spatial Resolution Mapping of Stoma Oxygen Emission →

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

Response of stomatal conductance to plant water stress in buffalograss seed production: Observation with UAV thermal infrared imagery →

Stomatal conductance (gs) is an indicator that allows for direct evaluation of plant water status, but it is challenging to achieve rapid monitoring in large-scale fields due to limitations in observation methods. Here this study was conducted to identify the thresholds of gs with different target yields and develop a gs-based water stress diagnostic model for buffalograss (Buchloe dactyloides (Nutt.) Engelm.) using UAV thermal infrared imagery for buffalograss in 2022 and 2023. The results of the field experiment demonstrated that the gs rapidly response to changes in the water stress status of buffalograss. The thresholds of gs were 403 and 385 mmol m−2 s−1 for the vegetative and reproductive growth stages, respectively, with the target seed yield of 1224 kg ha−1. The gs values were classified into three levels for the vegetative growth and four levels for the in reproductive growth stage of buffalograss, respectively. The canopy temperature depression response to water stress is consistent with the gs. Based on this relationship, this study developed a gs-based diagnostic model with a random forest algorithm for buffalograss. Furthermore, a spital map of gs was created using UAV thermal infrared imagery. The modification test results indicated that the model made a good estimation of gs were good with normalized root mean square errors of 15% in the vegetative stage and 11% in the reproductive stage, respectively. Therefore, it is feasible to use thermal infrared imagery for monitoring gs and evaluating the water stress of plants in buffalograss fields.

Why it matches plant phenotyping methodsUAV熱赤外画像から気孔コンダクタンスと植物の水ストレスを推定する診断モデルを開発・検証しており、植物生理状態の取得手法が中心である。

abstractdevelop a gs-based water stress diagnostic model for buffalograss (Buchloe dactyloides (Nutt.) Engelm.) using UAV thermal infrared imagery

How the Anatomy of the Epidermal Cells Is Correlated to the Transient Response of Stomata →

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.

Direct Observation and Automated Measurement of Stomatal Responses to Pseudomonas syringae pv. tomato DC3000 in Arabidopsis thaliana. →

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

Assessing the High Temperature Effects on Stomatal Production. →

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.

Probing the in-situ volumes of Arabidopsis leaf plastids using 3D confocal and scanning electron microscopy →

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).

Predicting physiological traits of rice from hyperspectral data under CO 2 and drought treatments →

Using hyperspectral technology for high-throughput plant phenotyping is a potentially useful method in crop sciences. To examine its effectiveness, we collected leaf-level hyperspectral and ground-reference data from rice plants grown in controlled-environments under drought and CO 2 treatments at Ag Alumni Seed Phenotyping Facility at Purdue University. By applying RReliefF, we found that short-wave infrared region (SWIR) was important for leaf water potential (LWP), near-infrared region was linked with specific leaf area (SLA) and both red-edge and SWIR regions were related to gas exchange traits (net assimilation [A n ], stomatal conductance to water vapor [gsw] and transpiration [E mm ]). Based on those results, we found that LWP and SLA were moderately predictable and gas exchange traits were predictable (R 2 \(\geq\) 0.60 and root mean squared error of prediction for A n , gsw and E mm were 7.706 \(\mu\)molm -2 s -1 , 0.282 molm -2 s -1 , and 3.906 mmolm -2 s -1 in validation datasets, respectively) by using partial least squares regression. Furthermore, treatment effect on A n from cross-validation predictions agreed with ground-reference data. In contrast, photosynthetic parameters (V cmax and J max ) could not be estimated from hyperspectral data. Hyperspectral data can provide potential insights about plant growth and water status. When the effect of treatments is pronounced, model predictions are consistent with ground-reference data.

Why it matches plant phenotyping methodsイネの生理形質をハイパースペクトルデータから推定する方法を開発・検証しており、形質取得と予測性能評価が研究の中心である。

abstractUsing hyperspectral technology for high-throughput plant phenotyping is a potentially useful method in crop sciences.

Increasing the Throughput of Annotation Tasks Across Scales of Plant Phenotyping Experiments →

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

Automated parameterization of stomatal conductance models from thermal imagery by leveraging synthetic images generated from Helios 3D biophysical model simulations →

Stomatal conductance ( g s ) is a critical plant biophysical variable that reflects plant regulation of CO 2 uptake and associated water loss, yet its direct measurement is often prohibitively time-consuming. Estimating the impacts of g s indirectly through leaf temperature ( T leaf ) is a common practice, but is complicated by confounding factors such as ambient conditions, measurement aggregation scale, sample size, and measurement time. Using T leaf measurements to instead determine parameters of a model for g s that can remove these external factors can provide quasi-traits that are more reliable and heritable. Our objective was to develop an automated pipeline for g s model parameterization using thermal data, which could be applied within a 3D biophysical model to predict the impacts of trait variation on canopy-level processes related to water-use efficiency. Field experiments were conducted on common bean, cowpea, and sorghum crops, involving high-resolution thermal measurements obtained from a robotic sensing platform. Subsequently, a deep learning algorithm was trained using synthetic thermography data generated using Helios 3D model simulations encompassing canopy structure, ambient conditions, and T leaf , enabling the prediction of long-wave radiation and incident shortwave radiation for each thermal image pixel. Following this, a leaf-surface energy budget analysis was applied to the collected field thermal data to predict g s parameters. Validation of these predictions was performed through comparisons with ground-truth leaf-level gas exchange data. This pipeline offers a promising pathway to predictive simulations of water status and transpiration-related traits, regardless of environmental variation, ultimately enhancing our understanding of plant responses to changing environmental conditions.

Why it matches plant phenotyping methods熱画像とロボットセンシングを用いて気孔コンダクタンス関連形質を推定する自動パイプラインを開発し、ガス交換データで検証しており、植物フェノタイピング手法が中心である。

abstractOur objective was to develop an automated pipeline for g s model parameterization using thermal data

Automated parameterization of stomatal conductance models from thermal imagery by leveraging synthetic images generated from Helios 3D biophysical model simulations →

Stomatal conductance ( g ) is a critical plant biophysical variable that reflects plant regulation of CO uptake and associated water loss, yet its direct measurement is often prohibitively time-consuming. Estimating the impacts of g indirectly through leaf temperature ( T ) is a common practice, but is complicated by confounding factors such as ambient conditions, measurement aggregation scale, sample size, and measurement time. Using T measurements to instead determine parameters of a model for g that can remove these external factors can provide quasi-traits that are more reliable and heritable. Our objective was to develop an automated pipeline for g model parameterization using thermal data, which could be applied within a 3D biophysical model to predict the impacts of trait variation on canopy-level processes related to water-use efficiency. Field experiments were conducted on common bean, cowpea, and sorghum crops, involving high-resolution thermal measurements obtained from a robotic sensing platform. Subsequently, a deep learning algorithm was trained using synthetic thermography data generated using Helios 3D model simulations encompassing canopy structure, ambient conditions, and T , enabling the prediction of long-wave radiation and incident shortwave radiation for each thermal image pixel. Following this, a leaf-surface energy budget analysis was applied to the collected field thermal data to predict g parameters. Validation of these predictions was performed through comparisons with ground-truth leaf-level gas exchange data. This pipeline offers a promising pathway to predictive simulations of water status and transpiration-related traits, regardless of environmental variation, ultimately enhancing our understanding of plant responses to changing environmental conditions.

Why it matches plant phenotyping methods熱画像とロボットセンシングを用いて気孔コンダクタンス関連形質を推定する自動パイプラインを開発し、葉レベルのガス交換データで検証しており、植物表現型取得法が中心である。

abstractOur objective was to develop an automated pipeline for g model parameterization using thermal data

Increasing the Throughput of Annotation Tasks Across Scales of Plant Phenotyping Experiments →

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

Automated estimation of stomatal number and aperture in haskap (Lonicera caerulea L.). →

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.

StomataTracker: Revealing circadian rhythms of wheat stomata with in-situ video and deep learning →

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.

Leaf relative water content at 50% stomatal conductance measured by noninvasive NMR is linked to climate of origin in nine species of eucalypt. →

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

High‐resolution thermal imagery reveals how interactions between crown structure and genetics shape plant temperature →

Abstract Understanding interactions between environmental stress and genetic variation is crucial to predict the adaptive capacity of species to climate change. Leaf temperature is both a driver and a responsive indicator of plant physiological response to thermal stress, and methods to monitor it are needed. Foliar temperatures vary across leaf to canopy scales and are influenced by genetic factors, challenging efforts to map and model this critical variable. Thermal imagery collected using unoccupied aerial systems (UAS) offers an innovative way to measure thermal variation in plants across landscapes at leaf‐level resolutions. We used a UAS equipped with a thermal camera to assess temperature variation among genetically distinct populations of big sagebrush (Artemisia tridentata), a keystone plant species that is the focus of intensive restoration efforts throughout much of western North America. We completed flights across a growing season in a sagebrush common garden to map leaf temperature relative to subspecies and cytotype, physiological phenotypes of plants, and summer heat stress. Our objectives were to (1) determine whether leaf‐level stomatal conductance corresponds with changes in crown temperature; (2) quantify genetic (i.e., subspecies and cytotype) contributions to variation in leaf and crown temperatures; and (3) identify how crown structure, solar radiation, and subspecies‐cytotype relate to leaf‐level temperature. When considered across the whole season, stomatal conductance was negatively, non‐linearly correlated with crown‐level temperature derived from UAS. Subspecies identity best explained crown‐level temperature with no difference observed between cytotypes. However, structural phenotypes and microclimate best explained leaf‐level temperature. These results show how fine‐scale thermal mapping can decouple the contribution of genetic, phenotypic, and microclimate factors on leaf temperature dynamics. As climate‐change‐induced heat stress becomes prevalent, thermal UAS represents a promising way to track plant phenotypes that emerge from gene‐by‐environment interactions.

Why it matches plant phenotyping methodsUAS搭載熱カメラによる葉・樹冠温度の高解像度推定と、遺伝型・構造・微気候との関係評価が研究の中心であり、植物表現型の取得手法を実質的に適用している。

abstractThermal imagery collected using unoccupied aerial systems (UAS) offers an innovative way to measure thermal variation in plants across landscapes at leaf‐level resolutions.

Incorporating photosynthetic acclimation improves stomatal optimisation models →

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.

Field phenomics reveals genetic variation for transpiration response to vapor pressure deficit in sorghum →

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.

Localized measurements of water potential reveal large loss of conductance in living tissues of maize leaves →

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.

Combining thermal imaging and soil water content sensors to assess tree water status in pear trees. →

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

Abaxial leaf surface-mounted multimodal wearable sensor for continuous plant physiology monitoring →

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.

A simple system for phenotyping of plant transpiration and stomatal conductance response to drought →

Plant breeding for increased crop water use efficiency or drought stress resistance requires methods to quickly assess the transpiration rate (E) and stomatal conductance (gₛ) of a large number of individual plants. Several methods to measure E and gₛ exist, each of which has its own advantages and shortcomings. To add to this toolbox, we developed a method that uses whole-plant thermal imaging in a controlled environment, where aerial humidity is changed rapidly to induce changes in E that are reflected in changes in leaf temperature. This approach is based on a simplified energy balance equation, without the need for a reference material or complicated calculations. To test this concept, we built a double-sided, perforated, open-top plexiglass chamber that was supplied with air at a high flow rate (35 L min⁻¹) and whose relative humidity (RH) could be switched rapidly. Measurements included air and leaf temperature as well as RH. Using several well-watered and drought stressed genotypes of Arabidopsis thaliana that were exposed to multiple cycles in RH (30–50 % and back), we showed that leaf temperature as measured in our system correlated well with E and gₛ measured in a commercial gas exchange system. Our results demonstrate that, at least within a given species, the differences in leaf temperature under several RH can be used as a proxy for E and gₛ. Given that this method is fairly quick, noninvasive and remote, we envision that it could be upscaled for work within rapid plant phenotyping systems.

Why it matches plant phenotyping methods植物の蒸散速度と気孔コンダクタンスを推定する熱画像ベースの表現型計測法を開発し、ガス交換システムとの相関で検証しているため、方法が中心的である。

abstractwe developed a method that uses whole-plant thermal imaging in a controlled environment

Rapid non-destructive method to phenotype stomatal traits. →

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.

Salinity-specific stomatal conductance model parameters are reduced by stomatal saturation conductance and area via leaf nitrogen. →

Modeling stomatal behavior is necessary for accurate stomatal simulation and predicting the terrestrial water‑carbon cycle. Although the Ball-Berry and Medlyn stomatal conductance (g s ) models have been widely used, variations and the drivers of their key slope parameters (m and g 1 ) remain poorly understood under salinity stress. We measured leaf gas exchange, physiological and biochemical traits, soil water content and electrical conductivity of saturation extract (EC e ), and fitted slope parameters of two genotypes of maize growing in two water and two salinity levels. We found m was different between the genotypes, but no difference in g 1 . Salinity stress reduced m and g 1 , saturated stomatal conductance (g sat ), the fraction of leaf epidermis area allocation to stomata (f s ), and leaf nitrogen (N) content, and increased EC e , but no marked decrease in slope parameters under drought. Both m and g 1 were positively correlated with g sat , f s , and leaf N content, and negatively correlated with EC e in the same fashion among the two genotypes. Salinity stress altered m and g 1 by modulating g sat and f s via leaf N content. The prediction accuracy of g s was improved using salinity-specific slope parameters, with root mean square error (RMSE) being decreased from 0.056 to 0.046 and 0.066 to 0.025 mol m -2 s -1 for the Ball-Berry and Medlyn models, respectively. This study provides a modeling approach to improving the simulation of stomatal conductance under salinity.

Why it matches plant phenotyping methods塩ストレス下の気孔コンダクタンスを推定・予測するモデルのパラメータ化と精度改善が中心であり、植物の生理形質を扱う方法開発に該当する。

abstractThe prediction accuracy of g s was improved using salinity-specific slope parameters

Estimating stomatal conductance of citrus under water stress based on multispectral imagery and machine learning methods. →

Introduction Canopy stomatal conductance (Sc) indicates the strength of photosynthesis and transpiration of plants. In addition, Sc is a physiological indicator that is widely employed to detect crop water stress. Unfortunately, existing methods for measuring canopy Sc are time-consuming, laborious, and poorly representative. Methods To solve these problems, in this study, we combined multispectral vegetation index (VI) and texture features to predict the Sc values and used citrus trees in the fruit growth period as the research object. To achieve this, VI and texture feature data of the experimental area were obtained using a multispectral camera. The H (Hue), S (Saturation) and V (Value) segmentation algorithm and the determined threshold of VI were used to obtain the canopy area images, and the accuracy of the extraction results was evaluated. Subsequently, the gray level co-occurrence matrix (GLCM) was used to calculate the eight texture features of the image, and then the full subset filter was used to obtain the sensitive image texture features and VI. Support vector regression, random forest regression, and k-nearest neighbor regression (KNR) Sc prediction models were constructed, which were based on single and combined variables. Results The analysis revealed the following: 1) the accuracy of the HSV segmentation algorithm was the highest, achieving more than 80%. The accuracy of the VI threshold algorithm using excess green was approximately 80%, which achieved accurate segmentation. 2) The citrus tree photosynthetic parameters were all affected by different water supply treatments. The greater the degree of water stress, the lower the net photosynthetic rate (Pn), transpiration rate (Tr), and Sc of the leaves. 3) In the three Sc prediction models, The KNR model, which was constructed by combining image texture features and VI had the optimum prediction effect (training set: R 2 = 0.91076, RMSE = 0.00070; validation set; R 2 = 0.77937, RMSE = 0.00165). Compared with the KNR model, which was only based on VI or image texture features, the R 2 of the validation set of the KNR model based on combined variables was improved respectively by 6.97% and 28.42%. Discussion This study provides a reference for large-scale remote sensing monitoring of citrus Sc by multispectral technology. Moreover, it can be used to monitor the dynamic changes of Sc and provide a new technique for gaining a better understanding of the growth status and water stress of citrus crops.

Why it matches plant phenotyping methodsマルチスペクトル画像から気孔コンダクタンスという植物生理形質を抽出・予測する画像処理および機械学習手法が研究の中心であり、分割精度と予測性能も検証している。

abstractwe combined multispectral vegetation index (VI) and texture features to predict the Sc values

A simple system for phenotyping of plant transpiration and stomatal conductance response to drought. →

Plant breeding for increased crop water use efficiency or drought stress resistance requires methods to quickly assess the transpiration rate (E) and stomatal conductance (gs) of a large number of individual plants. Several methods to measure E and gs exist, each of which has its own advantages and shortcomings. To add to this toolbox, we developed a method that uses whole-plant thermal imaging in a controlled environment, where aerial humidity is changed rapidly to induce changes in E that are reflected in changes in leaf temperature. This approach is based on a simplified energy balance equation, without the need for a reference material or complicated calculations. To test this concept, we built a double-sided, perforated, open-top plexiglass chamber that was supplied with air at a high flow rate (35 L min−1) and whose relative humidity (RH) could be switched rapidly. Measurements included air and leaf temperature as well as RH. Using several well-watered and drought stressed genotypes of Arabidopsis thaliana that were exposed to multiple cycles in RH (30–50 % and back), we showed that leaf temperature as measured in our system correlated well with E and gs measured in a commercial gas exchange system. Our results demonstrate that, at least within a given species, the differences in leaf temperature under several RH can be used as a proxy for E and gs. Given that this method is fairly quick, noninvasive and remote, we envision that it could be upscaled for work within rapid plant phenotyping systems.

Why it matches plant phenotyping methods植物の蒸散速度・気孔コンダクタンスを推定する熱画像ベースの表現型計測法を開発し、ガス交換測定と比較検証しているため、方法が研究の中心である。

abstractwe developed a method that uses whole-plant thermal imaging in a controlled environment

Hyperspectral Remote Sensing for Phenotyping the Physiological Drought Response of Common and Tepary Bean →

Proximal remote sensing offers a powerful tool for high-throughput phenotyping of plants for assessing stress response. Bean plants, an important legume for human consumption, are often grown in regions with limited rainfall and irrigation and are therefore bred to further enhance drought tolerance. We assessed physiological (stomatal conductance and predawn and midday leaf water potential) and ground- and tower-based hyperspectral remote sensing (400 to 2,400 nm and 400 to 900 nm, respectively) measurements to evaluate drought response in 12 common bean and 4 tepary bean genotypes across 3 field campaigns (1 predrought and 2 post-drought). Hyperspectral data in partial least squares regression models predicted these physiological traits ( R 2 = 0.20 to 0.55; root mean square percent error 16% to 31%). Furthermore, ground-based partial least squares regression models successfully ranked genotypic drought responses similar to the physiologically based ranks. This study demonstrates applications of high-resolution hyperspectral remote sensing for predicting plant traits and phenotyping drought response across genotypes for vegetation monitoring and breeding population screening.

Why it matches plant phenotyping methodsハイパースペクトルセンシングとPLS回帰により、植物の生理形質を推定し、遺伝子型間の干ばつ応答を表現型解析する手法を実証しており、方法が中心的である。

abstractProximal remote sensing offers a powerful tool for high-throughput phenotyping of plants for assessing stress response.

NAPPN Annual Conference Abstract: Integrating Live Confocal Microscope Imagery of Stomata with Measurement of Leaf-Level Photosynthetic Gas Exchange →

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.

Deep Transfer Learning-Based Multi-Object Detection for Plant Stomata Phenotypic Traits Intelligent Recognition →

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.

Image-based quantification of Arabidopsis thaliana stomatal aperture from leaf images →

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

Development of Two-Dimensional Model of Photosynthesis in Plant Leaves and Analysis of Induction of Spatial Heterogeneity of CO 2 Assimilation Rate under Action of Excess Light and Drought. →

Photosynthesis is a key process in plants that can be strongly affected by the actions of environmental stressors. The stressor-induced photosynthetic responses are based on numerous and interacted processes that can restrict their experimental investigation. The development of mathematical models of photosynthetic processes is an important way of investigating these responses. Our work was devoted to the development of a two-dimensional model of photosynthesis in plant leaves that was based on the Farquhar-von Caemmerer-Berry model of CO 2 assimilation and descriptions of other processes including the stomatal and transmembrane CO 2 fluxes, lateral CO 2 and HCO 3 - fluxes, transmembrane and lateral transport of H + and K + , interaction of these ions with buffers in the apoplast and cytoplasm, light-dependent regulation of H + -ATPase in the plasma membrane, etc. Verification of the model showed that the simulated light dependences of the CO 2 assimilation rate were similar to the experimental ones and dependences of the CO 2 assimilation rate of an average leaf CO 2 conductance were also similar to the experimental dependences. An analysis of the model showed that a spatial heterogeneity of the CO 2 assimilation rate on a leaf surface should be stimulated under an increase in light intensity and a decrease in the stomatal CO 2 conductance or quantity of the open stomata; this prediction was supported by the experimental verification. Results of the work can be the basis of the development of new methods of the remote sensing of the influence of abiotic stressors (at least, excess light and drought) on plants.

Why it matches plant phenotyping methods葉面CO2同化速度の空間分布を推定する二次元数理モデルを開発し、実験データで検証しているため、植物生理状態の取得・推定手法が研究の中心です。

abstractOur work was devoted to the development of a two-dimensional model of photosynthesis in plant leaves

High-throughput microscopy image analysis of plant stomata →

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

Rapid non-destructive method to phenotype stomatal traits →

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.

Century-long timelines of herbarium genomes predict plant stomatal response to climate change →

Abstract Dissecting plant responses to the environment is key to understanding if and how plants adapt to anthropogenic climate change. Stomata, plants’ pores for gas exchange, are expected to decrease in density following increased CO 2 concentrations, a trend already observed in multiple plant species. However, it is unclear if 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 A. thaliana historical herbarium specimens collected over the last 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 polygenic score based on known effects of gene knock-out on stomatal development that recovers a classic pattern of stomatal density decrease over the last centuries without requiring direct phenotype observation of historical samples. This approach combining historical genomics with functional experimental knowledge could allow further investigations of how different, even in historical samples unmeasurable, cellular plant phenotypes have already responded to climate change through adaptive evolution. One sentence summary Using a molecular-knowledge based genetic phenotype proxy, historical whole-genome A. thaliana timelines compared with contemporary data indicate a shift of stomatal density following climate-associated predictions.

Why it matches plant phenotyping methods遺伝情報から過去試料では直接測定できない気孔密度を推定するポリジェニックスコアを開発し、既知の気孔密度パターンで検証しており、植物形質推定法が中心です。

abstractWe then develop a polygenic score based on known effects of gene knock-out on stomatal development that recovers a classic pattern of stomatal density decrease over the last centuries without requiring direct phenotype observation of historical samples.

Adapting and Optimizing a Machine Learning Tool for Automated Cell Detection in Setaria viridis →

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

Integration of high-throughput phenotyping with anatomical traits of leaves to help understanding lettuce acclimation to a changing environment →

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

Thermal remote sensing for plant ecology from leaf to globe →

Surface temperatures are mechanistically linked to vegetation biophysical and physiological processes. Although remote sensing in the thermal infrared (TIR) domain can offer novel insights into the impacts of changing surface temperatures on vegetation, the transformative potential of remote sensing for plant ecology has not yet been realized. Remotely sensed surface temperatures can be used to derive stomatal behaviour and identify stressful environmental conditions in near‐real time. Plant species, traits and structural characteristics can be evaluated with high spectral resolution TIR emissivity. Beyond canopy scales, thermal remote sensing can enhance the inferences obtained from manipulative experiments and empirical evidence, providing unique insight into shifts in species ranges and phenology with changing climate conditions. Scaling leaf traits, canopy structure and regional patterns require an integrated understanding of both process and technology. Theory linking surface temperatures to vegetation dynamics is summarized from an energy balance perspective. We outline scaling considerations including the impacts of morphology on leaf energy balance, canopy structure influences on convective heat exchange and potential confounding impacts of non‐vegetated surfaces. Synthesis. We introduce a unifying framework to link leaf to globe through thermal remote sensing. Recent and emerging advances in sensors, data availability and analytics, together with synergies between TIR remote sensing and other data sources, present a timely opportunity for ecologists to advance our understanding of plant physiology, ecology and biogeography with thermal remote sensing.

Why it matches plant phenotyping methods熱赤外リモートセンシングにより植物の温度、気孔挙動、形質、生理状態を推定する技術を葉から全球規模まで体系化した方法論レビューであり、植物表現型の取得・推定が中心である。

abstractRemotely sensed surface temperatures can be used to derive stomatal behaviour and identify stressful environmental conditions in near‐real time.

Comparison of various approaches for estimating leaf water content and stomatal conductance in different plant species using hyperspectral data →

Water deficit stress is a frequent phenomenon that inhibits plant growth. This study explores the performance of hyperspectral data for estimating the leaf water content of ten tree species under different water conditions. The three most commonly used leaf water content indicators (relative water content, equivalent water thickness, and fuel moisture content) and stomatal conductance were assessed using narrow-band indices (single band, band ratio, band subtraction, and band difference) and multivariate analyses (partial least squares regression (PLSR), support vector regression, artificial neural network, and random forest) within the 350–2500 nm spectral reflectance range. The results indicated that the best bands and band combinations were mainly concentrated in the short-wavelength infrared region, which is sensitive regarding plant water content. Compared to a single band, dual-band indices exhibited better overall performance among the four kinds of indices. Multivariate analyses are more accurate than narrow-band indices. Among these, PLSR is the most robust and can be considered the optimal technique for predicting the water content of all tree species, except for conifer species. However, accurately predicting stomatal conductance is difficult to predict using these methods. This study shows that the PLSR model can accurately estimate leaf water content in multiple tree species, and hyperspectral technology, such as hyperspectral remote sensing, has potential regarding the estimation of leaf water content.

Why it matches plant phenotyping methodsハイパースペクトル計測と複数の回帰手法を比較・評価し、葉の含水量や気孔コンダクタンスという植物生理形質を推定する方法が研究の中心である。

abstractThis study explores the performance of hyperspectral data for estimating the leaf water content of ten tree species under different water conditions.

Genomic regions associate with major axes of variation in gas exchange and leaf construction traits in cultivated sunflower (Helianthus annuus L.) →

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.

A simple system for phenotyping of plant transpiration and stomatal conductance response to drought →

Abstract Plant breeding for increased crop water use efficiency or drought stress resistance requires methods to quickly assess the transpiration rate (E) and stomatal conductance (gs) of a large number of individual plants. Several methods to measure E and gs exist, each of which has its own drawbacks and shortcomings. To add to this toolbox, we developed a method that uses whole-plant thermal imaging in a controlled environment, where aerial humidity is changed rapidly to induce changes in E that are reflected in changes in leaf temperature. This approach is based on a simplified energy balance equation, without the need for a reference material or complicated calculations. To test this concept, we built a double-sided, perforated, open-top plexiglass chamber that was supplied with air at a high flow rate (35 L min− 1) and whose relative humidity (RH) could be switched rapidly. Measurements included air and leaf temperature as well as RH. Using several well-watered and drought stressed genotypes of Arabidopsis thaliana that were exposed to multiple cycles in RH (30 to 50% and back), we showed that leaf temperature as measured in our system correlated well with E and gs measured in a commercial gas exchange system. Our results demonstrate that, at least within a given species, the differences in leaf temperature under several RH can be used as a proxy for E and gs. Given that this method is fairly quick, noninvasive and remote, we envision that it could be upscaled for work within rapid plant phenotyping systems.

Why it matches plant phenotyping methods植物の蒸散・気孔コンダクタンスを熱画像から推定する新規手法を開発し、ガス交換測定との相関で検証しており、フェノタイピング手法が研究の中心である。

abstractwe developed a method that uses whole-plant thermal imaging in a controlled environment

A new device for continuous non-invasive measurements of leaf water content using NIR-transmission allowing dynamic tracking of water budgets →

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

Non-destructive analysis of plant physiological traits using hyperspectral imaging: A case study on drought stress →

Conventional methods to access plant physiological traits are based on destructive measurements by means of biochemical extraction or leaf clipping, thereby limiting the throughput capability. With advances in hyperspectral imaging sensor, fast, non-invasive and non-destructive measurements of a plant’s physiological status became feasible. In this work, a non-destructive method for the characterization of a plant’s status from hyperspectral images is presented. A supervised data-driven method based on Machine Learning Regression (MLR) algorithms was developed to generate prediction models of four targeted physiological traits: water potential, effective quantum yield of photosystem II, transpiration rate and stomatal conductance. Standard Normal Variate (SNV) transformed reflectance spectra were used as the input variables for building the regression model. Three MLR algorithms: Gaussian Process Regression (GPR), Kernel Ridge Regression (KRR), and Partial Least Squares Regression (PLSR) were explored as candidate methods for building the prediction model of the targeted physiological traits. Validation results show that the non-linear prediction models, developed based on the GPR algorithm produced the best estimation accuracy on all plant traits. The best prediction models were applied to a small-scale phenotyping experiment to study drought stress responses in maize plants. Results show that all estimated traits revealed a significant difference between plants under drought stress and normal growth dynamics as early as after 3 days of drought induction.

Why it matches plant phenotyping methods植物の生理形質をハイパースペクトル画像から非破壊推定する機械学習手法を開発し、複数形質で検証しているため、方法が研究の中心である。

abstractIn this work, a non-destructive method for the characterization of a plant’s status from hyperspectral images is presented.

Plantenna Demonstrator: novel sensors for monitoring plant health →

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.

Combination of high-throughput phenotyping with plant anatomical trait measurements to understand lettuce morpho-physiological acclimation under changing VPDs and watering regimes. →

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)

High-throughput phenotyping reveals differential transpiration behaviour within the banana wild relatives highlighting diversity in drought tolerance. →

Crop wild relatives, the closely related species of crops, may harbour potentially important sources of new allelic diversity for (a)biotic tolerance or resistance. However, to date, wild diversity is only poorly characterized and evaluated. Banana has a large wild diversity but only a narrow proportion is currently used in breeding programmes. The main objective of this study was to evaluate genotype-dependent transpiration responses in relation to the environment. By applying continuous high-throughput phenotyping, we were able to construct genotype-specific transpiration response models in relation to light, VPD and soil water potential. We characterized and evaluated six (sub)species and discerned four phenotypic clusters. Significant differences were observed in leaf area, cumulative transpiration and transpiration efficiency. We confirmed a general stomatal-driven 'isohydric' drought avoidance behaviour, but discovered genotypic differences in the onset and intensity of stomatal closure. We pinpointed crucial genotype-specific soil water potentials when drought avoidance mechanisms were initiated and when stress kicked in. Differences between (sub)species were dependent on environmental conditions, illustrating the need for high-throughput dynamic phenotyping, modelling and validation. We conclude that the banana wild relatives contain useful drought tolerance traits, emphasising the importance of their conservation and potential for use in breeding programmes.

Why it matches plant phenotyping methods連続ハイスループット表現型解析により蒸散応答モデルを構築し、動的フェノタイピング・モデリング・検証を主要手法として実施しているため。

abstractBy applying continuous high-throughput phenotyping, we were able to construct genotype-specific transpiration response models in relation to light, VPD and soil water potential.

Effect of Different Water Treatments in Soil-Plant-Atmosphere Continuum Based on Intelligent Weighing Systems →

In order to meet the needs of dynamic continuous monitoring of soil-plant-atmosphere continuum (SPAC), a new soil, plant, atmosphere analysis system has been established based on an intelligent weighing system (IWS). Four types of irrigation treatments (90%, 80%, 70%, and 60% of field capacity (FC)) were conducted on lettuce (Lactuca sativa var. ramosa Hort.) for two-season planting experiments. Regarding the soil, the relative system weight of IWS showed a significant linear correlation with the soil volumetric moisture content (SWC) (R2 = 0.64–0.94). When the SWC increased by 1.00%, the soil weight increased by 56–62 g. Regarding plants, the IWS also clearly reflected the changes in plant weight gain, transpiration rate, and stomatal conductance at different growth stages. After verification, the relative errors of the transpiration rate and stomatal conductance measured by the IWS were −9.60–22.30% and −7.20–22.20%, respectively. Regarding the atmospheric environment, the variation trend of the crop evapotranspiration (ETc) based on the IWS and the reference crop evapotranspiration (ET0) calculated with meteorological parameters were consistent. However, the numerical difference was in the uncertainty of the crop coefficient (Kc). The ETc of lettuce under the 80% FC treatment was the highest. Accordingly, a daily online measurement method for Kc was established. The Kc values of lettuce at different growth stages were 0.88, 1.22, and 2.43, respectively. The growth, yield, and water use efficiency (WUE) of crops under 80% FC treatment compared with other treatments significantly increased by 11.07–21.05%, 0.91–9.89%, and 2.16–15.80%, respectively. Therefore, the 80% FC was adopted as the irrigation low limit of potted lettuce. The experimental results provide a theoretical basis for further guiding crop irrigation.

Why it matches plant phenotyping methodsインテリジェント重量計を用いた土壌・植物・大気の連続モニタリングシステムを開発し、植物重量、蒸散速度、気孔コンダクタンス、ETcなどの測定値を検証しているため、植物表現型取得法が研究の中心である。

abstracta new soil, plant, atmosphere analysis system has been established based on an intelligent weighing system (IWS).

SAI: Fast and automated quantification of stomatal parameters on microscope images →

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.

Recognizing and overcoming context dependency in the application of a machine learning tool for counting stomata in Setaria versus maize →

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.

Scaling Up from Leaf to Whole-Plant Level for Water Use Efficiency Estimates Based on Stomatal and Mesophyll Behaviour in Platycladus orientalis →

Prediction of whole-plant short-term water use efficiency (WUEs,P) is essential to indicate plant performance and facilitate comparison across different temporal and spatial scales. In this study, an isotope model was scaled up from the leaf to the whole-plant level, in order to simulate the variation in WUEs,P in response to different CO2 concentrations (Ca; 400, 600, and 800 μmol·mol−1) and soil water content (SWC; 35–100% of field capacity). For WUEs,P modelling, leaf gas exchange information, plant respiration, and “unproductive” water loss were taken into account. Specifically, in shaping the expression of the WUEs,P, we emphasized the role of both stomatal (gsw) and mesophyll conductance (gm). Simulations were compared with the measured results to check the model’s applicability. The verification showed that estimates of gsw from the coupled photosynthesis (Pn,L)-gsw model accounting for the effect of soil water stress slightly outperformed the model neglecting the soil water status effect. The established coupled Pn,L-gm model also proved more effective in estimating gm than the previously proposed model. Introducing the two diffusion control functions into the whole-plant model, the developed model for WUEs,P effectively captured its response pattern to different Ca and SWC conditions. Overall, this study confirmed that the accurate estimation of WUEs,P requires an improved predictive accuracy of gsw and gm. These results have important implications for predicting how plants respond to climate change.

Why it matches plant phenotyping methods葉から個体全体への水利用効率推定モデルを開発し、実測値との比較で適用性を検証しており、植物の生理形質推定法が研究の中心です。

abstractan isotope model was scaled up from the leaf to the whole-plant level

Assessing Abscisic Acid-Mediated Changes in Stomatal Aperture Through High-Quality Leaf Impressions. →

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.

Stomata Detector: High-throughput automation of stomata counting in a population of African rice (Oryza glaberrima) using transfer learning. →

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.

A Deep Learning Method for Fully Automatic Stomatal Morphometry and Maximal Conductance Estimation. →

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.

Phenotyping stomatal closure by thermal imaging for GWAS and TWAS of water use efficiency-related genes →

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.

Crop Water Stress Index as a Proxy of Phenotyping Maize Performance under Combined Water and Salt Stress →

The crop water stress index (CWSI), based on canopy temperature (Tc), has been widely used in evaluating plant water status and planning irrigation scheduling, but whether CWSI can diagnose the stress status of crops and predict the physiological traits and growth under combined water and salt stress remains to be further studied. Here, a model of CWSI was established based on the continuous measurements of Tc for two maize genotypes (ZD958 and XY335) under two water and salt conditions, combined with growth stage-specific non-water-stressed baselines (NWSB). The relationships between physiology, growth, and yield of maize with CWSI were analyzed. There were significant differences in NWSB between the two maize genotypes at the same and different growth stages; thus, growth stage-specific NWSBs were used. The difference in NWSB was due to the difference and change in effective leaf width. CWSI was closely related to leaf water potential, stomatal conductance, and net photosynthetic rate under different water and salt stress, and also explained the variations in leaf area index, biomass, water use, and yield. Collectively, CWSI can be used as a proxy indicator of high-throughput phenotyping maize performance under combined water and salt stress, which will be valuable for predicting yield and improving water use efficiency.

Why it matches plant phenotyping methods連続的な冠層温度からCWSIモデルと生育段階・遺伝子型別基準を構築し、ストレス状態や生理・生育・収量を推定する高スループット表現型手法が研究の中心である。

abstractHere, a model of CWSI was established based on the continuous measurements of Tc for two maize genotypes (ZD958 and XY335) under two water and salt conditions, combined with growth stage-specific non-water-stressed baselines (NWSB).

Recognizing and overcoming context dependency in the application of a machine learning tool for counting stomata in Setaria versus maize →

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

Demonstration of laser biospeckle method for speedy in vivo evaluation of plant-sound interactions with arugula →

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

Unraveling the Genetic Architecture of Two Complex, Stomata-Related Drought-Responsive Traits by High-Throughput Physiological Phenotyping and GWAS in Cowpea ( Vigna. Unguiculata L. Walp). →

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

A reverse phenotyping approach identifies physiological differences associated with yield under water stress in leading tomato introgression lines. →

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.

Robust estimates of cuticular conductance to water on a stomatous leaf surface →

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.

Scaling-up From Leaf to Whole-plant Level for Water Use Efficiency Estimates Based on Stomatal and Mesophyll Behavior →

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.

Estimating Evapotranspiration from Commonly Occurring Urban Plant Species Using Porometry and Canopy Stomatal Conductance →

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.

Deep learning-based high-throughput phenotyping accelerates gene discovery for stomatal traits. →

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

High-throughput phenotyping reveals differential transpiration behavior within the banana wild relatives highlighting diversity in drought tolerance →

Crop wild relatives, the closely related species of crops, may harbor potentially important sources of new allelic diversity for (a)biotic tolerance or resistance. However, to date wild diversity is only poorly characterized and evaluated. Banana has a large wild diversity but only a narrow proportion is currently used in breeding programs. The main objective of this work was to evaluate genotype-dependent transpiration responses in relation to the environment. By applying continuous high-throughput phenotyping, we were able to construct genotype-specific transpiration response models in relation to light, VPD and soil water potential. We characterized and evaluated 6 (sub)species and discerned four phenotypic clusters. Significant differences were observed in leaf area, cumulative transpiration and transpiration efficiency. We confirmed a general stomatal-driven ‘isohydric’ drought avoidance behavior, but discovered genotypic differences in the onset and intensity of stomatal closure. We pinpointed crucial genotype specific environmental conditions when drought avoidance mechanisms were initiated and when stress kicked in. Differences between (sub)species were more pronounced under certain environmental conditions, illustrating the need for high-throughput dynamic phenotyping, modelling and validation. We conclude that the banana wild relatives contain useful drought tolerance traits, emphasizing the importance of their conservation and potential for use in breeding programs.

Why it matches plant phenotyping methods連続的なハイスループット表現型計測とモデル構築により、遺伝型特異的な蒸散応答や乾燥耐性形質を抽出・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。

abstractBy applying continuous high-throughput phenotyping, we were able to construct genotype-specific transpiration response models in relation to light, VPD and soil water potential.

Determining leaf stomatal properties in citrus trees utilizing machine vision and artificial intelligence →

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.

Incorporating cultivar-specific stomatal traits into stomatal conductance models improves the estimation of evapotranspiration enhancing greenhouse climate management →

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

Dependence of CWSI-Based Plant Water Stress Estimation with Diurnal Acquisition Times in a Nectarine Orchard →

Unmanned aerial vehicle (UAV) remote sensing has become a readily usable tool for agricultural water management with high temporal and spatial resolutions. UAV-borne thermography can monitor crop water status near real-time, which enables precise irrigation scheduling based on an accurate decision-making strategy. The crop water stress index (CWSI) is a widely adopted indicator of plant water stress for irrigation management practices; however, dependence of its efficacy on data acquisition time during the daytime is yet to be investigated rigorously. In this paper, plant water stress captured by a series of UAV remote sensing campaigns at different times of the day (9h, 12h and 15h) in a nectarine orchard were analyzed to examine the diurnal behavior of plant water stress represented by the CWSI against measured plant physiological parameters. CWSI values were derived using a probability modelling, named ‘Adaptive CWSI’, proposed by our earlier research. The plant physiological parameters, such as stem water potential (ψstem) and stomatal conductance (gs), were measured on plants for validation concurrently with the flights under different irrigation regimes (0, 20, 40 and 100 % of ETc). Estimated diurnal CWSIs were compared with plant-based parameters at different data acquisition times of the day. Results showed a strong relationship between ψstem measurements and the CWSIs at midday (12 h) with a high coefficient of determination (R2 = 0.83). Diurnal CWSIs showed a significant R2 to gs over different levels of irrigation at three different times of the day with R2 = 0.92 (9h), 0.77 (12h) and 0.86 (15h), respectively. The adaptive CWSI method used showed a robust capability to estimate plant water stress levels even with the small range of changes presented in the morning. Results of this work indicate that CWSI values collected by UAV-borne thermography between mid-morning and mid-afternoon can be used to map plant water stress with a consistent efficacy. This has important implications for extending the time-window of UAV-borne thermography (and subsequent areal coverage) for accurate plant water stress mapping beyond midday.

Why it matches plant phenotyping methodsUAV熱画像からCWSIを算出してネクタリン樹の水ストレスを推定し、取得時刻依存性を生理指標で検証することが研究の中心である。

titleDependence of CWSI-Based Plant Water Stress Estimation with Diurnal Acquisition Times in a Nectarine Orchard

Phenotyping stomatal closure by thermal imaging for GWAS and TWAS of water use efficiency-related genes →

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.

Thermal Analysis of Stomatal Response under Salinity and High Light. →

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.

The relationship between plant growth and water consumption: a history from the classical four elements to modern stable isotopes →

Abstract Key message The history of the relationship between plant growth and water consumption is retraced by following the progression of scientific thought through the centuries: from a purely philosophical question, to conceptual and methodological developments, towards a research interest in plant functioning and the interaction with the environment. Context The relationship between plant growth and water consumption has for a long time occupied the minds of philosophers and natural scientists. The ratio between biomass accumulation and water consumption is known as water use efficiency and is widely relevant today in fields as diverse as plant improvement, forest ecology and climate change. Defined at scales varying from single leaf physiology to whole plants, it shows how botanical investigations changed through time, generally in tandem with developing disciplines and improving methods. The history started as a purely philosophical question by Greek philosophers of how plants grow, progressed through thought and actual experiments, towards an interest in the functioning of plants and the relationship to the environment. Aims This article retraces this history by following the progression of scientific questions posed through the centuries, and presents not only the main methodological and conceptual developments on biomass growth and transpiration but also the development of the carbon isotopic method of estimation. The history of research on photosynthesis is only touched briefly, but the development of research on transpiration and stomatal conductance is presented with more detail. Conclusion Research on water use efficiency, following a path from the whole plant to leaf-level functioning, was strongly involved in the historical development of the discipline of plant ecophysiology and is still a very active research field across nearly all levels of botanical research.

Why it matches plant phenotyping methods植物の成長、バイオマス、水消費、蒸散、気孔コンダクタンス、水利用効率を対象に、測定・推定法の歴史的発展を体系的にレビューしており、植物形質の取得方法が中心である。

abstractpresents not only the main methodological and conceptual developments on biomass growth and transpiration but also the development of the carbon isotopic method of estimation

Classical phenotyping and deep learning concur on genetic control of stomatal density and area in sorghum →

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.

Toward accurate estimating of crop leaf stomatal conductance combining thermal IR imaging, weather variables, and machine learning →

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.

Stomatal conductance limited the CO 2 response of grassland in the last century. →

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

Hyperspectral sensing of photosynthesis, stomatal conductance, and transpiration for citrus tree under drought condition →

Abstract Obtaining variation in water use and photosynthetic capacity is a promising route toward yield increases, but it is still too laborious for large-scale rapid monitoring and prediction. We tested the application of hyperspectral reflectance as a high-throughput phenotyping approach for early identification of water stress and rapid assessment of leaf photosynthetic traits in citrus trees. To this end, photosynthetic CO 2 assimilation rate ( Pn ), stomatal conductance ( Cond ) and transpiration rate ( Trmmol ) were measured with gas-exchange approaches alongside measurements of leaf hyperspectral reflectance from citrus grown across a gradient of soil drought levels. Water stress caused Pn, Cond and Trmmol rapid and continuous decreases in whole drought period. Upper layer was more sensitive to drought than middle and lower layers. Original reflectance spectra of three drought treatments were surprisingly of low diversity and could not track drought responses, whereas specific hyperspectral spectral vegetation indices (SVIs) and absorption features or wavelength position variables presented great potential. Performance of four machine learning algorithms were assessed and random forest (RF) algorithm yielded the highest predictive power for predicting photosynthetic parameters. Our results indicated that leaf hyperspectral reflectance was a reliable and stable method for monitoring water stress and yield increasing in large-scale orchards. Highlight An efficient and stable methods using hyperspectral features for early and pre-visual identification of drought and machine learning techniques for predicting photosynthetic capacity.

Why it matches plant phenotyping methods柑橘の光合成・気孔コンダクタンス・蒸散をハイパースペクトル反射と機械学習で推定する手法の開発・評価が中心であり、植物生理形質のハイスループット表現型計測に該当する。

abstractWe tested the application of hyperspectral reflectance as a high-throughput phenotyping approach for early identification of water stress and rapid assessment of leaf photosynthetic traits in citrus trees.

Vegetation indices and NIR-SWIR spectral bands as a phenotyping tool for water status determination in soybean →

Drought is one of the main limiting factors of soybean production. The great deal of time and effort that current available phenotyping methods demand hampers the selection of tolerant genotypes. Therefore, the development of techniques capable of determining the water status of plants in a fast and practical way may improve the ability to distinguish genotypes under water deficit conditions. The aim of this study was to correlate physiological variables such as relative water content and gas exchange measurements, with vegetation indices (VIs) and spectral bands in order to optimize tools for plant phenotyping. Two trials were carried out, one in a climatic chamber and one in the field. The soybean genotypes were submitted to water deficit and control (irrigated) conditions. The variables measured were relative water content, leaf temperature, photosynthesis, transpiration, stomatal conductance and internal CO₂ content. The VIs NDWI₍₁₀₀₀–₁₆₀₀₎, NDWI₍₁₀₀₀–₂₃₀₀₎, NMDI, MSI and the spectral bands SWIR₁₆₀₀, SWIR₂₃₀₀, ρ1440, ρ1920, ρ1440+ρ1920, ρ1920−ρ1440 and SWIR−ρ1440 were obtained using a hyperspectral sensor. According to the results, the physiological measurements, the VIs and the spectral bands were able to differentiate the water conditions to which the genotypes were submitted and, in some cases, the indices and bands were more sensitive than the physiological measures to detect genotype effect. All indices and bands were efficient in determining the water status of soybean plants. However, the SWIR indices were the most sensitive, allowing the differentiation of a greater number of genotypes with high accuracy.

Why it matches plant phenotyping methodsダイズの水分状態という植物形質を、ハイパースペクトルセンサーと植生指数・スペクトル帯で高速推定する手法の開発・最適化が研究の中心であり、単なる生理測定ではない。

abstractTherefore, the development of techniques capable of determining the water status of plants in a fast and practical way may improve the ability to distinguish genotypes under water deficit conditions.

A Stomata Classification and Detection System in Microscope Images of Maize Cultivars →

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.

A relook into plant wilting: observational evidence based on unsaturated soil-plant-photosynthesis interaction. →

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.

Machine learning enabled phenotyping for GWAS and TWAS of WUE traits in 869 field-grown sorghum accessions →

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).

LabelStoma: A tool for stomata detection based on the YOLO algorithm →

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.

Optical topometry and machine learning to rapidly phenotype stomatal patterning traits for QTL mapping in maize →

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.

One-step and large-scale fabrication of flexible and wearable humidity sensor based on laser-induced graphene for real-time tracking of plant transpiration at bio-interface →

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).

Accelerating Automated Stomata Analysis Through Simplified Sample Collection and Imaging Techniques. →

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.

Novel perspectives on stomatal impressions: Rapid and non-invasive surface characterization of plant leaves by scanning electron microscopy →

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.

Penetration of foliar-applied Zn and its impact on apple plant nutrition status: in vivo evaluation by synchrotron-based X-ray fluorescence microscopy →

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

Racing against stomatal attenuation: rapid CO2 response curves more reliably estimate photosynthetic capacity than steady state curves in a low conductance species →

A/Ci curves are an important gas-exchange-based approach to understanding the regulation of photosynthesis, describing the response of net CO2 assimilation (A) to leaf internal concentration of CO2 (Ci). Low stomatal conductance species pose a challenge to the measurement of A/Ci curves by reducing the signal-to-noise ratio of gas exchange measures. Additionally, the stomatal attenuation effect of elevated ambient CO2 leads to further reduction of conductance and may lead to erroneous interpretation of high Ci responses of A. Rapid A/Ci response (RACiR) curves offer a potential practice to develop A/Ci curves faster than the stomatal closure response to elevated CO2. We used the moderately low conductance Citrus to compare traditional steady state (SS) A/Ci curves with RACiR curves. SS curves failed more often than RACiR curves. Overall parameter estimates were the same between SS and RACiR curves. When low stomatal conductance values were removed, triose-phosphate utilization (TPU) limitation estimates increased. Overall RACiR stomatal conductance values began and remained higher than SS values. Based on the comparable resulting parameter estimates, higher likelihood of success and reduced measurement time, we propose RACiR as a valuable tool to measure A/Ci responses in low conductance species.

Why it matches plant phenotyping methods低コンダクタンス植物の光合成能力を測定するA/Ciガス交換法について、従来法との比較検証と測定手順の改善を中心に扱っているため、植物フェノタイピング手法として含める。

abstractRapid A/Ci response (RACiR) curves offer a potential practice to develop A/Ci curves faster than the stomatal closure response to elevated CO2.

Crop Management in Controlled Environment Agriculture (CEA) Systems Using Predictive Mathematical Models. →

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.

Minimum conductance in leaves-cuticle, leaky stomata, or water vapor saturation? →

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)

Robust estimates of cuticle conductance on stomatous leaf surfaces during the light induction of photosynthesis →

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.

Effect of ploidy level on guard cell length and use of stomata to discard diploids among putative haploids in maize →

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

Quantifying Light Response of Leaf-Scale Water-Use Efficiency and Its Interrelationships With Photosynthesis and Stomatal Conductance in C 3 and C 4 Species. →

Light intensity ( I ) is the most dynamic and significant environmental variable affecting photosynthesis ( A n ), stomatal conductance ( g s ), transpiration ( T r ), and water-use efficiency (WUE). Currently, studies characterizing leaf-scale WUE- I responses are rare and key questions have not been answered. In particular, (1) What shape does the response function take? (2) Are there maximum intrinsic (WUE i ; WUE i-max ) and instantaneous WUE (WUE inst ; WUE inst-max ) at the corresponding saturation irradiances ( I i-sat and I inst-sat )? This study developed WUE i - I and WUE inst - I models sharing the same non-asymptotic function with previously published A n - I and g s - I models. Observation-modeling intercomparison was conducted for field-grown plants of soybean (C 3 ) and grain amaranth (C 4 ) to assess the robustness of our models versus the non-rectangular hyperbola models (NH models). Both types of models can reproduce WUE- I curves well over light-limited range. However, at light-saturated range, NH models overestimated WUE i-max and WUE inst-max and cannot return I i-sat and I inst-sat due to its asymptotic function. Moreover, NH models cannot describe the down-regulation of WUE induced by high light, on which our models described well. The results showed that WUE i and WUE inst increased rapidly within low range of I , driven by uncoupled photosynthesis and stomatal responsiveness. Initial response rapidity of WUE i was higher than WUE inst because the greatest increase of A n and T r occurred at low g s . C 4 species showed higher WUE i-max and WUE inst-max than C 3 species-at similar I i-sat and I inst-sat . Our intercomparison highlighted larger discrepancy between WUE i - I and WUE inst - I responses in C 3 than C 4 species, quantitatively characterizing an important advantage of C 4 photosynthetic pathway-higher A n gain but lower T r cost per unit of g s change. Our models can accurately return the wealth of key quantities defining species-specific WUE- I responses-besides A n - I and g s - I responses. The key advantage is its robustness in characterizing these entangled responses over a wide I range from light-limited to light-inhibitory light intensities, through adopting the same analytical framework and the explicit and consistent definitions on these responses. Our models are of significance for physiologists and modelers-and also for breeders screening for genotypes concurrently achieving maximized photosynthesis and optimized WUE.

Why it matches plant phenotyping methods葉スケールの水利用効率と光応答を定量化する新規モデルを開発し、観測値との比較および既存モデルとの頑健性検証を行っており、植物形質の取得・抽出法が研究の中心である。

abstractThis study developed WUE i - I and WUE inst - I models sharing the same non-asymptotic function with previously published A n - I and g s - I models.

Visualization of spatial gene expression in plants by modified RNAscope fluorescent in situ hybridization →

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.

C-dots as non-toxic, non-destructive novel tracers to measure biochemical cycles in the soil-plant-atmosphere continuum →

Tracers provide a way to determine, follow and quantify biochemical cycles and energy fluxes within the soil-plant-atmosphere continuum (SPAC). Thereby, different tracers, such as dyes, carbonyl sulfite or stable isotopes are employed. One major disadvantage of many tracers is, that very often, plants have to be destructively harvested to analyze the tracer concentration, making it difficult to measure continuous fluxes. Additionally, for stable isotope studies, fractionation or exchange effects can make interpretation and quantification of biogeochemical fluxes difficult. Novel tracers that are already frequently used in animal systems, are fluorescent C-dots. These nanoparticles (5-50 nm diameter) provide a non-destructive imaging option using “in vivo imaging systems” (IVIS). We examined a first approach to apply and measure C-dots as possible tracers in tree saplings of three species ( Picea glauca , Pinus strobus , Tsuga canadensis ). Roots were excavated from soils and exposed to 20 µmol/l liquid silica-based nanoparticles (diameter of 5.1 nm) labeled with a near-infrared fluorophore, cyanine 5.5 (excitation maximum 646 nm, emission maximum 662 nm). Subsequent continuous IVIS imaging showed real-time uptake and transport of the C-dots by the trees. Respective fluorescence intensity revealed the concentration of C-dots in each of the tissues at measured time steps. Subsequent cross-sectioning of roots, stems and leaves elucidated the internal transport pathway of the C-dots inside the saplings’ tissues. Finally, measurements of stomatal conductance, photosynthesis, transpiration rate, stem hydraulic conductivity and pre-dawn leaf water potentials in comparison to control saplings revealed no phytotoxic effect by the C-dots on plant functioning. In conclusion, C-dots provide a non-toxic new technique for measuring biochemical cycles within the SPAC. Future applications include high resolution tracing of water flow cycles and turnover times within the SPAC, compound specific analyses of root exudation or determining mechanisms of pest influences on plant metabolism.

Why it matches plant phenotyping methods植物体内のトレーサー濃度・輸送を非破壊IVIS画像で連続測定する手法が研究の中心であり、植物の生理状態・組織内輸送を定量化している。

abstractThese nanoparticles (5-50 nm diameter) provide a non-destructive imaging option using “in vivo imaging systems” (IVIS).

Estimation of Stomatal Conductance using Crop Water Stress Index based on the Thermal Image at a Leaf Scale →

In 1980s, Crop Water Stress Index (CWSI) is suggested to indicate the water stress of crops. CWSI is based on the leaf energy balance, which is closely related to leaf temperature. To calculate CWSI, meteorological factors such as air temperature and vapor pressure deficit should be measured besides leaf temperature. As recent technology has been developed, leaf temperature can be easily observed by thermal camera or infrared thermometer. Stomatal conductance (g s , mmol m -2 s -1 ) is one of the critical factors to understand crop photosynthesis and water demand. In addition, the behaviors of g s can represent the biotic and abiotic plant stresses. In abnormal condition, such as drought, insects or disease, g s getting lower. The observation of g s will make better to evaluate and predict crop growth and conditions. Therefore, the time series data of g s is useful for the monitoring of crop growth and the quick detection of abnormal crop condition in smart-farming system but there are some limitations to measure g s continuously and easily. We assume that there is some relationship between CWSI and g s because both has strong relation to leaf temperature. Thus, the aim of this study is to investigate possibility of estimation of g s using CWSI which is derived from thermal image. Through the data collected from literatures, negative correlations between CWSI and g s were revealed. The slope of correlation was changed according to crop types. In addition, as a result of simulation, there is almost linear negative relationship between CWSI and g s , and the slope was determined by maximum stomatal conductance (g s_max ). Field measurement in this study was also demonstrated to identify such correlation. Further, various methods to measure CWSI were tested. This relationship will contribute to not only monitoring of crop stress for irrigation scheduling in smart farm system but also estimating evapotranspiration, photosynthesis, and crop yield.

Why it matches plant phenotyping methods熱画像からCWSIを算出し、植物の生理形質である気孔コンダクタンスを推定する手法を検討・シミュレーション・圃場実測で検証しており、フェノタイピング手法が中心である。

abstractthe aim of this study is to investigate possibility of estimation of g s using CWSI which is derived from thermal image.

Prediction and monitoring of leaf water content in soybean plants using terahertz time-domain spectroscopy →

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.

Optimized Protocol for OnGuard2 Software in Studying Guard Cell Membrane Transport and Stomatal Physiology. →

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.

Spatial and Temporal Variability of Plant Leaf Responses Cascade after PSII Inhibition: Raman, Chlorophyll Fluorescence and Infrared Thermal Imaging →

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.

Identification of Novel Regulators of Plant Transpiration by Large-Scale Thermal Imaging Screening in Helianthus Annuus →

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の開発と実証が中心であり、単なる生物学的測定ではない。

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.

Identification of Novel Regulators of Plant Transpiration by Large-Scale Thermal Imaging Screening in Helianthus Annuus →

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.

Flux-Based Ozone Risk Assessment for a Plant Injury Index (PII) in Three European Cool-Temperate Deciduous Tree Species →

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.

DNA content equivalence in haploid and diploid maize leaves. →

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.

Maize Canopy Temperature Extracted From UAV Thermal and RGB Imagery and Its Application in Water Stress Monitoring. →

To identify drought-tolerant crop cultivars or achieve a balance between water use and yield, accurate measurements of crop water stress are needed. In this study, the canopy temperature (Tc) of maize at the late vegetative stage was extracted from high-resolution red-green-blue (RGB, 1.25 cm) and thermal (7.8 cm) images taken by an unmanned aerial vehicle (UAV). To reduce the number of parameters for crop water stress monitoring, four simple methods that require only Tc were identified: Tc, degrees above non-stress, standard deviation of Tc, and variation coefficient of Tc. The ground-truth temperatures obtained using a handheld infrared thermometer were used to calibrate the temperature obtained from the UAV thermal images and to evaluate the Tc extraction results. Measured leaf stomatal conductance values were used to evaluate the performance of the four Tc-based crop water stress indicators. The results showed a strong correlation between ground-truth Tc and Tc extracted by the red-green ratio index (RGRI)-Otsu method proposed in this study, with a coefficient of determination of 0.94 ( n = 15) and root mean square error value of 0.7°C. The RGRI-Otsu method was most accurate for estimating temperatures around 32.9°C, but the magnitude of residuals increased above and below this value. This phenomenon may be attributable to changes in canopy cover (leaf curling) under water stress, resulting in changes in the proportion of exposed sunlit soil in UAV thermal orthophotographs. Therefore, to improve the accuracy of maize canopy detection and extraction, optimal methods and better strategies for eliminating mixed pixels are needed. This study demonstrates the potential of using high-resolution UAV RGB images to supplement UAV thermal images for the accurate extraction of maize Tc.

Why it matches plant phenotyping methodsUAVのRGB・熱画像からトウモロコシ群落温度を抽出する手法を開発・校正・検証しており、植物の水ストレス状態を測定する方法が研究の中心です。

abstractthe canopy temperature (Tc) of maize at the late vegetative stage was extracted from high-resolution red-green-blue (RGB, 1.25 cm) and thermal (7.8 cm) images taken by an unmanned aerial vehicle (UAV).

Dynamic measurement of cytosolic pH and uncovers the role of the vacuolar transporter AtCLCa in the control of cytosolic pH. →

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.

StomataCounter: a neural network for automatic stomata identification and counting. →

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.

Automatic segmentation and measurement methods of living stomata of plants based on the CV model. →

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.

Fruit and Leaf Sensing for Continuous Detection of Nectarine Water Status. →

Continuous assessment of plant water status indicators provides the most precise information for irrigation management and automation, as plants represent an interface between soil and atmosphere. This study investigated the relationship of plant water status to continuous fruit diameter (FD) and inverse leaf turgor pressure rates ( p p ) in nectarine trees [ Prunus persica (L.) Batsch] throughout fruit development. The influence of deficit irrigation treatments on stem ( Ψ stem ) and leaf water potential, leaf relative water content, leaf stomatal conductance, and fruit growth was studied across the stages of double-sigmoidal fruit development in 'September Bright' nectarines. Fruit relative growth rate (RGR) and leaf relative pressure change rate (RPCR) were derived from FD and p p to represent rates of water in- and outflows in the organs, respectively. Continuous RGR and RPCR dynamics were independently and jointly related to plant water status and environmental variables. The independent use of RGR and RPCR yielded significant associations with midday Ψ stem , the most representative index of tree water status in anisohydric species. However, a combination of nocturnal fruit and leaf parameters unveiled an even more significant relationship with Ψ stem , suggesting a changing behavior of fruit and leaf water flows in response to pronounced water deficit. In conclusion, we highlight the suitability of a dual-organ sensing approach for improved prediction of tree water status.

Why it matches plant phenotyping methods果実径と葉の膨圧を連続センシングし、水分状態指標を抽出・予測する手法が研究の中心であるため、植物フェノタイピング手法として含める。

abstractContinuous assessment of plant water status indicators provides the most precise information for irrigation management and automation

Integrated metabolic strategy: A framework for predicting the evolution of carbon‐water tradeoffs within plant clades →

The fundamental tradeoff between carbon gain and water loss has long been predicted as an evolutionary driver of plant strategies across environments. Nonetheless, challenges in measuring carbon gain and water loss in ways that integrate over leaf lifetime have limited our understanding of the variation in and mechanistic bases of this tradeoff. Furthermore, the microevolution of plant traits within species versus the macroevolution of strategies among closely related species may not be the same, and accordingly, the latter must be addressed using comparative phylogenetic analyses. Here we introduce the concept of ‘integrated metabolic strategy’ (IMS) to describe the ratio between carbon isotope composition (δ¹³C) and oxygen isotope composition above source water (Δ¹⁸O) of leaf cellulose. IMS is a measure of leaf‐level conditions that integrate several mechanisms contributing to carbon gain (δ¹³C) and water loss (Δ¹⁸O) over leaf lifespan, with larger values reflecting higher metabolic efficiency and hence less of a tradeoff. We tested how IMS evolves among closely related yet ecologically diverse milkweed species, and subsequently addressed phenotypic plasticity in response to water availability in species with divergent IMS. Integrated metabolic strategy varied strongly among 20 Asclepias species when grown under controlled conditions, and phylogenetic analyses demonstrate species‐specific tradeoffs between carbon gain and water loss. Larger IMS values were associated with species from dry habitats, with larger carboxylation capacity, smaller stomatal conductance and smaller leaves; smaller IMS was associated with wet habitats, smaller carboxylation capacity, larger stomatal conductance and larger leaves. The evolution of IMS was dominated by changes in species’ demand for carbon (δ¹³C) more so than water conservation (Δ¹⁸O). Although some individual physiological traits showed phylogenetic signal, IMS did not. In response to experimental decreases in soil moisture, three species maintained similar IMS across levels of water availability because of proportional increases in δ¹³C and Δ¹⁸O (or little change in either), while one species increased IMS due to disproportional changes in δ¹³C relative to Δ¹⁸O. Synthesis. IMS is a broadly applicable mechanistic tool; IMS variation among and within species may shed light on unresolved questions relating to the evolution and ecology of plant ecophysiological strategies.

Why it matches plant phenotyping methods葉の同位体組成を統合して炭素獲得・水損失という植物生理状態を定量するIMS指標を新たに導入し、複数種で適用・評価しているため、単なる生物学的測定ではなく方法開発を含む実質的な植物フェノタイピング研究である。

abstractHere we introduce the concept of ‘integrated metabolic strategy’ (IMS) to describe the ratio between carbon isotope composition (δ¹³C) and oxygen isotope composition above source water (Δ¹⁸O) of leaf cellulose.

In-situ Real-time Field Imaging and Monitoring of Leaf Stomata by High-resolution Portable Microscope →

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.

Early prediction models for cassava root yield in different water regimes →

The development of cassava (Manihot esculenta Crantz) varieties with greater tolerance of water deficit depends on optimized phenotyping tools. The objective of this work was to develop early prediction models of final root yield (12 months after planting - MAP) using physiological and agronomic data obtained at 4 MAP under two water regimes. Nine genotypes of cassava were evaluated under two treatments (irrigated and with water deficit), using a complete randomized block design, in a factorial scheme of 2 harvest periods (at 4 and 12 MAP) × 9 genotypes, with four replications. Both treatment groups were irrigated until 3 MAP. After this period, irrigation was interrupted for the water deficit treatment group. Fourteen physiological and agronomic traits were evaluated in all harvest periods. Four prediction models were evaluated: linear regression with stepwise selection (LRSS), linear regression with backward selection (LRBS), Bayesian ridge regression (BRR), and partial least squares (PLS). Most of the models presented a high predictive ability for final root yield (R2 ranging from 0.83 to 0.91). However, in all prediction scenarios, the PLS model presented a high R2 (0.84 to 0.91) associated with the lowest root-mean-square error (RMSE) (0.82 to 1.60). Differences in the predictive ability of the models may have occurred due to the relative importance of the early traits. In the case of PLS, the most important traits for the model were stomatal conductance, root yield at 4 MAP, leaf area index and number of roots. Regardless of the water condition, the physiological and agronomic data collected at an early stage could successfully be used to predict the final root yield with great efficiency. This strategy can reduce the cost of phenotyping, increasing the capacity for analysis and optimization of genetic gains for tolerance to drought in cassava.

Why it matches plant phenotyping methodsカッサバの早期形質から最終根収量を予測するモデルを開発・比較し、予測性能を評価しており、植物表現型取得・推定の方法論が中心である。

abstractThe objective of this work was to develop early prediction models of final root yield (12 months after planting - MAP) using physiological and agronomic data obtained at 4 MAP under two water regimes.

Infrared Radiometry as a Tool for Early Water Deficit Detection: Insights into Its Use for Establishing Irrigation Calendars for Potatoes Under Humid Conditions →

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.

Genetic Diversity in Stomatal Density among Soybeans Elucidated Using High-throughput Technique Based on an Algorithm for Object Detection. →

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

Review: High-throughput phenotyping to enhance the use of crop genetic resources →

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

Dynamic leaf energy balance: deriving stomatal conductance from thermal imaging in a dynamic environment →

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.

Fossil leaf traits as archives for the past — and lessons for the future? →

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

Early diagnosis of ploidy status in doubled haploid production of maize by stomata length and flow cytometry measurements →

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.

LSM-W 2 : laser scanning microscopy worker for wheat leaf surface morphology. →

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.

The accuracy and utility of a low cost thermal camera and smartphone-based system to assess grapevine water status →

Smartphones have several advantages over specialist monitoring systems including ubiquity, price, and ease of implementing updates. Thermal imaging can be used to assess plant water status and allow more informed irrigation decisions; unfortunately, this technique has not been widely adopted due to the high cost of equipment and the lack of a system to provide analysis and results in real-time. Several inexpensive thermal cameras that connect to smartphones have recently been released and one of these (FLIR One) was evaluated as part of a system to assess grapevine water status. Irrigation treatments were established on Cabernet Sauvignon and Chardonnay vines in an arid region. Thermal images were taken from the shaded side of the grapevine canopy and software was developed to automatically determine the temperature of the canopy and artificial reference leaves. The temperature readings and metrological inputs were used to calculate five indices of water status including the Crop Water Stress Index (CWSI) and the stomatal conductance index. The best performing was the CWSI, which does not require input from a weather station. Over 30 days of assessment, and a range of irrigation levels, measurements collected with the thermal camera were correlated with stem water potential (R2 = 0.61) and stomatal conductance (R2 = 0.74). Windy conditions appeared to be the major cause of variation between CWSI and stomatal conductance. Inexpensive thermal cameras have the potential to be an easy and accessible tool for the assessment of plant water status and to make better irrigation decisions.

Why it matches plant phenotyping methods低価格熱画像カメラとスマートフォンを用いたブドウ樹の水分状態推定システムを開発・評価しており、植物生理状態の取得方法が研究の中心である。

abstractone of these (FLIR One) was evaluated as part of a system to assess grapevine water status.

The use of quantitative imaging to investigate regulators of membrane trafficking in Arabidopsis stomatal closure. →

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

Natural Selection For Disease Resistance In Hybrid Poplars Targets Stomatal Patterning Traits And Regulatory Genes. →

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.

Image Analysis: Basic Procedures for Description of Plant Structures. →

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

Factors influencing the measurement of assimilation and stomatal conductance with the LI-COR 6400XT gas exchange system. →

Although CO2 and H2O exchange rates are often measured in experiments as indicators of physiological plant responses these \"gas exchange\" measurements are prone to large experimental error. Gas exchange equipment and technology have improved greatly over the past two decades which supports scrutinizing current issues of experimental error in measuring plant photosynthesis and stomatal conductance. This report shows results of a greenhouse experiment with the goal of identifying lessor understood sources of experimental error and variation in measurements with the LI-COR 6400XT gas exchange system. A variety of plant types were used to encompass differing species variation. We found significant sources of experimental error in 1) the time for initial adjustment when placing a leaf in the leaf chamber 2) the time-of-day when measuring 3) leaf age 4) having the chamber window full vs. partially full with leaf tissue 5) using a leaf chamber environment that greatly diverges from the whole plant environment 6) differing degree of experimental error depending upon plant species. A situation with multiple contributors to error would result in useless gas-exchange data. Recommendations for minimizing these experimental errors are given.

Why it matches plant phenotyping methodsLI-CORガス交換システムによる光合成・気孔コンダクタンス測定の誤差要因を実験的に検証し、測定改善策を提示しており、植物表現型取得法が中心である。

abstractThis report shows results of a greenhouse experiment with the goal of identifying lessor understood sources of experimental error and variation in measurements with the LI-COR 6400XT gas exchange system.

Non-functional and weak alleles of FRIGIDA and FLOWERING LOCUS C reduce lifetime water-use independent of leaf-level water-use-efficiency traits in Arabidopsis thaliana →

Natural selection driven by water availability has resulted in considerable variation for traits associated with drought tolerance and leaf level water-use efficiency ( WUE ). In Arabidopsis, little is known about the variation of whole-plant water use (PWU) and whole-plant WUE (TE). To investigate the genetic basis of PWU, we developed a novel proxy trait by combining flowering time and rosette water use to estimate lifetime PWU. We validated its usefulness for large scale screening of mapping populations in a subset of ecotypes. This parameter subsequently facilitated the screening of water-use but also drought tolerance traits in a recombinant inbred line population derived from two Arabidopsis accessions with distinct water use strategies, namely C24 (low PWU) and Col-0 (high PWU). Subsequent quantitative trait loci (QTL) mapping and validation through near-isogenic lines identified two causal QTLs, which showed that a combination of weak and non-functional alleles of the FRIGIDA (FRI) and FLOWERING LOCUS C (FLC) genes substantially reduced plant water-use without penalising reproductive performance. Drought tolerance traits, stomatal conductance, intrinsic water use efficiency (δ 13 C) and rosette water-use were independent of allelic variation at FRI and FLC , suggesting that flowering is critical in determining life-time plant water use, but not leaf-level traits.

Why it matches plant phenotyping methods生涯の全植物水利用量を推定する新規プロキシ形質を開発し、大規模スクリーニングへの有用性を検証しており、表現型取得・推定手法が実質的に中心である。

abstractwe developed a novel proxy trait by combining flowering time and rosette water use to estimate lifetime PWU.

Rapid Estimation of Stomatal Density and Stomatal Area of Plant Leaves Based on Object-Oriented Classification and Its Ecological Trade-Off Strategy 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.

An introductory guide to gas exchange analysis of photosynthesis and its application to plant phenotyping and precision irrigation to enhance water use efficiency →

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.

A remote sensing-based two-leaf canopy conductance model: Global optimization and applications in modeling gross primary productivity and evapotranspiration of crops →

The temporal dynamics of optimum stomatal conductance (gsmax), as well differences between C3 and C4 crops, have rarely been considered in previous remote sensing (RS)-based Jarvis-type canopy conductance (Gc) models. To address this issue, a RS-based two-leaf Jarvis-type Gc model, RST-Gc, was optimized and validated for C3 and C4 crops using 19 crop flux sites across Europe, North America, and China. RST-Gc included restrictive functions for air temperature, vapor pressure deficit, and soil water deficit, and it used satellite-retrieved NDVI to formulate the temporal variation of gsmax defined at a photosynthetic photon flux density (PPFD) of 2000 μmol m−2 s−1 (gsm, 2000). Results showed that the parameters of RST-Gc differed between C3 and C4 crops. RST-Gc successfully simulated variations in Penman–Monteith (PM)-derived daytime Gc with R2 = 0.57 for both C3 and C4 crops. RST-Gc was incorporated into a revised evapotranspiration (ET) model and a new gross primary productivity (GPP) model. The two models were validated at 19 crop flux sites. Daily mean inputs were generally incorporated into a PM approach to model daily transpiration. This is inappropriate because available energy and stomatal conductance vary significantly on a diurnal basis, with both non-linearly regulating transpiration rate. The PM approach with daily mean inputs produced unreasonable transpiration rate estimates. Efforts were made in the revised ET model (denoted as RS-WBPM2), which was modified from the water balance based RS-PM (RS-WBPM) model of Bai et al. (2017), to address this issue by calculating transpiration using daytime inputs. The photosynthesis-based stomatal conductance model, developed by Ball et al. (1987a) and improved by Leuning (1995) (BBL model), was inverted to calculate GPP using canopy conductance; the inverted model was denoted as IBBL. Cross validation showed good agreement between flux tower measurements and modeled ET (R2 = 0.79, RMSE (root mean standard error) = 20.66 W m−2 for daily ET and R2 = 0.87, RMSE = 15.32 W m−2 for 16-day ET) and GPP (R2 = 0.83, RMSE = 2.49 gC m−2 d−1 for daily GPP and R2 = 0.86, RMSE = 1.96 gC m−2 d−1 for 16-day GPP) for the two models. Within-site validations demonstrated the successful performance of the two models at 18 sites (albeit with one outlier). Inter-site variations in ET and GPP were also successfully reproduced by the models. NDVI-derived gsm, 2000 outperformed the fixed gsm, 2000 in both ET and GPP estimates. The results imply that the RS-WBPM2 and IBBL models are useful tools for modeling regional and global ET and GPP.

Why it matches plant phenotyping methods衛星NDVIと気象・土壌情報から作物キャノピーコンダクタンスという生理状態を推定するモデルを開発し、19地点で最適化・検証しており、植物状態の取得手法が中心である。

abstracta RS-based two-leaf Jarvis-type Gc model, RST-Gc, was optimized and validated for C3 and C4 crops using 19 crop flux sites across Europe, North America, and China.

On the minimum leaf conductance: its role in models of plant water use, and ecological and environmental controls →

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

Flexible PI-Based Plant Drought Stress Sensor for Real-Time Monitoring System in Smart Farm →

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.

A stomatal control model based on optimization of carbon gain versus hydraulic risk predicts aspen sapling responses to drought. →

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.

Proximal hyperspectral sensing of stomatal conductance to monitor the efficacy of exogenous abscisic acid applications in apple trees →

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.

Quantification of Starch in Guard Cells of Arabidopsis thaliana . →

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.

Technical Note: A simple theoretical model framework to describe plant stomatal sluggishness in response to elevated ozone concentrations →

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.

Two Inexpensive and Non-destructive Techniques to Correct for Smaller-Than-Gasket Leaf Area in Gas Exchange Measurements. →

The development of technology, like the widely-used off-the-shelf portable photosynthesis systems, for the quantification of leaf gas exchange rates and chlorophyll fluorescence offered photosynthesis research a massive boost. Gas exchange parameters in such photosynthesis systems are calculated as gas exchange rates per unit leaf area. In small chambers ( 2 ), the leaf area used by the system for these calculations is actually the internal gasket area (A G ), provided that the leaf covers the entire A G . In this study, we present two inexpensive and non-destructive techniques that can be used to easily quantify the enclosed leaf area (A L ) of plant species with leaves of surface area much smaller than the A G , such as that of cereal crops. The A L of the cereal crop species studied has been measured using a standard image-based approach ( i A L ) and estimated using a leaf width-based approach ( w A L ). i A L and w A L did not show any significant differences between them in maize, barley, hard and soft wheat. Similar results were obtained when the w A L was tested in comparison with i A L in different positions along the leaf in all species studied. The quantification of A L and the subsequent correction of leaf gas exchange parameters for A L provided a precise quantification of net photosynthesis and stomatal conductance especially with decreasing A L . This study provides two practical, inexpensive and non-destructive solutions to researchers dealing with photosynthesis measurements on small-leaf plant species. The image-based technique can be widely used for quantifying A L in many plant species despite their leaf shape. The leaf width-based technique can be securely used for quantifying A L in cereal crop species such as maize, wheat and barley along the leaf. Both techniques can be used for a wide range of gasket shapes and sizes with minor technique-specific adjustments.

Why it matches plant phenotyping methods小葉の葉面積を画像法・葉幅法で非破壊推定し、ガス交換測定を補正する手法の開発・比較検証が研究の中心である。

abstractwe present two inexpensive and non-destructive techniques that can be used to easily quantify the enclosed leaf area (A L )

Measurement of Arabidopsis thaliana Plant Traits Using the PHENOPSIS Phenotyping Platform. →

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

Analysis of Arabidopsis thaliana Growth Behavior in Different Light Qualities. →

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.

In field quantification and discrimination of different vineyard water regimes by on-the-go NIR spectroscopy →

Precise and rapid methods to assess plant water status are needed in agriculture. The goal of this work was to evaluate the capability of a new plant-based method based on proximal near-infrared (NIR) spectroscopy acquired on-the-go from a moving vehicle to quantify and discriminate different water regimes in a commercial vineyard. Proximal on-the-go NIR spectroscopy (1100–2100 nm) was acquired at solar noon on five days from veraison (onset of ripening) to harvest 2015 in a commercial Tempranillo vineyard. Spectral measurements were taken at ∼0.30 m from the canopy, on both canopy sides, from a vehicle moving at 5 km h⁻¹. Measurements of midday stem water potential (Ψₛ) and leaf stomatal conductance (gₛ) were simultaneously acquired to be used as reference indicators of plant water status. Partial least squares (PLS) was used to build calibration, cross validation and predictive models for Ψₛ and gₛ. The determination coefficients of prediction (R²ₚ) were above 0.86 for Ψₛ and above 0.66 for gₛ, while the root mean square errors of prediction (RMSEP) were less than 0.18 MPa and 93.7 mmol [H₂O] m⁻² s⁻¹, respectively. PLS-Discriminant Analysis (PLS-DA) was applied to classify the data into three different water regimes, according to Ψₛ or gₛ. The average correctly classified percentage was greater than 72% for Ψₛ and gₛ. This discriminant capability, together with the large number of measurements that the on-the-go NIR spectroscopy can provide, enables the quantification and mapping of the variability of a vineyard water status and may help to define precise irrigation strategies in viticulture.

Why it matches plant phenotyping methodsブドウ樹の水分状態という植物生理形質を、移動式近接NIR分光で定量・識別する手法を開発・検証しており、手法が研究の中心である。

abstractThe goal of this work was to evaluate the capability of a new plant-based method based on proximal near-infrared (NIR) spectroscopy acquired on-the-go from a moving vehicle to quantify and discriminate different water regimes in a commercial vineyard.

The Assay of Abscisic Acid-Induced Stomatal Movement in Leaf Senescence. →

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.

Assessing the Effects of Water Deficit on Photosynthesis Using Parameters Derived from Measurements of Leaf Gas Exchange and of Chlorophyll a Fluorescence. →

Water deficit (WD) is expected to increase in intensity, frequency and duration in many parts of the world as a consequence of global change, with potential negative effects on plant gas exchange and growth. We review here the parameters that can be derived from measurements made on leaves, in the field, and that can be used to assess the effects of WD on the components of plant photosynthetic rate, including stomatal conductance, mesophyll conductance, photosynthetic capacity, light absorbance, and efficiency of absorbed light conversion into photosynthetic electron transport. We also review some of the parameters related to dissipation of excess energy and to rerouting of electron fluxes. Our focus is mainly on the techniques of gas exchange measurements and of measurements of chlorophyll a fluorescence (ChlF), either alone or combined. But we put also emphasis on some of the parameters derived from analysis of the induction phase of maximal ChlF, notably because they could be used to assess damage to photosystem II. Eventually we briefly present the non-destructive methods based on the ChlF excitation ratio method which can be used to evaluate non-destructively leaf contents in anthocyanins and flavonols.

Why it matches plant phenotyping methods植物の光合成・水分欠 deficit影響を評価するガス交換およびクロロフィル蛍光測定法と導出パラメータを中心にレビューしており、植物生理形質の取得手法が主題である。

abstractWe review here the parameters that can be derived from measurements made on leaves, in the field, and that can be used to assess the effects of WD on the components of plant photosynthetic rate

Phenomics allows identification of genomic regions affecting maize stomatal conductance with conditional effects of water deficit and evaporative demand. →

Stomatal conductance is central for the trades-off between hydraulics and photosynthesis. We aimed at deciphering its genetic control and that of its responses to evaporative demand and water deficit, a nearly impossible task with gas exchanges measurements. Whole-plant stomatal conductance was estimated via inversion of the Penman-Monteith equation from data of transpiration and plant architecture collected in a phenotyping platform. We have analysed jointly 4 experiments with contrasting environmental conditions imposed to a panel of 254 maize hybrids. Estimated whole-plant stomatal conductance closely correlated with gas-exchange measurements and biomass accumulation rate. Sixteen robust quantitative trait loci (QTLs) were identified by genome wide association studies and co-located with QTLs of transpiration and biomass. Light, vapour pressure deficit, or soil water potential largely accounted for the differences in allelic effects between experiments, thereby providing strong hypotheses for mechanisms of stomatal control and a way to select relevant candidate genes among the 1-19 genes harboured by QTLs. The combination of allelic effects, as affected by environmental conditions, accounted for the variability of stomatal conductance across a range of hybrids and environmental conditions. This approach may therefore contribute to genetic analysis and prediction of stomatal control in diverse environments.

Why it matches plant phenotyping methodsフェノタイピングプラットフォームのデータからPenman–Monteith式を逆算して全植物体の気孔コンダクタンスを推定し、ガス交換測定との相関で検証しているため、表現型取得・推定法が研究上の主要な技術要素です。

abstractWhole-plant stomatal conductance was estimated via inversion of the Penman-Monteith equation from data of transpiration and plant architecture collected in a phenotyping platform.

Microscope image based fully automated stomata detection and pore measurement method for grapevines. →

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.

Persistent drought monitoring using a microfluidic-printed electro-mechanical sensor of stomata in planta. →

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

Integrative field scale phenotyping for investigating metabolic components of water stress within a vineyard →

Background There is currently a high requirement for field phenotyping methodologies/technologies to determine quantitative traits related to crop yield and plant stress responses under field conditions. Methods We employed an unmanned aerial vehicle equipped with a thermal camera as a high-throughput phenotyping platform to obtain canopy level data of the vines under three irrigation treatments. High-resolution imagery ( g c ) via the leaf energy balance model. In parallel, physiological stress measurements at leaf and stem level as well as leaf sampling for primary and secondary metabolome analysis were performed. Results Aerial g c correlated significantly with leaf stomatal conductance ( g s ) and stem sap flow, benchmarking the quality of our remote sensing technique. Metabolome profiles were subsequently linked with g c and g s via partial least square modelling. By this approach malate and flavonols, which have previously been implicated to play a role in stomatal function under controlled greenhouse conditions within model species, were demonstrated to also be relevant in field conditions. Conclusions We propose an integrative methodology combining metabolomics, organ-level physiology and UAV-based remote sensing of the whole canopy responses to water stress within a vineyard. Finally, we discuss the general utility of this integrative methodology for broad field phenotyping.

Why it matches plant phenotyping methodsUAV搭載熱カメラによるブドウ樹冠の水ストレス形質推定を中心に、地上生理測定とのベンチマークまで行う統合的フィールド表現型解析である。

abstractWe employed an unmanned aerial vehicle equipped with a thermal camera as a high-throughput phenotyping platform to obtain canopy level data of the vines under three irrigation treatments.

Using Silicon Polymer Impression Technique and Scanning Electron Microscopy to Measure Stomatal Aperture, Morphology, and Density. →

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.

Adaptive Estimation of Crop Water Stress in Nectarine and Peach Orchards Using High-Resolution Imagery from an Unmanned Aerial Vehicle (UAV) →

The capability to monitor water status from crops on a regular basis can enhance productivity and water use efficiency. In this paper, high-resolution thermal imagery acquired by an unmanned aerial vehicle (UAV) was used to map plant water stress and its spatial variability, including sectors under full irrigation and deficit irrigation over nectarine and peach orchards at 6.12 cm ground sample distance. The study site was classified into sub-regions based on crop properties, such as cultivars and tree training systems. In order to enhance the accuracy of the mapping, edge extraction and filtering were conducted prior to the probability modelling employed to obtain crop-property-specific (‘adaptive’ hereafter) lower and higher temperature references (Twet and Tdry respectively). Direct measurements of stem water potential (SWP, ψstem) and stomatal conductance (gs) were collected concurrently with UAV remote sensing and used to validate the thermal index as crop biophysical parameters. The adaptive crop water stress index (CWSI) presented a better agreement with both ψstem and gs with determination coefficients (R2) of 0.72 and 0.82, respectively, while the conventional CWSI applied by a single set of hot and cold references resulted in biased estimates with R2 of 0.27 and 0.34, respectively. Using a small number of ground-based measurements of SWP, CWSI was converted to a high-resolution SWP map to visualize spatial distribution of the water status at field scale. The results have important implications for the optimal management of irrigation for crops.

Why it matches plant phenotyping methodsUAV熱画像から作物の水分ストレスを推定する適応型CWSIを開発し、茎水ポテンシャルと気孔コンダクタンスで検証しており、植物生理状態の取得手法が研究の中心である。

abstracthigh-resolution thermal imagery acquired by an unmanned aerial vehicle (UAV) was used to map plant water stress and its spatial variability

Application of thermography for monitoring stomatal conductance of Coffea arabica under different shading systems. →

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 )

Thermal phenotyping of stomatal sensitivity in spring barley →

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 in Arabidopsis thaliana Using Silicone Rubber Imprints. →

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.

Hyperspectral Technologies for Assessing Seed Germination and Trifloxysulfuron-methyl Response in Amaranthus palmeri (Palmer Amaranth). →

Weed infestations in agricultural systems constitute a serious challenge to agricultural sustainability and food security worldwide. Amaranthus palmeri S. Watson (Palmer amaranth) is one of the most noxious weeds causing significant yield reductions in various crops. The ability to estimate seed viability and herbicide susceptibility is a key factor in the development of a long-term management strategy, particularly since the misuse of herbicides is driving the evolution of herbicide response in various weed species. The limitations of most herbicide response studies are that they are conducted retrospectively and that they use in vitro destructive methods. Development of a non-destructive method for the prediction of herbicide response could vastly improve the efficacy of herbicide applications and potentially delay the evolution of herbicide resistance. Here, we propose a toolbox based on hyperspectral technologies and data analyses aimed to predict A. palmeri seed germination and response to the herbicide trifloxysulfuron-methyl. Complementary measurement of leaf physiological parameters, namely, photosynthetic rate, stomatal conductence and photosystem II efficiency, was performed to support the spectral analysis. Plant response to the herbicide was compared to image analysis estimates using mean gray value and area fraction variables. Hyperspectral reflectance profiles were used to determine seed germination and to classify herbicide response through examination of plant leaves. Using hyperspectral data, we have successfully distinguished between germinating and non-germinating seeds, hyperspectral classification of seeds showed accuracy of 81.9 and 76.4%, respectively. Sensitive and resistant plants were identified with high degrees of accuracy (88.5 and 90.9%, respectively) from leaf hyperspectral reflectance profiles acquired prior to herbicide application. A correlation between leaf physiological parameters and herbicide response (sensitivity/resistance) was also demonstrated. We demonstrated that hyperspectral reflectance analyses can provide reliable information about seed germination and levels of susceptibility in A. palmeri . The use of reflectance-based analyses can help to better understand the invasiveness of A. palmeri , and thus facilitate the development of targeted control methods. It also has enormous potential for impacting environmental management in that it can be used to prevent ineffective herbicide applications. It also has potential for use in mapping tempo-spatial population dynamics in agro-ecological landscapes.

Why it matches plant phenotyping methods種子発芽と除草剤応答という植物状態を、ハイパースペクトル反射と画像解析で非破壊推定・分類する方法が研究の中心であり、精度も評価している。

abstractHere, we propose a toolbox based on hyperspectral technologies and data analyses aimed to predict A. palmeri seed germination and response to the herbicide trifloxysulfuron-methyl.

Can the Responses of Photosynthesis and Stomatal Conductance to Water and Nitrogen Stress Combinations Be Modeled Using a Single Set of Parameters? →