In situ monitoring of plant responses to stress is one of the most challenging aspects of precision agriculture, and the dynamic control of crop growth according to fluctuating environmental factors. Although fluorescence imaging provides a nondestructive approach for monitoring stress-related biomarkers, its performance is often hindered by the low abundance of endogenous signaling molecules and strong tissue autofluorescence. Here, we report a microneedle-integrated sensing platform that enables sensitive detection of endogenous hydrogen peroxide (H 2 O 2 ) in living plants. The platform incorporates a second near-infrared fluorescent nanoprobe composed of Er 3+ -doped lanthanide nanoparticles emitting at 1550 nm and Mo-doped polymetallic oxomolybdates serving as the H 2 O 2 -responsive unit. Embedding the nanoprobe into custom-fabricated microneedles allows precise positioning on plant midribs for continuous monitoring of H 2 O 2 dynamics. Under stress conditions, the system successfully visualized spatiotemporal fluctuations of H 2 O 2 in living tomato, spinach, and tobacco plants. This work establishes a strategy for early stress diagnosis and developing universal plant health monitoring technologies.
Why it matches plant phenotyping methods植物ストレス状態を生体内H2O2として連続取得・可視化するマイクロニードル統合センシング基盤の開発が中心であり、単なる生物学的測定ではない。
abstractHere, we report a microneedle-integrated sensing platform that enables sensitive detection of endogenous hydrogen peroxide (H 2 O 2 ) in living plants.
Plant phenotyping relevance match · UnverifiedOpenAlex · Europe PMC · checked 14 Sept 2026
ABSTRACT Water-use efficiency (WUE), the ratio of accumulated plant biomass to water lost through transpiration has conventionally been determined using a destructive single-point measurement. Recent advances in high-throughput phenotyping now enable repeated, non-destructive estimation of biomass and WUE. However, these digital measurements must be statistically validated against conventional destructive methods to validate their use as reliable proxies. Therefore, we compared digital biomass determined point clouds produced from multispectral camera scanners with destructive harvests across eight harvests using Samsun tobacco grown under both drought and high-water conditions. WUE efficiency, calculated using the digital biomass estimated from a point cloud and gravimetric water use determinations, were compared to destructive harvest determinations. The coefficient of variation (CV) showed there were no significant differences in digital and destructive measurements for either biomass or WUE. Indicating that digital measurements can be used in place of destructive measurements. Drought plants used significantly less water and were significantly smaller than high-water plants from Harvests 4 through 8. However, there were no significant differences in the ratio of evapotranspiration to leaf area or WUE, indicating that drought plants were simply smaller and used less water than the high-water plants. This work validates that estimating plant biomass from a digital point coupled with continuous gravimetric determination of water use provides a reliable nondestructive measure of WUE in high-throughput measurements across the full plant life cycle. PLAIN LANGUAGE SUMMARY We grew tobacco plants under either a drought or high-water treatment and harvested a portion of the plants every few days for a total of eight harvests. Throughout the experiment, we collected 3D images of the plants and continuously measured pot weight to track plant growth and water use across different developmental stages. Destructive biomass served as the gold-standard measurement. We then compared biomass and water-use estimates generated from the digital measurements with the destructive measurements. The digital approach provided accurate estimates of plant biomass and water use while requiring little hands-on labor and no plant destruction. These nondestructive methods could help plant breeders identify water-efficient plants earlier in the breeding process, accelerating the development of crops that use water more efficiently.
Why it matches plant phenotyping methods3D画像による非破壊バイオマス推定と連続的な重量測定からWUEを推定する手法を、破壊収穫と比較して検証しており、植物表現型取得法が中心である。
abstractRecent advances in high-throughput phenotyping now enable repeated, non-destructive estimation of biomass and WUE. However, these digital measurements must be statistically validated against conventional destructive methods to validate their use as reliable proxies.
Plant phenotyping relevance match · UnverifiedOpenAlex · Europe PMC · Crossref · bioRxiv · checked 5 Sept 2026
Abstract Non-invasive, high-throughput phenotyping tools are needed that can identify environmental effects on plant structure and function to diagnose factors responsible for reduced growth in commercial and non-commercial settings. In this study, we explored whether the integration of 3D-multispectral (3D) and 2D-hyperspectral imaging (HSI), aided by machine learning (ML), could be used to identify environmental stress treatments imposed during plant growth. Controlled environment-grown Nicotiana Benthamiana plants were subjected to a range of abiotic treatments – including different growth irradiances, heat treatment and drought stress – with the treatments resulting in differences in shoot height, biomass, leaf area and spectral reflectance. ML models were trained to identify these treatments using morphological and spectral traits measured at 27, 29, 31, and 34 days after sowing (DAS). A 3D-multispectral scanner was used to obtain information on plant height, biomass, and leaf area. A visible and near-infrared (VNIR) HSI camera provided detailed spectral information for deriving spectral indices including the Normalised Difference Vegetation Index (NDVI), Photochemical Reflectance Index (PRI) and Normalized Difference Red Edge (NDRE). Manual measurements provided baseline comparative data. The 3D-multispectral scanner reliably estimated above-ground traits, with high correlations between manual and scanner-derived measurements. The ML models accurately differentiated among environmental stress treatments, with the fused 3D+HSI model achieving the best overall predictive performance across all evaluated metrics compared with models based on either imaging modality alone. Results demonstrated the effectiveness of combining 3D-multispectral and 2D-HSI data with ML analyses for non-destructive, high-throughput phenotyping. The integration of these techniques enabled non-destructive, high-throughput identification of environmental stress treatments imposed during plant growth.
Why it matches plant phenotyping methods3Dマルチスペクトル画像・ハイパースペクトル画像と機械学習を統合し、植物形態・スペクトル形質を非破壊かつ高スループットに取得・検証する方法が研究の中心である。
abstractNon-invasive, high-throughput phenotyping tools are needed that can identify environmental effects on plant structure and function
While multispectral sensors offer a cost-effective and robust solution for monitoring plant responses to environmental stress, their limited spectral resolution, largely dependent on vegetation indices, can hinder accurate classification of stress severity using machine learning. This paper aims at overcoming these limitations by introducing a multispectral device for plant stress early detection that is 1) affordable for a wide range of end-users, 2) robust to environmental factors, 3) capable of automatically finding the most meaningful features that maximize the stress detection accuracy, and 4) capable of discriminating different plant stress severity. The device integrates a broadband LED and a VIS-NIR multispectral sensor to early predict plant stress through machine learning algorithms (i.e., SelectKBest, kNN, SVM, and LDA). It was trained on spectral measurements acquired from tobacco plants under salinity stress. The results demonstrated its high capability to discriminate with high accuracy different stress severity (average accuracy of 91.0 ± 3.1%).
Why it matches plant phenotyping methods植物ストレスの重症度を推定する低コスト multispectral デバイスと機械学習手法を開発・評価しており、植物状態の取得・判別が研究の中心である。
abstractThis paper aims at overcoming these limitations by introducing a multispectral device for plant stress early detection
MangoTobaccoMultispectral / hyperspectralPhysiological trait estimationWater status / transpiration
This study proposed a novel multimodal hierarchical fusion framework integrating a convolutional neural network (CNN) and a transformer. The approach enhanced model performance by fusing spectral features with some auxiliary factors of the samples, such as the locality of growth (region), type of produce (cultivar), and sample temperature (temp). Spectral data were extracted using one-dimensional CNN to capture local spectral features, while auxiliary factors underwent sine-cosine or label encoding before being embedded into the same feature space as spectral data via a fully connected network. Ultimately, a transformer was employed to achieve global interaction and fusion between spectral features and auxiliary factors rather than merely concatenating different feature types. The fusion strategy was validated using the ultraviolet (UV)-visible (vis)-near-infrared (NIR) spectra of mango and tobacco data sets. Compared to single-modal models using spectra only, the multimodal model using spectra coupled with the auxiliary factors achieved improved prediction performance on both validation and test sets for the mango dry matter content (DMC). The RMSE decreased from 0.984 and 1.03 to 0.577 and 0.613, respectively. These results outperformed those of the other 11 machine learning models. SHAP analysis revealed that the CNN-transformer framework successfully captured the underlying relationships between auxiliary factors (region, temp, and cultivar) and spectral features near 960 nm (due to the O-H absorption signal) with DMC, with the former contributing more significantly to the model than the latter. Similar observations were obtained in the tobacco data set. The results demonstrated the advantages of the CNN-transformer multimodal model in overcoming the limitations of single-modal information, providing novel technical support for quantitative analysis.
Why it matches plant phenotyping methodsCNN-Transformerによるスペクトルと補助情報の融合モデルを開発・検証し、マンゴーの乾物含量という植物器官の形質を定量推定しているため、方法が中心的である。
abstractThis study proposed a novel multimodal hierarchical fusion framework integrating a convolutional neural network (CNN) and a transformer.
Accurate detection of tobacco plants in complex field environments is critical for precision agriculture, crop monitoring, and yield estimation. Traditional manual counting methods are time-consuming, labor-intensive, and susceptible to environmental and subjective factors. In this study, we propose an improved YOLO11-based framework for automated tobacco plant detection, specifically designed to address challenges such as scale variation, dense distribution, and background interference. The framework integrates four key modules: the Edge-Enhanced Feature Stem (EEFS) to strengthen low-level feature extraction, the Multi-Scale Kernel Interaction (MSKI) to capture multi-scale contextual information, the Adaptive Weighted Feature Fusion (AWFF) to optimize feature aggregation, and the Global–Local Synergistic Attention (GLSA) to enhance feature discrimination by jointly modeling local details and global context. A comprehensive UAV-based tobacco dataset was constructed, encompassing multiple lighting conditions, collection heights, and observation angles. Experimental results demonstrate that the proposed method significantly outperforms the YOLO11 baseline and achieves superior performance compared to mainstream YOLO variants. Ablation studies and heatmap visualizations confirm the effectiveness of each module. Furthermore, the model exhibits robust performance under multi-dimensional environmental perturbations, including varying illumination, scale, and camera angles. The proposed framework provides a practical and efficient solution for automated tobacco plant counting, offering potential applications in UAV-based precision agriculture and large-scale crop monitoring.
Why it matches plant phenotyping methodsUAV画像からタバコ個体を自動検出・計数する手法を開発し、専用データセット、比較実験、アブレーション、頑健性評価まで行っており、植物個体数の取得方法が中心です。
abstractwe propose an improved YOLO11-based framework for automated tobacco plant detection
Plants engineered with synthetic genetic programs can transform how we monitor and manage the extension of crop pests and diseases. Here, we establish a bioluminescent platform in Nicotiana benthamiana for autonomous viral sensing based on the fungal bioluminescence pathway (FBP). We first demonstrate that recombinant viruses can deliver missing pathway components, establishing a methodological framework for spatially resolved tracking of infection dynamics. Leveraging this starting point, we develop a dual-output sentinel circuit that uses a protease-responsive bioluminescence resonance energy transfer (BRET) module to report infection through a virus-triggered spectral shift in luminescence. In the absence of infection, plants emit a stable yellow glow indicating system integrity. Upon infection with potyviruses, cleavage of the BRET fusion by the virus-encoded NIa-protease activates a distinct colour change detectable with low-cost imaging. This modular design is compatible with other pathogens carrying specific proteases and supports future multiplexing strategies. Our results highlight the potential of synthetic sentinel gene circuits as autonomous biosensors for precision crop protection.
Why it matches plant phenotyping methods植物のウイルス感染状態を発光・画像で検出するセンチネル回路とプラットフォームの開発が中心であり、単なる生物学的測定ではない。
abstractwe establish a bioluminescent platform in Nicotiana benthamiana for autonomous viral sensing
Plant phenotyping relevance match · UnverifiedCrossref · Europe PMC · checked 5 Sept 2026
Abstract Key message CRaman imaging combined with a multi-layer perceptron neural network enables non-destructive, label-freeclassifi cation of tobacco BY-2 cells based on carotenoid composition. Abstract Carotenoids are natural tetraterpenoid pigments with important nutritional properties and broad industrial applications. Enhancing their production in plant-based biofactories offers a sustainable alternative to current manufacturing processes. In this work, we developed a label-free, single-cell analytical platform combining Raman imaging with a multi-layer perceptron neural network to classify tobacco BY-2 cells based on their carotenoid content. Carotenoid standards analysis, including astaxanthin, canthaxanthin, and β-carotene, was performed by surface-enhanced Raman scattering using hydrophobic gold nanostars due to the low concentration available. This analysis allowed the assignment of characteristic Raman peaks, specifically at 1160 cm −1 and 1520 cm −1 , of key carotenoids and their identification inside of the cells by Raman imaging. The Raman fingerprints were correlated with carotenoid profiles obtained by HPLC, enabling accurate differentiation between wild-type and transgenic cell lines. In the analyzed transgenic lines, carotenoids accumulated in vesicle-like structures near the nucleus and along the cytoplasmic membrane. This method provides a non-destructive, label-free approach with high classification accuracy and sorting potential based on carotenoid composition, and may be a useful tool for plant synthetic biology and metabolic engineering.
Why it matches plant phenotyping methods植物細胞内のカロテノイド組成をラマンイメージングと機械学習で非破壊・単細胞レベルに推定する分析プラットフォームを開発しており、植物表現型取得法が中心である。
abstractwe developed a label-free, single-cell analytical platform combining Raman imaging with a multi-layer perceptron neural network to classify tobacco BY-2 cells based on their carotenoid content.
Conventional two-dimensional image-based methods are limited in measuring the three-dimensional morphology of tobacco stems, especially thickness and curved geometry. This study proposes a point-cloud-based method for three-dimensional tobacco stem measurement using a dual-laser scanning system. The method combines improved centerline skeleton extraction, upper–lower surface registration, and skeleton-guided cross-sectional analysis to estimate length, width, thickness, and fineness. Adaptive neighborhood re-weighting and curvature-constrained regularization are introduced to improve skeleton extraction, and reference-assisted registration is used to support thickness measurement. For a standard gauge block, the proposed method achieved mean absolute errors below 0.009 mm and root mean square errors below 0.011 mm for length, width, and thickness measurements. Validation on 30 tobacco stem samples showed good agreement with the YC image-based method for length and width, with correlation coefficients of 0.998 and 0.997, respectively. The results demonstrate the feasibility of thickness-aware three-dimensional morphological measurement of tobacco stems under the tested conditions.
Why it matches plant phenotyping methodsタバコ茎の長さ・幅・厚さ・細さという植物形態形質を、点群・レーザースキャン・骨格抽出で測定する手法の開発と検証が研究の中心である。
abstractThis study proposes a point-cloud-based method for three-dimensional tobacco stem measurement using a dual-laser scanning system.
Abstract Temperature fundamentally impacts plants growth and physiology. However, the mechanisms by which plants sense and response to environmental changes remain unclear due to the lack of effective methods for measuring internal plant temperatures. Here, by combining lab-made nanothermometric probes with time-gated imaging technique, we accurately detected the change in internal plant temperature in response to environmental temperature variations. We discovered a multilevel temperature regulation mechanism during the process by which plants establish thermal homeostasis. In Nicotiana benthamiana leaves, when environment temperature changes from approximately 24°C to 45°C, the maximum of internal plant temperature change is only approximately 10°C near cell wall, and less than 7°C in cytoplasm, while remaining nearly constant in chloroplasts (ΔTchl ≈ 1°C). Similar compartment-specific thermal regulation was observed in Arabidopsis thaliana and tomato, indicating that hierarchical regulation represents a conserved strategy for maintaining internal temperature stability in plants. Together, these findings provide direct evidence for multiscale thermal homeostasis in plants and establish a framework for understanding how cellular and subcellular organization contributes to temperature regulation.
Why it matches plant phenotyping methodsナノ温度計プローブと時間ゲート imaging により植物内部温度を測定する手法が研究の中心であり、植物の生理状態を直接定量している。
abstractby combining lab-made nanothermometric probes with time-gated imaging technique, we accurately detected the change in internal plant temperature
Soil salinization threatens global food security by disrupting plant ion homeostasis and triggering complex hormonal signaling cascades. Early detection of salt stress and real-time tracking of stress-responsive physiological dynamics remain major technical bottlenecks, as current methods rely on destructive sampling or single-parameter sensing that obscure the crosstalk between ionic and hormonal networks. Here, we report a wearable plant electronic system that enables noninvasive, in situ monitoring of K + /Na + homeostasis and salicylic acid (SA) accumulation, two key indicators of plant salt stress adaptation. The integrated platform combines flexible laser-induced graphene (LIG) ion-selective electrodes enhanced with a SnS 2 -MoS 2 heterostructure for stable potentiometric sensing, a reverse iontophoresis module for noninvasive analyte extraction, and a biocompatible flexible zinc-air battery for long-term power supply. Deployed on tobacco plants, the wearable device detected salt stress within 24 h by capturing the initial disruption of the K + /Na + ratio and the subsequent accumulation of SA, far preceding visible symptoms. The temporally resolved data revealed a dynamic correlation where SA accumulation coincided with a partial recovery of the K + /Na + ratio, suggesting an active role of SA in modulating ion homeostasis. This wearable plant electronic system transcends the limitations of conventional single-parameter detection, providing a powerful tool for early stress diagnosis, deciphering plant adaptive mechanisms, and advancing precision agriculture to mitigate the impact of soil salinization.
Why it matches plant phenotyping methods植物の塩ストレス状態を非破壊・リアルタイムに測定するウェアラブルセンサー基盤の開発が研究の中心であり、K+/Na+恒常性とサリチル酸蓄積という生理表現型を直接取得している。
abstractHere, we report a wearable plant electronic system that enables noninvasive, in situ monitoring of K + /Na + homeostasis and salicylic acid (SA) accumulation, two key indicators of plant salt stress adaptation.
Elucidating gene function in highly redundant genetic programs such as signaling pathways is challenging in model and nonmodel plants with current whole-plant genetic screening tools. Many of these challenges could be overcome if screens were instead carried out using individual cells harboring genetic perturbations. Here we report a single-cell screening platform, PIVOT (protoplast isolation after virus overexpression in planta), to accelerate identification and functional characterization of plant genes. We use Nicotiana benthamiana as a heterologous host to test gene libraries arrayed in a single leaf. PIVOT harnesses viral superinfection exclusion to ensure single multiplicity of infection per cell during pooled library delivery. Additionally, we engineer a cell-surface protein as a phenotypic marker for isolating cells of interest from a heterogeneous population. Using this system, we recover regulators of cytokinin signaling from an Arabidopsis open reading frame library. We anticipate PIVOT will be broadly applicable for high-throughput, single-cell functional genetic screening across the plant kingdom.
Why it matches plant phenotyping methods植物の単一細胞スクリーニング基盤を開発し、細胞表面の表現型マーカーで関心細胞を分離する技術が研究の中心であるため、植物表現型取得・選別法として含める。
abstractHere we report a single-cell screening platform, PIVOT (protoplast isolation after virus overexpression in planta), to accelerate identification and functional characterization of plant genes.
Background: This study investigates the construction and optimization of the You Only Look Once (YOLO) deep learning model for high-precision identification of suitable tobacco leaves.(2) Methods: Using tobacco fields in Xiaoxin Street, Niulanjiang Town, Songming County, Kunming as the study area, a total of 1200 UAV images collected during the planting, growth, and harvesting stages were employed as the training dataset to train object detection models such as YOLO v3.After 200 training iterations, the recognition performance of each model was compared and analyzed.(3) Results: YOLO v5 and YOLO v7 were selected as baseline models, and a channel attention mechanism was integrated to develop the improved YOLO v5-EN model.Ablation experiments were conducted by incorporating the attention module, dynamic rectified linear unit (DReLu) activation function, and a feature refinement module.YOLO v7 en was designed as a backbone network, and metrics such as precision, recall, and accuracy were comprehensively evaluated to assess the performance of both the baseline and improved models in identifying the number of tobacco plants.Compared to the baseline, the improved YOLO v5 model demonstrated a 0.36% increase in precision and a 1.55% increase in recall, achieving an overall recognition accuracy of 91.41%.The improved YOLO v7 model achieved a precision of 99.16% and a mean average precision (map) of 95.86%.These results indicate that the enhanced YOLO v5 model with channel attention effectively addresses the issues of missed and false detections in tobacco plant recognition.Furthermore, the improved YOLO v7 model, integrated with collaborative optimization strategies and an enhanced backbone, significantly improves the performance and efficiency of the detection model, particularly in terms of accuracy and processing speed for complex visual tasks.(4) Conclusions: The improved YOLO models significantly enhance the accuracy of tobacco plant count recognition and offer a practical solution for efficient tobacco plant statistics, serving as a reference for intelligent agriculture.
Why it matches plant phenotyping methodsUAV画像からタバコ植物数を推定するYOLOモデルの改良・比較・アブレーション評価が中心であり、植物個体数という形態的状態の抽出手法を開発している。
titleEnhanced YOLO Architecture with Attention Mechanism for Accurate Tobacco Plant Counting from UAV Images
The tobacco plant counting is an important aspect in tobacco production management, traditional manual methods are time-consuming, labor-intensive and inaccurate, failing to meet the efficiency demands of modern agriculture. To enhance the accuracy and efficiency of tobacco plant counting in the field environment, this study utilizes high-resolution remote sensing imagery collected by drones to construct a sample dataset and proposes an improved YOLOv8-based target detection model (YOLOv8-CSD). YOLOv8-CSD model, based on YOLOv8, incorporates a coordinate attention mechanism (CA) to improve the extraction ability of the model to tobacco plant features. It also optimizes the feature pyramid network (FPN) and adds a small target detection layer to enhance the detection ability for the small target tobacco plants. Additionally, the shape intersection over ratio (SIoU) loss function is used to accelerate model convergence, and the slice-assisted hyper inference (SAHI) strategy is introduced to improve the accuracy and inference efficiency of small target detection by slicing high-resolution images. The experimental results show that the YOLOv8-CSD model achieves a precision of 97.96%, a recall rate of 97.93%, and an average accurate mean (mAP0.5) of 99.32%, significantly outperforming the original YOLOv8 and other 5 commonly used target recognition models. In addition, the efficiency of YOLOv8-CSD model is only lower than YOlOv11, indicating good overall performance. The YOLOv8-CSD model has good adaptability and robustness in tobacco plant detection at different growth stages, with a low missed detection rate, and the YOLOv8-CSD model can effectively meet the requirements of tobacco plant counting in complex field scenarios.
Why it matches plant phenotyping methodsUAV画像からタバコ個体を検出・計数する画像解析手法を開発し、複数モデルとの性能比較で検証しており、植物形態・個体数の取得が研究の中心である。
abstractproposes an improved YOLOv8-based target detection model (YOLOv8-CSD)
Hyperspectral remote sensing provides a rapid and non-destructive approach for monitoring plant nutrient status; however, its application for magnesium (Mg) estimation in flue-cured tobacco remains limited. In this study, two cultivars, Yunyan 87 and Zhongyan 100, were grown in a hydroponic system with five Mg concentration gradients (0, 0.2, 1, 5, and 25 mmol L -1 ). Hyperspectral reflectance data of fresh leaves were collected at different growth stages. Three preprocessing methods, including first derivative (FD), standard normal variate (SNV), and multiplicative scatter correction (MSC), were applied, and partial least squares regression (PLSR) was used to identify the optimal preprocessing strategy. Characteristic wavelengths were selected using competitive adaptive reweighted sampling (CARS), successive projections algorithm (SPA), and genetic algorithm (GA), and were further combined with extreme learning machine (ELM), support vector regression (SVR), and radial basis function (RBF) neural network models to estimate Mg content. The results showed that spectral preprocessing significantly improved the relationship between hyperspectral data and Mg content, with optimal methods varying across cultivars and growth stages. Selected wavelengths were mainly located in the near-infrared region. The developed models achieved high prediction accuracy, particularly during the middle and late growth stages, where the coefficients of determination (R 2 ) of all test sets exceeded 0.90. In addition, Yunyan 87 exhibited higher prediction accuracy than Zhongyan 100. These findings demonstrate that hyperspectral technology combined with feature wavelength selection and machine learning enables accurate and non-destructive estimation of Mg content in flue-cured tobacco leaves, providing a reliable tool for Mg nutrition diagnosis and precision management. However, further validation under diverse field conditions is required to enhance model robustness.
Why it matches plant phenotyping methodsタバコ葉のMg含量という植物生理状態を、ハイパースペクトル計測と波長選択・機械学習で非破壊推定する方法が研究の中心であり、モデル精度評価も行っている。
abstractThe developed models achieved high prediction accuracy, particularly during the middle and late growth stages, where the coefficients of determination (R 2 ) of all test sets exceeded 0.90.
The unfolded protein response (UPR) is a critical mechanism for maintaining endoplasmic reticulum (ER) homeostasis under stress. Here, we developed a bioluminescent reporter system, AtbZIP60-LUC, in Arabidopsis to dynamically monitor ER stress by coupling IRE1-mediated splicing of bZIP60 mRNA to firefly luciferase (LUC) expression. Under ER stress, IRE1 removes a 23-bp sequence from bZIP60u, producing a spliced bZIP60s transcript in-frame with LUC, enabling luciferin-dependent luminescence. Transgenic AtbZIP60-LUC lines exhibited specificity for canonical ER stressors (heat, DTT, tunicamycin) but not osmotic stressors (NaCl, mannitol), confirmed by bioluminescence, qPCR, and immunoblotting. Time-course assays revealed rapid LUC induction by DTT (peak at 1 h) and delayed activation by tunicamycin (peak at 1-2 h), followed by signal decline, reflecting adaptive UPR dynamics. Heat stress optimization identified 38°C as optimal, inducing robust LUC expression after 2-3 h without compromising viability, while 42°C caused irreversible damage. Genetic validation in ire1a ire1b mutants abolished LUC induction, confirming IRE1 dependency, whereas constitutive UPR activation via maize 16-kDa γ-zein (16γz) overexpression triggered LUC expression without stress. Extending this system to tobacco and tomato, we engineered NbbZIP60-LUC and SlbZIP60-LUC, which similarly responded to heat (38°C), DTT, tunicamycin, and ER-localized protein aggregation (16γz, zeolin) in transient and stable assays. This work establishes bZIP60-LUC as versatile, non-invasive tools for real-time UPR monitoring in plants, offering insights into ER stress dynamics and enabling cross-species studies of stress adaptation mechanisms.
Why it matches plant phenotyping methods植物のERストレス状態を非侵襲的・リアルタイムに測定するルシフェラーゼレポーター法を開発し、ストレス特異性、時間応答、遺伝的依存性、複数種での性能を検証しており、表現型取得法が研究の中心である。
abstractHere, we developed a bioluminescent reporter system, AtbZIP60-LUC, in Arabidopsis to dynamically monitor ER stress by coupling IRE1-mediated splicing of bZIP60 mRNA to firefly luciferase (LUC) expression.
Abstract Photosynthetic organisms have evolved multiple non-photochemical quenching (NPQ) processes, providing photoprotection by safely dissipating excess excitation energy. These processes involve various molecular players functioning on overlapping timescales from seconds to days, making it challenging to isolate and quantify their individual kinetics. In this study, we perform whole-leaf chlorophyll fluorescence lifetime and xanthophyll concentration measurements on wild-type and various newly characterized NPQ mutants of Nicotiana benthamiana , a vascular land plant. Based on these measurements, we construct a fluorescence lifetime-based quantitative kinetic model that disentangles individual photoprotection components and, when integrated additively, accurately predicts wild-type and mutant NPQ behaviors under various light-dark regimes. Additionally, the model quantifies the per-molecule quenching effectiveness of various xanthophylls and the contributions of six quenching components (qE V , qE A, qE Z, qE L, qZ, and qI) across different genotypes. It also suggests improved overall quenching efficiency at specific VDE:ZEP:PsbS overexpression stoichiometries, aligning with previous studies and supporting translational efforts to optimize photoprotection and enhance crop yields under dynamic light environments.
Why it matches plant phenotyping methods葉の蛍光寿命測定を基盤に、NPQ成分を分離・定量するモデルを構築しており、植物の光防護状態を取得・抽出する方法が研究の中心です。
abstractBased on these measurements, we construct a fluorescence lifetime-based quantitative kinetic model that disentangles individual photoprotection components and, when integrated additively, accurately predicts wild-type and mutant NPQ behaviors under various light-dark regimes.
Reproduction assets foundThe paper's fluorescence lifetime/pigment phenotyping data and the NPQ model code are both publicly deposited on Zenodo (DOI 10.5281/zenodo.16755870), per explicit Data availability and Code availability statements.Dataset · publicThe data supporting the findings of this study are available within the article and at https://doi.org/10.5281/zenodo.16755870 .Open asset ↗Zenodo · 10.5281/zenodo.16755870lines:171-237Code · publicThe codes for NPQ models used in this study are available at https://doi.org/10.5281/zenodo.16755870 .Open asset ↗Zenodo · 10.5281/zenodo.16755870lines:171-237Plant phenotyping relevance match · UnverifiedEurope PMC · checked 5 Sept 2026
The movement, distribution, and interactions of organelles are cell-type specific, responding to fluctuating metabolic and environmental cues and governing the efficiency of plant physiology and stress response. The directional motility of various plant organelles is predominantly driven by the actomyosin system, yet the distinct functionality of these organelles across plant tissues presupposes organelle-specific regulation of motility, which requires the detection of subtle shifts in dynamics. Meanwhile, studies that comprehensively characterize and directly compare the simultaneous movement of multiple types of organelles within the same cell are limited. Here, we visualized peroxisomes, mitochondria, chloroplasts, Golgi bodies, and actin filaments simultaneously in tobacco (Nicotiana tabacum) to evaluate organelle organization and motility within the context of one another. Quantitative analysis of multiple motility factors enabled us to identify peroxisome motility in tobacco mesophyll as distinct from other organelles. Further analysis in Arabidopsis (Arabidopsis thaliana) revealed that both mitochondria and peroxisomes are slower in mesophyll cells compared to epidermis in normal growth conditions, but their motility patterns are unique from one another across leaf tissue after plants experienced conditions that induce photorespiration, a metabolic pathway requiring the concerted action of chloroplasts, peroxisomes, and mitochondria. Our quantitative analysis of thousands of organelles across species, cell type, and physiological conditions unveils distinct modulation of motility according to organelle identity and function. The extensive combinatorial characterizations of plant organelle movement provide a fundamental resource for the future discovery of molecular mechanisms driving the movement and distribution of diverse organelles.
Why it matches plant phenotyping methods複数オルガネラを同時可視化し、運動性を定量抽出する画像解析ワークフローが研究の中心で、植物細胞の生理状態を表す測定法として実質的に適用されている。
abstractHere, we visualized peroxisomes, mitochondria, chloroplasts, Golgi bodies, and actin filaments simultaneously in tobacco (Nicotiana tabacum) to evaluate organelle organization and motility within the context of one another.
To address the inefficiency and high cost of manual counting of tobacco leaves, this study proposes a UAV-based method for automatic leaf counting in field-grown tobacco using 3D point clouds and an improved PointNext network. Although UAV imagery has been applied to crop phenotyping, most existing UAV-based leaf-counting methods still rely on 2D images or hand-crafted features and rarely exploit 3D point clouds with dedicated leaf-level segmentation, which limits accuracy and robustness under leaf overlap, variable viewing angles, and complex field backgrounds. In this work, oblique UAV photogrammetry is used to reconstruct individual plants into 3D point clouds, and a segmentation network, SRW-PointNext, is developed by integrating an SCSA attention mechanism and a Residual-SegHead to enhance feature extraction and segmentation performance, while a re-weighted loss alleviates class imbalance. Leaf point clouds are then clustered using MeanShift to obtain leaf counts. Experiments on field-grown tobacco demonstrate that the proposed method achieves a point-cloud segmentation precision of 92.09%, a MIoU of 76.13%. Compared with the original PointNext baseline, SRW-PointNext increased MIoU and overall precision by 3.34% and 2.42% respectively. The final accuracy rate of leaf counting was 92.61%, effectively achieving accurate and stable leaf counting under actual field conditions, and providing technical support for digital management, yield estimation and seedling breeding in tobacco production.
Why it matches plant phenotyping methodsUAV三次元画像と改良セグメンテーション手法により圃場タバコの葉数を推定する方法を開発・検証しており、表現型取得が研究の中心である。
abstractthis study proposes a UAV-based method for automatic leaf counting in field-grown tobacco using 3D point clouds and an improved PointNext
Reproduction assets foundThe paper reports a UAV-based tobacco leaf counting method with an annotated 1000-plant point cloud dataset and SRW-PointNext code, both explicitly declared publicly available at author-provided Zenodo and GitHub URLs matching the allowed list.Dataset · publicData supporting the reported results can be found at: https://zenodo.org/records/15130271 .Open asset ↗zenodo · 15130271lines:531-564Code · publicThe code used in this study is available at: https://github.com/Nan20377/SRW-Pointnext.git .Open asset ↗github · Nan20377/SRW-Pointnextlines:531-564Code / dataset availability confirmedEurope PMC · checked 5 Sept 2026
Tobacco transplanting quality inspection is crucial for tobacco production, as it directly affects crop yield and quality of tobacco leaves. Accurate transplanting status detection and assessment provide essential support for replanting decisions and transplanting machine optimization. Traditional methods rely on manual inspection, which suffer from high cost, low efficiency, and unstable results. To tackle the aforementioned issues, this paper proposes a Deep Learning and Digital Twin driven Online Quality Inspection Method for Tobacco Transplanting, which consists of four core modules: Transplanting Status Detection, Multi-sensor Data Fusion, Digital Twin Visualization, and Operational Optimization Feedback. This paper proposes a lightweight improved YAN-YOLO11 algorithm capable of assessing normal, exposed-root, and buried seedlings. By fusing GNSS positioning data with visual detection results, the system estimates in-row spacing and performs status assessment for missed planting and double planting. The system establishes a virtual-real interactive closed-loop of "collection-detection-mapping-feedback" via the digital twin. By visualizing operational status in real-time and generating replanting path suggestions, it provides guidance for operation management and significantly improves inspection efficiency. Field experiments demonstrate that, compared with YOLO11n, YAN-YOLO11 improves precision and recall by 2.4% and 2.5%, respectively; mAP@50 increased by 3% to 80.9% ± 1.4%, and mAP@0.5:0.95 increased by 5.8% to 54.2% ± 1.0%, while significantly reducing model complexity. The system achieves a real-time performance of 30 FPS in the field, with an overall recognition accuracy of 90.74%, meeting practical application requirements. This study effectively enhances the digitalization, automation, and refined management of tobacco transplanting operations, providing a theoretical foundation and practical solution for the intelligent transformation of transplanting machinery and precision crop management.
Why it matches plant phenotyping methodsタバコ苗の植栽状態、株間、欠株・二株植えを画像とセンサーで推定する手法を開発・検証しており、植物状態の取得が研究の中心である。
abstractThis paper proposes a lightweight improved YAN-YOLO11 algorithm capable of assessing normal, exposed-root, and buried seedlings.
Reproduction assets foundThe paper's authors explicitly state that the demonstration video and code for the tobacco transplanting quality inspection system are publicly available via a Zenodo DOI link, which matches an allowed URL. This is a paper-specific, publicly actionable code asset. The arXiv URLs are cited prior work, not paper assets.Code · publicThe related demonstration video and code has been made publicly available on GitHub: https://doi.org/10.5281/zenodo.17075402.Open asset ↗GitHub · 10.5281/zenodo.17075402html-lines:294-405Plant phenotyping relevance match · UnverifiedEurope PMC · checked 15 Sept 2026
Background Hydrogen sulfide (H 2 S) is a key endogenous gasotransmitter involved in plant physiological regulation and stress responses. Monitoring its dynamic changes in plants is essential for understanding related signaling mechanisms. However, due to its chemical instability and the complexity of plant tissues, developing a reliable method for accurate quantification and real-time visualization of H 2 S in living plants remains challenging. Although fluorescent probes have been developed for H 2 S imaging, most still face critical limitations, such as emission in the visible region being susceptible to background fluorescence interference, and insufficient quantitative reliability of single-wavelength-based signal output modes in complex samples. Therefore, developing novel probes with near-infrared emission and rationetric response characteristics is of great significance. Results We constructed a near-infrared rationetric fluorescent probe, NIR-Cou-H 2 S, for H 2 S detection. The probe itself emits at 716 nm, and after specific reaction with H 2 S, a new emission peak appears at 552 nm, resulting in a distinct dual-emission rationetric response (716 nm/552 nm) accompanied by a visible color change. Using chemometrics-based fluorescence analysis, the probe successfully enabled direct quantitative detection of H 2 S in river and lake water samples. Its near-infrared emission effectively reduced interference from plant autofluorescence, thereby achieving high-contrast dual-channel fluorescence imaging. The probe was successfully applied for high-quality in situ visualization of H 2 S in living cells and tobacco seedling roots. More importantly, using NIR-Cou-H 2 S, we observed and recorded in real time the dynamic upregulation trend of endogenous H 2 S levels in tobacco roots under both drought and flooding stress conditions. Significance As a novel near-infrared rationetric probe, NIR-Cou-H 2 S provides a powerful tool for monitoring endogenous H 2 S dynamics in plants. Its characteristics significantly improve the reliability of imaging and quantification in complex plant samples, offering a key methodological approach for further elucidating the regulatory mechanisms of H 2 S in plant stress resistance.
Why it matches plant phenotyping methods植物体内のH₂S動態を可視化・定量する近赤外蛍光プローブを開発し、植物根でのストレス応答測定に適用しており、表現型・生理状態の取得法が中心である。
abstractWe constructed a near-infrared rationetric fluorescent probe, NIR-Cou-H 2 S, for H 2 S detection.
Understanding how plants perceive and respond to environmental and developmental cues requires tools capable of monitoring molecular signals in vivo, across whole tissues, and in real time. Genetically encoded fluorescent indicators, coupled with fluorescence microscopy, have transformed plant biology, but their application remains largely confined to small model organisms and specialized microscopy instrumentation. Here, we present MAcro Plant Projection Imaging (MAPPI), an open-source, low-cost, and modular fluorescence imaging platform for soil-grown plants beyond the model organism or seedling stage. MAPPI enables wide field-of-view, dual-projection imaging of fluorescent reporters, supporting real-time visualization of systemic signals under near-physiological conditions. We validate MAPPI by tracking calcium and l -glutamate dynamics in adult Nicotiana benthamiana plants, revealing developmentally regulated long-distance calcium waves triggered by wounding, burning, or submergence, including bidirectional shoot-to-root and root-to-shoot signaling. By democratizing access to whole-plant functional imaging, MAPPI provides a scalable tool for dissecting signal propagation, stress adaptation, and systemic communication in both model and nonmodel species.
Why it matches plant phenotyping methods植物全体の蛍光シグナルをリアルタイム取得する低コスト・オープンな画像プラットフォームを開発し、成体植物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。
abstractHere, we present MAcro Plant Projection Imaging (MAPPI), an open-source, low-cost, and modular fluorescence imaging platform for soil-grown plants beyond the model organism or seedling stage.
Reproduction assets foundThe authors publicly deposit raw imaging data and MAPPI analysis code on Zenodo, host the MAPPI acquisition/analysis code on GitHub, and release the napari-roi-registration image registration plugin on GitHub. All are paper-specific, public, and actionable.Dataset · publicThe raw data for the images presented in the manuscript and the code to run the MAPPI system are available on Zenodo ( https://doi.org/10.5281/zenodo.15845576 ).Open asset ↗Zenodo · 10.5281/zenodo.15845576lines:170-466Code · publicThe code to run the MAPPI system is also available on the dedicated GitHub repository ( https://github.com/micropolimi/MAPPI ) along with the code used to analyze the data.Open asset ↗GitHub · micropolimi/MAPPIlines:170-466Code · publicThe software is open-source and available on GitHub ( https://github.com/GiorgiaTortora/napari-roi-registration ) and the napari-hub ( www.napari-hub.org/plugins/napari-roi-registration ).Open asset ↗GitHub · GiorgiaTortora/napari-roi-registrationlines:156-169Plant phenotyping relevance match · UnverifiedEurope PMC · checked 5 Sept 2026
Tobacco leaf diseases significantly affect yield and quality, underscoring the need for rapid and non-destructive diagnostic tools. Although hyperspectral imaging (HSI) has been applied in tobacco pathology, most existing studies focus on single diseases and lack generalized, interpretable frameworks for multi-class identification. In this study, hyperspectral images of healthy leaves and four major diseases-brown spot, wildfire, Tobacco Mosaic Virus (TMV), and Potato virus Y (PVY)-were collected to construct a balanced, leaf-independent dataset. Pixels were grouped by leaf ID, and the entire dataset was strictly partitioned at the leaf level to prevent pixel-level data leakage and ensure generalization to unseen leaves. Multiple preprocessing techniques, wavelength-selection methods, and machine-learning classifiers were systematically compared. A compact ANN model integrating Savitzky-Golay preprocessing and SPA-based wavelength selection achieved the best overall performance while requiring only a small number of informative wavelengths. A Transformer model provided slightly stronger predictive capacity but depended on full-spectrum inputs and substantially higher computational cost. Pixel-level predictions enabled lesion-area-based severity estimation for the two leaf-spot diseases. SHAP analysis highlighted physiologically meaningful spectral regions associated with pigment absorption and structural variation. Overall, this study presents an efficient and interpretable HSI framework for multi-disease tobacco diagnosis, supporting the development of practical hyperspectral or multispectral systems.
Why it matches plant phenotyping methodsタバコ葉の病徴をハイパースペクトル画像と機械学習で識別し、病斑面積に基づく重症度推定まで行う診断フレームワークの開発が中心である。
abstractthis study presents an efficient and interpretable HSI framework for multi-disease tobacco diagnosis
Abstract Specific generation of reactive oxygen species (ROS) is important for signalling and defence in many organisms. In plants, different types of ROS serve useful biological functions in the extracellular space (apoplast), influencing polymer structures as well as signalling during immune responses. The current knowledge of apoplastic ROS dynamics is limited, as dynamic monitoring of extracellular redox processes in vivo remains difficult. We employed evolutionary distant land plant model species from bryophytes and flowering plants to test whether the genetically encoded redox biosensor roGFP2‐Orp1 can be used to assess extracellular redox dynamics. Secreted roGFP2‐Orp1 can provide information about local diffusion barriers and protein cysteinyl oxidation rate in the apoplast, after pre‐reduction. Observed re‐oxidation rates were slow – within the range of hours. Compared to Physcomitrium patens , re‐oxidation in Arabidopsis thaliana was faster and increased after triggering an immune response. Comparing roGFP2‐Orp1 signals in tip‐growing P. patens protonema and Nicotiana tabacum pollen tubes, we consistently find no intracellular redox gradient, but a partially reduced extracellular sensor in pollen tubes. Our data indicate differences in extracellular oxidative processes between species and within a species, depending on cell type and immune signalling.
Why it matches plant phenotyping methods植物アポプラストの酸化還元動態という生理状態を、遺伝子コード型センサーで生体測定する手法の適用可能性と性能を中心に評価しているため。
abstractdynamic monitoring of extracellular redox processes in vivo remains difficult
Problems Tomato Spotted Wilt Virus (TSWV) severely affects tobacco yield and quality, creating an urgent need for accurate, rapid, non-destructive monitoring to support disease management. While existing TSWV detection methods perform well at the leaf scale, their field-scale application remains challenging. Due to complex crop canopy structures, spectral characteristics at the field level differ significantly from leaf-level observations, and TSWV-sensitive spectral features are still unclear. This study therefore aims to develop a field-scale TSWV identification model using UAV-based hyperspectral imaging to enable targeted disease control. Methodology A UAV-mounted hyperspectral camera (400-1000 nm) was deployed to capture imagery of tobacco plants at the rosette stage, enabling comparative spectral analysis between healthy and infected specimens. To identify sensitive features associated with tobacco plants infected with TSWV, six distinct feature extraction methodologies encompassing traditional statistical approaches (spectral ratio, correlation analysis, and principal component analysis [PCA]), machine learning-based techniques (relevant features [Relief], successive projections algorithm) and vegetation indices were utilized. Subsequently, we conducted a systematic evaluation of 18 classification models developed using three machine learning algorithms-support vector machine (SVM), k-nearest neighbors, and extreme gradient boosting -with the derived feature variables. Results This study demonstrates that while all integrated models combining Relief- and Correlation- selected feature bands with three machine learning algorithms delivered excellent performance, the SVM-Relief model achieved the most outstanding results (OA = 97.3%, AUC = 0.994, Kappa=0.947). Based on the SVM-Relief combination, a proposed method called RPR -which integrates PCA with recursive feature elimination- was further employed to reduce the number of feature indicators from 15 to 4 (775.6/772.9/781.1/756.4 nm). The resulting SVM-RPR combination model achieved performance (OA = 97.3%, AUC = 0.990, Kappa=0.947) comparable to that of the SVM-Relief model. Contribution This indicated that red-edge bands were of significant value in distinguishing healthy and TSWV-infected tobacco plants. Our study indicates the significant potential of integrating UAV-based hyperspectral imaging with machine learning techniques for rapid, non-destructive detection of tobacco TSWV at the field scale. The proposed approach offers a novel and efficient pathway for remote sensing-based monitoring of viral diseases in crops, with implications for precision agriculture and plant disease management.
Why it matches plant phenotyping methodsUAVハイパースペクトル画像と機械学習により、タバコ個体のウイルス感染状態を圃場規模で推定する手法の開発・評価が研究の中心であるため。
abstractThis study therefore aims to develop a field-scale TSWV identification model using UAV-based hyperspectral imaging to enable targeted disease control.
Reproduction assets foundThe paper states its tobacco TSWV UAV hyperspectral dataset is publicly available on GitHub, matching an allowed URL.Dataset · publicThe datasets are available in the GitHub repository ( https://github.com/smith22357/hyperspectral-dataset : tobacco TSWV hyperspectral-dataset).Open asset ↗smith22357/hyperspectral-dataset · tobacco TSWV hyperspectral-datasetlines:369-377Plant phenotyping relevance match · UnverifiedEurope PMC · checked 15 Sept 2026
Black shank disease and root rot disease represent the most destructive diseases of tobacco. Once it occurs, it will spread rapidly, endangering the health of tobacco plants, and even killing them. The stem near the root of tobacco plant is the first part that can exhibit observable signs of root disease. Monitoring the dynamic variations of in-situ stem water content (SₜWC) near the root is beneficial for the early detection of tobacco root diseases. Therefore, we developed a wearable plant sensor with a flexible interdigitated-electrodes (IE) probe design for in-situ monitoring of StWC and early identification and warning of tobacco root diseases. The IE probe of wearable sensor was securely affixed to the stem, and the soil moisture (SM) sensors were buried in the corresponding root area. The results demonstrated a clear inconsistency in the observed trend between the SₜWC near roots of diseased and healthy tobacco plants. About 60 h before the blackening of the stems near roots, the SM of diseased tobacco plants (0.007 cm³/cm³) indicated a slower decrease compared to healthy tobacco plants (0.021 cm³/cm³). In accordance with this phenomenon, the daily variation of SₜWC near roots of diseased tobacco plants (0.023 cm³/cm³) was significantly less than that of healthy tobacco plants (0.048 cm³/cm³). Moreover, the abnormal changes of SₜWC near roots of diseased tobacco plants after blackening further validated the availability of the wearable sensor in the early detection and warning of tobacco root diseases. The tobacco plant may have been in early diseased stage when the daily change of SₜWC was continuously less than 0.037 cm³/cm³. Future research will focus on the mechanism of water conduction between soil and stem near the root of tobacco plants, and the potential application of the wearable sensor in early disease detection.
Why it matches plant phenotyping methods根域付近の茎水分量という植物の生理状態を測定し、根病害の早期検出に用いるウェアラブルセンサーを開発・検証しており、表現型取得手法が中心である。
abstractwe developed a wearable plant sensor with a flexible interdigitated-electrodes (IE) probe design for in-situ monitoring of StWC and early identification and warning of tobacco root diseases.
As an economically important crop, tobacco requires the precise extraction of phenotypic characterization data, which is crucial for breeding, cultivation practices, physiological research, and industrial applications. However, there is currently a lack of automated algorithms for extracting key basic phenotypic traits such as plant height, leaf number, leaf area, and stem-leaf angle. In this study, we developed a set of computational methods for fully automated extraction of these phenotypic features from 3D tobacco point cloud data. Specifically, our pipeline includes: (1) preprocessing the 3D point cloud data, involving operations such as downsampling, denoising, normal vector estimation, and coordinate transformation; (2) integrating a graph neural network with a region-growing algorithm to segment leaves, stems, and other organs, and refining the segmentation results to address the challenge of overlapping leaves; and (3) calculating fundamental phenotypic attributes including plant height, leaf count, leaf area, and stem-leaf angle based on the segmentation output. Additionally, to address potential gaps in the scanned point cloud, we implemented perforation detection and repair operations. The effectiveness and accuracy of the proposed algorithm were validated through mathematical model simulations. Distinct from traditional statistical discriminative methods, this approach provides a novel framework for the precise extraction of tobacco phenotypic data.
Why it matches plant phenotyping methods3D点群から植物器官を分割し、草丈・葉数・葉面積・茎葉角を自動抽出する計算手法の開発と検証が中心である。
abstractwe developed a set of computational methods for fully automated extraction of these phenotypic features from 3D tobacco point cloud data.
Black shank disease and root rot disease represent the most destructive diseases of tobacco. Once it occurs, it will spread rapidly, endangering the health of tobacco plants, and even killing them. The stem near the root of tobacco plant is the first part that can exhibit observable signs of root disease. Monitoring the dynamic variations of in-situ stem water content (S t WC) near the root is beneficial for the early detection of tobacco root diseases. Therefore, we developed a wearable plant sensor with a flexible interdigitated-electrodes (IE) probe design for in-situ monitoring of StWC and early identification and warning of tobacco root diseases. The IE probe of wearable sensor was securely affixed to the stem, and the soil moisture (SM) sensors were buried in the corresponding root area. The results demonstrated a clear inconsistency in the observed trend between the S t WC near roots of diseased and healthy tobacco plants. About 60 h before the blackening of the stems near roots, the SM of diseased tobacco plants (0.007 cm 3 /cm 3 ) indicated a slower decrease compared to healthy tobacco plants (0.021 cm 3 /cm 3 ). In accordance with this phenomenon, the daily variation of S t WC near roots of diseased tobacco plants (0.023 cm 3 /cm 3 ) was significantly less than that of healthy tobacco plants (0.048 cm 3 /cm 3 ). Moreover, the abnormal changes of S t WC near roots of diseased tobacco plants after blackening further validated the availability of the wearable sensor in the early detection and warning of tobacco root diseases. The tobacco plant may have been in early diseased stage when the daily change of S t WC was continuously less than 0.037 cm 3 /cm 3 . Future research will focus on the mechanism of water conduction between soil and stem near the root of tobacco plants, and the potential application of the wearable sensor in early disease detection. • A wearable plant sensor is developed for early warning of tobacco root diseases. • The sensors were used to monitor diseased and healthy tobacco plants in the field. • The sensor can achieve early in-situ detection of tobacco root diseases.
Why it matches plant phenotyping methods植物茎内水分状態を測定し、根部病害の早期検出へ用いるウェアラブルセンサーを開発・検証しており、植物状態の取得法が中心である。
abstractTherefore, we developed a wearable plant sensor with a flexible interdigitated-electrodes (IE) probe design for in-situ monitoring of StWC and early identification and warning of tobacco root diseases.
Overcoming the strong chlorophyll background poses a significant challenge for measuring and optimizing plant growth. This research investigates the novel application of specialized quantum light emitters introduced into intact leaves of tobacco ( Nicotiana tabacum ), a well-characterized model plant system for studies of plant health and productivity. Leaves were harvested from plants cultivated under two distinct conditions: low light (LL), representing unhealthy leaves with reduced photosynthesis and high light (HL), representing healthy leaves with highly active photosynthesis. Higher-order correlation data were collected and analyzed using machine learning (ML) techniques, specifically a Convolutional Neural Network (CNN), to classify the photon emitter states. This CNN efficiently identified unique patterns and created distinct fingerprints for Nicotiana leaves grown under LL and HL, demonstrating significantly different quantum profiles between the two conditions. These quantum fingerprints serve as a foundation for a novel unified analysis of plant growth parameters associated with different photosynthetic states. By employing CNN, the emitter profiles were able to reproducibly classify the leaves as healthy or unhealthy. This model achieved high probability values for each classification, confirming its accuracy and reliability. The findings of this study pave the way for broader applications, including the application of advanced quantum and machine learning technologies in plant health monitoring systems.
Why it matches plant phenotyping methods量子発光体による葉の光子プロファイル取得とCNN解析を組み合わせ、光合成状態および植物の健康状態を分類する手法が研究の中心である。
abstractThis CNN efficiently identified unique patterns and created distinct fingerprints for Nicotiana leaves grown under LL and HL
Reproduction assets foundThe article's Data availability statement explicitly deposits the paper's time-tagged photon correlation data (the raw measurements underlying the quantum fingerprinting and CNN analysis) in the Dryad Digital Repository, a public, paper-specific dataset.Dataset · publicay: conceptualization, funding acqui-
sition, supervision, project administration, visualization,
writing – original draft, writing – review & editing.
Conflicts of interest
There are no conflicts to declare.
Data availability
Data for this article, including time tangled photon data, are
available at Dryad Digital Repository at https://doi.org/10.5061/dryad.1rn8pk15f.Supplementary information is available. See DOI: https://
doi.org/10.1039/d5an00326a.
Acknowledgements
This research was funded in part by the Faculty Industry
Applied Research (FIAR) program, by the University at
Buffalo’s Center of Excellence in Materials Informatics.
References
1 E. Murchie and T. Lawson, J. Exp. Bot., 2013, 6Open asset ↗Dryad Digital Repository · 10.5061/dryad.1rn8pk15fpdf-raw-page:7 lines:1-96Plant phenotyping relevance match · UnverifiedCrossref · Europe PMC · checked 6 Sept 2026
Abstract Microfluidic technologies offer powerful tools for miniaturized and highly controlled biological experiments, yet their application in plant research remains underexploited. In this study, we present a droplet-based microfluidic platform tailored for the encapsulation and cultivation of plant protoplasts, enabling long-term observation of cell development at nearly single-cell resolution. Protoplasts isolated from leaves of Nicotiana tabacum , Brassica juncea , and Kalanchoe daigremontiana were used to evaluate the platform’s suitability across diverse plant species. Our results demonstrate species-dependent responses to microfluidic cultivation, with tobacco protoplasts showing the highest viability. The system permits dynamic tracking of cell fate within individual droplets and supports the quantification of stochastic and concentration-dependent responses to chemical stimuli. Using tobacco protoplasts, we further investigated the effect of low concentrations of cytokinins (BAP) and auxins (NAA) for the early protoplast culture, up to the first division. Low concentrations (20–80 µg·L⁻¹) significantly enhanced cell survival and cell growth, while higher doses did not yield additional benefits. This work underscores the potential of droplet-based microfluidics as a high-resolution, low-volume platform for protoplast-based assays and dose-response screening, with applications across diverse plant biotechnology studies.
Why it matches plant phenotyping methods植物プロトプラストの生存、成長、細胞運命を高解像度で追跡・定量するドロplet型マイクロ流体プラットフォームが研究の中心であり、植物状態の取得・解析手法を開発・評価している。
abstractwe present a droplet-based microfluidic platform tailored for the encapsulation and cultivation of plant protoplasts, enabling long-term observation of cell development at nearly single-cell resolution.
Introduction The cigar leaves moisture content (CLMC) is a critical parameter for controlling curing barn conditions. Along with the continuous advancement of deep learning (DL) technologies, convolutional neural networks (CNN) have provided a way of thinking for the non-destructive estimation of CLMC during the air-curing process. Nevertheless, relying merely on single-perspective imaging makes it difficult to comprehensively capture the complementary morphological features of the front and back sides of cigar leaves during the air-curing process. Methods This study constructed a dual-view image dataset covering the air-curing process, and proposes a regression framework named CADFFNet (channel attention weight-based dual-branch feature fusion network) for the non-destructive estimation of CLMC during the curing process based on dual-view RGB images. Firstly, the model utilizes two independent and parallel ResNet as its backbone structure to capture the heterogeneous features of dual-view images. Secondly, the Dual Efficient Channel Attention (DECA) module is introduced to dynamically adjust the channel attention weights of the features, thereby facilitating interaction between the two branches. Lastly, a Multi-scale convolutional feature fusion (MSCFF) module is designed for the deep fusion of features from the front and back images to aggregate multi-scale features for robust regression. Results On five-fold cross-validation, CADFFNet attains R2 of 0.974±0.007 and mean absolute error (MAE) of 3.80±0.37%. On an independent cross-region, cross-variety testing set, it maintains strong generalization (R2=0.899, MAE=5.82%), compared with the classic CNN models ResNet18, GoogLeNet, VGG19Net, DenseNet121, and MobileNetV2, its R2 value has increased by 0.047, 0.041, 0.055, 0.098, and 0.090 respectively. Discussion Generally, the proposed CADFFNet offers an efficient and convenient method for non-destructive detection of CLMC, providing a theoretical basis for automating the air-curing process. It also provides a new perspective for moisture content prediction during the drying process of other crops, such as tea, asparagus, and mushrooms.
Why it matches plant phenotyping methods葉の水分含量という植物状態を、二視点RGB画像と新規深層学習回帰モデルで非破壊推定する手法を開発し、交差検証および独立試験で性能評価しており、植物フェノタイピング手法が中心である。
abstractproposes a regression framework named CADFFNet (channel attention weight-based dual-branch feature fusion network) for the non-destructive estimation of CLMC during the curing process based on dual-view RGB images.
Accurately obtaining the total nitrogen and nicotine content of tobacco plants and their vertical distribution within the canopy is crucial for smart management and quality assessment. However, the complex field environment and uneven vertical distribution pose significant challenges for precise estimation. This study proposed a spectral and texture feature fusion method based on deep learning to improve estimation accuracy, and an improved YOLOv8 model (AO-YOLOv8) was developed for tobacco leaf instance segmentation. After segmentation, the average spectral features from six image channels were extracted, and 474 texture features were obtained using Gray Level Co-occurrence Matrix (GLCM), Local Binary Pattern (LBP), Fourier Transform, Gabor Filter, and Wavelet Transform. Four deep neural networks, including LSTM, RNN, MLP, and FCNN, were then applied to establish estimation models of nitrogen and nicotine content at both the leaf and plant scales. The results showed that AO-YOLOv8 achieved an mAP50 of 87.3 and an mIoU of 83.4 in the leaf instance segmentation task, representing improvements of 6.99% and 8.88% over the original YOLOv8, and effectively detected and separated overlapping leaves under complex conditions. The fusion of spectral and texture features significantly improved prediction accuracy, with the LSTM network achieving the best performance, yielding R 2 values of 0.8634 and 0.8735 for nitrogen and nicotine prediction at the leaf scale in laboratory conditions. In the field environment, the LSTM-based models for plant-scale nitrogen and nicotine estimation achieved R 2 values of 0.6771 and 0.5735, respectively, which outperformed models using spectral features alone. In conclusion, this study enabled accurate estimation and visualization of the vertical distribution of nitrogen and nicotine content in field-grown tobacco plants, providing an efficient, low-cost, and non-destructive solution for tobacco production and quality control.
Why it matches plant phenotyping methodsタバコ葉・植物の窒素およびニコチン含量という植物形質を、マルチスペクトル画像、テクスチャ特徴、葉インスタンス分割、深層学習により推定・可視化する方法が研究の中心である。
abstractThis study proposed a spectral and texture feature fusion method based on deep learning to improve estimation accuracy, and an improved YOLOv8 model (AO-YOLOv8) was developed for tobacco leaf instance segmentation.
Flow cytometry (FCM) and genome sequencing are complementary methods for estimating plant genome size (GS). However, discrepancies between the GS estimates derived from genome assemblies and FCM create ambiguity regarding the accuracy of these approaches. Approximately 12,000 plant GS measurements have been reported, with hardly any of them based on genome assemblies. Currently, FCM is the most frequently used method. Accurate GS estimation by FCM relies on internal standards with known GS values. However, previous GS calibrations, often based on incomplete reference genome assemblies, have led to significant discrepancies in GS estimates. Historically, the GS of a diploid plant species was estimated by doubling the size of a consensus genome assembly. However, consensus assemblies collapse homologous chromosomes into a single sequence, typically favouring the larger haplotype and potentially overestimating GS, especially in highly heterozygous species. Here, we applied haplotype-resolved genome assemblies to accurately recalibrate the reference standards. We utilized a recent gapless, telomere-to-telomere (T2T) consensus and the most complete phased genome assemblies of the Nipponbare rice as a primary standard to recalibrate five commonly used plant standards. Using the consensus genome as a reference revealed an overestimation of over 30% in widely used previous GS estimates for Pisum sativum and Nicotiana benthamiana , approximately 18% for Arabidopsis thaliana , and 5% for Sorghum bicolor and Gossypium hirsutum . The GS estimates based on phased haplotype assemblies suggested an additional 6%–7% overestimation. Haplotype-resolved genome assemblies allow the recalibration of GS estimates with the potential to yield more accurate values by capturing haplotype-specific variations previously missed in consensus assemblies.
Why it matches plant phenotyping methods植物のゲノムサイズ推定に用いるフローサイトメトリー標準の再校正が研究の中心であり、測定精度の検証・改善に該当する。
titleRe-calibration of flow cytometry standards for plant genome size estimation
Accurate plant counting is essential for tobacco yield estimation and planting density regulation. However, manual quadrat surveys are inefficient and error-prone, and conventional detectors often suffer from feature loss and background confusion under complex field conditions. This study proposes an improved YOLOv8n framework, YOLOv8-AKConv-MLCA, tailored for UAV imagery of plain-field and mountain-field tobacco. First, an Alterable Kernel Convolution (AKConv) module is embedded inside the original C2f blocks to replace all conventional convolutions, enabling adaptive sampling and richer multi-scale representation of small and densely distributed targets. Second, a mixed local channel attention (MLCA) module is inserted between the last C2f-AKConv block and SPPF to fuse local spatial cues with global channel dependencies, suppressing clutter and occlusion effects. Extensive experiments on UAV datasets show that the proposed model achieves counting accuracies of 97.20% (plain-field) and 96.13% (mountain-field), improving over baseline YOLOv8 by 3.98% and 3.25%, respectively. Detection metrics likewise improve: mean average precision (mAP) reaches 0.936 and 0.914 in the two scenarios, surpassing SSD (0.844, 0.827), Faster R-convolutional neural network (0.865, 0.842), and a Transformer-based variant (YOLOv8-Trans, 0.923, 0.907). Relative to YOLOv8, maximum gains of 12.1% in precision, 1.9% in recall, and 7.3% in mAP are observed. Crucially, real-time throughput is preserved, with inference speeds of 219-227 frames per second across datasets. Grad-CAM visualizations further confirm that YOLOv8-AKConv-MLCA concentrates attention on canopy regions and suppresses background interference, offering intuitive evidence of enhanced feature learning. Overall, the proposed framework delivers a strong accuracy-efficiency trade-off and robust generalization under complex terrain, providing an effective solution for automated tobacco plant counting and supporting precision cultivation and smart agricultural management. Code and trained weights are available upon reasonable request for replication and evaluation.
Why it matches plant phenotyping methodsUAV画像からタバコ個体数を推定する検出・計数手法を開発し、複数条件で比較検証しており、植物表現型取得が中心である。
abstractThis study proposes an improved YOLOv8n framework, YOLOv8-AKConv-MLCA, tailored for UAV imagery of plain-field and mountain-field tobacco.
Sustainable and low-nicotine production of tobacco requires rapid and accurate on-site assessment of the leaf nitrogen (N) status. This issue can be supported by fluorescence-based sensors, which are promising tools for precision N management. We then aimed to 1) evaluate the suitability of the Multiplex® fluorescence sensor (Mx) to predict, at an early stage, the final nicotine content of tobacco leaves; 2) develop a model for in-season tobacco foliar N estimation using the Partial Least Square (PLS) multivariate regression technique; and finally, 3) test the effectiveness of a Mx map-based Variable Rate Nitrogen Fertilization (VRNF) in reducing the spatial variability in leaf Nitrogen Balance Index (NBI), that is the N status, of a commercial field of Virginia Bright tobacco. The NBI measured by the Mx about two months after transplanting was found to linearly relate to the nicotine content measured after curing (R² = 0.72, P < 0.001) over a nicotine range of 0.25 – 4.12 %. NBI, defined as the ratio between the leaf chlorophyll (SFRR) and Flavonoids (FLAV) indices better related to nicotine than the single SFRR and FLAV indices (R² = 0.47, P < 0.001 and R² = 0.52, P < 0.001, respectively. Furthermore, the NBI estimated the actual leaf N content before flowering better (R² = 0.33) than single SFRR and FLAV indices (R² = 0.28), over a range of 21 – 37.6 mgg⁻¹. Leaf fluorescence sensor indices were thus combined with growth stages and weather variables across diverse varieties and sites. The resulting PLS model successfully predicted leaf N (R² = 0.72, RMSEP = 2.73 mgg⁻¹ and relMAE = 7.75 %) over a range of 20.6–28.0 mgg⁻¹. The most significant variables, primarily related to solar radiation, were identified for a robust general model development. Finally, the spatial pattern of the NBI was mapped over a 2.04 ha commercial plot of the ITB 6118 variety, and used to produce a three-zone prescription map. Two weeks after the intervention of VRNF based on the defined prescription map, the overall NBI variability had dropped from 23.5 % coefficient of variation (CV) to 7.9 % CV. Our results show the feasibility of using the Mx sensor for precision fertilization of Virginia Bright tobacco and highlight its potential to support future developments aimed at more sustainable production of plants with reduced nicotine content.
Why it matches plant phenotyping methods蛍光センサーによる葉のニコチン含量・窒素状態の推定モデルを開発・検証し、NBIマッピングと可変施肥へ応用しており、植物形質の取得・推定手法が中心である。
abstractevaluate the suitability of the Multiplex® fluorescence sensor (Mx) to predict, at an early stage, the final nicotine content of tobacco leaves
While tobacco plays a significant role in the global economy, research on regional tobacco growth simulation remains limited. This study integrates the WOFOST crop model with satellite remote sensing data, focusing on the data assimilation (DA) of leaf area index (LAI) to enhance the accuracy of regional tobacco growth simulations. Field survey data were used for model calibration, providing the foundation for the analysis. The performance of four 4-Dimensional Variational Assimilation algorithms (4DVAs)—Particle Swarm Optimization (PSO), Simulated Annealing (SA), Shuffled Complex Evolution-University of Arizona (SCE-UA), and Gray Wolf Optimization (GWO)—was compared with four sequential DA algorithms (SDAs)—Ensemble Kalman Filter (EnKF), Ensemble Variational (EnVar), Ensemble Square Root Filter (EnSRF), and Particle Filter (PF). The 4DVAs were developed by integrating constraint DA Algorithms (CDAs) into the 4D-Var framework, enhancing their capability to optimize model states over a time window. Additionally, the performance of their coupled DA algorithms was evaluated. The results indicated that the coupled of SA and PF (SA-PF) achieved the best performance in terms of model accuracy. Compared to field survey data for biomass, stem mass and leaf mass, our method achieved the coefficient of determination (R²) values of 0.89, 0.86, and 0.81, respectively, with normalized root mean square error (NRMSE) values of 0.12, 0.10, and 0.09. The SA-PF coupling algorithm also performs better than some new DA algorithms. This study provides a valuable reference for regional tobacco growth simulation and data assimilation, improving the accuracy and applicability of crop growth models.
Why it matches plant phenotyping methods衛星リモートセンシングのLAIを作物モデルへ同化し、バイオマス・茎重・葉重を推定するデータ同化手法を開発・比較・検証しており、植物形質推定が中心である。
abstractThis study integrates the WOFOST crop model with satellite remote sensing data, focusing on the data assimilation (DA) of leaf area index (LAI) to enhance the accuracy of regional tobacco growth simulations.
The application of in-field and aerial spectroscopy to assess functional and phylogenetic variation in plants has led to novel ecological insights and supports global assessments of plant biodiversity. Understanding how plant genetic variation influences reflectance spectra will help harness this potential for biodiversity monitoring and improve understanding of why plants differ in functional responses to environmental change. Here, we use a well-resolved genetic mapping population derived from Multi-parent Advanced Generation Inter-cross (MAGIC) lines of Nicotiana attenuata to associate genetic differences with differences in leaf spectra between plants in a field experiment in their natural environment. We analyzed the leaf reflectance spectra using a hand-held spectroradiometer (350-2500 nm) on 616 fully genotyped plants of N. attenuata grown in a randomized block design. We tested three approaches to conducting Genome-Wide Association Studies on spectral variants. We introduce a new Hierarchical Spectral Clustering with Parallel Analysis (HSC-PA) method. This method efficiently captured the variation in our high-dimensional dataset and allowed us to discover a novel association, between a locus on chromosome 1 and the 734-1143 nm spectral range, spanning the red-edge and near-infrared regions that are sensitive to leaf structure and photosynthetic activity. This locus contains a candidate gene annotated as carbonic anhydrase, an enzyme involved in CO2 hydration and regulation of photosynthetic efficiency, suggesting a physiological link between variation in leaf optical properties and carbon assimilation. In contrast, an approach treating single wavelengths as phenotypes identified the same associations as HSC-PA, but without the statistical power to pinpoint significant associations. An index-based approach, which reduces complex spectra to a few dimensionless variables, detected two significant associations for ARDSI_Cw (a water-content-related index) with loci on chromosome 1 near genes annotated as a Zeta toxin domain-containing protein, and an Exocyst subunit Exo70 family protein. While these findings are biologically plausible, they represent a very narrow subset of the spectral variation captured by HSC-PA. The HSC-PA approach supports a comprehensive understanding of the genetic determinants of leaf spectral variation which is data-driven but human-interpretable, and lays a robust foundation for future research in linking plant genetics with biodiversity monitoring, large-scale ecological assessment and remote-sensing applications.
Why it matches plant phenotyping methods葉の反射スペクトルを植物表現型として取得し、高次元スペクトルを解析する新規HSC-PA手法を導入・評価しており、植物フェノタイピング手法が研究の中心的な技術的貢献である。
abstractWe analyzed the leaf reflectance spectra using a hand-held spectroradiometer (350-2500 nm) on 616 fully genotyped plants of N. attenuata
Accurate segmentation of key tobacco structures is essential for enabling automated harvesting. However, complex backgrounds, variable lighting conditions, and blurred boundaries between the stem and petiole significantly hinder segmentation accuracy in field environments. To overcome these challenges, we propose an enhanced instance segmentation approach based on YOLOv8-seg, incorporating depth-based background filtering and architectural improvements. Specifically, depth information from RGB-D images is employed to spatially filter non-target background regions, thereby enhancing foreground clarity. In addition, a Hybrid Dilated Residual Attention Block (HDRAB) is integrated into the YOLOv8-seg backbone to improve boundary discrimination between petioles and stems, while a Lightweight Shared Detail-Enhanced Convolution Detection Head (LSDECD) is designed to efficiently capture fine-grained texture features. Experimental results demonstrate that depth filtering increases mAP50 bb and mAP50 seg by 7.9% and 6.3%, respectively, while the architectural enhancements further raise them to 89.5% and 91.1%, surpassing the YOLOv8-seg baseline by 5.2% and 10.0%. Compared with mainstream models such as Mask R-CNN and SOLOv2, the proposed method achieves superior segmentation accuracy with low computational cost, highlighting its potential for practical deployment in automated tobacco harvesting.
Why it matches plant phenotyping methodsタバコ植物の茎・葉柄などの構造をRGB-D画像からセグメンテーションする手法を開発・比較しており、植物器官形態の取得が中心である。
abstractwe propose an enhanced instance segmentation approach based on YOLOv8-seg, incorporating depth-based background filtering and architectural improvements.
The classification of plant leaf diseases is critical for ensuring agricultural productivity and sustainability.Recent improvements in deep learning algorithms have shown a lot of promise for correctly identifying and diagnosing plant diseases by looking at images of leaves.To address the challenge of plant leaf disease classification using deep learning algorithms is critical for minimizing agricultural losses.The primary objective of this comparative analysis is to evaluate the effectiveness of various deep learning algorithms in classifying plant leaf diseases.To contribute to the development of a userfriendly classification tool that can be utilized by farmers and agricultural professionals, thus promoting early disease detection and intervention.The primary goal is to identify the most accurate and robust algorithm for classifying plant leaf diseases using images.To evaluate several prominent deep learning models, including Convolutional Neural Networks (CNNs), Median-Modified Wiener Filter (MMWF) reduces noise and enhances image quality, improving feature preservation for plant leaf classification.Hybrid Deep Segmentation Convolutional Neural Network (Hybrid-DSCNN) enhances feature extraction and segmentation, improving disease detection accuracy in plant leaves.It enables robust comparative analysis against other deep learning models, optimizing classification performance.Southern Leaf Blight (SLB) serves as a critical case study in deep learning for plant disease classification, highlighting model accuracy, feature extraction, and real-time diagnosis in agricultural applications.The test results show that the suggested method works better than current ones, and it got an F1-score of 92%, an accuracy of 95%, a precision of 92%, a recall of 90%, and a recall of 90%.The programming language Python was used to create the model.Future research in plant leaf disease classification using deep learning could explore hybrid models that combine multiple algorithms for improved accuracy.
Why it matches plant phenotyping methods植物葉画像から病害状態を推定する深層学習分類法を比較・評価し、分類性能を報告しており、病害フェノタイピング手法が中心です。
abstractThe primary objective of this comparative analysis is to evaluate the effectiveness of various deep learning algorithms in classifying plant leaf diseases.
SorghumTobaccoTomatoLiDAR / point cloudLeafStem / branchSegmentation
Accurate plant organ segmentation is essential for enabling high-throughput extraction of plant phenotypes, as it provides the foundational data for both trait measurement and structural modeling. Although existing studies have made significant progress, plant semantic segmentation (stems and leaves) across multiple species remains underexplored. To this end, this paper introduces an edge-aware downsampling algorithm and a novel network for segmenting plant point clouds at multiple scales, named MSPlantSegNet. Experimental results on a dataset of tobacco, tomato, and sorghum demonstrate that MSPlantSegNet attained superior performance across all four key metrics-precision (97.13 %), recall (95.63 %), F1-score (96.20 %), and IoU (93.14 %). MSPlantSegNet surpasses a set of leading models, including PointNet++, PointNet, ASIS, DGCNN, PlantNet, PSegNet, and PointNeXt. This research has valuable implications for plant phenotyping, the development of smart agriculture, and ideal type selection.
Why it matches plant phenotyping methods植物点群から茎・葉を分割する新規ネットワークとダウンサンプリング法を開発し、複数種データで性能比較しており、表現型抽出の基盤手法が中心である。
abstractAccurate plant organ segmentation is essential for enabling high-throughput extraction of plant phenotypes
Plant phenotyping relevance match · UnverifiedOpenAlex · Crossref · Europe PMC · checked 6 Sept 2026
Accurate acquisition of tobacco phenotypic traits is crucial for growth monitoring, cultivar selection, and other scientific management practices. Traditional manual measurements are time-consuming and labor-intensive, making them unsuitable for large-scale, high-throughput field phenotyping. The integration of 3D reconstruction and stem-leaf segmentation techniques offers an effective approach for crop phenotypic data acquisition. In this study, we propose a tobacco phenotyping method that combines unmanned aerial vehicle (UAV) remote sensing with an improved PointNet++ model. First, a 3D point-cloud dataset of field-grown tobacco plants was generated using multi-view UAV imagery. Next, the PointNet++ architecture was enhanced by incorporating a Local Spatial Encoding (LSE) module and a Density-Aware Pooling (DAP) module to improve the accuracy of stem and leaf segmentation. Finally, based on the segmentation results, an automated pipeline was developed to compute key phenotypic traits, including plant height, leaf length, leaf width, leaf number, and internode length. Experimental results demonstrated that the improved PointNet++ model achieved an overall accuracy (OA) of 95.25% and a mean intersection over union (mIoU) of 93.97% for tobacco plant segmentation-improvements of 5.12% and 5.55%, respectively, over the original PointNet++ model. Moreover, using the segmentation results from the improved PointNet++ model, the predicted phenotypic values exhibited strong agreement with ground-truth measurements, with coefficients of determination (R²) ranging from 0.86 to 0.95 and root mean square errors (RMSE) between 0.31 and 2.27 cm. This study provides a technical foundation for high-throughput phenotyping of tobacco and presents a transferable framework for phenotypic analysis in other crops.
Why it matches plant phenotyping methodsUAV 3D点群、改良PointNet++による茎葉分割と形質推定パイプラインが研究の中心であり、複数のタバコ形質を自動取得・検証している。
abstractIn this study, we propose a tobacco phenotyping method that combines unmanned aerial vehicle (UAV) remote sensing with an improved PointNet++ model.
This work demonstrates the biological detection of low-level by measuring electrochemical impedances of stem tissues in tobacco and tomato plants, both indoor and outdoor. Ozone concentrations as low as 30 above ambient levels were detected via physiological responses, enabling the use of phytosensors as biodetectors of environmental pollutants. exposure affects stomatal regulation that in turn alters the hydrodynamics of fluid transport system in plants. The measurement results indicate a reaction of hydrodynamic system to changes in concentration with a delay of 10-20 min between the onset of exposure and biological response. The probability of false-negative responses from a plant is 0.15 ± 0.06. Pooling data from at least three plants allows for 92% confidence in detecting excess . Measurements on days with low and high ozone levels of 80 to 130 result in a 2.33-fold difference in sensor readings at these levels, underscoring the sensitivity of the method. Statistical robustness is supported by 948 plant-sensor measurements with 9 plants over 51 days, totaling 10 7 samples via automated monitoring. Long-term field tests demonstrate the reliability of electrochemical methods. This approach has applications in environmental monitoring, biological pollution detection and biosensing.
Why it matches plant phenotyping methods植物組織の電気化学インピーダンスからオゾン曝露に対する生理応答を検出するセンサー手法を開発・検証しており、植物状態の取得方法が研究の中心である。
abstractThis work demonstrates the biological detection of low-level by measuring electrochemical impedances of stem tissues in tobacco and tomato plants
Hyperspectral imaging (HSI) is a powerful tool for crop phenotypic component analysis, but developing efficient collaborative extraction and modeling methods for image and spectral features is a challenge. This study proposed a novel spatial-spectral fusion detection method for nicotine content utilizing hyperspectral imaging (HSI) and deep learning. Spectra of different regions and multi-channel images extracted by two-dimensional correlation analysis (2D-COS) were employed as inputs. A dual-branch spatial-spectral attention fusion model (DSSAM) was developed to enhance the expression ability of different modal information. Among them, two branches designed based on the residual module were used to extract spatial and spectral features, respectively. For the spectral branch, a multi-region spectral attention encoder (MSAE) was added to dynamically adjust the weights of the spectrum across leaf regions. For the spatial branch, a swin window attention (SWA) module was introduced to improve local feature extraction and spatial structure learning. The results demonstrated that MSAE and SWA could improve the spatial-spectral information fusion ability of the DSSAM. Compared with the dual-branch model without the attention modules, the coefficient of determination (R²) and relative prediction deviation (RPD) of the DSSAM model on the test set increased by 7.85% and 2.64%, respectively, and the Root Mean Square Error (RMSE) decreased by 39.29%. In addition, the DSSAM outperformed traditional chemometric and single-modal models, with a R² of 0.893, a RMSE of 0.289, and a RPD of 3.054. These findings provide a valuable approach for the quality nondestructive detection of cured tobacco leaves and other crop phenotypic components.
Why it matches plant phenotyping methodsハイパースペクトル画像からタバコ葉のニコチン含量を推定する空間・スペクトル融合モデルを開発し、既存モデルとの性能比較で検証しており、表現型取得・推定手法が中心である。
abstractThis study proposed a novel spatial-spectral fusion detection method for nicotine content utilizing hyperspectral imaging (HSI) and deep learning.
Microfluidic technologies offer powerful tools for miniaturized and highly controlled biological experiments, yet their application in plant research remains underexploited. In this study, we present a droplet-based microfluidic platform tailored for the encapsulation and cultivation of plant protoplasts, enabling long-term observation of cell development at nearly single-cell resolution. Protoplasts isolated from leaves of Nicotiana tabacum, Brassica juncea , and Kalanchoe daigremontiana were used to evaluate the platform’s suitability across diverse plant species. Our results demonstrate species-dependent responses to microfluidic cultivation, with tobacco protoplasts showing the highest viability. The system permits dynamic tracking of cell fate within individual droplets and supports the quantification of stochastic and concentration-dependent responses to chemical stimuli. Using tobacco protoplasts, we further investigated the effect of low concentrations of cytokinins (BAP) and auxins (NAA) for the early protoplast culture, up to the first division. Low concentrations (20–80 µg·L −1 ) significantly enhanced cell survival and cell growth, while higher doses did not yield additional benefits. This work underscores the potential of droplet-based microfluidics as a high-resolution, low-volume platform for protoplast-based assays and dose-response screening, with applications across diverse plant biotechnology studies.
Why it matches plant phenotyping methods植物プロトプラストの生存、成長、細胞運命を単一細胞レベルで追跡・定量するマイクロ流体プラットフォームが研究の中心であり、植物状態の取得手法として適格。
abstractwe present a droplet-based microfluidic platform tailored for the encapsulation and cultivation of plant protoplasts, enabling long-term observation of cell development at nearly single-cell resolution.
Genetic screens in whole plants are a powerful tool for functional genetics. However, elucidating gene function in highly redundant genetic programs such as signaling pathways remains challenging in both model and non-model plants. Here, we report a single-cell screening platform, PIVOT (Protoplast Isolation after Virus Overexpression in planTa ), to accelerate identification and functional characterization of plant genes. We used Nicotiana benthamiana as a heterologous host to test gene libraries arrayed in a single leaf. Two elements of our system made pooled screens possible in planta : (1) we harnessed viral superinfection exclusion to ensure single multiplicity of infection per cell during pooled library delivery, and (2) we engineered a cell surface protein as a phenotypic marker for isolating cells of interest from a heterogeneous population. Using this system, we recovered known and new regulators of cytokinin signaling from an Arabidopsis open reading frame library. We anticipate PIVOT will be broadly applicable for high-throughput, single-cell functional genetic screening across the plant kingdom.
Why it matches plant phenotyping methods植物細胞の表現型マーカーを利用して関心細胞を単離する単一細胞スクリーニング基盤そのものの開発であり、表現型取得・選別が研究の中心です。
abstractHere, we report a single-cell screening platform, PIVOT (Protoplast Isolation after Virus Overexpression in planTa ), to accelerate identification and functional characterization of plant genes.
This study proposes a novel, robust and scalable approach for color phenotype modeling, providing a new research perspective for crop phenotype analysis. The color gradation skewness-distribution (CGSD) parameters, as a color characterization indicator that can be widely applied to different crops and ecological environments, possess significant theoretical value and practical application potential. Specifically, we explore the relationship between accumulated temperature—the primary heat factor driving crop development—and crop canopy leaf color, aiming to identify canopy color parameters that can consistently describe crop responses to environmental changes. In this study, we developed and tested inversion models for predicting accumulated temperature in wheat and tobacco crops grown in both laboratory and natural environments across various ecological regions. These models utilized color gradation skewness-distribution parameters derived from digital canopy images. Our results show that some inversion models can predict accumulated temperature responses with high accuracy, achieving 88.95% accuracy for wheat and 77.38% for tobacco. Statistical analysis revealed that, compared to models using parameters related to color depth, those incorporating parameters related to leaf color distribution as independent variables provided more consistent predictions across crops from different ecological regions. This can be explained from the fact that the leaf color distribution parameters are relative values, which are less affected by regional ecological variations than parameters associated with the image color depth, which depend on the absolute value of the color level of leaf image pixels. Our study confirms that the CGSD parameters extracted from the color information contained in digital canopy images can provide a novel approach for accurate monitoring and evaluation of crop growth in diverse ecological environments.
Why it matches plant phenotyping methodsデジタル群落画像から葉色分布指標を抽出し、環境応答の推定モデルを開発・検証しており、植物表現型の取得・計算手法が中心である。
abstractThis study proposes a novel, robust and scalable approach for color phenotype modeling, providing a new research perspective for crop phenotype analysis.
Plants engineered with synthetic genetic programs can transform how we monitor and manage the extension of crop pests and diseases. Here, we establish a bioluminescent platform in Nicotiana benthamiana for autonomous viral sensing based on the fungal bioluminescence pathway (FBP). We first demonstrate that recombinant viruses can deliver missing pathway components, enabling spatially resolved tracking of infection dynamics. Leveraging this starting point, we developed a dual-output sentinel circuit that uses a protease-responsive Bioluminescence Resonance Energy Transfer (BRET) module to report infection through a virus-triggered spectral shift in luminescence. In the absence of infection, plants emit a stable yellow glow indicating system integrity. Upon infection with potyviruses, cleavage of the BRET fusion by the virus-encoded NIa-Pro protease activates a distinct colour change detectable with low-cost imaging. This modular design is compatible with other pathogens carrying specific proteases and supports future multiplexing strategies. Our results highlight the potential of synthetic sentinel gene circuits as autonomous biosensors for precision crop protection.
Why it matches plant phenotyping methods植物のウイルス感染状態を発光変化として検出するセンチネル回路と低コスト画像診断プラットフォームの開発が中心であり、病害状態のフェノタイピング手法に該当する。
abstractHere, we establish a bioluminescent platform in Nicotiana benthamiana for autonomous viral sensing based on the fungal bioluminescence pathway (FBP).
SUMMARY Monitoring endogenous nutrient levels is crucial for maximizing crop yields and optimizing fertilizer use. Here, focusing on phosphorus, an essential nutrient for plant growth, we developed a low-cost and non-invasive biosensor to visualize and predict early stress signaling in plants. By combining plant phosphate (Pi)-deficiency-induced promoter systems with fungal self-sustained bioluminescence systems genetically engineered into tobacco plants, we created sensor plants that emitted more light when experiencing Pi deficiency. This light emission correlated with the expressions of known phosphate-responsive genes and the total phosphorus content in plants, and decreased during Pi recovery conditions, demonstrating the responsiveness and robustness of the sensor plants in reflecting endogenous phosphorus deficiency. The sensor plants responded primarily to Pi deficiency rather than nitrogen or potassium deficiencies and were sensitive to different ranges of external Pi concentrations. Additionally, when grafted onto tomato and chili pepper plants, the sensor plants responded to external phosphorus deficiency, showing promise for monitoring stress signals in different crop species. Using deep-learning-based image analysis techniques, auto-luminescent signals of sensor plants could be detected and used to predict phosphorus deficiency. This study outlines a strategy of creating a self-luminous biosensor to visualize phosphate dynamics in planta and predict nutrient deficiency for sustainable agriculture.
Why it matches plant phenotyping methods植物内リン欠乏状態を自己発光センサーと画像解析で可視化・予測する手法を開発し、応答性・頑健性を検証しているため、植物フェノタイピング手法が中心です。
abstractwe developed a low-cost and non-invasive biosensor to visualize and predict early stress signaling in plants
ABSTRACT Polyamines (PAs) are essential for plant development and stress responses, requiring tight homeostatic regulation. Many PA enzymes are regulated post-transcriptionally, making traditional transcript-based methods ineffective in determining their abundance, highlighting the need for alternative approaches to study PA homeostasis. Here, we refined a liquid chromatography-mass spectrometry (LC-MS) based method to simultaneously quantify activities of two key PA synthesizing enzymes – arginine decarboxylase (ADC) and ornithine decarboxylase (ODC) – from plant tissues using stable isotope substrates. By optimizing substrate concentrations, we increased assay sensitivity >10-fold in tomato leaf tissue. We further adapted this protocol for Nicotiana benthamiana , a model plant widely used for transient recombinant protein expression. Expression of epitope-tagged ADCs in this system revealed a direct correlation between protein abundance and enzymatic activity, demonstrating that ADC activity can infer its protein abundance in native tissues. Proof-of-principle experiments with the N. benthamiana expression system, confirm substrate specificity of tomato ADC and ODC enzymes and essential catalytic residues of tomato ADCs. Beyond enzymatic activities, our LCMS-based method also permits quantification of 11 PA network metabolite concentrations from the same LCMS sample. Visualizing this data as a heatmap pathway diagram, alongside ADC/ODC activities provides a comprehensive overview of PA metabolism in plant tissues. We also studied tomato CRISPR-Cas9-induced mutants deficient in ADC or ODC, complemented by phenotypic analysis. LC-MS analysis of an adc1/adc2 double mutant – an embryo lethal genotype in Arabidopsis – had no detectable agmatine, the product of ADCs. Additionally, despite a reduction in putrescine, no impact on the downstream PAs, spermidine and spermine, was found. The adc1/adc2 double mutant showed severe developmental abnormalities, including complete flower loss, demonstrating the indispensable role of ADCs in flower development. In summary, our optimized LC-MS approach for simultaneous quantification of ADC/ODC enzyme activity and PA-pathway metabolites, the ability to transiently express and functionally analyze recombinant ADC/ODC proteins in planta , and a collection of tomato CRISPR mutants deficient in these enzymes collectively establish a versatile new experimental toolkit to dissect PA homeostasis and PA-dependent developmental processes in plants.
Why it matches plant phenotyping methods植物組織中の酵素活性と代謝物を同時定量するLC-MS法を改良・検証し、発生異常との関連も評価しており、測定法が研究の中心である。
abstractHere, we refined a liquid chromatography-mass spectrometry (LC-MS) based method to simultaneously quantify activities of two key PA synthesizing enzymes – arginine decarboxylase (ADC) and ornithine decarboxylase (ODC) – from plant tissues using stable isotope substrates.
- Specific generation of reactive oxygen species (ROS) is important for signalling and defence in many organisms. In plants, different types of ROS serve useful biological functions in the extracellular space (apoplast), influencing polymer structures as well as signaling during immune responses. The current knowledge of apoplastic ROS dynamics is limited, as dynamic monitoring of extracellular redox processes in vivo remains difficult. - We employed evolutionary distant land plant model species from bryophytes and flowering plants to test whether the genetically encoded redox biosensor roGFP2-Orp1 can be used to assess extracellular redox dynamics. - Secreted roGFP2-Orp1 can inform about local diffusion barriers and protein cysteinyl oxidation rate in the apoplast, after pre-reduction. Observed re-oxidation rates were slow, within the range of hours. Compared to Physcomitrium patens, re-oxidation in Arabidopsis thaliana was faster and increased after triggering an immune response. Comparing roGFP2-Orp1 signals in tip-growing P. patens protonema and Nicotiana tabacum pollen tubes, we consistently find no intracellular redox gradient, but partially reduced extracellular sensor in pollen tubes. - Our data indicate differences in extracellular oxidative processes between species and within a species, depending on cell type and immune signalling.
Why it matches plant phenotyping methods植物アポプラストの酸化還元動態を生体センサーで定量する手法の適用可能性と技術的情報を評価しており、センサーによる生理状態の取得が中心です。
abstractdynamic monitoring of extracellular redox processes in vivo remains difficult
Plant phenotyping relevance match · UnverifiedCrossref · Europe PMC · checked 6 Sept 2026
The widespread applications of fluorescence imaging in plant science still suffer from challenges including strong auto-fluorescence (chlorophyll) and tissue light scattering, resulting in low signal-to-background ratio (SBR) for in vivo bioimaging. Moreover, the relationship between the transport efficacy of fluorescence probes in plants and their sizes has been rarely investigated. To address these bottlenecks, we developed an ingenious PEG-engineering strategy on the second near-infrared (NIR-II) donor-acceptor-donor (D-A-D) emissive dye (CCNU1020) to adjust the self-assembly nanosizes of NIR-II fluorescence probes, resulting in three variants: SYH1 (170 nm), SYH2 (80 nm), and SYH3 (60 nm). As the polyethylene glycol (PEG) chain length increased, the probes' nanosize decreased from 170 to 60 nm. Among them, SYH3 exhibited the fastest entry velocity into Epipremnum Aureum leaf and spread over the leaf veins evenly than the other two probes, of which SYH1 even could hardly entry into the leaf. Meanwhile, SYH3 demonstrated high-contrast imaging of leaf vein with an exceptional signal to background ratio (SBR, ~ 18.6) superior to that of classical NIR-I indocyanine green (ICG) (~ 3.0) and SYH2. This promising imaging ability of leaf veins achieved by size optimization laid the foundation for the early diagnosis of viral infections. In vivo experiments further confirmed that SYH3 effectively accumulated and monitored in the lesion of Tobacco mosaic virus (TMV)-infected Arabidopsis thaliana, which matched well with the green fluorescent protein (GFP)-labeled results. This work represents a significant step forward in plant bioimaging in the cutting-edge NIR-II region.
Why it matches plant phenotyping methods植物体内の葉脈・ウイルス病変を高コントラストに可視化するNIR-II蛍光プローブを開発し、サイズ最適化と既存色素との性能比較を行っているため、植物表現型取得法が中心である。
abstractTo address these bottlenecks, we developed an ingenious PEG-engineering strategy on the second near-infrared (NIR-II) donor-acceptor-donor (D-A-D) emissive dye (CCNU1020) to adjust the self-assembly nanosizes of NIR-II fluorescence probes, resulting in three variants: SYH1 (170 nm), SYH2 (80 nm), and SYH3 (60 nm).
Abstract To safely dissipate excess excitation energy, photosynthetic organisms have evolved multiple photoprotection mechanisms. These mechanisms involve various molecular players functioning on overlapping timescales from seconds to days, making it challenging to isolate and quantify their individual kinetics. In this study, we perform whole-leaf chlorophyll fluorescence lifetime and xanthophyll concentration measurements on wild-type and various newly characterized non-photochemical quenching mutants of Nicotiana benthamiana , an allotetraploid vascular land plant. Based on these measurements, we construct a fluorescence lifetime-based quantitative kinetic model that disentangles individual photoprotection components and, when integrated additively, accurately predicts wild-type and mutant quenching/recovery behaviors under various light-dark regimes. Additionally, the model quantifies the per-molecule quenching efficiencies of various xanthophylls and the contributions of six quenching pathways across different mutants. It also suggests that enhancing VDE, ZEP, and PsbS expression improves overall quenching efficiency, aligning with previous studies and supporting translational efforts to optimize photoprotection and enhance crop yields under dynamic light environments.
Why it matches plant phenotyping methods葉の蛍光寿命測定に基づく定量的速度論モデルを構築し、光防護・消光状態を分解、予測、定量する手法が研究の中心である。
abstractBased on these measurements, we construct a fluorescence lifetime-based quantitative kinetic model that disentangles individual photoprotection components and, when integrated additively, accurately predicts wild-type and mutant quenching/recovery behaviors under various light-dark regimes.
Nonphotochemical quenching (NPQ) is a critical photoprotective mechanism in plants, safeguarding photosystem II (PSII) and PSI from photodamage under abiotic stress. However, it is unclear if different stressors lead to similar NPQ phenotypes, and the magnitude of natural variation (between and within plant species) in NPQ response to abiotic stress is unknown. Testing a semi-high-throughput leaf-disc approach for examining the NPQ kinetics parameters, we investigated NPQ under chilling, drought and low nitrogen stress across multiple species and/or genotypes. Our results show substantial variation in NPQ phenotypes across species, genotypes and treatments. In C3 crops, tobacco and soybean, multiple NPQ parameters generally increased under chilling and drought, while in C4 crops, maize and sorghum, NPQ traits were more variable including a decrease of multiple NPQ parameters. Low-N stress revealed genotype- and developmental stage-specific effects on NPQ, potentially reflecting distinct adaptive strategies and regulatory changes in NPQ stress response. A significant effect of ecotype and stress treatment was detected on most NPQ kinetics traits in Arabidopsis thaliana , however, the interaction between ecotype and treatment was stronger in drought than in chilling. Differential regulation of NPQ could be associated with a combination of changes in proton motive, ATPase synthase activity, and PSI redox state. Our findings highlight that interpreting relative changes in NPQ under abiotic stress is inherently complex and demands a broader integration of physiological data across multiple regulatory layers.
Why it matches plant phenotyping methods半高速スループットの葉ディスク法を用いてNPQ動態形質を測定し、複数種・遺伝子型・ストレス条件で適用しているため、植物生理フェノタイピング手法の実質的応用と判断します。
abstractTesting a semi-high-throughput leaf-disc approach for examining the NPQ kinetics parameters, we investigated NPQ under chilling, drought and low nitrogen stress across multiple species and/or genotypes.
The ability to efficiently screen plant pathogen effectors is crucial for understanding plant-pathogen interactions and developing disease-resistant crops. Traditional methods are often labor-intensive and time-consuming. Here, we present a robust, high-throughput screening assay using the tobacco mosaic virus-green fluorescent protein (TMV-GFP) vector system. The screening system combines the TMV-GFP vector and Agrobacterium -mediated transient expression in the model plant Nicotiana benthamiana . This system enables the rapid identification of effectors that interfere with plant immunity (both activation and suppression). The biological function of these effectors can be easily evaluated within six days by observing the GFP fluorescence signal using a UV lamp. This protocol significantly reduces the time required for screening and increases the throughput, making it suitable for large-scale studies. The method is versatile, cost-effective, and can be adapted to effectors with immune interference activity from various pathogens. Key features • A robust, cost-effective, and high-throughput functional screening system for plant pathogen effectors. • Utilizes the TMV-GFP vector for rapid monitoring of effector activity. • Evaluates the function of effectors within a few days using just a UV lamp. • Adaptable to both apoplastic and cytoplasmic effectors from various phytopathogens.
Why it matches plant phenotyping methods植物免疫干渉をGFP蛍光で迅速・高スループットに評価する機能スクリーニング法が研究の中心であり、植物の免疫状態を表現型として取得する方法に該当する。
abstractHere, we present a robust, high-throughput screening assay using the tobacco mosaic virus-green fluorescent protein (TMV-GFP) vector system.
Abstract Precise prediction of crop disease trends and accurate grading identification of diseases based on information technology represent a significant challenge in the application of IoT devices in agricultural production. Addressing this issue necessitates the development of predictive models for the efficient representation of Internet of Things (IoT) data. In this context, a graded precision forecasting model for Tobacco weather fleck has been developed. Additionally, a graded recognition model has been constructed to identify different levels of disease severity based on the aforementioned grading system. We utilized meteorological data collected from field Internet of Things (IoT) devices and employed the Generalized Additive Model (GAM) to identify factors significantly associated with the occurrence of this disease. The proposed composite model, which leverages the search capability of the Grey Wolf Optimizer (GWO) and the feature extraction advantage of Convolutional Neural Networks (CNN), optimized the Long Short-Term Memory (LSTM) model (GWO-CNN-LSTM) for the best grading prediction effect of tobacco climate spot disease (accuracy rate of 85.46%). The GoogleNet model, optimized with the Convolutional Block Attention Module (CBAM) and based on the Inception-ResNet-v2, achieved the highest accuracy rate for disease grading recognition (92.40%), which was significantly higher than the manual recognition accuracy rate (83.00%; P
Why it matches plant phenotyping methodsタバコ葉の病害状態・重症度を自動予測および画像認識するモデルを開発・比較しており、植物病害表現型の取得・評価手法が研究の中心である。
abstracta graded recognition model has been constructed to identify different levels of disease severity
Plant phenotyping relevance match · UnverifiedCrossref · Europe PMC · checked 15 Sept 2026
Methylglyoxal (MG) can be produced via various pathways in plants. MG is toxic for plant cells at high levels, however it acts as a signaling molecule at low levels, just as H 2 O 2 in plants. Therefore, MG detection is very important for investigating its roles in plant cells, especially in plants under environmental stresses. The near-infrared fluorescent probe SWJT-2 is a novel probe with high sensitivity for the rapid detection of MG in human HeLa cells, but at present it is not clear whether the probe can be used to determine MG levels in plant tissues. In this present research, we tried to apply the probe in plant research. Our results showed that 40 min treatment of SWJT-2 (80 μM) can be applied to the detection and imaging of MG levels in tobacco (Nicotiana benthamiana) tissues.
Why it matches plant phenotyping methods植物組織中のメチルグリオキサール濃度を近赤外蛍光プローブで検出・画像化する手法の植物への適用が中心であり、植物の生理状態を測定するフェノタイピング手法に該当する。
abstractThe near-infrared fluorescent probe SWJT-2 is a novel probe with high sensitivity for the rapid detection of MG in human HeLa cells, but at present it is not clear whether the probe can be used to determine MG levels in plant tissues.
Pollen is a male gametophyte of angiosperms. Following meiosis, the microspore undergoes an asymmetric division called pollen mitosis I (PMI), which produces two cells of different sizes: a large vegetative cell and a small generative cell. Polarized nuclear migration and positioning during PMI are important for successful pollen development and cell differentiation. However, analyzing the pollen development process in real-time is challenging in many model plants with tricellular pollen, including Arabidopsis and rice. In this study, we established a method for live confocal imaging of microtubule and actin dynamics using suspension cultures with biolistic delivery of plasmid DNAs during PMI in Nicotiana benthamiana (Benthams tobacco), containing bicellular pollen. Pharmacological studies have indicated that actin filaments are crucial for microspore nuclear positioning before PMI, cell plate expansion during cytokinesis, and chromatin dispersion in vegetative cell nucleus after PMI. By contrast, inhibition of microtubule assembly resulted in abnormal chromosome segregation and nuclear behavior after PMI, although nuclear positioning and asymmetric division were observed. Our in vitro live cell imaging system for PMI provides insights into the importance of cytoskeletal regulation in asymmetric division and differentiation during pollen development.
Why it matches plant phenotyping methods花粉の細胞分裂・核動態をリアルタイム取得するライブ共焦点イメージング法を確立しており、画像取得系自体が研究の中心である。
abstractwe established a method for live confocal imaging of microtubule and actin dynamics using suspension cultures with biolistic delivery of plasmid DNAs during PMI
This study introduces a plant sensor utilizing an array of microneedles to monitor hydrogen peroxide (H 2 O 2 ) in tobacco and soybean plants under biotic stress response. The microneedle array features a biohydrogel layer composed of the natural biopolymer chitosan (Cs) and reduced graphene oxide (rGO), functionalized with horseradish peroxidase (HRP) (HRP/Cs-rGO). This HRP/Cs-rGO biohydrogel combines biocompatibility, hydrophilicity, porosity, and electron transfer ability, making it a suitable bioelectrode material for an electrochemical sensor. The sensor detects H 2 O 2 through the catalytic reaction of the enzyme, either by direct attachment to the plant leaf with the inserted microneedle or by exposure to the solution extracted from plant parts such as leaves. Utilizing chronoamperometry, the sensor demonstrates high sensitivity of 14.7 μA/μM across a concentration range of 0.1-4500 μM with a low detection limit of 0.06 μM. The sensor enables rapid detection of H 2 O 2 levels by exposing the sensor to extracted leaf solutions. For in situ measurements within the leaf, results are obtained in approximately 1 min, eliminating the need for sample preparation. H 2 O 2 levels in leaves following bacterial pathogen inoculation are evaluated alongside results from qualitative histological staining and quantitative fluorescence-based Amplex Red Assay, validating the ability of the sensor to detect changes in H 2 O 2 concentrations during plant defense responses. This sensor technology has the potential to function as a portable device for on-site measurement of reactive oxygen species in plants, providing a rapid and cost-effective solution for H 2 O 2 quantification.
Why it matches plant phenotyping methods植物体内の過酸化水素濃度という生理状態を測定するマイクロニードル電気化学センサーを開発し、既存アッセイ等で妥当性を検証しており、測定手法が研究の中心である。
abstractThis study introduces a plant sensor utilizing an array of microneedles to monitor hydrogen peroxide (H 2 O 2 ) in tobacco and soybean plants under biotic stress response.
High lipid producing (HLP) tobacco (Nicotiana tabacum) is a potential biofuel crop that produces an excess of 30% dry weight as lipid bodies in the form of triacylglycerol. While using HLP tobacco as a sustainable fuel source is promising, it has not yet been tested for its tolerance to warmer environments that are expected in the near future as a result of climate change. We found that HLP tobacco had reduced stomatal conductance, which results in increased leaf temperatures up to 1.5°C higher under control and high temperature (38°C day/28°C night) conditions, reduced transpiration, and reduced CO 2 assimilation. We hypothesize this reduction in stomatal conductance is due to the presence of excessive, large lipid droplets in HLP guard cells imaged using confocal microscopy. High temperatures also significantly reduced total fatty acid levels by 55% in HLP plants; thus, additional engineering may be needed to maintain high titers of leaf oil under future climate conditions. High-throughput image analysis techniques using open-source image analysis platform PlantCV for thermal image analysis (plant temperature), stomata microscopy image analysis (stomatal conductance), and fluorescence image analysis (photosynthetic efficiency) were developed and applied in this study. A corresponding set of PlantCV tutorials are provided to enable similar studies focused on phenotyping future crops under adverse conditions.
Why it matches plant phenotyping methodsPlantCVを用いた熱画像・気孔顕微鏡画像・蛍光画像の高スループット解析手法を開発・適用し、植物温度、気孔関連指標、光合成効率を推定しているため、表現型取得手法が中心的です。
abstractHigh-throughput image analysis techniques using open-source image analysis platform PlantCV for thermal image analysis (plant temperature), stomata microscopy image analysis (stomatal conductance), and fluorescence image analysis (photosynthetic efficiency) were developed and applied in this study.
Reproduction assets foundThe paper's raw phenotyping image data (thermal, fluorescence, stomata, confocal microscopy) are deposited on Zenodo, and the authors' PlantCV analysis workflows and R scripts are on GitHub, including three PlantCV tutorials for thermal, stomata, and photosynthesis analysis.Dataset · publicaxial side of the leaf rather than a cross section. While small lipid droplets were present in the WT stomatal guard cells and epidermis, large lipid droplets were present in the HLP guard cells under both control and after 7 days of treatment (representative control images in Figure 8A–D , complete dataset available on Zenodo, https://zenodo.org/records/10711864 ). In addition, while HLP oil appeared to form spherical droplets, it did not “line” the stomatal opening as in WT (Figure 8C,D ).
Figure 8
High lipid producing (HLP) had excessive oil droplets in stomatal guard cells.
Representative confocal microscopy images, shown as focused Z‐stack, of tobacco leaf tissue fixed in paraformaOpen asset ↗Zenodolines:115-123Code · publicmated marginal means (LSMEANS) to determine which sample types were significantly different from others. Means are reported in text with standard error. Plots were made using ggplot2 package (v.3.5.0) in R. Jupyter notebooks associated with PlantCV analyses and R scripts associated with this manuscript are available on Github ( https://github.com/danforthcenter/tobacco‐heat‐paper ).
AUTHOR CONTRIBUTIONS
DKA, MAG, PDB, BSJ and KMM designed experiments. KMM and BSJ performed experiments and data analysis. KJC designed and aided KMM in confocal and brightfield microscopy experiments and advised TEM experiments. JW performed TEM experiments, and KG‐O and SK performed data analysis of TEM images.Open asset ↗GitHublines:171-182Code · publictification was used to isolate only individual plants in each mask. Then, the mask was applied to the registered thermal image to calculate the average plant temperature, as well as a histogram of pixel temperatures for each plant. A PlantCV workflow was used to analyze the images in parallel. A tutorial is available on GitHub: https://github.com/danforthcenter/plantcv‐tutorial‐thermal?tab=readme‐ov‐file (Acosta‐Gamboa et al., 2024 ). Scripts for this project are available at https://github.com/danforthcenter/tobacco‐heat‐paper . Raw image data are available on Zenodo, https://zenodo.org/records/10711864 .
Stomatal aperture measurements
To measure stomatal number and aperture, leaf impressioOpen asset ↗GitHublines:142-146Code · publicpackage was then used to calculate the number of stomata and the area of the aperture. A limitation of this method is that it does not provide the width and length of stomata, or measurements of the guard cells themselves; instead, it provides the aperture area (a result of length and width). A tutorial is available on GitHub: https://github.com/danforthcenter/plantcv‐stomata‐tutorial‐pcv4 (Murphy, 2024 ). Scripts for this project are available at https://github.com/danforthcenter/tobacco‐heat‐paper . Raw image data are available on Zenodo, https://zenodo.org/records/10711864 .
Photosynthesis and gas exchangeOpen asset ↗GitHublines:142-146Code · publicPlantCV (Gehan et al., 2017 ) using the photosynthesis package; the chlorophyll fluorescence image was used to mask the image for only plant pixels, and average F
v / F
m , F q ′ / F m ′ , NPQ, chlorophyll index, and anthocyanin index were calculated as an average per plant at each timepoint. A tutorial is available on GitHub: https://github.com/danforthcenter/plantcv‐tutorial‐photosynthesis?tab=readme‐ov‐file (Schuhl et al., 2024 ). Scripts for this project are available at https://github.com/danforthcenter/tobacco‐heat‐paper . Raw image data are available on Zenodo, https://zenodo.org/records/10711864 .
Microscopy imaging of lipids
Leaf samples analyzed for lipid content were taken from thOpen asset ↗GitHublines:156-164Plant phenotyping relevance match · UnverifiedEurope PMC · checked 14 Sept 2026
Abiotic stress severely hinders plant growth and development, resulting in a considerable reduction in crop yields. Salicylic acid (SA) serves as a central signal mediating abiotic stress responses in plants. Real-time fluorescence tracking using specific probes can enhance our understanding of the SA-triggered modulation underlying these events. However, in complicated living plant microenvironments, selective recognition and bioimaging of SA is a great challenge for scientists due to the severe background interference and SA analogues. Herein, an efficient fluorescence probing technology employing a highly selective rhodamine probe-phoxrodam was developed, which realizes the precise bioimaging of SA in salt-stressed plant seedlings. Experimental findings reveal that phoxrodam demonstrates exceptional selectivity (fluorescence intensity: I Phoxrodam+SA /I Phoxrodam+SA analogues > 4.29-fold), high sensitivity (limit of detection = 6.42 nM, fluorescence quantum yield: Φ Phoxrodam+SA = 0.36) and good anti-interference properties. Furthermore, we confirmed that phoxrodam accurately detects SA in the roots of salt-stressed wheat seedlings, the low-temperature resistance of Nicotiana benthamiana and the heavy metal resistance of pea seeds, using in vivo confocal imaging. This study provides a feasible strategy for efficiently tracking plant signalling molecules and promotes the in-depth research of SA-mediated physiological mechanisms, laying a key foundation for the future development of new immune activation inducers.
Why it matches plant phenotyping methods植物体内のサリチル酸を選択的に可視化・定量する蛍光プローブ技術の開発が中心であり、植物の生理状態を取得する実質的なフェノタイピング手法に該当する。
abstractHerein, an efficient fluorescence probing technology employing a highly selective rhodamine probe-phoxrodam was developed, which realizes the precise bioimaging of SA in salt-stressed plant seedlings.
Purpose Hyperspectral imaging combined with machine learning offers a promising, cost-effective alternative to invasive chemical analysis for early plant disease detection. In this study, the use of 3D Convolutional Neural Networks (3D-CNNs) was explored to detect presymptomatic viral infections in the model plant Nicotiana benthamiana L. and assess the generalization of these models across different plant genotypes. Methods Four genotypes of Nicotiana benthamiana L. (wild-type, DCL2/4, AGO2, and NahG) were inoculated with different potexviruses (PepMV mild or severe strain, PVX, BaMV). Viral infection was verified via northern blot analysis at 5 and 10 days post inoculation (DPI). Hyperspectral images were captured over 10 days following inoculation, focusing on the top 3 leaves where symptoms typically appear. The dataset was carefully processed to remove errors, and raster masks were generated to isolate only the leaf pixels. The Extremely Randomized Trees algorithm was used for Effective Wavelength selection, and a novel 3D-CNN architecture was developed to classify 16 × 16 × 16 nonoverlapping cubes extracted from the unmasked leaf surfaces. The aim was to classify each cube into healthy or diseased for each of the four viruses at different time points. Results Accuracies of 0.78 - 0.87 were achieved for AGO2 mutants at the cube level, and overall plant-level accuracies of 0.68 - 0.89 . The model's generalization capabilities were tested across other genotypes, yielding accuracies of up to 0.75 for DCL2/4, 0.83 for NahG, and 0.78 for the wild-type. The timing of disease detection was also assessed, finding that accuracies approached 0.8 as early as 6 - 8 DPI depending on the virus. The results were validated against northern blot analyses and benchmarked against another state-of-the-art methodology for Nicotiana benthamiana viral infections, achieving superior overall classification accuracies. Conclusion The proposed patch-based method demonstrated key advantages: (a) exploiting both spectral and textural information, (b) deriving a large training dataset from few hyperspectral images, (c) providing localized classification explainability within leaf regions, and (d) achieving high accuracy for early detection of viral infections.
Why it matches plant phenotyping methods葉のハイパースペクトル画像からウイルス感染による植物病徴を抽出する3D-CNN手法を開発し、異なる遺伝子型で検証・ベンチマークしており、植物フェノタイピング手法が研究の中心である。
abstractHyperspectral imaging combined with machine learning offers a promising, cost-effective alternative to invasive chemical analysis for early plant disease detection.
Plant phenotyping relevance match · UnverifiedCrossref · Europe PMC · checked 6 Sept 2026
Abstract Plant bioengineering is a time-consuming and labor-intensive process with no guarantee of achieving desired traits. Here, we present a fast, automated, scalable, high-throughput pipeline for plant bioengineering (FAST-PB) in maize (Zea mays) and Nicotiana benthamiana. FAST-PB enables genome editing and product characterization by integrating automated biofoundry engineering of callus and protoplast cells with single-cell matrix-assisted laser desorption/ionization mass spectrometry (MALDI-MS). We first demonstrated that FAST-PB could streamline Golden Gate cloning, with the capacity to construct 96 vectors in parallel. Using FAST-PB in protoplasts, we found that PEG2050 increased transfection efficiency by over 45%. For proof-of-concept, we established a reporter-gene-free method for CRISPR editing and phenotyping via mutation of high chlorophyll fluorescence 136. We show that diverse lipids were enhanced up to 6-fold using CRISPR activation of lipid controlling genes. In callus cells, an automated transformation platform was employed to regenerate plants with enhanced lipid traits through introducing multigene cassettes. Lastly, FAST-PB enabled high-throughput single-cell lipid profiling by integrating MALDI-MS with the biofoundry, protoplast, and callus cells, differentiating engineered and unengineered cells using single-cell lipidomics. These innovations massively increase the throughput of synthetic biology, genome editing, and metabolic engineering and change what is possible using single-cell metabolomics in plants.
Why it matches plant phenotyping methods植物の遺伝子改変と連動した自動フェノタイピング基盤を開発し、単一細胞MALDI-MSによる脂質プロファイリングと葉緑素蛍光を用いた表現型評価を中核的に扱っているため。
abstractHere, we present a fast, automated, scalable, high-throughput pipeline for plant bioengineering (FAST-PB)
Plant phenotyping relevance match · UnverifiedCrossref · Europe PMC · checked 15 Sept 2026
Abstract The applicability of a deep learning model for the virtual staining of plant cell structures using bright-field microscopy was investigated. The training dataset consisted of microscopy images of tobacco BY-2 cells with the plasma membrane stained with the fluorescent dye PlasMem Bright Green and the cell nucleus labeled with Histone-red fluorescent protein. The trained models successfully detected the expansion of cell nuclei upon aphidicolin treatment and a decrease in the cell aspect ratio upon propyzamide treatment, demonstrating its utility in cell morphometry. The model also accurately documented the shape of Arabidopsis pavement cells in both wild type and the bpp125 triple mutant, which has an altered pavement cell phenotype. Metrics such as cell area, circularity, and solidity obtained from virtual staining analyses were highly correlated with those obtained by manual measurements of cell features from microscopy images. Furthermore, the versatility of virtual staining was highlighted by its application to track chloroplast movement in Egeria densa . The method was also effective for classifying live and dead BY-2 cells using texture-based machine learning, suggesting that virtual staining can be applied beyond typical segmentation tasks. Although this method still has some limitations, its non-invasive nature and efficiency make it highly suitable for label-free, dynamic, and high-throughput analyses in quantitative plant cell biology.
Why it matches plant phenotyping methods植物細胞構造の仮想染色を用いた画像ベースの形態計測法を開発・検証しており、細胞面積や形状などの表現型抽出が研究の中心である。
abstractThe applicability of a deep learning model for the virtual staining of plant cell structures using bright-field microscopy was investigated.
Cigar leaf is a special type of tobacco plant, which is the raw material for producing high-quality cigars. The content and proportion of nicotine and other composite substances of cigar leaves have a crucial impact on their quality and vary greatly with the time of harvest. Hyperspectral remote sensing technology has been widely used in the field of crop monitoring because of its advantages of large area coverage, fast information acquisition, short cycle turnover, strong real-time performance and high efficiency. Therefore, it is important to accurately monitor nicotine content of field crops in a timely manner in the production of high-quality cigar leaf. To this end, this study set out to measure crop reflectance spectra acquired by UAV drones from tobacco field crops by hyperspectral image acquisition. MSC, SG, and SNV were combined and applied to the raw data. The output of these operations was then further processed by CARS, SPA, and UVE algorithms to determine the nicotine sensitive bands. Three machine learning algorithms were then used to analyze the data: PLS, BP, RF, and the SVM. An inversion model of the content of nicotine was established, and the model was evaluated for accuracy. The main research conclusions are as follows: (1) With the increase in the rate of application of nitrogen fertilizer, the nicotine content of cigar leaves increased; (2) Processing data by the CARS, SPA, and UVE methods reduces the degree of data redundancy and information co-linearity in the screening of the content of nicotine sensitive bands; (3) The MSC-SNV-SG-CARS-BP model has the best predictive accuracy on the nicotine content. The prediction accuracy of the testing set was R 2 = 0.797, RMSE = 0.078,RPD = 2.182.
Why it matches plant phenotyping methodsUAVハイパースペクトル画像からタバコ葉のニコチン含量を推定する取得・前処理・機械学習ワークフローを構築し、精度評価しており、植物形質の計測手法が中心である。
abstractthis study set out to measure crop reflectance spectra acquired by UAV drones from tobacco field crops by hyperspectral image acquisition.
Phragmoplasts are plant-specific intracellular structures composed of microtubules, actin microfilaments (AFs), membranes, and associated proteins. Importantly, they are involved in the formation and expansion of cell plates that partition daughter cells during cell division. While previous studies have revealed the important role of cytoskeletal dynamics in the proper functioning of the phragmoplast, the localization and role of AFs in the initial phase of cell plate formation remain controversial. Here, we used deep learning-based image restoration to achieve high-resolution 4D imaging with minimal laser-induced damage, enabling us to investigate the dynamics of AFs during the initial phase of cell plate formation in transgenic tobacco BY-2 cells labeled with Lifeact-RFP or RFP-ABD2 (actin binding domain 2). This computational approach overcame the limitation of conventional imaging, namely laser-induced photobleaching and phototoxicity. The restored images indicated that RFP-ABD2 labeled AFs were predominantly localized near the daughter nucleus, whereas Lifeact-RFP labeled AFs were found not only near the daughter nucleus but also around the initial cell plate. These findings, validated by imaging with a long exposure time, highlight distinct localization patterns between the two AF probes and suggest that Lifeact-RFP labeled AFs play a role in initiating cell plate formation.
Why it matches plant phenotyping methods深層学習による画像復元を開発・検証し、植物細胞内のアクチン局在と動態を高解像度4D画像から取得しているため、植物表現型取得法が中心である。
abstractwe used deep learning-based image restoration to achieve high-resolution 4D imaging with minimal laser-induced damage
Identification and diagnosis of tobacco diseases are prerequisites for the scientific prevention and control of these ailments. To address the limitations of traditional methods, such as weak generalization and sensitivity to noise in segmenting tobacco leaf lesions, this study focused on four tobacco diseases: angular leaf spot, brown spot, wildfire disease, and frog eye disease. Building upon the Unet architecture, we developed the Multi-scale Residual Dilated Segmentation Model (MD-Unet) by enhancing the feature extraction module and integrating attention mechanisms. The results demonstrated that MD-Unet achieved 92.75%, 90.94%, 84.93%, and 91.81% for the lesion CPA, recall, IoU, and F1 metrics, respectively, with an overall Dice score of 94.67%. Furthermore, the model parameters, floating-point operations, and inference time per single image for MD-Unet were 4.65 × 10 7 , 2.3392 × 10 11 , and 65.096 ms, respectively. Compared to Unet, PSP, DeepLab v3+, FCN, SegNet, UNET++, and DoubleU-Net, MD-Unet significantly improved accuracy while effectively managing model complexity, achieving optimal overall performance. This work provides the theoretical foundations and technical support for precise segmentation of tobacco lesions, with potential applications in the segmentation of other plant diseases.
Why it matches plant phenotyping methodsタバコ葉の病斑を画像から分割・定量する深層学習手法を開発し、複数モデルと精度・計算量・推論時間を比較検証しており、植物病害状態の取得方法が中心である。
abstractwe developed the Multi-scale Residual Dilated Segmentation Model (MD-Unet)
Reproduction assets foundThe paper's tobacco leaf disease image dataset is explicitly stated as publicly available via a Kaggle DOI in the Data availability statement. Labelme is a generic annotation tool, not a paper-specific asset; no author code or model checkpoint is released.Dataset · publicThe dataset was publicly available and the linkage is https://doi.org/10.34740/kaggle/dsv/10393041.Open asset ↗kaggle · 10.34740/kaggle/dsv/10393041pdf-page:15 lines:1-61Plant phenotyping relevance match · UnverifiedEurope PMC · checked 6 Sept 2026
In recent years, image processing technology has been increasingly studied on intelligent unmanned platforms, and the differences in the shooting environment during tobacco baking pose challenges to image processing algorithms. To address this problem, an ensemble multi-dimensional randomization network (EMRNet) for intelligent recognition of tobacco baking stage is proposed. The first is to obtain the tobacco leaf area during the baking process. Then, a multi-dimensional randomization network (MRNet) is designed to recognize tobacco baking stage. The effectiveness of MRNet lies in multi-scale hidden layer feature extraction, which can effectively enhance the expression ability of features to overcome the impact of differences between different environments on the tobacco baking stage. Finally, MRNet is used as component learner for constructing an ensemble randomization network structure to distinguish the tobacco baking stage. On the constructed tobacco baking stage dataset, EMRNet achieves 89.14% accuracy with 642.96MFLOPs. Compared with SVM, MLP, BP, ELM, CRVFL and other algorithms, EMRNet shows excellent performance in accuracy and model complexity. The proposed method explores the application of image processing technology in crop baking and drying, providing theoretical support for intelligent baking technology.
Why it matches plant phenotyping methodsタバコ葉の面積取得と画像処理による乾燥・ベーキング段階認識を中心に、環境差に対する認識手法を開発・評価しており、葉の状態を推定する植物フェノタイピング手法に該当する。
abstractTo address this problem, an ensemble multi-dimensional randomization network (EMRNet) for intelligent recognition of tobacco baking stage is proposed.
Microscopic analyses of cytoskeleton organization are crucial for understanding various cellular activities, including cell proliferation and environmental responses in plants. Traditionally, assessments of cytoskeleton dynamics have been qualitative, relying on microscopy-assisted visual inspection. However, the transition to quantitative digital microscopy has introduced new technical challenges, with segmentation of cytoskeleton structures proving particularly demanding. In this study, we examined the utility of a deep learning-based segmentation method for accurate quantitative evaluation of cytoskeleton organization using confocal microscopic images of the cortical microtubules in tobacco BY-2 cells. The results showed that, although conventional methods sufficed for measurement of cytoskeleton angles and parallelness, the deep learning-based method significantly improved the accuracy of density measurements. To assess the versatility of the method, we extended our analysis to physiologically significant models in the context of changes in cytoskeleton density, namely Arabidopsis thaliana guard cells and zygotes. The deep learning-based method successfully improved the accuracy of cytoskeleton density measurements for quantitative evaluations of physiological changes in both stomatal movement in guard cells and intracellular polarization in elongating zygotes, confirming its utility in these applications. The results demonstrate the effectiveness of deep learning-based segmentation in providing precise and high-throughput measurements of cytoskeleton density, and has the potential to automate and expedite analyses of large-scale image datasets.
Why it matches plant phenotyping methods植物細胞の画像から細胞骨格密度を定量抽出する深層学習セグメンテーション法が研究の中心であり、精度評価と複数の植物細胞モデルへの適用も行っている。
abstractwe examined the utility of a deep learning-based segmentation method for accurate quantitative evaluation of cytoskeleton organization using confocal microscopic images
Due to the constraints of the tobacco leaf curing environment and computational resources, current image classification models struggle to balance recognition accuracy and computational efficiency, making practical deployment challenging. To address this issue, this study proposes the development of a lightweight classification network model for recognizing tobacco leaf curing stages (TCSRNet). Firstly, the model utilizes an Inception structure with parallel convolutional branches to capture features at different receptive fields, thereby better adapting to the appearance variations of tobacco leaves at different curing stages. Secondly, the incorporation of Ghost modules significantly reduces the model's computational complexity and parameter count through parameter sharing, enabling efficient recognition of tobacco leaf curing stages. Lastly, the design of the Multi-scale Adaptive Attention Module (MAAM) enhances the model's perception of key visual information in images, emphasizing distinctive features such as leaf texture and color, which further improves the model's accuracy and robustness. On the constructed tobacco leaf curing stage dataset (with color images sized 224×224 pixels), TCSRNet achieves a classification accuracy of 90.35% with 158.136 MFLOPs and 1.749M parameters. Compared to models such as ResNet34, GhostNet, ShuffleNetV2×1.5, EfficientNet-b0, MobileViT-xs, MobileNetV2, MobileNetV3-large, and MobileNetV3-small, TCSRNet demonstrates superior performance in terms of accuracy, FLOPs, and parameter count. Furthermore, when evaluated on the public V2 Plant Seedlings dataset, TCSRNet maintains an impressive accuracy of 97.15% compared to other advanced network models. This research advances the development of lightweight models for recognizing tobacco leaf curing stages, providing theoretical support for smart tobacco curing technologies and injecting new momentum into the digital transformation of the tobacco industry.
Why it matches plant phenotyping methodsタバコ葉の画像から硬化段階という植物状態を認識する軽量画像分類モデルを開発し、専用データセットおよび公開データセットで性能比較・評価しているため、フェノタイピング手法開発が中心である。
abstractthis study proposes the development of a lightweight classification network model for recognizing tobacco leaf curing stages (TCSRNet).
The plant cell wall, composed mainly of polysaccharides, lignin, and structural proteins, supports the architecture, mechanics, and functions of plants. Developing appropriate chemical imaging methods to study spatiotemporal changes of cell wall structural components at the microscopic level is important for understanding plant growth and senescence. In this study, tobacco (Nicotiana tabacum L.), a widely cultivated economic crop and model plant, was selected as the research object. Based on Raman confocal imaging combined with a multivariate curve resolution model, a label-free, in situ, high-throughput and high specificity imaging method for cellulose, high methylated pectin, and low methylated pectin in tobacco leaf cell wall was established to study their microscopic spatiotemporal changes during leaf growth and senescence (flue-curing) processes. The results based on the proposed method revealed that cellulose and pectin levels in the midrib cell wall gradually increased as the leaves matured, from appeared mainly at the cell corners and middle lamella respectively, to appeared in the cell corners, middle lamella, and cell wall. The same trend was observed in the lateral vein cell walls, where cellulose and pectin levels gradually increased. During the flue-curing process, cellulose and highly methylated pectin degraded. The proposed chemical imaging method is expected to provide a label-free, in situ, and high-throughput cell imaging technique for investigating the microscopic spatiotemporal distribution of the main structural components of the leaf cell wall.
Why it matches plant phenotyping methods葉の細胞壁成分を対象に、ラマン顕微鏡画像と多変量曲線分解による化学イメージング手法を開発し、セルロース・ペクチンの空間および時系列分布を抽出しているため、植物表現型取得法が中心である。
abstractBased on Raman confocal imaging combined with a multivariate curve resolution model, a label-free, in situ, high-throughput and high specificity imaging method for cellulose, high methylated pectin, and low methylated pectin in tobacco leaf cell wall was established
The tobacco seedling stage is a crucial period for tobacco cultivation. Accurately extracting tobacco seedlings from satellite images can effectively assist farmers in replanting, precise fertilization, and subsequent yield estimation. However, in complex Karst mountainous areas, it is extremely challenging to accurately segment tobacco plants due to a variety of factors, such as the topography, the planting environment, and difficulties in obtaining high-resolution image data. Therefore, this study explores an accurate segmentation model for detecting tobacco seedlings from UAV RGB images across various geomorphic partitions, including dam and hilly areas. It explores a family of tobacco plant seedling segmentation networks, namely, U-Net, U-Net++, Linknet, PSPNet, MAnet, FPN, PAN, and DeepLabV3+, using the Hill Seedling Tobacco Dataset (HSTD), the Dam Area Seedling Tobacco Dataset (DASTD), and the Hilly Dam Area Seedling Tobacco Dataset (H-DASTD) for model training. To validate the performance of the semantic segmentation models for crop segmentation in the complex cropping environments of Karst mountainous areas, this study compares and analyzes the predicted results with the manually labeled true values. The results show that: (1) the accuracy of the models in segmenting tobacco seedling plants in the dam area is much higher than that in the hilly area, with the mean values of mIoU, PA, Precision, Recall, and the Kappa Coefficient reaching 87%, 97%, 91%, 85%, and 0.81 in the dam area and 81%, 97%, 72%, 73%, and 0.73 in the hilly area, respectively; (2) The segmentation accuracies of the models differ significantly across different geomorphological zones; the U-Net segmentation results are optimal for the dam area, with higher values of mIoU (93.83%), PA (98.83%), Precision (93.27%), Recall (96.24%), and the Kappa Coefficient (0.9440) than those of the other models; in the hilly area, the U-Net++ segmentation performance is better than that of the other models, with mIoU and PA of 84.17% and 98.56%, respectively; (3) The diversity of tobacco seedling samples affects the model segmentation accuracy, as shown by the Kappa Coefficient, with H-DASTD (0.901) > DASTD (0.885) > HSTD (0.726); (4) With regard to the factors affecting missed segregation, although the factors affecting the dam area and the hilly area are different, the main factors are small tobacco plants (STPs) and weeds for both areas. This study shows that the accurate segmentation of tobacco plant seedlings in dam and hilly areas based on UAV RGB images and semantic segmentation models can be achieved, thereby providing new ideas and technical support for accurate crop segmentation in Karst mountainous areas.
Why it matches plant phenotyping methodsUAV画像からタバコ苗を抽出するセマンティックセグメンテーション手法を複数モデル・データセットで比較検証しており、植物状態の取得方法が研究の中心です。
abstractTherefore, this study explores an accurate segmentation model for detecting tobacco seedlings from UAV RGB images across various geomorphic partitions
Covalent organic frameworks designed as chromatic sensors offer opportunities to probe biological interfaces, particularly when combined with biocompatible matrices. Particularly compelling is the prospect of chemical tomography - or the 3D spatial mapping of chemical detail within the complex environment of living systems. Herein, we demonstrate a chromic Covalent Organic Framework (COF) integrated within silk fibroin (SF) microneedles that probe plant vasculature, sense the alkalization of vascular fluid as a biomarker for drought stress, and provide a 3D in-vivo mapping of chemical gradients using smartphone technology. A series of Schiff base COFs with tunable pKa ranging from 5.6 to 7.6 enable conical, optically transparent SF microneedles with COF coatings of 120 to 950 nm to probe vascular fluid and the surrounding tissues of tobacco and tomato plants. The conical design allows for 3D mapping of the chemical environment (such as pH) at standoff distances from the plant, enabling in-vivo chemical tomography. Chromatic COF sensors of this type will enable multidimensional chemical mapping of previously inaccessible and complex environments.
Why it matches plant phenotyping methods植物維管束のpH変化を乾燥ストレスの指標として測定し、COFマイクロニードルとスマートフォンによる3D化学マッピング手法を開発しているため、植物表現型取得法が中心です。
abstractsense the alkalization of vascular fluid as a biomarker for drought stress, and provide a 3D in-vivo mapping of chemical gradients using smartphone technology
The applicability of a deep learning model for the virtual staining of plant cell structures using bright-field microscopy was investigated. The training dataset consisted of microscopy images of tobacco BY-2 cells with the plasma membrane stained with the fluorescent dye PlasMem Bright Green and the cell nucleus labeled with Histone-red fluorescent protein. The trained models successfully detected the expansion of cell nuclei upon aphidicolin treatment and a decrease in the cell aspect ratio upon propyzamide treatment, demonstrating its utility in cell morphometry. The model also accurately documented the shape of Arabidopsis pavement cells in both wild type and the bpp125 triple mutant, which has an altered pavement cell phenotype. Metrics such as cell area, circularity, and solidity obtained from virtual staining analyses were highly correlated with those obtained by manual measurements of cell features from microscopy images. Furthermore, the versatility of virtual staining was highlighted by its application to track chloroplast movement in Egeria densa . The method was also effective for classifying live and dead BY-2 cells using texture-based machine learning, suggesting that virtual staining can be applied beyond typical segmentation tasks. Although this method still has some limitations, its non-invasive nature and efficiency make it highly suitable for label-free, dynamic, and high-throughput analyses in quantitative plant cell biology.
Why it matches plant phenotyping methods植物細胞構造の仮想染色と画像解析モデルを開発・評価し、細胞面積・形状・核拡大・葉緑体運動・生死などの表現型を定量化しているため、フェノタイピング手法が中心である。
abstractThe applicability of a deep learning model for the virtual staining of plant cell structures using bright-field microscopy was investigated.
Reproduction assets foundThe paper publicly releases its virtual-staining training/test image sets (bright-field inputs with paired confocal reference images) for BY-2 vacuole, BY-2 nucleus/plasma membrane, and E. densa chloroplast models on figshare under CC BY 4.0, via three DOIs listed in the Data Availability section. No author analysis orDataset · publicata pertaining to this article will be shared on reasonable request to the corresponding author. The
training and test image sets for BY-2 cells and E. densa, which are publicly accessible on figshare under the
CC BY 4.0 license, include images of wild-type tobacco BY-2 cells stained with BCECF for vacuolar lumen
visualization (https://doi.org/10.6084/m9.figshare.27247629.v1), transgenic tobacco BY-2 cells with
.
CC-BY-NC-ND 4.0 International license
perpetuity. It is made available under a
preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in
The copyright holder for this
this version posted October 25, 2024.
;
hOpen asset ↗figshare · 10.6084/m9.figshare.27247629.v1pdf-raw-page:21 lines:1-32Plant phenotyping relevance match · UnverifiedCrossref · checked 15 Sept 2026
TobaccoStem / branchPhysiological trait estimationWater status / transpiration
Environmental sustainability has become a significant topic, especially in recent years. Extreme natural phenomena and food insecurity related to the rising world population have highlighted the need for a new approach to agriculture. Smart Agriculture solutions may represent a viable answer to boost productivity, reduce emissions, and optimize human labor through the utilization of several new technologies and techniques in the climate-change scenario. From this perspective, the following paper proposes a sensor system capable of evaluating the plant's health status based on stem electrical impedance from a local and global point of view. In particular, the receiving system is able to sense the global stem impedance, monitoring the amplitude of a signal transmitted inside the plant itself. The system injects a square wave into the plant, and thanks to the proposed sensor, it is possible to read a frequency proportional to the amplitude of this signal collected from another point of the stem. The developed system has been tested on a tobacco plant, showing correlations of 0.94 and -0.97, respectively, for the local sensor and global sensor with respect to the soil water potential.
Why it matches plant phenotyping methods植物体内信号を用いて茎の電気インピーダンスから水ストレス／健康状態を評価するセンサーシステムを開発し、土壌水ポテンシャルとの相関で検証しており、植物表現型取得が中心である。
abstractthe following paper proposes a sensor system capable of evaluating the plant's health status based on stem electrical impedance
The RUBY reporter system has demonstrated great potential as a visible marker to monitor gene expression in both transiently and stably transformed plant tissues. Ectopic expression of the RUBY reporter leads to bright red pigmentation in plant tissues that do not naturally accumulate betalain. Unlike traditional visual markers such as β-glucuronidase (GUS), luciferase (LUC), and various fluorescent proteins, the RUBY reporter system does not require sample sacrifice or special equipment for visualizing the gene expression. However, a robust quantitative analysis method for betalain content has been lacking, limiting accurate comparative analyses. In this work, we present a simple and rapid protocol for quantitative evaluation of RUBY expression in transgenic plant tissues. Using this method, we demonstrate that differential RUBY expression can be quantified in transiently transformed leaf tissues, such as agroinfiltrated Nicotiana benthamiana leaves, and in stable transgenic maize tissues, including seeds, leaves, and roots. We found that grinding fresh tissues with a hand grinder and plastic pestle, without the use of liquid nitrogen, is an effective method for rapid betalain extraction. Betalain contents estimated by spectrophotometric and High-Performance Liquid Chromatography (HPLC) analyses were highly consistent, validating that our rapid betalain extraction and quantification method is suitable for comparative analysis. In addition, betalain content was strongly correlated with RUBY expression level in agroinfiltrated N. benthamiana leaves, suggesting that our method can be useful for monitoring transient transformation efficiency in plants. Using our rapid protocol, we quantified varying levels of betalain pigment in N. benthamiana leaves, ranging from 110 to 1066 mg/kg of tissue, and in maize samples, ranging from 15.3 to 1028.7 mg/kg of tissue. This method is expected to streamline comparative studies in plants, providing valuable insights into the effectiveness of various promoters, enhancers, or other regulatory elements used in transgenic constructs.
Why it matches plant phenotyping methods植物組織の betalain 含量を定量する抽出・測定プロトコルの開発と、分光法およびHPLCによる検証が研究の中心であり、植物の色素状態・RUBY発現量を測定する方法である。
abstractIn this work, we present a simple and rapid protocol for quantitative evaluation of RUBY expression in transgenic plant tissues.
Abstract Translation is a fundamental process for every living organism. In plants, the rate of translation is tightly modulated during development and in response to environmental cues. However, it is difficult to measure the actual translation state of the tissues in vivo . Here, we report the implementation of an in vivo translation marker based on bimolecular fluorescence complementation, the Ribo-BiFC. We combined method originally developed for fruit-fly with an improved low background split-mVenus BiFC system previously described in plants. We labelled Arabidopsis thaliana small subunit ribosomal protein (RPS) and large subunit ribosomal protein (RPL) with fragments of the mVenus fluorescent protein. Upon the assembly of the 80S ribosome, the mVenus fragments complemented and were detected by fluorescent microscopy. We show that these recombinant proteins are in close proximity in the tobacco epidermal cells, although the signal is reduced when compared to BiFC signal from known interactors. This Ribo-BiFC method system can be used in stable transgenic lines to enable visualisation of translational rate in plant tissues and could be used to study translation dynamics and its changes during plant development, under abiotic stress or in different genetic backgrounds.
Why it matches plant phenotyping methods植物組織内の翻訳速度という生理状態を可視化するRibo-BiFC法を実装・検証しており、表現型取得法が研究の中心である。
abstractHere, we report the implementation of an in vivo translation marker based on bimolecular fluorescence complementation, the Ribo-BiFC.
Meloidogyne spp. (root-knot nematodes [RKNs]) are a major threat to a wide range of agricultural crops worldwide. Breeding crops for RKN resistance is an effective management strategy, yet assaying large numbers of breeding lines requires laborious bioassays that are time-consuming and require experienced researchers. In these bioassays, quantifying nematode eggs through manual counting is considered the current standard for quantifying establishing resistance in plant genotypes. Counting RKN eggs is highly laborious, and even experienced researchers are subject to fatigue or misclassification, leading to potential errors in phenotyping. Here, we present three automated egg counting models that rely on machine learning and image analysis to quantify RKN eggs extracted from tobacco and sweet potato plants. The first method relied on convolutional neural networks trained using annotated images to identify eggs ( M. enterolobii R 2 = 0.899, M. incognita R 2 = 0.927, M. javanica R 2 = 0.886), whereas a second contour-based approach used image analysis to identify eggs from their morphological characteristics and did not rely on neural networks ( M. enterolobii R 2 = 0.977, M. incognita R 2 = 0.990, M. javanica R 2 = 0.924). A third hybrid model combined these approaches and was able to detect and count eggs nearly as well as human raters ( M. enterolobii R 2 = 0.985, M. incognita R 2 = 0.992, M. javanica R 2 = 0.983). These automated counting protocols have the potential to provide significant time and resource savings annually for breeders and nematologists and may be broadly applicable to other nematode species.
Why it matches plant phenotyping methods植物の抵抗性評価に用いる線虫卵数という表現型を、画像解析・機械学習で自動取得する手法を開発し、複数モデルを比較検証しているため、方法が研究の中心である。
abstractHere, we present three automated egg counting models that rely on machine learning and image analysis to quantify RKN eggs extracted from tobacco and sweet potato plants.
Nicotiana tabacum is a kind of plant cultivated for its leaves used for manufacturing medicine and cigarettes. With the common name, the Tobacco plant is grown in many countries including China, Indonesia, Malawi and Tanzania just to mention a few. Literatures suggest a technical gap in the proper identification of grade labels for various parts of the plant. In addition, manual grading has resulted in various gaps and biases. To mitigate this, a data-driven grading solution is necessary. However, relevant datasets to train grade classifiers from various countries become of the essence. This article presents images concentrated on tobacco leaf plant position namely Leaf position which normally carries 23 grade labels. Due to high rainfall which swiped away the applied fertilizer on the tobacco plants in the farms, we failed to get images of one grade. Therefore, this research could capture and label 22 grade labels. Images of tobacco leaves based on the tobacco plant position were collected in Tanzania through participatory community research. Canon 5D mark III cameras with 100 mm micro lens were used to take pictures of tobacco leaves based on the tobacco plant position. Domain experts were used for image labelling and cleaning according to tobacco grade labels identified in Tanzania. The dataset carries 49,779 images, which can be used to develop machine learning models for tobacco leaf grade label identification. The collected dataset can be used to train models and enhance the performance of pre-trained models in any country of interest.
Why it matches plant phenotyping methodsタバコ葉の位置・等級を画像として収集し、専門家ラベル付きデータセットを構築しており、植物器官の状態・品質を画像から分類する再利用可能な方法資源が中心である。
abstractThis article presents images concentrated on tobacco leaf plant position namely Leaf position which normally carries 23 grade labels.
Reproduction assets foundThe paper is a data descriptor whose own tobacco leaf image dataset (49,779 images, 22 grade labels) is publicly deposited in the Harvard Dataverse with an explicit direct URL, making it a paper-specific, publicly actionable phenotyping image dataset.Dataset · publicr tobacco leaves image sample, the names of each grade label within leaf position in the dataset were identified.
Data source location
Tanzania Tobacco Board (TTB), Tobacco Research Institute of Tanzania (TORITA)
City/Town/Region: Tabora
Country: Tanzania
Data accessibility
Repository name: Harvard Dataverse
Direct URL to data: https://dataverse.harvard.edu/dataset.xhtml?persistentId=doi:10.7910/DVN/TTPLFT
1
Value of the Data
•Open asset ↗Harvard Dataverse · doi:10.7910/DVN/TTPLFTlines:1-49Plant phenotyping relevance match · UnverifiedCrossref · checked 15 Sept 2026
Flue-cured tobacco is a main economic crop, and leaves are the direct product of tobacco plant. Monitoring leaf area index (LAI) of tobacco plant is important to field management, yield prediction, and industry regulation. Hyperspectral data have hundreds of narrow spectral bands in continuous spectral ranges, significantly advancing monitoring of LAI. However, considering spectral responses of LAI vary with wavelength, the use of the full spectral range in estimation of LAI is redundant. To reduce spectral redundancy and improve estimation of LAI, spectral subsets were selected based on importance of spectral bands. Variable importance in the projection (VIP) score obtained by partial least squares regression (PLSR) was adopted to measure the importance. The study was conducted in Yunnan Province, China. Canopy reflectance spectra of tobacco plant were measured in two consecutive growth seasons. Genetic algorithm (GA) and PLSR were used for model calibration. The identified important spectral regions for estimation of LAI were red edge, near-infrared (NIR), and green regions. In estimation of LAI of tobacco plant, compared with the estimation using the full spectral range of VNIR reflectance spectra, normalized root mean square error (NRMSE) and coefficient of determination (R2) values were improved from 10.30% and 0.83 to 7.68% and 0.90 and from 18.78% and 0.43 to 7.65% and 0.90 by using the reflectance spectra in identified spectral regions in the growth seasons in 2021 and 2022 separately. In addition to the identified continuous spectral regions, central bands of the identified spectral regions also achieved estimation of LAI, with the highest R2 value reaching 0.72. The selected spectral subsets improved estimation accuracy and reduced model complexity. The results indicate that selected spectral subsets are effective and promising in estimation of LAI, providing an alternative for estimation of LAI using hyperspectral data.
Why it matches plant phenotyping methodsタバコのキャノピー反射スペクトルからLAIを推定するセンシング・モデル手法が研究の中心であり、スペクトル選択と推定精度を評価しているため。
abstractTo reduce spectral redundancy and improve estimation of LAI, spectral subsets were selected based on importance of spectral bands.
Redox regulations and antioxidant defence play a central role in the acclimation of plants to their environment. Glutathione represents an essential component of the cellular antioxidant defence system, which keeps levels of reactive oxygen species (ROS) under control. High-performance liquid chromatography (HPLC) separation with fluorescence detection is a sensitive method that enables analysis of reduced and oxidised glutathione levels in small samples of plant tissues or plant cell culture. We aimed to optimise the method to obtain more accurate information about the total level of glutathione and the proportion of the reduced form (GSH) by choosing the most suitable reduction reagent and the conditions under which the reduction occurs. The applicability of the developed method was verified by analysing tobacco cells treated with hydrogen peroxide, which caused a decrease in the GSH/total glutathione ratio. Significant changes in the level of glutathione as well as in the GSH/total glutathione ratio were also observed during tobacco cell culture development.
Why it matches plant phenotyping methods植物細胞の酸化ストレス状態を推定するグルタチオンHPLC測定法の条件最適化と適用検証が研究の中心であり、単なる生物学的実験のルーチン測定ではない。
titleThe optimised method of HPLC analysis of glutathione allows to determine the degree of oxidative stress in plant cell culture.
Nitroxyl (HNO), a reactive nitrogen species (RNS), is essential for plant growth. However, the action of HNO in plants has been difficult to understand due to the lack of highly sensitive and real-time in-situ monitoring tools. Herein, we presented a near-infrared fluorescent probe, DCI-HNO, based on dicyanoisophorone fluorophore, for real-time mapping HNO in plants. The introduction of a phosphine moiety as a specific HNO recognition unit can inhibit the intramolecular charge transfer (ICT) of probe DCI-HNO. However, in the presence of HNO, the ICT process occurred, leading to the emission at 665 nm. Probe DCI-HNO exhibited high sensitivity (97 nM), rapid response time (8 min), large Stokes shift (135 nm) for detection of HNO in plants. The novel developed probe has successfully imaged endogenous HNO produced during NO/H 2 S cross-talk in plant tissues. Additionally, the up-regulated in HNO levels during tobacco aging and in response to stress has been confirmed. Therefore, probe DCI-HNO has provided a reliable method for monitoring the NO/H 2 S cross-talk and revealing the role of HNO in plants.
Why it matches plant phenotyping methods植物組織内のHNOをリアルタイム可視化・定量する蛍光プローブを開発し、感度や応答時間を評価しているため、植物の生理状態を取得する方法開発が中心である。
abstractwe presented a near-infrared fluorescent probe, DCI-HNO, based on dicyanoisophorone fluorophore, for real-time mapping HNO in plants.
Abstract Background Automating stomatal trait measurement has gained popularity because of their inherent importance for field phenotyping application as stomata are critical for both carbon capture and water use efficiency in plants. Such tool has been reported for rice, wheat, tomato, barley and oil palm. However, none exist yet for canola, which is an important economic and agronomic crop globally. Results We developed a new toolkit called Stomatal Comprehensive Automated Neural Network or SCAN by combining the use of high-resolution portable digital microscopy with machine learning based on You Only Look Once algorithm (YOLOv8). Digital micrographs of leaf surfaces enter the SCAN pipeline, which includes stomata detection, stomata segmentation and stomatal pore segmentation models, to measure stomatal density, stomatal size and stomatal pore area, respectively. In addition to SCAN’s ability to measure leaf stomatal traits in canola at 89 to 94% accuracy, we also showed that SCAN can be used to predict stomatal density even in species not included in the training set such as Arabidopsis, tobacco, rice, wheat, maize and proso millet. SCAN was designed for the biological science community with the premise that users are not required to possess advanced programming capabilities to manage dependency prerequisites, execute the models, and integrate the analysis. This was achieved by packaging the models into a desktop application system that can be accessed offline. Conclusion Overall, SCAN provides a non-destructive, real-time, portable, and high-throughput measurement of leaf stomatal traits in canola. The minimised hardware requirement and user-friendly desktop application system make SCAN suitable for field phenotyping application.
Why it matches plant phenotyping methodsカノーラ葉の気孔形質を画像と機械学習で自動抽出するツールを開発し、精度検証と他種での適用性評価を行っており、フェノタイピング手法が中心である。
abstractWe developed a new toolkit called Stomatal Comprehensive Automated Neural Network or SCAN by combining the use of high-resolution portable digital microscopy with machine learning based on You Only Look Once algorithm (YOLOv8).
Reproduction assets foundThe paper's authors publicly deposit the SCAN pipeline's model weights, hyperparameters, training scripts, and datasets in the FD_detection GitHub repository, and provide the SCAN application itself (with download and demonstration) in a second GitHub repository. Both are paper-specific, public, and actionable.Code · publicin Table S1.
123
124
The training tasks were carried out on an Ubuntu 20.04 Linux server at the Research School of Biology in
125
Australian National University, using two Nvidia A30 (24G) Graphic Processing Units (GPUs). The full
126
details of models’ weights, hyperparameters, training scripts and datasets can be found at
127
https://github.com/William-Yao0993/FD_detection.128
129
Model evaluation
130
131
Mean Average Precision (mAP, Fig. 3) and F1 score were used to assess model ability (Fig. 4). mAP is
132
calculated as the mean value of each class area under the precision-recall curve over thresholds, and the
133
F1 score is the harmonic mean of precision and recall. The formulas are deOpen asset ↗William-Yao0993/FD_detectionpdf-raw-page:4 lines:1-81Plant phenotyping relevance match · UnverifiedEurope PMC · bioRxiv · checked 15 Sept 2026
Microscopic analyses of cytoskeleton organization are crucial for understanding various cellular activities, including cell proliferation and environmental responses in plants. Traditionally, assessments of cytoskeleton dynamics have been qualitative, relying on microscopy-assisted visual inspection. However, the transition to quantitative digital microscopy has introduced new technical challenges, with segmentation of cytoskeleton structures proving particularly demanding. In this study, we examined the utility of a deep learning-based segmentation method for accurate quantitative evaluation of cytoskeleton organization using confocal microscopic images of the cortical microtubules in tobacco BY-2 cells. The results showed that, although conventional methods sufficed for measurement of cytoskeleton angles and parallelness, the deep learning-based method significantly improved the accuracy of density measurements. To assess the versatility of the method, we extended our analysis to physiologically significant models in the context of changes in cytoskeleton density, namely Arabidopsis thaliana guard cells and zygotes. The deep learning-based method successfully improved the accuracy of cytoskeleton density measurements for quantitative evaluations of physiological changes in both stomatal movement in guard cells and intracellular polarization in elongating zygotes, confirming its utility in these applications. The results demonstrate the effectiveness of deep learning-based segmentation in providing precise and high-throughput measurements of cytoskeleton density, and has the potential to automate and expedite analyses of large-scale image datasets.
Why it matches plant phenotyping methods植物細胞画像から細胞骨格密度を定量化する深層学習セグメンテーション法を開発・評価しており、植物状態の表現型抽出が研究の中心である。
abstractwe examined the utility of a deep learning-based segmentation method for accurate quantitative evaluation of cytoskeleton organization using confocal microscopic images
TobaccoMicroscopyLeafTissueVisualization / data management
Abstract Traditional deep fluorescence imaging has primarily focused on red‐shifting imaging wavelengths into the near‐infrared (NIR) windows or implementation of multi‐photon excitation approaches. Here, the advantages of NIR and multiphoton imaging are combined by developing a dual‐infrared two‐photon microscope that enables high‐resolution deep imaging in biological tissues. This study first computationally identifies that photon absorption, as opposed to scattering, is the primary contributor to signal attenuation. A NIR two‐photon microscope is constructed next with a 1640 nm femtosecond pulsed laser and a NIR PMT detector to image biological tissues labeled with fluorescent single‐walled carbon nanotubes (SWNTs). Spatial imaging resolutions are achieved close to the Abbe resolution limit and eliminate blur and background autofluorescence of biomolecules, 300 µm deep into brain slices and through the full 120 µm thickness of a Nicotiana benthamiana leaf. NIR‐II two‐photon microscopy can also measure tissue heterogeneity by quantifying how much the fluorescence power law function varies across tissues, a feature this study exploits to distinguish Huntington's Disease afflicted mouse brain tissues from wildtype. These results suggest dual‐infrared two‐photon microscopy can accomplish in‐tissue structural imaging and biochemical sensing with a minimal background, and with high spatial resolution, in optically opaque or highly autofluorescent biological tissues.
Why it matches plant phenotyping methods植物組織を対象に、深部構造イメージングと組織不均一性の測定を可能にする二光子顕微鏡を開発しており、植物組織への適用も明示されているため、方法開発として中心的である。
abstractA NIR two‐photon microscope is constructed next with a 1640 nm femtosecond pulsed laser and a NIR PMT detector to image biological tissues
Why it matches plant phenotyping methods植物のプラズモデスマル・カロース沈着を対象に、染色・蛍光顕微鏡・画像解析手法を比較評価しており、表現型取得法の検証が研究の中心である。
abstractIn this study, three commonly used methods for identifying and quantifying plasmodesmal callose by aniline blue staining were evaluated to determine the most effective in the Nicotiana benthamiana leaf model.
To survive and grow, plant cells must regulate the properties of their cellular microenvironment in response to ever changing external factors. How the biomechanical balance across the cells internal structures is established and maintained during environmental variations remains a nurturing question. To provide insight into this issue we used two micro-mechanical imaging techniques, namely Brillouin light scattering and BODIPY-based molecular rotors Fluorescence Lifetime Imaging, to study Nicotiana tabacum suspension BY-2 cells long-term adapted to high concentrations of NaCl and mannitol. We discuss our results in terms of molecular crowding in cytoplasm and vacuoles, as well as tension in plasma membrane. The viscoelastic behavior was elucidated relative to cells external environments revealing the difference between the responses of cytoplasm and vacuole in the adapted cells. To understand how sudden changes in osmolarity affect cellular mechanics, the response of control and already adapted cells to further short-term osmotic stimulus was also examined. The applied correlative approach provides evidence that adaptation to hyperosmotic stress leads to different ratios of protoplast and environmental qualities that help to maintain cell integrity. Presented results demonstrate that the viscoelastic properties of protoplasts are an element of plant cells adaptation to high osmolarity.
Why it matches plant phenotyping methods植物細胞の粘弾性という生理・力学的形質を、Brillouin光散乱と蛍光寿命イメージングで測定する手法の実質的な適用が研究の中心であり、単なるルーチン測定ではない。
abstractwe used two micro-mechanical imaging techniques, namely Brillouin light scattering and BODIPY-based molecular rotors Fluorescence Lifetime Imaging, to study Nicotiana tabacum suspension BY-2 cells
Abstract Tissue clearing is a technique to make the inner structure of opaque tissue visible to achieve 3-dimensional (3-D) tissue imaging by unifying the refractive indexes of most of the cell components. Tissue clearing is widely used in animal tissue imaging, where whole body 3-D imaging has been realized. However, it has not been widely used in plant research. Most plant tissue clearing protocols have their disadvantages, including low efficiency, not being fluorescence-friendly and poor transparency on tissues with a high degree of lignification. In this work, we developed a new plant tissue clearing method for whole plant imaging, named Plant Accessible Tissue Clearing Solvent System (PATCSOS), which was based on the Polyethylene Glycol-associated Solvent System (PEGASOS). The PATCSOS method realized extensive transparency of plant tissues, including the flower, leaf, stem, root, and seed of Arabidopsis thaliana, with high efficiency. The PATCSOS method consists of four main steps: fixation, decolorization/delipidation, dehydration, and clearing. Subsequently a rapid and efficient clearing of mature plant tissue can be achieved. With PATCSOS, we can image Arabidopsis seedling in their entirety in 3-D using endogenous cellulose autofluorescence. What’s more, the PATCSOS method is compatible with fluorescence protein imaging and GUS staining, which greatly expands the applicability of this method. We also imaged intact Nicotiana benthamiana leaf and Zea mays embryos. Our results showed that the PATCSOS clearing method is an excellent tool to study plant development and cell biology.
Why it matches plant phenotyping methods植物組織を透明化して全身の3D構造を可視化する新規手法を開発しており、植物表現型の画像取得が研究の中心である。
abstractIn this work, we developed a new plant tissue clearing method for whole plant imaging, named Plant Accessible Tissue Clearing Solvent System (PATCSOS)
Uncovering the function of phytopathogen effectors is crucial for understanding mechanisms of pathogen pathogenicity and for improving our ability to protect plants from diseases. An increasing number of effectors have been predicted in various plant pathogens. Functional characterization of these effectors has become a major focus in the study of plant-pathogen interactions. In this study, we designed a novel screening system that combines the TMV (tobacco mosaic virus)-GFP vector and Agrobacterium-mediated transient expression in the model plant Nicotiana benthamiana. This system enables the rapid identification of effectors that interfere with plant immunity. The biological function of these effectors can be easily evaluated by observing the GFP fluorescence signal using a UV lamp within just a few days. To evaluate the TMV-GFP system, we initially tested it with well-described virulence and avirulence type III effectors from the bacterial pathogen Ralstonia solanacearum. After proving the accuracy and efficiency of the TMV-GFP system, we successfully screened a novel virulence effector, RipS1, using this approach. Furthermore, using the TMV-GFP system, we reproduced consistent results with previously known cytoplasmic effectors from a diverse array of pathogens. Additionally, we demonstrated the effectiveness of the TMV-GFP system in identifying apoplastic effectors. The easy operation, time-saving nature, broad effectiveness, and low technical requirements of the TMV-GFP system make it a promising approach for high-throughput screening of effectors with immune interference activity from various pathogens.
Why it matches plant phenotyping methods植物免疫干渉をGFP蛍光で評価する高スループットスクリーニング系を開発・検証しており、植物状態の取得法が研究の中心である。
abstractwe designed a novel screening system that combines the TMV (tobacco mosaic virus)-GFP vector and Agrobacterium-mediated transient expression in the model plant Nicotiana benthamiana.
Tobacco is a valuable crop, but its disease identification is rarely involved in existing works. In this work, we use few-shot learning (FSL) to identify abnormalities in tobacco. FSL is a solution for the data deficiency that has been an obstacle to using deep learning. However, weak feature representation caused by limited data is still a challenging issue in FSL. The weak feature representation leads to weak generalization and troubles in cross-domain. In this work, we propose a feature representation enhancement network (FREN) that enhances the feature representation through instance embedding and task adaptation. For instance embedding, global max pooling, and global average pooling are used together for adding more features, and Gaussian-like calibration is used for normalizing the feature distribution. For task adaptation, self-attention is adopted for task contextualization. Given the absence of publicly available data on tobacco, we created a tobacco leaf abnormality dataset (TLA), which includes 16 categories, two settings, and 1,430 images in total. In experiments, we use PlantVillage, which is the benchmark dataset for plant disease identification, to validate the superiority of FREN first. Subsequently, we use the proposed method and TLA to analyze and discuss the abnormality identification of tobacco. For the multi-symptom diseases that always have low accuracy, we propose a solution by dividing the samples into subcategories created by symptom. For the 10 categories of tomato in PlantVillage, the accuracy achieves 66.04% in 5-way, 1-shot tasks. For the two settings of the tobacco leaf abnormality dataset, the accuracies were achieved at 45.5% and 56.5%. By using the multisymptom solution, the best accuracy can be lifted to 60.7% in 16-way, 1-shot tasks and achieved at 81.8% in 16-way, 10-shot tasks. The results show that our method improves the performance greatly by enhancing feature representation, especially for tasks that contain categories with high similarity. The desensitization of data when crossing domains also validates that the FREN has a strong generalization ability.
Why it matches plant phenotyping methods植物葉の異常・病徴を画像から識別する few-shot 学習法を開発し、専用データセットを作成・検証しており、植物状態の取得・推定手法が中心である。
abstractIn this work, we use few-shot learning (FSL) to identify abnormalities in tobacco.
Reproduction assets foundThe authors created the tobacco leaf abnormality dataset (TLA) used in this paper and state it is publicly available via a Google Drive link in the data availability statement.Dataset · publicion of diseases is an important research direction. For multidisease identification, the classification method is not an optimal choice. Semantic segmentation is a good solution and worthy of study.
Data availability statement
The original contributions presented in the study are publicly available. This data can be found here: https://drive.google.com/drive/folders/1Qn5UjATDaDpRoF1dCTdp62tlnAXJv0MF?usp=sharing .
Author contributions
HL: Conceptualization, Data curation, Formal Analysis, Investigation, Methodology, Project administration, Resources, Software, Supervision, Validation, Visualization, Writing – original draft, Writing – review & editing. ZQ: Conceptualization, Funding acquisitiOpen asset ↗lines:1208-1229Plant phenotyping relevance match · UnverifiedEurope PMC · bioRxiv · checked 14 Sept 2026
Summary We employed hyperspectral imaging to detect chloroplast positioning in Nicotiana benthamiana and Arabidopsis thaliana leaves and assess its influence on commonly used vegetation indices. In low light, chloroplasts move to cell walls perpendicular to the direction of the incident light. In high light, they move to cell walls parallel to the light direction. Chloroplast movements result in significant changes in leaf transmittance and reflectance. The changes in leaf reflectance offer a way to examine chloroplast positioning in a non-contact way. At the same time, they may confound remote sensing of other physiological traits. The shape of reflectance spectra recorded on irradiated and non-irradiated parts of N. benthamiana and A. thaliana leaves indicated the specific position of chloroplasts. Low blue light resulted in a decrease in leaf reflectance in the green-yellow region of the spectrum. High blue light irradiation caused an increase in leaf reflectance in the visible range. The differential spectra, showing the effect of high light on leaf reflectance, exhibited a characteristic saddle in the green-yellow region and a peak at around 695 nm. Results obtained for A. thaliana mutants with disrupted chloroplast movements suggest that the observed spectral changes are mostly due to the chloroplast relocations. The reflectance spectra were used to train machine learning methods in the classification of leaves according to the chloroplast positioning. The convolutional network showed low levels of misclassification of leaves irradiated with high light even when different species were used for training and testing. This suggests that reflectance spectra may be used to detect the chloroplast avoidance response in heterogeneous patches of vegetation. We also examined the correlation between chloroplast positioning and values of indices of normalized-difference type for various combinations of wavelengths and proposed a chloroplast movement index for validation of chloroplast positions in leaves. The analysis of commonly used vegetation indices showed that their values may be altered due to chloroplast rearrangements. Our work indicates that changes in leaf reflectance due to chloroplast movements may be substantial and should be taken into account in remote sensing studies.
Why it matches plant phenotyping methodsハイパースペクトル反射を用いて葉内の葉緑体位置を非接触検出し、機械学習分類と新しい指標を提案・検証しており、表現型取得手法が研究の中心です。
abstractThe changes in leaf reflectance offer a way to examine chloroplast positioning in a non-contact way.
Plant phenotyping relevance match · UnverifiedCrossref · Europe PMC · checked 13 Sept 2026
Abstract Accurate quantification of hypersensitive response (HR) programmed cell death is imperative for understanding plant defense mechanisms and developing disease‐resistant crop varieties. Here, a phenotyping platform for rapid, continuous‐time, and quantitative assessment of HR is demonstrated: Parallel Automated Spectroscopy Tool for Electrolyte Leakage (PASTEL). Compared to traditional HR assays, PASTEL significantly improves temporal resolution and has high sensitivity, facilitating detection of microscopic levels of cell death. Validation is performed by transiently expressing the effector protein AVRblb2 in transgenic Nicotiana benthamiana (expressing the corresponding resistance protein Rpi‐blb2) to reliably induce HR. Detection of cell death is achieved at microscopic intensities, where leaf tissue appears healthy to the naked eye one week after infiltration. PASTEL produces large amounts of frequency domain impedance data captured continuously. This data is used to develop supervised machine‐learning (ML) models for classification of HR. Input data (inclusive of the entire tested concentration range) is classified as HR‐positive or negative with 84.1% mean accuracy (F1 score = 0.75) at 1 h and with 87.8% mean accuracy (F1 score = 0.81) at 22 h. With PASTEL and the ML models produced in this work, it is possible to phenotype disease resistance in plants in hours instead of days to weeks.
Why it matches plant phenotyping methods植物組織のプログラム細胞死・過敏感反応を電気インピーダンスで連続定量するフェノタイピング基盤を開発し、機械学習分類も検証しているため、方法が研究の中心である。
abstractHere, a phenotyping platform for rapid, continuous‐time, and quantitative assessment of HR is demonstrated: Parallel Automated Spectroscopy Tool for Electrolyte Leakage (PASTEL).
ArabidopsisTobaccoLaboratory / benchtopMicroscopyCell / cellular structureTrackingVisualization / data management
Super-resolution microscopy techniques have revolutionized cell biology by providing insights into single-molecule dynamics and nanoscale organization within living cells. However, the application of dynamic live-cell methods in plants remains limited by the availability of suitable fluorophores for simultaneous visualization of multiple proteins. To address this challenge, we implemented a dual-color single-particle tracking photoactivated localization microscopy (sptPALM) approach based on codon-optimized photoactivatable fluorescent proteins PA-GFP and PATagRFP. Recently, we demonstrated their individual performance in single-color experiments in Nicotiana benthamiana and Arabidopsis thaliana cells. Here, we establish their combined use for dual-color sptPALM, enabling the simultaneous tracking of two distinct protein species within the same plant cell. This approach provides a framework to investigate the coordinated dynamics, interactions, and spatial organization of multiple proteins in living plant cells.
Why it matches plant phenotyping methods植物細胞内の2種類のタンパク質を同時追跡するデュアルカラーsptPALM法を開発・確立した研究であり、生細胞の動態・空間配置という植物状態の取得法が中心です。
abstractHere, we establish their combined use for dual-color sptPALM, enabling the simultaneous tracking of two distinct protein species within the same plant cell.
Canopy photosynthesis (A c ), rather than leaf photosynthesis, is critical to gaining higher biomass production in the field because the daily or seasonal integrals of A c correlate with the daily or seasonal integrals of biomass production. The canopy photosynthesis and transpiration measurement system (CAPTS) was developed to enable measurement of canopy photosynthetic CO 2 uptake, transpiration, and respiration rates. CAPTS continuously records the CO 2 concentration, water vapor concentration, air temperature, air pressure, air relative humidity, and photosynthetic photon flux density (PPFD) inside the chamber, which can be used to derive CO 2 and H 2 O fluxes of a canopy covered by the chamber. This system can also be used to measure the fluxes of greenhouse gases when integrating with CH 4 and N 2 O analyzers. Here, we describe the protocol for using CAPTS to perform experiments on rice (Oryza sativa L.) in paddy field, wheat (Triticum aestivum L.) in upland field, and tobacco (Nicotiana tabacum L.) in pots.
Why it matches plant phenotyping methodsCAPTSというキャノピーの光合成・蒸散・呼吸フラックスを測定するシステムの開発と使用プロトコルが中心であり、植物の生理状態を定量化するフェノタイピング手法に該当する。
abstractThe canopy photosynthesis and transpiration measurement system (CAPTS) was developed to enable measurement of canopy photosynthetic CO 2 uptake, transpiration, and respiration rates.
Rice stripe disease caused by the rice stripe virus (RSV), which infects many Poaceae species in nature, is one of the most devastating plant viruses in rice that causes enormous losses in production. Ailanthone is one of the typical C 20 quassinoids synthesized by the secondary metabolism of Ailanthus altissima , which has been proven to be a biologically active natural product with promising prospects and great potential for use as a lead structure for pesticide development. Based on the achievement of the systemic infection and replication of RSV in Nicotiana benthamiana plants and rice protoplasts, the antiviral properties of Ailanthone were investigated by determining its effects on viral-coding RNA gene expression using reverse transcription polymerase chain reaction, and Western blot analysis. Ailanthone exhibited a dose-dependent inhibitory effect on RSV NSvc3 expression in the assay in both virus-infected tobacco plants and rice protoplasts. Further efforts revealed a potent inhibitory effect of Ailanthone on the expression of seven RSV protein-encoding genes, among which NS3 , NSvc3 , NS4 , and NSvc4 are the most affected genes. These facts promoted an extended and greater depth of understanding of the antiviral nature of Ailanthone against plant viruses, in addition to the limited knowledge of its anti-tobacco mosaic virus properties. Moreover, the leaf disc method introduced and developed in the study for the detection of the antiviral activity of Ailanthone facilitates an available and convenient screening method for anti-RSV natural products or synthetic chemicals.
Why it matches plant phenotyping methods抗ウイルス活性評価を主目的とする研究だが、植物葉ディスクを用いたRSV抗ウイルス活性スクリーニング法を導入・開発しており、感染植物の病態を評価する方法的貢献が明示されている。
abstractMoreover, the leaf disc method introduced and developed in the study for the detection of the antiviral activity of Ailanthone facilitates an available and convenient screening method for anti-RSV natural products or synthetic chemicals.
Background: Estimating tobacco leaf yield is a crucial task. The number of leaves is directly related to yield. Therefore, it is important to achieve intelligent and rapid high-throughput statistical counting of field tobacco leaves. Unfortunately, the current method of counting the number of tobacco leaves is expensive, imprecise, and inefficient. It heavily relies on manual labor and also faces challenges of mutual shading among the field tobacco plants during their growth and maturity stage, as well as complex environmental background information. This study proposes an efficient method for counting the number of tobacco leaves in a large field based on unmanned aerial vehicle (UAV) image data. First, a UAV is used to obtain high-throughput vertical orthoimages of field tobacco plants to count the leaves of the tobacco plants. The tobacco plant recognition model is then used for plant detection and segmentation to create a dataset of images of individual tobacco plants. Finally, the improved algorithm YOLOv8 with Squeeze-and-Excitation (SE) and bidirectional feature pyramid network (BiFPN) and GhostNet (YOSBG) algorithm is used to detect and count tobacco leaves on individual tobacco plants. Results: Experimental results show YOSBG achieved an average precision (AP) value of 93.6% for the individual tobacco plant dataset with a model parameter (Param) size of only 2.5 million (M). Compared to the YOLOv8n algorithm, the F1 (F1-score) of the improved algorithm increased by 1.7% and the AP value increased by 2%, while the model Param size was reduced by 16.7%. In practical application discovery, the occurrence of false detections and missed detections is almost minimal. In addition, the effectiveness and superiority of this method compared to other popular object detection algorithms have been confirmed. Conclusions: This article presents a novel method for high-throughput counting of tobacco leaves based on UAV image data for the first time, which has a significant reference value. It solves the problem of missing data in individual tobacco datasets, significantly reduces labor costs, and has a great impact on the advancement of modern smart tobacco agriculture.
Why it matches plant phenotyping methodsUAV画像と物体検出モデルを用いて圃場タバコの葉数という植物形態・収量関連形質を高スループットに抽出する手法を開発・評価しており、フェノタイピング手法が中心です。
abstractFinally, the improved algorithm YOLOv8 with Squeeze-and-Excitation (SE) and bidirectional feature pyramid network (BiFPN) and GhostNet (YOSBG) algorithm is used to detect and count tobacco leaves on individual tobacco plants.
Early detection of pathogens can significantly reduce yield losses and improve the quality of agricultural products. This study compares the efficiency of hyperspectral (HS) imaging and pulse amplitude modulation (PAM) fluorometry to detect pathogens in plants. Reflectance spectra, normalized indices, and fluorescence parameters were studied in healthy and infected areas of leaves. Potato virus X with GFP fluorescent protein was used to assess the spread of infection throughout the plant. The study found that infection increased the reflectance of leaves in certain wavelength ranges. Analysis of the normalized reflectance indices (NRIs) revealed indices that were sensitive and insensitive to infection. NRI 700/850 was optimal for virus detection; significant differences were detected on the 4th day after the virus arrived in the leaf. Maximum (F v /F m ) and effective quantum yields of photosystem II ( Φ PSII ) and non-photochemical fluorescence quenching (NPQ) were almost unchanged at the early stage of infection. Φ PSII and NPQ in the transition state (a short time after actinic light was switched on) showed high sensitivity to infection. The higher sensitivity of PAM compared to HS imaging may be due to the possibility of assessing the physiological changes earlier than changes in leaf structure.
Why it matches plant phenotyping methodsタバコ葉の感染状態を検出するため、ハイパースペクトル画像とPAM蛍光法の性能を比較・評価しており、植物状態の取得手法が研究の中心である。
abstractThis study compares the efficiency of hyperspectral (HS) imaging and pulse amplitude modulation (PAM) fluorometry to detect pathogens in plants.
Tobacco Mosaic Virus (TMV) and Potato Virus Y (PVY) pose significant threats to crop production. Non-destructive and accurate surveillance is crucial to effective disease control. In this study, we propose the adoption of hyperspectral and machine learning technologies to discern the type and severity of tobacco leaves affected by PVY and TMV infection. Initially, we applied three preprocessing methods - Multivariate Scattering Correction (MSC), Standard Normal Variate (SNV), and Savitzky-Golay smoothing filter (SavGol) - to corrected the leaf full-length spectral sheet data (350-2500nm). Subsequently, we employed two classifiers, support vector machine (SVM) and random forest (RF), to establish supervised classification models, including binary classification models (healthy/diseased leaves or PVY/TMV infected leaves) and six-class classification models (healthy and various severity levels of diseased leaves). Based on the core evaluation index, our models achieved accuracies in the range of 91-100% in the binary classification. In general, SVM demonstrated superior performance compared to RF in distinguishing leaves infected with PVY and TMV. Different combinations of preprocessing methods and classifiers have distinct capabilities in the six-class classification. Notably, SavGol united with SVM gave an excellent performance in the identification of different PVY severity levels with 98.1% average precision, and also achieved a high recognition rate (96.2%) in the different TMV severity level classifications. The results further highlighted that the effective wavelengths captured by SVM, 700nm and 1800nm, would be valuable for estimating disease severity levels. Our study underscores the efficacy of integrating hyperspectral technology and machine learning, showcasing their potential for accurate and non-destructive monitoring of plant viral diseases.
Why it matches plant phenotyping methodsハイパースペクトル計測と機械学習により、タバコ葉のウイルス感染および重症度という植物状態を非破壊推定する方法を開発・評価しており、方法が中心的である。
abstractwe propose the adoption of hyperspectral and machine learning technologies to discern the type and severity of tobacco leaves affected by PVY and TMV infection.
Abstract Remote sensing of vegetation by spectroscopy is increasingly used to characterize trait distributions in plant communities. How leaves interact with electromagnetic radiation is determined by their structure and contents of pigments, water, and abundant dry matter constituents like lignins, phenolics, and proteins. High-resolution (“hyperspectral”) spectroscopy can characterize trait variation at finer scales, and may help to reveal underlying genetic variation—information important for assessing the potential of populations to adapt to global change. Here, we use a set of 360 inbred genotypes of the wild coyote tobacco Nicotiana attenuata : wild accessions, recombinant inbred lines (RILs), and transgenic lines (TLs) with targeted changes to gene expression, to dissect genetic versus non-genetic influences on variation in leaf spectra across three experiments. We calculated leaf reflectance from hand-held field spectroradiometer measurements covering visible to short-wave infrared wavelengths of electromagnetic radiation (400–2500 nm) using a standard radiation source and backgrounds, resulting in a small and quantifiable measurement uncertainty. Plants were grown in more controlled (glasshouse) or more natural (field) environments, and leaves were measured both on- and off-plant with the measurement set-up thus also in more to less controlled environmental conditions. Entire spectra varied across genotypes and environments. We found that the greatest variance in leaf reflectance was explained by between-experiment and non-genetic between-sample differences, with subtler and more specific variation distinguishing groups of genotypes. The visible spectral region was most variable, distinguishing experimental settings as well as groups of genotypes within experiments, whereas parts of the short-wave infrared may vary more specifically with genotype. Overall, more genetically variable plant populations also showed more varied leaf spectra. We highlight key considerations for the application of field spectroscopy to assess genetic variation in plant populations.
Why it matches plant phenotyping methods葉の反射スペクトルを用いて植物の遺伝的変異を評価する測定法を、異なる環境・測定条件で検証・評価しており、植物フェノタイピング手法が中心です。
titleEvaluating potential of leaf reflectance spectra to monitor plant genetic variation
Reproduction assets foundThe paper's leaf reflectance spectral measurements and analysis code are publicly available: processed spectral data, metadata, and code are on the authors' GitHub repository, and the raw spectral measurement dataset is published in SPECCHIO.Code · publicAll processed spectral data, metadata and code are provided at the GitHub repository: https://github.com/licheng1221/How-leaves-reflect-genetic-variation .Open asset ↗https://github.com/licheng1221/How-leaves-reflect-genetic-variationlines:218-235Plant phenotyping relevance match · UnverifiedEurope PMC · bioRxiv · checked 7 Sept 2026
Callose, a beta-(1,3)-D-glucan polymer, is essential for regulating intercellular trafficking via plasmodesmata (PD). Pathogens manipulate PD-localized proteins to enable intercellular trafficking by removing callose at PD, or conversely by increasing callose accumulation at PD to limit intercellular trafficking during infection. Plant defense hormones like salicylic acid regulate PD-localized proteins to control PD and intercellular trafficking during innate immune defense responses such as systemic acquired resistance. Measuring callose deposition at PD in plants has therefore emerged as a popular parameter for assessing the intercellular trafficking activity during plant immunity. Despite the popularity of this metric there is no standard for how these measurements should be made. In this study, three commonly used methods for identifying and quantifying PD callose by aniline blue staining were evaluated to determine the most effective in the Nicotiana benthamiana leaf model. The results reveal that the most reliable method used aniline blue staining and fluorescent microscopy to measure callose deposition in fixed tissue. Manual or semi-automated workflows for image analysis were also compared and found to produce similar results although the semi-automated workflow produced a wider distribution of data points.
Why it matches plant phenotyping methods植物葉のPDカロース沈着という防御関連形質の画像測定法について、複数手法と画像解析ワークフローを比較・評価しており、測定法の検証が中心である。
abstractIn this study, three commonly used methods for identifying and quantifying PD callose by aniline blue staining were evaluated to determine the most effective in the Nicotiana benthamiana leaf model.
The accurate quantification of hypersensitive response (HR) programmed cell death is imperative for understanding plant defense mechanisms and developing disease-resistant crop varieties. In this study, we report an accelerated phenotyping platform for the continuous-time, rapid and quantitative assessment of HR: Parallel Automated Spectroscopy Tool for Electrolyte Leakage (PASTEL). Compared to traditional HR assays, PASTEL significantly improves temporal resolution and has high sensitivity, facilitating the detection of microscopic levels of cell death. We validated PASTEL by transiently expressing the effector protein AVRblb2 in transgenic lines of the model plant Nicotiana benthamiana (expressing the corresponding resistance protein Rpi-blb2) to reliably induce HR. We were able to detect cell death at microscopic intensities, where leaf tissue appeared healthy to the naked eye one week after infiltration. PASTEL produces large amounts of frequency domain impedance data captured continuously (sub-seconds to minutes). Using this data, we developed a supervised machine learning models for classification of HR. We were able to classify input data (inclusive of our entire tested concentration range) as HR-positive or negative with 84.1% mean accuracy (F 1 score = 0.75) at 1 hour and with 87.8% mean accuracy (F 1 score = 0.81) at 22 hours. With PASTEL and the ML models produced in this work, it is possible to phenotype disease resistance in plants in hours instead of days to weeks.
Why it matches plant phenotyping methods植物組織の過敏感反応による細胞死を連続的・定量的に測定する分光計測プラットフォームと機械学習分類モデルを開発・検証しており、植物表現型取得が研究の中心である。
abstractwe report an accelerated phenotyping platform for the continuous-time, rapid and quantitative assessment of HR: Parallel Automated Spectroscopy Tool for Electrolyte Leakage (PASTEL).
Abstract Remote sensing of vegetation by spectroscopy is increasingly used to characterize trait distributions in plant communities. How leaves interact with electromagnetic radiation is determined by their structure and contents of pigments, water, and abundant dry matter constituents like lignins, phenolics, and proteins. High-resolution ("hyperspectral") spectroscopy can characterize trait variation at finer scales, and may help to reveal underlying genetic variation – information important for assessing the potential of populations to adapt to global change. Here, we use a set of 360 inbred genotypes of the wild coyote tobacco Nicotiana attenuata: wild accessions, recombinant inbred lines (RILs), and transgenic lines (TLs) with targeted changes to gene expression, to dissect genetic versus non-genetic influences on variation in leaf spectra across three experiments. We calculated leaf reflectance from hand-held field spectroradiometer measurements covering visible to short-wave infrared wavelengths of electromagnetic radiation (400-2500 nm) using a standard radiation source and backgrounds, resulting in a small and quantifiable measurement uncertainty. Plants were grown in more controlled (glasshouse) or more natural (field) environments, and leaves were measured both on- and off-plant with the measurement set-up thus also in more to less controlled environmental conditions. Entire spectra varied across genotypes and environments. We found that the greatest variance in leaf reflectance was explained by between-experiment and non-genetic between-sample differences, with subtler and more specific variation distinguishing groups of genotypes. The visible spectral region was most variable, distinguishing experimental settings as well as groups of genotypes within experiments, whereas parts of the short-wave infrared may vary more specifically with genotype. Overall, more genetically variable plant populations also showed more varied leaf spectra. We highlight key considerations for the application of field spectroscopy to assess genetic variation in plant populations.
Why it matches plant phenotyping methods葉の反射スペクトルを用いて遺伝的変異を評価する分光計測法を、複数環境・遺伝子型で検証し、測定不確実性や適用上の考慮点も評価しており、植物表現型取得法が研究の中心である。
titleEvaluating potential of leaf reflectance spectra to monitor plant genetic variation
Reproduction assets foundThe paper's leaf reflectance spectral measurement data are published in SPECCHIO, and all processed spectral data, metadata, and analysis code (e.g., RawDataProcess.R, Plots PCA.R, Plots Models.R) are provided in the authors' public GitHub repository.Code · publicilability of data and code
All plant lines are available from the Max Planck Institute for Chemical Ecology. The spectral measurement data
underlying the results presented in this paper are available as a published dataset [71]. All processed spectral data,
metadata and code for this study are provided at the GitHub repository:
https://github.com/licheng1221/How-leaves-reflect-genetic-variationOpen asset ↗https://github.com/licheng1221/How-leaves-reflect-genetic-variationpdf-raw-page:32 lines:1-45Plant phenotyping relevance match · UnverifiedEurope PMC · checked 14 Sept 2026
Due to technical limitations, research to date has mainly focused on the role of abiotic and biotic stress-signalling molecules in the aerial organs of plants, including the whole shoot, stem, and leaves. Novel experimental platforms including the dual-flow-RootChip (dfRC), PlantChip, and RootArray have since expanded this to plant-root cell analysis. Based on microfluidic platforms for flow stream shaping and force sensing on tip-growing organisms, the dfRC has further been expanded into a bi-directional dual-flow-RootChip (bi-dfRC), incorporating a second adjacent pair of inlets/outlet, enabling bi-directional asymmetric perfusion of treatments towards plant roots (shoot-to-root or root-to-shoot). This protocol outlines, in detail, the design and use of the bi-dfRC platform. Plant culture on chip is combined with guided root growth and controlled exposure of the primary root to solute changes. The impact of surface treatment on root growth and defence signals can be tracked in response to abiotic and biotic stress or the combinatory effect of both. In particular, this protocol highlights the ability of the platform to culture a variety of plants, such as Arabidopsis thaliana , Nicotiana benthamiana , and Solanum lycopersicum , on chip. It demonstrates that by simply altering the dimensions of the bi-dfRC, a broad application basis to study desired plant species with varying primary root sizes under microfluidics is achieved. Key features Expansion of the method developed by Stanley et al. (2018a) to study the directionality of defence signals responding to localised treatments. Description of a microfluidic platform allowing culture of plants with primary roots up to 40 mm length, 550 μm width, and 500 μm height. Treatment with polyvinylpyrrolidone (PVP) to permanently retain the hydrophilicity of partially hydrophobic bi-dfRC microchannels, enabling use with surface-sensitive plant lines. Description of novel tubing array setup equipped with rotatable valves for switching treatment reagent and orientation, while live-imaging on the bi-dfRC. Graphical overview Graphical overview of bi-dfRC fabrication, plantlet culture, and setup for root physiological analysis. (a) Schematic diagram depicting photolithography and replica molding, to produce a PDMS device. (b) Schematic diagram depicting seed culture off chip, followed by sub-culture of 4-day-old plantlets on chip. (c) Schematic diagram depicting microscopy and imaging setup, equipped with a media delivery system for asymmetric treatment introduction into the bi-dfRC microchannel root physiological analysis under varying conditions.
Why it matches plant phenotyping methods植物根の生理状態を観察・解析するマイクロ流体プラットフォームの設計、改良、培養、ライブイメージング手順が論文の中心であり、植物フェノタイピング手法に該当する。
abstractThis protocol outlines, in detail, the design and use of the bi-dfRC platform.
Why it matches plant phenotyping methods植物オルガネラ間相互作用という植物状態を、TIRF光ピンセットによる画像取得・自動計測で定量化するプロトコルを中心に扱っており、校正、再現性、自動化、定量手順が明示されているため。
abstractAutomated routines (such as setting trapping laser power and controlling the stage speed and distance) mean we can quantify organelle interactions in a repeatable and reproducible manner.
Two and three-dimensional (2D and 3D) imaging of plant samples with the scanning and transmission electron microscope (SEM, TEM) can reveal important information regarding physiological and anatomical adaptations of plants to environmental (stress) situations [1-3]. This study provides an overview of SEM and TEM techniques for the rapid evaluation of 2D ultrastructural changes in plants. Additionally, methods for the 3D reconstruction and volume extraction of plant cells based on serial section TEM (ssTEM), focused ion beam SEM (FIB-SEM) are demonstrated. Leaves of Nicotiana tabacum and Cucurbita pepo were prepared conventionally and with the help of microwave irradiation [1]. Additionally, for SEM investigations leaf replicas were made by applying dental putty [2]. SEM investigations were performed with a Versa 3D SEM (FEI, Hillsboro, OR, USA). For TEM investigations ultrathin sections (80 nm) were imaged with a JEOL 1010 TEM (JEOL, Akishima, Japan). For ssTEM, 71 sections of tobacco cells were imaged with a Zeiss EM 902 TEM (Zeiss, Oberkochen, Germany) while FIB-SEM was used to image 126 slices of pumpkin cells. Track EM (Image J) was used for 3D reconstructions and volume extractions. The use of microwave irradiation strongly reduced sample preparation time from 6 to 2h for SEM and from 3d to 5h for TEM investigations. SEM revealed that the surface of samples prepared with microwave irradiation was well preserved and comparable to those prepared conventionally (Figure 1 a & b). Stomatal and epidermal cells could be clearly distinguished (Figure 1 a & b). Samples showed signs of shrinkage (Figure 1 b) which was not observed on leaf replicas which showed a smooth surface (Figure 1 c). TEM revealed that the ultrastructure of samples prepared with microwave irradiation was well preserved and similar to those prepared conventionally (Figure 2 a & b). The cytoplasm contained chloroplasts with thylakoids and starch grains, nuclei with eu- and hetero-chromatin, mitochondria, peroxisomes, vacuoles and cell walls (Figure 2a & b). 3D reconstruction by FIB-SEM was faster and less sophisticated than 3D reconstruction by ssTEM [3]. Nevertheless, both methods delivered adequate results (Figure 2 c & d). Volume extraction revealed that tobacco cells were larger (31410 μm3) than pumpkin cells (20697 μm3) and contained more chloroplasts (175 vs. 124), mitochondria (1317 vs. 291) and peroxisomes (745 vs. 79). While individual chloroplasts, mitochondria, peroxisomes were larger in pumpkin plants (25, 53, and 50%) they covered more total volume in tobacco plants (5390, 395, 374 μm3) when compared to pumpkin plants (4762, 134, 59 μm3). Summing up, microwave-assisted sample preparation and the production of leaf replicas enabled the rapid evaluation of 2D ultrastructure of plant cells for TEM and SEM investigations. 3D reconstructions based on FIB-SEM and TEM were well suited to extract volume data of whole plant cells. These techniques are well suited to study the effects of environmental stress situations on plant ultrastructure. SEM micrographs of the surface of tobacco leaves showing stomatal (arrows) and epidermal cells. While samples prepared with the help of microwave irradiation (a) and conventionally (b) showed signs of shrinkage (arrowheads in b), leaf surface replicas with dental putty (c) showed a smooth surface. Bars = 50 μm. TEM micrographs of the 2D ultrastructure of pumpkin (a) and tobacco (b) plant leaf cells with chloroplasts (C), mitochondria (M), nuclei (N), and vacuoles (V). 3D reconstructions [modified according to 4] of pumpkin (c) and tobacco (d) plant cells using FIB-SEM (c) and ssTEM (d). Cell wall (gray), chloroplasts (green), mitochondria (red), nucleus (brown), peroxisomes (purple), and vacuole (blue). Bars=1 μm. Cubes = 3 and 4 μm3.
Why it matches plant phenotyping methods植物細胞の2D/3D画像取得、再構成、体積抽出を中心に、SEM/TEMおよびFIB-SEM手法と試料調製法を比較・評価しているため、植物フェノタイピング手法として適格。
abstractThis study provides an overview of SEM and TEM techniques for the rapid evaluation of 2D ultrastructural changes in plants.
Abstract Changes in cytosolic calcium (Ca2+) concentration are among the earliest reactions to a multitude of stress cues. While a plethora of Ca2+-permeable channels may generate distinct Ca2+ signatures and contribute to response specificities, the mechanisms by which Ca2+ signatures are decoded are poorly understood. Here, we developed a genetically encoded Förster resonance energy transfer (FRET)-based reporter that visualizes the conformational changes in Ca2+-dependent protein kinases (CDPKs/CPKs). We focused on two CDPKs with distinct Ca2+-sensitivities, highly Ca2+-sensitive Arabidopsis (Arabidopsis thaliana) AtCPK21 and rather Ca2+-insensitive AtCPK23, to report conformational changes accompanying kinase activation. In tobacco (Nicotiana tabacum) pollen tubes, which naturally display coordinated spatial and temporal Ca2+ fluctuations, CPK21-FRET, but not CPK23-FRET, reported oscillatory emission ratio changes mirroring cytosolic Ca2+ changes, pointing to the isoform-specific Ca2+-sensitivity and reversibility of the conformational change. In Arabidopsis guard cells, CPK21-FRET-monitored conformational dynamics suggest that CPK21 serves as a decoder of signal-specific Ca2+ signatures in response to abscisic acid and the flagellin peptide flg22. Based on these data, CDPK-FRET is a powerful approach for tackling real-time live-cell Ca2+ decoding in a multitude of plant developmental and stress responses.
Why it matches plant phenotyping methods植物体内のCa2+依存的な生理状態をリアルタイム可視化するFRETレポーターを開発しており、植物表現型取得法が研究の中心である。
abstractHere, we developed a genetically encoded Förster resonance energy transfer (FRET)-based reporter that visualizes the conformational changes in Ca2+-dependent protein kinases (CDPKs/CPKs).
Plant phenotyping relevance match · UnverifiedOpenAlex · Europe PMC · checked 15 Sept 2026
Hyperspectral technology offers significant potential for non-invasive monitoring and prediction of morphological parameters in plants. In this study, UV−VIS−NIR−SWIR reflectance hyperspectral data were collected from Nicotiana tabacum L. plants using a spectroradiometer. These plants were grown under different light and gibberellic acid (GA3) concentrations. Through spectroscopy and multivariate analyses, key growth parameters, such as height, leaf area, energy yield, and biomass, were effectively evaluated based on the interaction of light with leaf structures. The shortwave infrared (SWIR) bands, specifically SWIR1 and SWIR2, showed the strongest correlations with these growth parameters. When classifying tobacco plants grown under different GA3 concentrations in greenhouses, artificial intelligence (AI) and machine learning (ML) algorithms were employed, achieving an average accuracy of over 99.1% using neural network (NN) and gradient boosting (GB) algorithms. Among the 34 tested vegetation indices, the photochemical reflectance index (PRI) demonstrated the strongest correlations with all evaluated plant phenotypes. Partial least squares regression (PLSR) models effectively predicted morphological attributes, with R2CV values ranging from 0.81 to 0.87 and RPDP values exceeding 2.09 for all parameters. Based on Pearson’s coefficient XYZ interpolations and HVI algorithms, the NIR−SWIR band combination proved the most effective for predicting height and leaf area, while VIS−NIR was optimal for optimal energy yield, and VIS−VIS was best for predicting biomass. To further corroborate these findings, the SWIR bands for certain morphological characteristic wavelengths selected with s−PLS were most significant for SWIR1 and SWIR2, while i−PLS showed a more uniform distribution in VIS−NIR−SWIR bands. Therefore, SWIR hyperspectral bands provide valuable insights into developing alternative bands for remote sensing measurements to estimate plant morphological parameters. These findings underscore the potential of remote sensing technology for rapid, accurate, and non-invasive monitoring within stationary high-throughput phenotyping systems in greenhouses. These insights align with advancements in digital and precision technology, indicating a promising future for research and innovation in this field.
Why it matches plant phenotyping methods植物の形態形質をハイパースペクトル反射データと機械学習で非侵襲的に推定・分類する手法が研究の中心であり、温室高スループットフェノタイピングへの適用も明示されている。
abstractHyperspectral technology offers significant potential for non-invasive monitoring and prediction of morphological parameters in plants.
Volume electron microscopy techniques play an important role in plant research from understanding organelles and unicellular forms to developmental studies, environmental effects and microbial interactions with large plant structures, to name a few. Due to large air voids central vacuole, cell wall and waxy cuticle, many plant tissues pose challenges when trying to achieve high quality morphology, metal staining and adequate conductivity for high-resolution volume EM studies. Here, we applied a robust conventional chemical fixation strategy to address the special challenges of plant samples and suitable for, but not limited to, serial block-face and focused ion beam scanning electron microscopy. The chemistry of this protocol was modified from an approach developed for improved and uniform staining of large brain volumes. Briefly, primary fixation was in paraformaldehyde and glutaraldehyde with malachite green followed by secondary fixation with osmium tetroxide, potassium ferrocyanide, thiocarbohydrazide, osmium tetroxide and finally uranyl acetate and lead aspartate staining. Samples were then dehydrated in acetone with a propylene oxide transition and embedded in a hard formulation Quetol 651 resin. The samples were trimmed and mounted with silver epoxy, metal coated and imaged via serial block-face scanning electron microscopy and focal charge compensation for charge suppression. High-contrast plant tobacco and duckweed leaf cellular structures were readily visible including mitochondria, Golgi, endoplasmic reticulum and nuclear envelope membranes, as well as prominent chloroplast thylakoid membranes and individual lamella in grana stacks. This sample preparation protocol serves as a reliable starting point for routine plant volume electron microscopy.
Why it matches plant phenotyping methods植物組織の高品質な細胞形態を取得するための体積電子顕微鏡試料調製法を開発・適用しており、植物形態の画像取得が中心的な方法論的貢献である。
abstractHere, we applied a robust conventional chemical fixation strategy to address the special challenges of plant samples and suitable for, but not limited to, serial block-face and focused ion beam scanning electron microscopy.
Different diseases are observed in vegetables, fruits, cereals, and commercial crops by farmers and agricultural experts. Nonetheless, this evaluation process is time-consuming, and initial symptoms are primarily visible at microscopic levels, limiting the possibility of an accurate diagnosis. This paper proposes an innovative method for identifying and classifying infected brinjal leaves using Deep Convolutional Neural Networks (DCNN) and Radial Basis Feed Forward Neural Networks (RBFNN). We collected 1100 images of brinjal leaf disease that were caused by five different species (Pseudomonas solanacearum, Cercospora solani, Alternaria melongenea, Pythium aphanidermatum, and Tobacco Mosaic Virus) and 400 images of healthy leaves from India's agricultural form. First, the original plant leaf is preprocessed by a Gaussian filter to reduce the noise and improve the quality of the image through image enhancement. A segmentation method based on expectation and maximization (EM) is then utilized to segment the leaf's-diseased regions. Next, the discrete Shearlet transform is used to extract the main features of the images such as texture, color, and structure, which are then merged to produce vectors. Lastly, DCNN and RBFNN are used to classify brinjal leaves based on their disease types. The DCNN achieved a mean accuracy of 93.30% (with fusion) and 76.70% (without fusion) compared to the RBFNN (82%-without fusion, 87%-with fusion) in classifying leaf diseases.
Why it matches plant phenotyping methodsブリンジャル葉の病斑領域を画像から抽出・分類する手法が研究の中心であり、植物病害状態の表現型測定に該当する。
abstractThis paper proposes an innovative method for identifying and classifying infected brinjal leaves using Deep Convolutional Neural Networks (DCNN) and Radial Basis Feed Forward Neural Networks (RBFNN).
Light-field three-dimensional (3D) fluorescence microscopes can acquire 3D fluorescence images in a single shot, and followed numerical reconstruction can realize cross-sectional imaging at an arbitrary depth. The typical configuration that uses a lens array and a single image sensor has the trade-off between depth information acquisition and spatial resolution of each cross-sectional image. The spatial resolution of the reconstructed image degrades when depth information increases. In this paper, we use U-net as a deep learning model to improve the quality of reconstructed images. We constructed an optical system that integrates a light-field microscope and an epifluorescence microscope, which acquire the light-field data and high-resolution two-dimensional images, respectively. The high-resolution images from the epifluorescence microscope are used as ground-truth images for the training dataset for deep learning. The experimental results using fluorescent beads with a size of 10 µm and cultured tobacco cells showed significant improvement in the reconstructed images. Furthermore, time-lapse measurements were demonstrated in tobacco cells to observe the cell division process.
Why it matches plant phenotyping methods植物細胞の3D蛍光画像再構成を深層学習で改善する光学・画像解析手法を開発し、タバコ細胞の細胞分裂をタイムラプス観察しているため、植物状態の取得方法が中心です。
abstractIn this paper, we use U-net as a deep learning model to improve the quality of reconstructed images.
Abstract Remote sensing of vegetation by its reflection of light is increasingly used to characterize trait distributions in plant communities. How leaves reflect light is determined by their structure and contents of pigments, water, and abundant dry matter constituents like lignins, phenolics, and proteins. High-resolution ("hyperspectral") spectroscopy can characterize trait variation at finer scales, and may help to reveal underlying genetic variation: information important for assessing the potential of populations to adapt to global change. Here, we use a set of 360 inbred genotypes of the wild coyote tobacco Nicotiana attenuata: wild accessions, recombinant inbred lines (RILs), and transgenic lines (TLs) with targeted changes to gene expression, to dissect genetic versus non-genetic influences on variation in leaf spectra across three experiments. We calculated leaf reflectance from field spectroradiometer measurements covering visible to short-wave infrared wavelengths of electromagnetic radiation (400-2500 nm) using a standard radiation source and backgrounds, resulting in a small and quantifiable measurement uncertainty. Plants were grown in more controlled (glasshouse) or more natural (field) environments, and leaves were measured both on- and off-plant with the measurement set-up thus also in more to less controlled environmental conditions. Entire spectra varied across genotypes and environments. We found that the greatest variance in leaf reflectance was explained by between-experiment and non-genetic between-sample differences, with subtler and more specific variation distinguishing groups of genotypes. The visible spectral region was most variable, distinguishing experimental settings as well as groups of genotypes within experiments, whereas parts of the short-wave infrared may vary more specifically with genotype. Overall, more genetically variable plant populations also had more varied leaf spectra. We highlight key considerations for the application of field spectroscopy to assess genetic variation in plant populations.
Why it matches plant phenotyping methods葉の反射スペクトルを用いて遺伝的変異を評価する分光計測法を検討し、測定不確かさや環境条件の影響も評価しており、植物表現型取得法が研究の中心である。
titleEvaluating potential of leaf reflectance spectra to monitor plant genetic variation in nature
Cellulose is an important component of tobacco (Nicotiana tabacum L.) cell walls, which can be precursors for many harmful compounds in smoke. Traditional cellulose content analysis methods involve sequential extraction and separation steps, which are time-consuming and environmentally unfriendly. In this study, a novel method was first introduced to analyze cellulose content in tobacco via two-dimensional heteronuclear single quantum coherence (2D HSQC) NMR spectroscopy. The method was based on derivatization approach to allow the dissolution of insoluble polysaccharide fractions of tobacco cell walls in DMSO‑d 6 /pyridine-d 5 (4:1 v/v) for NMR analysis. The NMR results suggested that besides the main NMR signals of cellulose, partial signals of hemicellulose including mannopyranose, arabinofuranose, and galactopyranose units could also be identified. In addition, the utilization of relaxation reagents has proved to be an effective way to improve the sensitivity of 2D NMR spectroscopy, which was beneficial for quantification of biological samples with limited quantities. To overcome the limitations of quantification using 2D NMR, the calibration curve of cellulose with 1,3,5-trimethoxybenzene as internal reference was constructed and thus the accurate measurement of cellulose in tobacco was achieved. Compared with the chemical method, the interesting method was simple, reliable, and environmentally friendly, which provided a new insight for quantitative determination and structure analysis of plant macromolecules in complex samples.
Why it matches plant phenotyping methodsタバコ植物中のセルロース含量を定量するNMR測定法の開発が研究の中心であり、植物の化学的形質を直接測定する再利用可能な方法を提示している。
abstractIn this study, a novel method was first introduced to analyze cellulose content in tobacco via two-dimensional heteronuclear single quantum coherence (2D HSQC) NMR spectroscopy.
Tobacco is an important economic crop and the main raw material of cigarette products. Nowadays, with the increasing consumer demand for high-quality cigarettes, the requirements for their main raw materials are also varying. In general, tobacco quality is primarily determined by the exterior quality, inherent quality, chemical compositions, and physical properties. All these aspects are formed during the growing season and are vulnerable to many environmental factors, such as climate, geography, irrigation, fertilization, diseases and pests, etc. Therefore, there is a great demand for tobacco growth monitoring and near real-time quality evaluation. Herein, hyperspectral remote sensing (HRS) is increasingly being considered as a cost-effective alternative to traditional destructive field sampling methods and laboratory trials to determine various agronomic parameters of tobacco with the assistance of diverse hyperspectral vegetation indices and machine learning algorithms. In light of this, we conduct a comprehensive review of the HRS applications in tobacco production management. In this review, we briefly sketch the principles of HRS and commonly used data acquisition system platforms. We detail the specific applications and methodologies for tobacco quality estimation, yield prediction, and stress detection. Finally, we discuss the major challenges and future opportunities for potential application prospects. We hope that this review could provide interested researchers, practitioners, or readers with a basic understanding of current HRS applications in tobacco production management, and give some guidelines for practical works.
Why it matches plant phenotyping methodsタバコの品質・収量・ストレスなどの植物形質を推定するハイパースペクトルリモートセンシングの原理、取得プラットフォーム、方法、応用を包括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。
abstractIn this review, we briefly sketch the principles of HRS and commonly used data acquisition system platforms. We detail the specific applications and methodologies for tobacco quality estimation, yield prediction, and stress detection.
Plants rely on innate immune systems to defend against a wide variety of biotic attackers. Key components of innate immunity include cell-surface pattern-recognition receptors (PRRs), which recognize pest- and pathogen-associated molecular patterns (PAMPs). Unlike other classes of receptors that often have visible cell-death immune outputs upon activation, PRRs generally lack rapid methods for assessing function. Here, we describe a genetically encoded bioluminescent reporter of immune activation by heterologously expressed PRRs in the model organism Nicotiana benthamiana. We characterized N. benthamiana transcriptome changes in response to Agrobacterium tumefaciens and subsequent PAMP treatment to identify pattern-triggered immunity (PTI)-associated marker genes, which were then used to generate promoter-luciferase fusion fungal bioluminescence pathway (FBP) constructs. A reporter construct termed pFBP_2xNbLYS1::LUZ allows for robust detection of PTI activation by heterologously expressed PRRs. Consistent with known PTI signaling pathways, reporter activation by receptor-like protein (RLP) PRRs is dependent on the known adaptor of RLP PRRs, i.e., SOBIR1. The FBP reporter minimizes the amount of labor, reagents, and time needed to assay function of PRRs and displays robust sensitivity at biologically relevant PAMP concentrations, making it ideal for high throughput screens. The tools described in this paper will be powerful for investigations of PRR function and characterization of the structure-function of plant cell-surface receptors. [Formula: see text] The author(s) have dedicated the work to the public domain under the Creative Commons CC0 “No Rights Reserved” license by waiving all of his or her rights to the work worldwide under copyright law, including all related and neighboring rights, to the extent allowed by law, 2023.
Why it matches plant phenotyping methods植物免疫活性という植物状態を発光レポーターで検出・定量する方法を開発し、感度や既知経路依存性を検証しているため、植物フェノタイピング手法が中心である。
abstractHere, we describe a genetically encoded bioluminescent reporter of immune activation by heterologously expressed PRRs in the model organism Nicotiana benthamiana.
The quantitative study of plasmodesmata (PD) frequency is routine in plant science for providing information on the potential of intercellular transportation. Here, we report quantification of plasmodesmatal frequency in virus-infected tobacco vascular tissues using serial sectioning and image analysis. The image datasets were collected by focused ion beam-scanning electron microscopy (FIB-SEM), and the measurements of plasmodesmatal frequency were performed after image analysis with commercial computational programs. With a 5-nm step size (less than half the diameter of PD) during FIB sectioning, exhaustive PD sampling was performed in regions of interest. Segmentation of cell wall (CW) and PD from the background densities was performed manually, and PD were assigned automatically to individual CW interfaces by image analysis and then quantified. The PD quantification results were used to compare the plamodesmatal frequencies among different CW interfaces of individual cells and the average frequencies among different cell types were calculated. CWs lacking PD distribution were found in several cellular types, and the PD frequency were used to determine the possible pathways of PD-based symplasmic transportation. The method enables imaging of samples of several cells containing multiple CW interfaces and minimizes PD omission during sectioning and imaging.
Why it matches plant phenotyping methodsFIB-SEMと画像解析を用いた植物細胞間の原形質連絡頻度の定量法が中心であり、取得・セグメンテーション・自動割当・定量の技術的手順を提示している。
abstractHere, we report quantification of plasmodesmatal frequency in virus-infected tobacco vascular tissues using serial sectioning and image analysis.
Global climate change and exponential population growth pose a challenge to agricultural outputs. In this scenario, novel techniques have been proposed to improve plant growth and increase crop yields. Wearable sensors are emerging as promising tools for the non-invasive monitoring of plant physiological and microclimate parameters. Features of plant wearables, such as easy anchorage to different organs, compliance with natural surfaces, high flexibility, and biocompatibility, allow for the detection of growth without impacting the plant functions. This work proposed two wearable sensors based on fiber Bragg gratings (FBGs) within silicone matrices. The use of FBGs is motivated by their high sensitivity, multiplexing capacities, and chemical inertia. Firstly, we focused on the design and the fabrication of two plant wearables with different matrix shapes tailored to specific plant organs (i.e., tobacco stem and melon fruit). Then, we described the sensors' metrological properties to investigate the sensitivity to strain and the influence of environmental factors, such as temperature and humidity, on the sensors' performance. Finally, we performed experimental tests to preliminary assess the capability of the proposed sensors to monitor dimensional changes of plants in both laboratory and open field settings. The promising results will foster key actions to improve the use of this innovative technology in smart agriculture applications for increasing crop products quality, agricultural efficiency, and profits.
Why it matches plant phenotyping methods植物の茎・果実の寸法変化を測定するFBGウェアラブルセンサーを設計・製作し、性能評価と実証を行った、中心的な植物フェノタイピング手法研究である。
abstractThis work proposed two wearable sensors based on fiber Bragg gratings (FBGs) within silicone matrices.
Considering the availability of serological and molecular biological methods, the bioassay has been paled into insignificance, although it is the only experimental method that can be used to demonstrate the infectivity of a virus. We compared goodness-of-fit and predictability power of five models for the quantification of tomato brown rugose fruit virus (ToBRFV) based on local lesion assays: the Kleczkowski model, Furumoto and Mickey models I and II, the Gokhale and Bald model (growth curve model), and the modified Poisson model. For this purpose, mechanical inoculations onto Nicotiana tabacum L. cv. Xanthi nc and N. glutionosa L. with defined virus concentrations were first performed with half-leaf randomization in a Latin square design. Subsequently, models were implemented using Python software and fitted to the number of local lesions. All models could fit to the data for quantifying ToBRFV based on local lesions, among which the modified Poisson model had the best prediction of virus concentration in spike samples based on local lesions, although data of individual indicator plants showed variations. More accurate modeling was obtained from the test plant N. glutinosa than from N. tabacum cv. Xanthi nc. The position of the half-leaves on the test plants had no significant effect on the number of local lesions.
Why it matches plant phenotyping methods局部病斑数という感染植物の可視的病徴を用いたウイルス定量モデルを比較・予測検証しており、病徴の取得・定量法が研究の中心である。
abstractWe compared goodness-of-fit and predictability power of five models for the quantification of tomato brown rugose fruit virus (ToBRFV) based on local lesion assays
We used the NanoLuc luciferase bioluminescent reporter system to detect turnip yellows virus (TuYV) in infected plants. For this, TuYV was genetically tagged by replacing the C-terminal part of the RT protein with full-length NanoLuc (TuYV-NL) or with the N-terminal domain of split NanoLuc (TuYV-N65-NL). Wild-type and recombinant viruses were agro-infiltrated in Nicotiana benthamiana , Montia perfoliata , and Arabidopsis thaliana . ELISA confirmed systemic infection and similar accumulation of the recombinant viruses in N. benthamiana and M. perfoliata but reduced systemic infection and lower accumulation in A. thaliana . RT-PCR analysis indicated that the recombinant sequences were stable in N. benthamiana and M. perfoliata but not in A. thaliana . Bioluminescence imaging detected TuYV-NL in inoculated and systemically infected leaves. For the detection of split NanoLuc, we constructed transgenic N. benthamiana plants expressing the C-terminal domain of split NanoLuc. Bioluminescence imaging of these plants after agro-infiltration with TuYV-N65-NL allowed the detection of the virus in systemically infected leaves. Taken together, our results show that NanoLuc luciferase can be used to monitor infection with TuYV.
Why it matches plant phenotyping methodsNanoLucを用いた生物発光イメージングによる植物ウイルス感染状態の検出・モニタリング法を開発し、複数植物種で評価しているため、植物の病態を対象とするフェノタイピング手法が中心である。
abstractBioluminescence imaging detected TuYV-NL in inoculated and systemically infected leaves.
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
For image classification of crops, most convolutional neural network (CNN) models have low accuracy, especially in modern agricultural environments. Furthermore, crop disease images create more difficulties for classification owing to the morphological and physiological changes of organs, tissues, and cells. Here, we propose a CNN model named CAMFFNet (coordinate attention-based multiple feature fusion network) for tobacco disease identification under field conditions. The CAMFFNet model has three multiple feature fusion (MFF) modules. Each module is composed of two residual blocks. The MFF module is concatenated by max-pooling downsampling layers at different locations in the residual blocks to realize a fusion between features of multiple depths, thereby reducing the loss of tobacco disease information. Furthermore, to enhance the ability to extract effective feature information of tobacco diseases and to alleviate the impact of the field environment, coordinate attention (CA) modules are included between each multiple feature fusion module. The obtained results show that the CAMFFNet model achieved an accuracy of 89.71 % on the tobacco disease test set. The accuracy was 3.36 %, 4.7 %, 4.7 %, 2.91 %, 8.05 %, 4.92 %, 10.07 %, and 2.91 % higher than those of the classic CNN models VGG16, GoogLeNet, DenseNet121, ResNet34, MobbileNetV2, MobbileNetV3 Large, ShuffleNetV2 1.0×, and EfficientNetV2 Small, respectively. In addition, the CAMFFNet model’s number of parameters is only 2.37 million. The results demonstrate that the CAMFFNet model has a high potential for tobacco disease recognition in mobile and embedded devices.
Why it matches plant phenotyping methodsタバコの病害状態を画像から認識するCNNモデルを開発し、精度比較と性能評価を行っており、植物表現型取得・判定手法が研究の中心である。
abstractHere, we propose a CNN model named CAMFFNet (coordinate attention-based multiple feature fusion network) for tobacco disease identification under field conditions.
MaizeTobaccoTomatoLiDAR / point cloudLeafImage / point-cloud registrationSegmentationTrackingGrowth / development / phenology
Plant phenotypic analysis is of great importance to the development of agricultural engineering, and is one of the core issues in crop science and plant breeding. Since plant growth is spatio-temporal and synchronous, understanding the growth and development of individual plants can help to reveal the growth potential of the whole plot and thus improve planting methods. In recent years, the technical means to analyze the growth situation using 3D point cloud data has received extensive attention. The plant point cloud obtained by scanning plants with LiDAR has the characteristics of high resolution, high precision, etc. Periodic scanning of the same plant for spatio-temporal point cloud data sets allows monitoring of growth through subtle changes of plant organs. Organ tracking of growing plants remains challenging due to the lightward nature of growth, the potential for topological changes and the unpredictability of plant growth over time, with the possibility of new leaf growth and leaf death. This paper designs a plant organ growth tracking method based on point cloud. First of all, for growing plants, we have established a crop point cloud spatio-temporal dataset based on two publicly available point cloud datasets. The data set includes four species, tomato, tobacco, sorghum and maize, each species contains complete organ instance labels, and each organ of the same plant has a unique label, which means that the labels of the same organ of an individual at different scan dates correspond one-to-one. Second, this paper proposes a point cloud data-based plant organ growth tracking method, which uses a cost correlation matrix to automatically track growing plant organs. Finally, based on the set of quantitative evaluation metrics, our algorithm achieves a matching accuracy of 82.89% on the plant spatio-temporal dataset and good growth tracking results in the qualitative analysis.
Why it matches plant phenotyping methods植物器官の成長を3D点群から追跡・定量化する手法を開発し、ラベル付き時系列データセットと評価指標で検証しており、植物フェノタイピング手法が中心である。
abstractThis paper designs a plant organ growth tracking method based on point cloud.
High-throughput and large-scale data are part of a new era of plant remote sensing science. Quantification of the yield, energetic content, and chlorophyll a fluorescence (ChlF) remains laborious and is of great interest to physiologists and photobiologists. We propose a new method that is efficient and applicable for estimating photosynthetic performance and photosystem status using remote sensing hyperspectroscopy with visible, near-infrared and shortwave spectroscopy (Vis-NIR-SWIR) based on rapid multivariate partial least squares regression (PLSR) as a tool to estimate biomass production, calorimetric energy content and chlorophyll a fluorescence parameters. The results showed the presence of typical inflections associated with chemical and structural components present in plants, enabling us to obtain PLSR models with R2P and RPDP values greater than >0.82 and 3.33, respectively. The most important wavelengths were well distributed into 400 (violet), 440 (blue), 550 (green), 670 (red), 700−750 (red edge), 1330 (NIR), 1450 (SWIR), 1940 (SWIR) and 2200 (SWIR) nm operating ranges of the spectrum. Thus, we report a methodology to simultaneously determine fifteen attributes (i.e., yield (biomass), ΔH°area, ΔH°mass, Fv/Fm, Fv’/Fm’, ETR, NPQ, qP, qN, ΦPSII, P, D, SFI, PI(abs), D.F.) with high accuracy and precision and with excellent predictive capacity for most of them. These results are promising for plant physiology studies and will provide a better understanding of photosystem dynamics in tobacco plants when a large number of samples must be evaluated within a short period and with remote acquisition data.
Why it matches plant phenotyping methodsVis-NIR-SWIRハイパースペクトル計測とPLSRにより、収量・バイオマス・蛍光・光合成関連形質を高速推定する方法の開発が中心である。
abstractWe propose a new method that is efficient and applicable for estimating photosynthetic performance and photosystem status using remote sensing hyperspectroscopy with visible, near-infrared and shortwave spectroscopy (Vis-NIR-SWIR) based on rapid multivariate partial least squares regression (PLSR)
Plants rely on innate immune systems to defend against a wide variety of biotic attackers. Key components of innate immunity include cell-surface pattern recognition receptors (PRRs), which recognize pest/pathogen-associated molecular patterns (PAMPs). Unlike other classes of receptors which often have visible cell death immune outputs upon activation, PRRs generally lack rapid methods for assessing function. Here, we describe a genetically encoded bioluminescent reporter of immune activation by heterologously-expressed PRRs in the model organism Nicotiana benthamiana. We characterized N. benthamiana transcriptome changes in response to Agrobacterium tumefaciens (Agrobacterium) and subsequent PAMP treatment to identify PTI-associated marker genes, which were then used to generate promoter-luciferase fusion fungal bioluminescence pathway (FBP) constructs. A reporter construct termed pFBP_2xNbLYS1::LUZ allows for robust detection of PTI activation by heterologously expressed PRRs. Consistent with known PTI signaling pathways, activation by receptor-like protein (RLP) PRRs is dependent on the known adaptor of RLP PRRs, SOBIR1. This system minimizes the amount of labor, reagents, and time needed to assay function of PRRs and displays robust sensitivity at biologically relevant PAMP concentrations, making it ideal for high throughput screens. The tools described in this paper will be powerful for studying PRR function and investigations to characterize the structure-function of plant cell surface receptors.
Why it matches plant phenotyping methods植物の免疫活性化状態を生物発光で定量する遺伝子コード型レポーターを開発した研究であり、植物状態の取得法が中心的な貢献である。
abstractHere, we describe a genetically encoded bioluminescent reporter of immune activation by heterologously-expressed PRRs in the model organism Nicotiana benthamiana.
To keep up with population growth, precision farming technologies must be implemented to sustainably increase agricultural output. The impact of such technologies can be expanded by monitoring phytohormones, such as salicylic acid. In this study, we present a plant-wearable electrochemical sensor for in situ detection of salicylic acid. The sensor utilizes microneedle-based electrodes that are functionalized with a layer of salicylic acid selective magnetic molecularly imprinted polymers. The sensor's capability to detect the phytohormone is demonstrated both in vitro and in vivo with a limit of detection of 2.74 μM and a range of detection that can reach as high as 150 μM. Furthermore, the selectivity of the sensor is verified by testing the sensor on commonly occurring phytohormones. Finally, we demonstrate the capability of the sensor to detect the onset of fungal infestation in Tobacco 5 min post-inoculation. This work shows that the sensor could serve as a promising platform for continuous and non-destructive monitoring in the field and as a fundamental research tool when coupled with a portable potentiostat.
Why it matches plant phenotyping methods植物体内の植物ホルモンを非破壊・連続測定するウェアラブル電気化学センサーを開発し、in vitro/in vivo性能と真菌感染開始の検出を検証しており、植物状態の取得手法が中心である。
abstractwe present a plant-wearable electrochemical sensor for in situ detection of salicylic acid.
Deep learning-based methods have recently provided a means to rapidly and effectively extract various plant traits due to their powerful ability to depict a plant image across a variety of species and growth conditions. In this study, we focus on dealing with two fundamental tasks in plant phenotyping, i.e., plant segmentation and leaf counting, and propose a two-steam deep learning framework for segmenting plants and counting leaves with various size and shape from two-dimensional plant images. In the first stream, a multi-scale segmentation model using spatial pyramid is developed to extract leaves with different size and shape, where the fine-grained details of leaves are captured using deep feature extractor. In the second stream, a regression counting model is proposed to estimate the number of leaves without any pre-detection, where an auxiliary binary mask from segmentation stream is introduced to enhance the counting performance by effectively alleviating the influence of complex background. Extensive pot experiments are conducted CVPPP 2017 Leaf Counting Challenge dataset, which contains images of Arabidopsis and tobacco plants. The experimental results demonstrate that the proposed framework achieves a promising performance both in plant segmentation and leaf counting, providing a reference for the automatic analysis of plant phenotypes.
Why it matches plant phenotyping methods植物画像からのセグメンテーションと葉数推定を中核とする深層学習フェノタイピング手法の開発であり、植物形質の自動抽出性能も評価しているため。
abstractwe focus on dealing with two fundamental tasks in plant phenotyping, i.e., plant segmentation and leaf counting, and propose a two-steam deep learning framework
Reproduction assets foundThe paper's experiments use the public CVPPP 2017 Leaf Counting Challenge dataset (Arabidopsis and tobacco plant images with segmentation masks and leaf counts), which the authors explicitly link in the data availability statement. No author code or trained models are disclosed.Dataset · publicPublicly available datasets were analyzed in this study. This data can be found here: https://www.plant-phenotyping.org/CVPPP2017 .Open asset ↗CVPPP2017 · CVPPP2017lines:527-601Code / dataset availability confirmedOpenAlex · Europe PMC · checked 14 Sept 2026
BACKGROUND: Although quantitative single-cell analysis is frequently applied in animal systems, e.g. to identify novel drugs, similar applications on plant single cells are largely missing. We have exploited the applicability of high-throughput microscopic image analysis on plant single cells using tobacco leaf protoplasts, cell-wall free single cells isolated by lytic digestion. Protoplasts regenerate their cell wall within several days after isolation and have the potential to expand and proliferate, generating microcalli and finally whole plants after the application of suitable regeneration conditions. RESULTS: High-throughput automated microscopy coupled with the development of image processing pipelines allowed to quantify various developmental properties of thousands of protoplasts during the initial days following cultivation by immobilization in multi-well-plates. The focus on early protoplast responses allowed to study cell expansion prior to the initiation of proliferation and without the effects of shape-compromising cell walls. We compared growth parameters of wild-type tobacco cells with cells expressing the antiapoptotic protein Bcl2-associated athanogene 4 from Arabidopsis (AtBAG4). CONCLUSIONS: AtBAG4-expressing protoplasts showed a higher proportion of cells responding with positive area increases than the wild type and showed increased growth rates as well as increased proliferation rates upon continued cultivation. These features are associated with reported observations on a BAG4-mediated increased resilience to various stress responses and improved cellular survival rates following transformation approaches. Moreover, our single-cell expansion results suggest a BAG4-mediated, cell-independent increase of potassium channel abundance which was hitherto reported for guard cells only. The possibility to explain plant phenotypes with single-cell properties, extracted with the single-cell processing and analysis pipeline developed, allows to envision novel biotechnological screening strategies able to determine improved plant properties via single-cell analysis.
Why it matches plant phenotyping methods植物プロトプラストの成長・増殖特性を大量画像から抽出する自動顕微鏡解析と画像処理パイプラインを開発・適用しており、表現型取得手法が研究の中心である。
abstractHigh-throughput automated microscopy coupled with the development of image processing pipelines allowed to quantify various developmental properties of thousands of protoplasts
Reproduction assets found保存済みの本文根拠を更新済みルールで再検証し、公開資産1件を確認しました。Code · publicAll the scripts and codes used in the analysis pipeline, additional downloaded plugins used in processing the images as well as sample data can be found in our Github page https://github.com/jodawson/cell_seg_tracking_analysis .Open asset ↗jodawson/cell_seg_tracking_analysislines:143-159Plant phenotyping relevance match · UnverifiedCrossref · Europe PMC · OpenAlex · checked 15 Sept 2026
Main conclusion Focused ion beam scanning electron microscopy is well suited for volumetric extractions and 3D reconstructions of plant cells and its organelles. The three-dimensional (3D) reconstruction of individual plant cells is an important tool to extract volumetric data of organelles and is necessary to fully understand ultrastructural changes and adaptations of plants to their environment. Methods such as the 3D reconstruction of cells based on light microscopical images often lack the resolution necessary to clearly reconstruct all cell compartments within a cell. The 3D reconstruction of cells through serial sectioning transmission electron microscopy (ssTEM) and focused ion beam scanning electron microscopy (FIB-SEM) are powerful alternatives but not widely used in plant sciences. Here, we present a method for the 3D reconstruction and volumetric extraction of plant cells based on FIB milling and compare the results with 3D reconstructions obtained with ssTEM. When compared to 3D reconstruction based on ssTEM, FIB-SEM delivered similar results. The data extracted in this study demonstrated that tobacco cells were larger (31410 µm 3 ) than pumpkin cells (20697 µm 3 ) and contained more chloroplasts (175 vs. 124), mitochondria (1317 vs. 291) and peroxisomes (745 vs. 79). While individual chloroplasts, mitochondria, peroxisomes were larger in pumpkin plants (25, 53, and 50%, respectively) they covered more total volume in tobacco plants (5390, 395, 374 µm 3 , respectively) due to their higher number per cell when compared to pumpkin plants (4762, 134, 59 µm 3 , respectively). While image acquisition with FIB-SEM was automated, software controlled, and less difficult than ssTEM, FIB milling was slower and sections could not be revised or re-imaged as they were destroyed by the ion beam. Nevertheless, the results in this study demonstrated that both, FIB-SEM and ssTEM, are powerful tools for the 3D reconstruction of and volumetric extraction from plant cells and that there were large differences in size, number, and organelle composition between pumpkin and tobacco cells.
Why it matches plant phenotyping methods植物細胞の3D画像再構成と体積・オルガネラ形質抽出法を開発し、FIB-SEMをssTEMと比較検証しており、表現型取得法が研究の中心である。
abstractHere, we present a method for the 3D reconstruction and volumetric extraction of plant cells based on FIB milling and compare the results with 3D reconstructions obtained with ssTEM.
Plant phenotyping relevance match · UnverifiedCrossref · Europe PMC · checked 8 Sept 2026
In this study, we developed and applied a new spectroscopic fluorescence method for the in vivo detection of the early events in the interaction between tobacco ( Nicotiana tabacum L.) plants and pathogenic bacteria. The leaf disks were infiltrated with a bacterial suspension in sterile physiological solution (SPS), or with SPS alone as control. The virulent Pseudomonas syringae pv. tabaci strain ATCC 11528, its non-pathogenic ΔhrpA mutant, and the avirulent P. syringae pv. tomato strain DC3000 were used. At different post-infiltration time–points, the in vivo fluorescence spectra on leaf disks were acquired by a fiber bundle-spectrofluorimeter. The excitation spectra of the leaf blue emission at 460 nm, which is mainly due to the accumulation of coumarins following a bacterial infiltration, were processed by using a two-bands Gaussian fitting that enabled us to isolate the scopoletin (SCT) contribution. The pH-dependent fluorescence of SCT and scopolin (SCL), as determined by in vitro data and their intracellular localization, as determined by confocal microscopy, suggested the use of the longer wavelength excitation band at 385 nm of 460 nm emission (F 385_460 ) to follow the metabolic evolution of SCT during the plant–bacteria interaction. It was found to be directly correlated ( R 2 = 0.84) to the leaf SCT content, but not to that of SCL, determined by HPLC analysis. The technique applied to the time-course monitoring of the bacteria–plant interaction clearly showed that the amount and the timing of SCT accumulation, estimated by F 385_460 , was correlated with the resistance to the pathogen. As expected, this host defense response was delayed after P. syringae pv. tabaci ATCC 11528 infiltration, in comparison to P. syringae pv. tomato DC3000. Furthermore, no significant increase of F 385_460 (SCT) was observed when using the non-pathogenic ΔhrpA mutant of P. syringae pv. tabaci ATCC 11528, which lacks a functional Type Three Secretion System (TTSS). Our study showed the reliability of the developed fluorimetric method for a rapid and non-invasive monitoring of bacteria-induced first events related to the metabolite-based defense response in tobacco leaves. This technique could allow a fast selection of pathogen-resistant cultivars, as well as the on-site early diagnosis of tobacco plant diseases by using suitable fluorescence sensors.
Why it matches plant phenotyping methodsタバコ葉の病原体応答を、蛍光分光法でスコポレチン蓄積として非侵襲的に測定する手法を開発し、HPLCとの相関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 developed and applied a new spectroscopic fluorescence method for the in vivo detection of the early events in the interaction between tobacco ( Nicotiana tabacum L.) plants and pathogenic bacteria.
Selecting transformed plants is generally time consuming and laborious. To develop a method for transgenic plant selection without the need for antibiotics or herbicides, we evaluated the suitability of the R2R3 MYB transcription factor gene CaAN2 from purple chili pepper ( Capsicum annuum ) for use as a visible selection marker. CaAN2 positively regulates anthocyanin biosynthesis. Transient expression assays in tobacco ( Nicotiana tabacum ) leaves revealed that CaAN2 actively induced sufficient pigment accumulation for easy detection without the need for a basic helix-loop-helix (bHLH) protein as a cofactor; similar results were obtained for tobacco leaves transiently co-expressing the anthocyanin biosynthesis regulators bHLH B-Peru from maize and R2R3 MYB mPAP1D from Arabidopsis. Tobacco plants harboring CaAN2 were readily selected based on their red color at the shoot regeneration stage due to anthocyanin accumulation without the need to impose selective pressure from herbicides. Transgenic tobacco plants harboring CaAN2 showed strong pigment accumulation throughout the plant body. The ectopic expression of CaAN2 dramatically promoted the transcription of anthocyanin biosynthetic genes as well as regulators of this process. The red coloration of tobacco plants harboring CaAN2 was stably transferred to the next generation. Therefore, anthocyanin accumulation due to CaAN2 expression is a useful visible trait for stable transformation, representing an excellent alternative selection system for transgenic plants.
Why it matches plant phenotyping methodsCaAN2によるアントシアニン蓄積と赤色を利用し、抗生物質・除草剤なしで形質転換植物を非破壊選抜する可視表現型ベースの方法を開発しており、表現型取得・判定が研究の中心である。
abstractTo develop a method for transgenic plant selection without the need for antibiotics or herbicides, we evaluated the suitability of the R2R3 MYB transcription factor gene CaAN2 from purple chili pepper ( Capsicum annuum ) for use as a visible selection marker.
Abstract Copper-based fungicides are largely used in agriculture in the control of a wide range of plant diseases. Applied on plants, they remain deposited on leaf surfaces and are not absorbed into plant tissues. Because of accumulation problems and their ecotoxicological profiles in the soil, their use needs to be monitored and controlled, also by using modern technologies to better optimize the efficacy rendering minimum the amount of copper per season used. In this work, we test a novel approach based on pulsed thermography to evaluate the persistence of the copper on plant leaves so that the time between two applications should be the minimum needs. We monitored the thermal response observed on different treatments of both grapevine and tobacco plants over a 3-week period. Our experimental results demonstrate that the new methodological approach based on pulsed thermography can be an effective tool to evaluate in real time the presence of copper on differently treated plants allowing a tentative quantification and, therefore, to optimize its use in the agricultural practices, according also to the European Regulation n. 1107/2009.
Why it matches plant phenotyping methods植物葉面上の銅残留をパルスサーモグラフィーで評価・定量する新規手法が研究の中心であり、植物状態のセンサー計測法として該当する。
abstractIn this work, we test a novel approach based on pulsed thermography to evaluate the persistence of the copper on plant leaves
TobaccoMicroscopyCell / cellular structureGrowth / time-series analysisVisualization / data management
Transgenic tobacco BY-2 cell lines stably expressing fluorescent protein-tagged marker proteins have been used to visualize the dynamic behaviors of cytoskeletons and organelles during plant cell division. Using time-lapse confocal imaging, we recently revealed that the pharmacological disruption of actin filaments results in the abnormal organization of phragmoplast microtubules during the early phase of cytokinesis in cell cycle-synchronized BY-2 cells. Additionally, disrupting the actin filaments shortens the time from cell plate emergence to the accumulation of green fluorescent protein-tagged NACK1 kinesin on the cell plate, suggesting that there are two functionally diverse types of microtubules in the phragmoplast. We herein describe a protocol for the cell cycle synchronization of BY-2 cells and the time-lapse confocal imaging of cytokinesis combined with a treatment with an actin polymerization inhibitor and the visualization of an emerging cell plate with a vital stain. This protocol is useful for examining the dynamic changes in protein localization or the intracellular architecture and the effects of actin disruption during plant cell division.
Why it matches plant phenotyping methods植物細胞分裂中の細胞板形成や細胞内構造の動態を取得するタイムラプス共焦点 imaging protocol が論文の中心であり、植物細胞状態の画像ベース計測法として収載可能。
abstractthe time-lapse confocal imaging of cytokinesis combined with a treatment with an actin polymerization inhibitor and the visualization of an emerging cell plate with a vital stain
Chlorophyll fluorescence imaging was used to study potato virus X (PVX) infection of Nicotiana benthamiana . Infection-induced changes in chlorophyll fluorescence parameters (quantum yield of photosystem II photochemistry ( Φ PSII ) and non-photochemical fluorescence quenching (NPQ)) in the non-inoculated leaf were recorded and compared with the spatial distribution of the virus detected by the fluorescence of GFP associated with the virus. We determined infection-related changes at different points of the light-induced chlorophyll fluorescence kinetics and at different days after inoculation. A slight change in the light-adapted steady-state values of Φ PSII and NPQ was observed in the infected area of the non-inoculated leaf. In contrast to the steady-state parameters, the dynamics of Φ PSII and NPQ caused by the dark-light transition in healthy and infected areas differed significantly starting from the second day after the detection of the virus in a non-inoculated leaf. The coefficients of correlation between chlorophyll fluorescence parameters and virus localization were 0.67 for Φ PSII and 0.76 for NPQ. In general, the results demonstrate the possibility of reliable pre-symptomatic detection of the spread of a viral infection using chlorophyll fluorescence imaging.
Why it matches plant phenotyping methods葉緑素蛍光イメージングにより、植物の感染状態を症状出現前に検出する方法を評価しており、植物状態の取得・判定が研究の中心である。
abstractChlorophyll fluorescence imaging was used to study potato virus X (PVX) infection of Nicotiana benthamiana .
Improving photosynthesis has been considered critical to increasing crop yield to meet food demands from a growing population. To achieve this goal, high-throughput phenotyping techniques are highly needed to explore both natural and genetic variation in photosynthetic performance among crop cultivars. Due to the non-invasive nature of hyperspectral imaging, there is an increasing use of hyperspectral imaging for phenotyping of photosynthesis or photosynthetic physiology. The use of hyperspectral sensors has resulted in the accumulation of large amounts of data, shifting the research efforts into efficiently mining spectral information for high-throughput phenotyping. In this presentation, we will introduce data pipelines developed to leverage proximal sensing platforms and data sources including both reflectance spectra and solar-induced fluorescence (SIF) for quantifying photosynthetic performance at the canopy level. Photosynthetic performance was represented by the maximum carboxylation rate (Vcmax) and the maximum electron transport rate (Jmax). The experiments were conducted using eleven tobacco cultivars grown in field conditions during 2017 and 2018 at Energy Farm at University of Illinois. Time-synchronized hyperspectral images from 400 to 900 nm and irradiance measurements of sunlight under clear-sky conditions were collected for capturing reflectance spectra and SIF (and SIF related parameters). Within 30 minutes of spectral measurements, ground-truth Vcmax and Jmax were obtained from portable leaf gas exchange system. Our results suggested both reflectance spectra and SIF can provide accurate estimations of Vcmax and Jmax. The presented data pipelines have potential to relieve bottleneck in phenotyping of photosynthesis for breeding cultivars of enhanced photosynthesis.
Why it matches plant phenotyping methods反射スペクトルとSIFを用いて、キャノピーの光合成性能（Vcmax、Jmax）を推定するデータパイプラインを開発・評価しており、植物表現型取得法が中心である。
abstracthigh-throughput phenotyping techniques are highly needed to explore both natural and genetic variation in photosynthetic performance among crop cultivars
When dealing with plant roots, a multiscale description of the functional root structure is needed. Since the beginning of 21st century, new devices such as laser confocal microscopes have been accessible for coarse root structure measurements, including three-dimensional (3D) reconstruction. Most researchers are familiar with using simple 2D geometry visualization that does not allow quantitative determination of key morphological features from an organ-like perspective. We provide here a detailed description of the quantitative methods available for 3D analysis of root features at single-cell resolution, including root asymmetry, lateral root analysis, cell size and nuclear organization, cell-cycle kinetics, and chromatin structure analysis. Quantitative maps of the root apical meristem (RAM) are shown for different species, including Arabidopsis thaliana (L.), Heynh, Nicotiana tabacum L., Medicago sativa L., and Setaria italica (L.) P. Beauv. The 3D analysis of the RAM in these species showed divergence in chromatin organization and cell volume distribution that might be used to study root zonation for each root tissue. Detailed protocols and possible pitfalls in the usage of the marker lines are discussed. Therefore, researchers who need to improve their quantitative root biology portfolio can use them as a reference.
Why it matches plant phenotyping methods根の3D形態・細胞特性を定量化する方法とプロトコルを中心に扱う方法論的レビューであり、植物フェノタイピング手法が主題である。
abstractWe provide here a detailed description of the quantitative methods available for 3D analysis of root features at single-cell resolution
Abstract The ability to visualize cell and tissue morphology at a high magnification using scanning electron microscopy (SEM) has revolutionized plant sciences research. In plant–insect interactions studies, SEM-based imaging has been of immense assistance to understand plant surface morphology including trichomes [plant hairs; physical defense structures against herbivores], spines, waxes, and insect morphological characteristics such as mouth parts, antennae, and legs, that they interact with. While SEM provides finer details of samples, and the imaging process is simpler now with advanced image acquisition and processing, sample preparation methodology has lagged. The need to undergo elaborate sample preparation with cryogenic freezing, multiple alcohol washes, and sputter coating makes SEM imaging expensive, time consuming, and warrants skilled professionals, making it inaccessible to majority of scientists. Here, using a desktop version of SEM (SNE- 4500 Plus Tabletop), we show that the “plug and play” method can efficiently produce SEM images with sufficient details for most morphological studies in plant–insect interactions. We used leaf trichomes of Solanum genus as our primary model, and oviposition by tobacco hornworm (Manduca sexta; Lepidoptera: Sphingidae) and fall armyworm (Spodoptera frugiperda; Lepidoptera: Noctuidae), and leaf surface wax imaging as additional examples to show the effectiveness of this instrument and present a detailed methodology to produce the best results with this instrument. While traditional sample preparation can still produce better resolved images with less distortion, we show that even at a higher magnification, the desktop SEM can deliver quality images. Overall, this study provides detailed methodology with a simpler “no sample preparation” technique for scanning fresh biological samples without the use of any additional chemicals and machinery.
Why it matches plant phenotyping methods植物表面形態（葉毛・ワックス等）の取得を目的に、デスクトップSEMの簡便な試料調製・撮像法を開発し、実例で有効性を検証しているため。
abstractusing a desktop version of SEM (SNE- 4500 Plus Tabletop), we show that the “plug and play” method can efficiently produce SEM images with sufficient details for most morphological studies in plant–insect interactions.
Plant phenotyping relevance match · UnverifiedCrossref · Europe PMC · checked 14 Sept 2026
In recent years, the plant morphology has been well studied by multiple approaches at cellular and subcellular levels. Two-dimensional (2D) microscopy techniques offer imaging of plant structures on a wide range of magnifications for researchers. However, subcellular imaging is still challenging in plant tissues like roots and seeds. Here we use a three-dimensional (3D) imaging technology based on the X-ray microscope (XRM) and analyze several plant tissues from different plant species. The XRM provides new insights into plant structures using non-destructive imaging at high-resolution and high contrast. We also utilized a workflow aiming to acquire accurate and high-quality images in the context of the whole specimen. Multiple plant samples including rice, tobacco, Arabidopsis and maize were used to display the differences of phenotypes. Our work indicates that the XRM is a powerful tool to investigate plant microstructure in high-resolution scale. Our work also provides evidence that evaluate and quantify tissue specific differences for a range of plant species. We also characterize novel plant tissue phenotypes by the XRM, such as seeds in Arabidopsis, and utilize them for novel observation measurement. Our work represents an evaluated spatial and temporal resolution solution on seed observation and screening.
Why it matches plant phenotyping methodsX線顕微鏡による非破壊3D画像取得とワークフローを用いて植物組織の表現型を定量・比較し、種子観察とスクリーニングへ応用しており、フェノタイピング手法が中心である。
abstractThe XRM provides new insights into plant structures using non-destructive imaging at high-resolution and high contrast.
Modern plant phenotyping requires tools that are robust to noise and missing data, while being able to efficiently process large numbers of plants. Here, we studied the skeletonization of plant architectures from 3D point clouds, which is critical for many downstream tasks, including analyses of plant shape, morphology, and branching angles. Specifically, we developed an algorithm to improve skeletonization at branch points (forks) by leveraging the geometric properties of cylinders around branch points. We tested this algorithm on a diverse set of high-resolution 3D point clouds of tomato and tobacco plants, grown in five environments and across multiple developmental timepoints. Compared to existing methods for 3D skeletonization, our method efficiently and more accurately estimated branching angles even in areas with noisy, missing, or non-uniformly sampled data. Our method is also applicable to inorganic datasets, such as scans of industrial pipes or urban scenes containing networks of complex cylindrical shapes.
Why it matches plant phenotyping methods植物の3D点群から分枝構造を骨格化し、分枝角度を推定するアルゴリズムを開発・比較評価しており、植物表現型の抽出手法が研究の中心です。
abstractHere, we studied the skeletonization of plant architectures from 3D point clouds
Reproduction assets foundThe paper's Data Availability Statement explicitly states that data and code executable are publicly available at the authors' GitHub repository iziamtso/P3D, which is an allowed URL. This covers the paper-specific plant point cloud data and skeletonization analysis code.Code · publicData Availability Statement: Data and code executable are available at: https://github.com/iziamtso/P3D.Open asset ↗iziamtso/P3Dpdf-page:14 lines:1-59Plant phenotyping relevance match · UnverifiedCrossref · Europe PMC · checked 15 Sept 2026
Summary Soil fungi establish mutualistic interactions with the roots of most vascular land plants. Arbuscular mycorrhizal (AM) fungi are among the most extensively characterised mycobionts to date. Current approaches to quantifying the extent of root colonisation and the abundance of hyphal structures in mutant roots rely on staining and human scoring involving simple yet repetitive tasks which are prone to variation between experimenters. We developed Automatic Mycorrhiza Finder (AMFinder) which allows for automatic computer vision‐based identification and quantification of AM fungal colonisation and intraradical hyphal structures on ink‐stained root images using convolutional neural networks. AMFinder delivered high‐confidence predictions on image datasets of roots of multiple plant hosts ( Nicotiana benthamiana , Medicago truncatula , Lotus japonicus , Oryza sativa ) and captured the altered colonisation in ram1‐1 , str , and smax1 mutants. A streamlined protocol for sample preparation and imaging allowed us to quantify mycobionts from the genera Rhizophagus , Claroideoglomus , Rhizoglomus and Funneliformis via flatbed scanning or digital microscopy, including dynamic increases in colonisation in whole root systems over time. AMFinder adapts to a wide array of experimental conditions. It enables accurate, reproducible analyses of plant root systems and will support better documentation of AM fungal colonisation analyses. AMFinder can be accessed at https://github.com/SchornacklabSLCU/amfinder .
Why it matches plant phenotyping methods根の画像から菌根菌の定着状態と菌糸構造を自動定量する画像解析手法・ソフトウェアを開発しており、植物状態の取得と定量が研究の中心です。
abstractWe developed Automatic Mycorrhiza Finder (AMFinder) which allows for automatic computer vision‐based identification and quantification of AM fungal colonisation and intraradical hyphal structures on ink‐stained root images using convolutional neural networks.
Ethylene (ET) is an important gaseous plant hormone. It is highly desirable to develop fluorescent probes for monitoring ethylene in living cells. We report an efficient Rh III -catalysed coupling of N-phenoxyacetamides to ethylene in the presence of an alcohol. The newly discovered coupling reaction exhibited a wide scope of N-phenoxyacetamides and excellent regioselectivity. We successfully developed three fluorophore-tagged Rh III -based fluorogenic coumarin-ethylene probes (CEPs) using this strategy for the selective and quantitative detection of ethylene. CEP-1 exhibited the highest sensitivity with a limit of detection of ethylene at 52 ppb in air. Furthermore, CEP-1 was successfully applied for imaging in living CHO-K1 cells and for monitoring endogenous-induced changes in ethylene biosynthesis in tobacco and Arabidopsis thaliana plants. These results indicate that CEP-1 has great potential to illuminate the spatiotemporal regulation of ethylene biosynthesis and ethylene signal transduction in living biological systems.
Why it matches plant phenotyping methods植物内エチレンを選択的・定量的に検出し、植物体内での内生変化を可視化・モニタリングする蛍光プローブを開発した研究であり、植物の生理状態取得法が中心である。
abstractWe successfully developed three fluorophore-tagged Rh III -based fluorogenic coumarin-ethylene probes (CEPs) using this strategy for the selective and quantitative detection of ethylene.
Agroinfiltration in Nicotiana benthamiana is widely used to transiently express heterologous proteins in plants. However, the state of Agrobacterium itself is not well studied in agroinfiltrated tissues, despite frequent studies of immunity genes conducted through agroinfiltration. Here, we generated a bioluminescent strain of Agrobacterium tumefaciens GV3101 to monitor the luminescence of Agrobacterium during agroinfiltration. By integrating a single copy of the lux operon into the genome, we generated a stable 'AgroLux' strain, which is bioluminescent without affecting Agrobacterium growth in vitro and in planta. To illustrate its versatility, we used AgroLux to demonstrate that high light intensity post infiltration suppresses both Agrobacterium luminescence and protein expression. We also discovered that AgroLux can detect Avr/Cf-induced immune responses before tissue collapse, establishing a robust and rapid quantitative assay for the hypersensitive response (HR). Thus, AgroLux provides a non-destructive, versatile and easy-to-use imaging tool to monitor both Agrobacterium and plant responses.
Why it matches plant phenotyping methodsAgroLuxという生物発光イメージング法を開発し、植物の免疫応答・過敏感反応を非破壊かつ定量的に測定することが中心である。
abstractwe generated a bioluminescent strain of Agrobacterium tumefaciens GV3101 to monitor the luminescence of Agrobacterium during agroinfiltration.
Assessing immune responses and cell death in Nicotiana benthamiana leaf agro-infiltration assays is a powerful and widely used experimental approach in molecular plant pathology. Here, we describe a reliable high-throughput protocol to quantify strong, macroscopically visible cell death responses in N. benthamiana agro-infiltration assays. The method relies on measuring the reduction of leaf autofluorescence in the red spectrum upon cell death induction and provides quantitative data suitable for straightforward statistical analysis. Two different well-established model nucleotide-binding and leucine-rich repeat domain proteins (NLRs) were used to ensure the genericity of the approach. Its accuracy and versatility were compared to visual scoring of the cell death response and standard methods commonly used to characterize NLR activities in N. benthamiana. A discussion of the advantages and limitations of our method compared to other protocols demonstrates its robustness and versatility and provides an effective means to select the best-suited protocol for a defined experiment.
Why it matches plant phenotyping methods植物葉の細胞死という状態を蛍光画像から定量する高スループット手法を開発し、既存法と比較検証しており、植物フェノタイピング手法が中心である。
abstractwe describe a reliable high-throughput protocol to quantify strong, macroscopically visible cell death responses in N. benthamiana agro-infiltration assays.
Soil fungi establish mutualistic interactions with the roots of most vascular land plants. Arbuscular mycorrhizal (AM) fungi are among the most extensively characterised mycobionts to date. Current approaches to quantifying the extent of root colonisation and the abundance of hyphal structures in mutant roots rely on staining and human scoring involving simple, yet repetitive tasks prone to variations between experimenters. We developed AMFinder which allows for automatic computer vision-based identification and quantification of AM fungal colonisation and intraradical hyphal structures on ink-stained root images using convolutional neural networks. AMFinder delivered high-confidence predictions on image datasets of roots of multiple plant hosts (Nicotiana benthamiana, Medicago truncatula, Lotus japonicus, Oryza sativa) and captured the altered colonisation in ram1-1, str and smax1 mutants. A streamlined protocol for sample preparation and imaging allowed us to quantify mycobionts from the genera Rhizophagus, Claroideoglomus, Rhizoglomus and Funneliformis via flatbed scanning or digital microscopy including dynamic increases in colonisation in whole root systems over time. AMFinder adapts to a wide array of experimental conditions. It enables accurate, reproducible analyses of plant root systems and will support better documentation of AM fungal colonisation analyses. AMFinder can be accessed here: https://github.com/SchornacklabSLCU/amfinder.git
Why it matches plant phenotyping methods植物根の菌根菌感染状態を画像から自動識別・定量する手法とソフトウェアを開発しており、表現型取得・抽出が研究の中心です。
abstractWe developed AMFinder which allows for automatic computer vision-based identification and quantification of AM fungal colonisation and intraradical hyphal structures on ink-stained root images using convolutional neural networks.
Reproduction assets foundThe paper's AMFinder analysis software (amf/amfbrowser) and pre-trained CNN models are publicly available on the authors' GitHub repository under the MIT license. The training image datasets are not public and must be requested from the authors.Code · publicmanuscript. All authors
have read and approved the manuscript.
Data Availability
AMFinder is released under the terms of the open-source MIT license
(https://opensource.org/licenses/MIT) allowing unrestricted usage. Source code, pre-trained models
and detailed installation instructions are available on AMFinder GitHub webpage
(https://github.com/SchornacklabSLCU/amfinder.git). Training datasets are available upon request.
References
Abadi M, Agarwal A, Barham P, Brevdo E, Chen Z, Citro C, Corrado GS, Davis A, Dean J,
Devin M, et al. 2016. TensorFlow: Large-Scale Machine Learning on Heterogeneous Distributed
Systems. arXiv.
Bally J, Jung H, Mortimer C, Naim F, Philips JG, Hellens R, BombarelyOpen asset ↗SchornacklabSLCU/amfinderpdf-raw-page:21 lines:1-72Plant phenotyping relevance match · UnverifiedEurope PMC · OpenAlex · bioRxiv · checked 13 Sept 2026
ABSTRACT When dealing with plant roots, a multi-scale description of the functional root structure is needed. Since the beginning of XXI century, new devices like laser confocal microscopes have been accessible for coarse root structure measurements, including 3D reconstruction. Most researchers are familiar with using simple 2D geometry visualization that does not allow quantitatively determination of key morphological features from an organ-like perspective. We provide here a detailed description of the quantitative methods available for three-dimensional (3D) analysis of root features at single cell resolution, including root asymmetry, lateral root analysis, xylem and phloem structure, cell cycle kinetics, and chromatin determination. Quantitative maps of the distal and proximal root meristems are shown for different species, including Arabidopsis thaliana , Nicotiana tabacum and Medicago sativa . A 3D analysis of the primary root tip showed divergence in chromatin organization and cell volume distribution between cell types and precisely mapped root zonation for each cell file. Detailed protocols are also provided. Possible pitfalls in the usage of the marker lines are discussed. Therefore, researchers who need to improve their quantitative root biology portfolio can use them as a reference.
Why it matches plant phenotyping methods根の3D形態・細胞構造を定量化する方法と詳細プロトコルが中心であり、植物フェノタイピング手法として適格です。
abstractWe provide here a detailed description of the quantitative methods available for three-dimensional (3D) analysis of root features at single cell resolution
Plant phenotyping relevance match · UnverifiedbioRxiv · OpenAlex · Europe PMC · checked 15 Sept 2026
Plant root development is a complex spatial-temporal process that originates in the root apical meristem (RAM). To shape the organs structure signaling between the different cells and cell files must be highly coordinated. Thereby, diverging kinetics of chromatin remodeling and cell growth in these files need to be integrated and balanced by differential cell growth and local differences in cell proliferation frequency. Understanding the local differences in cell cycle duration in the RAM and its correlation with chromatin organization is crucial to build a holistic view on the different regulatory processes and requires a quantitative estimation of the chromatin geometry and underlying mitotic cell cycle phases timing at every cell file and every position. Unfortunately, so far precise methods for such analysis are missing. This study presents a robust and straightforward pipeline to determine in parallel the duration of cell cycles key stages in all cell layers of a plants root and their nuclei organization. The methods combine marker-free techniques based on the detection of the nucleus, deep analysis of the chromatin phase transition, incorporation of 5-ethynyl-2'-deoxyuridine (EdU), and mitosis with a deep-resolution plant phenotyping platform to analyze all key cell cycle events kinetics. In the Arabidopsis thaliana L. RAM S-phase duration was found to be as short as 20-30 minutes in all cell files. The subsequent G2-phase duration however depends on the cell type/position and varies from 3.5 hours in the pericycle to more than 4.5 hours in the epidermis. Overall, S+G2+M duration in Arabidopsis under our condition is 4 hours in the pericycle and up to 5.5 hours in the epidermis. Endocycle duration was determined as the time required to achieve 100% EdU index in the transition zone and estimated to be in the range of 3-4 hours. Besides Arabidopsis, we show that the presented technique is applicable also to root tips of other dicot and monocot plants (tobacco (Nicotiana tabacum L.), tomato (Lycopersicon esculentum L.) and wheat (Triticum aestivum L.).
Why it matches plant phenotyping methods植物根の細胞周期と核・クロマチン状態を定量取得するパイプラインおよび高解像度フェノタイピング基盤が研究の中心であるため。
abstractThis study presents a robust and straightforward pipeline to determine in parallel the duration of cell cycles key stages in all cell layers of a plants root and their nuclei organization.
A breeder’s panacea would be to manipulate plant traits to their “gusto e piacere” (taste and pleasure). Tolerance to drought and salinity, resistance to pests and pathogens, selective resistance to herbicides, increased yield through timed flowering, and better nutritional or productive traits are some of the attributes crop breeders continuously aim to improve. The development of recent genome editing tools, most notable CRISPR/Cas9, promises faster and less expensive crop breeding. Many traits, including plant height and flowering time, are controlled by complex, multiloci genetic programs with both activation and repression mechanisms to tune plant development (Eshed and Lippman, 2019). Thus, there is a need for tools that allow rapid evaluation of spatial and temporal phenotypic outcomes resulting from gene expression level reprogramming. In this issue of Plant Physiology, Khakhar et al. (2021) report a system called VipariNama (ViN) that accelerates phenotypic assessment of genetic changes by minimizing the use of stable transgenic plants. The ViN system exploits the customizable DNA recognition, cutting, and scaffolding properties of Cas9 and single-guide RNAs (sgRNAs), together with the ability of RNA viral vectors, to quickly spread in the plant and persist for extended periods of time. ViN seeks to alter plant attributes by subtly manipulating gene expression levels of transcription factors. As a proof of concept, Khakhar and colleagues demonstrated altered growth and leaf-pigmentation effects in Nicotiana benthamiana, Arabidopsis (Arabidopsis thaliana), and tomato (Solanum lycopersicum) plants. The authors carried out a series of experiments to demonstrate the capabilities of the ViN 1.0 system. They used transgenic N. benthamiana plants constitutively expressing a gibberellin biosensor consisting of inactive Cas9 (dCas9) fused to a gibberellin (GA) degron (a targeting sequence for degradation in the presence of GA) and to a repressor domain (Khakhar et al., 2018). ViN vectors carrying sgRNAs designed to bind to the upstream regulatory region of five putative GA 20-oxidase (GA20ox) gibberellin biosynthesis genes (Spielmeyer et al., 2002) were delivered via Agrobacterium infiltration of young leaves of the transgenic N. benthamiana. The delivery of the customized ViN vectors into the plant was sufficient to trigger the recruitment of the biosensor to the upstream regulatory region of at least two GA20ox genes and repress their transcription, lowering GA levels and resulting in smaller leaves. ViN 1.0 was further tested using Arabidopsis plants with constitutive expression of an active Cas9 fused to an RNA-binding protein and a regulatory domain. These plants were infiltrated with a sgRNA to direct the regulatory activity of Cas9 to repress the expression of the three GIBBERELLIN-INSENSITIVE DWARF 1 genes (GID1a, GID1b, and GID1c) that encode GA receptors while activating the expression of PAP1, a positive regulator of anthocyanin accumulation in the plant (Griffiths et al., 2006). The transgenic plants were consequently smaller and contained more anthocyanin in their leaf tissues. With the idea of increasing the flexibility of the ViN system, the authors designed ViN 2.0. In this second system, only Cas9 protein (either active or inactive) needs to be stably transformed in the plant with all other components delivered by ViN vectors; at least one of these ViN vectors must contain the sgRNA and another must contain an RNA-binding protein fused to an activator or repressor regulatory domain. This strategy allows phenotypes to be easily tuned with a mix and match of protein effectors and sgRNAs. For these synthetic transcription factors to be active, the vectors containing the sgRNAs and the effector must colocalize within the same cell. To ensure colocalization, Khakhar and collaborators also introduced a short tRNA-like motif derived from the Arabidopsis FLOWERING LOCUST T known to improve RNA systemic mobility in the plant (Li et al., 2009). ViN 2.0 was tested in several settings demonstrating its ability to allow easy swapping of effectors and targets and to regulate multiple genes. In addition, the system was tested for its systemic mobility by measuring temporal and spatial gene expression levels and vector abundance. As a final proof of concept, and to move beyond model plants into crops, the ViN 2.0 system was tested in tomato. Tomato plants with Cas9 integrated in the genome were Agro-infiltrated with ViN vectors carrying a functional N-terminal fragment of the TOPLESS transcriptional repressor from Arabidopsis and sgRNAs targeting the promoter region of the gene coding for PROCERA, the single DELLA protein in tomato. The delivery of the ViN vectors resulted in a reduction of the DELLA protein in the plant and concomitant increase in internode length. Generating improved crop cultivars can take several years. Modern technologies derived from next-generation sequencing, accelerated breeding strategies (Watson et al., 2018), and precise genome engineering, such as CRISPR/Cas9, have the capability of accelerating breeding programs. However, phenotyping, a critical step in crop improvement, is still slow since it is affected by both genotype and environmental variables. The viral-vector-based systems developed by Khakhar et al. demonstrate possibilities to complement others derived from the synthetic CRISPR-based transcriptional regulators toolbox (Zalatan et al., 2015). The effectiveness of infiltrating viral-derived vectors comes from their ease of introduction (by leaf infiltration) combined with their capacity of systemic spread in the plant (Figure 1). Although the ViN systems still depend on a transgenic plant expressing different versions of Cas9, and/or assemblies of effectors and repressors, they represent a step towards the availability of a fully customizable tool for fast phenotypic evaluation. Strategies that do not depend on integration of Cas9 into the plant genome would introduce possibilities for a new generation of tools to study and engineer crops. ViN offers a tool to accelerate plant phenotyping. The ViN vector system was tested to induce rapid effects on plant growth and metabolism. This graph compares the pipeline of traditional transgenesis (top), ViN 1.0 (middle), where ViN vectors deliver sgRNAs to a transgenic plant expressing Cas9‐based transcription factors, and ViN 2.0 (bottom), using multiple ViN vectors to deliver sgRNA scaffolds and an RNA binding effector to a Cas9-expressing plant. RBP, RNA-binding protein; AD, activating domain; RD, repressing domain; TRV, Tobacco Rattle Virus (Figure from Khakhar et al., 2021). ViN offers a tool to accelerate plant phenotyping. The ViN vector system was tested to induce rapid effects on plant growth and metabolism. This graph compares the pipeline of traditional transgenesis (top), ViN 1.0 (middle), where ViN vectors deliver sgRNAs to a transgenic plant expressing Cas9‐based transcription factors, and ViN 2.0 (bottom), using multiple ViN vectors to deliver sgRNA scaffolds and an RNA binding effector to a Cas9-expressing plant. RBP, RNA-binding protein; AD, activating domain; RD, repressing domain; TRV, Tobacco Rattle Virus (Figure from Khakhar et al., 2021).
Why it matches plant phenotyping methodsCRISPRとウイルスベクターを組み合わせ、遺伝子発現変化による植物形質を迅速に評価するViNシステムを開発・実証しており、表現型取得・評価のための技術が中心である。
abstractreport a system called VipariNama (ViN) that accelerates phenotypic assessment of genetic changes
Extracellular ATP (eATP) is an important biological signal transduction molecule. Although a variety of detection methods have been extensively used in ATP sensing and analysis, accurate detection of eATP remains difficult due to its extremely low concentration and spatiotemporal distribution. Here, an eATP measurement strategy based on tetrahedral DNA (T-DNA)-modified electrode sensing platform and hybridization chain reaction (HCR) combined with G-quadruplex/Hemin (G4/Hemin) DNAzyme dual signal amplification is proposed. In this strategy, ATP aptamer and RNA-cleaving DNAzyme were combined to form a split aptazyme. In the presence of ATP, this aptazyme hydrolyzes the cleaving substrate strand with high selectivity, releasing cleaved ssDNA, which are captured by the T-DNA assembled on the electrode surface, triggering an HCR on the electrode surface to form numerous linker sequences of the HCR dsDNA product. When G-quadruplex@AuNPs (G4) spherical nucleic acid enzymes (SNAzymes) with other linkers are used as nanocatalyst tags, they are captured by HCR dsDNA through sticky linkers present on the electrode surface. An amplified electrochemical redox current signal is generated through SNAzyme-mediated catalysis of H 2 O 2 , enabling easy detection of picomole amounts of ATP. Using this strategy, eATP levels released by tobacco suspension cells were accurately measured and the distribution and concentration of eATP released on the surface of an Arabidopsis leaf was determined.
Why it matches plant phenotyping methods植物の細胞外ATPという生理状態を測定する電気化学的センシング手法の開発が中心で、植物細胞および葉での測定にも適用している。
abstractHere, an eATP measurement strategy based on tetrahedral DNA (T-DNA)-modified electrode sensing platform and hybridization chain reaction (HCR) combined with G-quadruplex/Hemin (G4/Hemin) DNAzyme dual signal amplification is proposed.
Plateauing yield and stressful environmental conditions necessitate selecting crops for superior physiological traits with untapped potential to enhance crop performance. Plant productivity is often limited by carbon fixation rates that could be improved by increasing maximum photosynthetic carboxylation capacity (Vcmax). However, Vcmax measurements using gas exchange and biochemical assays are slow and laborious, prohibiting selection in breeding programs. Rapid hyperspectral reflectance measurements show potential for predicting Vcmax using regression models. While several hyperspectral models have been developed, contributions from different spectral regions to predictions of Vcmax have not been clearly identified or linked to biochemical variation contributing to Vcmax. In this study, hyperspectral reflectance data from 350-2500 nm were used to build partial least squares regression models predicting in vivo and in vitro Vcmax. Wild-type and transgenic tobacco plants with antisense reductions in Rubisco content were used to alter Vcmax independent from chlorophyll, carbon, and nitrogen content. Different spectral regions were used to independently build partial least squares regression models and identify key regions linked to Vcmax and other leaf traits. The greatest Vcmax prediction accuracy used a portion of the shortwave infrared region from 2070 nm to 2470 nm, where the inclusion of fewer spectral regions resulted in more accurate models.
Why it matches plant phenotyping methods短波赤外ハイパースペクトル反射からVcmaxという植物生理形質を予測する回帰モデルを開発・比較しており、形質取得法とモデル精度評価が研究の中心である。
abstractRapid hyperspectral reflectance measurements show potential for predicting Vcmax using regression models.
The plant cell mechanics, including turgor pressure and wall mechanical properties, not only determine the growth of plant cells, but also reflect the functional and structural changes of plant cells under biotic and abiotic stresses. However, there are currently no appropriate techniques allowing to monitor the complex mechanical properties of living plant cells non-invasively and continuously. In this work, quartz crystal microbalance with dissipation (QCM-D) monitoring technique with overtones (3-9) was used for the dynamic monitoring of adhesions of living tobacco BY-2 cells onto positively charged N,N-dimethyl-N-propenyl-2-propen-1-aminiumchloride homopolymer (PDADMAC)/SiO 2 QCM crystals under different concentrations of mannitol (C M ) and the subsequent effects of osmotic stresses. The cell viscoelastic index (CVI n ) (CVI n = ΔD⋅n/ΔF) was used to characterize the viscoelastic properties of BY-2 cells under different osmotic conditions. Our results indicated that lower overtones of QCM could detect both the cell wall and cytoskeleton structures allowing the detection of plasmolysis phenomena; whereas higher overtones could only detect the cell wall's mechanical properties. The QCM results were further discussed with the morphological changes of the BY-2 cells by an optical microscopy. The dynamic changes of cell's generated forces or cellular structures of plant cells caused by external stimuli (or stresses) can be traced by non-destructive and dynamic monitoring of cells' viscoelasticity, which provides a new way for the characterization and study of plant cells. QCM-D could map viscoelastic properties of different cellular structures in living cells and could be used as a new tool to test the mechanical properties of plant cells.
Why it matches plant phenotyping methodsQCM-Dによる生きた植物細胞の粘弾性・機械特性を非侵襲的かつ動的に測定する手法の開発・実証が中心であり、浸透圧ストレス下の細胞状態を表現型として評価している。
abstractthere are currently no appropriate techniques allowing to monitor the complex mechanical properties of living plant cells non-invasively and continuously.
Abstract To understand how the body of plants is made, it is essential to observe the morphology, structure and arrangement of constituent cells. However, the opaque nature of the plant body makes it difficult to observe the internal structures directly under a microscope. To overcome this problem, we developed a reagent, ClearSee, that makes plants transparent, allowing direct observation of the inside of a plant body without inflicting damage on it, e.g. through physical cutting. However, because ClearSee is not effective in making some plant species and tissues transparent, in this study, we further improved its composition to prevent oxidation, and have developed ClearSeeAlpha, which can be applied to a broader range of plant species and tissues. Sodium sulfite, one of the reductants, prevented brown pigmentation due to oxidation during clearing treatment. Using ClearSeeAlpha, we show that it is possible to obtain clear chrysanthemum leaves, tobacco and Torenia pistils and fertilized Arabidopsis thaliana fruits—tissues that have hitherto been challenging to clear. Moreover, we show that the fluorescence intensity of purified fluorescent proteins emitting light of various colors was unaffected in the ClearSeeAlpha solution; only the fluorescence intensity of TagRFP was reduced by about half. ClearSeeAlpha should be useful in the discovery and analysis of biological phenomena occurring deep inside the plant tissues.
Why it matches plant phenotyping methods植物組織内部の形態・構造を可視化するための光学クリアリング試薬を改良・開発した研究であり、植物画像取得法が中心的です。
abstractwe further improved its composition to prevent oxidation, and have developed ClearSeeAlpha, which can be applied to a broader range of plant species and tissues.
Large datasets' availability is catalyzing a rapid expansion of deep learning in general and computer vision in particular. At the same time, in many domains, a sufficient amount of training data is lacking, which may become an obstacle to the practical application of computer vision techniques. This paper challenges small and imbalanced datasets based on the example of a plant phenomics domain. We introduce an image augmentation framework, which enables us to extremely enlarge the number of training samples while providing the data for such tasks as object detection, semantic segmentation, instance segmentation, object counting, image denoising, and classification. We prove that our augmentation method increases model performance when only a few training samples are available. In our experiment, we use the DeepLabV3 model on semantic segmentation tasks with Arabidopsis and Nicotiana tabacum image dataset. The obtained result shows a 9% relative increase in model performance compared to the basic image augmentation techniques.
Why it matches plant phenotyping methods植物フェノミクス画像を対象に、少数データでのセグメンテーション等を改善する画像拡張フレームワークを開発・評価しており、植物表現型取得・抽出の計算手法が中心である。
abstractThis paper challenges small and imbalanced datasets based on the example of a plant phenomics domain.
Plasmodesmata are unique channel structures in plants that link the fluid cytoplasm between adjacent cells. Plants have evolved these microchannels to allow trafficking of nutritious substances as well as signaling molecules for intercellular communication. However, tracking the behavior of plasmodesmata in real time is difficult because they are located inside tissues. Hence, we developed a microfluidic device that traps cultured cells and fixes their positions to allow testing of plasmodesmata permeability. The device has 112 tandemly aligned trap zones in the flow channel. Cells of the tobacco line BY-2 were cultured for 7 days and filtered using a sieve and a cell strainer before use to isolate short cell clusters consisting of only a few cells. The isolated cells were introduced into the flow channel, resulting in entrapment of cell clusters at 25 out of 112 trap zones (22.3%). Plasmodesmata permeability was tested from 1 to 4 days after trapping the cells. During this period, the cell numbers increased through cell division. Fluorescence recovery after photobleaching experiments using a transgenic marker line expressing nuclear-localized H2B-GFP demonstrated that cell-to-cell movement of H2B-GFP protein occurred within 200 min of photobleaching. The transport of H2B-GFP protein was not observed when sodium chloride, a compound known to cause plasmodesmata closure, was present in the microfluid channel. Thus, this microfluidic device and one-dimensional plant cell samples allowed us to observe plasmodesmata behavior in real time under controllable conditions.
Why it matches plant phenotyping methods植物細胞のプラズモデスマ透過性をリアルタイム測定するマイクロ流体デバイスを開発し、蛍光回復実験で検証しており、植物状態の取得手法が中心である。
abstractHence, we developed a microfluidic device that traps cultured cells and fixes their positions to allow testing of plasmodesmata permeability.
Plant phenotyping relevance match · UnverifiedCrossref · Europe PMC · checked 15 Sept 2026
Published1 Jan 2021Spectrochimica Acta Part A: Molecular and Biomolecular SpectroscopyCited by 73 · OpenAlex ↗
Accurate detection of heavy metal stress on the growth status of plants is of great concern for agricultural production and management, food security, and ecological environment. A proximal hyperspectral imaging (HSI) system covered the visible/near-infrared (Vis/NIR) region of 400-1000 nm coupled with machine learning methods were employed to discriminate the tobacco plants stressed by different concentration of heavy metal Hg. After acquiring hyperspectral images of tobacco plants stressed by heavy metal Hg with concentration solutions of 0 mg·L -1 (non-stressed groups), 1, 3, and 5 mg·L -1 (3 stressed groups), regions of interest (ROIs) of canopy in tobacco plants were identified for spectra processing. Meanwhile, tobacco plant's appearance and microstructure of mesophyll tissue in tobacco leaves were analyzed. After that, clustering effects of the non-stressed and stressed groups were revealed by score plots and score images calculated by principal component analysis (PCA). Then, loadings of PCA and competitive adaptive reweighted sampling (CARS) algorithm were employed to pick effective wavelengths (EWs) for discriminating non-stressed and stressed samples. Partial least squares discriminant analysis (PLS-DA) and least-squares support vector machine (LS-SVM) were utilized to estimate the stressed tobacco plants status with different concentrations Hg solutions. The performances of those models were evaluated using confusion matrixes (CMes) and receiver operating characteristics (ROC) curves. Results demonstrated that PLS-DA models failed to offer relatively good result, and this algorithm was abandoned to classify the stressed and non-stressed groups of tobacco plants. Compared to LS-SVM model based on full spectra (FS-LS-SVM), the LS-SVM model established EWs selected by CARS (CARS-LS-SVM) carried 13 variables provided an accuracy of 100%, which was promising to achieve the qualitative discrimination of the non-stressed and stressed tobacco plants. Meanwhile, for revealing the discrepancy between 3 stressed groups of tobacco plants, the other FS-LS-SVM, PCA-LS-SVM, and CARS-LS-SVM models were setup and offered relatively low accuracies of 55.56%, 51.11% and 66.67%, respectively. Performance of those 3 LS-SVM discriminative models was also poorly performing to differentiate 3 stressed groups of tobacco plants, which might be caused by low concentration of heavy metal and similar canopy (especially in fresh leaves) of plant. The achievements of the research indicated that HSI coupled with machine learning methods had a powerful potential to discriminate tobacco plant stressed by heavy metal Hg.
Why it matches plant phenotyping methodsタバコ植物のHgストレス状態を、近接ハイパースペクトル画像と機械学習で識別する方法が研究の中心であり、植物状態の取得・推定を技術的に評価している。
abstractA proximal hyperspectral imaging (HSI) system covered the visible/near-infrared (Vis/NIR) region of 400-1000 nm coupled with machine learning methods were employed to discriminate the tobacco plants stressed by different concentration of heavy metal Hg.
Reverse genetics approaches have revolutionized plant biology and agriculture. Phenomics has the prospect of bridging plant phenotypes with genes, including transgenes, to transform agricultural fields. Genetically encoded fluorescent proteins (FPs) have revolutionized plant biology paradigms in gene expression, protein trafficking and plant physiology. While the first instance of plant canopy imaging of green fluorescent protein (GFP) was performed over 25 years ago, modern phenomics has largely ignored fluorescence as a transgene expression device despite the burgeoning FP colour palette available to plant biologists. Here, we show a new platform for stand-off imaging of plant canopies expressing a wide variety of FP genes. The platform-the fluorescence-inducing laser projector (FILP)-uses an ultra-low-noise camera to image a scene illuminated by compact diode lasers of various colours, coupled with emission filters to resolve individual FPs, to phenotype transgenic plants expressing FP genes. Each of the 20 FPs screened in plants were imaged at >3 m using FILP in a laboratory-based laser range. We also show that pairs of co-expressed fluorescence proteins can be imaged in canopies. The FILP system enabled a rapid synthetic promoter screen: starting from 2000 synthetic promoters transfected into protoplasts to FILP-imaged agroinfiltrated Nicotiana benthamiana plants in a matter of weeks, which was useful to characterize a water stress-inducible synthetic promoter. FILP canopy imaging was also accomplished for stably transformed GFP potato and in a split-GFP assay, which illustrates the flexibility of the instrument for analysing fluorescence signals in plant canopies.
Why it matches plant phenotyping methods植物キャノピーの蛍光シグナルを取得して形質化する新規イメージング基盤を開発し、複数FP・遺伝子型で実証しているため、方法が中心的です。
abstractHere, we show a new platform for stand-off imaging of plant canopies expressing a wide variety of FP genes.
The responses of plants to their environment often hinge on the spatiotemporal dynamics of transcriptional regulation. While live-imaging tools have been used extensively to quantitatively capture rapid transcriptional dynamics in living animal cells, lack of implementation of these technologies in plants has limited concomitant quantitative studies. Here, we applied the PP7 and MS2 RNA-labeling technologies for the quantitative imaging of RNA polymerase II activity dynamics in single cells of living plants as they respond to experimental treatments. Using this technology, we count nascent RNA transcripts in real-time in Nicotiana benthamiana (tobacco) and Arabidopsis thaliana (Arabidopsis). Examination of heat shock reporters revealed that plant tissues respond to external signals by modulating the number of cells engaged in transcription rather than the transcription rate of active cells. This switch-like behavior, combined with cell-to-cell variability in transcription rate, results in mRNA production variability spanning three orders of magnitude. We determined that cellular heterogeneity stems mainly from the stochasticity intrinsic to individual alleles. Taken together, our results demonstrate that it is now possible to quantitatively study the dynamics of transcriptional programs in single cells of living plants.
Why it matches plant phenotyping methods植物の生細胞における転写活性を定量画像化するPP7/MS2法の植物適用と技術的実証が中心であり、単なる生物学的測定ではない。
abstractHere, we applied the PP7 and MS2 RNA-labeling technologies for the quantitative imaging of RNA polymerase II activity dynamics in single cells of living plants as they respond to experimental treatments.
Deriving crop information from remotely sensed data is an important strategy for precision agriculture. Small unmanned aerial systems (UAS) have emerged in recent years as a versatile remote sensing tool that can provide precisely-timed, fine-grained data for informing management responses to intra-field crop variability (e.g., nutrient status and pest damage). UAS sensors with high spectral resolution used to compute informative vegetation indices, however, are practically limited by high cost and data dimensionality. This research extends spectral analysis for remote crop monitoring to investigate the relationship between crop health and 3D canopy structure using low-cost UAS equipped with consumer-grade RGB cameras. We used flue-cured tobacco as a case study due to its known sensitivity to fertility variation and nutrient-specific symptomology. Fertilizer treatments were applied to induce plant health variability in a 0.5 ha field of flue-cured tobacco. Multi-view stereo images from three UAS surveys collected during crop development were processed into orthoimages used to compute a visible band spectral index and photogrammetric point clouds using Structure from Motion (SfM). Plant structural metrics were then computed from detailed high resolution canopy surface models (0.05 m resolution) interpolated from the photogrammetric point clouds. The UAS surveys were complimented by nutrient status measurements obtained from plant tissues. The relationships between foliar nitrogen (N), phosphorus (P), potassium (K), and boron (B) concentrations and the UAS-derived metrics were assessed using multiple linear regression. Symptoms of N and K deficiencies were well captured and differentiated by the structural metrics. The strongest relationship observed was between canopy shape and N foliar concentration (adj. r2 = 0.59, increasing to adj. r2 = 0.81 when combined with the spectral index). B foliar concentration was consistently better predicted by canopy structure with a maximum adj. r2 = 0.41 observed at the latest growth stage surveyed. Overall, combining information about canopy structure and spectral reflectance increased model fit for all measured nutrients compared to spectral alone. These results suggest that an important relationship exists between relative canopy shape and crop health that can be leveraged to improve the usefulness of low cost UAS for precision agriculture.
Why it matches plant phenotyping methods低コストUASのRGB画像からSfM点群・キャノピー表面モデルを生成し、キャノピー構造形質を抽出して栄養状態を推定する手法が研究の中心であり、技術的な形質推定の評価も行っている。
abstractMulti-view stereo images from three UAS surveys collected during crop development were processed into orthoimages used to compute a visible band spectral index and photogrammetric point clouds using Structure from Motion (SfM).
SunflowerTobaccoLeafStomata / guard-cell complexPhysiological trait estimationStomatal traitsWater status / transpiration
O_LIMinimum conductance (gw,min) in leaves is important for water relations in land plants. Yet, its regulation is unclear due to measurement constraints. C_LIO_LICuticle conductance to water vapor (gcw) was estimated from the difference between calculated and direct measurement of CO2 concentration in the leaf airspace (Ci) of amphi-stomatous tobacco and sunflower. We estimated gcw in a series of light and dark experiments, and partitioned gw,min into cuticle and stomatal components. Some leaves were detached to simulate severe drought through desiccation conditions where gw,min is generally determined. C_LIO_LIBetween light and dark experiments each gcw was in close agreement, and successfully corrected the discrepancies of calculations from direct measurements. In the dark, either stomatal or cuticle conductance dominated the gw,min, suggesting either of them can control the minimum water loss. In the detached leaves, gcw could not be estimated likely due to unsaturation in the leaf airspace, and gw,min was progressively underestimated. C_LIO_LIBesides cuticle, leaf water status is a potential pitfall of the standard gas exchange model. Our technique is useful to study the minimal gas exchange as well as to refine the model. C_LI
Why it matches plant phenotyping methods葉の最小コンダクタンスを分解・推定する測定技術を開発し、光・暗条件で検証しており、植物の生理形質取得が中心である。
abstractCuticle conductance to water vapor (gcw) was estimated from the difference between calculated and direct measurement of CO2 concentration in the leaf airspace (Ci)
O_LICuticle conductance (gcw) can bias calculations of intercellular CO2 concentration inside the leaf (Ci) when stomatal conductance (gsw) is small. C_LIO_LIWe examined how the light induction of photosynthesis impacts calculations by directly measuring Ci along with standard gas exchange in sunflower and tobacco leaves. C_LIO_LIWhen photosynthesis was induced from dark to saturating light (1200 mol m-2 s-1 PAR) the calculated Ci was significantly larger than measured Ci and the difference decreased as gsw increased. This difference could lead to over-estimation of rubisco deactivation by limited CO2 supply during early induction of photosynthesis. However, only small differences in Ci were observed during the induction from shade (50 mol m-2 s-1 PAR) because gsw was sufficiently large. The induction from dark also allowed robust estimations of gcw when combined with direct Ci measurements. These gcw estimates succeeded in correcting the calculation, suggesting that the cuticle was the major source of error. C_LIO_LIDespite a technical restriction to amphi-stomatous leaves, the presented technique has a potential to provide insights into the cuticle conductance on intact stomatous leaf surfaces. C_LI
Why it matches plant phenotyping methods光合成誘導中の直接Ci測定とガス交換を組み合わせ、葉面のクチクラコンダクタンスを推定・補正する技術が研究の中心であり、植物の生理形質を取得する方法として適格です。
abstractThe induction from dark also allowed robust estimations of gcw when combined with direct Ci measurements.
Development of live imaging techniques for providing information how chromatin is organized in living cells is pivotal to decipher the regulation of biological processes. Here, we demonstrate the improvement of a live imaging technique based on CRISPR/Cas9. In this approach, the sgRNA scaffold is fused to RNA aptamers including MS2 and PP7. When the dead Cas9 (dCas9) is co-expressed with chimeric sgRNA, the aptamer-binding proteins fused to fluorescent protein (MCP-FP and PCP-FP) are recruited to the targeted sequence. Compared to previous work with dCas9:GFP, we show that the quality of telomere labelling was improved in transiently transformed Nicotiana benthamiana using aptamer-based CRISPR-imaging constructs. Labelling is influenced by the copy number of aptamers and less by the promoter types. The same constructs were not applicable for labelling of repeats in stably transformed plants and roots. The constant interaction of the RNP complex with its target DNA might interfere with cellular processes. HighlightAptamer-based CRISPR imaging: an opportunity for improving live-cell imaging in plants
Why it matches plant phenotyping methods植物細胞内のテロメアを可視化するCRISPRライブイメージング手法の改良と比較検証が研究の中心であり、植物の細胞状態を画像取得する方法に該当する。
abstractDevelopment of live imaging techniques for providing information how chromatin is organized in living cells is pivotal to decipher the regulation of biological processes.
Photosynthesis is currently measured using time-laborious and/or destructive methods which slows research and breeding efforts to identify crop germplasm with higher photosynthetic capacities. We present a plot-level screening tool for quantification of photosynthetic parameters and pigment contents that utilizes hyperspectral reflectance from sunlit leaf pixels collected from a plot (~2 m×2 m) in <1 min. Using field-grown Nicotiana tabacum with genetically altered photosynthetic pathways over two growing seasons (2017 and 2018), we built predictive models for eight photosynthetic parameters and pigment traits. Using partial least squares regression (PLSR) analysis of plot-level sunlit vegetative reflectance pixels from a single visible near infra-red (VNIR) (400-900 nm) hyperspectral camera, we predict maximum carboxylation rate of Rubisco (Vc,max, R2=0.79) maximum electron transport rate in given conditions (J1800, R2=0.59), maximal light-saturated photosynthesis (Pmax, R2=0.54), chlorophyll content (R2=0.87), the Chl a/b ratio (R2=0.63), carbon content (R2=0.47), and nitrogen content (R2=0.49). Model predictions did not improve when using two cameras spanning 400-1800 nm, suggesting a robust, widely applicable and more 'cost-effective' pipeline requiring only a single VNIR camera. The analysis pipeline and methods can be used in any cropping system with modified species-specific PLSR analysis to offer a high-throughput field phenotyping screening for germplasm with improved photosynthetic performance in field trials.
Why it matches plant phenotyping methods近接ハイパースペクトル画像とPLSRにより、圃場プロットから光合成パラメータや色素形質を高速推定するスクリーニング手法を開発しており、表現型取得・抽出法が研究の中心である。
abstractWe present a plot-level screening tool for quantification of photosynthetic parameters and pigment contents that utilizes hyperspectral reflectance from sunlit leaf pixels collected from a plot (~2 m×2 m) in <1 min.
Background Reactive oxygen species (ROS) such as hydrogen peroxide and superoxide anions significantly accumulate during biotic and abiotic stress and cause oxidative damage and eventually cell death. There is accumulating evidence that ROS are also involved in regulating beneficial plant-microbe interactions, signal transduction and plant growth and development. Due to the relevance of ROS throughout the life cycle and for interaction with the multifactorial environment, the physiological phenotyping of the mechanisms controlling ROS homeostasis is of general importance. Results In this study, we have developed a robust and resource-efficient experimental platform that allows the determination of the activities of the nine key ROS scavenging enzymes from a single extraction that integrates posttranscriptional and posttranslational regulations. The assays were optimized and adapted for a semi-high throughput 96-well assay format. In a case study, we have analyzed tobacco leaves challenged by pathogen infection, drought and salt stress. The three stress factors resulted in distinct activity signatures with differential temporal dynamics. Conclusions This experimental platform proved to be suitable to determine the antioxidant enzyme activity signature in different tissues of monocotyledonous and dicotyledonous model and crop plants. The universal enzymatic extraction procedure combined with the 96-well assay format demonstrated to be a simple, fast and semi-high throughput experimental platform for the precise and robust fingerprinting of nine key antioxidant enzymatic activities in plants.
Why it matches plant phenotyping methods植物の生理表現型として抗酸化酵素活性を測定する、抽出法と96ウェル半高スループット測定プラットフォームの開発・最適化が中心であるため。
abstractwe have developed a robust and resource-efficient experimental platform that allows the determination of the activities of the nine key ROS scavenging enzymes from a single extraction
Traditional functional genetic studies in crops are time consuming, complicated and cannot be readily scaled up. The reason is that mutant or transformed crops need to be generated to study the effect of gene modifications on specific traits of interest. However, many crop species have a complex genome and a long generation time. As a result, it usually takes several months to over a year to obtain desired mutants or transgenic plants, which represents a significant bottleneck in the development of new crop varieties. To overcome this major issue, we are currently establishing a versatile plant genetic screening platform, amenable to high throughput screening in almost any crop species, with a unique workflow. This platform combines protoplast transformation and fluorescence activated cell sorting. Here we show that tobacco protoplasts can accumulate high levels of lipid if transiently transformed with genes involved in lipid biosynthesis and can be sorted based on lipid content. Hence, protoplasts can be used as a predictive tool for plant lipid engineering. Using this newly established strategy, we demonstrate the major role of ABI3 in plant lipid accumulation. We anticipate that this workflow can be applied to numerous highly valuable metabolic traits other than storage lipid accumulation. This new strategy represents a significant step toward screening complex genetic libraries, in a single experiment and in a matter of days, as opposed to years by conventional means.
Why it matches plant phenotyping methods植物の脂質蓄積という形質をFACSで測定・選別する高スループット表現型スクリーニング基盤の開発が中心であり、単なる生物学的測定ではない。
abstractwe are currently establishing a versatile plant genetic screening platform, amenable to high throughput screening in almost any crop species, with a unique workflow.
Background Image processing techniques have been widely used in the analysis of leaf characteristics. Earlier techniques for processing digital RGB color images of plant leaves had several drawbacks, such as inadequate de-noising, and adopting normal-probability statistical estimation models which have few parameters and limited applicability. Results We confirmed the skewness distribution characteristics of the red, green, blue and grayscale channels of the images of tobacco leaves. Twenty skewed-distribution parameters were computed including the mean, median, mode, skewness, and kurtosis. We used the mean parameter to establish a stepwise regression model that is similar to earlier models. Other models based on the median and the skewness parameters led to accurate RGB-based description and prediction, as well as better fitting of the SPAD value. More parameters improved the accuracy of RGB model description and prediction, and extended its application range. Indeed, the skewed-distribution parameters can describe changes of the leaf color depth and homogeneity. Conclusions The color histogram of the blade images follows a skewed distribution, whose parameters greatly enrich the RGB model and can describe changes in leaf color depth and homogeneity.
Why it matches plant phenotyping methods葉画像のRGB色ヒストグラムから葉色の深さ・均一性を推定する画像解析モデルを開発しており、植物表現型の取得・抽出が研究の中心です。
abstractImage processing techniques have been widely used in the analysis of leaf characteristics.
Background The unfolded protein response (UPR) is a highly conserved process in eukaryotic organisms that plays a crucial role in adaptation and development. While the most ubiquitous components of this pathway have been characterized, current efforts are focused on identifying and characterizing other UPR factors that play a role in specific conditions, such as developmental changes, abiotic cues, and biotic interactions. Considering the central role of protein secretion in plant pathogen interactions, there has also been a recent focus on understanding how pathogens manipulate their host's UPR to facilitate infection. Results We developed a high-throughput screening assay to identify proteins that interfere with UPR signaling in planta . A set of 35 genes from a library of secreted proteins from the maize pathogen Ustilago maydis were transiently co-expressed with a reporter construct that upregulates enhanced yellow fluorescent protein (eYFP) expression upon UPR stress in Nicotiana benthamiana plants. After UPR stress induction, leaf discs were placed in 96 well plates and eYFP expression was measured. This allowed us to identify a previously undescribed fungal protein that inhibits plant UPR signaling, which was then confirmed using the classical but more laborious qRT-PCR method. Conclusions We have established a rapid and reliable fluorescence-based method to identify heterologously expressed proteins involved in UPR stress in plants. This system can be used for initial screens with libraries of proteins and potentially other molecules to identify candidates for further validation and characterization.
Why it matches plant phenotyping methods植物体内のUPRストレス状態を蛍光レポーターで定量する高スループット測定法を開発・検証しており、表現型（生理状態）の取得が研究の中心である。
abstractThis allowed us to identify a previously undescribed fungal protein that inhibits plant UPR signaling, which was then confirmed using the classical but more laborious qRT-PCR method.
Inorganic polyphosphates (polyPs) are linear polymers of orthophosphate units linked by phosphoanhydride bonds. Polyphosphates represent important stores of phosphate and energy, and are abundant in many pro- and eukaryotic organisms. In plants, the existence of polyPs has been established using microscopy and biochemical extraction methods that are now known to produce artifacts. Here we use a polyP-specific dye and a polyP-binding domain to detect polyPs in plant and algal cells. To develop the staining protocol, we induced polyP granules in Nicotiana benthamiana and Arabidopsis cells by heterologous expression of Escherichia coli polyphosphate kinase 1 (PPK1). Over-expression of PPK1 but not of a catalytically impaired version of the enzyme leads to severe growth phenotypes, suggesting that ATP-dependent synthesis and accumulation of polyPs in the plant cytosol is toxic. We next crossed stable PPK1-expressing Arabidopsis lines with plants expressing the polyP-binding domain of E. coli exopolyphosphatase (PPX1c), which co-localized with PPK1-generated polyP granules. These granules were stained by the polyP-specific dye JC-D7 and appeared as electron-dense structures in transmission electron microscopy sections. Using the polyP staining protocol derived from these experiments, we screened for polyP stores in different organs and tissues of both mono- and dicotyledonous plants. While we could not detect polyP granules in higher plants, we could visualize the polyP-rich acidocalcisomes in the green alga Chlamydomonas reinhardtii.
Why it matches plant phenotyping methods植物細胞内のポリリン酸貯蔵を可視化・検出する染色プロトコルを開発し、遺伝学的手法と電子顕微鏡で検証しており、表現型取得法が研究の中心である。
abstractHere we use a polyP-specific dye and a polyP-binding domain to detect polyPs in plant and algal cells.
The hyperspectral imaging technique was used for the non-destructive detection of tomato spotted wilt virus (TSWV) infection in tobacco at an early stage. Spectra ranging from 400 to 1000 nm with 128 bands from inoculated and healthy tobacco plants were analyzed by using three wavelength selection methods (successive projections algorithm (SPA), boosted regression tree (BRT), and genetic algorithm (GA)), and four machine learning (ML) techniques (boosted regression tree (BRT), support vector machine (SVM), random forest (RF), and classification and regression tress (CART)). The results indicated that the models built by the BRT algorithm using the wavelengths selected by SPA as the input variables obtained the best outcome for the 10-fold cross-validation with the mean overall accuracy of 85.2% and area under receiver operating curve (AUC) of 0.932. The band selection results and variable contribution analysis in BRT modeling jointly showed that the near-infrared (NIR) spectral region is informative and important for the differentiation of infected and healthy tobacco leaves. Different stages of post-inoculation were split according to the molecular identification and visual observation. The classification results at different stages indicated that the hyperspectral imaging data combined with ML methods and wavelength selection algorithms can be used for the early detection of TSWV in tobacco, both at the presymptomatic stage and during the period before the systematic infection can be detected by the molecular identification approach.
Why it matches plant phenotyping methodsハイパースペクトル画像と機械学習により、タバコ個体のウイルス感染状態を非破壊・早期に推定する手法を開発・評価しており、植物病徴状態の取得が研究の中心である。
abstractThe hyperspectral imaging technique was used for the non-destructive detection of tomato spotted wilt virus (TSWV) infection in tobacco at an early stage.
Traditional functional genetic studies in crops are time-consuming, complicated and cannot be readily scaled up. The reason is that mutant or transformed crops need to be generated to study the effect of gene modifications on specific traits of interest. However, many crop species have a complex genome and a long generation time. As a result, it usually takes several months to over a year to obtain desired mutants or transgenic plants, which represents a significant bottleneck in the development of new crop varieties. To overcome this major issue, we are currently establishing a versatile plant genetic screening platform, amenable to high throughput screening in almost any crop species, with a unique workflow. This platform combines protoplast transformation and fluorescence-activated cell sorting. Here we show that tobacco protoplasts can accumulate high levels of lipids if transiently transformed with genes involved in lipid biosynthesis and can be sorted based on lipid content. Hence, protoplasts can be used as a predictive tool for plant lipid engineering. Using this newly established strategy, we demonstrate the major role of ABI3 in plant lipid accumulation. We anticipate that this workflow can be applied to numerous highly valuable metabolic traits other than storage lipid accumulation. This new strategy represents a significant step towards screening complex genetic libraries, in a single experiment and in a matter of days, as opposed to years by conventional means.
Why it matches plant phenotyping methods植物プロトプラストの脂質含量を蛍光活性化セルソーティングで測定・選別する高スループット表現型スクリーニング基盤の開発が中心であり、単なる生物学的測定ではない。
abstractwe are currently establishing a versatile plant genetic screening platform, amenable to high throughput screening in almost any crop species, with a unique workflow
Developing automated methods to efficiently process large volumes of point cloud data remains a challenge for three-dimensional (3D) plant phenotyping applications. Here, we describe the development of machine learning methods to tackle three primary challenges in plant phenotyping: lamina/stem classification, lamina counting, and stem skeletonization. For classification, we assessed and validated the accuracy of our methods on a dataset of 54 3D shoot architectures, representing multiple growth conditions and developmental time points for two Solanaceous species, tomato ( Solanum lycopersicum cv 75 m82D ) and Nicotiana benthamiana Using deep learning, we classified lamina versus stems with 97.8% accuracy. Critically, we also demonstrated the robustness of our method to growth conditions and species that have not been trained on, which is important in practical applications but is often untested. For lamina counting, we developed an enhanced region-growing algorithm to reduce oversegmentation; this method achieved 86.6% accuracy, outperforming prior methods developed for this problem. Finally, for stem skeletonization, we developed an enhanced tip detection technique, which ran an order of magnitude faster and generated more precise skeleton architectures than prior methods. Overall, our improvements enable higher throughput and accurate extraction of phenotypic properties from 3D point cloud data.
Why it matches plant phenotyping methods3D点群から葉・茎の分類、葉数計測、茎骨格化を行う機械学習・画像解析手法の開発と検証が中心であり、植物形態形質の抽出に直接関わる。
abstractwe describe the development of machine learning methods to tackle three primary challenges in plant phenotyping: lamina/stem classification, lamina counting, and stem skeletonization.
Recent studies have shown that computer vision techniques developed to boost the count of plant leaves brings significant improvements. In this paper, a proposal was presented for plant leaf counting using Convolutional Neural Networks (CNNs). To accomplish the training process, CNNs architectures were adapted to solve regression problems. To evaluate the proposed method, an image dataset with 810 images of three species (Arabidopsis, Tobacco and one mutation) was used. The results showed that Xception architecture obtained the best results with R2 of 0.96 and MAE (mean absolute error) of 0.46.
Why it matches plant phenotyping methodsCNNを用いた植物葉数の画像ベース推定手法を提案し、複数アーキテクチャとデータセットで性能評価しており、表現型取得・抽出法が研究の中心である。
abstractIn this paper, a proposal was presented for plant leaf counting using Convolutional Neural Networks (CNNs).
Spectroscopy is becoming an increasingly powerful tool to alleviate the challenges of traditional measurements of key plant traits at the leaf, canopy, and ecosystem scales. Spectroscopic methods often rely on statistical approaches to reduce data redundancy and enhance useful prediction of physiological traits. Given the mechanistic uncertainty of spectroscopic techniques, genetic modification of plant biochemical pathways may affect reflectance spectra causing predictive models to lose power. The objectives of this research were to assess over two separate years, whether a predictive model can represent natural and imposed variation in leaf photosynthetic potential for different crop cultivars and genetically modified plants, to assess the interannual capabilities of a partial least square regression (PLSR) model, and to determine whether leaf N is a dominant driver of photosynthesis in PLSR models. In 2016, a PLSR analysis of reflectance spectra coupled with gas exchange data was used to build predictive models for photosynthetic parameters including maximum carboxylation rate of Rubisco ( V c , max ), maximum electron transport rate ( J max ) and percentage leaf nitrogen ([N]). The model was developed for wild type and genetically modified plants that represent a wide range of photosynthetic capacities. Results show that hyperspectral reflectance accurately predicted V c ,max , J max and [N] for all plants measured in 2016. Applying these PLSR models to plants grown in 2017 resulted in a strong predictive ability relative to gas exchange measurements for V c ,max , but not for J max , and not for genotypes unique to 2017. Building a new model including data collected in 2017 resulted in more robust predictions, with R 2 increases of 17% for V c , max . and 13% J max . Plants generally have a positive correlation between leaf nitrogen and photosynthesis, however, tobacco with reduced Rubisco (SSuD) had significantly higher [N] despite much lower V c ,max . The PLSR model was able to accurately predict both lower V c , max and higher leaf [N] for this genotype suggesting that the spectral based estimates of V c , max and leaf nitrogen [N] are independent. These results suggest that the PLSR model can be applied across years, but only to genotypes used to build the model and that the actual mechanism measured with the PLSR technique is not directly related to leaf [N]. The success of the leaf-scale analysis suggests that similar approaches may be successful at the canopy and ecosystem scales but to use these methods across years and between genotypes at any scale, application of accurately populated physical based models based on radiative transfer principles may be required.
Why it matches plant phenotyping methods植物の光合成生理形質をハイパースペクトル反射とPLSRで推定する手法を開発・検証し、年次・遺伝子型間の予測性能を評価しているため、フェノタイピング手法が中心です。
titleHigh-throughput field phenotyping using hyperspectral reflectance and partial least squares regression (PLSR)
ABSTRACT Background In recent years, the plant morphology has been well studied by multiple approaches at cellular and subcellular levels. Two-dimensional (2D) microscopy techniques offer imaging of plant structures on a wide range of magnifications for researchers. However, subcellular imaging is still challenging in plant tissues like roots and seeds. Results Here we use a three-dimensional (3D) imaging technology based on the ZEISS X-ray microscope (XRM) Versa and analyze several plant tissues from different plant species. The XRM provides new insights into plant structures using non-destructive imaging at high-resolution and high contrast. We also developed a workflow aiming to acquire accurate and high-quality images in the context of the whole specimen. Multiple plant samples including rice, tobacco, Arabidopsis and maize were used to display the differences of phenotypes, which indicates that the XRM is a powerful tool to investigate plant microstructure. Conclusions Our work provides a novel observation method to evaluate and quantify tissue specific differences for a range of plant species. This new tool is suitable for non-destructive seed observation and screening.
Why it matches plant phenotyping methods植物組織・種子の微細構造をX線顕微鏡で非破壊撮像し、ワークフロー開発と組織差の定量評価・スクリーニングを行うことが中心であり、植物フェノタイピング手法に該当する。
abstractWe also developed a workflow aiming to acquire accurate and high-quality images in the context of the whole specimen.
ABSTRACT Automatically scoring plant traits using a combination of imaging and deep learning holds promise to accelerate data collection, scientific inquiry, and breeding progress. However, applications of this approach are currently held back by the availability of large and suitably annotated training datasets. Early training datasets targeted arabidopsis or tobacco. The morphology of these plants quite different from that of grass species like maize. Two sets of maize training data, one real-world and one synthetic were generated and annotated for late vegetative stage maize plants using leaf count as a model trait. Convolutional neural networks (CNNs) trained on entirely synthetic data provided predictive power for scoring leaf number in real-world images. This power was less than CNNs trained with equal numbers of real-world images, however, in some cases CNNs trained with larger numbers of synthetic images outperformed CNNs trained with smaller numbers of real-world images. When real-world training images were scarce, augmenting real-world training data with synthetic data provided improved prediction accuracy. Quantifying leaf number over time can provide insight into plant growth rates and stress responses, and can help to parameterize crop growth models. The approaches and annotated training data described here may help future efforts to develop accurate leaf counting algorithms for maize.
Why it matches plant phenotyping methodsトウモロコシの葉数という植物形質を対象に、合成・実画像データセットとCNNによる画像ベース計測手法を開発・評価しており、フェノタイピング手法が中心である。
abstractTwo sets of maize training data, one real-world and one synthetic were generated and annotated for late vegetative stage maize plants using leaf count as a model trait.
Reproduction assets foundThe paper deposits its phenotyping analysis scripts/source code in a public GitHub repository and used a public Zooniverse project to crowd-score the real-world maize leaf-count images; both are paper-specific, public, and actionable.Code · publicAdditional Information
The scripts and source code employed in this study have been deposited at https://github.com/freemao/MaizeLeafCounting.Images and annotations used in this study have been deposited with CyVerse [27].
The authors declare no competing interests.
References
1. Houle, D., Govindaraju, D. R. & Omholt, S. Phenomics: the next challenge. Nat. reviews genetics 11, 855 (2010).
2. Furbank, R. T. & Tester, M. Phenomics–technologies to relieve the phenotyping bottOpen asset ↗freemao/MaizeLeafCounting.Imagespdf-raw-page:9 lines:1-56Plant phenotyping relevance match · UnverifiedEurope PMC · checked 9 Sept 2026
The frequencies of free oscillations of plants, or plant parts, depend on their geometries, stiffnesses, and masses. Besides direct biomechanical interest, free frequencies also provide insights into plant properties that can usually only be measured destructively or with low-throughput techniques (e.g., change in mass, tissue density, or stiffness over development or with stresses). We propose here a new high-throughput method based on the noncontact measurements of the free frequencies of the standing plant. The plant is excited by short air pulses (typically 100 ms). The resulting motion is recorded by a high speed video camera (100 fps) and processed using fast space and time correlation algorithms. In less than a minute the mechanical behavior of the plant is tested over several directions. The performance and versatility of this method has been tested in three contrasted species: tobacco (Nicotiana benthamian), wheat (Triticum aestivum L.), and poplar (Populus sp.), for a total of more than 4000 data points. In tobacco we show that water stress decreased the free frequency by 15%. In wheat we could detect variations of less than 1 g in the mass of spikes. In poplar we could measure frequencies of both the whole stem and leaves. The work provides insight into new potential directions for development of phenotyping.
Why it matches plant phenotyping methods植物の自由振動を非接触・高速に測定し、質量や水ストレスなどの植物形質を推定する高スループット手法を開発・検証しており、フェノタイピング手法が研究の中心です。
abstractWe propose here a new high-throughput method based on the noncontact measurements of the free frequencies of the standing plant.
Global agriculture production is challenged by increasing demands from rising population and a changing climate, which may be alleviated through development of genetically improved crop cultivars. Research into increasing photosynthetic energy conversion efficiency has proposed many strategies to improve production but have yet to yield real-world solutions, largely because of a phenotyping bottleneck. Partial least squares regression (PLSR) is a statistical technique that is increasingly used to relate hyperspectral reflectance to key photosynthetic capacities associated with carbon uptake (maximum carboxylation rate of Rubisco, V c,max ) and conversion of light energy (maximum electron transport rate supporting RuBP regeneration, J max ) to alleviate this bottleneck. However, its performance varies significantly across different plant species, regions, and growth environments. Thus, to cope with the heterogeneous performances of PLSR, this study aims to develop a new approach to estimate photosynthetic capacities. A framework was developed that combines six machine learning algorithms, including artificial neural network (ANN), support vector machine (SVM), least absolute shrinkage and selection operator (LASSO), random forest (RF), Gaussian process (GP), and PLSR to optimize high-throughput analysis of the two photosynthetic variables. Six tobacco genotypes, including both transgenic and wild-type lines, with a range of photosynthetic capacities were used to test the framework. Leaf reflectance spectra were measured from 400 to 2500 nm using a high-spectral-resolution spectroradiometer. Corresponding photosynthesis vs. intercellular CO 2 concentration response curves were measured for each leaf using a leaf gas-exchange system. Results suggested that the mean R 2 value of the six regression techniques for predicting V c,max ( J max ) ranged from 0.60 (0.45) to 0.65 (0.56) with the mean RMSE value varying from 47.1 (40.1) to 54.0 (44.7) μmol m -2 s -1 . Regression stacking for V c,max ( J max ) performed better than the individual regression techniques with increases in R 2 of 0.1 (0.08) and decreases in RMSE by 4.1 (6.6) μmol m -2 s -1 , equal to 8% (15%) reduction in RMSE . Better predictive performance of the regression stacking is likely attributed to the varying coefficients (or weights) in the level-2 model (the LASSO model) and the diverse ability of each individual regression technique to utilize spectral information for the best modeling performance. Further refinements can be made to apply this stacked regression technique to other plant phenotypic traits.
Why it matches plant phenotyping methods高スペクトル分解能の葉反射スペクトルから光合成能力を推定する機械学習フレームワークの開発・検証が中心であり、植物の生理形質を直接推定するフェノタイピング手法に該当する。
abstractThus, to cope with the heterogeneous performances of PLSR, this study aims to develop a new approach to estimate photosynthetic capacities.
CottonMaizeRapeseed / canolaRiceTobaccoWheatLiDAR / point cloudMultispectral / hyperspectralThermalWhole plant / canopy / plot / field
Three dimensional (3D) plant reconstructions, extended to four dimensions with the use of time series and accompanied by visual modelling, is being used for a number of purposes including the estimation of biovolume and as the basis for functional structural plant modelling (FSPM). This has been successfully applied to crop species such as cotton (Paproki et al. 2012). Measuring the growth pattern and arrangement of a pasture sward is a difficult task but can be used as an indirect measure of other variables of interest, such as growth rate, light interception, nutritional quality, herbivore intake, etc. (Laca and Lemaire 2000). Digital representation of individual plants in three dimensions is one way to determine sward structure. The High Resolution Plant Phenomics Centre (HRPPC) has developed PlantScan™ which combines robotics, image analysis and computing advances, to accelerate and automate the measurement of plant growth characteristics and allow discrimination of differences between individual plants within species. Image silhouettes and LiDAR (Light Detection And Ranging) are used and combined to digitise plant architecture in three dimensions with a high level of detail. Colour information, extracted from multispectral sensors, and thermal imaging from infra-red (IR) cameras are then overlaid on these 3D plant representations, thus providing a tool to link plant structure to plant function. Successful reconstructions using data collected by PlantScan™ in controlled conditions, have been conducted for a range of grasses such as wheat (Triticum aestivum), rice (Oryza sativa), corn (Zea mays) and broadleaf species such as canola (Brassica napus), cotton (Gossypium hirsutum) and tobacco (Nicotiana tabacum). This suggests that modelling the sward structure of grass and legume pasture species should be equally achievable. This study explores the use of PlantScanTM to reconstruct 3D images of the important and common pasture legume, subterranean clover (Trifolium subterraneum) with a view to analysing their 3D structure in-silico.
Why it matches plant phenotyping methodsPlantScanを用いたロボット・画像解析・LiDARによる植物体の3D構造再構成と成長特性測定が研究の中心であり、植物表現型取得手法の実質的な適用研究である。
abstractThe High Resolution Plant Phenomics Centre (HRPPC) has developed PlantScan™ which combines robotics, image analysis and computing advances, to accelerate and automate the measurement of plant growth characteristics and allow discrimination of differences between individual plants within species.
TobaccoStomata / guard-cell complexStress / disease detectionGrowth / development / phenologyStomatal traitsStress response / toleranceWater status / transpiration
Plant growth and development are negatively affected by a wide range of external stresses, including water deficits. Especially, plants generally reduce the stomatal aperture to decrease transpiration levels upon drought stress. Advanced technologies, such as wireless communications, the Internet of things (IoT), and smart sensors have been applied to practical smart farming and indoor planting systems to monitor plants’ signals effectively. In this study, we develop a flexible polyimide (PI)-based sensor for real-time monitoring of water conditions in tobacco plants. The stoma response, by which a plant adjusts to drought stress to maintain homeostasis, can be confirmed through the examination of evaporated water. Using a flexible PI-based sensor, a plant’s response variation is translated into an electrical signal. The sensors are integrated with a Bluetooth (BLE) module and a processing module and show potential as smart real-time water sensors in smart farms.
Why it matches plant phenotyping methodsタバコの乾燥ストレスに伴う気孔応答・水分状態を電気信号として取得する柔軟センサーを開発しており、植物状態の取得法が研究の中心です。
abstractIn this study, we develop a flexible polyimide (PI)-based sensor for real-time monitoring of water conditions in tobacco plants.
SUMMARY Boron (B) is an essential micronutrient for plants. To maintain B concentration in tissues at appropriate levels, plants use boric acid channels belonging to the NIP subfamily of aquaporins and BOR borate exporters. To regulate B transport, these transporters exhibit different cell‐type specific expression, polar localization, and B‐dependent post‐transcriptional regulation. Here, we describe the development of genetically encoded biosensors for cytosolic boric acid to visualize the spatial distribution and temporal dynamics of B in plant tissues. The biosensors were designed based on the function of the NIP 5 ; 1 5′‐untranslated region ( UTR ), which promotes mRNA degradation in response to an elevated cytosolic boric acid concentration. The signal intensities of the biosensor coupled with Venus fluorescent protein and a nuclear localization signal ( uNIP 5 ; 1‐Venus ) showed negative correlation with intracellular B concentrations in cultured tobacco BY ‐2 cells. When expressed in Arabidopsis thaliana , uNIP 5 ; 1‐Venus enabled the quantification of B distribution in roots at single‐cell resolution. In mature roots, cytosolic B levels in stele were maintained under low B supply, while those in epidermal, cortical, and endodermal cells were influenced by external B concentrations. Another biosensor coupled with a luciferase protein fused to a destabilization PEST sequence ( uNIP 5 ; 1‐Luc ) was used to visualize changes in cytosolic boric acid concentrations. Thus, uNIP 5 ; 1‐Venus / Luc enables visualization of B transport in various plant cells/tissues.
Why it matches plant phenotyping methods植物細胞内ホウ酸濃度の空間分布と時間変化を可視化・定量する遺伝子コード型バイオセンサーを開発しており、植物状態の取得手法が研究の中心です。
abstractHere, we describe the development of genetically encoded biosensors for cytosolic boric acid to visualize the spatial distribution and temporal dynamics of B in plant tissues.
Volatile organic compounds (VOCs) have been proven to be important biomarkers for predicting pathogen/pest-induced diseases in plants 1 . Methyl salicylate (MeSA) has been identified as one such important biomarker for biotic stress in plants 2-4 . Advanced detection of MeSA could help in early identification of plant diseases and has a profound significance for precision agriculture industry to maintain effective use for disease precautions. Previous research has demonstrated the development of biosensor consisting salicylate hydroxylase (SH) / tyrosinase (TYR) for salicylate detection. However, the method requires high temperature hydrolysis and pH neutralization steps before detection, rendering it more complex for device miniaturization 5 . In this project, we aim to eliminate these additional steps by developing a tri-enzyme detection platform consisting of esterase (ES), SH and TYR for direct MeSA detection without additional hydrolysis or pH neutralization steps. Two different immobilization strategies were used and compared using a lab-on-chip model and the sensitivity and specificity were determined to be 3.1 ± 0.2 µA·cm -2 ·µM -1 and 0.8 ± 0.2 µM respectively. An open source computer hardware and software Arduino was used for fabricating the computer-controlled automatic collection device for VOC collection. The prototype of a portable device for MeSA detection device was designed and fabricated, and the measurement was carried with the enzymatic biosensor strip. Reference (1) Laothawornkitkul, J., et al., Discrimination of plant volatile signatures by an electronic nose: a potential technology for plant pest and disease monitoring. Environmental Science & Technology, 2008. 42 (22): p. 8433-8439. (2) Buttery, R., et al., Characterization of some volatile constituents of bell peppers. Journal of Agricultural and Food Chemistry, 1969. 17 (6): p. 1322-1327. (3) Seskar, M., V. Shulaev, and I. Raskin, Endogenous methyl salicylate in pathogen-inoculated tobacco plants. Plant Physiology, 1998. 116 (1): p. 387-392. (4) Zhu, J. and K.-C. Park, Methyl salicylate, a soybean aphid-induced plant volatile attractive to the predator Coccinella septempunctata. Journal of chemical ecology, 2005. 31 (8): p. 1733-1746. (5) Fang, Y., et al., Detection of methyl salicylate using bi-enzyme electrochemical sensor consisting salicylate hydroxylase and tyrosinase. Biosensors and Bioelectronics, 2016. 85 : p. 603-610.
Why it matches plant phenotyping methods植物の病害関連揮発性物質MeSAを直接検出する携帯型センサーと自動VOC収集装置の開発が中心であり、植物の病害状態に関連する生理形質の取得法に該当する。
abstractwe aim to eliminate these additional steps by developing a tri-enzyme detection platform consisting of esterase (ES), SH and TYR for direct MeSA detection without additional hydrolysis or pH neutralization steps.
Summary There is a need for flexible and affordable plant phenotyping solutions for basic research and plant breeding. We demonstrate our open source plant imaging and processing solution (‘PhenoBox’/‘PhenoPipe’) and provide construction plans, source code and documentation to rebuild the system. Use of the PhenoBox is exemplified by studying infection of the model grass Brachypodium distachyon by the head smut fungus Ustilago bromivora , comparing phenotypic responses of maize to infection with a solopathogenic Ustilago maydis (corn smut) strain and effector deletion strains, and studying salt stress response in Nicotiana benthamiana . In U. bromivora ‐infected grass, phenotypic differences between infected and uninfected plants were detectable weeks before qualitative head smut symptoms. Based on this, we could predict the infection outcome for individual plants with high accuracy. Using a PhenoPipe module for calculation of multi‐dimensional distances from phenotyping data, we observe a time after infection‐dependent impact of U. maydis effector deletion strains on phenotypic response in maize. The PhenoBox/PhenoPipe system is able to detect established salt stress responses in N. benthamiana . We have developed an affordable, automated, open source imaging and data processing solution that can be adapted to various phenotyping applications in plant biology and beyond.
Why it matches plant phenotyping methods植物画像取得・処理システム自体の開発、オープンソース化、再構築可能な設計とコード提供が中心であり、植物表現型の測定・解析に直接対応するため。
abstractWe demonstrate our open source plant imaging and processing solution (‘PhenoBox’/‘PhenoPipe’) and provide construction plans, source code and documentation to rebuild the system.
Reproduction assets foundThe paper explicitly states that the complete PhenoBox/PhenoPipe source code, documentation, and analysis modules (including the R code for classification and multidimensional distance calculation) are publicly available in the authors' GitHub repository under the GNU General Public Licence v.2. This is a paper-phenotCode · publicThe complete source code to run the PhenoBox and PhenoPipe, together with a detailed documentation in wiki format, can be found at https://github.com/Gregor-Mendel-Institute/PhenoBox-System .Open asset ↗Gregor-Mendel-Institute/PhenoBox-Systemlines:40-50Plant phenotyping relevance match · UnverifiedEurope PMC · checked 15 Sept 2026
Main conclusion The software FlowerMorphology is designed for automatic morphometry of actinomorphic flowers. The novel complex parameters of flowers calculated by FlowerMorphology allowed us to quantitatively characterize a polyploid series of tobacco. Morphological differences of plants representing closely related lineages or mutants are mostly quantitative. Very often, there are only very fine variations in plant morphology. Therefore, accurate and high-throughput methods are needed for their quantification. In addition, new characteristics are necessary for reliable detection of subtle changes in morphology. FlowerMorphology is an all-in-one software package to automatically image and analyze five-petal actinomorphic flowers of the dicotyledonous plants. Sixteen directly measured parameters and ten calculated complex parameters of a flower allow us to characterize variations with high accuracy. The program was developed for the needs of automatic characterization of Nicotiana tabacum flowers, but is applicable to many other plants with five-petal actinomorphic flowers and can be adopted for flowers of other merosity. A genetically similar polyploid series of N. tabacum plants was used to investigate differences in flower morphology. For the first time, we could quantify the dependence between ploidy and size and form of the tobacco flowers. We found that the radius of inner petal incisions shows a persistent positive correlation with the chromosome number. In contrast, a commonly used parameter-radius of outer corolla-does not discriminate 2n and 4n plants. Other parameters show that polyploidy leads to significant aberrations in flower symmetry and are also positively correlated with chromosome number. Executables of FlowerMorphology, source code, documentation, and examples are available at the program website: https://github.com/Deyneko/FlowerMorphology .
Why it matches plant phenotyping methods花の画像から形態形質を自動抽出・定量するソフトウェアの開発が中心であり、植物表現型計測手法として明確に該当する。
abstractThe software FlowerMorphology is designed for automatic morphometry of actinomorphic flowers.
Canopy photosynthesis (A c ), rather than leaf photosynthesis, is critical to gaining higher biomass production in the field because the daily or seasonal integrals of A c correlate with the daily or seasonal integrals of biomass production. The canopy photosynthesis and transpiration measurement system (CAPTS) was developed to enable measurement of canopy photosynthetic CO 2 uptake, transpiration, and respiration rates. CAPTS continuously records the CO 2 concentration, water vapor concentration, air temperature, air pressure, air relative humidity, and photosynthetic photon flux density (PPFD) inside the chamber, which can be used to derive CO 2 and H 2 O fluxes of a canopy covered by the chamber. Here we describe the protocol of using CAPTS to perform experiments on rice (Oryza sativa L.) in paddy field, wheat (Triticum aestivum L.) in upland field, and tobacco (Nicotiana tabacum L.) in pots.
Why it matches plant phenotyping methodsCAPTSは作物キャノピーの光合成、蒸散、呼吸を定量する測定システムであり、植物生理形質の取得プロトコル自体が論文の中心である。
abstractThe canopy photosynthesis and transpiration measurement system (CAPTS) was developed to enable measurement of canopy photosynthetic CO 2 uptake, transpiration, and respiration rates.
Analysis of cellular ultrastructure has been dominated by transmission electron microscopy (TEM), so images collected by this technique have shaped our current understanding of cellular structure. More recently, three-dimensional (3D) analysis of organelle structures has typically been conducted using TEM tomography. However, TEM tomography application is limited by sample thickness. Focused ion beam-scanning electron microscopy (FIB-SEM) uses a dual beam system to perform serial sectioning and imaging of a sample. Thus FIB-SEM is an excellent alternative to TEM tomography and serial section TEM tomography. Animal tissue samples have been more intensively investigated by this technique than plant tissues. Here, we show that FIB-SEM can be used to study the 3D ultrastructure of plant tissues in samples previously prepared for TEM via commonly used fixation and embedding protocols. Reconstruction of FIB-SEM sections revealed ultra-structural details of the plant tissues examined. We observed that organelles packed tightly together in Nicotiana benthamiana Domin leaf cells may form membrane contacts. 3D models of soybean nodule cells suggest that the bacteroids in infected cells are contained within one large membrane-bound structure and not the many individual symbiosomes that TEM thin-sections suggest. We consider the implications of these organelle arrangements for intercellular signalling.
Why it matches plant phenotyping methodsFIB-SEMを植物組織の3次元超微細構造取得へ適用・実証しており、植物細胞の形態状態の画像ベース計測が研究の中心です。
abstractHere, we show that FIB-SEM can be used to study the 3D ultrastructure of plant tissues in samples previously prepared for TEM via commonly used fixation and embedding protocols.
In this chapter, we describe different methods for phenotyping strains or mutants of the bacterial wilt agent, Ralstonia solanacearum, on four different host plants: Arabidopsis thaliana, tomato (Solanum lycopersicum), tobacco (Nicotiana benthamiana), or Medicago truncatula. Methods for preparation of high volume or low volume inocula are first described. Then, we describe the procedures for inoculation of plants by soil drenching, stem injection or leaf infiltration, and scoring of the wilting symptoms development. Two methods for measurement of bacterial multiplication in planta are also proposed: (1) counting the bacterial colonies upon serial dilution plating and (2) determining the bacterial concentration using a qPCR approach. In this chapter, we also describe a competitive index assay to compare the fitness of two strains coinoculated in the same plant. Lastly, specific protocols describe in vitro and hydroponic inoculation procedures to follow disease development and bacterial multiplication in both the roots and aerial parts of the plant.
Why it matches plant phenotyping methods植物病原性の評価手順を体系的に記述し、接種後の萎凋症状や植物体内での病原菌増殖を測定する方法が中心であるため、植物病害表現型の方法論として収載する。
abstractIn this chapter, we describe different methods for phenotyping strains or mutants of the bacterial wilt agent, Ralstonia solanacearum, on four different host plants
The plant cell wall is an important and abundant biomass with great potential for use as a modern recyclable resource. For effective utilization of this cellulosic biomass, its ability to degrade efficiently is key point. With the aim of modifying the cell wall to allow easy decomposition, we used chemical biological technology to alter its structure. As a first step toward evaluating the chemicals in the cell wall we employed a phenotype-based approach using high-throughput screening. As the plant cell wall is essential in determining cell morphology, phenotype-based screening is particularly effective in identifying compounds that bring about alterations in the cell wall. For rapid and reproducible screening, tobacco BY-2 cell is an excellent system in which to observe cell morphology. In this chapter, we provide a detailed chemical biological methodology for studying cell morphology using tobacco BY-2 cells.
Why it matches plant phenotyping methods植物細胞形態を用いた高スループット表現型スクリーニングの再現可能な方法論を詳細に提示しており、形態表現型の取得が中心である。
abstractFor rapid and reproducible screening, tobacco BY-2 cell is an excellent system in which to observe cell morphology.
Plants have evolved a sophisticated innate immune system to contend with potential infection by various pathogens. Understanding and manipulation of key molecular mechanisms that plants use to defend against various pathogens are critical for developing novel strategies in plant disease control. In plants, resistance to attempted pathogen infection is often associated with hypersensitive response (HR), a form of rapid programmed cell death (PCD) at the site of attempted pathogen invasion. In this chapter, we describe a method for rapid identification of genes that are essential for plant innate immunity. It combines virus-induced gene silencing (VIGS), a tool that is suitable for studying gene function in high-throughput, with the utilization of immunity-associated PCD, particularly HR-linked PCD as the readout of changes in plant innate immunity. The chapter covers from the design of gene fragment for VIGS, the agroinfiltration of the Nicotiana benthamian plants, to the use of immunity-associated PCD induced by twelve elicitors as the indicator of activation of plant immunity.
Why it matches plant phenotyping methods植物免疫に関連するプログラム細胞死を表現型 readout として、VIGSと組み合わせて免疫関連遺伝子を迅速・高スループットに同定する方法を中心に記述したプロトコルであり、単なる生物学的測定ではない。
abstractwe describe a method for rapid identification of genes that are essential for plant innate immunity.
Segmentation of a region of interest is an important pre-processing step for many colour image analysis techniques. Similarly segmentation of plant in digital images is an important preprocessing step in phenotying plants by image analysis. In this paper we present an analytical study to statistically determine the suitability of colour space representation of an image to best detect plant pixels and separate them from background pixels. Our hypothesis is that the colour space representation in which the separation of the distributions representing plant pixels and background pixels is maximized would be the best for detection of plant pixels. The two classes of pixels are modelled as a Gaussian mixture model (GMM). In our GM modelling we don't make any prior assumption about the number of Gaussians in the model. Rather a constant bandwidth mean-shift filter is used to cluster the data and the number of clusters and hence the number of Gaussians is automatically determined. Here we have analysed following representative colour spaces like $RGB$, $rgb$, $HSV$, $Ycbcr$ and $CIE-Lab$. This is because these colour spaces represent several other similar colour spaces and also an exhaustive study of all the colour space will be too voluminous. We also analyse the colour space feature from the two-class variance ratio perspective and compare the results of our hypothesis with this metric. The dataset for this empirical study consist of 378 digital images of plants and their manual segmentation. Dataset consist of various species of plants (arabidopsi, tobacco, wheat, rye grass etc.) imaged under different lighting conditions, indoor and outdoor, controlled and uncontrolled background. In results we obtain better segmentation of the plants in $HSV$ colour space, which is supported by its Earth mover distance (EMD) on the GMM distribution of plant and background pixels.
Why it matches plant phenotyping methods植物画像から植物画素を抽出する色空間・分割手法を比較分析し、植物フェノタイピングの前処理を技術的に開発・評価しているため、方法が中心である。
abstractIn this paper we present an analytical study to statistically determine the suitability of colour space representation of an image to best detect plant pixels and separate them from background pixels.
Plant phenotyping relevance match · UnverifiedbioRxiv · Europe PMC · checked 10 Sept 2026
The number of leaves a plant has is one of the key traits (phenotypes) describing its development and growth. Here, we propose an automated, deep learning based approach for counting leaves in model rosette plants. While state-of-the-art results on leaf counting with deep learning methods have recently been reported, they obtain the count as a result of leaf segmentation and thus require per-leaf (instance) segmentation to train the models (a rather strong annotation). Instead, our method treats leaf counting as a direct regression problem and thus only requires as annotation the total leaf count per plant. We argue that combining different datasets when training a deep neural network is beneficial and improves the results of the proposed approach. We evaluate our method on the CVPPP 2017 Leaf Counting Challenge dataset, which contains images of Arabidopsis and tobacco plants. Experimental results show that the proposed method significantly outperforms the winner of the previous CVPPP challenge, improving the results by a minimum of 50% on each of the test datasets, and can achieve this performance without knowing the experimental origin of the data (i.e. \"in the wild\" setting of the challenge). We also compare the counting accuracy of our model with that of per leaf segmentation algorithms, achieving a 20% decrease in mean absolute difference in count (|DiC|).
Why it matches plant phenotyping methods植物画像から葉数という形態形質を推定する深層学習手法を開発し、既存手法・ベンチマークデータセットで性能評価しており、表現型取得・抽出法が研究の中心である。
abstractHere, we propose an automated, deep learning based approach for counting leaves in model rosette plants.
Transport networks serve critical functions in biological and engineered systems, and yet their design requires trade-offs between competing objectives. Due to their sessile lifestyle, plants need to optimize their architecture to efficiently acquire and distribute resources while also minimizing costs in building infrastructure. To understand how plants resolve this design trade-off, we used high-precision three-dimensional laser scanning to map the architectures of tomato, tobacco, or sorghum plants grown in several environmental conditions and through multiple developmental time points, scanning in total 505 architectures from 37 plants. Using a graph-theoretic algorithm that we developed to evaluate design strategies, we find that plant architectures lie along the Pareto front between two simple length-based objectives-minimizing total branch length and minimizing nutrient transport distance-thereby conferring a selective fitness advantage for plant transport processes. The location along the Pareto front can distinguish among species and conditions, suggesting that during evolution, natural selection may employ common network design principles despite different optimization trade-offs.
Why it matches plant phenotyping methods高精度3Dレーザースキャンによる植物構造の取得と、構造を評価する新規グラフ理論アルゴリズムが研究の中心であり、植物アーキテクチャという形態形質を定量化している。
abstractwe used high-precision three-dimensional laser scanning to map the architectures of tomato, tobacco, or sorghum plants
Bernd Zechmann, Günther Zellnig; 3D Reconstruction of Zucchini- and Tobacco Yellow Mosaic Virus Induced Ultrastructural Changes in Plants, Microscopy and M
Why it matches plant phenotyping methods植物のウイルス誘導性超微細構造変化を3D再構成する画像ベースの表現型取得・解析が題名上の中心であり、病害状態の形態計測に該当する。
title3D Reconstruction of Zucchini- and Tobacco Yellow Mosaic Virus Induced Ultrastructural Changes in Plants
Laser ablation-inductively coupled plasma-optical emission spectroscopy (LA-ICP-OES) is presented as a valuable tool for elemental bioimaging of alkali and earth alkali elements in plants. Whereas LA-ICP-OES is commonly used for micro analysis of solid samples, laser ablation-inductively coupled plasma-mass spectrometry (LA-ICP-MS) has advanced to the gold standard for bioimaging. However, especially for easily excitable and ubiquitous elements such as alkali and earth alkali elements, LA-ICP-OES holds some advantages regarding simultaneous detection, costs, contamination, and user-friendliness. This is demonstrated by determining the calcium, sodium and potassium distribution in tobacco plant stem and leaf petiole tissues. A quantification of the calcium contents in a concentration range up to 1000 μg g -1 using matrix-matched standards is presented as well. The method is directly compared to a LA-ICP-MS approach by analyzing parallel slices of the same samples.
Why it matches plant phenotyping methods植物組織内元素分布を取得するLA-ICP-OES bioimaging法を提示し、定量とLA-ICP-MSとの直接比較で技術的有用性を検証しており、測定法が中心である。
abstractLaser ablation-inductively coupled plasma-optical emission spectroscopy (LA-ICP-OES) is presented as a valuable tool for elemental bioimaging of alkali and earth alkali elements in plants.
Image-based approaches to plant phenotyping are gaining momentum providing fertile ground for several interesting vision tasks where fine-grained categorization is necessary, such as leaf segmentation among a variety of cultivars, and cultivar (or mutant) identification. However, benchmark data focusing on typical imaging situations and vision tasks are still lacking, making it difficult to compare existing methodologies. This paper describes a collection of benchmark datasets of raw and annotated top-view color images of rosette plants. We briefly describe plant material, imaging setup and procedures for different experiments: one with various cultivars of Arabidopsis and one with tobacco undergoing different treatments. We proceed to define a set of computer vision and classification tasks and provide accompanying datasets and annotations based on our raw data. We describe the annotation process performed by experts and discuss appropriate evaluation criteria. We also offer exemplary use cases and results on some tasks obtained with parts of these data. We hope with the release of this rigorous dataset collection to invigorate the development of algorithms in the context of plant phenotyping but also provide new interesting datasets for the general computer vision community to experiment on. Data are publicly available at http://www.plant-phenotyping.org/datasets.
Why it matches plant phenotyping methods植物フェノタイピング用の画像データセットとアノテーション、評価基準を整備したベンチマーク研究であり、方法開発を支えるデータ基盤が中心です。
abstractThis paper describes a collection of benchmark datasets of raw and annotated top-view color images of rosette plants.
ArabidopsisTobaccoRootSeed / grainPhysiological trait estimationGrowth / development / phenologyRoot system architecture
Background Small synthetic molecules provide valuable tools to agricultural biotechnology to circumvent the need for genetic engineering and provide unique benefits to modulate plant growth and development. Results We developed a method to explore molecular mechanisms of plant growth by high-throughput phenotypic screening of haploid populations of pollen cells. These cells rapidly germinate to develop pollen tubes. Compounds acting as growth inhibitors or stimulators of pollen tube growth are identified in a screen lasting not longer than 8 h high-lighting the potential broad applicability of this assay to prioritize chemicals for future mechanism focused investigations in plants. We identified 65 chemical compounds that influenced pollen development. We demonstrated the usefulness of the identified compounds as promotors or inhibitors of tobacco and Arabidopsis thaliana seed growth. When 7 days old seedlings were grown in the presence of these chemicals twenty two of these compounds caused a reduction in Arabidopsis root length in the range from 4.76 to 49.20 % when compared to controls grown in the absence of the chemicals. Two of the chemicals sharing structural homology with thiazolidines stimulated root growth and increased root length by 129.23 and 119.09 %, respectively. The pollen tube growth stimulating compound (S-02) belongs to benzazepin-type chemicals and increased Arabidopsis root length by 126.24 %. Conclusions In this study we demonstrate the usefulness of plant pollen tube based assay for screening small chemical compound libraries for new biologically active compounds. The pollen tubes represent an ultra-rapid screening tool with which even large compound libraries can be analyzed in very short time intervals. The broadly applicable high-throughput protocol is suitable for automated phenotypic screening of germinating pollen resulting in combination with seed germination assays in identification of plant growth inhibitors and stimulators.
Why it matches plant phenotyping methods花粉管成長を指標とする高スループットの表現型スクリーニング法を開発・実証しており、植物の成長表現型の取得が研究の中心である。
abstractWe developed a method to explore molecular mechanisms of plant growth by high-throughput phenotypic screening of haploid populations of pollen cells.
BarleyTobaccoChlorophyll fluorescenceLeafPhysiological trait estimationWater status / transpiration
In the context of global climate change, drought is one of the major stress factors with negative effect on photosynthesis and plant productivity. Currently, chlorophyll fluorescence parameters are widely used as indicators of plant stress, mainly owing to the rapid, non-destructive and simple measurements this technique allows. However, these parameters have been shown to have limited sensitivity for the monitoring of water deficit as leaf desiccation has relatively small effect on photosystem II photochemistry. In this study, we found that blue light-induced increase in leaf transmittance reflecting chloroplast avoidance movement was much more sensitive to a decrease in relative water content (RWC) than chlorophyll fluorescence parameters in dark-desiccating leaves of tobacco (Nicotiana tabacum L.) and barley (Hordeum vulgare L.). Whereas the inhibition of chloroplast avoidance movement was detectable in leaves even with a small RWC decrease, the chlorophyll fluorescence parameters (F V/F M, V J, Ф PSII, NPQ) changed markedly only when RWC dropped below 70 %. For this reason, we propose light-induced chloroplast avoidance movement as a sensitive indicator of the decrease in leaf RWC. As our measurement of chloroplast movement using collimated transmittance is simple and non-destructive, it may be more suitable in some cases for the detection of plant stresses including water deficit than the conventionally used chlorophyll fluorescence methods.
Why it matches plant phenotyping methods葉の相対含水率を推定するための、光誘導クロロプラスト運動の透過光測定法を提案し、従来の蛍光指標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。
abstractblue light-induced increase in leaf transmittance reflecting chloroplast avoidance movement was much more sensitive to a decrease in relative water content (RWC) than chlorophyll fluorescence parameters
Abstract A number of fluorescence microscopy techniques are described to study dynamics of fluorescently labeled proteins, lipids, nucleic acids, and whole organelles. However, for studies of plant plasma membrane (PM) proteins, the number of these techniques is still limited because of the high complexity of processes that determine the dynamics of PM proteins and the existence of cell wall. Here, we report on the usage of raster image correlation spectroscopy (RICS) for studies of integral PM proteins in suspension-cultured tobacco cells and show its potential in comparison with the more widely used fluorescence recovery after photobleaching method. For RICS, a set of microscopy images is obtained by single-photon confocal laser scanning microscopy (CLSM). Fluorescence fluctuations are subsequently correlated between individual pixels and the information on protein mobility are extracted using a model that considers processes generating the fluctuations such as diffusion and chemical binding reactions. As we show here using an example of two integral PM transporters of the plant hormone auxin, RICS uncovered their distinct short-distance lateral mobility within the PM that is dependent on cytoskeleton and sterol composition of the PM. RICS, which is routinely accessible on modern CLSM instruments, thus represents a valuable approach for studies of dynamics of PM proteins in plants.
Why it matches plant phenotyping methods植物細胞膜タンパク質の動態を定量するRICSを導入し、FRAPと比較して植物試料での有用性を示した方法研究である。
abstractHere, we report on the usage of raster image correlation spectroscopy (RICS) for studies of integral PM proteins in suspension-cultured tobacco cells and show its potential in comparison with the more widely used fluorescence recovery after photobleaching method.
Plant phenotyping relevance match · UnverifiedOpenAlex · Europe PMC · checked 15 Sept 2026
New technologies, such as three-dimensional (3D) laser scanning and stereo imaging, have recently been adopted for quantifying plant structure. The datasets collected using such technologies offer realistic representations of the morphological characteristics of the studied plant organs. The datasets, however, are very large and occupy excessive amount of storage space. Moreover, the computation time is also very long when these datasets are made the subject of further analysis and simulation. Some dataset simplification is essential if the balance between storage cost and computation time vs the accuracy of the plant geometry description is to be optimised. In this study, the surface morphologies of field-grown maize and tobacco leaves were measured using 3D laser scanning and were progressively simplified using two different methods – Vertex removal and Edge collapse. To evaluate the impacts of simplification on the accuracy of the leaf-surface morphological descriptions, several error metrics were developed. These metrics are able to quantify these impacts in various respects. The statistical results show that most error metrics increase only marginally, even with moderate simplifications of the leaf surfaces. The errors, however, increase quickly with over-simplification. The simulation results of light distribution in canopies indicate that over-simplification of the leaf-surface meshes results in significant deviations in the simulated leaf light-capture efficiency compared with the original leaf surfaces. Compared with the Vertex removal method, the Edge collapse method is better at retaining the original leaf-surface morphology, but loses more of the leaf-edge information. This study provides valuable information in the analysis of high-precision plant-structure data, relevant in a range of research fields including functional–structural plant modelling and high-throughput phenotyping.
Why it matches plant phenotyping methods3Dレーザースキャンで取得した葉面形状データの簡略化アルゴリズムと誤差指標を開発・評価しており、植物形態の表現精度と計算効率を扱う方法論が中心である。
abstractThe datasets collected using such technologies offer realistic representations of the morphological characteristics of the studied plant organs.
ArabidopsisTobaccoCell / cellular structureRootVisualization / data management
Summary In plants, 3‐deoxy‐ d ‐ manno ‐oct‐2‐ulosonic acid (Kdo) is a monosaccharide that is only found in the cell wall pectin, rhamnogalacturonan‐ II ( RG ‐ II ). Incubation of 4‐day‐old light‐grown Arabidopsis seedlings or tobacco BY ‐2 cells with 8‐azido 8‐deoxy Kdo (Kdo‐N 3 ) followed by coupling to an alkyne‐containing fluorescent probe resulted in the specific in muro labelling of RG ‐ II through a copper‐catalysed azide–alkyne cycloaddition reaction. CMP ‐Kdo synthetase inhibition and competition assays showing that Kdo and D‐Ara, a precursor of Kdo, but not L‐Ara, inhibit incorporation of Kdo‐N 3 demonstrated that incorporation of Kdo‐N 3 occurs in RG ‐ II through the endogenous biosynthetic machinery of the cell. Co‐localisation of Kdo‐N 3 labelling with the cellulose‐binding dye calcofluor white demonstrated that RG ‐ II exists throughout the primary cell wall. Additionally, after incubating plants with Kdo‐N 3 and an alkynated derivative of L‐fucose that incorporates into rhamnogalacturonan I, co‐localised fluorescence was observed in the cell wall in the elongation zone of the root. Finally, pulse labelling experiments demonstrated that metabolic click‐mediated labelling with Kdo‐N 3 provides an efficient method to study the synthesis and redistribution of RG ‐ II during root growth.
Why it matches plant phenotyping methods植物細胞壁中のRG-IIの局在・合成・再分布を蛍光クリック標識で可視化する手法を開発し、根の成長に伴う状態の解析へ適用しており、測定法が研究の中心である。
titlePlant cell wall imaging by metabolic click‐mediated labelling of rhamnogalacturonan II
TobaccoMultispectral / hyperspectralLeafPhysiological trait estimationWater status / transpiration
Fast identification of moisture content in tobacco plant leaves plays a key role in the tobacco cultivation industry and benefits the management of tobacco plant in the farm. In order to identify moisture content of tobacco plant leaves in a fast and nondestructive way, a method involving Mahalanobis distance coupled with Monte Carlo cross validation(MD-MCCV) was proposed to eliminate outlier sample in this study. The hyperspectral data of 200 tobacco plant leaf samples of 20 moisture gradients were obtained using FieldSpc(®) 3 spectrometer. Savitzky-Golay smoothing(SG), roughness penalty smoothing(RPS), kernel smoothing(KS) and median smoothing(MS) were used to preprocess the raw spectra. In addition, Mahalanobis distance(MD), Monte Carlo cross validation(MCCV) and Mahalanobis distance coupled to Monte Carlo cross validation(MD-MCCV) were applied to select the outlier sample of the raw spectrum and four smoothing preprocessing spectra. Successive projections algorithm (SPA) was used to extract the most influential wavelengths. Multiple Linear Regression (MLR) was applied to build the prediction models based on preprocessed spectra feature in characteristic wavelengths. The results showed that the preferably four prediction model were MD-MCCV-SG (Rp(2) = 0.8401 and RMSEP = 0.1355), MD-MCCV-RPS (Rp(2) = 0.8030 and RMSEP = 0.1274), MD-MCCV-KS (Rp(2) = 0.8117 and RMSEP = 0.1433), MD-MCCV-MS (Rp(2) = 0.9132 and RMSEP = 0.1162). MD-MCCV algorithm performed best among MD algorithm, MCCV algorithm and the method without sample pretreatment algorithm in the eliminating outlier sample from 20 different moisture gradients of tobacco plant leaves and MD-MCCV can be used to eliminate outlier sample in the spectral preprocessing.
Why it matches plant phenotyping methodsタバコ葉の含水量という植物形質を、ハイパースペクトル計測と外れ値除去・前処理・予測モデルで非破壊推定する方法が研究の中心であり、手法開発および技術評価に該当する。
abstractIn order to identify moisture content of tobacco plant leaves in a fast and nondestructive way, a method involving Mahalanobis distance coupled with Monte Carlo cross validation(MD-MCCV) was proposed to eliminate outlier sample in this study.