Volatile organic compounds (VOCs) released by plant leaves play key roles in stress signaling, plant-atmosphere interactions, and plant defense. Among these, wound-induced VOCs (wVOCs) are emitted within seconds of mechanical damage, herbivory, or environmental disturbance. Their emission dynamics depend strongly on the timing, severity, and method of tissue disruption. Yet, accurate quantification remains challenging due to mechanical artifacts, variable exposure conditions, and delays between injury and measurement. This study presents a standardized within-chamber leaf excision protocol for real-time monitoring of wVOCs and gas exchange. A surgical-grade cutter was integrated into a portable gas-exchange chamber to enable clean, controlled cuts within a sealed chamber under stable light, humidity, CO2, and temperature conditions. A proton-transfer-reaction time-of-flight mass spectrometer (PTR-TOF-MS) continuously measured volatile emissions at the chamber outlet, minimizing delay and signal distortion. This setup resolves emission onset, peak timing, maximum rise rate, and total release with high temporal fidelity. Application of the method to Quercus rubra, Acer platanoides, and Gossypium hirsutum demonstrated its ability to resolve distinct wound-induced emission patterns across contrasting leaf types. By eliminating delays associated with conventional sampling, this method resolves the full kinetic trajectory of wound-induced emissions and overcomes major limitations of previous approaches. It provides a robust framework for studying rapid stress responses in plant physiology, ecological biochemistry, and plant-atmosphere interactions.
Why it matches plant phenotyping methods植物葉の創傷誘導揮発性物質とガス交換をリアルタイム定量する測定系を開発しており、植物のストレス生理状態の取得が研究の中心である。
abstractThis study presents a standardized within-chamber leaf excision protocol for real-time monitoring of wVOCs and gas exchange.
Autonomous, cross-facility science requires capabilities that no individual project should have to build for itself: managed execution for long-lived services, versioned distribution of models to remote compute systems, governed access to large language models, a shared substrate for experimental data, and end-to-end provenance. The U.S. Department of Energy Genesis Mission platform, delivered through the American Science Cloud, provides these as reusable services. This paper reports how the Genesis platform enables cross-facility experiments and accelerates scientific discovery. We explore the plant phenotyping workflow of the Orchestrated Platform for Autonomous Laboratories as the exemplar: it couples Oak Ridge National Laboratory's Advanced Plant Phenotyping Laboratory with the Frontier supercomputer. In a 40-day nickel-treatment campaign, the resulting workflow replaced roughly twelve hours of manual analysis with interactive queries returning in seconds to minutes.
Why it matches plant phenotyping methods植物フェノタイピングのワークフローを実証例として、実験施設とスーパーコンピュータを連携する再利用可能なプラットフォーム基盤を報告しており、フェノタイピング基盤が中心的である。
abstractWe explore the plant phenotyping workflow of the Orchestrated Platform for Autonomous Laboratories as the exemplar: it couples Oak Ridge National Laboratory's Advanced Plant Phenotyping Laboratory with the Frontier supercomputer.
Mangrove ecosystems are frequently exposed to high concentrations of iron (Fe) in sediments, resulting in Fe accumulation in plant tissues. Although Fe is an essential micronutrient involved in several metabolic processes, its excess requires efficient mechanisms of compartmentalization and storage to maintain cellular homeostasis. Histochemical detection using the Perls reaction has usually been applied to identify ferric iron (Fe 3+ ) in biological tissues; however, the combination of this technique with ultrastructural and elemental analyses remains relatively unexplored in plant cells. In this study, we investigated Fe localization in leaf tissues of Rhizophora mangle L. (Rhizophoraceae), a dominant mangrove species, by combining complementary approaches, including Perls cytochemical reaction, transmission electron microscopy (TEM), scanning transmission electron microscopy coupled with high-angle annular dark-field imaging (STEM-HAADF), and energy-dispersive X-ray spectroscopy (EDS). Perls-positive electron-dense deposits were visualized at the ultrastructural level, and their elemental composition was further characterized by EDS analyses. Fe-containing deposits were detected in the epidermis, mesophyll parenchyma, mucilage cells, and vascular tissues, as well as in multiple cellular compartments, including plastids, mitochondria, vacuoles, cell walls, intercellular spaces, and plasmodesmata, whereas sclerenchyma cells showed no detectable Fe-containing deposits. The combination of Perls reaction with TEM, STEM-HAADF, and EDS provides a complementary approach for high-resolution visualization and elemental characterization of Fe-containing deposits at the subcellular level. This integrated methodology may facilitate the investigation of Fe distribution and compartmentalization in plant tissues under contrasting conditions of Fe availability.
Why it matches plant phenotyping methods植物葉の鉄分布・細胞内区画化という生理状態を対象に、複数の顕微鏡・元素分析法を統合した可視化および特性評価手法が研究の中心であるため。
abstractThe combination of Perls reaction with TEM, STEM-HAADF, and EDS provides a complementary approach for high-resolution visualization and elemental characterization of Fe-containing deposits at the subcellular level.
Overlapping cells in plant suspension-culture microscopy pose a particular challenge, for instance, segmentation because a single pixel may belong to more than one cell. Most standard instance-segmentation methods are not designed for this setting and tend to treat overlapping objects as mutually exclusive regions. We instead represent each cell as an independent full-cell instance and introduce MC-SlotNet, an architecture that separates competitive object-slot feature assignment from mask decoding. This allows multiple predicted masks to occupy the same image region. We further introduce a mask-level multiplicity-consistency loss that encourages the predicted number of masks covering a pixel to agree with the underlying cell occupancy. We evaluate MC-SlotNet on a newly annotated dataset of 53 Siraitia grosvenorii suspension-culture micrographs containing 4131 full-cell instances acquired at 4×–40× magnification. Using grouped five-fold cross-validation and an overlap-preserving evaluation protocol, we compare the method with Mask R-CNN, SOLOv2, and Mask2Former. MC-SlotNet achieves the best performance on AP50 (0.800), mAP50:95 (0.565), F150 (0.842), all-ground-truth Dice (0.761), AJI+ (0.754), overlap-region Dice (0.705), and overlap-instance recall (0.828). Its AP75 (0.649) is comparable to Mask2Former’s (0.651). MC-SlotNet also has the lowest inference time among the evaluated methods, at 1.113 s/image. These results indicate that decoding full-cell masks independently, rather than enforcing an exclusive partition of image pixels, is well-suited to instance segmentation in plant suspension-culture microscopy images with substantial cell overlap.
Why it matches plant phenotyping methods植物細胞の重複画像から個々の細胞を分割・抽出する新規モデルを開発し、データセット上で既存手法と比較評価しており、植物表現型取得法が研究の中心である。
abstractWe instead represent each cell as an independent full-cell instance and introduce MC-SlotNet, an architecture that separates competitive object-slot feature assignment from mask decoding.
The automation and standardization of hyperspectral imaging, particularly at high spatial resolution, are essential for advancing plant phenomics and supporting diverse plant science research. We developed HyperBird, a hyperspectral microscopic imaging robot, to automate the acquisition of up to 351 leaf-disc samples in a single tray within approximately 2.4 h and thereby support scalable plant experiments. System calibration established a spatial resolution of 24.3 μ m (full width at half maximum; FWHM), a spectral resolution of 1.78 nm (FWHM), and a depth of field of 4.53 mm, producing hyperspectral data cubes of 2195 × 2000 × 950 pixels across the 400–1000 nm spectral range. System-level and biological-sample consistency assessments confirmed stable spectral measurements during extended scanning sessions and across independently prepared trays. Thermal evaluation confirmed that the 150 W illumination design introduced minimal sample heating during scanning. HyperBird was first evaluated using grape downy and powdery mildews, where no consistent measurable effect of repeated imaging on pathosystem development was detected under the tested experimental conditions. Subsequently, HyperBird was used to characterize spatiotemporal spectral progression in grapevine leaves inoculated with Plasmopara viticola . An automated regional spectral tracing pipeline was developed to isolate spectra from retrospectively traced regions and compare them with whole-leaf averaged spectra across 0–9 days post-inoculation (DPI). Categorical mixed-effects modeling showed that these spatially resolved regional spectra exhibited significant disease-associated spectral change by 5 DPI, with a sharp transition at 6 DPI, whereas whole-leaf spectra showed delayed and weaker disease-aligned changes beginning at 7 DPI. These results demonstrate that HyperBird enables high-throughput, spatially resolved hyperspectral imaging for quantifying localized plant disease progression and provides a scalable platform for studying spectral biology across plant phenotyping applications.
Why it matches plant phenotyping methodsHyperBirdの高スループット・ハイパースペクトル画像取得ロボットを開発し、校正、再現性、加熱影響、病害進展の定量性能を検証しており、植物フェノタイピング手法が中心である。
abstractWe developed HyperBird, a hyperspectral microscopic imaging robot, to automate the acquisition of up to 351 leaf-disc samples in a single tray within approximately 2.4 h and thereby support scalable plant experiments.
Reproduction assets foundThe paper's Data Availability statement points to a public GitHub repository containing the authors' code and processed data supporting the phenotyping analyses; raw hyperspectral image data are request-only.Code · publicData Availability
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Supplementary Figure S1. Representative GSAM-based segOpen asset ↗HyperBird-Robotpdf-raw-page:39 lines:1-75Code / dataset availability confirmedEurope PMC · checked 15 Sept 2026
Surface phenotyping underpins plant science, preclinical animal research and entomology, yet across all three the measurement is almost always a photograph, which records a projection and not the surface itself. Here we present the Gentschinator3000 , an open structured-light platform that brings high-end metric surface measurement within reach of laboratories with no optics expertise, combining documented open hardware, open reconstruction software and analysis workflows for under 4000 Euro in components. It resolves a planar reference to 45 µm local flatness, registers full rotations to a loop closure of 156 µm, and performs stably across acquisition ranges that we define. Applying one workflow to a leaf before and after desiccation, to murine anatomy and to a spread lepidopteran, we find that projection underestimates surface area by 11 to 41 %. That error grows with the condition under study, with the evaluation scale and with the direction of view, so it can confound phenotype comparisons dramatically. In murine limbs a 15-degree change of viewing direction shifts a projected inter-segment angle by up to 23.2 degrees, while the three-dimensional angle does not move. Projection geometry can therefore contribute as much to a measured phenotype as the biology it is meant to quantify.
Why it matches plant phenotyping methods植物表面の三次元形状を測定するオープンな構造化光プラットフォームと再構成・解析ワークフローを開発し、葉で投影バイアスを評価しており、表現型取得手法が中心である。
abstractHere we present the Gentschinator3000 , an open structured-light platform that brings high-end metric surface measurement within reach of laboratories with no optics expertise, combining documented open hardware, open reconstruction software and analysis workflows for under 4000 Euro in components.
Reproduction assets foundThe paper explicitly deposits three public Zenodo records: reconstructed 3D surfaces of all specimens (including the leaf and hop cone phenotyping measurements), the authors' analysis notebooks with derived and per-panel source data, and the reconstruction software with build documentation and working examples. All areDataset · publicData availability
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hereOpen asset ↗Zenodo · 10.5281/zenodo.22167598pdf-raw-page:35 lines:1-52Plant phenotyping relevance match · UnverifiedbioRxiv · checked 11 Sept 2026
The increasing world population necessitates new sustainable nutrient sources, making microalgae like Chlorella sorokiniana interesting due to its rich nutrient profile and sustainable cultivation methods. With genetic optimization tools like CRISPR/Cas9, microalgae as a nutrient source can be improved even further. However, degradation of the rigid cell wall of microalgae, and thereby developing protoplasts, is often necessary prior to transformation, but monitoring protoplast development in spherical, single-celled organisms like C. sorokiniana is challenging using bright-field microscopy. Carbotrace 480 and 630 were tested as fluorescent markers of the cell wall of a C. sorokiniana mutant for protoplast detection, and Carbotrace 480 was successfully used to distinguish protoplast from normal cells in a cell suspension. The enzymes Driselase, Glucanex, Snailase, and Saczyme were tested in different combinations to degrade the cell wall of the mutant, with Snailase as the most effective yielding ~60 % protoplasts. This study provides a quick and easy tool for monitoring protoplast development in the microalgae C. sorokiniana, the first step to improve C. sorokiniana as a sustainable nutrient source using genetic optimization tools like CRISPR/Cas9.
Why it matches plant phenotyping methods微細藻類の細胞壁状態とプロトプラスト形成を蛍光マーカーで識別する方法の開発・検証が研究の中心であり、植物状態の取得手法に該当する。
abstractCarbotrace 480 and 630 were tested as fluorescent markers of the cell wall of a C. sorokiniana mutant for protoplast detection, and Carbotrace 480 was successfully used to distinguish protoplast from normal cells in a cell suspension.
The intrinsic properties of plants offer numerous opportunities for scientific and technological advancement. Considerable efforts have been directed toward developing plant-on-chip platforms to investigate cellular responses to external stimuli, including chemical, mechanical, and electrical cues. In this study, we present a fluidic platform using polydimethylsiloxane (PDMS) and a printed circuit board (PCB), integrated with electrochemical impedance spectroscopy (EIS) detection. Various experimental conditions were examined, including ionic and pH stimulation, as well as membrane dimensions, with the onion inner membrane treated as a black-box system. The measurement results are presented as Nyquist plots, and a resistance model incorporating multifactorial influences is proposed. Impedance variations in plant cells serve as a basis for electrical modulation. To explore these properties, we converted acoustic signals into electrical inputs and recorded the outputs after being modulated by onion inner epidermal cells. A transfer function analysis was subsequently performed. Our results indicate that the plant cell-on-chip (PCOC) platform holds promise for further investigations into plant cell properties. The impedance results suggest that plant cells can respond to different external stimuli, enabling modulation of the electrical properties. These findings lay the groundwork for future studies on cellular electrical characteristics and the development of preliminary bioelectrical circuits.
Why it matches plant phenotyping methods植物細胞の電気的生理状態を測定・解析するEISベースのオンチップ基盤を開発しており、植物状態の取得方法が研究の中心である。
abstractwe present a fluidic platform using polydimethylsiloxane (PDMS) and a printed circuit board (PCB), integrated with electrochemical impedance spectroscopy (EIS) detection
Abstract Deep convolutional networks now classify leaf images on curated benchmarks such as PlantVillage with accuracies close to the measurement ceiling of those datasets, which has shifted the open questions away from raw accuracy toward two under-reported issues: which components of a composite pipeline actually cause the result, and whether the components that matter on laboratory images are the same ones that matter on field photographs. We address both with a classification framework evaluated under a protocol that fixes every development decision before the independent test data are read. A Mask R-CNN stage localizes the dominant leaf, the accepted box is expanded by a validation-selected 5% margin and resized to a shared 224 by 224 input, and the same localized image is passed to ResNet50, InceptionV3, and MobileNetV2 together with an extractor that produces eighteen colour and shape descriptors. The forty-five class probabilities and eighteen descriptors form a sixty-three-dimensional input to a neural meta-classifier developed by three repetitions of stratified five-fold cross-validation. On a locked 3,101-image PlantVillage test partition of fifteen classes the framework reached 99.77% accuracy and 99.75% macro F1 with seven misclassifications, and a one-component-at-a-time ablation confirmed that every stage contributed. The same design was then trained and evaluated entirely within a separate thirteen-class PlantDoc field-image dataset, where it reached 90.03% accuracy and 89.40% macro F1. This is a within-PlantDoc experiment and not a controlled-to-field transfer test, so the figure measures how the pipeline behaves on field imagery rather than how a PlantVillage-trained model survives a domain shift. The central finding comes from running the identical ablation on both datasets: on clean images the ensemble breadth and descriptors provide the incremental gains, but under field conditions the ordering changes, and leaf localization and learned fusion become the decisive components. Removing localization cost 2.83 accuracy points and replacing the neural fusion with soft voting cost a further 2.08 points, the two largest effects on PlantDoc. The contribution is a controlled and transparent account of where each component of a localization-aware plant disease classifier earns its place, and of how that ordering shifts between laboratory and field acquisition.
Why it matches plant phenotyping methods葉画像から植物病害状態を推定する分類パイプラインの開発・アブレーション検証が中心であり、単なる病害実験や routine measurement ではない。
titleA Localization-Aware Heterogeneous CNN Ensemble with Neural Meta- Fusion for Plant Disease Classification, with a Component Analysis on Laboratory and Field Images
Reproduction assets found保存済みの本文根拠を更新済みルールで再検証し、公開資産1件を確認しました。Dataset · publicThe classification subset used here (20,638 images spanning fifteen pepper-bell, potato, and tomato classes) was obtained from PlantVillage, which is openly accessible at https://www.kaggle.com/datasets/emmarex/plantdisease.Open asset ↗Kaggle · emmarex/plantdiseaselines:314-336Plant phenotyping relevance match · UnverifiedCrossref · checked 5 Sept 2026
Plant diseases destroy 20–40% of global food production annually, posing a critical threat to food security for a projected population of 9.7 billion by 2050. Conventional diagnostic approaches relying on expert visual assessment are slow, costly, and unsuitable for modern agricultural scales. While deep convolutional neural networks demonstrated early promise, single-modality, image-centric systems consistently fail under real-world field conditions characterized by variable lighting, co-occurring infections, and cultivar diversity. This review synthesizes a decade of progress across four interconnected frontiers: the evolution of deep learning architectures for plant disease detection; the adaptation of foundation models including CLIP, SAM, and DINOv2 to agricultural contexts; the development of multimodal fusion frameworks integrating imagery, environmental, genomic, and hyperspectral data; and the transition from static disease diagnosis to descriptive comparison of reported metrics, which suggested that multimodal approaches frequently reported improved diagnostic performance relative to corresponding single-modality baselines, although direct cross-study comparison was limited by methodological heterogeneity. A systematic review following PRISMA guidelines identifies eligible comparative studies. Descriptive comparison of reported performance metrics across these studies indicated that multimodal approaches generally achieved higher accuracy and sensitivity than single-modality models, particularly for pre-symptomatic disease detection. Eight critical research gaps are identified, including the absence of a unified agricultural foundation model and limited climate-aware forecasting under non-stationary climate projections. A structured research agenda is proposed to accelerate translation from laboratory performance to globally equitable, field-deployable crop protection systems.
Why it matches plant phenotyping methods植物病害の画像ベース検出・予測手法を対象とする系統的レビューであり、植物の病徴・病害状態を観測から推定するフェノタイピング手法のレビューとして中心的です。
titleMultimodal Deep Learning and Foundation Models for Early Detection and Forecasting of Plant Diseases
Background Global soybean production is constrained by scarce arable land, and standardized evaluation tools remain lacking for natural mixed saline-alkali stress, the predominant abiotic stress under field conditions. Objective This study aimed to establish a comprehensive saline-alkali tolerance evaluation system for soybean germplasms via integrated multivariate statistical methods, and screen core and auxiliary indicators for efficient germplasm identification. Methods Seventy-one soybean germplasms were tested under 90 mmol/L mixed saline-alkali stress (NaCl:Na 2 SO 4 :NaHCO 3 :Na 2 CO 3 = 1:9:9:1, pH 8.2) simulating natural saline-alkali soil. We quantified the saline-alkali tolerance coefficients (SATC) of 13 morphological and physiological indicators, followed by coefficient of variation (CV), principal component analysis (PCA), subordinate function, cluster analysis and regression modeling. Results Significant inter-germplasm variations in saline-alkali tolerance were detected, and indicators with CV > 0.35 ( e.g ., root length (RL), root fresh weight (RFW)) were screened as primary indices. PCA extracted five principal components with 87.18% cumulative variance contribution, and the integrated analytical pipeline categorized germplasms into five tolerance grades: eight highly tolerant, 24 moderately tolerant, 13 generally tolerant, 16 sensitive and 10 highly sensitive accessions. A high-precision prediction model was constructed ( D = 0.290 X 1 - 0.026 X 2 + 0.438 X 3 + 0.402 X 4 + 0.180 X 5 + 0.153 X 6 + 0.813 X 7 - 1.123; R 2 = 0.998, where X 1 - X 7 represent the SATC of germination rate (GR), RL, RFW, total fresh weight (TFW), shoot dry weight (SDW), root dry weight (RDW), and total dry weight (TDW), respectively). A Chi-squared Automatic Interaction Detection (CHAID) decision tree model was further developed and validated using 10-fold cross-validation, yielding a cross-validation risk value of 0.003, which was comparable to the resubstitution risk value (0.002), indicating good generalization ability and low risk of overfitting. A novel five-dimensional overlapping analysis identified RFW as the core evaluation indicator, with RDW, TFW and R/S as key auxiliary indicators. Conclusion This study delivers a standardized, reproducible technical framework for large-scale screening of saline-alkali-tolerant soybean germplasms. It facilitates global saline-alkali land utilization, accelerates worldwide soybean stress-tolerance breeding, and provides a transferable paradigm for stress tolerance evaluation in other major crops.
Why it matches plant phenotyping methodsダイズの耐塩・耐アルカリ性を評価するための形態・生理形質の統合評価体系、予測モデル、指標選定、交差検証を中心的に開発・検証しており、再利用可能な植物表現型評価手法に該当する。
abstractThis study aimed to establish a comprehensive saline-alkali tolerance evaluation system for soybean germplasms via integrated multivariate statistical methods, and screen core and auxiliary indicators for efficient germplasm identification.
Cell polarity and tip growth rely on the dynamic spatial organization of signaling and structural components. Quantitative characterization of these spatiotemporal dynamics is critical for understanding polarized cell growth, yet manual quantification is labor-intensive and existing computational tools often lack the flexibility and robustness needed to analyze molecular and structural dynamics in tip-growing cells. Tip Quantification (TipQuant) identifies the cell apex by detecting the site of maximum expansion and automatically quantifies fluorescence distribution along the plasma membrane and within the apical cytoplasm from live-cell imaging data, enabling analysis of the spatiotemporal dynamics of molecular and structural components in tip-growing cells. TipQuant accurately identified cell apices and quantified the spatiotemporal behavior of fluorescently labeled proteins and cellular structures in Arabidopsis thaliana pollen tubes and Fusarium graminearum hyphae, reproducing manual measurements while reducing user bias and improving efficiency, consistency, and analytical flexibility. The tool also revealed a strong positive correlation between rho-like GTPase from plants activity and apical Ca 2+ influx in Arabidopsis pollen tubes, demonstrating its utility for analyzing dynamic cellular processes. TipQuant is a robust analytical tool for quantifying spatiotemporal dynamics in tip-growing cells, providing a flexible alternative to manual image analysis and enabling studies of the molecular mechanisms underlying polarized growth.
Why it matches plant phenotyping methodsTipQuantはライブセル画像から植物の細胞先端位置、膜上の蛍光分布、先端細胞質内の動態を自動定量する解析ツールであり、画像ベースの植物表現型・状態取得が研究の中心です。
abstractTip Quantification (TipQuant) identifies the cell apex by detecting the site of maximum expansion and automatically quantifies fluorescence distribution along the plasma membrane and within the apical cytoplasm from live-cell imaging data
Plant diseases remain a threat to global agricultural productivity, food security and livelihoods, especially in developing countries where the availability of experts in agriculture is still limited. The recent progress in AI, particularly deep learning and computer vision, has ushered in new possibilities for automated plant disease diagnosis, especially for plant image-based systems. This paper provides a systematic review of the deep learning methods employed for plant disease diagnosis, highlighting CNN-based methods, the application of transfer learning, explainable AI (XAI) methods and deployment issues. In the framework of PRISMA 2020, the relevant peer reviewed literature from 2016 to 2025 was systematically identified, screened and analysed on the most important academic databases. The review compared some of the most popular architectures such as GoogLeNet, DenseNet-121, MobileNetV2, EfficientNet, Attention-CNNs and Vision Transformers. Results showed very high classification accuracy in controlled lab conditions with DenseNet-121 achieving ~99.75% accuracy with good computational efficiency. But it also revealed a big gap between the lab and the field, mainly due to environmental variations, domain shifts, and dependence on datasets. Some innovative and emerging technologies like explainable AI, hyperspectral imaging, few-shot learning, and lightweight mobile architectures showed promise of enhancing the interpretability, early detection of disease, and the use of smart phones in low-resource agricultural settings. In conclusion, the study suggests that in order to be implementable in the field, future intelligent agricultural diagnosis systems must be able to balance predictive accuracy, explainability, computational efficiency and field adaptability. The results enrich the existing knowledge on precision agriculture and serve as useful information for researchers, agricultural technologists, and policymakers working on the creation of AI-based systems for crop protection.
Why it matches plant phenotyping methods植物病害を画像から診断する深層学習手法を体系的に比較・レビューしており、植物の病徴・病害状態の推定方法が中心である。
abstractThis paper provides a systematic review of the deep learning methods employed for plant disease diagnosis
Rice seed vigor is a key determinant of germination performance and final crop yield, making its rapid and non-destructive assessment essential for seed quality evaluation. Conventional vigor detection methods are often destructive, labor-intensive, and time-consuming. Hyperspectral imaging provides a promising non-destructive alternative, but hyperspectral data are typically high-dimensional, redundant, and susceptible to noise and scattering interference. Moreover, existing models still have limited ability to discriminate subtle spectral differences among seed vigor levels. To address these challenges, this study proposes a gated recurrent unit (GRU)-guided closed-loop CNN-Transformer network (GCT-BCLN) for accurate, non-destructive identification of rice seed vigor. The model establishes bidirectional information flow between CNN and Transformer via the GRU, enabling dynamic and synergistic optimization of local spectral features and global spectral representations. In addition, a combined preprocessing strategy integrating adaptive iteratively reweighted penalized least squares (AirPLS), Savitzky-Golay (SG) smoothing, and multiplicative scatter correction (MSC) was adopted to improve spectral quality. Experimental results showed that GCT-BCLN achieved a test accuracy of 0.9795 for hybrid indica rice, outperforming the CNN-Transformer fusion model by 1.37%. The model also achieved accuracies of 0.9793 and 0.9758 on conventional japonica rice and glutinous japonica rice, respectively, showing consistent performance across the three evaluated variety-specific datasets under the controlled experimental protocol. These results support the feasibility of GCT-BCLN for laboratory-scale, non-destructive discrimination of aging-induced rice seed categories under controlled conditions, while practical application requires further external validation.
Why it matches plant phenotyping methodsイネ種子の活力という植物状態を、ハイパースペクトル画像と新規深層学習モデルで非破壊推定する手法開発が研究の中心である。
abstractthis study proposes a gated recurrent unit (GRU)-guided closed-loop CNN-Transformer network (GCT-BCLN) for accurate, non-destructive identification of rice seed vigor.
Abstract Cross-sectional analysis is considered the reference method for measuring cotton fiber fineness and maturity; however, its widespread use has been limited by labor-intensive sample preparation and manual image analysis. The objective of this study was to develop and validate a reproducible deep-learning-based workflow for automated segmentation and quantitative analysis of cotton fiber cross-sections. A total of 249 composite light microscopy images of cotton fiber cross-sections were collected and manually annotated to generate training and validation datasets. A YOLO11m instance segmentation model was developed to identify cotton fiber and lumen regions and automatically extract quantitative traits, including fiber area, lumen area, fiber perimeter, and lumen perimeter. The workflow integrates automated image segmentation, post-processing, quantitative trait extraction, and data export to facilitate reproducible cotton fiber phenotyping. Model performance was evaluated using mean Average Precision (mAP), and workflow outputs were validated against Adobe Photoshop using descriptive comparisons of six cross-sectional traits. The model achieved Box mAP50 scores of 0.984 for cotton fiber regions and 0.789 for lumen regions, demonstrating high segmentation accuracy. The automated workflow substantially reduced manual analysis time while producing measurements with central tendencies comparable to those obtained using Adobe Photoshop. To facilitate reproducibility and adoption, the workflow, trained model weights, and supporting documentation are publicly available through GitHub and a Hugging Face web application. The workflow substantially increases analytical throughput while providing a reproducible and publicly accessible method for automated cotton fiber cross-sectional phenotyping, facilitating quantitative analysis for cotton genetics and breeding research.
Why it matches plant phenotyping methods綿繊維横断面の画像分割、形質抽出、検証を目的とした再現可能な深層学習ワークフローの開発であり、植物フェノタイピング手法が研究の中心である。
abstractThe objective of this study was to develop and validate a reproducible deep-learning-based workflow for automated segmentation and quantitative analysis of cotton fiber cross-sections.
Reproduction assets foundThe paper's cotton fiber cross-section phenotyping workflow (YOLO11m segmentation pipeline, trained model weights, example images/outputs) is explicitly stated as publicly available via a GitHub repository and a Hugging Face web application, with URLs matching the allowed list.Code · publicThe complete source code, training scripts, dataset configuration, example input images, example outputs,
and supporting documentation are publicly available through the GitHub repository:
https://github.com/RifeLab/cotton-lumen-microOpen asset ↗RifeLab/cotton-lumen-micropdf-page:11 lines:1-47Code · publicThe cotton fiber image analysis workflow is publicly available through a web-based application hosted on
Hugging Face at: https://huggingface.co/spaces/chaneylc/cotton_fiber_microscopy_measureOpen asset ↗pdf-page:11 lines:1-47Plant phenotyping relevance match · UnverifiedEurope PMC · bioRxiv · checked 5 Sept 2026
White lupin ( Lupinus albus L.) is a cool-season grain legume with seed crude protein of 33-47%, competitive with soybean ( Glycine max L.) meal. It also fixes nitrogen and mobilizes soil phosphorus. Because soybean is a summer crop, white lupin can occupy Southeastern winter fields as a complementary protein source. Breeding for seed protein is limited by the cost and throughput of reference phenotyping. To determine how each is best deployed, we compared the utility of near-infrared spectroscopy (NIRS)-based phenomic selection with genomic selection based on 246,847 SNPs from low-pass, whole genome sequencing in a panel of Auburn University breeding lines and USDA National Plant Germplasm System germplasm. A handheld NIR calibration against Dumas reference protein reached screening-grade accuracy (R 2 = 0.81). Under common cross-validation, phenomic predictive ability was 0.93 and genomic was 0.12. The low genomic value was consistent with moderate heritability (H 2 = 0.33) and strong genotype-by-year interaction. Beyond predictive ability, NIRS recovered superior accessions the strictest selection intensity, and 40 to 60 reference assays sufficed to calibrate the model. Handheld NIRS is a low-cost tool for protein calibration and early-generation screening, while genomic prediction remains suited to parental selection, together supporting a complementary strategy for legume breeding Plain Language Summary Soybean meal is the main protein source for livestock and fish farms in the United States. Because soybean is a summer crop, many Southeastern fields sit idle or grow low-value cover crops in winter. White lupin, a cool-season legume whose seeds are as protein-rich as soybean meal, makes a good complementary winter crop: it yields high-protein grain while serving as a cover crop that fixes nitrogen and frees up soil phosphorus for later crops. In our early-stage lupin breeding program, measuring seed protein by standard lab methods is slow and costly. We built a calibration that lets a handheld scanner estimate protein from light, and compared it with predicting protein from the plant’s DNA. The scanner gave accurate, low-cost protein screening from only about 40-60 lab tests, while DNA-based prediction remains suited to guiding parent selection. Used together, these tools offer breeders a practical path to develop high-protein white lupin. Core ideas Handheld NIRS provides screening-grade prediction of white lupin seed crude protein. Spectra carried more usable protein signal than markers by measuring seed chemistry directly. NIRS and genomic prediction serve different stages of a white lupin breeding program. About 40 to 60 reference assays sufficed to calibrate NIRS to near-full accuracy.
Why it matches plant phenotyping methods携帯型NIRSによる種子タンパク質形質の推定・校正・精度検証が研究の中心であり、育種スクリーニングへの実質的応用も評価している。
abstractA handheld NIR calibration against Dumas reference protein reached screening-grade accuracy (R 2 = 0.81).
Root anatomical barriers, including the suberized and lignified walls of the endodermis and exodermis, and cortical aerenchyma, regulate water and nutrient transport, gas exchange, and rhizosphere interaction. Their adaptive function places them as an important target for breeding environmentally resilient plant species. Quantifying these structures at high resolution is a manual bottleneck that limits experimental scale. We present RADIX (Root Anatomy Deep- learning Image segmentation across species and platforms), a framework that adapts a large self-supervised vision-transformer foundation encoder (DINOv3), pre-trained on billions of natural images, to root anatomy by fine-tuning its encoder with a dense-prediction-transformer decoder. Transferring these general-purpose vision encoders to a specialized biological domain with a high-quality annotated dataset is what allows RADIX to generalize across species and imaging platforms. We train and evaluate it on the first expert-annotated benchmark of root anatomical structures at scale, comprising 1,695 high-quality fluorescence images spanning 17 monocot and dicot species, six anatomical structures, and three imaging platforms. RADIX segments all six structures at inter-annotator-level accuracy and generalizes to unseen species, genotypes, growth conditions, and an imaging platform from an independent laboratory. A single unified model surpasses monocot- and dicot-specialist models without sacrificing in-group accuracy. Predicted masks yield aerenchyma and suberin/lignin measurements matching expert annotation at ∼1.2 s per image with a single GPU, reducing weeks of manual analysis to minutes. Applying RADIX across genotypes, microbial treatments, and growth systems, we show that these cell type features form a coordinated, multidimensional, and context-dependent system shaped by genetic and environmental factors.
Why it matches plant phenotyping methods根の解剖学的構造を画像から自動抽出・定量する深層学習フレームワークを開発し、注釈付きベンチマーク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。
abstractQuantifying these structures at high resolution is a manual bottleneck that limits experimental scale.
Plant phenotyping relevance match · UnverifiedCrossref · Europe PMC · OpenAlex · checked 15 Sept 2026
Grass seed crops are susceptible to yellow dwarf viruses transmitted by aphids. The Willamette Valley in Oregon, United States, is the leading producer of cool-season grass seed crops globally, and industry reports have attributed seed yield loss and shortened stand longevity to aphid-transmitted yellow dwarf viruses. Genetic resources are needed for effective and sustainable management of this pest, specifically the Rhopalosiphum padi–PAV pathosystem, in grass seed production to reduce foliar insecticide applications and maintain optimum seed yield potential. High-throughput phenotyping methods are needed to screen grass seed cultivars to identify resistant traits for traditional breeding programs. An automated video tracking procedure was optimized to evaluate host plant resistance in cool-season grass seed crops to R. padi–PAV with live plants and viruliferous and nonviruliferous aphid populations. Feeding behavior recorded with automated video tracking was strongly correlated with “ground-truthed” observations by human observers. Partial resistance (antixenosis and antibiosis) and tolerance traits were detected in select perennial ryegrass and tall fescue cultivars evaluated with traditional phenotyping methods in a greenhouse setting and with high-throughput phenotyping using automated video tracking in the laboratory. Across grass cultivars, nonviruliferous aphids had greater fitness and preference for noninfected grass plants compared with viruliferous aphids. Automated video tracking can be used as a high-throughput phenotyping method for continued evaluation of host plant resistance in grasses grown for seed production, as well as to identify resistant genotypes in other grass crops susceptible to aphid–yellow dwarf virus virus–vector systems.
Why it matches plant phenotyping methods自動動画追跡を用いてアブラムシ媒介ウイルスに対する植物抵抗性を高スループットに評価する手法を最適化・検証し、従来観察との相関および抵抗性形質の検出を示しており、表現型取得法が研究の中心である。
abstractHigh-throughput phenotyping methods are needed to screen grass seed cultivars to identify resistant traits for traditional breeding programs.
Germination percentage is an endpoint measure and therefore does not describe when an individual seed begins visible growth or how rapidly its radicle and plumule expand. We developed a time-resolved phenotyping workflow to quantify rice seed germination continuously in shallow-water culture. A single industrial camera moved along a 1 m rail and imaged three culture boxes at 1 h intervals for up to 80 h. The archive comprised 1,062 full-frame images and 6,372 seed-level repeated observations under the six-seed field-of-view configuration. A physical grid maintained seed identity through time and enabled individual regions of interest to be extracted. Whole-seed foregrounds were obtained with a pretrained U 2 -Net, and a masked RGB intensity rule separated newly emerging tissue from the darker hull. For each tracked seed, projected emerging-tissue area and interval growth rate were calculated. Three representative normally germinating seeds first showed measurable tissue at 48 h, yet subsequently followed distinct trajectories: final projected areas ranged from 2,605 to 4,700 pixels and peak interval growth rates ranged from 106.88 to 287.92 pixels h −1 . B-1 accumulated 63.71% of its final visible area during 72–80 h, whereas B-3 accumulated 73.51% during 60–72 h. Thus, seeds with the same observed emergence interval can differ substantially in the timing and magnitude of post-emergence expansion. The workflow converts repeated images into biologically interpretable temporal phenotypes and provides a basis for nondestructive studies of rice seed vigor and germination heterogeneity.
Why it matches plant phenotyping methods連続画像から個々のイネ種子の発芽・組織面積・成長速度を抽出する時間分解フェノタイピングワークフローを開発しており、表現型取得と解析手法が研究の中心である。
abstractWe developed a time-resolved phenotyping workflow to quantify rice seed germination continuously in shallow-water culture.
Background: Plant diseases significantly threaten global food security, reducing potential harvests and sometimes causing total crop failure. Traditional detection methods, which rely on manual inspection and laboratory testing, are time-consuming, costly and prone to human error. Methods: To address these challenges, this study applies transfer learning techniques using deep convolutional neural networks for accurate and efficient plant disease detection. A comparative analysis of nine pretrained models, VGG16, VGG19, ResNet50, ResNet101V2, MobileNetV2, InceptionV3, DenseNet121, InceptionResNetV2 and Xception was conducted on the PlantVillage dataset, focusing on apple, potato and peach leaf images. Result: Results show that DenseNet121 and ResNet101V2 achieved the highest accuracy, particularly for potato leaves with 98.5%, while MobileNetV2 also performed well with up to 99% accuracy for apple and peach leaves. The study demonstrates that transfer learning effectively enhances plant disease classification, enabling faster, more reliable and resource efficient detection for precision agriculture.
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手法を比較評価しており、病害表現型の取得・分類が研究の中心である。
abstractthis study applies transfer learning techniques using deep convolutional neural networks for accurate and efficient plant disease detection.
Identifying plant health conditions is an emerging precision-agriculture and food-security challenge, intensified by deploying deep-learning models on memory- and power-constrained edge devices. We present a TinyMLOps pipeline spanning model design, optimization, quantization, and deployment across diverse edge devices, evaluated under controlled laboratory conditions. Using a dataset derived from the PlantVillage benchmark, 39 fine-grained classes are aggregated into three superclasses: healthy leaf, unhealthy leaf, and no leaves. The resulting system therefore performs plant health-status classification and background filtering rather than diagnosing specific diseases. We train a MobileNet-based convolutional neural network jointly optimized for classification accuracy and computational efficiency, adopting state-of-the-art hyperparameter optimization (HPO) tools. Five models are selected, four from the Pareto Front and one as the biggest evaluated model during HPO, converted to LiteRT and ONNX, and evaluated at float32 and post-training int8 precision on a Raspberry Pi Zero 2 W and an STM32H743ZI microcontroller. At float32, LiteRT is 1.87–2.65× faster than ONNX Runtime on the Raspberry Pi across all five models. Relative to their float32 LiteRT counterparts, the int8 LiteRT models are 2.83–3.67× smaller on disk and 21.3–31.2% faster on the same board, at a cost in F1-score of between 0.0010 and 0.0068. On the microcontroller, only the two smallest models deploy at both precisions; for these, the fully quantized int8 variants are 4.3× faster and 3.85× smaller in MCU flash footprint than the float32 counterparts. The mid-range model fits the 2 MB flash and 1 MB RAM budget only when quantized, while the two largest models exceed it in every configuration tested.
Why it matches plant phenotyping methods葉画像から植物の健康状態を推定する分類手法と、エッジデバイス向けの最適化・量子化・展開パイプラインが研究の中心であり、植物状態の画像ベースフェノタイピングに該当する。
abstractWe present a TinyMLOps pipeline spanning model design, optimization, quantization, and deployment across diverse edge devices, evaluated under controlled laboratory conditions.
Reproduction assets foundThe paper's plant-phenotyping measurements are based on a derived PlantVillage dataset (39 classes aggregated into three superclasses) that the authors explicitly state is openly available in their own GitHub repository, also catalogued in the AgrifoodTEF Data Space. No author analysis code or trained model checkpointsDataset · publicThe data used in this study are derived from the openly available GitHub repository available at https://github.com/FBK-OpenIoT/PlantVillage-AugNoLeaves , accessed on 1 July 2026.Open asset ↗FBK-OpenIoT/PlantVillage-AugNoLeaveslines:1355-1403Dataset · publicPlantVillage-AugNoLeaves—AgrifoodTEF Data Space Catalogue. 2025. [(accessed on 1 July 2026)]. Available online: https://dataspace.agrifoodtef.eu/asset/did:op:3091fdcf83a05784416e585e4e45ea24afaf8c925a891921a3949acd98426f65Open asset ↗did:op:3091fdcf83a05784416e585e4e45ea24afaf8c925a891921a3949acd98426f65lines:1424-1474Plant phenotyping relevance match · UnverifiedEurope PMC · checked 15 Sept 2026
Root lodging, the agronomic term for plant mechanical failure, causes yield loss in crops, including maize. Brace roots can provide structural support and assist in preventing root lodging. While the mechanics of brace roots (e.g., stiffness and strength) can play a role in their ability to prevent root lodging, there has been limited characterization of individual brace root mechanical properties. Methods to quantify root mechanics can thus be useful for characterizing maize mechanical traits and breeding new varieties with improved root anchorage and lodging resistance. Here, we describe a protocol for evaluating mechanical properties of maize brace roots. Specifically, we outline the steps necessary to perform three-point bend mechanical testing of maize brace roots using an Instron Universal Testing Stand. We describe root preparation, instrument setup, method establishment, testing, and data analysis. While we exemplify the protocol using maize brace roots, the approach can be adapted for assessing the mechanics of other plants or root types.
Why it matches plant phenotyping methodsトウモロコシの根の力学特性という植物形質を定量する三点曲げ測定プロトコルの開発・手順化が中心であり、育種利用可能な表現型測定法に該当する。
abstractHere, we describe a protocol for evaluating mechanical properties of maize brace roots.
Idesia polycarpa Maxim. is a premier woody oil species in Guizhou Province, China, whose fruit yield and oil quality largely depend on effective pollination and fertilization. However, limited research on pollen viability and germination has hindered industrial progress. To address this gap, a comprehensive evaluation framework for elite I. polycarpa germplasm was developed, integrating micromorphological analysis, optimized staining protocols, and in vitro germination assay. Scanning electron microscopy (SEM) revealed that I. polycarpa pollen, while genetically conserved at the genus level-characterized by prolate shapes, tricolporate apertures, and reticulate exine ornamentation-exhibits notable micromorphological variation among genotypes. Of the nine staining protocols tested (2,3,5-triphenyl tetrazolium chloride [TTC], carbol fuchsin, acetocarmine, methylene blue, Alexander, peroxidase, 2,5-diphenylmonotetrazolium bromide [MTT], I2-KI, and red ink), TTC and red ink were the most effective, offering clear chromatic distinction between viable and non-viable pollen. Through orthogonal experimental designs, genotype-specific optimal media for in vitro germination were identified: 0.40 g/L H3BO3, 0.01 g/L KNO3, 0.02 g/L Ca(NO3)2·4H2O, and 0.20 g/L KH2PO4 for STZ-6; and 0.20 g/L H3BO3, 0.02 g/L KNO3, 0.02 g/L Ca(NO3)2·4H2O, and 0.10 g/L KH2PO4 for STZ-9. Regression analysis confirmed a highly significant positive correlation (P < 0.01) between in vitro germination rates and the staining results from both TTC and red ink across various concentrations. Notably, 5% TTC and 30% red ink exhibited the highest coefficients of determination. A hierarchical evaluation strategy is thus proposed: the 5% TTC method is recommended for precise laboratory quantification due to its stability, while the 30% red ink method, due to its ease of use, is suited for rapid field-based screening. This study provides valuable insights into the morphological characteristics of I. polycarpa pollen and establishes a standardized evaluation framework, supporting germplasm innovation and optimizing pollination management.
Why it matches plant phenotyping methods花粉の生存性・発芽という植物の生殖形質を対象に、染色法とin vitro発芽法を最適化・検証し、標準化した評価フレームワークを開発しているため、方法論が中心である。
abstracta comprehensive evaluation framework for elite I. polycarpa germplasm was developed, integrating micromorphological analysis, optimized staining protocols, and in vitro germination assay.
Reproduction assets foundThe article's Data Availability statement points to a public Biostudies deposit containing the study's data (pollen morphology measurements, staining viability counts, and in vitro germination results). No author analysis code or trained models are mentioned.Dataset · publicData Availability: The data that support the findings of this study are openly available in Biostudies at https://doi.org/10.6019/S-BSST3125 .Open asset ↗Biostudies · S-BSST3125lines:176-186Plant phenotyping relevance match · UnverifiedEurope PMC · checked 5 Sept 2026
Amino acids are important nutrients in maize grain used for food and feed. Because all 20 amino acids are required for growth and development, a deficiency in a single essential amino acid limits the utilization of dietary protein. In monogastric animals, 10 amino acids must be supplied by the diet and therefore are considered essential. The remaining amino acids can be made from the 10 essential amino acids. Lysine, tryptophan, and methionine are frequently limiting essential amino acids in grain-based diets. Therefore, increasing levels of limiting essential amino acids in grain is an important objective in crop improvement. Standard chromatographic methods for assessing levels of amino acids in grain are extremely accurate, but very expensive. Here, we present a protocol for high-throughput analysis of amino acids in grains, using microbial assays, conducted in 96 well plates, that can be carried out for a fraction of the cost of the standard chromatographic methods. We use Escherichia coli strains that have mutations in the biosynthetic pathway of the amino acid of interest. These strains are auxotrophic, so their growth is proportional to the amount of a specific amino acid in the media. The level of the amino acid of interest in a corn extract is determined by adding the corn extract to the microbial growth medium and measuring the growth of the culture as turbidity in a 96 well plate reader. This protocol is designed for analysis of methionine, but can be adapted for the analysis of any amino acid, by substitution of an appropriate auxotrophic strain of E. coli .
Why it matches plant phenotyping methodsトウモロコシ種子のアミノ酸含量という育種関連形質を、96ウェルで高スループット測定する新規プロトコル自体が中心であり、単なる生物学実験のルーチン測定ではない。
abstractHere, we present a protocol for high-throughput analysis of amino acids in grains, using microbial assays, conducted in 96 well plates, that can be carried out for a fraction of the cost of the standard chromatographic methods.
Annotation scarcity, poor model generalization and lagged data processing remain key bottlenecks hindering the practical deployment of phenotyping robots. To address these issues, we developed a novel phenotyping robot capable of online 3D reconstruction and zero-shot segmentation directly on the edge. Diverging from conventional semantic SLAM, our core contribution is RT-ZSDR, a framework featuring two key methodological novelties. First, we introduce the ForeCut pipeline for target extraction, which innovatively fuses DINO features with 3D geometric spatial information, leveraging multi-view semantic-spatial consistency to achieve annotation-free, zero-shot dense segmentation and reconstruction. Second, we designed a hardware-coupled loop closure strategy utilizing the robotic arm's kinematic feedback as prior constraints to significantly improve loop closure recall. Supported by edge computing Jetson Orin NX, the tracking and segmentation process takes approximately 0.24 s per frame after an initialization period of 1.82 s. RT-ZSDR's phenotypic measurements demonstrated strong correlations with reference baseline in both laboratory settings (n=90, PlantEye measurements as reference baseline; R 2 =0.990, 0.939, 0.725, and 0.861 for plant height, projected leaf area, surface area, and volume) and practical greenhouse environments (n=48, manual measurements as reference baseline; R 2 =0.965, 0.862 for plant height and stem diameter). Additionally, evaluated against COLMAP benchmarks (n=24), the system achieved a mean 3D reconstruction F1-score of 0.816.
Why it matches plant phenotyping methods植物フェノタイピングロボット向けに、ゼロショット分割・3D再構成・エッジ処理を開発し、植物形質を基準測定およびベンチマークと比較検証しており、取得・抽出手法が研究の中心である。
abstractwe developed a novel phenotyping robot capable of online 3D reconstruction and zero-shot segmentation directly on the edge.
Low-cost uncooled snapshot hyperspectral sensors mounted on UAV platforms offer new opportunities for field-scale remote sensing high-throughput phenotyping, but their reliability is constrained by sensor-intrinsic artefacts, particularly dark current. In this study, we present the first spatio-spectro-temporal characterisation and correction of dark current in the Senop HSC-2 dual-CMOS Fabry-Perot snapshot hyperspectral camera. Six controlled dark experiments (∼6,100 image cubes) revealed that dark current in the sensor is highly structured and reproducible, exhibiting CMOS-specific baseline offsets, monotonic temporal drift, pixel-wise dark signal non-uniformity (DSNU) with wavelength-dependent structure, and persistent hot pixels, and exposure-dependent baseline shifts that do not scale linearly with integration time. These results confirm that conventional single-frame dark subtraction is insufficient for quantitative analysis in uncooled snapshot hyperspectral sensors. Building on this characterisation, a modular correction framework was developed to stabilise the dark signal across spatial, spectral, and temporal domains. Across laboratory datasets, the framework reduced temporal drift by 70-85%, DSNU variance by approximately 37.5%, and suppressed >99.9% of persistent hot pixels, substantially improving radiometric stability. Corrected data exhibited simultaneous spatial uniformity, temporal stability, and spectral integrity, enabling downstream radiometric processing without introducing spectral distortion. Application of Senop HSC-2 to UAV-acquired wheat canopy imagery demonstrated effective transfer to field conditions, improving spectral continuity and robustness of vegetation indices after dark current correction. These results establish a transferable calibration approach for affordable snapshot hyperspectral sensors and demonstrate that rigorous dark current correction is essential for achieving quantitative radiometric performance in UAV-based phenotyping and precision agriculture applications without active thermal control, extending calibration principles traditionally applied in satellite hyperspectral systems to low-cost UAV snapshot sensors.
Why it matches plant phenotyping methodsUAVハイパースペクトルセンサの暗電流を空間・スペクトル・時間的に補正する手法の開発と検証が中心であり、圃場のコムギ群落画像への適用も行っているため、植物フェノタイピング手法として採用する。
abstractIn this study, we present the first spatio-spectro-temporal characterisation and correction of dark current in the Senop HSC-2 dual-CMOS Fabry-Perot snapshot hyperspectral camera.
The efficacy of biological control agents is often inconsistent across pathogen isolate panels, yet conventional dual-culture screening often reduces antagonism to single endpoint measurements such as radial growth or colony area. Here, we developed a quantitative phenotyping framework to evaluate interactions between three Trichoderma antagonists and Colletotrichum isolates associated with coffee and cacao. Dual-culture assays were used to quantify antagonist and pathogen morphology after 96 h, and the combined morphology dataset was analyzed using machine learning to test whether host-associated isolate panels could be classified from colony-level interaction traits. To address potential information leakage and basal-growth confounding, we evaluated control-only pathogen morphology models and leave-one-pathogen-isolate-out validation. Under random 5-fold validation, Random Forest models achieved similar balanced accuracy using control-only pathogen morphology and full dual-culture interaction morphology, 0.840 and 0.864, respectively. Under the more conservative leave-one-pathogen-isolate-out validation, performance decreased but the full dual-culture model, balanced accuracy = 0.725, outperformed the control-only model, balanced accuracy = 0.578, indicating that basal pathogen morphology contributes to host-associated differences while interaction-level traits add information beyond basal growth alone. Hyperspectral imaging was then used as a proof-of-concept, non-invasive tool to characterize selected interaction interfaces. In the complete 11C-65-1 × P24-83/P24-192 subset, VNIR reflectance residuals showed interface-specific deviations from within-plate colony-side spectral mixing axes. These residual wavelength features are presented as candidate spectral correlates rather than validated biochemical mechanisms. Overall, morphology-based machine learning and hyperspectral interface phenotyping provide a scalable framework for controlled biocontrol screening, while emphasizing the importance of isolate-aware validation and cautious spectral interpretation.
Why it matches plant phenotyping methods植物コロニーの形態を定量化し、機械学習とハイパースペクトル画像で相互作用表現型を抽出する枠組みを開発・検証しており、表現型取得と解析手法が研究の中心である。
abstractHere, we developed a quantitative phenotyping framework to evaluate interactions between three Trichoderma antagonists and Colletotrichum isolates associated with coffee and cacao.
ArabidopsisLaboratory / benchtopMicroscopyStem / branchVisualization / data management
Fluorescent stains for lignified plant walls must operate in chemically heterogeneous, autofluorescent matrices while remaining compatible with confocal multiplexing. Here, we evaluated two canonical Ru(ii) tris-polypyridyl luminophores, Ru1 ([Ru(deeb) 3 ] 2+ ) and Ru2 ([Ru(phen) 3 ] 2+ ), as non-derivatizing stains for fixed Arabidopsis thaliana stem sections. In situ spectral profiling defined practical 405-nm confocal detection windows, and both probes produced reproducible wall-associated photoluminescence enriched in secondary-wall-rich vascular domains, especially xylem vessels and interfascicular fibers. Their anatomical distribution showed qualitative concordance with Wiesner/Mäule lignin histochemistry and condition-validated Safranin O maps, supporting their use as spatial reporters of matrix-associated enrichment within anatomically defined lignified secondary-wall territories. The molecular determinants of this enrichment, including the relative contribution of lignin and other wall polymers, remain to be resolved. Sequential co-staining with Calcofluor White separated broad β-glucan-rich wall architecture from Ru-enriched secondary-wall domains, while spectral-overlap analysis identified far-red Alexa Fluor 647 excitation at 638 nm as the most orthogonal tested third-label configuration. Ligand-comparative DFT descriptors provided structure-property fingerprints summarizing differences in π-surface continuity and electrostatic anisotropy. Overall, these results position canonical Ru(ii) polypyridyl luminophores as confocal-compatible, chemically tractable scaffolds for anatomical imaging of lignified plant-wall territories.
Why it matches plant phenotyping methods植物の木化二次細胞壁を可視化・空間評価する共焦点蛍光染色法の開発と検証が中心であり、単なる生物学的測定ではない。
abstractFluorescent stains for lignified plant walls must operate in chemically heterogeneous, autofluorescent matrices while remaining compatible with confocal multiplexing.
Introduction Timely and accurate detection of plant diseases is essential for ensuring global food security and supporting sustainable agriculture. Conventional diagnostic approaches, such as manual inspection and laboratory testing, are often time-consuming, labor-intensive, and impractical for large-scale or remote agricultural environments. Although deep learning models, particularly Convolutional Neural Networks (CNNs), have significantly improved automated plant disease classification, they often lack interpretability and struggle to generalize under diverse field conditions. Methods This study proposes AgriX-SENet, an explainable deep learning framework that integrates Squeeze-and-Excitation (SE) blocks with a DenseNet121 backbone to enhance disease classification performance. The SE blocks recalibrate channel-wise feature responses to emphasize disease-relevant information while suppressing background noise. To improve model transparency, Grad-CAM, SHAP, and LIME were incorporated to provide visual and feature-level explanations of the model’s predictions. The framework was trained and evaluated using the Plant Pathology 2020 dataset containing four classes: healthy, rust, scab, and multiple diseases. Results AgriX-SENet achieved a training accuracy of 97.47% and a validation accuracy of 95.07%, outperforming fourteen state-of-the-art deep learning models. The classification report demonstrated high precision and recall across most disease categories, although the scab class exhibited comparatively lower recall, indicating an opportunity for further improvement. The explainability analyses consistently showed that the model focused on pathologically relevant regions of leaf images, validating the reliability of its predictions. Discussion The proposed AgriX-SENet framework effectively combines high classification performance with model interpretability, addressing a key limitation of existing CNN-based plant disease detection systems. Its ability to provide accurate and explainable predictions makes it a promising solution for scalable agricultural diagnostics. Future work will focus on improving classification performance for challenging disease categories and optimizing the framework for deployment on mobile and edge computing devices to enable real-time field applications.
Why it matches plant phenotyping methods葉画像から植物病害状態を推定する説明可能な深層学習フレームワークを開発・評価しており、植物表現型取得・判定手法が中心である。
abstractThis study proposes AgriX-SENet, an explainable deep learning framework that integrates Squeeze-and-Excitation (SE) blocks with a DenseNet121 backbone to enhance disease classification performance.
Reproduction assets foundThe paper trains and evaluates AgriX-SENet on the public Plant Pathology 2020 (FGVC7) Kaggle image dataset, which is the paper-specific plant image input for its disease-classification measurements. No author analysis code, trained model checkpoints, or supplementary code/data deposit is mentioned; the data statement (Dataset · publicPlant Pathology 2020 - Fgvc7 . Available online at: https://www.kaggle.com/competitions/plant-pathology-2020-fgvc7/data .Open asset ↗Kaggle · plant-pathology-2020-fgvc7lines:895-974Plant phenotyping relevance match · UnverifiedEurope PMC · Crossref · checked 5 Sept 2026
Abstract Deep learning has achieved remarkable success in rice disease diagnosis; however, existing methods often suffer from limited interpretability and poor robustness against open-world environmental noise. To address these challenges, this study proposes the Knowledge-Guided Multi-Task Rice Network (MTRNet) built upon a ResNet-50 backbone. Unlike conventional "black-box" models, MTRNet employs expert knowledge injection via a phytopathological matrix to explicitly disentangle disease features into Shape, Color, and Location attributes within a multi-head architecture. Furthermore, to mitigate false positives in complex field scenarios, a non-parametric Cascade Inference System (CIS)—comprising a biological grayscale filter and a visual consistency check—is introduced for robust Out-of-Distribution (OOD) detection and anomaly rejection. Experiments on a benchmark dataset of 5,932 field images, which primarily comprises four main rice diseases (Rice Leaf Blast, Brown Spot, Bacterial Leaf Blight, and Tungro), demonstrate that MTRNet achieves a diagnostic accuracy of 99.83%. Crucially, in an open-world robustness evaluation involving 1,000 non-agricultural noise samples, the proposed system achieved an 81.80% OOD rejection rate. By balancing diagnostic accuracy with structural transparency, this framework effectively narrows the gap between laboratory benchmarks and real-world agricultural applications.
Why it matches plant phenotyping methodsイネ病害の画像から病徴を診断する深層学習・OOD検出手法を提案し、実画像データで性能評価しており、植物の病害状態の取得・推定が研究の中心である。
abstractthis study proposes the Knowledge-Guided Multi-Task Rice Network (MTRNet) built upon a ResNet-50 backbone.
Leaf disease diagnosis needs models that are accurate enough for agronomic use yet small enough for constrained computing settings. This study examines a late-attention MobileNetV2 design in which one Convolutional Block Attention Module (CBAM) is inserted between the last MobileNetV2 convolutional map and global average pooling. The experiments use 54,306 controlled-background PlantVillage images spanning 38 classes. Under a uniform saved-model re-evaluation, MobileNetV2 + CBAM obtained 97.17% accuracy and 97.15% weighted F1-score, whereas MobileNetV2 obtained 96.78% and 96.73%. On the converted models, paired testing gave a 0.64-percentage-point accuracy advantage for the CBAM variant (95% CI: 0.31–0.96; exact McNemar p
Why it matches plant phenotyping methods植物葉の病徴を画像から検出する軽量深層学習モデルを開発・比較しており、植物病害状態の画像ベース表現型取得が中心である。
titleAn Efficient Attention-Enhanced MobileNetV2 Framework for Plant Disease Detection on Resource-Constrained Devices
GrapevineLaboratory / benchtopStem / branchPhysiological trait estimationWater status / transpiration
Non-invasive, real-time monitoring of plant water status is critical for precision agriculture and plant physiology. However, existing methods often lack continuous in situ measurement capability or are limited by temporal resolution. This paper proposes a novel non-invasive method based on xylem electrical conductivity, inspired by industrial non-contact fluid measurement. As a ground-based complement to remote sensing, this approach demonstrates the feasibility of online, in situ, and non-invasive monitoring of water stress in grapevine stems under controlled laboratory conditions. The industrial C4D sensing system is adaptively modified into a specialized Plant-C4D sensor with an array-based design for batch signal acquisition. To validate the electrical response to water loss, a gravimetric natural dehydration experiment was conducted, demonstrating a clear correlation between electrical signals and water content changes in detached stem samples. Full-day dynamic experiments are conducted under three conditions: normal water supply, varying water stress, and plant inactivation. Sensitive characteristic parameters are extracted through signal analysis, and a pattern recognition framework is established to eliminate environmental interference and suppress individual differences. Experimental results on 24 plant samples (Shine Muscat) show that the method accurately discriminates viable from inactivated plants with an accuracy of 91.67% (22/24 correct). Furthermore, the Fuzzy C-Means (FCM) clustering algorithm successfully quantifies the severity of water stress in viable plants, yielding results consistent with actual water supply conditions. While these findings demonstrate the capability of Plant-C4D sensor to capture stem water status-related information, the current results do not establish full physiological validation, warranting further exploration with in vivo experiments.
Why it matches plant phenotyping methods植物の水分状態を直接推定する非侵襲センサーと解析手法の開発・検証が研究の中心であり、明確な植物フェノタイプ測定に該当する。
abstractThe industrial C4D sensing system is adaptively modified into a specialized Plant-C4D sensor with an array-based design for batch signal acquisition.
Blackberry micropropagation enables the rapid production of pathogen-free and genetically uniform plant material, although the evaluation of in vitro shoot development still relies on destructive and time-consuming measurements. This study investigated a low-cost smartphone-based 3D imaging approach for the non-destructive characterization of in vitro blackberry shoots (cultivar ‘Thornfree’) grown under different sucrose concentrations in the media (0, 7.5, 15, and 30 g L−1). Explants were cultured for 30 days under controlled environmental conditions in ventilated vessels containing 15 explants. Three-dimensional reconstructions generated using the viDoC RTK rover system coupled with an Apple iPhone 15 Pro Max were used to extract geometric traits, including shoot height, projected area, and shoot volume estimated through three complementary approaches, together with voxel-derived structural descriptors of shoot spatial organization and compactness. The proposed approach enabled the quantitative assessment of shoot architectural responses to sucrose availability, revealing differences in volumetric development and internal structural organization among treatments that would not be detectable by conventional measurements. The results highlight the potential of smartphone-based 3D phenotyping as a rapid, low-cost, and non-destructive tool for monitoring structural traits in micropropagated plant material and for supporting the optimization of in vitro culture conditions.
Why it matches plant phenotyping methodsスマートフォン由来の3D再構成を用いてシュート形態・構造形質を抽出する手法が研究の中心であり、非破壊植物フェノタイピングへの実質的応用である。
abstractThree-dimensional reconstructions generated using the viDoC RTK rover system coupled with an Apple iPhone 15 Pro Max were used to extract geometric traits, including shoot height, projected area, and shoot volume estimated through three complementary approaches, together with voxel-derived structural descriptors of shoot spatial organization and compactness.
Premise Accurate species identification is crucial for ecological restoration and can be especially challenging for understudied non-model species. Quercus garryana is the only native oak species in the Pacific Northwest and is an important component of the endangered oak savanna ecosystem. Quercus robur is an imported ornamental species from Europe and has been found to be mistakenly planted as Q. garryana in habitat restoration projects. Methods We measured leaf morphological traits sampled from herbarium collections in their native ranges using the digital morphometric tools MorphoLeaf and Tomato Analyzer. We then used Lasso logistic analysis to generate a predictive model and tested it on leaves from Portland, Oregon. To streamline this species detection process, we developed Garryanalyzer, an ImageJ plug-in that automatically measures leaf traits and outputs species predictions. Results Garryanalyzer demonstrated 95% accuracy in predicting the species identity of herbarium specimens of oaks. Garryanalyzer correctly identified all Q. robur individuals sampled in Portland but showed lower accuracy for Q. garryana . Discussion Many existing morphometric software are not open source, which makes them unable to be customized to specific study systems. Garryanalyzer is built upon the widely used open-source ImageJ platform. This study also demonstrates a viable workflow for developing similar tools for other ecologically important non-model plant species.
Why it matches plant phenotyping methods葉の形態形質を自動測定し、種予測まで行うImageJプラグインとワークフローの開発・評価が中心であり、植物フェノタイピング手法として適格です。
abstractTo streamline this species detection process, we developed Garryanalyzer, an ImageJ plug-in that automatically measures leaf traits and outputs species predictions.
Reproduction assets foundThe paper's authors publicly released the Garryanalyzer ImageJ plug-in source code on GitHub, all original and modified leaf images used in the morphometric analyses on Zenodo, and the full leaf morphometric measurement dataset plus R Lasso analysis code in a second Zenodo repository. All are paper-specific, public,可直接Code · publicThe source code and installation instructions for Garryanalyzer can be accessed on GitHub at https://github.com/zxie8561/Garryanalyzer.Open asset ↗https://github.com/zxie8561/Garryanalyzer · zxie8561/Garryanalyzerhtml-lines:210-274Dataset · publicAll images used in the morphometric analyses, both original and modified, are available on Zenodo (https://doi.org/10.5281/zenodo.17462266).Open asset ↗https://doi.org/10.5281/zenodo.17462266 · 10.5281/zenodo.17462266html-lines:210-274Dataset · publicThe full dataset of leaf morphometric measurements of both GBIF and Portland samples, R code for Lasso analysis, and other miscellaneous files are available on a separate Zenodo repository (https://doi.org/10.5281/zenodo.17546152).Open asset ↗https://doi.org/10.5281/zenodo.17546152 · 10.5281/zenodo.17546152html-lines:210-274Plant phenotyping relevance match · UnverifiedEurope PMC · checked 15 Sept 2026
Drought and water deficit have severely restricted melon ( Cucumis melo L.) production in Xinjiang, and large-scale systematic evaluations of drought tolerance at the germination stage are still extremely limited. Physiological and biochemical indicators related to the germination stage, including osmotic adjustment substances and antioxidant enzyme activities, have not yet been incorporated into prediction models for the rapid identification of germplasm drought resistance. To address these research gaps, this study selected 60 accessions of local melon germplasm resources in Xinjiang and used polyethylene glycol (PEG) solutions at four different concentrations (0%, 10%, 20% and 30%) to simulate drought stress conditions. Drought tolerance was evaluated to develop a method for the rapid screening of drought-tolerant germplasms. The findings demonstrated that PEG stress significantly suppressed seed germination and had both stimulatory and inhibitory effects on radicle growth. With the increase in PEG concentration, germination indices consistently exhibited a downward trend. Under 10% PEG treatment, the variation among different germplasms was relatively small, while 30% PEG completely inhibited seed germination. Notably, 20% PEG fell within the semi-lethal concentration range for all tested germplasms and yielded the maximum coefficient of variation for germination rate, which could maximally differentiate the drought resistance differences among germplasms. Therefore, 20% PEG was determined to be the optimal screening concentration. Under 20% polyethylene glycol (PEG) stress, the degree of membrane lipid peroxidation (malondialdehyde, MDA), contents of osmotic regulators (proline, Pro; soluble protein, SP), and activities of antioxidant enzymes (superoxide dismutase, SOD; peroxidase, POD; catalase, CAT; ascorbate peroxidase, APX) in the radicles of melon germplasms were universally elevated. However, the variation ranges and trends of biochemical indices among different germplasms exhibited significant differences. The proline content of melon accessions with strong drought resistance increased, the malondialdehyde (a product of membrane damage) was low, and the enzyme activities increased significantly. The proline content of non-drought-tolerant melon accessions increased less, malondialdehyde accumulated in large amounts, and the activity of some protective enzymes decreased. Correlation analysis demonstrated that Pro exerted a synergistic effect in conjunction with antioxidant enzymes (SOD, CAT) to mitigate drought stress. Cluster analysis classified the germplasm into 14 high-tolerance types, 10 medium-tolerance types, and 9 low-tolerance types. Based on extreme germination phenotypes, 27 germplasms were identified as drought-sensitive types. A prediction model for drought tolerance was established via stepwise regression: D = -0.309 + 0.053 × Pro (proline content) + 0.319 × RL (radicle length) + 0.469 × MDA (malondialdehyde) + 0.137 × SOD (superoxide dismutase), with four core indicators (RL, MDA, Pro, SOD) identified. These findings provide a scientific basis and technical support for drought tolerance breeding, parental selection, and large-scale, precise, and rapid drought tolerance screening of melon germplasms in the arid regions of Xinjiang.
Why it matches plant phenotyping methodsメロン遺伝資源の乾燥耐性を迅速にスクリーニングするため、最適PEG濃度の決定、指標選定、予測モデル構築を中心的に行っており、表現型取得・抽出法の開発に該当する。
abstractused polyethylene glycol (PEG) solutions at four different concentrations (0%, 10%, 20% and 30%) to simulate drought stress conditions. Drought tolerance was evaluated to develop a method for the rapid screening of drought-tolerant germplasms.
Plant hormones play critical roles in many aspects of plant life cycles including development, growth, reproduction and responses to environmental stimuli. These processes are often associated with changes in endogenous plant hormone levels and locations. Therefore, to understand the modes of action of plant hormones, it is important to accurately quantify these chemical compounds in a high-definition tissue map. In this study, we developed a system to quantify indole-3-acetic acid (IAA), the major endogenous auxin, from small tissue samples using laser microdissection (LMD) coupled with nano-flow liquid chromatography (nano-LC)-mass spectrometry (MS), which improved detection limits, allowing quantification of IAA from a single 10 μm cryosection of maize coleoptile. Our results reveal that IAA is actively synthesized in the apical 400 μm region of the coleoptiles and is preferentially accumulated in vascular tissues. This technique can provide a precise view of the spatiotemporal distribution of plant hormones and their significance in regulating physiological responses at tissue or cellular levels.
Why it matches plant phenotyping methods植物組織中のIAAの空間分布を高感度に定量するLMD-nano-LC-MS法そのものを開発しており、植物の生理状態を組織・細胞レベルで取得する技術が研究の中心である。
abstractIn this study, we developed a system to quantify indole-3-acetic acid (IAA), the major endogenous auxin, from small tissue samples using laser microdissection (LMD) coupled with nano-flow liquid chromatography (nano-LC)-mass spectrometry (MS)
Plant phenotyping relevance match · UnverifiedCrossref · Europe PMC · checked 13 Sept 2026
Cassava brown streak disease (CBSD) threatens food security for millions in East Africa, yet its control remains limited by the absence of field-deployable molecular diagnostics. Here, we introduce ELLA (Electrochemical Lateral flow assay with Linked Analytics), a battery-free, smartphone-powered electrochemical lateral flow assay that delivers enzyme-linked immunosorbent assay (ELISA)-grade protein detection directly in the field. ELLA integrates near-field communication, a single-chip potentiostat, metal-pin electrodes, and ferrocene-labeled nanoparticles into a fully disposable cassette, enabling quantitative immunoassays without optical instrumentation or centralized laboratory infrastructure. Validated across laboratory studies and extensive field trials in Tanzania, ELLA achieved 95% agreement with ELISA and 89% agreement with RT-qPCR, outperforming ELISA’s limit of detection while maintaining a material cost below US$1. By coupling molecular test results with cloud-linked analytics, we further trained DeepELLA, a smartphone-based image classification model that enables scalable surveillance from field-acquired leaf images. Together, these advances unify electrochemical sensing, digital connectivity, and AI-assisted interpretation, enabling portable, ELISA-level diagnostics for plant, environmental, and health monitoring in resource-limited regions.
Why it matches plant phenotyping methods主軸は分子診断ですが、植物葉画像から病害状態を分類するDeepELLAも開発され、植物病害表現型の直接推定を含むため対象に含める。
abstractwe further trained DeepELLA, a smartphone-based image classification model that enables scalable surveillance from field-acquired leaf images
Traditional manual measurement of garlic bulb phenotypic traits is inefficient, subjective, poorly reproducible, and may cause sample damage. To improve the adaptability of three-dimensional reconstruction to garlic bulb morphology and grading-related parameter extraction, this study developed a non-destructive phenotypic measurement workflow based on multi-view image-based three-dimensional reconstruction. Four garlic materials with distinct bulb morphologies and epidermal characteristics were used to demonstrate the feasibility of the reconstruction workflow, and 40 Lanling white-skinned garlic bulbs were used for quantitative accuracy validation. Multi-view images were acquired using a high-resolution camera, a motorized turntable, and a controlled illumination system. Three-dimensional models were reconstructed using ContextCapture, and the resulting point clouds were processed in CloudCompare through cropping, denoising, downsampling, and pose correction. Maximum longitudinal diameter, maximum transverse diameter, and volume were extracted from the processed point clouds according to GB/T 45244-2025 (Grades and Specifications of Garlic) and validated against manual reference measurements. The coefficients of determination for maximum longitudinal diameter, maximum transverse diameter, and volume were 0.9935, 0.9909, and 0.9924, respectively, with RMSE values of 0.0529 cm, 0.0520 cm, and 0.8874 cm3, and MAPE values of 0.6647%, 0.7765%, and 1.9149%. Additional MAE, bias, confidence interval, and Bland–Altman analyses further supported the agreement between model-derived and manual reference measurements. These results demonstrate the feasibility of multi-view image-based three-dimensional reconstruction for non-destructive garlic bulb phenotypic measurement and provide a methodological basis for future grading-related assessment and three-dimensional phenotyping of bulbous horticultural crops.
Why it matches plant phenotyping methodsニンニク球の形態形質を非破壊的に取得する3D画像計測ワークフローを開発し、手動測定と定量検証しており、フェノタイピング手法が中心である。
abstractthis study developed a non-destructive phenotypic measurement workflow based on multi-view image-based three-dimensional reconstruction.
Grapevine yellows diseases, including Flavescence Dorée (FD) and Bois Noir (BN), severely affect vineyards, making reliable detection methods essential. Detecting symptoms, particularly in Chardonnay variety, remains challenging due to year-to-year vine variability and the presence of symptoms that can be misleading. We present a dataset of multispectral images of grapevine leaves collected over four years (2021–2024). The dataset includes healthy leaves, grapevine yellows - overwhelmingly BN, and three other diseases (Esca, Discoloration, and Leafroll) with visually similar symptoms particularly for Leafroll, providing a diverse benchmark for disease recognition. Ground-truth labels were assigned based on field inspections conducted by Comité Champagne expert viticulturists who selected the vines before acquisitions and analyzed the images after acquisition. Images were acquired under laboratory conditions using a DJI Phantom 4 Multispectral camera from leaves collected in the Plumecoq Comité Champagne experimental vineyards. Leaves were collected on designated vines and placed on polystyrene boards on a 2x2 grid. One RGB image and five multispectral images corresponding to the Blue (B - 450 nm ± 16 nm), Green (G - 560 nm ± 16 nm), Red (R - 650 nm ± 16 nm), Red-Edge (RE - 730 nm ± 16 nm), and Near-Infrared (NIR - 840 nm ± 26 nm) bands were captured using the same acquisition system (camera-to-board distance of approximately 90 cm) over the four years.. For the vast majority, ambient light was used; for some acquisitions, indirect halogen spotlights were used. The 512x512 leaf images were manually cropped from the acquired original images with the same offsets between the bands; offsets were already present due to the parallax phenomenon. The dataset is organized into six folders (B, G, R, RE, NIR, and RGB). Each leaf is represented by one RGB image and five corresponding multispectral band images, enabling multimodal analysis and machine learning for grapevine disease detection. Each image filename encodes acquisition and annotation information as follows: Characters 1–2: acquisition year (21 = 2021, 22 = 2022, 23 = 2023, 24 = 2024). Character 3: image type/band (0 = RGB, 1 = B, 2 = G, 3 = R, 4 = RE, 5 = NIR). Character 4: class (J = Grapevine Yellows, T = Healthy, S = Esca, E = Leafroll, D = Discoloration). Character 5: illumination condition (0 = ambient light, G = additional left halogen spotlight, D = additional right halogen spotlight, 2 = additional both left and right halogen spotlightsl). Characters 6–9: image identifier, numbered sequentially from 0000. Character 10: relabelling status. After image acquisition, all leaves were independently reviewed by expert viticulturists on the recorded RGB images. A value of 1 indicates that the expert confirmed the original field label, 0 indicates that the original label was considered incorrect based on the information visible in the RGB image, and D indicates that the sample was reclassified as Discoloration. This dataset complements the "Multi-annual spectral data of Chardonnay grapevine leaves" dataset published on Recherche Data Gouv. While the previous dataset contains spectral measurements, the present dataset provides multispectral images acquired from the same grapevine leaves, enabling multimodal analyses that combine spectral signatures with image-based information. Note, however, that there is no bijection between the respective files.
Why it matches plant phenotyping methodsブドウ葉の病徴をマルチスペクトル画像で取得し、専門家ラベル付きの疾病認識用データセット／ベンチマークとして提供しており、植物状態の画像ベース表現型計測が中心である。
abstractWe present a dataset of multispectral images of grapevine leaves collected over four years (2021–2024).
BarleyLaboratory / benchtopRootMorphology / geometry measurementSegmentationGrowth / development / phenologyRoot system architecture
Root system architecture and root hairs highly influence plant resource uptake, yet their simultaneous quantification at the whole-plant scale remains challenging due to the conflicting requirements of high-resolution imaging and non-destructive, repeated measurements. Here, we present a plant growth and imaging system based on A3 sized flatbed rhizotrons, combined with a non-machine-learning image analysis workflow implemented in R, that enables repeated, in situ quantification of barley root system architecture and accompanied visible projected root hair area (VP root hair area) over three weeks of growth. The system produces integrated outputs highlighting both whole-root architecture and the spatial distribution of surrounding root hairs from single images. Validation of the image analysis algorithm showed good segmentation performance, with average Matthews Correlation Coefficient values of 0.68 for projected root area and 0.65 for VP root hair area. To demonstrate its experimental applicability, the system was used to assess root growth and root hair responses under controlled environmental conditions, combining three irrigation regimes (2, 4, and 6 irrigation events per day) with three dry bulk density levels (1.4, 1.5, and 1.6 g cm -3 ). In addition to whole-system metrics, the approach enables analysis of root hair expansion at individual root tips. This methodology provides a training-free method for integrated analysis of root traits e.g. projected root size, root growth, root diameter and root hairs under controlled physical conditions similar to soil, facilitating studies of root-soil interactions that require both spatial resolution and temporal continuity.
Why it matches plant phenotyping methods根系形態と根毛面積を画像から定量する画像解析ワークフローおよび生育・撮像システムを開発し、アルゴリズム性能も検証しているため、植物フェノタイピング手法が中心である。
abstractwe present a plant growth and imaging system based on A3 sized flatbed rhizotrons, combined with a non-machine-learning image analysis workflow implemented in R
Ustilago maydis is a biotrophic fungus that causes smut disease in maize, leading to tumor formation on aerial parts of the plant. While U. maydis has been a model for plant-fungal interaction studies, no tool has existed to automatically quantify infection symptoms under laboratory conditions for deep learning analysis. To address this, we developed a rotating camera system that captures videos of plants under customized lighting and shutter settings. These videos were used to train machine learning models to distinguish between healthy and infected plants. Two detection approaches have been presented. In the first approach, by employing a naive masking technique and combining classical machine learning classifiers utilizing handcrafted features, the model achieved a reasonable performance, with an Area Under the Curve (AUC) of maximum 0.90 on the Receiver Operating Characteristic in one of the classifiers, showing relatively high sensitivity and specificity. The second approach utilizes pre-trained YOLO11 model for object detection and further classification. The YOLO11-based approach outperforms traditional methods, achieving near-perfect validation accuracy (AUC: 0.99-1.00), demonstrating its superiority for real-time, scalable applications. Our toolset, featuring a cost-efficient and customizable scanning platform with open building-blocks design, provides a valuable resource as a proof-of-concept for unbiased disease symptom detection and scoring, with potential applications in other plant pathology studies. This point enables easy replication and adaptation by other research laboratories which makes the platform robust, scalable and practical beyond our specific application.
Why it matches plant phenotyping methodsトウモロコシの感染症状を画像から自動検出・スコア化する低コスト撮像プラットフォームと機械学習手法の開発が中心であり、植物病害表現型の取得・抽出方法に該当する。
abstractwe developed a rotating camera system that captures videos of plants under customized lighting and shutter settings.
This study presents a large-scale framework for the digitalization of wheat mold odor based on controlled volatile release and standardized gas acquisition. Seven wheat-varieties harvested from 2022 to 2025 in three provinces of China were cultivated into four spoilage levels (normal, mild, moderate, and severe mildew), and a graphene-based sensor array was developed for multidimensional odor detection. A dual pre-treatment strategy integrating temperature-regulated volatilization and cooling-assisted dehumidification was implemented to ensure stable and comparable signal acquisition. Under optimized conditions, 1491 odor response curves from the first three batches were used for machine learning modeling, while an independent fourth batch (n = 503) was used for external validation. For binary classification (normal vs. moldy wheat), the optimized Light Gradient Boosting Machine achieved 95.8% accuracy, 95.5% sensitivity, 96.8% specificity, and an AUC of 0.984. This proposed approach enables rapid, non-destructive mold assessment and supports standardized grain quality monitoring.
Why it matches plant phenotyping methods小麦穀粒のカビ状態を対象に、グラフェンセンサーアレイと揮発成分取得前処理を開発し、機械学習分類と独立バッチ検証まで行っており、植物状態の取得・判定法が研究の中心である。
abstracta graphene-based sensor array was developed for multidimensional odor detection
Quantitative imaging of plant tissues by laser ablation inductively coupled plasma mass spectrometry (LA-ICP-MS) is hindered by the lack of matrix-matched calibration standards. Established approaches, such as gelatine or homogenized tissue blocks, do not replicate plant matrices accurately. Here, we introduce a nano-dispenser-based calibration strategy that deposits nanolitre volumes of elemental standards directly onto paraffin-embedded grain sections, exploiting the low endogenous metal content of the endosperm to generate in situ calibration curves. Calibration performance for Mg, Mn, Cu, Zn and Mo was assessed using LA-ICP-MS imaging and Iolite data processing. The method demonstrated excellent linearity (R2 > 0.98), reproducibility across multiple grains, and sub-ppm limits of detection. Comparative analysis with in-house homogenized blocks and NIST wheat reference material confirmed superior accuracy and reproducibility of the nano-dispenser approach. As a proof-of-concept we have applied the method for the quantitative imaging of metals in a whole barley grain section and the results show excellent agreement with published data obtained by conventional liquid-mode ICP MS. The technique reported here provides a robust, scalable solution for quantitative metallomics studies of plants tissues, enabling improved assessment of nutrient distribution and supporting the development of standardised protocols for LA-ICP-MS Imaging
Why it matches plant phenotyping methods植物組織中の元素分布を定量画像化するための校正手法を開発し、直線性・再現性・精度を検証している。栄養元素という植物形質の取得法が研究の中心である。
abstractHere, we introduce a nano-dispenser-based calibration strategy that deposits nanolitre volumes of elemental standards directly onto paraffin-embedded grain sections
Field / plotLaboratory / benchtopLeafPhysiological trait estimationSegmentationWater status / transpiration
Abstract Climate-change-driven drought intensification increasingly threatens forest ecosystems, highlighting an urgent need for accurate monitoring of forest water stress. Leaf water potential (Ψleaf) is a key integrative indicator, yet conventional measurements are destructive and unsuitable for large-scale or high-frequency monitoring. Hyperspectral remote sensing offers a promising alternative, but robust canopy-level Ψleaf estimation remains constrained by limited labeled data and heterogeneous environmental conditions. Here, we develop a cross-scale framework integrating supervised contrastive learning with deep transfer learning to translate robust leaf-scale pretraining into canopy-scale Ψleaf estimation from hyperspectral data in a Populus × euramericana ‘I-214’ plantation. Hyperspectral imagery was captured at the leaf scale under controlled laboratory conditions (n = 229) and at the canopy scale using a UAV-based platform (n = 200), together with paired Ψleaf measurements. Reflectance consistently increased with declining Ψleaf at both scales, supporting the feasibility of cross-scale modeling. At the leaf scale, physics-consistent spectral augmentation coupled with contrastive learning enhanced feature discrimination and predictive stability under small-sample conditions (R2 = 0.8030). Transfer learning via progressive fine-tuning enabled efficient scaling of the leaf-level pretrained model to canopy-level prediction despite structural and environmental complexity and restricted field data ranges, achieving R2 = 0.7605 and RMSE = 0.1056 MPa. Coupling with individual-tree crown segmentation further enabled spatially explicit mapping of canopy Ψleaf and plot-level forest water stress dynamics. These results demonstrate that combining contrastive representation learning with cross-scale transfer provides a practical pathway for physiological monitoring and scalable, climate-smart forest phenotyping in data-constrained forested environments.
Why it matches plant phenotyping methodsUAVハイパースペクトル画像と深層学習により、ポプラの葉の水ポテンシャルを推定する手法を開発・評価しており、植物生理形質の取得が研究の中心である。
abstractHere, we develop a cross-scale framework integrating supervised contrastive learning with deep transfer learning to translate robust leaf-scale pretraining into canopy-scale Ψleaf estimation from hyperspectral data
Understanding elemental distributions in plants is critical for agricultural productivity, nutritional quality, and limiting the transfer of toxic elements into the food chain. Conventional elemental mapping techniques typically require thin sectioning or complex tomographic reconstructions, making three-dimensional analysis labor-intensive and destructive. Here, we present a non-destructive approach for three-dimensional elemental mapping in intact plant tissues using confocal X-ray fluorescence (XRF) microscopy with a collimating channel array (CCA) [1]. This method defines a localized 3D detection volume within the sample, enabling the generation of elemental virtual cross-sections without physical sectioning while preserving native spatial relationships. We demonstrate the capability of this technique by mapping Fe distributions in carrot (Daucus carota) roots and shoots and Cd distributions in root tips of near-isogenic wheat (Triticum aestivum) lines. Integration of multiple virtual sections enabled three-dimensional reconstructions that reveal distinct Cd translocation pathways between accumulating and non-accumulating wheat lines, tracing elemental movement from the epidermis through cortical layers into vascular tissues. The method is applicable to diverse plant morphologies, including cylindrical roots and irregular leaf and stem tissues. This approach enables high-sensitivity, non-destructive 3D elemental imaging, providing a powerful tool for studying elemental transport in plants with direct relevance to crop breeding, food safety, and agricultural sustainability [2].
Why it matches plant phenotyping methods植物組織内の元素分布という生理状態を、非破壊3D XRFで取得・再構成する手法の開発と植物試料での実証が中心である。
abstractHere, we present a non-destructive approach for three-dimensional elemental mapping in intact plant tissues using confocal X-ray fluorescence (XRF) microscopy with a collimating channel array (CCA) [1].
High-throughput plant phenotyping now generates image derived datasets far faster than scientists can analyze them. At Oak Ridge National Laboratory's Advanced Plant Phenotyping Laboratory (APPL), automated stations image hundreds of plants daily across multiple remote sensing modalities; yet, trait extraction and interpretation remain manual, expert-bound, and strictly post-hoc, making analysis, not acquisition, the binding constraint on discovery. We present an end-to-end agentic AI framework that turns the facility from a data factory into an interactive autonomous, discovery platform, where scientists partner with AI agents to accelerate time to insight. A conversational Co-Scientist Agent translates a scientist's natural-language question into a structured analysis plan, and a headless Compute Agent dispatches Vision Transformer segmentation and trait extraction on the Frontier exascale supercomputer. The two agents run in separate security and resource domains and communicate over a secure, token-authenticated streaming channel, a design that accounts for the federation, data-movement, and provenance realities cloud-native agentic frameworks ignore, ensuring end-to-end provenance is captured for every interaction. The framework turns a days- to weeks-long analysis process into an interactive loop where agents reason over results, recommend next analyses, and respond to follow-up questions in seconds.
Why it matches plant phenotyping methods植物画像からの形質抽出を自動化するエージェント型AIフレームワークの開発であり、解析・抽出手法が研究の中心である。
abstractWe present an end-to-end agentic AI framework that turns the facility from a data factory into an interactive autonomous, discovery platform, where scientists partner with AI agents to accelerate time to insight.
ABSTRACT Premise Seed size and morphology are critical traits in agriculture, ecology, and genetics, but high-throughput quantification of these traits is often limited by labor-intensive manual measurements or expensive, platform-specific imaging software. Methods and Results We developed SeedMeasure, a lightweight, open-source, and cross-platform command-line tool written in Python that automates the measurement of seed area, length, and width from images. Using a simple imaging setup, the program processes images by correcting for perspective skew, filtering debris, and exports quantitative data alongside quality-check images. We validated SeedMeasure across nine diverse species, ranging from small Arabidopsis thaliana seeds to large Zea mays kernels. The tool quickly handles images using multithreading and demonstrates high reproducibility, yielding low coefficients of variation across repeated runs. Conclusions Compared to existing software, SeedMeasure is free, offers faster processing through parallel computing, and provides standalone executables that require no programming dependencies. SeedMeasure offers an accessible, cost-effective, and high-throughput approach for rapid phenotypic profiling, making advanced seed morphological analysis available to researchers without specialized laboratory hardware.
Why it matches plant phenotyping methods種子画像から面積・長さ・幅を自動抽出するソフトウェアを開発し、複数種で検証しており、植物表現型取得法が研究の中心である。
abstractWe validated SeedMeasure across nine diverse species
Introduction Stomata are vital gatekeepers of plants that regulate the fundamental trade-off between carbon gain and water loss. Precise, high-throughput identification of stomatal traits is therefore essential for assessing plant stress tolerance and water-use efficiency. However, conventional bounding box detection struggles to accurately localize densely distributed and arbitrarily oriented stomata. Methods This study proposes DFA-YOLO, an enhanced YOLOv11-OBB (Oriented Bounding Box) model for orientation-aware maize stomatal localization and preliminary OBB-derived trait extraction. Based on a maize stomatal dataset expanded from 1,053 to 3,597 microscopic images, DFA-YOLO integrates three task-specific components: (1) a cross-dataset MGD distillation strategy that transfers structural priors from single-stomata images to dense multi-stomata scenes; (2) a fixed-threshold Focaler-CIoU localization-loss reweighting strategy (u stomata = 0.95, d stomata = 0.00) to emphasize hard positive samples during OBB regression; and (3) a C3k2_AssemFormer feature-aggregation module that combines convolutional local feature extraction with linear attention-based context aggregation. Results DFA-YOLO achieved 94.1% mAP50, 84.8% mAP75, 74.1% mAP50-95, and 90.0% recall, with higher recall, mAP50, mAP75, and mAP50-95 than the YOLOv11-OBB baseline under the same OBB evaluation protocol. When deployed on an automated platform, the system processed images at 44.9 FPS and supported stomatal localization, density estimation, and preliminary orientation-aware size description. Discussion Under the tested maize microscopic imaging workflow, DFA-YOLO enables rapid extraction of detection-oriented stomatal traits and provides a prototype tool for high-throughput maize stomatal phenotyping.
Why it matches plant phenotyping methodsトウモロコシ気孔の画像検出・配向局在化と形質抽出を目的とするYOLOベース手法を開発し、データセット、精度比較、自動化プラットフォームでの性能を評価しているため、植物フェノタイピング手法が中心である。
abstractThis study proposes DFA-YOLO, an enhanced YOLOv11-OBB (Oriented Bounding Box) model for orientation-aware maize stomatal localization and preliminary OBB-derived trait extraction.
Background Cell geometry plays a central role in determining division orientation and body axis formation during early embryogenesis in Arabidopsis thaliana . However, quantitative analysis of dynamic three-dimensional (3D) morphology remains challenging because live-imaging studies often rely on two-dimensional (2D) projections, while existing 3D reconstruction approaches, including mesh-based methods, often lose the original orientation information relative to the ovule and require labor-intensive mesh correction. In addition, embryo positional fluctuation caused by floating in liquid medium and continuous growth makes it difficult to analyze temporal morphological changes within a common coordinate system. Results We developed a robust framework for quantitative 3D and four-dimensional (4D; 3D + time) analysis of embryo initial cell (apical cell) morphology. The method first establishes a standardized 3D coordinate system by normalizing cell orientation based on the bottom plane and the optical axis of the observation. Cell morphology is then reconstructed through ellipse-based approximation of serial cross-sections extracted from stacked imaging data, enabling accurate geometric characterization without the need for complex surface mesh reconstruction. To evaluate shape anisotropy, we quantified the apical cell shape in 3D. The framework further supports the characterization of volumetric features of subsequent division, providing a basis for quantifying 3D embryogenesis. Conclusion Our framework provides a simple and noise-reduced approach for quantitative analysis of living cell morphology in 3D. We named the integrated method of combining coordinate normalization with elliptical cross-section-based reconstruction Apical3DTip. This method enables consistent comparison of cell shapes without extensive manual corrections. The method overcomes key limitations of 2D projection-based and mesh-dependent analyses and offers a practical platform for quantifying cell shape and daughter cell shapes in 3D. More broadly, it provides a quantitative foundation for exploring the relationship between cell geometry, morphodynamics, and developmental patterning in living plant embryos.
Why it matches plant phenotyping methods植物胚の細胞形態を3D・4D画像から定量化する再構成手法を開発しており、表現型取得・抽出法が研究の中心である。
abstractWe developed a robust framework for quantitative 3D and four-dimensional (4D; 3D + time) analysis of embryo initial cell (apical cell) morphology.
Reproduction assets foundThe paper's Methods availability statement explicitly deposits the Apical3DTip analysis code and associated datasets on two public GitHub repositories (main implementation and ImageJ plugin). These are paper-specific author assets for the 3D/4D apical cell reconstruction and phenotyping analysis. No separate phenotype/Code · publicctor of the fitted vertical plane:
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Availability of data and materials
The code for Apical3DTip, along with all associated datasets, is available on Github:
https://github.com/blues0910/Apical3DTip.
Apical3DTip is also available as an ImageJ plugin:
https://github.com/YusukeKimata-Moo/Apical3DTip.
Ethics approval and consent to participate
Not applicable.
Consent for publication
Not applicable.
Competing interests
The authors declare no competing interests.
Funding
This work was supported by a Japan Society for the PromotOpen asset ↗https://github.com/blues0910/Apical3DTippdf-layout-page:12 lines:1-49Code · publictroid s, the offset e was calculated as
N
MQ.
Then, the fitted plane was represented as
O G N O
MQ 0.
Availability of data and materials
The code for Apical3DTip, along with all associated datasets, is available on Github:
https://github.com/blues0910/Apical3DTip.
Apical3DTip is also available as an ImageJ plugin:
https://github.com/YusukeKimata-Moo/Apical3DTip.
Ethics approval and consent to participate
Not applicable.
Consent for publication
Not applicable.
Competing interests
The authors declare no competing interests.
Funding
This work was supported by a Japan Society for the Promotion of Science (JSPS) KAKENHI
Grant (No. JP22K15135 to H.M., JP25H01809 to Y.K., JP26K02023 tOpen asset ↗https://github.com/YusukeKimata-Moo/Apical3DTippdf-layout-page:12 lines:1-49Code / dataset availability confirmedEurope PMC · checked 5 Sept 2026
Objective This study presents an integrated, multi-scale approach for the non-destructive estimation of phenological stages and carotenoid content in carrots by combining spectral techniques, colorimetry, and artificial intelligence. Methods Six commercial varieties, including orange, yellow, white, and purple genotypes, were evaluated under field and laboratory conditions using multispectral drone imagery, high-resolution spectroradiometric signatures, red green blue (RGB) images, and CIELAB color measurements. A hierarchical modeling framework was developed across two phases: (i) spectral modeling using uncrewed aerial vehicle (UAV)-based multispectral indices, textural and geometric metrics, and laboratory-generated hyperspectral signatures; and (ii) a colorimetric index from RGB images. Results Using UAV-based multispectral field data, phenological prediction indices achieved high classification performance (F1-scores > 0.90) when modeled with a Random Forest classifier, supported by distinct spectral signatures associated with canopy development and senescence. In parallel, carotenoid content estimation using a Random Forest regression model demonstrated strong predictive accuracy ( R 2 = 0.897; RMSE = 0.584), with the Plant Senescence Reflectance Index (PSRI) and Carotenoid Reflectance Index (CRI) identified as the most influential predictors. A complementary laboratory-based Random Forest regression model using high-resolution spectral signatures achieved near-perfect predictive performance ( R 2 = 0.987). SHapley Additive exPlanations (SHAP) analysis identified physiologically relevant wavelengths in the green (540-550 nm) and red-edge (∼700 nm) regions as the primary drivers of carotenoid concentration. Likewise, a novel colorimetric index (ICarot), derived from CIELAB parameters, enabled accurate image-based carotenoid estimation ( R 2 = 0.85). Conclusion This study introduces an innovative multi-sensor framework for precision agriculture and automated postharvest quality control, enabling rapid, objective, and scalable phenotyping in carrot production systems. Through the integration of spectral, colorimetric, and AI-based approaches, the proposed methodology effectively captures both internal nutritional attributes and external quality traits within a unified, non-destructive assessment pipeline.
Why it matches plant phenotyping methods複数センサー画像・分光計測とAIを統合し、ニンジンの生育段階およびカロテノイド含量を非破壊推定する手法を開発・評価しており、表現型取得が研究の中心である。
abstractThis study presents an integrated, multi-scale approach for the non-destructive estimation of phenological stages and carotenoid content in carrots by combining spectral techniques, colorimetry, and artificial intelligence.
Reproduction assets foundThe paper's Data Availability section explicitly deposits the study's data (and project materials) on GitHub and Zenodo, both with authors' public URLs matching allowed_urls. These qualify as paper-specific public assets for the carrot phenotyping measurements and analysis.Dataset · publicThe data is available at GitHub and Zenodo:
- https://github.com/agrocompuepidemlab/Carrot-value-chain-proyect/tree/mainOpen asset ↗github.com/agrocompuepidemlab/Carrot-value-chain-proyectlines:184-307Plant phenotyping relevance match · UnverifiedOpenAlex · checked 11 Sept 2026
Accurate and non-destructive volume estimation of agricultural products is essential for precision agriculture, yet remains challenging when transitioning from controlled laboratory conditions to complex orchard environments. Although 2D image-based volume estimation methods provide a cost-effective and scalable solution, existing studies are fragmented and lack a unified perspective on their real-world applicability. This review presents a systematic synthesis of 2D image-based volume estimation methods, explicitly framed through the laboratory-to-orchard transition. We categorized existing volume estimation approaches according to the sensing modality into monocular RGB-based approaches and depth-assisted methods, and further reviewed them based on the image processing methods. A key finding is that high-precision geometric estimation can be achieved in laboratory environments, whereas deep learning and RGB-D fusion have driven a shift from conventional geometric modeling toward data-driven and hybrid learning frameworks in orchard settings. However, 2D image-based volume estimation remains fundamentally limited by scale ambiguity, severe occlusion, and sensitivity to illumination and background variability in real orchard environment. Overall, this review provides a unified perspective for understanding volume estimation methodology across environments and offers guidance for developing robust, scalable, and field-deployable volume estimation systems for real-world agricultural applications.
Why it matches plant phenotyping methods農産物の2D画像から体積を推定する手法を体系的にレビューしており、植物器官・果実の形態形質取得方法が中心である。
abstractThis review presents a systematic synthesis of 2D image-based volume estimation methods, explicitly framed through the laboratory-to-orchard transition.
Tomographic microscopy enables three-dimensional internal imaging but often requires expensive optical or X-ray instrumentation. Here we present an ultra-low-cost continuous-wave diffusive tomography (CWDT) system for biological samples. The system uses a smartphone microscope, a white LED coupled into an optical fiber, 3D-printed micropositioners, and a physics-based forward model optimized with machine learning. We demonstrate full-color volumetric reconstructions from a tartrazine-cleared poplar section, a scattering phantom, fungal mycelium near an Arabidopsis root, and thick poplar branch imaging with an inserted side-emitting fiber. The current results are qualitative and exploratory, but they show that scanned fiber illumination and inexpensive hardware can produce useful three-dimensional reconstruction outputs for low-cost microscopy experiments.
Why it matches plant phenotyping methods低コスト三次元断層イメージング法そのものを開発し、ポプラ組織・枝やシロイヌナズナ根近傍を対象に植物の内部構造を可視化しているため、植物形態の取得法として中心的です。
abstractHere we present an ultra-low-cost continuous-wave diffusive tomography (CWDT) system for biological samples.
Reproduction assets foundThe paper's raw imaging inputs, configurations, and reconstruction outputs for Figures 2–5 are publicly deposited on Kaggle. The analysis code repository is only 'prepared for release' (no confirmed public deposit yet), so it is listed as request-only. Hardware CAD mirrors are public but are instrument designs, not theDataset · publicFigure-level raw inputs, model configurations, selected outputs, and manifests are available through the Kaggle dataset https://www.kaggle.com/datasets/alingold/continuous-wave-diffusive-tomography .Open asset ↗continuous-wave-diffusive-tomographylines:108-129Code / dataset availability confirmedEurope PMC · checked 5 Sept 2026
Manually counting sunflower seeds on capitula is labor-intensive, requiring approximately one person-hour per head, and can be inconsistent for densely packed heads. Existing phenotyping approaches often depend on laboratory-based equipment, limiting their accessibility. In this study, we developed a benchtop image-based pipeline for rapid, non-destructive estimation of developed and aborted seeds on intact dried sunflower heads. A dataset of 1093 sunflower capitula was imaged under fixed indoor lighting, and individual seeds were annotated as developed or aborted. A YOLOv8m one-stage object detector was trained and evaluated using a counting-focused protocol, in which a single confidence threshold was selected on the validation set and then applied unchanged to an independent test set of 109 images. The baseline model was compared with recent YOLO variants and different augmentation strategies. On the test set, the model achieved a mean absolute count error of 61.3 seeds per image, a mean relative error of 12.0%, and an mAP50 of 0.18 at the locked confidence threshold of 0.15. Only 13.8% of test images had relative errors below 2%. Larger YOLO models and augmentation variants did not improve performance. These findings show that the proposed system provides approximate, non-destructive seed-count estimation under controlled imaging conditions, while highlighting the need for improved localization in dense regions and domain adaptation for fresh heads or field conditions. The annotated dataset and trained model weights are made available to support reproducible research.
Why it matches plant phenotyping methodsヒマワリ頭花の発達・不稔種子数という植物形質を、画像取得とYOLOによる推定パイプラインで定量化する手法を開発・評価しており、方法が研究の中心である。
abstractwe developed a benchtop image-based pipeline for rapid, non-destructive estimation of developed and aborted seeds on intact dried sunflower heads.
Reproduction assets foundThe authors state the source code is available on GitHub and the CVAT-annotated dataset is available via a public share link; the GitHub repository URL is explicitly provided and matches an allowed URL. The dataset link itself is not given, so only the code/checkpoint repository qualifies as an actionable public asset.Code · publicThe developed system is available as a Telegram bot [ 19 ] and the source code is available on GitHub [ 20 ]. The CVAT annotated dataset is available via a public share link.Open asset ↗lines:84-103Plant phenotyping relevance match · UnverifiedCrossref · checked 15 Sept 2026
Abstract Objective Minimizing crop losses through the early detection of plant diseases is vital for enhancing global agricultural efficiency. While deep learning has emerged as a promising solution, a significant gap exists between laboratory performance and practical, in-field utility. This study evaluates this discrepancy through a dual-methodological approach. Methods First, a tertiary systematic literature review was conducted, synthesizing 22 secondary reviews encompassing over 750 unique primary studies to establish the current state of the art. Second, an empirical validation was performed using a VGG16 transfer learning model trained on three distinct dataset types, which vary in scale (small vs. large), environment (laboratory vs. in-field), and condition (raw vs. pre-processed). Results The tertiary review identifies Convolutional Neural Networks, particularly VGG architectures, as the leading model but highlights a critical reliance on private and unrealistic datasets. Furthermore, the analysis reveals that Accuracy, the most common metric, is often insufficient for evaluating the imbalanced datasets typical of the field. Empirical results corroborate these findings, demonstrating that VGG16 performance is highly dependent on dataset characteristics; models perform significantly better on large, pre-processed laboratory data than on realistic in-field datasets. Conclusion These findings suggest that many current models remain inapplicable to real-world agricultural scenarios. To bridge this reality gap, future research must prioritize the development of open-source, standardized, and validated in-field datasets to ensure the reliability and scalability of automated disease detection systems.
Why it matches plant phenotyping methods植物病害を画像から検出する深層学習手法を体系的にレビューし、VGG16を異なるデータセット条件で実証評価しており、病害状態の取得・推定方法が中心である。
titleA tertiary systematic literature review and experimental evaluation of deep learning models for plant disease detection
LeafScans-Orchard is a curated, multi-year RGB image dataset of orchard plant leaves designed to support research in computer vision, machine learning, and plant phenotyping. The dataset comprises 9708 high-quality leaf scans acquired during collection campaigns conducted between 2015 and 2025, covering seven orchard crop species: apple, pear, sweet cherry, sour cherry, plum, peach, and apricot. In total, the dataset includes 67 cultivar labels. All samples were acquired using flatbed scanning under controlled conditions on a uniform background, ensuring high visual consistency and minimal background variability. The original scans were captured at 1200 dpi and subsequently converted into a public release format at 300 dpi, stored as lossless TIFF images to preserve morphological and textural details. Each image corresponds to a single leaf and is organized in a hierarchical directory structure by species, cultivar, and acquisition year, accompanied by image-level metadata and aggregated species–cultivar–year counts. LeafScans-Orchard is suitable for plant species classification, cultivar recognition, leaf morphology analysis, texture analysis, and general visual feature extraction. In addition to the main release, a representative subset of 300 original 1200 dpi scans is provided to support high-resolution analyses. The dataset is particularly suited for fine-grained classification, morphology-driven analysis, and methodological studies under controlled imaging conditions.
Why it matches plant phenotyping methods果樹葉のRGBスキャン画像を収録した公開データセットで、植物フェノタイピングおよび葉形態解析を目的とする。標準化された画像取得と再利用可能なデータ構成が中心であり、フェノタイピング用データセットとして適格。
abstractLeafScans-Orchard is a curated, multi-year RGB image dataset of orchard plant leaves designed to support research in computer vision, machine learning, and plant phenotyping.
Reproduction assets foundThe paper's core asset is the LeafScans-Orchard dataset itself (9708 RGB leaf scans, 300 dpi TIFF release plus 1200 dpi subset, image-level metadata and summary counts), openly deposited on Zenodo with an explicit DOI and CC BY 4.0 license. This is a paper-specific, public, actionable phenotyping image dataset. No codeDataset · publicthe published version of the manuscript.
Funding: This research received no external funding.
Institutional Review Board Statement: Not applicable.
Informed Consent Statement: Not applicable.
Data Availability Statement: The dataset described in this article is openly available in Zenodo
as LeafScans-Orchard Dataset (v1.0.0) at https://doi.org/10.5281/zenodo.20187966 (accessed on
10 May 2026). The repository includes the 300 dpi image release, the 1200 dpi high-resolution subset,
image-level metadata, aggregated species–cultivar–year counts, and supporting documentation. The
complete archive of original 1200 dpi scans is retained locally by the authors but is not included in
the current pubOpen asset ↗Zenodo · 10.5281/zenodo.20187966pdf-raw-page:12 lines:1-46Plant phenotyping relevance match · UnverifiedEurope PMC · checked 5 Sept 2026
Phytophthora cinnamomi is considered as one of the world's worst plant pathogens, infecting about 5,000 plant species including those of agricultural and environmental significance. Disease management is largely dependent on chemical control, particularly synthetic fungicides such as phosphonic acid-based fungicides, e.g., phosphite/potassium phosphonate. While phosphonic-acid-based fungicides have been highly effective for more than 40 years, their prolonged use has led to the development of tolerance and decreased sensitivity in P. cinnamomi . Novel control agents that are effective but environmentally sustainable are therefore urgently needed. RNA-based biopesticides, which use exogenously applied double-stranded RNA (dsRNA) specific to the target pest or pathogen to avoid off-target effects on other organisms in the environment including beneficials, have emerged as a potential novel disease management strategy against P. cinnamomi . Due to the limited availability of bioassays to study the efficacy of this novel control agent against P. cinnamomi , we developed water-based lupin and pineapple bioassays using readily available plastic cups and glassware with mycelial plugs as inoculum. Infection rate was assessed 3 to 7 days post-inoculation (dpi) for lupin and 7 to 14 dpi for pineapple by measuring root lesion length and rating root rot. Potassium phosphonate (Agri Fos 600) and dsRNA were tested as example control agents, with dsRNA uptake tested via northern blotting. The bioassays were found suitable for P. cinnamomi pathogenicity assays, with one mycelial plug an effective inoculum; fungicide sensitivity testing, with doses as low as 0.45 g L -1 Agri Fos® 600 providing protection; and exogenous dsRNA studies targeting root pathogens, with dsRNA able to be taken up by germinating lupin seeds. Overall, the assays are soil-free and thus overcome dsRNA stability issues in the soil and enable the collection of intact clean roots for molecular analyses. Furthermore, the bioassays are non-destructive, allowing root lesion symptoms to be visually monitored and repeatedly measured across different timepoints.
Why it matches plant phenotyping methods植物病害の根病徴を測定する土壌フリー・バイオアッセイを開発し、その適用性を検証しており、表現型取得法が研究の中心です。
abstractwe developed water-based lupin and pineapple bioassays using readily available plastic cups and glassware with mycelial plugs as inoculum.
Three-dimensional (3D) reconstruction based on structure from motion and multi-view stereo (SfM-MVS) is increasingly used in plant phenotyping, but its performance is influenced by crop architecture, viewpoint configuration, and image preprocessing. For compact crops such as peanut, dense branching and severe within-canopy occlusion make reliable reconstruction challenging. This study evaluated the effects of reconstruction pipeline, angular interval, and image preprocessing on 3D reconstruction of peanut plants under controlled rotary imaging. A total of 10,800 RGB images from 30 plants were used to compare representative implementations of incremental and global SfM-MVS pipelines in terms of geometric quality, phenotypic accuracy, and processing efficiency. At the 1° baseline, the tested global pipeline implementation reduced the root mean square reprojection error (RMSRE), point-density coefficient of variation (CV), vertical root mean square error (VRMSE), and the 95th percentile of the absolute point-cloud distance values (P95) by 15.05%, 14.08%, 39.87%, and 33.33%, respectively, and increased average phenotypic accuracy from 96.08% to 97.37%, compared with the tested incremental pipeline implementation. In contrast, the tested incremental implementation showed a lower voxel void ratio and shorter processing time. In both pipelines, increasing the angular interval reduced processing time but also reduced geometric stability, internal voxel filling, and phenotypic accuracy. In the present dataset, angular intervals of 3°–5° provided a favourable balance between reconstruction accuracy and efficiency. Cropping reduced peripheral redundancy, whereas cropping combined with background removal produced the best overall results, with the lowest reprojection error and the highest phenotypic accuracy. These results provide practical guidance for selecting reconstruction pipeline, viewpoint configuration, and preprocessing strategy in close-range indoor 3D phenotyping of peanut plants and crops with similar canopy architectures.
Why it matches plant phenotyping methods落花生の3D表現型取得について、SfM-MVSパイプライン、視点間隔、画像前処理を比較・検証しており、フェノタイピング手法が研究の中心である。
abstractThis study evaluated the effects of reconstruction pipeline, angular interval, and image preprocessing on 3D reconstruction of peanut plants under controlled rotary imaging.
Plant phenotyping relevance match · UnverifiedCrossref · Europe PMC · checked 5 Sept 2026
Plant diseases result in the estimated loss of 20–40% of the world’s crop production annually, amounting to more than $220 billion in economic losses and threatening food security for a rapidly expanding world population. While the conventional methods for detecting plant diseases rely on visual inspection of the symptoms, they are resource-consuming. For effective plant disease detection at a pre-mature stage, hyperspectral imaging (HSI) represents a paradigm shift in technology. It can be used to obtain subtle spectral signatures outside the visible spectrum, which enables pre-symptomatic and highly specific plant disease diagnosis. Concurrently, deep learning (DL) has become the prevalent analytical paradigm for decoding the complex and high-dimensional data that HSI produces. This paper covers a comprehensive narrative review of the intersection of these two transformative technologies from 2008 to 2026. We first set out the biological and physical principles by which HSI is uniquely suited to detecting plant–pathogen interactions in the absence of visible symptoms. We then present a detailed taxonomy of deep learning architectures for Vision Imaging and HSI data, ranging from basic 1D and 3D convolutional neural networks (CNNs) to hybrid models with attention mechanisms and, most recently, vision transformers, which have achieved greater robustness to real-world conditions. There is currently a major and consistent “lab-to-field” performance gap. A critical analysis of various studies reveals a persistent and significant performance gap between models that perform well on controlled lab datasets (ranging from 95 to 99%) and field-collected data (typically 70–85%). This paper also addresses the practical gap of environmental variability, image noise, and the domain gap between the controlled environment and the real dataset. Finally, this review concludes by providing strategic research recommendations and a roadmap, highlighting that the future of the field is contingent upon not only architectural innovation but also a holistic approach, with robustness, scalability, affordability, and interpretability as the main focus to bring the proven potential of HSI-DL systems from the lab to the field, ultimately contributing to global food security.
Why it matches plant phenotyping methods植物病害の症状・状態をハイパースペクトル画像と深層学習で推定する手法を中心に扱うレビューであり、植物フェノタイピング手法レビューに該当する。
abstractThis paper covers a comprehensive narrative review of the intersection of these two transformative technologies from 2008 to 2026.
Hyperspectral sensing data processing pipeline, originally developed for the early diagnosis of rust diseases in grain crops, was assessed for its applicability for the task of phenotyping of healthy plants of wheat Triticum aestivum, barley Hordeum vulgare, and rye Secale cereale. Hyperspectral images of healthy plants, obtained under laboratory conditions using a Cubert Ultris 20 camera (450–874 nm range, 106 channels), were utilized. The effectiveness of various preprocessing schemes was compared: full (including normalization, smoothing, calculation of derivatives, and identification of extreme features), reduced, and minimal. Machine learning models were exploited for classification: logistic regression, support vector machine, and gradient boosting, trained on averaged spectra. It is shown that the use of a full pipeline optimized for phytopathological diagnostics leads to reduced classification accuracy in phenotyping tasks. The best results (F1 = 0.97 ± 0.025) were achieved using the original averaged spectral curves without additional transformations. It is concluded that for healthy wheat, barley, and rye phenotyping, absolute reflectance levels are informative, whereas for disease diagnostics, changes in the shape of the spectral curve are more important. The obtained results clarify the applicability limits of pipelines developed for phytosanitary purposes and can inform the development of remote monitoring and phenotyping systems for cereal crops.
Why it matches plant phenotyping methods穀類の健全植物フェノタイピングに対するハイパースペクトル画像処理パイプラインの適用性を比較評価しており、前処理と分類性能の検証が研究の中心である。
abstractHyperspectral sensing data processing pipeline, originally developed for the early diagnosis of rust diseases in grain crops, was assessed for its applicability for the task of phenotyping of healthy plants of wheat Triticum aestivum, barley Hordeum vulgare, and rye Secale cereale.
Estimating the nutritional status of rice leaves is crucial for efficient nutrient management and yield enhancement. Traditional wet lab analyses are time-consuming and labor-intensive. This study presents a novel deep learning-based approach utilizing multispectral images captured by unmanned aerial vehicles (UAVs) to estimate macro/micro nutrients in rice leaves. The proposed framework integrates a differentiable neural search technique using polynomial function approximators and an adaptive activation mechanism, which not only provides improved predictive performance but also deals efficiently with limited training data. The model performance is evaluated across different treatments and crop growth stages using mean absolute error (MAE) and [Formula: see text] values. Experiments were conducted at the Punjab Agricultural University. The results demonstrate that the proposed model achieves MAE values in the range of 0.06-0.11 for SAS-I and 0.06-0.16 for SAS-II across eleven leaf macro/micro nutrients. To further evaluate the reliability of the predicted nutrients beyond the prediction error analysis, uncertainty estimation of nutrients is also performed. Comparative analysis shows that the proposed framework outperforms conventional deep learning baselines and machine learning methods in terms of accuracy and robustness. Furthermore, the t-SNE visualization of learned feature representations effectively clusters similar nutrient values while separating dissimilar ones. The robustness of the proposed framework is further validated through ablation studies, treatment-wise and plot-wise cross-validation, highlighting the contribution of individual components and their performance under varying field conditions. These findings highlight the proposed NAS-based framework for precise and reliable nutrient assessment in precision agriculture.
Why it matches plant phenotyping methods稲葉のマクロ・微量栄養素という植物状態をマルチスペクトル画像から推定する深層学習手法を開発し、比較検証・不確実性評価・アブレーション試験まで行っており、表現型取得・推定法が中心である。
abstractThis study presents a novel deep learning-based approach utilizing multispectral images captured by unmanned aerial vehicles (UAVs) to estimate macro/micro nutrients in rice leaves.
Real-time monitoring of H 2 O 2 in plant tissues is useful for evaluating oxidative changes during postharvest storage, but direct on-site detection in vegetables remains difficult because most assays still require tissue disruption and laboratory instruments. In this study, a dual-signal microneedle biosensor was developed by integrating polydopamine-coated Fe/Zr-MOF nanozyme (PDA@Fe/Zr-MOF) into a gelatin/sodium alginate microneedle patch for H 2 O 2 detection in lettuce. The polydopamine coating improved the peroxidase-like response of Fe/Zr-MOF through •OH generation and also contributed to photothermal conversion under 808 nm near-infrared (NIR) irradiation. After contact with lettuce leaves, the microneedles extracted interstitial fluid and allowed H 2 O 2 -triggered TMB oxidation to be read by both colorimetric imaging and thermal imaging. The two outputs were not independent recognition mechanisms, but they provided mutually supportive information and helped reduce the influence of sample color and environmental fluctuations. The sensor achieved detection limits of 0.42 μM for the colorimetric mode and 0.34 μM for the photothermal mode. During 15 days of storage at 4°C, the sensor tracked H 2 O 2 accumulation in lettuce and showed a clear relationship with spoilage progression. These results indicate that PDA@Fe/Zr-MOF-based microneedle sensing is a feasible approach for monitoring oxidative freshness changes in postharvest vegetables.
Why it matches plant phenotyping methodsレタス組織内H2O2という植物の生理状態を、マイクロニードルとカラー・熱画像で現場測定するセンサーを開発しており、取得手法が研究の中心である。
abstracta dual-signal microneedle biosensor was developed by integrating polydopamine-coated Fe/Zr-MOF nanozyme (PDA@Fe/Zr-MOF) into a gelatin/sodium alginate microneedle patch for H 2 O 2 detection in lettuce.
Plant phenotyping relevance match · UnverifiedOpenAlex · Europe PMC · checked 5 Sept 2026
Introduction: Root anatomical traits and spatial architecture play a critical role in crop water acquisition and utilization, directly impacting drought tolerance. However, comprehensive studies examining the synergistic effects of deep root configuration and cortical tissue organization under drought stress during the seedling stage remain scarce. Additionally, the underlying physiological mechanisms are not yet well understood. Methods: In this study, we utilized a high-throughput, paper-based phenotyping platform to simulate drought stress using 10% PEG. An efficient, multi-trait evaluation framework was employed to classify the 28 tested genotypes into five drought tolerance categories. Results: This approach enabled the identification of drought-tolerant cultivars "Ruichun 1," "Ningchun 11," and "Ningchun 57," as well as the drought-sensitive cultivar "Dingxi 48." Root traits, including maximum depth, convex hull area, and plant height, demonstrated strong explanatory power and could serve as valuable phenotypic indicators for seedling stage screening. Our findings suggest that drought adaptation in spring wheat involves a strategic coupling in which specific cortical configurations facilitate the development of deep root architecture. While previous studies have often focused on individual parameters, we show that drought-tolerant genotypes optimize root growth in deeper segments of the growth medium by adjusting cortical tissue proportions, potentially minimizing metabolic costs. Discussion: This integrated perspective offers a detailed physiological framework for understanding drought resilience and moves toward a mechanism-based interpretation of resource reallocation. However, it is important to note that these results were obtained using a paper-based phenotyping platform under PEG-induced osmotic stress, reflecting the genotypic potential at the seedling stage rather than actual field drought tolerance. In conclusion, combining the paper-based high-throughput phenotyping platform with a multi-trait evaluation framework allows for the accurate classification of drought tolerance types and the efficient identification of representative spring wheat cultivars. The findings emphasize the importance of deep root configuration and optimized cortical allocation as fundamental components of the root structural basis for drought adaptation in spring wheat. These results provide clear phenotypic targets for early-stage screening, which should be further validated at later developmental stages and under field conditions before being applied in breeding programs.
Why it matches plant phenotyping methods紙ベースのハイスループット表現型解析プラットフォームと多形質評価フレームワークが、根形態を用いた耐乾性分類の中心的手法として明示されているため。
abstractwe utilized a high-throughput, paper-based phenotyping platform
Worldwide, crop health still suffers despite constant watch – diseases linger, cutting harvests and weakening quality across regions. Early detection shifts outcomes once outbreaks begin; yet current approaches lean heavily on trained eyes examining symptoms up close – a resource often missing at critical moments. Enter AgroVision: an imaging tool powered by deep learning that scans leaf photos to catch signs of illness quickly. Instead of relying on rigid rules, it leans on Convolutional Neural Networks, uncovering subtle clues linked to specific ailments while learning on its own. What sets it apart? It learns patterns naturally, spotting threats without step-by-step instructions. Every now and then, working outside or under lab lights shows how tricky shifting conditions can be - this slip between environments is the core of what folks call the domain gap. Designed tight and with intent, the model moves fast yet expands smoothly if demands grow. Learning from earlier jobs helps it start faster, still hitting close even on fresh, unfamiliar inputs. A browser tab opens, farm photos go in, answers show up instantly, no lagging behind. Tests back its steady precision, all while staying light on computing load. Most older devices handle it without slowing down. Where connections drop often, that matters more than speed. Smart programming tackles messy farm decisions anywhere. Clarity comes when software respects tough conditions on the ground.
Why it matches plant phenotyping methods葉画像から植物病徴を認識する深層学習画像手法を開発し、実験室・圃場間のドメインギャップと性能を評価しているため、植物フェノタイピング手法が中心である。
abstractan imaging tool powered by deep learning that scans leaf photos to catch signs of illness quickly
Why it matches plant phenotyping methods大豆貯蔵タンパク質比を測定するSDS-PAGEプロトコルを開発・比較検証し、育種系統の表現型判別に適用しており、測定法が中心的である。
abstractThis study presents an improved SDS-PAGE protocol optimized for quantifying the 11S/7S ratio with improved accuracy, reproducibility, and cost-effectiveness.
Abiotic stresses such as drought and salinity significantly constrain the productivity of in vitro-grown wheat (Triticum aestivum L.) by disrupting its biochemical and physiological homeostasis. Rapid, non-destructive, and data-driven diagnostic approaches are therefore essential for the early detection of stress conditions and for supporting sustainable crop management. In this study, Raman spectroscopy (RS) was integrated with conventional biochemical assays to investigate wheat responses under controlled drought and salinity stress treatments. Distinct Raman spectral features associated with pigments, proteins, carbohydrates, and lipids were analyzed alongside biochemical indicators, including proline, chlorophyll, and malondialdehyde levels. Overall, the integration of RS with machine learning provides a rapid, robust, and non-invasive framework for the early detection of drought and salinity stress in wheat. Notably, Raman intensity variations observed at 737, 996, 1051, 1064, and 1518 [Formula: see text] exhibited consistent spectral trends that closely mirrored changes in conventional biochemical stress markers, confirming that these spectral shifts directly reflect underlying physiological stress responses. To classify stress levels and to identify key Raman-derived biomarkers associated with each stress type, a machine learning approach was implemented, achieving a classification accuracy exceeding 85% in discriminating control, drought-stressed, and salinity-stressed plants. Furthermore, characteristic Raman bands, particularly those associated with C-H and amide vibrational modes, showed strong correlations with established biochemical indicators, underscoring their potential as reliable, non-invasive stress biomarkers. Collectively, these findings provide mechanistic insight into stress-induced structural and biochemical alterations and support the application of RS-machine learning integration for precision agriculture and resilient crop management under changing environmental conditions.
Why it matches plant phenotyping methodsラマン分光と機械学習により、コムギの乾燥・塩ストレス状態を非破壊的に検出・分類する方法を開発・検証しており、植物状態の取得が研究の中心である。
abstractRaman spectroscopy (RS) was integrated with conventional biochemical assays to investigate wheat responses under controlled drought and salinity stress treatments.
Because conventional vegetative propagation methods for Kiwifruit (Actinidia deliciosa (A.Chev.) C.F.Liang & A.R.Ferguson) often are constrained by their need for extensive plantation areas, high labour inputs, and intensive weed management. Therefore, in vitro micropropagation has emerged as an effective approach for the large-scale production of uniform, disease-free kiwifruit plant material. In the present study the shoot growth dynamics and spatial competition between explants for kiwifruit (cv. Hayward) in vitro-grown were investigated considering different explant densities (3, 5, and 7) and two subculture durations (30 and 45 days). Growth performance was assessed integrating traditional measurements (shoot viability, number and length, callus formation, fresh and dry biomass) with high-resolution three-dimensional photogrammetric reconstruction. Image acquisition was performed using a smartphone-based system (iPhone+viDoc RTK rover), and dense point clouds were generated through the Structure from Motion photogrammetric processing, providing the basis for quantitative geometric analysis. Consistent correlations were observed between manually measured growth traits and smartphone-derived morphometric parameters at both 30 and 45 days of subculture. Specifically, point cloud–based estimates of surface area, height, and volume were significantly associated with shoot number, shoot length, and biomass accumulation, supporting the reliability of 3D photogrammetry as a non-destructive tool for phenotyping in vitro kiwifruit growth. The proposed approach demonstrates the potential of 3D photogrammetry to enhance the objectivity, resolution, and repeatability of growth assessment in in vitro culture systems, offering new insights into shoot development and density-dependent interactions. Graphical Abstract
Why it matches plant phenotyping methodsスマートフォン撮影とSfMによる3Dフォトグラメトリで、キウイフルーツ苗条の表面積・高さ・体積を非破壊推定し、手動測定およびバイオマスとの相関で信頼性を検証しており、表現型取得手法が中心である。
abstractdense point clouds were generated through the Structure from Motion photogrammetric processing, providing the basis for quantitative geometric analysis.
Aroma is a primary determinant of rice quality and market value, yet its evaluation in breeding programs remains constrained by labor-intensive milling, cooked-grain sensory methods, binary screening assays, and the limited seed availability of early generation selection. Moreover, aromatic rice breeding has historically focused narrowly on 2-acetyl-1-pyrroline-mediated popcorn aroma, potentially overlooking valuable alternative aromatic profiles. In this study, we developed a rapid sensory phenotyping approach for paddy rice that enables quantitative assessment of the aroma intensity and qualitative aroma characterization without milling or cooking. A diverse panel of 126 rice genotypes was evaluated using 1 g of ground paddy rice heated under controlled conditions coupled with sensory analysis and targeted HS-SPME-GC-MS/MS quantification of 164 volatile compounds. The method discriminated the aroma intensity and enabled characterization of aroma quality. Hierarchical clustering integrating sensory and chemical data resolved five distinct aroma classes, including popcorn-dominant, fruity-floral, nutty-grainy, woody-floral, and oxidation-driven phenotypes. While 2AP showed the strongest association with popcorn aroma and overall intensity ( r = 0.50), several high-intensity genotypes exhibited minimal 2AP, yet strong aroma perception driven by esters, alcohols, indole, and ketones. Interestingly, two genotypes (R125 and R126) showed strong popcorn perception despite much lower 2AP than typical aromatic rice, indicating the contribution of non-2AP popcorn-like aroma drivers. Conversely, genotypes with elevated lipid oxidation aldehydes exhibited high volatile abundance but poor aroma quality characterized by rancid, phenolic, and musty notes. These results demonstrate that superior rice aroma is a multivariate trait and is not related to only 2AP. The rapid phenotyping framework presented here provides breeding programs with an employable, information-rich tool for early generation screening, accelerating the identification of aromatic rice cultivars with expanded sensory diversity.
Why it matches plant phenotyping methodsイネ籾の香気を迅速・定量的に評価する感覚フェノタイピング手法を開発し、遺伝子型間で検証・適用した研究であり、表現型取得法が中心である。
abstractwe developed a rapid sensory phenotyping approach for paddy rice that enables quantitative assessment of the aroma intensity and qualitative aroma characterization without milling or cooking.
Here, we present a protocol to independently and concurrently evaluate salt and alkaline tolerance in maize at the seedling stage using a controlled sand-bed germination assay. We describe steps for preparing standardized stress solutions, establishing the sand-bed assay, applying stresses either independently or in combination, and measuring multiple phenotypic traits. Furthermore, we detail procedures for ranking germplasm using a comprehensive scoring system based on a membership function. This protocol enables reproducible, large-scale screening of maize germplasm at the seedling stage.
Why it matches plant phenotyping methods幼苗耐盐碱性状の再現可能な大規模フェノタイピング用アッセイとスコアリング手順を中心に提示しており、単なる生物学的処理実験ではない。
abstractwe present a protocol to independently and concurrently evaluate salt and alkaline tolerance in maize at the seedling stage using a controlled sand-bed germination assay
Plant leaf disease detection is a critical task in precision agriculture, where reliable diagnosis under real-world conditions is essential for reducing crop losses and supporting timely intervention. Although deep learning models have achieved high classification accuracy, their performance often degrades under domain shift between controlled laboratory datasets and real-field environments, while predictive uncertainty and confidence calibration remain largely unaddressed.This study presents an uncertainty-aware cross-domain evaluation framework based on a Hierarchical Vision Transformer (HViT) for plant leaf disease classification. The framework integrates multi-scale feature learning with Monte Carlo Dropout-based predictive uncertainty estimation and temperature-based calibration to systematically analyze model behavior in terms of accuracy, reliability, and robustness. Experiments were conducted on two complementary datasets: the New Plant Diseases Dataset (controlled conditions) and the PlantDoc dataset (field conditions), enabling bidirectional cross-domain evaluation. Results demonstrate that the proposed framework achieves superior performance, attaining 97.8% accuracy on controlled data and 93.6% on field data, while significantly improving calibration with lower Expected Calibration Error (ECE = 0.032 / 0.041), reduced Negative Log-Likelihood, and lower Brier score compared to baseline CNN and transformer models. Furthermore, the framework exhibits improved robustness under domain shift, with reduced performance degradation and stable uncertainty behavior. Overall, this study highlights the importance of integrating uncertainty estimation and calibration within a hierarchical transformer-based framework, providing a more reliable and deployment-ready solution for real-world agricultural disease diagnosis.
Why it matches plant phenotyping methods植物葉の病害状態を直接推定する不確実性-aware分類フレームワークの開発・評価が中心であり、異なる条件のデータセット間で精度、校正、頑健性を検証している。
abstractThis study presents an uncertainty-aware cross-domain evaluation framework based on a Hierarchical Vision Transformer (HViT) for plant leaf disease classification.
Reproduction assets foundThe paper's Data availability statement explicitly links the two public image datasets used for its cross-domain plant leaf disease classification experiments: the New Plant Diseases Dataset on Kaggle and the PlantDoc dataset on Dataset Ninja. No author analysis code, models, or checkpoints are reported as available.Dataset · publicThe New Plant Diseases Dataset can be obtained from Kaggle at [https://www.kaggle.com/datasets/vipoooool/new-plant-diseases-dataset]Open asset ↗Kaggle · vipoooool/new-plant-diseases-datasetlines:360-398Dataset · publicThe PlantDoc dataset is available for download at [https://datasetninja.com/plantdoc#download]Open asset ↗plantdoclines:360-398Plant phenotyping relevance match · UnverifiedEurope PMC · checked 5 Sept 2026
Accurate detection of early invisible apple bruises is important for post-harvest quality assessment. Although hyperspectral imaging (HSI) provides rich spectral information, its high dimensionality introduces substantial redundancy and weak-signal interference. This study proposes an integrated framework combining waveband optimization and discrete spectral modeling for efficient bruise detection. A Selection-Refined Improved Grey Wolf Optimization (SR-IGWO) algorithm was developed to select 18 bruise-sensitive wavebands from 273 channels (996-2501 nm), achieving a 93.4% reduction in spectral dimensionality. SHAP analysis was further used to interpret the selected bands in relation to biochemical responses associated with bruising. To address the mismatch between conventional CNNs and sparse discrete spectral inputs, a CNN-Transformer hybrid model (DSFormer) was designed using pointwise convolution for band embedding and a Transformer encoder to capture global dependencies. Experimental results across ten independent runs achieved a classification accuracy of 99.11% ± 0.08%, a recall of 96.04% ± 1.08%, and an F1-score of 95.95% ± 0.39% under the tested conditions. Ablation studies suggest that the proposed architecture supports effective detection under sparse spectral conditions. Although validation was limited to a single cultivar and controlled sampling, the proposed framework provides a promising preliminary exploration of reduced hyperspectral data for non-destructive fruit bruise detection.
Why it matches plant phenotyping methodsリンゴ果実の打撲状態を対象に、ハイパースペクトル波長選択とCNN-Transformerによる症状検出手法を開発・検証しており、植物器官の状態取得が研究の中心である。
abstractThis study proposes an integrated framework combining waveband optimization and discrete spectral modeling for efficient bruise detection.
Analysis of the element composition of plant materials has many applications, including monitoring nutrient status, detecting biogeochemical indications of mineral deposits and assessing the effectiveness of phytoremediation in contaminated soils. Portable X-ray fluorescence spectroscopy (pXRF) delivers the advantage of real time in situ multi-elemental analysis at low cost, but calibration is affected by factors that include the water content of plant organs. The effect of variation in moisture content on pXRF-determined concentrations of heavy metals Zn, Fe, Cu, Sr and Th, and nutrient elements Si, S, K and Ca, have been evaluated using the foliage of A. imperialis, D. excelsa and A. macrorhiza through a controlled continuous drying experiments. A segmented linear relationship between pXRF measurements and moisture content was observed for most elements with a rapid decrease followed by a moderate decrease as moisture content increased. The position of the inflection point is dependent on leaf thickness and energy of the main X-ray peak measured. Calibration issues related to variation in moisture content comprises a combination of dilution and spectral interference effects. Dilution accounts for most of the underestimation of pXRF-determined concentrations for fresh plant samples compared with laboratory methods on dried samples. As moisture contents increase, the relative influence of spectral interferences decreases. The single-layer thickness of plant sample affects the position of inflection point of linearity and the relative contribution of spectral interference effect. This study provides new insights into the effect of moisture on pXRF-determined elemental concentrations and offers practical suggestions and recommendations for in situ analysis of plant samples using pXRF.
Why it matches plant phenotyping methods植物試料の元素濃度を測定するpXRFについて、水分含量による測定誤差と校正特性を評価し、実 in situ 測定への実用的推奨を示す方法検証研究である。
abstractcalibration is affected by factors that include the water content of plant organs
PollenBB16 is an RGB pollen image dataset of Chilean flora with pixel-accurate instance segmentation masks, whose annotation was fully verified by an expert palynologist to guarantee the taxonomic reliability of every published instance. The dataset is designed to close a concrete gap in existing palynological datasets, which typically combine low taxonomic diversity, few samples per class, and low-resolution crops restricted to bounding boxes. PollenBB16 contains 16,198 brightfield optical microscopy images at the native resolution of 3088 × 2064 pixels and 36,383 pixel-accurate polygons across 16 species from the Biobío Region, spanning endemic, native and exotic species of high ecological and melliferous value such as Eucryphia glutinosa and Quillaja saponaria (endemic), Gevuina avellana and Aristotelia chilensis (native), and Medicago sativa and Brassica rapa (introduced). Each spatial position is recorded at three focal planes. The displacement along the z axis reveals features of the exine together with information on the internal structure of the grain that remain inaccessible on a single plane. From this multifocal information, more robust convolutional networks can be trained with more accurate classification. The operational quality of the dataset is backed by a leakage-safe partition that keeps the three focal planes of the same position in the same subset to avoid metric inflation, complemented by a YOLO11n-seg baseline trained for 50 epochs that reaches 0.985 mask mAP@50 on the validation set, establishing a reproducible reference point. Beyond deep learning, PollenBB16 enables interdisciplinary applications in aerobiology, biodiversity monitoring under climate change, ecological restoration of the South American temperate forest, and botanical-origin authentication of Chilean monofloral honeys.
Why it matches plant phenotyping methods植物由来の花粉粒を対象とした高解像度画像データセットとセグメンテーション基準を構築し、深層学習による画像解析を再現可能な形で検証しているため、植物フェノタイピング手法・データセットとして中心的です。
abstractPollenBB16 is an RGB pollen image dataset of Chilean flora with pixel-accurate instance segmentation masks
Reproduction assets foundThe paper's PollenBB16 pollen image dataset (16,198 multifocal RGB microscopy images with pixel-accurate instance segmentation masks) and the accompanying authors' script polygons_to_bboxes.py are publicly deposited on Zenodo (10.5281/zenodo.19830051), per the Data Availability Statement.Code · publicThe only custom code distributed with this Data Descriptor is the Python script polygons_to_bboxes.py, which regenerates the YOLO bounding-box labels in labels_bb/ from the polygon labels in labels/. The script is packaged inside the scripts/ folder of the same Zenodo repository that hosts the dataset (10.5281/zenodo.19830051) and is released under the Creative Commons Attribution 4.0 International (CC BY 4.0) license, with no restrictions on access.Open asset ↗Zenodo · 10.5281/zenodo.19830051html-lines:731-797Plant 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.
Abstract Purpose Leaf chlorophyll content is a critical indicator of plant health. On-farm monitoring is limited by the high cost and accessibility of specialised meters and laboratory assays. This study evaluates a smartphone-based imaging method (PhotoFolia) as a practical, low-cost alternative for accurately estimating leaf SPAD (Soil and Plant Analysis Development) values and chlorophyll content. Methods Image-based estimates from the PhotoFolia mobile application were validated against standard laboratory assays and SPAD-502+ meter readings. The validation was conducted across four commercial rice varieties cultivated in Thailand. Results The smartphone imaging method predicted SPAD values with a mean absolute error (MAE) of 1.2 units and chlorophyll concentrations with a mean absolute percentage error (MAPE) of 7.2% relative to laboratory benchmarks. Conclusion With SPAD error approaching the industry ±1 unit standard and chlorophyll estimation remaining below a 10% relative error threshold, this approach demonstrates that ordinary mobile phones can serve as highly accessible, cost-effective tools for routine on-farm crop monitoring, eliminating the need for dedicated hardware. Highlights Novel low-cost approach for chlorophyll assay and SPAD-value measurement using standard mobile phone. Achieves accuracy comparable to commercial tools. Eliminates need for specialised sensors or laboratory equipment. Impact This study demonstrates that mobile phone-based image analysis can accurately estimate leaf SPAD and chlorophyll levels in rice under ambient lighting conditions, offering a low-cost, accessible tool for monitoring plant health.
Why it matches plant phenotyping methodsスマートフォン画像から葉のSPAD値とクロロフィル量を推定する手法を開発・標準測定と検証しており、植物表現型取得が中心である。
abstractThis study evaluates a smartphone-based imaging method (PhotoFolia) as a practical, low-cost alternative for accurately estimating leaf SPAD (Soil and Plant Analysis Development) values and chlorophyll content.
Introduction Confidence calibration, selective prediction, out-of-distribution scoring, and deep ensembles are mature techniques in machine learning, yet their efficacy under the severe domain shift encountered when plant disease classifiers move from controlled laboratory imagery to heterogeneous field photographs has not been systematically benchmarked. Methods Models trained on PlantVillage were evaluated on PlantDoc leaf-level crop images under a parent-image-aware split protocol, and a suite of standard mitigation techniques was applied to characterize the reliability gap. Analyses included temperature scaling and selective prediction for a fine-tuned ResNet-50, quantitative image-level shift analysis, Grad-CAM visualization, simple target-aware adaptation baselines, frozen-feature backbone comparisons, and ensemble baselines. Results In the primary case study, a fine-tuned ResNet-50 suffered a 67.7-percentage-point accuracy collapse upon cross-domain transfer, while mean predicted confidence remained at 79.76%. Post-hoc temperature scaling reduced calibrated ECE to 0.3645 but left selective risk at 80% coverage at 64.30%. Quantitative image-level shift analysis confirmed large-effect-size differences in saturation ( d = 3.90), border edge density ( d = 3.33), and foreground-occupancy proxy ( d = 2.48) between the two domains, while Grad-CAM visualizations showed that the model shifts attention from lesion-centered regions in PlantVillage to background-dominated areas in PlantDoc. Simple target-aware mitigations, including adaptive batch normalization and feature moment matching, improved accuracy from 0.321 to 0.343 and 0.366, respectively, whereas DANN-style adversarial adaptation degraded performance to 0.252. A frozen-feature backbone comparison across five backbones showed that, within the energy-scoring frozen-backbone comparison, DINOv2-S/14 achieved the highest unknown-detection AUROC (0.764) and the lowest selective risk at 80% coverage (0.520), with paired Wilcoxon tests confirming statistically significant accuracy and macro-F1 differences across backbones. Two ensemble baselines were evaluated: a warm-start end-to-end ResNet-50 ensemble reduced calibrated ECE to 0.063 but achieved only 0.666 AUROC, while a lightweight DINOv2 linear-probe ensemble achieved 0.779 AUROC after calibration but under limited epistemic diversity. Discussion Neither ensemble established deployment-grade reliability: the best selective risk at 80% coverage across all configurations remained above 0.51. The principal contribution is a reproducible, deployment-oriented reliability characterization showing that standard post-hoc and lightweight adaptation techniques reduce but do not eliminate the severe reliability gap under controlled-to-field transfer in agricultural computer vision.
Why it matches plant phenotyping methods植物病害画像分類の信頼性・ドメインシフト・校正・選択的予測を体系的にベンチマークしており、病害状態を画像から推定する方法の技術評価が中心である。
abstracttheir efficacy under the severe domain shift encountered when plant disease classifiers move from controlled laboratory imagery to heterogeneous field photographs has not been systematically benchmarked.
Reproduction assets found本文中に内容が明示された植物フェノタイピング関連の補足表と、その公開リンクを確認しました。Supplement · publicSupplementary Table 1 ) was therefore constructed by normalizing all labels to a canonical Crop_Disease format and retaining only those categories for which an unambiguous semantic match existed in both datasets.Open asset ↗lines:335-337Plant phenotyping relevance match · UnverifiedCrossref · checked 15 Sept 2026
Robust in-field sensing technologies are essential for advancing precision agriculture and autonomous field robotics toward analysing internal quality attributes of fruits and vegetables. This study demonstrated in-the-field, non-contact near-infrared (NIR) spectroscopy for determining total soluble solids (TSS), a measure of sugar content, in on-the-plant strawberries under daytime conditions. A compact NIR interaction instrument (750–1020 nm), designed for robotic operation, was built and tested in a polytunnel environment under varying day- and night-time conditions. The instrument was calibrated using a partial least squares regression (PLSR) model built on laboratory data collected in 2025 from 200 strawberries of a single variety. It was tested on 100 strawberries of two varieties that were measured in 2024, while still attached to the plant. During night-time operation, TSS was predicted with a standard error of prediction ( SEP ) of 0.73 % TSS and a bias of 0.65 % TSS. Under challenging daytime conditions with strong and fluctuating ambient light, measurements were more affected by additional shot noise from the ambient light, resulting in SEP s up to 1.35 % TSS and biases up to 1.45 % TSS, both of which are acceptable for most applications. The measurement time was 12 s. Robust performance was achieved by implementing rapid and continuous ambient light sampling and correction, combined with outlier rejection of spectra of insufficient quality. These findings confirm the feasibility of in-field, on-the-plant NIR spectroscopy for assessing internal fruit quality and provide practical design guidelines to support further in-field implementations of NIR spectroscopy.
Why it matches plant phenotyping methodsイチゴ果実の糖度という植物器官形質を、ロボット搭載可能なNIRセンサーで非接触測定する手法を開発・検証しており、環境光補正や性能評価も中心的に扱っている。
abstractThis study demonstrated in-the-field, non-contact near-infrared (NIR) spectroscopy for determining total soluble solids (TSS), a measure of sugar content, in on-the-plant strawberries under daytime conditions.
Volume electron microscopy based on serial sectioning allows for three-dimensional (3D) visualization and analysis of the internal structures of tissues, cells, and organelles. One such technique, focused ion beam (FIB) scanning electron microscopy (SEM), has the advantages of nanoscale sectioning and high z-resolution, but the disadvantage of limited volume processing. Because of this limitation, targeting localized objects by FIB-SEM is difficult. Here, we developed a FIB-SEM observation workflow that enables the analysis of the filiform apparatus of synergid cells enclosed in the Arabidopsis ovule. In this protocol, plant samples are stained, embedded, trimmed, and carbon-coated while maintaining their orientation within the tissue. Then, sequential observations are performed using Cut & See function of FIB-SEM, followed by image processing for 3D reconstruction. Utilization of multi-scanning and image cropping from high-resolution data helps to identify localized targets within plant tissue. The filiform apparatus, which is an invaginated cell wall structure of the synergid cells, shows distinct contrast in each image, allowing for segmentation using brightness-based binarization. Such segmentation avoids the need to manually trace complex structures and facilitates 3D reconstruction by volume electron microscopy. Key features • Sampling and trimming of the resin block enable directionally loading in FIB-SEM. • Multi-scanning by FIB-SEM and target extraction by image processing software enable 3D reconstruction of local areas within the sample block. • Binarization using distinctive brightness of cellular structures enables segmentation without manual tracing of complex structures such as the filiform apparatus cell wall.
Why it matches plant phenotyping methods植物組織内の構造をFIB-SEMと画像処理で3D再構成・セグメンテーションするワークフローを開発しており、フィリフォーム装置形態という植物器官形質の取得が中心である。
abstractHere, we developed a FIB-SEM observation workflow that enables the analysis of the filiform apparatus of synergid cells enclosed in the Arabidopsis ovule.
Introduction Colorimetric analysis of food using the CIELab/Ch colour space (i.e., from digital images of samples) is an accessible, non-destructive method for carotenoid and anthocyanin content prediction. Literature presents very well-fit, but rudimentary, models for pigment estimation (e.g., single/multiple linear regressions). However, standardised methods that statistically account for the high multicollinearity between CIELab/Ch colour parameters, varying light conditions and colour calibration, and samples with high genotypic variability are lacking. Methods An image analysis optimisation was developed for the prediction of carotenoid and anthocyanin content of 16 carrot genotypes of different colours. Samples were photographed under six light conditions with a digital camera and image colour was calibrated before analysis with the CIELab/Ch colour space. Total pigment contents and individual carotenoid contents were analysed chemically via spectrophotometry and high-performance liquid chromatography, respectively. Partial least squares (PLS) regressions were used to assess the colour-pigment relationships to correct for high multicollinearity amongst the independent variables (CIELab/Ch colour parameters). Results/discussion The PLS models achieved satisfactory accuracy for the prediction of total carotenoid content ( ca. R 2 = 0.77) and total anthocyanin content ( ca. R 2 = 0.81) under all light conditions. The two models are suggested as robust approaches to total pigment prediction with multi-dimensional colour spaces, varying light conditions, and for a sample group of high genotypic variability. The carrot samples proved to have very high genetic diversity within each cultivar, resulting in unsatisfactory models for prediction of individual carotenoids ( ca. R 2 = 0.45) under the default light condition. However, all the results can be used to expand databases (towards artificial intelligence) and aid breeding programmes in search for higher concentrations of these interesting antioxidants for human health.
Why it matches plant phenotyping methodsニンジン試料の画像色解析を最適化し、化学分析値を用いてカロテノイド・アントシアニン含量を予測する手法を開発・検証しており、植物形質取得が研究の中心である。
abstractThe PLS models achieved satisfactory accuracy for the prediction of total carotenoid content ( ca. R 2 = 0.77) and total anthocyanin content ( ca. R 2 = 0.81) under all light conditions.
Reproduction assets foundThe authors deposited the paper's data and protocols in public repositories (DOI links in the Data availability statement). The anthocyanin quantification protocol is explicitly linked (10.34894/BTPTSV), and the other two DOIs (10.34894/P37WCL, 10.34894/OUURRH) are stated to hold the paper's data. No separate author's'Dataset · publicData and protocols are available in the following links: https://doi.org/10.34894/P37WCL , https://doi.org/10.34894/OUURRH , https://doi.org/10.34894/BTPTSV .Open asset ↗10.34894/P37WCLlines:641-686Dataset · publicData and protocols are available in the following links: https://doi.org/10.34894/P37WCL , https://doi.org/10.34894/OUURRH , https://doi.org/10.34894/BTPTSV .Open asset ↗10.34894/OUURRHlines:641-686Plant phenotyping relevance match · UnverifiedEurope PMC · checked 15 Sept 2026
Abstract Magnetic resonance imaging (MRI) enables non-invasive and non-destructive, three-dimensional anatomical and functional imaging of plant tissues and the quantitative investigation of dynamic processes such as water transport. Despite these advantages, MRI remains underutilized in plant and biomimetic research. One major limitation is the difficulty of maintaining physiologically suitable and stable environmental conditions during prolonged measurements, particularly when using ultra-high-field preclinical MRI scanners that were originally developed for small-animal imaging. In this work, we present a low cost, climate-controlled and MR-compatible growth chamber that includes an in-bore extension for preclinical MRI scanners. The system integrates growth and imaging conditions into a single setup, allowing continuous control of temperature, humidity, and illumination by the same system and removing the need to maintain separate commercial growth chambers alongside custom in-bore extensions. The implementation was optimized for the horizontal bore of a small animal scanner (Bruker PharmaScan 70/16) with 16 cm bore diameter and 72 mm free access but is applicable to other ultra-high-field preclinical MRI systems with comparable dimensions. The performance of the climate chamber and the in-bore extension was characterized with respect to temperature, humidity, and illumination stability. In addition, the potential negative impact of the insert and its electronics on the MRI signal (B0 homogeneity, RF attenuation as well as potential RF artefacts) were verified. Functional validation in form of sap flow measurements as well as anatomical validation was demonstrated in a naturally transpiring stem of Passiflora quadrangularis. Under controlled in-bore environmental conditions, changes in sap flow velocity were reliably detected using a pulsed field gradient spin-echo sequence. Specifically, increasing the light intensity in the extension resulted in a shift of the maximum flow velocity in individual vascular bundles from 0.21 mm/s and 0.39 mm/s to 1.37 mm/s and 1.17 mm/s, respectively. In addition, high-resolution anatomical imaging (1 mm slices with an in-plane resolution of 25 µm) of branching regions in Dracaena braunii was successfully performed without observable motion artifacts. The presented system provides a low-cost, open-source solution for conducting anatomical and functional MRI studies of intact plants using ultra-high field preclinical MRI scanners.
Why it matches plant phenotyping methods植物のMRI計測を可能にする環境制御・MR互換チャンバーを開発し、性能および植物の解剖・通道機能計測で検証しており、フェノタイピング手法と基盤が研究の中心である。
abstractIn this work, we present a low cost, climate-controlled and MR-compatible growth chamber that includes an in-bore extension for preclinical MRI scanners.
Accurate and reproducible assessment of foliar disease severity is essential for evaluating the performance of heterogeneous plant communities and understanding host-pathogen interactions. However, traditional visual scoring methods remain subjective, with limited precision, and difficult to scale in large phenotyping experiments. Here, we present a semi-automated image analysis workflow designed to quantify multiple foliar disease symptoms simultaneously on wheat flag leaves sampled from varietal mixtures. The workflow combines three methodological components: (i) a standardized protocol for leaf sampling and imaging, (ii) supervised machine learning segmentation using Random Forest implemented in Ilastik to classify multiple symptoms (powdery mildew and yellow rust), and (iii) a graphical user interface facilitating pipeline deployment by non-specialist operators. To evaluate the influence of image representation on classification performance, four color spaces (RGB, HSV, HLS, LAB) were systematically compared. The approach was validated using images of durum wheat flag leaves collected from a field experiment assessing eight-way varietal mixtures under natural fungal pressure. Cross-validation against manually annotated images demonstrated high segmentation accuracy across all symptom. Comparison among color spaces revealed only minor differences in performance. Overall, this workflow offers a cost-effective, annotation-efficient and reproducible alternative to deep learning approaches, leveraging open-source and actively maintained tools while requiring limited training data and enabling objective, reproducible and scalable disease phenotyping.
Why it matches plant phenotyping methods葉の病害症状を画像解析で定量化するワークフローを開発し、色空間比較と手動アノテーションによる検証を行っており、植物表現型取得法が中心である。
abstractwe present a semi-automated image analysis workflow designed to quantify multiple foliar disease symptoms simultaneously
Reproduction assets foundThe paper's authors explicitly state that all code implementing the leaf disease quantification workflow (SegLeaf, including the graphical interface and documentation) is hosted in a public GitHub repository. No separate public phenotype dataset or trained model checkpoint is described in the supplied blocks.Code · publicted by the Agence Nationale de
la Recherche (ANR) (project SCOOP, grant no. ANR-19-CE32-0011; and project MOBIDIV,
grant no. ANR-20-PCPA-0006).
Code and Data Availability
The method and associated scripts developed in this work are freely available to the re-
search community. All code is hosted in a public GitHub repository at https://github.com/titouanlegourrierec/SegLeaf, which includes the full implementation of the method includ-
ing the graphical interface and documentation to guide users through the analysis pipeline.
15
.
CC-BY 4.0 International license
made available under a
(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display tOpen asset ↗titouanlegourrierec/SegLeafpdf-raw-page:15 lines:1-39Plant phenotyping relevance match · UnverifiedEurope PMC · checked 15 Sept 2026
Published15 May 2026Spectrochimica acta. Part A, Molecular and biomolecular spectroscopyCited by 0 · OpenAlex ↗
Effectively imaging the variation of heavy metal induce stress (HMIS) in plant is significantly important for stress resistance research in the fields of environmental and plant biology. However, due to the absence of distinctive parameter to reveal the relationship between HMIS and plant homeostasis, the reported fluorescence sensors fail to assess HMIS. Herein, a new fluorescent sensor (quinoline-based viscosity probe, QVP) with prominent-responsive viscosity was first developed for evaluating HMIS in plants. Spectral experiments indicate that QVP exhibited selectivity, sensitive, photochemical stability, and pH adaptability for viscosity detection. Motivated by the robust detection capacities, QVP was further applied for clear fluorescence imaging of viscosity changes of plant cell (onion epidermis and scallion bulb) induced by HMIS (Cu 2+ , Au 3+ and Ag + ). Notably, the cellular viscosity was positively correlated with Cu 2+ concentration. More importantly, the sensor QVP had good penetration within plant tissues and enabled viscosity imaging of root hairs, leaves and other tissues. This work not only provides a novel molecular tool for understanding HMIS resistance of the plant by investigating the dynamic change of intracellular viscosity, but also provides an additional dimension for evaluating crop stress resistance.
Why it matches plant phenotyping methods植物細胞・組織の細胞内粘度を蛍光イメージングで測定し、金属イオンストレスを評価する新規センサーを開発・適用しており、植物状態の取得方法が研究の中心である。
abstractHerein, a new fluorescent sensor (quinoline-based viscosity probe, QVP) with prominent-responsive viscosity was first developed for evaluating HMIS in plants.
Potato crop is highly vulnerable to abiotic stresses like salinity and low nutrient availability. Rapid identification of stress-resilient genotypes is therefore essential for breeding, yet conventional phenotyping is often slow, space-demanding and expensive. We present LOCOPOTS — a LOw-COst high-throughput screening platform for in vitro POTatoes under abiotic Stress — which combines individual in vitro plant culture, low-cost RGB imaging and machine-learning-based automatic segmentation using a trained model of a convolutional neural network, based on U-Net architecture. LOCOPOTS enabled the automated extraction of growth, colour, and vegetation-index traits and demonstrated robust performance across independent phenotyping rounds. We screened 30 potato varieties under control, low-nutrient and saltinity conditions, identifying contrasting growth and physiological responses. Integrated traits such as final area and height, Area_AUC and height_AUC, together with GLI, Ch ol , cive and chlorophyll fluorescence parameters, discriminated genotype performance under stress. Metabolic profiling further revealed genotype-specific reprogramming in carbon and nitrogen metabolism under low nutrition and salt stress, including changes in fructose, myo-inositol, β-aminobutyric acid, γ-aminobutyric acid, proline, and certain polyamines, identifying them as specific chemical biomarkers of plant stress responses. LOCOPOTS provides a scalable, affordable and space-efficient platform for early screening of potato genetic diversity and identification of candidate traits associated with stress resilience.
Why it matches plant phenotyping methods低コストRGB撮像とU-Netによる自動セグメンテーションを中核とする、ジャガイモ表現型取得プラットフォームの開発・検証であり、形態・色・植生指数形質を自動抽出している。
abstractWe present LOCOPOTS — a LOw-COst high-throughput screening platform for in vitro POTatoes under abiotic Stress — which combines individual in vitro plant culture, low-cost RGB imaging and machine-learning-based automatic segmentation using a trained model of a convolutional neural network, based on U-Net architecture.
Given the increasing frequency, severity, and socioecological impacts of wildfires, there is an urgent need for robust frameworks to better characterize fire behavior and flammability patterns across ecosystems to support early warning, mitigation, and management strategies. However, flammability remains difficult to quantify and scale, as it involves multiple interacting components that are typically measured at the bench scale. This study aimed to establish empirical links between spectral information, plant traits, and flammability metrics, and to scale these relationships to satellite imagery to translate these metrics into a spatial context. We combined laboratory spectroscopy, plant trait measurements including leaf mass per area, carbon, and cellulose, and combustion experiments using a simple and reproducible burning device. In total, 84 samples were collected and analysed, allowing us to characterise how spectral signatures relate to vegetation traits and fire behaviour. Spectral indices were developed to estimate plant traits, which were subsequently used as predictors in flammability models. These models were then transferred to Environmental Mapping and Analysis Program (EnMAP) hyperspectral imagery to derive spatial estimates across eucalypt forests and grasslands of the Australian Capital Territory (ACT). Spectral information distinguished fuel types and captured variability of the plant traits, while these traits showed associations with combustion behaviour. Based on these links, the best-performing model predicted the rate of temperature increase, a combustibility metric, in eucalypt forests (R2 = 0.70; Root Mean Square Error = 32.48 °C/s). In contrast, grassland models showed limited predictive performance, likely due to weaker relationships between plant traits and flammability metrics. Overall, this study demonstrates a practical and scalable approach for deriving flammability maps from hyperspectral and in situ data, highlighting the potential of plant-trait-based remote sensing. The resulting maps should not be interpreted as standalone fire risk products, but rather as a characterization of the structural and biochemical drivers of flammability. The main constraint of this work is the limited sample size. Future research should expand spatial and temporal coverage to better capture vegetation variability and enable the inclusion of independent validation datasets. Exploring alternative combustion protocols and testing more advanced spectral modelling approaches for trait estimation would provide additional insights.
Why it matches plant phenotyping methods植物形質を分光情報から推定し、ハイパースペクトル画像へ展開して可燃性関連の植物状態を評価する手法が研究の中心であり、モデル性能も検証しているため。
abstractSpectral indices were developed to estimate plant traits, which were subsequently used as predictors in flammability models.
Reproduction assets foundThe paper's supplementary materials (hosted publicly by MDPI) contain the paper-specific plant phenotype measurements: sampled species lists, fractional cover, and measured vegetation traits across dates and plots, plus combustion replicate variability and trait–flammability relationship data. The raw underlying data,谱Supplement · publicbroader environmental coverage, improved plant trait retrieval meth-
ods, and independent validation. Future work should also explore non-linear modelling
frameworks to better capture the complexity of vegetation flammability across ecosystems.
Supplementary Materials: The following supporting information can be downloaded at:
https://www.mdpi.com/article/10.3390/rs18101546/s1, Supplementary Table S1 provides the list of
sampled plant species and their percentage cover across sites, paddocks, plots, and fuel types; Table
S2 presents the fractional cover of each species and litter component; Figure S1 shows the study-site
vegetation map; Figures S2–S6 show the measured vegetation traits acrosOpen asset ↗pdf-raw-page:22 lines:1-49Plant phenotyping relevance match · UnverifiedEurope PMC · checked 5 Sept 2026
Accurate identification of Allium seed genotypes is essential for cultivar authentication, breeding, and fraud prevention, yet remains challenging due to morphological similarities. This study evaluates the potential of a visible and near-infrared (Vis-NIR) spectrometer and a hyperspectral camera for non-destructive classification of seven closely related Allium genotypes, including shallot, red, white, and yellow onions, bon-sorkh, and two leek varieties. A total of 700 spectra and 70 images were acquired using the Vis-NIR spectrometer and hyperspectral camera, respectively, under controlled conditions and spectral preprocessing was applied to enhance signal quality. For spectrometer data, classification models were developed using soft independent modelling of class analogy (SIMCA), artificial neural networks (ANN), and histogram-based gradient boosting (HisGB). For hyperspectral data, pixel-level spectra were used to train ANN, HisGB, and deep convolutional neural networks (1D and 2D CNNs). Among the spectrometer models, the combination of second derivative preprocessing with HisGB achieved the highest performance (F1-score: 98.52%). For HSI, HisGB yielded the highest pixel-level classification accuracy (F1-score: 97.83%; error: 2.49%), followed by 1D CNN (F1-score: 96.85%). Spatial analysis revealed that HisGB and 1D CNN produced consistent classification maps across genotypes, whereas ANN and 2D CNN exhibited higher misclassification rates, particularly for morphologically similar classes such as shallot and bon-sorkh. At image level, the hyperspectral camera outperformed the Vis-NIR spectrometer, achieving perfect classification across all models. These results demonstrate the potential of hyperspectral imaging, especially when combined with ensemble and deep learning approaches, for high-throughput, non-destructive seed sorting and genotype purity assessment. The study also emphasizes the trade-off between the lower cost but reduced precision of the Vis-NIR spectrometer and the superior accuracy offered by the hyperspectral camera.
Why it matches plant phenotyping methodsAllium種子の遺伝型識別を対象に、Vis-NIR分光およびハイパースペクトル画像取得と分類ワークフローを比較・評価しており、非破壊的な表現型取得・判別手法が研究の中心です。
abstractThis study evaluates the potential of a visible and near-infrared (Vis-NIR) spectrometer and a hyperspectral camera for non-destructive classification of seven closely related Allium genotypes
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
Plant photosynthesis operates under naturally fluctuating light, yet its dynamic responses across timescales remain incompletely understood. Here, we apply sinusoidal light modulation as a controlled periodic input and analyze the response in the frequency domain, enabling quantitative system identification of photosynthetic dynamics. Using a minimal biochemical model of photosynthetic electron transport and regulation, we show that photosynthetic performance under fluctuating light differs systematically from that under constant illumination, even when the mean photon flux density is identical. Large-amplitude oscillations generate higher harmonics and alter time-averaged chlorophyll fluorescence, oxygen evolution, and non-photochemical quenching (NPQ), demonstrating that fluctuating light acts not merely as a perturbation but as a distinct physiological regime. For sufficiently small perturbations, the system behaves approximately linearly and can be characterized by transfer functions and Bode plots. We identify two dynamic regimes separated by a characteristic timescale of approximately 10 s. In the high-frequency domain, the response is governed by constitutive photochemical processes and reflects local steady-state properties, including the redox state of the plastoquinone pool. In the low-frequency domain, adaptive regulatory feedback dominates, particularly NPQ, which reshapes both the amplitude and phase of the photosynthetic response. Characteristic frequency-response features, including gain transitions and phase extrema, provide direct information about physiologically relevant quantities such as effective relaxation times and regulatory coupling strengths. We further introduce the concept of regulation fingerprints, defined as ratios of transfer functions between regulated and unregulated systems. These fingerprints reveal distinct spectral signatures of fast PsbS-dependent and slower zeaxanthin-dependent NPQ, enabling their quantitative separation and providing experimentally testable predictions for regulatory dynamics. Together, these results establish frequency-domain analysis as a general framework for probing, identifying, and testing the dynamic regulation of photosynthesis under fluctuating light. More broadly, they suggest that fluctuating illumination, often regarded as experimental noise, can instead serve as a structured probe of photosynthetic function in both laboratory and field environments.
Why it matches plant phenotyping methods植物の光合成動態を定量化する周波数領域解析を中心的に提案し、蛍光・酸素発生・NPQなどの生理状態を抽出する方法論研究であるため。
abstractTogether, these results establish frequency-domain analysis as a general framework for probing, identifying, and testing the dynamic regulation of photosynthesis under fluctuating light.
ArabidopsisLaboratory / benchtopMicroscopyCell / cellular structureVisualization / data management
While confocal fluorescence microscopy has provided valuable insights into chromatin organization in plant nuclei, its diffraction-limited resolution constrains the investigation of chromatin architecture, motivating the use of super-resolution techniques such as Single-Molecule Localization Microscopy (SMLM). Among these approaches, direct stochastic optical reconstruction microscopy (dSTORM) provides nanoscale resolution in individual cells, enabling precise visualization of chromatin domains, histone modifications, and nuclear organization. While such methods are increasingly applied in mammalian systems, their use in plant biology remains limited, largely due to technical challenges in sample preparation. Here, we present a streamlined and reproducible workflow for SMLM imaging of nuclei isolated from Arabidopsis thaliana. This protocol starts with seedling fixation to preserve nuclear morphology, followed by gentle tissue chopping and centrifugation to enrich intact nuclei. Isolated nuclei are then fluorophore-labeled in liquid medium and immobilized on low-melting agarose pads, a strategy that enhances stability during prolonged single-molecule imaging sessions. These steps collectively minimize background fluorescence, improve labeling consistency, and increase reproducibility across biological replicates. The resulting preparations provide enhanced clarity for visualizing chromatin modifications and nuclear architecture in plants. By lowering the technical barriers to implement SMLM imaging in Arabidopsis, this protocol provides a versatile means to investigate epigenetic regulation, chromatin organization, and nuclear topological variations at the nanoscale. This work establishes a methodological foundation for applying SMLM to plants, bridging the gap with mammalian cell biology and opening new opportunities to study how nuclear architecture contributes to genome regulation in response to developmental and environmental cues in plant systems.
Why it matches plant phenotyping methods植物核の形態・クロマチン構造を取得するSMLM画像化プロトコルの開発と再現性向上が研究の中心であり、植物フェノタイピング手法として適格です。
abstractHere, we present a streamlined and reproducible workflow for SMLM imaging of nuclei isolated from Arabidopsis thaliana.
Plant disease recognition from images is often reported as classification, although field inspection needs more than a class label. A farmer or agricultural officer must also know where the suspected leaf or disease region appears. This paper examines that localization problem using YOLO26n on the PlantDoc object detection dataset. PlantDoc is not a clean laboratory leaf dataset. It contains outdoor images with background clutter, uneven illumination, different leaf poses, overlapping objects, and visible symptom variation. YOLO26n was trained for 50 epochs with 416 × 416 input size and batch size 16. On the test set, the model obtained 0.534 precision, 0.560 recall, 0.547 F1-score, 0.573 mAP@0.50, and 0.417 mAP@0.50:0.95. Compared with the original PlantDoc detection benchmark, mAP@0.50 increased from 0.389 to 0.573. This result shows that a recent lightweight YOLO detector can improve object-level localization on PlantDoc. At the same time, the lower mAP@0.50:0.95 shows that precise bounding-box placement is still difficult. Most errors appear in visually similar symptoms, overlapping leaves, cluttered backgrounds, and under-represented classes. Thus, YOLO26n is better positioned as an initial baseline reference than as a deployable diagnostic model. Keywords: Object detection; Plant disease; PlantDoc; YOLO26n; Deep learning.
Why it matches plant phenotyping methods植物画像から病変領域を検出・局在化する手法をYOLOで評価し、ベンチマーク比較と性能検証を行っているため、病害状態の画像ベース表現型計測が中心です。
abstractThis paper examines that localization problem using YOLO26n on the PlantDoc object detection dataset.
Early-stage frost damage in citrus fruits is difficult to detect because external symptoms are often weak or absent, hindering intelligent robotic sorting in postharvest scenarios. To address this challenge, this study proposes a robotic multimodal tactile sensing approach inspired by human mechanoreception for frost-damage detection during grasping. A robotic gripper equipped with a 6×6 pressure matrix sensor and a piezoelectric vibration sensor was used to capture complementary tactile cues during standardized fruit handling, enabling the perception of subtle mechanical changes associated with early frost injury. Using 240 Citrus reticulata 'Hong Mei Ren' fruits under controlled experimental conditions, a Transformer-based multimodal fusion network was developed to jointly model pressure and vibration sequences for binary classification of normal and frost-damaged fruits. Across repeated stratified random-split experiments, the proposed method achieved a mean classification accuracy of 93.1%. Comparative experiments showed that the fusion model outperformed representative sequence-learning baselines, and ablation analysis confirmed that pressure-vibration fusion was more effective than either single modality alone. Attention-based temporal attribution further revealed that the most informative cues were concentrated in the initial contact and early loading stages, indicating the importance of early transient mechanical responses for frost-damage discrimination. Overall, the proposed approach demonstrates the feasibility of grasp-based robotic frost-damage detection under controlled experimental conditions.
Why it matches plant phenotyping methods柑橘果実の凍害状態を圧力・振動センサーで取得し、マルチモーダル融合により分類する手法の開発が中心であり、単なる生物学的実験の測定ではない。
abstractthis study proposes a robotic multimodal tactile sensing approach inspired by human mechanoreception for frost-damage detection during grasping.
Plant disease is a serious threat to agricultural productivity and food security worldwide. Traditional diagnostic methods such as manual observation and laboratory testing are time-consuming, labor-intensive and error prone. The emergence of artificial intelligence (AI) and deep learning (DL) offer scalable solutions for precision agriculture in plant disease detection using advanced computational techniques to process large datasets. Hybrid deep learning architecture integrates Convolutional Neural Networks (CNNs) along with Artificial Neural Networks (ANNs) can leverage both visual and contextual data to improve detection performance. The hybrid CNN-ANN model was developed to analyze visual data (plant images) and contextual data (environmental and soil metrics). The CNN module extracted spatial and textural features from plant images, while the ANN module processed environmental parameters. These outputs were fused into a unified feature vector for disease classification. A total of 15 plant species and their associated diseases were analyzed using 200-270 training samples and 150-190 testing samples for each disease across a total of 1000 images. The model was judged by metrics such as detection accuracy, AUC, sensitivity etc. Data augmentation, pre-trained architectures (e.g., ResNet50) and early stopping techniques were utilized to improvise model performance. The hybrid model saliently achieved detection accuracy consistently above 87% with majority of diseases surpassing 90%. Highperforming cases like Rice Blast (92.5%), Tomato Early Blight (93.8%), and Coffee Rust (93.0%), with AUC values of 0.93 or higher, sensitivity exceeding 94% and specifically above 90%. Diseases of Sugarcane Red Rot and Tea Blister Blight exhibited sensitivities of 92.4% and 92.1% and specificities of 91.1% and 90.5% respectively. Moderate accuracy for Coconut Bud Rot (87.5%) and Mustard Alternaria Blight (87.8%) was due to smaller training sample sizes.
Why it matches plant phenotyping methods植物画像から病害状態を分類するハイブリッドCNN-ANN手法を開発し、精度・AUC・感度などで評価しており、病害フェノタイピング手法が研究の中心である。
abstractThe hybrid CNN-ANN model was developed to analyze visual data (plant images) and contextual data (environmental and soil metrics).
Reproduction assets foundThe paper's plant disease detection model was trained on public plant image datasets: the PlantSeg dataset (Zenodo record 13958858, DOI 10.5281/zenodo.13293891) and the UCI Machine Learning Repository Plants dataset. Both are cited in Materials and Methods as sources of the visual data used for the CNN module. No code,Dataset · publicebao (ZiranKexueBan)/Journal of
Huazhong University of Science and Technology
(Natural Science Edition). 2021;49(8).
37. Ma C, Mu X, Sha D. Multi-Layers Feature Fusion of
Convolutional Neural Network for Scene
Classification of Remote Sensing. IEEE Access.
2019;7.
38. Hämäläinen W.Plants Dataset[Internet]. 2024.
Available from:
https://archive.ics.uci.edu/dataset/180/plants
39. WeiT. PlantSeg: A Large-Scale In-the-wild Dataset
for Plant Disease Segmentation [Internet]. 2018.
Available from: https://zenodo.org/records/13958858Open asset ↗180pdf-raw-page:15 lines:1-48Plant phenotyping relevance match · UnverifiedbioRxiv · checked 13 Sept 2026
In root nodule symbiosis, symbiosome compartments accommodate nitrogen-fixing rhizobia inside the plant cell. Differentiated into bacteroids, the rhizobia are surrounded by a peribacteroid space and a plant-derived peribacteroid membrane, which separates them from the plant cytoplasm but allows signal and nutrient exchange between host and microbe. The morphological features of symbiosomes are primarily determined by ultrastructural single focal plane imaging, with limited information about spatial details. This study combines 2D and 3D imaging, using transmission electron microscopy and focused ion beam scanning electron microscopy as complementary techniques to analyse the symbiosome ultrastructure and organisation in Lotus japonicus wild-type plants. The 3D model of a mature colonised root nodule cell region demonstrates a dense, puzzle-like arrangement of symbiosomes relative to one another and adjacent plant organelles. The symbiosome shape and size depends on the orientation and number of bacteroids within the compartment and features connective tubular structures. Furthermore, vesicular structures, some likely of bacterial origin, were present at the interface. The study presents a multi-angled analysis of symbiosome-related structures, highlighting their volumes, spatial distribution, and pronounced compactness. Interface associated vesicles, protrusions and connective structures hint towards a dynamic and flexible system that contributes to the plant-microbe crosstalk.
Why it matches plant phenotyping methods植物根粒内の共生体の形態・体積・空間分布を、2D/3D電子顕微鏡で解析することが研究の中心であり、植物組織の構造的表現型を取得する方法論的応用に該当する。
abstractThis study combines 2D and 3D imaging, using transmission electron microscopy and focused ion beam scanning electron microscopy as complementary techniques to analyse the symbiosome ultrastructure and organisation in Lotus japonicus wild-type plants.
Branched broomrape (Phelipanche ramosa) is an obligate root parasitic weed that threatens tomato production in many regions. Progress in understanding host resistance mechanisms has been hindered by the parasite's subterranean life cycle and the technical limitations of traditional soil-based assays. Here, we introduce an integrated experimental framework that enables molecular, genetic, and cellular analysis of broomrape parasitism in tomato under controlled conditions. We implemented a transparent, soil-less co-cultivation system for non-destructive, real-time monitoring of broomrape development on tomato roots, and a dual-compartment in vitro co-culture system supporting parasite infection of transgenic hairy roots. This methodology enabled rapid functional testing of candidate host resistance genes, exemplified by CRISPR-edited mutants of the tomato transcription factor SCHIZORIZA (SlSCZ), which displayed localized lignin accumulation at the parasite entry site in the root. The observed lignification suggests a role for this gene in regulating inducible cell wall lignification against broomrape. Together, these tomato-focused integrated methods enable reproducible imaging, genetic perturbation, and high-resolution analysis of host-parasite interfaces. These provide a scalable platform for dissecting broomrape resistance and accelerating resistance gene discovery in tomato and a critical tool for combating the devastating consequences of this parasite on agriculture.
Why it matches plant phenotyping methodsトマト根上の寄生進展を非破壊・リアルタイムに観察する共培養系と再現可能なイメージングを開発し、植物の感染状態を取得する基盤が研究の中心である。
abstractWe implemented a transparent, soil-less co-cultivation system for non-destructive, real-time monitoring of broomrape development on tomato roots
The article presents the developed algorithm and software for automated monitoring of strawberry plant growth using neural network technologies. The YOLO11x and YOLOx-seg models, pre-trained by transfer learning, are adapted to recognize and classify plants (plant class), leaves (leaf class), and a reference marker (ref_obj class) of a known size. Segmentation of strawberry leaves using the YOLO11x-seg model makes it possible to analyze the morphometric parameters of individual leaf plates (area, perimeter, roundness, aspect ratio). A set of RGB images (2000 pieces) obtained using a GoPro HERO11 camera under controlled laboratory conditions was formed and annotated, followed by augmentation to increase the model's resistance to variations in shooting conditions. The developed algorithm converts the coordinates of the bounding boxes and segmentation masks of recognized objects into metric units using calibration coefficients calculated from a marker of known size (100×100 mm). The software implemented using PyQt5, TensorFlow, Keras, and OpenCV libraries provides not only visualization of results but also data storage in a local SQLite database with the ability to export to JSON and Excel formats. Validation of the model showed high accuracy in detecting plant bounding boxes (mAP50 = 0.906) and leaf segmentation (mAP50 -mask = 0.625). The average processing speed was 20.3 ms/frame for detection and 34.5 ms/frame for segmentation. The measurement error was less than 3.5 % for the overall parameters of the plant and 5.2 % for the morphometric parameters of the leaves, confirming the effectiveness of the method for assessing the height, width and area of plants, as well as the analysis of the leaf apparatus. The research results show the promise of an approach for automating plant phenotyping in real time.
Why it matches plant phenotyping methods植物の成長・葉形態を画像から自動抽出するアルゴリズム、ソフトウェア、データセットを開発し、精度・処理速度・測定誤差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。
abstractThe article presents the developed algorithm and software for automated monitoring of strawberry plant growth using neural network technologies.
Plant phenotyping relevance match · UnverifiedOpenAlex · Crossref · Europe PMC · checked 15 Sept 2026
Desiccation tolerance is a critical adaptive trait that enables plants to survive extreme water loss, yet its physiological basis in tomato and its wild relatives remains poorly understood. In this study, chlorophyll a fluorescence imaging was used as a reliable tool to evaluate photosystem II (PSII) response to progressive desiccation. The analysis was conducted in cultivated tomato (Solanum lycopersicum) and five wild relatives (Solanum chilense, Solanum habrochaites, Solanum peruvianum, Solanum pimpinellifolium, and Solanum pennellii). Detached leaves were subjected to controlled desiccation for up to 50 h. During this period, tissue moisture content (TMC), relative water content (RWC), PSII photochemical efficiency [Fv/Fm; maximum quantum yield (QY_max)], minimal fluorescence (F0), maximal fluorescence (Fm), and variable fluorescence (Fv) were monitored to assess changes in photosynthetic performance. Desiccation caused a significant, moisture-dependent decline in PSII efficiency across all species, with QY_max showing a strong linear relationship with RWC (R2 = 0.80–0.90). Interspecific variation was evident as S. chilense, S. habrochaites, S. peruvianum, and S. pimpinellifolium exhibited rapid PSII impairment, while S. lycopersicum showed moderate tolerance. In contrast, S. pennellii maintained higher PSII stability, with 50% loss of efficiency occurring only at lower RWC (30–35%). Overall, chlorophyll fluorescence imaging effectively captured functional diversity in desiccation tolerance, highlighting S. pennellii as a valuable genetic resource for improving drought resilience in tomato.
Why it matches plant phenotyping methodsクロロフィル蛍光イメージングを用いた高スループット表現型解析と乾燥耐性スクリーニングが研究の中心であり、PSII効率などの植物生理形質を定量化している。
titleChlorophyll Fluorescence-Based High-Throughput Phenotyping Reveals Mechanisms and Enables Rapid Screening of Desiccation-Tolerant Wild Tomato Species.
Abstract Ustilago maydis is a biotrophic fungus that causes smut disease in maize, leading to tumor formation on aerial parts of the plant. While U. maydis has been a model for plant-fungal interaction studies, no tool has existed to automatically quantify infection symptoms under laboratory conditions for deep learning analysis. To address this, we developed a rotating camera system that captures videos of plants under customized lighting and shutter settings. These videos were used to train machine learning models to distinguish between healthy and infected plants. Two machine learning models have been presented. In the first approach, by employing a naive masking technique and combining classical machine learning with deep learning classifiers, the model achieved a reasonable performance, with an Area Under the Curve (AUC) of 0.90 on the Receiver Operating Characteristic (ROC), displaying high sensitivity and specificity. The second approach utilizes pre-trained YOLO11 model for object detection and further classification. The YOLO11-based approach outperforms traditional methods, achieving near-perfect accuracy (AUC: 0.99-1.00), demonstrating its superiority for real-time, scalable applications. Our toolset, featuring a cost-efficient and customizable scanning platform with open building-blocks design, provides a valuable resource for unbiased disease symptom detection and scoring, with potential applications in other plant pathology studies. This point enables easy replication and adaptation by other research laboratories which makes the platform robust, scalable and practical beyond our specific application.
Why it matches plant phenotyping methods植物の感染症状を画像から自動検出・定量する低コスト撮像プラットフォームと解析モデルを開発しており、植物表現型取得法が中心的である。
abstractwe developed a rotating camera system that captures videos of plants under customized lighting and shutter settings.
Accurately simulating shoot-scale light scattering in physically based radiative transfer models remains a key challenge for conifer ecosystems. This study evaluates the high-resolution three-dimensional (3D) radiative transfer capability of the Discrete Anisotropic Radiative Transfer (DART) model using laboratory reflectance measurements and detailed photogrammetric reconstructions of Norway spruce ( Picea abies (L.) H. Karst) shoots. Samples representing multiple age classes and crown positions were collected from temperate (Czech Republic) and hemiboreal (Estonia) Norway spruce stands. Their geometry was reconstructed with sub-millimetre accuracy using structured blue-light 3D scanning, while the optical properties of needles and twigs were measured using an integrating sphere. We measured shoot reflectance under controlled laboratory illumination and compared it to DART simulations based on the identical 3D structures and optical inputs. DART simulations accurately reproduced the measured spectral signatures (R 2 = 0.95; median spectral angle mapper = 4.8°), demonstrating the model's capacity to simulate shoot-scale reflectance across diverse viewing geometries. These results suggest that detailed 3D shoot representations can improve radiative transfer modelling accuracy, and that DART efficiently simulates shoot reflectance across diverse viewing geometries as an alternative to labour-intensive goniometer measurements. This work provides the first empirical evaluation of DART at the shoot-scale and establishes a transferable framework for integrating detailed 3D photogrammetry into radiative transfer modelling. This approach enables more accurate upscaling from the conifer needle to the canopy-level and can enhance future model intercomparison exercises, such as the Radiation Transfer Model Intercomparison benchmark. • First empirical validation of DART simulation of conifer shoots. • High-resolution blue-light photogrammetry captures realistic shoot architecture. • DART-simulated reflectance closely matches laboratory measurements. • Framework enables realistic needle-to-canopy upscaling in radiative transfer models.
Why it matches plant phenotyping methods針葉樹シュートの3D構造を高精度に取得し、反射率モデルを実測値で検証する技術研究であり、植物形質（シュート構造・反射特性）の取得とモデル評価が中心である。
abstractTheir geometry was reconstructed with sub-millimetre accuracy using structured blue-light 3D scanning
Sampling and reliable quantification of root exudates from undisturbed soil-grown plant roots remain challenging. We further developed a non-destructive method for the sampling, 2D mapping and quantification of seven carboxylates (aconitate, citrate, fumarate, lactate, malate, oxalate, succinate) exuded from rhizobox-grown plant roots. The method described here employs polyacrylamide zirconium hydroxide hydrogels (ZrOH hydrogels) that uptake all tested carboxylates and can be eluted with an efficiency ranging from 95.3% ± 3.12% to 111% ± 1.99% . The ZrOH hydrogels have a high binding capacity for carboxylates, up to 1.82 µmol cm -2 , depending on the solution pH and carboxylate species, a concentration higher than that usually available in the rhizosphere. Moreover, the bound carboxylates on the ZrOH hydrogels remain stable and can be stored for several weeks at 4 °C before analysis. For the application, plants are cultivated in soil-filled rhizoboxes that allow for easy access with minimal disturbance to the root system. To sample root-released carboxylates, ZrOH hydrogels are carefully applied to the region of interest for 24 h. After retrieving the ZrOH hydrogels, they are cut for mapping purposes, and the gel pieces are eluted for subsequent carboxylate analysis (e.g., via Ion Chromatography-Mass Spectrometry). Our findings indicate that ZrOH hydrogels are effective for capturing and determining carboxylate concentrations in the rhizosphere. The novelty of this method lies in its ability to sample root exudates from intact soil-grown plant roots, as well as the possibility of time-resolved sampling, compared to traditional methods (soil-hydroponic hybrid approach) that are often destructive and allow only single-time sampling. Most importantly, it enables the generation of quantitative, high-resolution, millimetre-scale 2D images, facilitating the visualisation of carboxylate exudation along the root axis and its spatial distribution within the rhizosphere. Additionally, this method facilitates the sampling of root exudates at various growth stages during the growth cycle.
Why it matches plant phenotyping methods根からのカルボキシレート放出という植物の生理状態を、非破壊・空間分解・定量的に取得するハイドロゲル法の開発が研究の中心であり、単なる化学測定のルーチン利用ではない。
abstractThe novelty of this method lies in its ability to sample root exudates from intact soil-grown plant roots
Abstract Current phytopathological diagnostic systems rely on manual inspections or laboratory analyses, which delay early detection and limit in-field responsiveness. Phytopathogenic fungal diseases pose a persistent threat to food security, directly affecting the productivity of essential crops such as potato ( Solanum tuberosum ) and tomato ( Solanum lycopersicum ) [1]–[5]. Among these diseases, Phytophthora infestans , the causal agent of late blight, is characterized by its high virulence and rapid spread, capable of generating significant losses in short periods when detection occurs too late [3], [4]. To address this issue, a computer vision and deep learning–based system for multistage detection of fungal infections in potato and tomato crops is proposed. The system comprises a convolutional neural network optimized for edge processing and a mobile robotic platform equipped with a manipulator arm for localized treatment application. The developed model was deployed on a Raspberry Pi 4 connected to a 12-MP Raspberry Pi Camera Module 3 NoIR, responsible for acquiring RGB images in the field. The proposed network was compared with reference architectures—ResNet-50, VGG16, MobileNetV2, and Inception-v3—within a four-stage detection pipeline: crop identification, health-state classification, infection diagnosis, and foliar severity estimation. A dataset of 18,200 images obtained from publicly accessible online sources, under diverse lighting and background conditions, was used, partitioned into 70% for training, 20% for validation, and 10% for testing. Preliminary results show an average accuracy in the range of 0.90–0.92, with inference latencies below 60 ms per image, ensuring smooth performance on the Raspberry Pi 4 without requiring cloud connectivity. Additionally, the network demonstrated higher sensitivity to visual variations compared to the baseline models.
Why it matches plant phenotyping methods植物病害の健康状態・感染・葉面重症度を画像から推定するコンピュータビジョン手法を開発・比較検証しており、植物表現型取得が中心である。
abstracta computer vision and deep learning–based system for multistage detection of fungal infections in potato and tomato crops is proposed
Reproduction assets foundThe paper's CNN training data are two publicly available third-party plant-disease image datasets (Mendeley Data and Kaggle Plant Village) with explicit URLs in the Data Availability Statement. The authors' field-test images and experimental records are only available on request, and no analysis code or trained model/сDataset · public10
Network, DOI: 10.17632/tywbtsjrjv.1, available at
https://data.mendeley.com/datasets/tywbtsjrjv/1, and the
Kaggle Plant Village dataset, available at
https://www.kaggle.com/datasets/emmarex/plantdisease.The field-test images and experimental records generated during
the current study during the real-world evaluation of the
embedded-vision system are available from the corresponding
author on reasonable request.
IX. REFERENCOpen asset ↗10.17632/tywbtsjrjv.1pdf-raw-page:11 lines:1-98Dataset · public10
Network, DOI: 10.17632/tywbtsjrjv.1, available at
https://data.mendeley.com/datasets/tywbtsjrjv/1, and the
Kaggle Plant Village dataset, available at
https://www.kaggle.com/datasets/emmarex/plantdisease.The field-test images and experimental records generated during
the current study during the real-world evaluation of the
embedded-vision system are available from the corresponding
author on reasonable request.
IX. REFERENCES
[1] P. W. Crous, A. Y. Rossman, M. C. Aime, W. C. Allen, T.
Burgess, J. Z. Groenewald y L. A. CastlebuOpen asset ↗Kagglepdf-raw-page:11 lines:1-98Plant phenotyping relevance match · UnverifiedCrossref · checked 15 Sept 2026
Published20 Apr 2026Uluslararası Tarım ve Yaban Hayatı Bilimleri DergisiCited by 0 · OpenAlex ↗
This study aimed to train a Random Forest regression model using pollen germination rate and pollen tube length data obtained after 3 h of in vitro germination at 0.005, 0.05, and 0.5 mM concentrations of 24-epibrassinolide, methyl jasmonate, spermidine, spermine, and putrescine, and to evaluate the model’s accuracy in predicting responses at 0.025, 0.25, and 2.5 mM concentrations. Experimental data were compared with Random Forest Regression model predictions, and model performance was assessed using Absolute Error and Root Mean Square Error. Prediction accuracy was classified as good, moderate, or low based on Absolute Error thresholds applied to both pollen germination and pollen tube length (0-6, 6-15, ≥15), and Root Mean Square Error thresholds defined separately for pollen germination (0-10, 10-20, ≥20) and pollen tube length (0-20, 20-40, ≥40). Results indicated that the Random Forest Regression model provided reliable predictions at low and moderate plant growth regülatör concentrations, with 24-epibrassinolide and putrescine treatments aligning closely with experimental data. However, for methyl jasmonate, spermidine, and spermine at higher concentrations, the model exhibited overestimations, particularly in predicting pollen germination rates at inhibitory doses. The study highlights the potential of machine learning approaches in pollen biology research and demonstrates the necessity of optimizing model parameters for high-dose predictions. These findings contribute to the integration of data-driven decision-making in artificial pollination and plant growth regulators treatment strategies.
Why it matches plant phenotyping methodsランダムフォレストによる花粉発芽率と花粉管長の予測モデルを構築・評価しており、植物形質の推定とモデル性能検証が研究の中心である。
abstractThis study aimed to train a Random Forest regression model using pollen germination rate and pollen tube length data
Pteris vittata, an arsenic-hyperaccumulating fern, is widely employed for phytoremediation of arsenic (As). Rapid, accurate assessment of As in P. vittata is crucial for evaluating its accumulation ability. In this study, P. vittata was analyzed using a spectral fusion of laser-induced breakdown spectroscopy (LIBS) and X-ray fluorescence (XRF). A total of 60 biological samples (roots and fronds) were collected and prepared as 180 compressed tablets for spectroscopic analysis, covering an As concentration range of 88-1956 mg kg -1 . Multivariate analysis methods were employed for full spectra and feature spectra, including partial least squares regression (PLSR), least squares support vector machine (LSSVM), extreme learning machine (ELM), random forest (RF), and adaptive weighting normalization-linear weighted network (AWN-LWNet). The best single-modality model, an XRF-based PLSR model built upon feature spectra selected by the Competitive Adaptive Reweighting Sampling (CARS) algorithm, achieved a prediction performance of R 2 P = 0.969, RMSE P = 54.13 mg kg -1 , and MAE P = 43.00 mg kg -1 . Spectral fusion further enhanced prediction accuracy, with high-level fusion outperforming low- and mid-level methods. The proposed feature-spectra-based decision fusion model achieved the best performance (R 2 P = 0.980, RMSE P = 43.69 mg kg -1 , MAE P = 32.49 mg kg -1 ), corresponding to reductions of 19.3% in RMSE P and 24.4% in MAE P compared to the best single-modality model. These results demonstrate that spectral fusion effectively integrates complementary information, improving the accuracy of As quantification in complex plant matrices. The proposed approach provides a rapid and non-destructive strategy for monitoring arsenic accumulation in phytoremediation plants.
Why it matches plant phenotyping methodsLIBS・XRFのスペクトル融合と機械学習により、植物組織中のヒ素蓄積量を非破壊推定する手法を開発・性能評価しており、植物状態の取得が中心です。
abstractThe proposed approach provides a rapid and non-destructive strategy for monitoring arsenic accumulation in phytoremediation plants.
Plant-based infection models provide cost effective and biologically relevant systems for investigating bacterial pathogenesis and virulence in living hosts. The mung bean seedling model enables the study of bacterial biofilms on living surfaces by allowing attachment and biofilm development on plants, but its broader use has been limited by methodological complexity and variability in experimental outcomes. Here, we present a modified mung bean seedling biofilm infection model for assessing Pseudomonas aeruginosa virulence that improves both consistency and practicality. The assay incorporates a bleach-based seed sterilization protocol that effectively reduces surface associated contaminants while maintaining high seed germination percentages. Additional refinements, including dehulling germinated seedlings, a shortened bacterial inoculation period, and plate-based incubation of seedlings at 37 °C, minimize variability in plant health outcomes while supporting development of gnotobiotic plants. Plant mortality, cotyledon emergence, and root branching were identified as rapid and quantitative measures of biofilm associated disease. Using this modified assay, reproducible differences in virulence were detected among P. aeruginosa strains, including reduced pathogenicity in a pqsR quorum sensing mutant. This simplified mung bean seedling model provides an accessible platform for studying biofilm associated virulence and screening genes involved in biofilm-mediated pathogenicity on a biotic surface.
Why it matches plant phenotyping methodsムングビーン幼植物を用いた感染・疾患表現型測定系の改良と再現性評価が研究の中心であり、植物死亡、子葉出現、根分枝を定量的な病徴指標として開発・検証している。
abstractHere, we present a modified mung bean seedling biofilm infection model for assessing Pseudomonas aeruginosa virulence that improves both consistency and practicality.
Imaging carbon movements in the rhizosphere is fundamentally limited by high soil heterogeneity, low signal levels, and lack of methodology. We present Rhizo-PET, a dedicated positron emission tomography (PET) imaging and analysis framework designed to characterize the 4D spatiotemporal patterns of tracer distribution in intact plant–soil systems. The system achieved a global energy resolution of 11.93 ± 0.02% FWHM at 511 keV and maintained stable performance over 8 h of continuous acquisition, with a coincidence rate variation of only 0.7%. Spatial resolution reached 1.06 mm near the center of the field of view, establishing a high-fidelity region for root-scale analysis. Dynamic datasets were acquired from live Phaseolus vulgaris plants ( N = 3) over 180 min following 11 CO 2 pulse labeling and reconstructed into 3 min temporal frames. Quantitative analysis across 243 independent regions of interest (ROI) revealed that cumulative tracer accumulation decreases monotonically with radial distance from the root axis, while axial transport delays increase systematically in lower root segments ( p < 0.001). Hierarchical variability analysis showed that within-plant spatial organization ( CV TTP = 0.03) is significantly more stable than inter-plant variation ( CV TTP = 0.14), proving that the observed heterogeneity reflects biological spatial organization rather than experimental instability. These results establish Rhizo-PET as a robust, reproducible platform for the non-invasive, time-resolved analysis of carbon dynamics in the rhizosphere under realistic soil conditions.
Why it matches plant phenotyping methods植物根圏における炭素動態を非侵襲・時系列で測定する専用PETシステムを開発し、性能・再現性・空間解析能力を検証した研究であり、植物状態の取得方法が中心である。
abstractWe present Rhizo-PET, a dedicated positron emission tomography (PET) imaging and analysis framework designed to characterize the 4D spatiotemporal patterns of tracer distribution in intact plant–soil systems.
Abstract Digital seed phenotyping offers an efficient and objective alternative to conventional, labor-intensive methods for morphological characterization in plant breeding programs. In castor bean, rapid and reliable tools are essential to support genetic improvement. This study evaluated the potential of digital phenotyping for seed characterization and its application in breeding. Seeds from 65 accessions (2023) and 51 accessions (2024) were photographed with an RGB camera and processed in ImageJ® for extraction of morphological traits. Agreement between digital and manual measurements was assessed by correlation and Bland–Altman analysis, while machine learning models were trained to predict hundred-seed weight (HSW). Genetic diversity was explored using principal component analysis (PCA) and clustering, and variance components and heritability were estimated with mixed linear models. Digital phenotyping showed strong agreement with manual measurements (r = 0.95–0.97) and enabled accurate HSW prediction, with Ridge Regression achieving the best performance (R 2 = 0.88; RMSE = 3.83; MAE = 3.19). PCA explained 85.7% of the variance and revealed three phenotypic clusters. Traits such as seed length (H 2 = 0.88) and aspect ratio (H 2 = 0.87) exhibited high heritability, while roundness (H 2 = 0.79), perimeter (H 2 = 0.72), and area (H 2 = 0.67) were moderate. These findings demonstrate that digital phenotyping is a reliable and high-throughput method for castor bean seed characterization, supporting genotype selection and the integration of machine learning approaches into breeding programs for greater precision and efficiency.
Why it matches plant phenotyping methods種子形態形質の画像取得・抽出、手動測定との技術検証、重量予測モデル評価が研究の中心であり、植物フェノタイピング手法として明確に適格。
abstractDigital seed phenotyping offers an efficient and objective alternative to conventional, labor-intensive methods for morphological characterization in plant breeding programs.
Near- and short-wave infrared spectroscopy has become increasingly relevant for the assessment of plant composition, yet the reproducibility of chemometric models across different instruments remains a major limitation. This work proposes a methodological approach for calibration transfer from a benchtop hyperspectral imaging (HSI) system to a portable SWIR spectrometer, using powdered grapevine leaves as a controlled case study. The strategy relies on Direct Standardization (DS), a technique that transforms spectra acquired under different conditions into a common space. By applying DS to a representative subset of samples, models trained under laboratory conditions can be adapted to spectra acquired with portable devices. Two complementary modelling strategies were adopted: an Error-Correcting Output Codes Support Vector Machine (ECOC-SVM) classifier, used to assess qualitative improvements in class discrimination before and after DS, and an eXtreme Gradient Boosting (XGB) regression model, developed for quantitative prediction of elemental concentrations measured by micro-XRF. Validation on an independent set of homogenized leaf powders confirmed that DS markedly reduced inter-instrument spectral divergence, improving class separability and enabling accurate regression of macro- and micronutrients (Fe, Mn, P, S, Zn, Ca, K, Si). Although limited to laboratory-scale samples, the study demonstrates that calibration transfer is effective in harmonizing spectral domains. The proposed workflow provides a reproducible and scalable methodology for cross-instrument adaptation, with potential applicability to diverse agricultural products and portable spectroscopy platforms.
Why it matches plant phenotyping methods植物葉のスペクトルから元素濃度を推定する測定ワークフローを対象に、装置間キャリブレーション転移を開発・検証しており、植物形質取得法が研究の中心である。
abstractThis work proposes a methodological approach for calibration transfer from a benchtop hyperspectral imaging (HSI) system to a portable SWIR spectrometer, using powdered grapevine leaves as a controlled case study.
Drought stress poses a significant threat to rose (Rosa spp.) cultivation, impacting plant vigor, floral quality and marketability. Traditional drought screening methods are often destructive and labor-intensive, limiting their application in large-scale breeding programs. This study presents a non-destructive and high-throughput phenotyping approach for assessing drought responses in rose using RGB-derived vegetation indices (VIs) obtained from multi-angle imaging. Twenty-eight diverse rose genotypes were evaluated under well-watered (WW) and induced drought (ID) conditions using a LemnaTec Scanalyzer 3D platform. A total of 56 indices from side-view (SV) and top-view (TV) images were computed to quantify canopy color, greenness and pigment-related traits. Analysis of variance revealed significant genotypic differences and strong genotype × treatment (G × T) interactions across most indices, demonstrating their sensitivity to drought-induced physiological changes. Multivariate analyses, including Principal Component Analysis (PCA) and Pearson correlation matrix evaluation, were performed to explore trait relationships and identify key traits associated with drought stress. These analyses effectively differentiated greenness-related and stress-responsive traits. In addition, the MGIDI analysis integrated all indices and identified ‘Queen Elizabeth’, ‘Jwala’, Rosa chinensis, ‘Sylvia’ and ‘Rose Sherbet’ as the top-performing drought-tolerant genotypes. Integration of leaf wilting scores validated the reliability of these indices as accurate indicators of drought response, with tolerant genotypes exhibiting lower LWS and higher greenness indices. Overall, the study demonstrates that RGB-based high-throughput phenotyping provides a rapid, efficient and scalable method for drought tolerance assessment in roses, offering a valuable tool for accelerating selection in ornamental breeding programs.
Why it matches plant phenotyping methodsRGB画像から植生指数を抽出する高スループット表現型解析手法の開発・実証が研究の中心であり、バラの干ばつ応答を評価する再利用可能なワークフローを提示している。
abstractThis study presents a non-destructive and high-throughput phenotyping approach for assessing drought responses in rose using RGB-derived vegetation indices (VIs) obtained from multi-angle imaging.
Abstract Common scab in potato is caused by multiple Streptomyces species that harbour various virulence factors. Varietal resistance is commonly evaluated with multi-year – multi-location field trials with known high infection potential or using the phytotoxin thaxtomin applied to in vitro mini tubers or potato tissue culture. In this study, we aimed to develop an efficient root inoculation assay to assess the resistance levels of potato genotypes and to evaluate whether the assay could identify resistant and susceptible genotypes, thus facilitating selection of scab resistant clones. We isolated 24 potential Streptomyces strains from fields in Ireland, of which 11 were identified as S. europaescabiei . All S. europaescabiei strains tested positive for txtAB gene but lacked n ec1 and t omA genes. The root inoculation assay resulted in plants exhibiting necrotic symptoms on roots and stunted root growth. Image analysis software was used to collect quantitative data from our assay. We observed a Spearman’s rank correlation of 0.61 between field data and our assay using a panel comprising 50 clones from the bi-parental cross Electra × Désirée and five control varieties. The root inoculation assay is rapid, as symptoms are observed within 6 to 10 days post-inoculation, and requires minimal manipulation since a bacterial suspension is applied instead of purified thaxtomin. Notably, this assay identifies resistant and susceptible progeny reliably, with some disparities between the resistance pattern in the field and the assay. This tool has potential to be useful for screening large numbers of genotypes and discarding the susceptible ones in a breeding program.
Why it matches plant phenotyping methodsジャガイモ根の壊死症状と生育阻害を定量化する根部接種アッセイを開発し、画像解析で表現型を取得、圃場データとの相関で検証しているため、フェノタイピング手法が中心です。
abstractIn this study, we aimed to develop an efficient root inoculation assay to assess the resistance levels of potato genotypes and to evaluate whether the assay could identify resistant and susceptible genotypes
Plant phenotyping relevance match · UnverifiedOpenAlex · Europe PMC · bioRxiv · checked 15 Sept 2026
Abstract High-throughput phenotyping of biological samples is essential for large-scale studies but is frequently bottlenecked by the need for accurate instance segmentation in crowded images. While deep learning offers powerful solutions, the high cost of manual annotation and the requirement for coding expertise often limit adoption in routine laboratory workflows. Here we present SegBio, a lightweight, open-source pipeline that enables end-to-end instance segmentation for non-expert users. The protocol features an interactive annotation GUI that extrapolates full masks from minimal centerline markings, significantly reducing manual labeling effort. It further integrates a configurable U-Net training module and a standalone inference application with a ‘human-in-the-loop’ editing workflow for rapid and intuitive error correction. We employ the pipeline to annotate and train the model on a novel dataset of crowded C. elegans images. Validated on independent datasets, SegBio achieves high segmentation performance (Panoptic quality ∼0.85) and accurately quantifies per-animal morphology and fluorescence. By eliminating external dependencies and streamlining the correction process, SegBio provides a scalable solution for routine phenotyping that is easily generalized to other crowded biological samples, such as cellular organelles, cells, and organisms.
Why it matches plant phenotyping methodsC. elegansの画像から個体インスタンスを分離し、形態と蛍光を定量するエンドツーエンドの表現型解析ツールを開発・検証しており、方法が研究の中心である。
abstractHere we present SegBio, a lightweight, open-source pipeline that enables end-to-end instance segmentation for non-expert users.
Maize is a globally significant crop for both food and feed, and its seed vigor directly impacts germination rate and yield. In the context of intelligent agriculture, there is an urgent need for rapid, non-destructive, and quantifiable methods for assessing seed vigor. This study proposes a novel multimodal fusion approach for maize seed vigor detection, integrating hyperspectral imaging (HSI), electronic nose (ENS), and machine vision (MV) technologies. Multisource data were systematically collected from seeds subjected to varying levels of artificial accelerated aging, thereby constructing a sample set encompassing five distinct vigor levels. Standard germination tests were employed as the ground truth for vigor labeling.Each modality was individually subjected to preprocessing procedures including calibration, denoising, feature extraction, and standardization. The processed data were then fused to construct a comprehensive dataset for model development. Among the unimodal models, classification accuracies of HSI, ENs, and MV reached 91.8%, 94.4%, and 92.0%, respectively. In contrast, the feature fusion network based on three-way cross attention (TCAF-Net) effectively utilizes the complementary information between spectral, olfactory, and morphological features. This model achieved an accuracy of 99.6%, demonstrating superior robustness and stability in distinguishing seed vigor levels.The results validate the efficacy of multimodal data fusion for rapid, non-invasive seed vigor assessment in maize, and provide a promising technical foundation for applications in smart agriculture and seed quality monitoring.
Why it matches plant phenotyping methodsトウモロコシ種子の活力という植物形質を、ハイパースペクトル画像・電子鼻・マシンビジョンと新規融合ネットワークで非破壊推定する方法研究であり、表現型取得・抽出が中心である。
abstractThis study proposes a novel multimodal fusion approach for maize seed vigor detection, integrating hyperspectral imaging (HSI), electronic nose (ENS), and machine vision (MV) technologies.
Plant phenotyping relevance match · UnverifiedOpenAlex · Europe PMC · bioRxiv · checked 5 Sept 2026
ABSTRACT Bioavailability of iron, an essential micronutrient to plants, is low in alkaline or calcareous soils, which are prevalent across semi-arid production regions. Breeding efforts to increase tolerance to iron deficiency chlorosis (IDC) in sorghum, a major crop of semi-arid regions, are confounded by spatial variation of stress severity in field trials. Here we developed and validated two high-throughput phenotyping approaches to address this challenge, with multi-spectral aerial imaging in the field and a controlled-environment assay to isolate the effects of iron bioavailability. In the field, severity and uniformity of stress are highly predictive of genetic signals for IDC tolerance ( R 2 > 0.6 for soil pH metrics and H 2 ). Plot-level data filtering for stress conditions based on control genotypes successfully addresses field spatial variation (unfiltered H 2 = 0.18 vs. filtered H 2 = 0.4). The controlled-environment assay proxies field stress using iron sources with differential bioavailability, evidenced by high heritability ( H 2 = 0.98) and phenotypic differential for hybrid control genotypes that matches field performance. Finally, we show that assay phenotypes are suitable for genome-wide association studies in global germplasm. Together, these field and lab phenomic approaches can be deployed to understand genetics of IDC tolerance and develop crops resilient to alkaline soils. HIGHLIGHT Stress severity and uniformity greatly impact detection of genetic signals underlying iron deficiency chlorosis tolerance in sorghum. A controlled-environment assay reduces spatial heterogeneity and improves assessment of tolerance genetics.
Why it matches plant phenotyping methods鉄欠乏性クロロシス耐性を評価するため、圃場マルチスペクトル空撮と管理環境アッセイという2つのハイスループット表現型計測法を開発・検証しており、フェノタイピング手法が研究の中心である。
abstractHere we developed and validated two high-throughput phenotyping approaches to address this challenge, with multi-spectral aerial imaging in the field and a controlled-environment assay to isolate the effects of iron bioavailability.
ABSTRACT 3D reconstruction has matured into a robust technology. However, small, flexible objects such as conifer seedlings remain challenging due to their fine‐scale structures, and susceptibility to movement. This study investigates and evaluates methods for reconstructing spruce ( Picea abies ) and pine ( Pinus sylvestris ) seedlings, with the aim of establishing a workflow capable of capturing geometry and texture for applications in machine learning and virtual testing environments. Two acquisition approaches were tested: photogrammetry using a RGB camera and a 3D scanner, both mounted on a robotic arm. While the scanner produced incomplete results, the photogrammetry approach successfully generated point clouds (pcl) with color information. Three different photogrammetry software were tested before relying on Agisoft Metashape and Meshroom for image processing and dense pcl generation, followed by pcl filtering in CloudCompare and meshing in Blender. Six seedlings were reconstructed to textured meshes and quantitatively evaluated using the metrics precision, recall, F1‐score, mask intersection‐over‐union (IoU), and boundary IoU. Results showed an average mask IoU of 75.7% and F1‐score of 86.1%. Pine seedlings yielded higher recall and F1‐scores, whereas spruce reconstructions demonstrated higher precision. The proposed semi‐automated workflow demonstrates the feasibility of reconstructing small and slender structured flexible objects, specifically conifer seedlings.
Why it matches plant phenotyping methods針葉樹苗の形状・テクスチャを取得する3D画像再構成ワークフローを開発・比較・定量評価しており、植物フェノタイピング手法が中心である。
abstractThis study investigates and evaluates methods for reconstructing spruce ( Picea abies ) and pine ( Pinus sylvestris ) seedlings, with the aim of establishing a workflow capable of capturing geometry and texture for applications in machine learning and virtual testing environments.
Reproduction assets foundThe paper's Data Availability Statement states that the raw seedling image data and finalized textured meshes (the paper's phenotyping/3D reconstruction inputs and outputs) are freely available on Zenodo under DOI 10.5281/zenodo.19823955, which appears in the allowed URL list.Dataset · publicand without adjusting the scanning parameters, while also re-
Data Availability Statement
taining texture and color. In contrast to prior approaches that
require manual intervention or do not preserve visual informa- Raw image data and finalized textured meshes are freely available at
Zenodo.org with https://doi.org/10.5281/zenodo.19823955.
tion, the proposed workflow enables a semi-automated recon-
struction process suitable for dataset generation. As shown, the
methodology is effective for the digital reconstruction of small References
and slender structured flexible objects and holds potential for
Abbood, S. A., H. A. Ajjah, A. H. H. Alboabidallah, M. U. MohaOpen asset ↗Zenodopdf-layout-page:14 lines:50-74Plant phenotyping relevance match · UnverifiedEurope PMC · checked 14 Sept 2026
Remotely Piloted Aircraft (RPAs) equipped with multispectral sensors have emerged as promising tools for estimating foliar nitrogen content (FNC). In this context, this study applied a methodological approach aimed at simulating UAV multispectral data using hyperspectral leaf data obtained in a controlled environment, with the objective of evaluating its predictive potential and its transferability to field data collected by UAVs for FNN estimation. To this end, spectral bands and indices equivalent to those of UAV-mounted sensors were simulated based on hyperspectral data acquired by a benchtop sensor, and subsequently used in modeling via Partial Least Squares Regres-sion (PLSR) and Random Forest (RF). The results showed similar performance across the levels, with R² values of 0.75 and 0.76 for PLSR and RF on the UAV data, and 0.75 and 0.74 for PLSR and RF on the simulated data, respectively. The RF model also performed well in cross-domain validation, with R² = 0.70 when calibrated with simulated data and ap-plied to UAV data. Furthermore, the simulated data maintained high predictive power even with a reduced sample size. It is concluded that spectral simulation constitutes a viable strategy for expanding the applicability of nutritional monitoring using multi-spectral sensors.
Why it matches plant phenotyping methodsUAV・ハイパースペクトルデータの統合とスペクトルシミュレーションにより、葉面窒素含量を推定する手法を開発・交差検証しており、植物形質取得が研究の中心である。
abstractthis study applied a methodological approach aimed at simulating UAV multispectral data using hyperspectral leaf data obtained in a controlled environment
In the European framework for assessing the ecological effects of plant protection products on aquatic organisms, standard tests usually rely on constant laboratory exposure. This Tier 1 approach may lead to overly conservative assessments for short-term exposures lasting only a few hours under realistic environmental conditions. To address this, the European Food Safety Authority proposed a Tier 2C assessment to incorporate more realistic dynamic exposure profiles through refined tests and toxicokinetic/toxicodynamic (TKTD) modeling. Currently, the only accepted TKTD model for macrophyte growth inhibition relates to the duckweed Lemna sp. We hypothesize that this model can be adapted for other macrophytes including sediment rooted macrophytes. We propose a Modeling Approach for Growth inhibition of MAcrophytes (MAGMA), to simulate both constant and dynamic exposure tests under laboratory conditions. The model was calibrated using experimental data from Myriophyllum spicatum exposed to two herbicides with different modes of action. Validation against single and two-pulse exposure experiments showed good agreement between model predictions and observed data. The modeling approach, MAGMA can therefore serve as a valuable Tier 2C tool for predicting macrophyte growth rates under dynamic exposure conditions.
Why it matches plant phenotyping methodsマクロファイトの成長阻害を動的曝露下で予測するモデルを開発し、実験データで較正・検証しており、植物表現型（成長率）の推定手法が研究の中心である。
abstractWe propose a Modeling Approach for Growth inhibition of MAcrophytes (MAGMA), to simulate both constant and dynamic exposure tests under laboratory conditions.
Why it matches plant phenotyping methods植物組織から総可溶性糖を抽出・定量する手順自体が論文の中心であり、植物の生理状態を測定する方法として小試料向けに適応されています。
abstractHere, we present a detailed protocol for extracting and quantifying total soluble sugars in pteridophytes (lycophytes and ferns) from small amounts of leaf tissue (10 mg fresh tissue) using the anthrone method
Abstract Fungal diseases such as Ascochyta pose major threats to chickpea production, causing significant losses if not detected early. Conventional diagnostic methods, including visual inspection and molecular assays, are often time-consuming, subjective, and ineffective for early detection of infection. This study investigates the use of hyperspectral imaging (HSI) combined with machine learning for early, non-destructive detection of Ascochyta blight in chickpea leaves, an application that remains underexplored in previous research. Hyperspectral data in the 400–1000 nm range were acquired under controlled laboratory conditions from artificially infected chickpea plants. In this study, we developed a new comprehensive processing pipeline to address critical challenges associated with hyperspectral data, including noise, artifacts, and illumination variations. Subsequently, unsupervised learning approaches, such as K-means clustering, were employed to construct a clean, well-labeled database of mean leaf spectra. Using this refined dataset, we evaluated a classification framework based on supervised learning models, leveraging selected vegetation indices, visible and infrared spectral bands, along with features derived from statistical analyses. The proposed approach achieved an overall classification accuracy exceeding 95% in distinguishing healthy chickpea plants from those infected with Ascochyta blight. Results demonstrate that HSI can capture subtle physiological changes in leaves before visible symptoms appear, offering a reliable and scalable tool for precision agriculture. This study contributes a promising step toward AI-powered early disease detection in chickpea farming, enabling timely interventions, reducing fungicide use, and supporting sustainable crop protection strategies. Future work will focus on real-world deployment and cost-effective integration into existing monitoring systems.
Why it matches plant phenotyping methodsHSIと機械学習による感染葉の生理変化・病害状態の非破壊推定パイプラインを開発・評価しており、植物フェノタイピング手法が中心である。
abstractThis study investigates the use of hyperspectral imaging (HSI) combined with machine learning for early, non-destructive detection of Ascochyta blight in chickpea leaves
Identifying the detection of plant diseases remains a chronic menace in contemporary farm settings, with farmers unable to manage the economic downturns caused by the lack of resources and long periods of waiting to get expert agronomic advice. Current diagnostic methods are highly biased towards in depth visual examination by trained staff as well as laboratory pathology analysis thus posing a bottleneck in terms of lengthy turnaround times, high cost and impractical situation in real agricultural practice. This paper outlines a smartphone-based, bilingual diagnostic tool, which combines the concept of computational simplified deep learning systems with user-friendly interface structure and speech functionality. The developed tool uses a Convolutional Neural Network redesigned as a TensorFlow Lite system, which allows conducting analysis computations on a handheld device without the constant need to have an internet connection. The images of the leaves that are taken with the cameras of mobile devices are processed and evaluated with the taxonomic indicators to determine the indicators of plant wellness or identify particular forms of illness. The tool inserts crop-targeted classification systems and alters the classification outcome, involved on the basis of the specified botanical specimens. With reference to the accessibility requirements, the instrument has a dual-language feature, which can operate in English and Tamil, in addition to speech synthesis features, which audio-visually details the diagnostic conclusions to the end users. The architecture has been modified to maintain the stability of operations and consistency of the user experience over repeated interactions by including verification processes, historical analysis archiving, and capabilities of preserving data in disconnected modes. The tool separates the impermissible and appropriate photographic contributions, a non-infected situation on the plants, and pathology, consequently minimizing the instances of erroneous evaluation. Combining image-based analytical algorithms, cross-language inter-user interaction, as well as audio-based assists to make decisions, this mobile tool will be a grounded and farmer-centered technological solution. The model with a validation accuracy of 93.04 in the training phase and the deployment model with a validation accuracy of 92.27 and a mean inference time of 28.28 m/s validates the use of the model in real-time smartphone-based agriculture. The deployment strategy attests to the feasibility of deploying the state-of-the-art agricultural diagnostic equipment on cost-efficient mobile devices, progressing fast disease-detection schedules, minimizing the undue use of chemicals, and enhancing agricultural practices that are sustainable to the environment.
Why it matches plant phenotyping methods葉画像から植物の健全性・病害状態を推定するスマートフォン画像解析システムを開発し、精度と推論時間を検証しており、植物フェノタイピング手法が中心である。
abstractThe developed tool uses a Convolutional Neural Network redesigned as a TensorFlow Lite system, which allows conducting analysis computations on a handheld device without the constant need to have an internet connection.
Abstract Root system architecture plays a critical role in water and nutrient acquisition, particularly in semi‐arid environments where drought stress limits crop productivity. Despite advances in three‐dimensional (3D) root phenotyping, no dedicated low‐cost imaging platform currently exists for sorghum ( Sorghum bicolor (L.) Moench) in the United States. The objective of this study was to design and construct an affordable laboratory‐based 3D imaging system for sorghum root phenotyping modeled after the digital imaging of root traits (3D) framework. The system consists of a rotating aluminum frame equipped with eight high‐resolution digital cameras controlled by Raspberry Pi microcomputers, uniform LED lighting, and background reference markers to ensure accurate image alignment. Approximately 2000–3000 overlapping images are captured in under 5 min and processed using structure‐from‐motion algorithms to generate colorized 3D point clouds. The total system cost was approximately $6000, substantially lower than commercial imaging technologies such as computed tomography or magnetic resonance imaging. Initial assembly demonstrated strong geometric alignment and minimal distortion, enabling measurement of key root traits including volume, nodal root angle, and whorl spacing. This platform provides a reproducible and scalable approach for sorghum root phenotyping and addresses a critical gap in crop research tools for semi‐arid production systems. The system also offers educational value by integrating engineering design, programming, and plant science, supporting interdisciplinary training and future genotype‐phenotype studies aimed at improving drought resilience.
Why it matches plant phenotyping methodsソルガム根の形態形質を取得する低コスト3D画像プラットフォームの設計・構築が研究の中心であり、根体積や根角度などの測定法を提供している。
abstractThe objective of this study was to design and construct an affordable laboratory‐based 3D imaging system for sorghum root phenotyping modeled after the digital imaging of root traits (3D) framework.
Abstract Plant disease detection systems face significant challenges in cross-domain generalization, particularly when transitioning from controlled laboratory settings to diverse field conditions. Traditional deep learning approaches exhibit severe performance degradation across different imaging environments, limiting practical deployment in real-world agricultural scenarios. This paper introduces a novel Flexible Contrastive Clustering (FCC) framework for zero-shot tomato disease classification that addresses fundamental generalization limitations through vision-language learning. Unlike standard CLIP’s one-to-one image-text pairing, our method leverages one-to-many relationships where each disease image is associated with multiple diverse textual descriptions, enabling robust representation learning across linguistic variations. The FCC framework optimizes class-based clustering in joint embedding space through a specialized loss function that treats all same-class descriptions as positives, facilitating effective handling of both seen and unseen disease categories during zero-shot evaluation. We evaluate our approach on PlantDoc training data (740 images) and test across four diverse tomato disease datasets totaling 17,313 images, spanning laboratory and field conditions. Experimental results demonstrate substantial improvements over state-of-the-art vision-language models, achieving an average of 30.15% accuracy and 28.05% weighted F1-score on average across all test datasets. Our method shows particularly strong performance on field datasets, achieving 59.70% accuracy on FieldPlant and 26.52% on Tomato Village, significantly outperforming existing approaches. Attention visualization analysis reveals effective disease localization capabilities for both seen and unseen categories, validating the practical applicability of our approach for real-world agricultural monitoring systems.
Why it matches plant phenotyping methods植物画像から病害状態を推定する新規視覚言語分類法を開発し、複数データセットで性能評価しており、病害フェノタイピング手法が研究の中心である。
abstractThis paper introduces a novel Flexible Contrastive Clustering (FCC) framework for zero-shot tomato disease classification
Rhizosphere microbial processes play a central role in soil function and plant health yet remain difficult to monitor noninvasively. Engineered sentinel plants that use bacterial-to-plant communication channels are promising. However, no such efforts have thus far enabled a detectable aboveground response in the sentinel plant. Here, we optimize a previously described synthetic bacteria-to-plant communication channel based on the p-coumaroyl-homoserine lactone (pC-HSL) signaling molecule in plants to function as aboveground sentinels of belowground microbial activities. Arabidopsis thaliana sentinel plants harboring this optimized circuit detect root-applied pC-HSL at concentrations as low as 30 nM in roots and 3 M in leaves, demonstrating long-distance signal transmission from below ground to aboveground tissues. Moreover, sentinel plants report pC-HSL production by engineered Escherichia coli and Pseudomonas putida colonizing plant roots in both plate and soil assays. These results establish an engineered plant platform that converts rhizosphere microbial activity into a visible aboveground signal, enabling a minimally invasive platform for monitoring rhizosphere microbial gene expression and for precision agriculture and soil management.
Why it matches plant phenotyping methods微生物活動を植物の可視的な地上部シグナルへ変換するセンチネル植物プラットフォームの最適化・実証が中心であり、植物状態の取得を伴う方法研究である。
abstractThese results establish an engineered plant platform that converts rhizosphere microbial activity into a visible aboveground signal
Precision greenhouse agriculture enhances plant health and crop yields by continuously monitoring key plant parameters. Stem diameter is such a parameter and is monitored to support decisions on plant care. However, traditional contact-based methods induce thigmomorphogenic effects that impact plant growth. Here, we introduce the Optical Caliper (OC), a novel contactless device for precise, non-invasive stem diameter measurement. The OC operates by projecting a collimated light beam to cast a shadow of the stem onto a high-resolution image sensor. The shadow size is a measure for the stem diameter. Controlled laboratory tests show the OC offers an accuracy comparable to that of a Digital Caliper (DC). Field trials on irregular tomato and cucumber stems demonstrate a repeatability of 0.1-0.2 mm. The OC's non-invasive design and high repeatability exceed the performance of a DC, making it particularly suited for accurately monitoring soft, variable plant structures. Bringing the advantage of avoiding thigmomophogenic effects and thus optimizing crop yield, the OC is a promising tool for high-throughput plant phenotyping and precision agriculture applications.
Why it matches plant phenotyping methods植物の茎径を非接触・高精度に測定する光学デバイスを開発し、実験室および圃場で精度・再現性を検証しており、植物表現型取得法が研究の中心です。
abstractHere, we introduce the Optical Caliper (OC), a novel contactless device for precise, non-invasive stem diameter measurement.
Reproduction assets foundThe paper's measurement data (optical caliper, digital caliper, and micrometer readings on reference cylinders and tomato/cucumber stems) is openly deposited on the SURF data repository of The Hague University of Applied Sciences. No author analysis code or trained models are explicitly deposited; other allowed URLs (DDataset · publicThe data gathered during this study is openly available via https://hhs.data.surf.nl/s/nqnFYBf42KPA75P (accessed on 10 February 2026).Open asset ↗hhs.data.surf.nlhtml-lines:282-314Plant phenotyping relevance match · UnverifiedEurope PMC · bioRxiv · checked 5 Sept 2026
Microbial transformations of nitrogen in soils strongly influence plant nutrition and ecosystem function, yet these processes remain difficult to monitor. Existing approaches rely largely on destructive soil sampling and laboratory analysis, limiting the ability to track nitrate dynamics in situ. Here, we engineer “sentinel plants,” genetically encoded plant biosensors that convert nitrate perception into a quantitative signal reporting plant-accessible nitrate. The sensor uses a synthetic nitrate-responsive promoter coupled to a ratiometric luciferase reporter, enabling high-dynamic-range measurements. Sentinel plants exhibit a dose-dependent, reversible nitrate response with high specificity over alternative nitrogen sources. In agricultural soils from multiple California field sites, sensor output closely tracked analytically measured nitrate concentrations and resolved nitrate amendments without destructive extraction. Beyond environmental sensing, sentinel plants enabled screening of nitrogen-fixing microbial communities and the detection of microbially generated nitrate in both liquid culture and soil systems. Using this platform, we identified a minimal three-member microbial consortium capable of converting atmospheric nitrogen into nitrate via sequential nitrogen fixation and nitrification. This consortium increased tissue nitrate accumulation and plant fresh weight, demonstrating that sentinel plants can both monitor nitrate availability and identify microbial communities that enhance plant growth. Significance Statement Nitrogen availability in soils fluctuates across space and time, yet most measurements rely on extracting soil samples and analyzing them in the laboratory. Such measurements provide only snapshots of nitrogen status and do not necessarily reflect the nitrogen that plants themselves experience. Here, we engineer plants that act as living nitrate sensors by converting nitrate perception into a measurable optical signal. Because these sensors operate within intact plants, they report nitrate availability as integrated through plant uptake and physiology rather than through chemical extraction alone. Using this platform, we tracked nitrate levels in agricultural soils and identified a minimal microbial consortium capable of converting atmospheric nitrogen into plant-available nitrate. This plant-based sensing strategy enables direct monitoring of nitrogen dynamics in soils and microbial environments, providing a platform for identifying microbial communities that enhance nitrogen availability for crops.
Why it matches plant phenotyping methods植物を用いた遺伝子 encoded センサーを開発し、硝酸可給性を定量する光学的表現型取得法として検証・応用しているため、植物フェノタイピング手法が中心である。
abstractHere, we engineer “sentinel plants,” genetically encoded plant biosensors that convert nitrate perception into a quantitative signal reporting plant-accessible nitrate.
Pesticides are widely used in agriculture to control weeds, insects, and diseases that threaten crop yields. However, their extensive use raises concerns about environmental impacts, particularly in aquatic ecosystems, which are vulnerable to contamination through runoff and leaching. To assess the toxicity of pesticides to aquatic plants, we applied an optimized automated duckweed (Wolffia globosa) frond-counting tool based on the StarDist technique. Using this method, we tested twenty-eight commonly used pesticides, including herbicides, fungicides, and insecticides effects on duckweed growth. The herbicide paraquat showed the strongest growth inhibition (IC 50 50 = 384.2 ppb). Simazine and pendimethalin exhibited moderate toxicity, while glyphosate, triclopyr, and glufosinate showed lower toxicity. Surprisingly, metamifop did not inhibit duckweed growth up to the highest tested concentration (10 6 ppb). Isoprothiolane was the only fungicide tested that exhibited significant toxic effects on duckweed (IC 50 ≈ 924.3 ppb). All others, including azoxystrobin, hexaconazole, difenoconazole, picoxystrobin, tebuconazole, and cyproconazole, only inhibited plant growth at unnaturally high concentrations. Interestingly, cyazofamid promoted duckweed growth under the test conditions. Among 12 insecticides tested, 8 exhibited relatively low toxicity to duckweed (IC 50 > 10 5 ppb). Cypermethrin, carbofuran, fenpropathrin, and nitenpyram showed very low toxicity, with IC 50 values exceeding 10 6 ppb. Our results both enhance understanding of agrochemical toxicity and demonstrate the utility of automated, high-throughput quantification of W. globosa growth, providing a rapid and effective approach for pesticide toxicity assessment in aquatic environments.
Why it matches plant phenotyping methodsStarDistによるウキクサ葉状体の自動カウントと成長定量が、農薬毒性評価のための中心的な方法として用いられているため。
abstractwe applied an optimized automated duckweed (Wolffia globosa) frond-counting tool based on the StarDist technique
Seed selection constitutes the initial and one of the most critical steps in agricultural productivity. The identification of high-quality seeds is a labor-intensive and costly process that requires considerable expertise. Within the scope of smart farming applications, this study proposes a deep learning–based model designed to automate the seed selection process by accurately predicting seed germination capacity from seed images. The proposed model determines whether a seed will germinate using RGB images and morphological traits automatically extracted from these images. The dataset used in this study comprises a total of 3,645 images belonging to three different seed types. For each seed type (okra, eggplant, and tomato), 405 seed images were acquired from three distinct imaging sources (digital microscope, camera, and scanner), labeled, and subsequently sown in seed trays. The germination status of each sown seed was systematically monitored and matched with its corresponding image data. The dataset was partitioned into 80% training and 20% testing subsets. Following 5-fold stratified cross-validation, the proposed model achieved an average weighted F1-score of 0.95 on the training set and 0.93 on the testing set for germination capacity prediction. The performance of the proposed model was further compared with widely used deep convolutional neural network architectures, including VGG19, ResNet50, and EfficientNetB5. Comparative results demonstrate that the proposed model provides competitive and robust performance for seed germination prediction. Overall, the findings indicate that the proposed approach can effectively be utilized for automated seed germination prediction. Future research should evaluate the generalizability of the model by conducting performance assessments on additional seed types.
Why it matches plant phenotyping methodsRGB画像から種子の形態形質を自動抽出し、発芽状態を予測する深層学習手法が研究の中心であるため、植物フェノタイピング手法として含める。
abstractthis study proposes a deep learning–based model designed to automate the seed selection process by accurately predicting seed germination capacity from seed images.
Common beanPigeon peaLaboratory / benchtopMultispectral / hyperspectralSeed / grainClassification
Reliable seed accession identification underpins germplasm conservation, traceability and breeding; however, conventional assays remain destructive, labour-intensive and difficult to scale. Here, visible-near-infrared-shortwave infrared (VIS-NIR-SWIR) hyperspectral imaging (HSI; 449.54-2399.17 nm; 563 bands) was used to classify 32 grain-legume accessions ( n = 3200 seeds; 100 seeds per accession), comprising 30 common bean ( Phaseolus vulgaris L.) landraces plus two outgroup legumes ( Vigna angularis (Willd.) Ohwi & Ohashi and Cajanus cajan (L.) Huth). Each seed was represented by one ROI-averaged spectrum obtained from mean representative pixels within a standardised 10 × 10 pixel window at the centre of each seed. A fixed stratified 70:30 seed-level training:test partition was used, with 70 seeds per accession ( n = 2240) reserved for fully independent training and 30 seeds per accession ( n = 960) reserved as a fully independent test set. Principal component analysis (PCA) captured 97.42% of the spectral variance in the first three components (PC1 = 63.34%, PC2 = 23.78%, and PC3 = 10.31%). One-versus-rest wavelength association mapping revealed a maximum R 2 of 0.775 at 461.37 nm, and ReliefF concentrated the strongest reduced-band signal within 449.54-456.30 nm and 577.02-597.54 nm. In the original ReliefF-selected 16-band benchmark, the subspace discriminant reached 68.25% macro-F1 and 68.54% balanced accuracy; after edge-band trimming, the alternative 16-band configuration decreased to 60.67% and 60.94%, respectively. With respect to the full-spectrum sensitivity benchmark, linear discriminant analysis achieved 96.35% balanced accuracy, followed by linear SVM (94.17%). Deep learning trained directly on the full 563-band spectra reached 84.90% test accuracy, 84.47% macro-F1, 86.27% precision and 84.90% recall, with MLP_Wide outperforming the convolutional, recurrent and attention-based alternatives. Overall, under controlled laboratory conditions, this benchmark shows that accession discrimination is driven mainly by visible-domain contrasts in the most compact representations, whereas the full spectral context remains important for the most confusable accessions and for cautious future sensor design. The reduced-band findings should therefore be interpreted as exploratory guidance for sensor design rather than as a validated deployment-ready specification.
Why it matches plant phenotyping methods豆類種子の識別・分類を目的に、ハイパースペクトル画像取得、波長選択、機械学習・深層学習をベンチマークしており、種子形質の計測・抽出手法が中心である。
abstractOverall, under controlled laboratory conditions, this benchmark shows that accession discrimination is driven mainly by visible-domain contrasts
Detecting postharvest tomato ripeness is essential for quality control. To reveal the evolution of complex conductivity σ ∗ and complex permittivityε ∗ during tomato ripening, this study integrates bioimpedance spectroscopy (BIS) and finite element method (FEM) to predict postharvest tomato maturity. Based on the Maxwell-Wagner equation, σ ∗ and ε ∗ were derived from the measured impedance and conductance data. BIS measurements were conducted on whole tomatoes at four ripening periods and their components (pericarp, chamber, core, cavity). A finite element model was implemented in COMSOL to simulate electrical field distribution and quantify tissue-specific differences. Continuous monitoring of white ripening period tomatoes was used to validate the model, yielding an average accuracy of 85.16%, peaking at 92.86% in red ripening period and dipping to 80.30% in color change period, elucidate the dynamic changes in electrical properties during tomato ripening and provide a basis for nondestructive maturity assessment.
Why it matches plant phenotyping methodsトマトの成熟状態を電気特性から非破壊推定するBIS・FEM手法を開発・検証しており、植物状態の取得・推定が研究の中心である。
abstractthis study integrates bioimpedance spectroscopy (BIS) and finite element method (FEM) to predict postharvest tomato maturity.
WheatLaboratory / benchtopX-ray / CTRootMorphology / geometry measurementStress / disease detectionGrowth / time-series analysisRoot system architectureWater status / transpiration
Soil structure creates spatial heterogeneity that shapes ecosystem functions, including water retention and root colonization. Chernozems – soils characterized by exceptionally stable aggregation resulting from millennia of root-soil co-evolution – offer a unique model to investigate how aggregate-scale pore architecture controls plant responses to drought. Using soil microcosms (4 × 10 cm, ~80 g soil) with aggregates from Native Steppe and Arable Chernozems, we established six experimental treatments (3 aggregate sizes × 2 soil types) with three replicates each. Root-soil dynamics were tracked through repeated X-ray computed tomography (Neoscan N80, Belgium) at 16 µm resolution. Imaging was synchronized with plant developmental stages – germination, first leaf, and third leaf stage at permanent wilting point – yielding a total of 54 soil tomograms for analysis.Preliminary processing of the data reveals distinct pore network architectures across aggregate size classes. Small aggregates exhibited low CT-visible porosity (24%) with high solid phase connectivity (6.60 mm⁻³), while medium aggregates showed moderate porosity (39%) with lower connectivity (0.64 mm⁻³), and large aggregates had the highest porosity (49%) but the lowest connectivity (0.51 mm⁻³). This structural gradient directly controlled root colonization: solid phase connectivity showed a strong negative correlation with root volume growth (r = −0.76), suggesting that matrix mechanical cohesion, rather than pore characteristics alone, limits root expansion. Medium aggregates – which naturally dominate in undisturbed steppe soils – provided optimal conditions for root development, with 90% greater root surface expansion compared to small aggregates. Root sphericity decreased 3–4 times more in medium aggregates (−0.14) than in small aggregates (−0.04), indicating greater architectural plasticity critical for water acquisition. Importantly, our preliminary results also show that medium aggregates provided the greatest drought resistance: plants in these microcosms reached the permanent wilting point latest, suggesting that this aggregate fraction optimizes both root development and water availability over time.These findings demonstrate that native Chernozem aggregate structure represents an optimized spatial configuration balancing root accessibility with water retention. The strong coupling between aggregate-scale heterogeneity and root response suggests that tillage-induced disruption of natural aggregate distributions may compromise this evolutionary optimization. Our approach – combining high-resolution CT with growth stage-synchronized imaging – offers a framework for quantifying how spatial heterogeneity translates into ecosystem-relevant soil functions. Data processing is ongoing, and final results will include expanded replication and additional root morphometric parameters.
Why it matches plant phenotyping methods高解像度X線CTを用いて根の体積成長、表面拡大、球形度などの形態形質を反復取得・定量する手法が研究の中心であり、植物フェノタイピングへの実質的応用に該当する。
abstractRoot-soil dynamics were tracked through repeated X-ray computed tomography (Neoscan N80, Belgium) at 16 µm resolution.
Early-stage fungal contamination in maize kernels is difficult to identify visually and it can cause severe quality and safety risks during storage and transportation. Short-wave infrared (SWIR) hyperspectral imaging offers a rapid, non-destructive approach by capturing chemical information related to water, proteins, and lipids. This study investigates the early detection and classification of Gibberella zeae contamination in maize kernels using SWIR hyperspectral imaging combined with machine learning. Two maize varieties were artificially inoculated and cultured under controlled conditions, followed by hyperspectral data collection over six contamination stages. Various preprocessing techniques including standard normal variate (SNV), second derivative (SD), multiplicative scatter correction (MSC), and derivatives were evaluated to enhance data quality. Feature wavelength selection was performed using successive projections algorithm (SPA), competitive adaptive reweighted sampling (CARS), and uninformative variable elimination (UVE), significantly reducing redundancy and improving classification performance. Multiple models, including linear discriminant analysis (LDA), multilayer perceptron (MLP), support vector machine (SVM), a convolutional neural network (CNN), long short-term memory (LSTM) network, and a hybrid architecture Transformer that integrated a CNN, a LSTM network, and a Transformer (abbreviated as CLT), were constructed for both binary (healthy vs. contaminated) and multiclass classification tasks. Specifically, the multiclass task consisted of six contamination stages corresponding to contamination time from Day 0 to Day 5. The best binary classification task accuracy of 100% was achieved using SNV-preprocessed data with the MLP model. For multiclass classification task, the SD-preprocessed LDA model reached a test accuracy of 92.56%. Combined with appropriate preprocessing, feature selection and modeling, these results demonstrate that hyperspectral imaging is a powerful tool for the non-destructive, early-stage identification of fungal contamination in maize kernels, offering strong support for food safety and quality monitoring.
Why it matches plant phenotyping methodsSWIRハイパースペクトル画像と機械学習を用いて、トウモロコシ種子の真菌汚染状態・汚染段階を非破壊推定する手法が研究の中心であり、植物器官の病態の測定に該当する。
abstractThis study investigates the early detection and classification of Gibberella zeae contamination in maize kernels using SWIR hyperspectral imaging combined with machine learning.
Abstract Background Apoplastic pH is a central regulator of plant growth, development, and environmental adaptation, influencing cell expansion, nutrient uptake, and extracellular signaling. Many studies have successfully used HPTS to monitor relative changes in apoplastic pH in plants. At the same time, research increasingly targets pH-dependent biochemical and biophysical processes. Many enzymatic activities, ion binding events, and receptor–ligand interactions depend on defined proton concentrations. Accordingly, the development of reliable approaches to measure absolute pH in living tissues is gaining importance. Methods A calibration-based workflow was developed to enable quantitative assessment of absolute apoplastic pH using ratiometric HPTS imaging. The approach integrates a simplified two-point normalization strategy with an in-vitro derived sigmoidal calibration model, thereby minimizing the need for extensive in-vivo calibration curves. Confocal imaging was performed using HPTS excited at two wavelengths followed by ratiometric image processing. Data analysis is supported by a custom Fiji plugin, Ratio2pH, which converts ratiometric images into pixel-resolved maps of absolute pH. Results In vitro characterization revealed a robust, non-linear relationship between normalized HPTS ratios and pH, enabling accurate pH estimation within the physiologically relevant range of pH 5.0–7.0. When applied in-vivo to Arabidopsis thaliana roots, the workflow yielded extracellular pH estimates consistent with the pH of the incubation medium and detected reproducible pH shifts in response to pharmacological treatments. Conclusions This workflow enables reproducible, spatially resolved measurement of absolute apoplastic pH in living plant tissues. By combining a simplified calibration strategy with accessible image analysis tools, it facilitates quantitative extracellular pH measurements and their integration into biochemical and biophysical analyses.
Why it matches plant phenotyping methods生きた植物組織の絶対アポプラストpHを画像から定量する校正ワークフローを開発・検証し、Fijiプラグインも提供しているため、植物状態の取得法が中心である。
abstractA calibration-based workflow was developed to enable quantitative assessment of absolute apoplastic pH using ratiometric HPTS imaging.
Reproduction assets foundThe paper deposits its authors' analysis code and data publicly: the Ratio2pH Fiji plugin (Zenodo 10.5281/zenodo.15599805), a Python script for sigmoidal calibration curve fitting (Zenodo 10.5281/zenodo.17303477), and source data files and raw confocal images (Freidata 10.60493/t29wb-7my86). The Zenodo 15658668 ratiom�Code · publicThe Python Script for generating a user-defined sigmoidal calibration curve is available at Zenodo: https://doi.org/10.5281/zenodo.17303477Open asset ↗Zenodo · 10.5281/zenodo.17303477lines:175-235Dataset · publicSource data files and raw images are uploaded at Freidata, the data server of the University of Freiburg, available under https://doi.org/10.60493/t29wb-7my86Open asset ↗Freidata · 10.60493/t29wb-7my86lines:175-235Plant phenotyping relevance match · UnverifiedOpenAlex · checked 14 Sept 2026
The need for more sustainable agricultural systems is becoming increasingly apparent. The global demand for agricultural products—food, feed, fuel and fiber—will continue to increase as the global population continues to grow. This challenge is compounded by climate change. Not only does a changing climate make it difficult to maintain stable yields but current agricultural systems are a major source of greenhouse gas emissions and continue to drive the problem further. Therefore, future agricultural systems must not only increase production but also significantly decrease negative environmental impacts. One approach to addressing this is to begin breeding and cultivating new plant species that have fundamental sustainability advantages over our existing crops. The Lemnaceae, commonly known as duckweeds, are one family of plants that have potential to increase output and reduce the negative environmental impacts of agricultural production. Herein we describe the Automated Lab‐scale PHenotyping Apparatus, ALPHA, for high‐throughput phenotyping of Lemnaceae. ALPHA is being used for selective breeding of one species, Lemna gibba , toward the goal of creating a new crop for use in sustainable agricultural systems. ALPHA can be used on many small aquatic plant species to assess growth rates in different environmental conditions. A proof of principle use case is demonstrated where ALPHA is used to determine saltwater tolerance of six different clones of L. gibba .
Why it matches plant phenotyping methods水生植物の成長率をハイスループットに定量するフェノタイピング装置を開発・適用しており、表現型取得システムが研究の中心である。
abstractHerein we describe the Automated Lab‐scale PHenotyping Apparatus, ALPHA, for high‐throughput phenotyping of Lemnaceae.
Laboratory / benchtopChlorophyll fluorescenceMicroscopyCell / cellular structureFlowerRootTissueVisualization / data management
Abstract Cryo‐scanning electron microscopy (CryoSEM) permits the preparation and detailed imaging of bulky samples while keeping them in a hydrated state. For plant biology, cryofractures give information on cell ultrastructure and tissue organisation within a much larger context that is the whole organ or organism. To date, a method to locate fluorescence reporters on the cryofracture has not been reported. Our approach uses a stereofluorescence microscope with an 80 mm working distance and a high‐zoom ratio to image the fracture through a viewing port of the cryopreparation chamber while the sample is still frozen and under vacuum. We have applied this method to look at fluorescent reporters of auxin transport and signalling in plant shoot apices and seedlings, the expression of a poorly characterised gene in the young floral pedicel and nitrogen‐fixing rhizobial bacteria, expressing GFP, in root nodules. This method is applicable to any cryopreserved bulky sample that has a fluorescent output and paves the way for correlative light‐electron microscopy for cryoSEM‐based imaging.
Why it matches plant phenotyping methods植物試料の蛍光レポーターを凍結破断面上で位置特定・画像化する新規CryoFluorSEM法の開発であり、植物の構造・組織状態を取得する方法が中心である。
abstractTo date, a method to locate fluorescence reporters on the cryofracture has not been reported.
Abstract Biofortification is a sustainable and cost‐effective strategy that uses plant breeding and agronomic approaches to improve the nutrient content of staple crops consumed by vulnerable populations. The approach requires high‐throughput phenotyping to effectively identify and develop nutrient‐rich genotypes. This study aimed to develop a multielement, nondestructive method to quantify calcium (Ca), manganese (Mn), iron (Fe), copper (Cu), and zinc (Zn) in whole seed wheat ( Triticum aestivum L.) samples using a benchtop energy dispersive x‐ray fluorescence (EDXRF) spectrometer. Grain samples from 29 and 41 wheat genotypes were used for the EDXRF calibration and validation, respectively. A microwave plasma–atomic emission spectrometer (MP‐AES) provided the analyte reference values for each sample. The EDXRF calibration showed moderate to high correlation with MP‐AES values for Ca, Mn, Cu, and Zn, while Fe exhibited a weak correlation. The limits of quantification (mg kg −1 ) were 103.9 for Ca, 8.5 for Mn, 3.5 for Fe, 4.7 for Zn, and 1.0 for Cu—all below the observed analyte range in wheat grain. The method is suitable for use in early generation selection, as indicated by standard errors of prediction (mg kg −1 ) of 36.4 for Ca, 3.3 for Mn, 2.5 for Fe, 0.3 for Cu, and 1.5 for Zn. This study builds upon previous nondestructive EDXRF methods by introducing additional elements that can be reliably phenotyped in wheat, supporting broader use in biofortification programs.
Why it matches plant phenotyping methods小麦種子の無破壊多元素組成を定量するEDXRF法を開発し、独立試料と基準法で校正・検証しており、植物形質取得法が研究の中心です。
abstractThis study aimed to develop a multielement, nondestructive method to quantify calcium (Ca), manganese (Mn), iron (Fe), copper (Cu), and zinc (Zn) in whole seed wheat ( Triticum aestivum L.) samples using a benchtop energy dispersive x‐ray fluorescence (EDXRF) spectrometer.
Field / plotLaboratory / benchtopNeRF / 3D Gaussian SplattingPhotogrammetry / SfM / MVSLiDAR / point cloudRGB-D / ToFWhole plant / canopy / plot / field2D/3D reconstruction
Three-dimensional point cloud (3DPC) data capture detailed geometric and structural plant traits beyond the capability of 2D imaging. When combined with artificial intelligence (AI), it offers a powerful, non-invasive tool for plant phenotyping, which is crucial for driving advancements in plant breeding and agriculture. However, challenges related to data complexity, limited datasets, and model generalization hinder 3DPC’s widespread adoption. To provide a comprehensive overview and guide future research in this area, we conducted a systematic literature review (SLR) following Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA) guidelines by analysing 381 papers published between January 2017 and October 2025 from major databases. Our review examines the advantages, current status, limitations, and future directions of AI applications in 3DPC-based plant phenotyping. Our findings indicate a rapid increase in publications since 2022, with deep learning (DL) methods, especially pointwise MLP-based networks, driving much of this growth, with a notable recent surge in Transformer-based, Graph-based, and particularly Hybrid models that combine their strengths. Furthermore, novel methods like Neural Radiance Fields (NeRF) and 3D Gaussian Splatting (3DGS) are emerging as powerful tools for 3D reconstruction and scene synthesis. Time-of-Flight (ToF) and Structure from Motion and Multi-View Stereo (SfM-MVS) technologies remain the predominant 3DPC data acquisition techniques. Research in this area focuses on trees/shrubs and cereals, typically involving single-species studies. Although the overall use of public datasets remains low (18.9%), their adoption has significantly increased since 2020. Key limitations identified include: (1) a lack of standardized data collection and formats, (2) insufficient model robustness and generalization, especially from lab to field, (3) high computational demands, and (4) a reliance on species-specific models. The future of AI-driven 3DPC phenotyping hinges on overcoming these bottlenecks. Priority should be given to: developing field-deployable, computationally efficient models; exploring the potential of the foundation model; establishing diverse and standardized public datasets; and strengthening the integration of 3D phenomics with genomics to bridge the genotype-to-phenotype gap. This review provides a foundational roadmap to guide research in plant phenomics, crop breeding, and plant science.
Why it matches plant phenotyping methods3D点群とAIによる植物形質取得・解析を中心に扱う体系的レビューであり、植物フェノタイピング手法のレビューとして明確に適格。
titleAI-driven 3D point cloud analysis in plant phenotyping: A Systematic Review
Published1 Mar 2026Journal of food composition and analysis : an official publication of the United Nations University, International Network of Food Data Systems
Waxiness is a critical textural factor determining the taste and consumer preference of postharvest Chinese chestnut (Castanea mollissima Blume) kernels. However, its genetic improvement is hindered by the lack of an accurate, high-throughput phenotyping method and a clear understanding of its inheritance. In this study, a texture analyzer-based method was developed to objectively evaluate waxiness and analyze the associated phenotypic variations. Sensory waxy scores, starch composition, and texture parameters of 43 chestnut germplasms were evaluated. Notably, high-waxiness germplasms (waxy score > 80) exhibited significantly higher amylopectin content but had lower hardness and chewiness after steaming. Integrated analyses, including UPGMA clustering, principal component analysis, and Pearson correlation, convergently identified hardness as the optimal parameter for predicting waxiness. A robust regression model (Waxiness = 106.3041–0.3055 × Hardness) was established to quantify waxiness based on instrumental hardness measurements. Notably, the method effectively quantified continuous waxiness variation among 20 F₁ hybrid kernel populations and uncovered significant parental effects, thereby demonstrating its power as a reliable phenotyping tool when breeding for chestnut quality. This study is an important exploration of the phenotypic variation characteristics of the chestnut waxiness trait and lays a foundation for breeding high-quality chestnut cultivars.
Why it matches plant phenotyping methods胸果仁のワキシネスという植物器官形質について、テクスチャーアナライザーを用いた高スループット表現型測定法を開発し、回帰モデルによる定量化とF₁集団での実証を行っており、表現型取得法が研究の中心である。
abstracta texture analyzer-based method was developed to objectively evaluate waxiness and analyze the associated phenotypic variations
Pseudomonas syringae pv. syringae ( Pss ) is the causal agent of bacterial canker, a disease that can result in yield losses, aerial tissue damage, and tree mortality in stone fruits worldwide. Peach, one of the major stone fruit crops, experiences significant yield losses and tree mortality attributed to bacterial canker in the United States. As the second-largest peach-producing state, South Carolina faces direct and significant impacts due to Pss . Early evaluations of peach scion responses to Pss infection have relied primarily on circumstantial field observations in rootstock trials. Although laboratory evaluations in peach have been reported, these studies primarily focused on pathogen virulence testing or small accession sets and did not establish a standardized, scalable detached twig protocol for systematic germplasm phenotyping. The absence of a clearly described laboratory assay has limited reproducible and large-scale evaluation of bacterial canker tolerance in peach. To address this gap, a detached dormant twig assay, previously developed for cherry, was adapted and optimized for peach. Dormant shoots from nine peach accessions were cut into 10 cm segments, surface-sterilized, and inoculated with a Pss suspension prepared in 10 mM MgCl 2 buffer or with the buffer alone. After six weeks of incubation, inner bark lesion size was evaluated visually and quantified using ImageJ. A newly developed visual rating scale was established and compared with quantitative lesion measurements. Spearman correlation analysis showed strong positive correlations between visual disease scores and ImageJ-based lesion measurements across two independent replicates (ρ = 0.80-1.00, p < 0.01), while shoot segment diameter showed weak-to-moderate negative correlations with disease severity. This adapted and consolidated dormant twig assay provides a practical, reproducible, and scalable method for phenotyping bacterial canker tolerance in peach and supports future germplasm screening and breeding efforts.
Why it matches plant phenotyping methodsモモの細菌性潰瘍病抵抗性を評価するため、切離枝アッセイを適応・最適化し、病斑測定と視覚評価を検証した中心的な表現型測定法の研究。
abstracta detached dormant twig assay, previously developed for cherry, was adapted and optimized for peach
Identifying nutritional deficiencies at an early stage is crucial for maximizing yield production and ensuring healthy plants. Conventional methods generally rely on time-consuming analysis conducted by agronomic experts. To address this challenge, this study presents a data-driven approach for the early identification of nutritional deficiencies in hazelnut orchards. Different custom datasets, composed of images acquired in a real hazelnut orchard as well as in a controlled laboratory environment, are collected, and the performance of five state-of-the-art machine learning models in early detecting nutritional deficiencies is compared. In particular, ResNet, DenseNet, MobileNet, EfficientNet, and ConvNext models, along with a baseline based on support vector machines, are considered. Data augmentation techniques are introduced to synthetically increase the datasets, and their effectiveness is extensively evaluated. Additionally, a pipeline is designed to carry out the early identification of nutritional deficiencies onboard an agricultural robot. Experimental results on the early identification show that ConvNext achieves the highest performance: 81.79% accuracy and 0.8168 F1 score on a real-world dataset with four classes, and 75.54% accuracy with 0.7552 F1 score for the more challenging six-class scenario. Furthermore, the effectiveness of the integrated system is validated in preliminary laboratory experiments using a Turtlebot2 mobile base and a Franka Research 3 arm, equipped with RGB-D cameras. • Data-driven pipeline detects hazelnut nutrient deficiencies from leaf images. • Real and lab-acquired hazelnut leaf image datasets collected and publicly released. • ConvNext achieves 85.50% accuracy on 4-class hazelnut deficiencies in lab conditions. • 75.54% accuracy on 6-class real orchard dataset validates field robustness. • Two-stage pipeline with leaf detection and classification enables onboard robot monitoring.
Why it matches plant phenotyping methods葉画像から植物の栄養欠乏状態を推定する画像解析・機械学習パイプラインと、ロボット搭載システム、データセットを中心的に開発・評価しているため。
abstractthis study presents a data-driven approach for the early identification of nutritional deficiencies in hazelnut orchards
We present a protocol to evaluate greening capacity in etiolated Arabidopsis seedlings during the critical dark-to-light transition. We describe steps for sample preparation and sowing and then detail procedures for quantifying protochlorophyllide accumulation in darkness, the greening rate upon illumination, and reactive oxygen species levels as an indicator of photo-oxidative stress. This protocol can be used for screening and phenotypic quantification across genetic backgrounds. For complete details of this protocol, please refer to Zhong et al. 1 and Zhong et al. 2 .
Why it matches plant phenotyping methods暗所から光への移行における植物の緑化能を定量するプロトコルで、表現型スクリーニングと遺伝背景間の定量が中心であるため。
abstractWe present a protocol to evaluate greening capacity in etiolated Arabidopsis seedlings during the critical dark-to-light transition.
Maize productivity is increasingly constrained by water deficit stress (WDS), particularly under erratic rainfall conditions. Efficient early-stage phenotyping coupled with field validation is critical for breeding WDS-tolerant genotypes. In this study, we developed a two-tier screening strategy integrating hydroponics-based root trait evaluation at pre-reproductive stage with subsequent field validation of maize inbreds under managed WDS at CIMMYT, Hyderabad. A set of 50 diverse maize inbreds were evaluated for root architectural traits and plant growth stages including grain yield components. Hydroponic screening applied PEG6000-induced osmotic stress to assess root length, tips, forks, segments and diameter, whereas field trials imposed pre-reproductive WDS through cumulative growing degree day-based irrigation withdrawal. Significant genotypic variation and genotype × trait interactions were observed across both environments, reflecting trait and environment-specific responses. Key root traits, including root tips, total length, forks and segments, showed strong positive correlations (r ≥ 0.70) with yield components and Normalized difference vegetation index (NDVI), underscoring their importance in WDS resilience. Multivariate analysis further confirmed the alignment of root vigor with kernel traits and canopy health as critical determinants of yield stability. Among the evaluated lines, introgressed ILM23 and ILM24 emerged as the principal donor lines, while PML1249, PML1275, and PML1285 were identified as promising donor sources, all exhibiting robust root systems, stable anthesis-silking interval (ASI) and superior stress tolerance indices. Spearman's rank correlation (ρ = 0.988) between hydroponics and field rankings highlighted the predictive reliability of controlled root phenotyping for field performance under WDS. This integrated hydroponics-to-field approach provides a rapid, efficient and cost-effective framework for the early identification of WDS-tolerant or high water-use-efficiency (WUE) maize hybrids, facilitating the accelerated breeding of resilient cultivars.
Why it matches plant phenotyping methods水耕栽培による根系形態フェノタイピングを開発し、圃場条件で予測信頼性を検証する二段階スクリーニング手法が研究の中心である。
abstractwe developed a two-tier screening strategy integrating hydroponics-based root trait evaluation at pre-reproductive stage with subsequent field validation
Laboratory / benchtopTissuePhysiological trait estimationWater status / transpiration
Measurement of xylem hydraulic conductance provides access to xylem hydraulic conductivity and vulnerability to cavitation, two key traits for assessing plant sensitivity to environmental stressors. We evaluated the performance of custom-built low-cost pressure drop flow meters through nearly 1200 measurements across devices, laboratories, reservoir heights (10, 25 and 45 cm, used to induce pressure head and drive water flux) and PEEK tubing of hydraulic contrasting resistances. Flow meters were interchangeable, with mean differences generally < 3.5% and never exceeding 5%, with 88.9% of comparative tests showing no significant difference. Under recommended conditions (25-45 cm pressure head, downstream-to-upstream pressure ratio ≈0.5), precision reached 1%-7% coefficient of variation. Accuracy, assessed against reference values obtained by water displacement, was also strong, with 68% of measurements deviating by < 5% from reference values and over 78% when measured at {greater than or equal to}25 cm. At 10 cm, performance declined because sensor deviations represented a larger fraction of pressure differential, and low-resistance PEEK tubing increased absolute but not relative error. Validated flow meters proved portable, affordable (≈2500 CAD), and reliable. Their low cost, open-source interface, and publicly available construction protocol make them accessible to laboratories with limited resources, enabling reproducible multi-laboratory studies of plant hydraulics and fostering international collaborations.
Why it matches plant phenotyping methods植物の木部油圧コンダクタンスという生理形質を測定する低コスト流量計を開発・検証し、精度・再現性・多施設間性能を評価しているため、植物フェノタイピング手法が研究の中心である。
abstractWe evaluated the performance of custom-built low-cost pressure drop flow meters through nearly 1200 measurements across devices, laboratories, reservoir heights (10, 25 and 45 cm, used to induce pressure head and drive water flux) and PEEK tubing of hydraulic contrasting resistances.
Background Apple scab (AS), caused by the fungal pathogen Venturia inaequalis, is a major disease of apple that manifests as lesions on leaves and fruits. The disease compromises fruit quality and yield, leading to substantial economic losses. Traditional AS assessment relies on visual scoring, which is labor-intensive, subjective, and poorly reproducible. This study proposes a deep learning-based framework to overcome these limitations and to enable an accurate, scalable AS phenotyping approach. Results Deep learning techniques were employed for the object detection and segmentation of AS symptoms in apple fruits. A two-stage fine-tuning process was applied to color images collected under orchard and laboratory conditions using the YOLO foundation model (YOLO11). The model was first trained to detect healthy apple fruits (Model 1) and subsequently refined to segment AS lesions (Model 2) using high-resolution imagery (864 × 864 pixels). Model 1 (Fruit Detection) achieved 0.98 precision, 0.95 recall, and 0.94 mAP50. Model 2 (Lesion Segmentation) achieved 0.64 precision, 0.75 recall, and 0.75 mAP50. The framework supports real-time processing of images and video. Despite challenges such as variable lighting and symptom heterogeneity, the use of high-resolution training data improved the segmentation accuracy (mAP50-95) of fine-scale lesions by over 50% compared to the previous YOLO architecture. Conclusion These results demonstrate that the proposed deep learning-based approach provides a reliable pipeline for automated AS phenotyping. By improving precision and efficiency in both controlled and field environments, the model enhances apple grading assessments and accelerates breeding efforts to identify AS-resistant genotypes. Furthermore, this work establishes a solid foundation for broader applications in real-time plant disease monitoring and future integration of additional apple diseases.
Why it matches plant phenotyping methodsリンゴ果実上の病斑を画像から検出・セグメント化し、植物病害の程度を自動推定する深層学習手法が研究の中心であるため、植物フェノタイピング手法として含める。
abstractThis study proposes a deep learning-based framework to overcome these limitations and to enable an accurate, scalable AS phenotyping approach.
Plant diseases continue to pose a serious challenge to agriculture, leading to substantial yield losses and posing a threat to global food security. Accurate and early identification of plant diseases is crucial for effective crop management. However, traditional manual inspection methods are time-consuming, subjective, and heavily dependent on expert knowledge. With recent advances in artificial intelligence and computer vision, automated plant disease detection systems have emerged as a reliable alternative. These systems depend strongly on large, well-annotated image datasets for training and evaluation. Among publicly available resources, the PlantVillage dataset is one of the most widely used benchmarks for plant disease research. It contains over 50,000 high-resolution RGB leaf images captured under controlled conditions and covers 14 crop species, including tomato, potato, and bell pepper, along with multiple other plants. The dataset represents 38 distinct classes encompassing both healthy and diseased leaf categories. All images are collected against uniform backgrounds, providing visual consistency and making the dataset suitable for benchmarking machine learning and deep learning–based plant disease classification models. This survey provides a comprehensive review of the PlantVillage dataset and its contribution to the advancement of automated plant disease detection. It traces the progression from traditional handcrafted feature–based classifiers to convolutional neural networks, transfer learning approaches, and recent transformer-based architectures. Various methods are compared in terms of classification accuracy, generalization ability, computational efficiency, and robustness. In the survey we have identified that Transfer Learning model showed 99.75% accuracy. The survey also discusses key limitations of the dataset, particularly its controlled imaging conditions and challenges related to real-field deployment. Finally, future research directions are highlighted, including domain adaptation, explainable artificial intelligence, multi-disease recognition, and real-world agricultural applications. This work aims to offer researchers a structured understanding of the PlantVillage dataset and support the development of next-generation intelligent crop disease diagnostic systems.
Why it matches plant phenotyping methodsPlantVillage画像を用いた植物病害状態の画像ベース推定手法とデータセットを中心に、複数の機械学習手法を比較・レビューしているため、植物フェノタイピング方法論のレビューとして含める。
abstractThis survey provides a comprehensive review of the PlantVillage dataset and its contribution to the advancement of automated plant disease detection.
Plants defend against pathogens such as fungi by initiating coordinated structural and chemical responses. Pathogen perception triggers rapid cytosolic calcium influx and calcium oscillations that drive defense gene expression, yet the mechanisms by which these signals encode stressor intensity and propagate systematically remain unclear. Here, we present a microfluidic system to characterize intracellular calcium dynamics in protonemal colonies of the moss Physcomitrium patens (Hedw.) upon precise and reversible exposure to fungal chitin oligosaccharides. Epifluorescent imaging of cells expressing the calcium indicator GCaMP6f revealed a rapid, coordinated calcium response to chitin addition, followed by stereotyped oscillations that subsided quickly upon stimulus removal. We implemented an unbiased image segmentation algorithm using pixel-based k -means clustering to automatically locate regions with specific oscillatory signatures. Calcium dynamics were distinct across adjacent cells, distinguishable by cell type, and significantly modulated by circadian rhythm, adaptation time within the device, and stimulus timing. Cytosolic calcium oscillations, which rose and fell symmetrically within about 60 s, occurred spontaneously during the subjective night and following short adaptation periods. Chitin elicited strong oscillations with increased frequency, amplitude, and duration, and repeated pulses entrained regular, colony-wide oscillations at the stimulation interval. This study complements prior investigations of whole plant and growth tip dynamics and provides a quantitative framework to study calcium signaling in plants, including mechanisms of signal propagation and the role of oscillation frequency on gene expression.
Why it matches plant phenotyping methods植物細胞のカルシウム動態を定量するマイクロ流体・蛍光イメージング系と自動画像セグメンテーションを開発し、植物の生理状態を抽出する方法が研究の中心である。
abstractHere, we present a microfluidic system to characterize intracellular calcium dynamics in protonemal colonies of the moss Physcomitrium patens
Reproduction assets foundThe paper's Data Availability Statement explicitly makes analysis scripts and sample data publicly available on the authors' GitHub repository (albrechtLab/moss_calcium), and the MDPI supplementary materials (plants-15-00582-s001.zip) contain the paper's timelapse calcium-imaging videos and supplementary figures. Raw/全Code · publicData are available upon request. Analysis scripts and sample data are publicly available at https://github.com/albrechtLab/moss_calcium (accessed on 1 January 2026).Open asset ↗albrechtLab/moss_calciumlines:188-251Plant phenotyping relevance match · UnverifiedOpenAlex · Crossref · Europe PMC · checked 5 Sept 2026
Aluminium (Al) toxicity is a potential constraint to maize productivity in acidic soils, primarily due to its inhibitory effect on root growth during its early establishment. In the present study, a hydroponic screening protocol was standardized using Modified Magnavaca-II solution at the seedling stage and applied to 250 tropical maize inbred lines. Five root traits-total root length (TRL), root surface area (RSA), root volume (RV), average root diameter (AD), and number of root tips (NRT)-were quantified using WinRHIZO. To assess differential tolerance, the Relative Root Tolerance Index (RRTI)-a ratio-based metric comparing root performance under stress versus control-was calculated along with percent reduction for all traits. Protocol optimization with seven elite inbreds exposed to graded AlCl₃ concentrations (0-1500 µM) identified 300 µM AlCl₃ at 11 days post-germination as optimal for differentiating genotypic responses. Under this optimized condition, the 250 inbreds showed highly significant genotypic variation and genotype × treatment interactions. Stress significantly reduced most root traits by 10-40%, while improving the average root diameter, indicating compensatory thickening. Substantial variability was observed for both RRTI and percent reduction indices, ranging from 3.83 to 533.88. Principal component analysis and composite indices identified IMR292, IMR592, IMR463, IMR621, IMR546, IMR534, IMR629 and IMR395 as tolerant due to high TRL, RSA and NRT under stress, while IMR388, IMR33, IMR58, IMR349 and IMR446 were highly susceptible. The tolerant inbreds offer promising genetic resources for breeding Al-tolerant maize, while the optimized hydroponic system provides a robust, scalable framework for future phenotyping and genetic dissection studies.
Why it matches plant phenotyping methodsアルミニウム耐性評価のための高スループット根系表現型測定プロトコルを標準化・最適化し、250系統へ適用しているため、表現型取得法が研究の中心です。
abstracta hydroponic screening protocol was standardized
ArabidopsisLaboratory / benchtopRootMorphology / geometry measurementGrowth / development / phenologyWater status / transpiration
Root hairs are outgrowths of the epidermal cells of plant roots. They increase the root's exchange surface with the soil and provide it with good anchorage in the soil. Root hairs are an emblematic model of apical growth, a process also used by yeasts and hyphae to invade their environment. From a mechanical perspective, the root hair is considered as an elastic cylinder under pressure, closed by a dome that behaves like a yield fluid. We introduce here two innovative mechanical setups and protocols to characterize the mechanical properties of single growing root hairs in Arabidopsis thaliana. In the first setup, root hairs grow against an elastic obstacle until buckling. By measuring the critical buckling force, we determine the surface modulus and estimate the Young's modulus of the cell wall, which aligns with previous measurements. Using a 1D elasto-viscoplastic model of root hair growth, we assess the excess pressure beyond the yield threshold (the driver of tip growth) and estimate the axial stiffness of the root hair, reflecting its elastic resistance to compression. For the second protocol, we designed a setup where a single root hair grows against a cantilever with variable stiffness, a technique adapted from our earlier work on rigidity sensing by animal cells. This method provides an independent estimate of the root hair's axial stiffness, confirming our initial findings and suggesting that this stiffness primarily involves tip compression and depends mainly on turgor pressure, at least within the low deformation regime explored.
Why it matches plant phenotyping methods単一の生長中根毛の力学特性を測定する革新的な実験系とプロトコルを開発・相互検証しており、植物表現型の取得法が研究の中心である。
abstractWe introduce here two innovative mechanical setups and protocols to characterize the mechanical properties of single growing root hairs in Arabidopsis thaliana.
Plant phenotyping relevance match · UnverifiedOpenAlex · Crossref · Europe PMC · checked 5 Sept 2026
3D phenotyping refers to the quantitative characterization of a plant's structural and morphological traits in three-dimensional space, allowing for a detailed analysis of plant architecture and growth patterns. In recent years, rapid advancements in non-destructive, high-throughput 3D imaging technologies have enabled the precise measurement of these traits. Initially focused on single-plant traits under controlled conditions, the field has now expanded towards robust applications in real-world field environments, enabling large-scale analyses of plant canopies and complex structures. This study focuses on the recent advancements in 3D crop phenotyping using point cloud technologies. It compares sensor technology and its application in controlled environments (Chamber-Crop Phenotyping, CCP) and field conditions (Field-Crop Phenotyping, FCP). Technologies such as Multiview stereo (MVS) reconstruction, LiDAR, and laser triangulation have enhanced plant phenomics by enabling high-throughput, non-destructive measurements of key traits such as canopy structure, leaf area, and stem diameter. This review highlights the strengths of the CCP, where environmental variables and flexibility are tightly controlled, facilitating precise trait measurement, and contrasts it with the challenges of the FCP, where unpredictable factors, such as occlusion, wind, light variability, and terrain complexity, complicate data acquisition. Various sensor platforms, including ground-based robotic systems and unmanned aerial vehicles (UAVs), have been discussed regarding their ability to overcome occlusion and limited sensor range in real-world conditions. The need to transition these technologies from laboratory environments to real-world agricultural applications is emphasized, highlighting their potential to improve crop management and plant breeding through accurate phenotypic trait extraction. Finally, current research gaps and future directions for integrating advanced sensor platforms and analytical techniques in both CCP and FCP settings are identified, emphasizing the need to enhance the scalability and robustness of 3D phenotyping for field applications.
Why it matches plant phenotyping methods3D作物フェノタイピングのセンサー技術、点群処理、対象形質、検証上の課題を中心に扱う方法論レビューであり、植物形質の取得手法が明確に中心である。
abstractThis study focuses on the recent advancements in 3D crop phenotyping using point cloud technologies.
Water deficit, salinity, and cadmium (Cd) contamination have generated an environmental problem worldwide, leading to damages to plant growth due to alteration in their metabolism. This study aimed to classify enzymatic and non-enzymatic antioxidant systems in Micro-Tom (MT) plants when subjected to two intensities (moderate and severe) of water deficit, salinity, and Cd exposure. The experimental design was a completely randomized 3 × 2 factorial, with the first factor representing the stress agents (water deficit, salinity, and Cd) and the second factor indicating stress intensities (moderate and severe), along with a control group. After an acclimation period, plants were exposed to 10 days of stress. Water deficit treatments were imposed using solutions adjusted to osmotic potentials of − 0.40 MPa and − 1.00 MPa; salinity stress was established with nutrient solutions containing 40 mM or 120 mM NaCl; and Cd stress was induced using nutrient solutions with 0.25 mM or 0.5 mM CdCl₂. Laboratory analyses included lipid peroxidation, hydrogen peroxide content, proline accumulation, protein quantification, and enzyme extraction. Descriptive analyses, and a Spearman’s correlation, identified the behavior of enzymatic and non-enzymatic systems for each stress agents and intensities, enabling the selection of key influencing factors. A factorial analysis of variance was performed to assess the mean differences among the treatments (α = 0.05) for enzymatic, non-enzymatic systems, MDA and, H₂O₂. Using this data, a decision tree model classified the stresses into four levels: low, low-medium, medium-high, and high. Variations in antioxidant response and stress biomarkers were detailed, with proline and superoxide dismutase identified as the primary variables of significance across stress indicators. Furthermore, the model achieved robust classification performance with Matthew’s correlation coefficients exceeding 0.80 in the extreme classes; however, it encountered limitations in distinguishing between classes with closely proximate values. The findings indicate the capability of the decision tree to classify stress levels in plants.
Why it matches plant phenotyping methods植物の抗酸化・生理指標から非生物的ストレス強度を推定・分類する決定木モデルが中心で、性能評価も行っているため、植物状態の計算的フェノタイピングに該当します。
abstractUsing this data, a decision tree model classified the stresses into four levels: low, low-medium, medium-high, and high.
Background and aims A major challenge in root exudation research is obtaining exudates samples that accurately reflect the exudation processes under natural soil growth conditions. Both growth environment and experimental setup can significantly influence root exudation dynamics. This study investigated how different experimental systems and growth conditions affect carbon exudation in maize ( Zea mays L.) roots and whether these factors could influence the detection of genotypic differences between the wild type (B73) and its hairless mutant, rth3. Methods Maize plants were grown under various experimental conditions, including soil-based and hydroponic systems. Root exudates were collected using a combination of traditional and innovative sampling approaches. Carbon exudation rates were compared across experimental setups and genotypes. Laboratory results were further compared with data from a separate field experiment. Results Exudation rates obtained from soil-based laboratory experiments were comparable to those observed in the field under similar growth temperatures. The contribution of root hairs to total carbon exudation was negligible compared to the effect of growth conditions and experimental setup. Large differences in root biomass introduced bias into exudation measurements, particularly when root to sampling volume ratio (RSVR) varied substantially. Conclusions Experimental setup and environmental conditions have a strong influence on root exudation measurement. Soil-based laboratory systems that closely replicate field conditions, particularly temperature, can serve as reliable proxies for field experiments, providing ecologically meaningful data. Maintaining a consistent RSVR is also essential for obtaining accurate and comparable results. These findings offer important methodological guidance for reliably quantifying root carbon exudation in maize. Supplementary information The online version contains supplementary material available at 10.1007/s11104-026-08324-x.
Why it matches plant phenotyping methodsトウモロコシ根からの炭素滲出量の測定について、実験系・環境条件・サンプリング法の影響を比較検証し、信頼性と再現性のための測定指針を提示している。測定法の評価が研究の中心である。
abstractA major challenge in root exudation research is obtaining exudates samples that accurately reflect the exudation processes under natural soil growth conditions.
Waxiness is a critical textural factor determining the taste and consumer preference of postharvest Chinese chestnut ( Castanea mollissima Blume) kernels. However, its genetic improvement is hindered by the lack of an accurate, high-throughput phenotyping method and a clear understanding of its inheritance. In this study, a texture analyzer-based method was developed to objectively evaluate waxiness and analyze the associated phenotypic variations. Sensory waxy scores, starch composition, and texture parameters of 43 chestnut germplasms were evaluated. Notably, high-waxiness germplasms (waxy score > 80) exhibited significantly higher amylopectin content but had lower hardness and chewiness after steaming. Integrated analyses, including UPGMA clustering, principal component analysis, and Pearson correlation, convergently identified hardness as the optimal parameter for predicting waxiness. A robust regression model (Waxiness = 106.3041 - 0.3055 × Hardness) was established to quantify waxiness based on instrumental hardness measurements. Notably, the method effectively quantified continuous waxiness variation among 20 F 1 hybrid kernel populations and uncovered significant parental effects, thereby demonstrating its power as a reliable phenotyping tool when breeding for chestnut quality. This study is an important exploration of the phenotypic variation characteristics of the chestnut waxiness trait and lays a foundation for breeding high-quality chestnut cultivars. • A novel, instrumental method for objective waxiness evaluation in postharvest Chinese chestnut kernels was developed. • Kernel hardness after steaming was identified as the optimal parameter for predicting sensory waxiness. • A robust regression model was established to convert instrumental hardness into a reliable waxiness index. • The method enabled high-throughput phenotyping, revealing continuous waxiness variation in F 1 hybrid populations. • Significant maternal effects on kernel waxiness were uncovered, providing crucial insights for breeding strategies.
Why it matches plant phenotyping methodsクリの子実のワキシネスという植物器官形質を対象に、テクスチャーアナライザーによる客観的・高スループット測定法と回帰モデルを開発し、F1集団で実証しているため、表現型取得法が中心です。
abstracta texture analyzer-based method was developed to objectively evaluate waxiness and analyze the associated phenotypic variations
Developing resilience against climate change and establishing food security will require significant research into the responses of multicellular organisms to their environment. New approaches, such as lab automation, can substantially increase the rate of data collection for organism‐level behavior. This report describes an automated robotic system for studying multicellular organisms to accelerate scientific experimentation. The robot can simultaneously image and deliver chemicals to each organism during multiday experiments, and uses a deep learning model to automatically obtain phenotypic data. This system's abilities are demonstrated by creating a plant growth strategy for food security applications that increases biomass while decreasing nutrient utilization. Furthermore, plant growth is characterized under high salt concentrations to better understand the effects of climate change on freshwater ecosystems. This robotic approach improves lab automation for studying multicellular organisms by increasing experimental throughput, and will enable researchers to improve crop yields under uncertain climates and predict the response of organisms in changing environments.
Why it matches plant phenotyping methodsロボットによる自動撮像と深層学習による表現型データ取得が研究の中心であり、植物の成長・バイオマスを高スループットに評価する表現型解析プラットフォームである。
abstractThe robot can simultaneously image and deliver chemicals to each organism during multiday experiments, and uses a deep learning model to automatically obtain phenotypic data.
Soybean ( Glycine max L. ) performs an important position as a main resource of protein in Indonesia. Its quality and productivity can be assessed based on the characteristics of its seed. Accordingly, the identification process through the observation of soybean seed traits is a crucial step in plant breeding and quality assurance. Manual approaches rely on manual observation, which is subjective, prone to human error and time-consuming. With the improvement of artificial intelligence, automated seed identification has appeared as a potential solution. However, progress is constrained by the lack of open and standardized image datasets, especially for locally bred varieties in developing countries. To address this gap, we propose an open image dataset of Indonesian soybean seeds from three widely cultivated and plant-bred varieties: Anjasmoro, Grobogan, and DEGA-1. The dataset consists of high-resolution seed images captured with an Epson L360 flatbed scanner, with the optical resolution fixed at 800 dots per inch, yielding images of 6800 × 9359 pixels. All raw images are saved in JPG format. No manually segmentation masks are released in this version, instead of using Deeplab V3+ with MobileNet as backbone to enable the automated seed image segmentation. The curated dataset is intended to support a broad range of applications, including computer vision tasks such as image classification and segmentation, as well as research in plant breeding, seed quality assessment, and agricultural informatics. By providing a standardized and publicly accessible resource, this dataset contributes to the advancement of interdisciplinary studies at the intersection of agriculture and artificial intelligence.
Why it matches plant phenotyping methods大豆種子画像を標準化して公開するデータセット研究であり、種子形質の自動画像解析・セグメンテーションを支援する方法論的資源が中心です。
titleAn open image dataset of Indonesian soybean seed varieties (Anjasmoro, Grobogan, DEGA-1) for agricultural research and machine learning applications.
Reproduction assets foundThe paper is a data descriptor for a public Mendeley Data repository containing the authors' own soybean seed image dataset (raw scans and segmented seed images) used for seed phenotyping, with an explicit direct URL and DOI.Dataset · publicData accessibility
Repository name: Mendeley Data
Data identification number: DOI: 10.17632/c733bjz4m3.3
Direct URL to data: https://data.mendeley.com/datasets/c733bjz4m3/3Open asset ↗Mendeley Data · 10.17632/c733bjz4m3.3html-lines:115-142Plant phenotyping relevance match · UnverifiedEurope PMC · checked 15 Sept 2026
MaizeLaboratory / benchtopRootMorphology / geometry measurementRoot system architecture
The rolled towel assay (RTA) is a soil-free method to evaluate juvenile phenotypes in crops such as maize and soybean. Here, we provide an updated RTA-based protocol to phenotype maize seedling responses to chemicals of interest. We exemplify the protocol with two synthetic auxin herbicides (2,4-dichlorophenoxyacetic acid and picloram), an auxin precursor (indole-3-butyric acid), and an auxin inhibitor ( N -1-naphthylphthalamic acid), but the method can be used with other hormones or plant growth regulators that are soluble in growth media. We also include instructions on how to annotate root traits and analyze primary root length trait data. The protocol can be scaled up for use in genetic screens, preparing tissue for gene expression analyses, carrying out genome-wide association studies (GWASs), and quantitative trait locus (QTL) identification.
Why it matches plant phenotyping methods根の形態・ホルモン応答を取得するロールドタオル法の更新プロトコルであり、根形質のアノテーションと解析も中心的に扱うため、植物フェノタイピング手法として適格です。
abstractThe rolled towel assay (RTA) is a soil-free method to evaluate juvenile phenotypes in crops such as maize and soybean.
A complex network method is introduced for high-resolution hyperspectral image analysis and classification. The method is applied to detecting environmental pollution with the Jacaranda caroba plant species. Using confocal laser scanning microscopy (CLSM), detailed spectral data were captured from leaves exposed to different levels of potassium fluoride. Unlike most studies that focus on pixel- or patch-level classification, this work targets the classification of entire high-resolution hyperspectral images, requiring a method capable of capturing global spatial-spectral and texture relationships. Therefore, the limited number of samples and the high dimensionality of the hyperspectral data make conventional deep learning methods unsuitable, motivating the need for a robust and efficient alternative. To address this, we developed the hand-engineered technique named Directed Network of Angular Similarity (DNAS) which models the hyperspectral pixels as complex network vertices connected based on the angular similarity of their spectral bands. This technique allows for effective and efficient feature extraction, computing a compact image representation with only 36 descriptors. Coupled with a supervised classifier, our method achieves a classification accuracy of 92.6% when distinguishing Jacaranda caroba pollutant levels, surpassing both traditional and deep learning approaches. By leveraging the structural, spectral, and texture properties of hyperspectral data, the DNAS method provides a novel framework for detecting pollutant-induced changes in leaf structure, offering significant advantages in resource-limited scenarios. The results demonstrate the potential of Jacaranda caroba leaves, analyzed with this innovative technique, to serve as indicators of air quality.
Why it matches plant phenotyping methods葉のハイパースペクトル画像から汚染誘発変化を抽出・分類するDNAS手法の開発が中心であり、植物状態の画像ベース表現型評価に該当する。
abstractTherefore, the limited number of samples and the high dimensionality of the hyperspectral data make conventional deep learning methods unsuitable, motivating the need for a robust and efficient alternative.
This study aimed to establish an efficient screening method for identifying biocontrol strains effective against soil-borne pathogens of plants. To achieve this objective, we developed a quantitatively-controlled potted plant testing (QC-PPT) system by optimizing cultivation devices, growth substrates, pathogen inoculation methods, and quantitative evaluation of root infection. This design effectively confines the roots and facilitates uniform pathogen infection. The optimal inoculation timing was determined to be between the 10th and 16th day of growth. Furthermore, a substrate composed of vermiculite with 4% organic compost was identified as ideal, supporting vigorous peanut growth while allowing sufficient pathogen infection required for reliable biocontrol evaluation. Using this system, seven strains with strong antagonistic effects against Sclerotium rolfsii were isolated from White-spotted Flower Chafer (WSFC, Protaetia brevitarsis) larval frass. In vitro assays showed that strain X13 inhibited Sclerotium rolfsii growth by 44.56%, while strain X15 performed more excellently in the QC-PPT system: it increased peanut root dry weight by 32.8% and reduced root lesion area by 51.2% compared to the control group. Genomic sequencing data revealed that the superior strain X15 possesses the most diverse set of secondary metabolite biosynthetic gene clusters. Collectively, strain X15 is a promising candidate for biopesticide development, and the QC-PPT system we established can be extended to other crop-soil-borne pathogen systems. This study not only provides an effective biocontrol resource for peanut southern blight but also facilitates the development of sustainable disease management strategies in agriculture.
Why it matches plant phenotyping methods植物根の感染状態を定量評価するQC-PPTシステムを開発・最適化し、根病斑面積などの植物病徴を測定する方法が研究の中心であるため。
abstractwe developed a quantitatively-controlled potted plant testing (QC-PPT) system by optimizing cultivation devices, growth substrates, pathogen inoculation methods, and quantitative evaluation of root infection.
This study presents a novel application of atomic force microscopy (AFM) for characterising the ultrastructure of dry (15 % moisture) native and mature wheat grains (Triticum aestivum L.). A key contribution is the standardisation of a meticulous sample preparation protocol that minimises artefacts. This protocol involves dry-cutting the grains using a device that enables precise surface smoothing via ultramicrotomy, ensuring perfect alignment for AFM scanning without the need for resin-embedding. The research provides a comprehensive histological description ranging from the outer pericarp to the starchy endosperm. The outer layers (pericarp, seed coat, and nucellar epidermis) appear as compact, continuous structures in the dry state, with stronger inter-layer adhesion. The study also discovered a previously undescribed left-handed helical twist in the tube cells of the inner pericarp, a feature that is hypothesised to be lost in conventional resin-embedding techniques. AFM is demonstrated to be a powerful tool for revealing intricate, hydration-dependent ultrastructural adaptations in plant tissues.
Why it matches plant phenotyping methods成熟コムギ粒の組織微細構造という植物形質を対象に、AFM imaging とアーティファクトを低減する試料調製法を中心的に開発・実証しているため、植物フェノタイピング手法として採用する。
abstractThis study presents a novel application of atomic force microscopy (AFM) for characterising the ultrastructure of dry (15 % moisture) native and mature wheat grains (Triticum aestivum L.).
The rapid and precise identification of apple leaf diseases is crucial for minimizing yield loss in precision agriculture. However, many existing deep learning methods struggle to be applicable in real-world settings, are not easily interpretable, and often lack sufficient statistical validation. To address these difficulties, we propose our solution approach LeafSightX . This dual-backbone architecture combines features from DenseNet201 and InceptionV3 using Multi-Head Self-Attention (MHSA) techniques, enhancing representational capability and spatial context reasoning. Our extensive procedure includes specialized preprocessing and limited data augmentation, improving model resilience in many scenarios. Furthermore, LeafSightX integrates explainable AI techniques with Grad-CAM visualizations to improve transparency. In assessments of a five-class apple leaf disease dataset featuring field and laboratory images, LeafSightX demonstrates exceptional performance, attaining a test accuracy of 99.64%, an F1-score of 0.9962, and AUC and PR-AUC scores of 1.000, far surpassing all baseline CNNs. Cross-validated Cohen's Kappa (mean = 0.9917, σ = 0.0020) and AUC (mean = 0.9998) indicate a significant level of predictive consistency. Despite its architectural complexity, the model offers real-time inference capabilities, ensuring per-sample latency suitable for edge device deployment. Additionally, the proposed LeafSightX framework was trained and evaluated on an additional independent apple leaf disease dataset, achieving a test accuracy of 99.69%, demonstrating its robustness and generalization. Our approach is a rigorously evaluated, clear, and highly accurate system for identifying plant diseases, providing a reproducible foundation for the actual application of AI in agriculture.
Why it matches plant phenotyping methodsリンゴ葉の病害状態を画像から識別するCNN手法を開発し、複数データセット・交差検証・ベースライン比較で性能を評価しており、植物病害表現型の取得・推定が中心である。
Reproduction assets foundThe paper uses two public Kaggle apple leaf disease image datasets as its phenotyping inputs; both are directly cited with public URLs. No author analysis code, trained model checkpoints, or supplementary code repository is deposited — the data availability statement only offers contact with corresponding authors.Dataset · publicThis research utilizes the Apple Tree Leaf Disease dataset, collected from Kaggle and made available by Nirmal (Kaggle, 2025).Open asset ↗Kagglehtml-lines:128-184Dataset · publicDhar S. (2023). Apple leaf disease classification dataset. Available online at: https://www.kaggle.com/datasets/showravdhar/apple-disease-dataset (Accessed November 1, 2023).Open asset ↗Kaggle · showravdhar/apple-disease-datasethtml-lines:1449-1484Plant phenotyping relevance match · UnverifiedOpenAlex · Crossref · Europe PMC · checked 5 Sept 2026
With the development of artificial intelligence (AI) in complicated imaging and remote sensing technologies, plant research is transitioning from manual measurements to automated data collecting. High-throughput image-based phenotyping enables the precise and automated acquisition of traits across various spatial and temporal scales, ranging from controlled laboratory settings to intricate field. Furthermore, AI facilitates the combination of satellite observations, unmanned aerial vehicle (UAV) imaging, soil and climate data, and spatiotemporal information to enhance the precision of trait monitoring and yield prediction. These advances enhance the ability to evaluate and predict crop performance under variable environmental conditions. This paper offers a cross-disciplinary paradigm for accurate and sustainable modern agriculture by merging AI methodologies with plant phenotyping and yield forecasting.
Why it matches plant phenotyping methods画像ベース・リモートセンシングによる植物形質取得とAI解析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中心的である。
abstractThis paper offers a cross-disciplinary paradigm for accurate and sustainable modern agriculture by merging AI methodologies with plant phenotyping and yield forecasting.
Quinoa ( Chenopodium quinoa Willd.) is a genetically diverse Andean crop valued for its nutrition and adaptability to varied agro-climatic conditions with potential for cultivation in European and Mediterranean, particularly on marginal lands. Low temperatures during early sowing can impair germination, while delayed sowing increases the risk of poor maturation due to unfavorable autumn weather. To assess the adaptation of quinoa to low temperature conditions, that reflect cold stress, we evaluated germination and phenotypic variation in 60 accessions from highland and coastal ecotypes across three sowing dates in South-Western Germany: late winter (S1), early spring (S2), and spring (S3). Early sowing under low temperature conditions in S1 delayed seedling-emergence and reduced emergence percentages, yet these plants produced the highest average seed yield per plot (64 g) compared to S2 (46 g) and S3 (35 g). Highland accessions showed earlier seedling-emergence and with higher emergence percentages, while coastal types matured earlier and gave higher yields across sowing dates. A complementary laboratory experiment assessed germination under cold (4.4 °C) and control (18.3 °C) conditions, using both manual scoring and image analysis via a Mask R Convolutional Neural Network, to track seedling growth. This confirmed the beneficial germination performance of highland accessions under low temperature conditions, with strong agreement between manual and automated scoring. Our findings suggest that quinoa demonstrates resilience to cold stress with highland quinoa exhibiting superior germination traits, and early sowing, despite reduced emergence, can lead to higher yields. We conclude that combining favorable traits such as faster maturity and higher yield of coastal ecotypes with superior germination traits of highland accessions is a promising avenue for breeding improved quinoa varieties for cold climatic regions.
Why it matches plant phenotyping methodsMask R-CNNによる発芽・幼植物成長の画像解析を手動評価と比較し、強い一致を検証しており、植物表現型取得法の技術的検証を含む。
abstractA complementary laboratory experiment assessed germination under cold (4.4 °C) and control (18.3 °C) conditions, using both manual scoring and image analysis via a Mask R Convolutional Neural Network, to track seedling growth.
Reproduction assets foundThe paper states that all phenotypic data and R analysis scripts are available as supplementary material (publicly hosted with the bioRxiv preprint), while raw seed germination images are only available upon request. No separate repository or trained model checkpoint is named.Dataset · publicData availability: All phenotypic data and R scripts used for the analysis are available
as supplementary material.Open asset ↗pdf-page:1 lines:1-52Code / dataset availability confirmedEurope PMC · checked 5 Sept 2026
Background The plant vacuole arises by orchestrated interplay of membrane trafficking, cytoskeletal rearrangements and a variety of signaling pathways. In the root, the characteristic large central vacuole develops by endomembrane reorganization occurring mainly in the transition zone. The vacuole's bounding membrane-the tonoplast-can be visualized in vivo using fluorescent protein markers, allowing for quantitative analysis of confocal microscopy images. Tonoplast organization can thus serve as a sensitive indicator of changes to any of the processes involved in vacuole biogenesis. The Vacuolar Morphology Index (VMI) is widely accepted as a quantitative measure of vacuole structure. However, this metric has two drawbacks-it only reflects the size of the largest vacuolar compartment (missing therefore possible differences in the organization of smaller compartments), and its determination is labor intensive, limiting its use on large datasets. Results We developed an alternative metric for describing vacuole organization, named the Tonoplast Topology Index (TTI), which overcomes the above-mentioned shortcomings of the VMI. We compared the performance of our protocol with VMI on a simulated dataset and on real data. To validate the methods´ performance, we used it to confirm the previously reported differences in vacuole shape and size between Arabidopsis thaliana roots grown on the surface of an agar medium compared to those embedded inside the agar. Both VMI and TTI could efficiently detect the relatively subtle changes in vacuole organization depending on the position of the root in the agar, and provided correlated results. However, only TTI produced data with close to normal value distribution, simplifying subsequent statistical evaluation. Conclusions We present the protocol for TTI determination as a two-stage semi-automated procedure involving microscopic image analysis employing an ImageJ macro and subsequent processing of numeric data in the Jupyter Notebook environment, together with benchmarking image data. Since this implementation is freeware-based, platform-independent and (relatively) user-friendly, we hope it will find its use as a high throughput, added value alternative to the VMI metric.
Why it matches plant phenotyping methods植物液胞構造を定量化する新規指標と半自動画像解析プロトコルを開発し、シミュレーションおよび実画像で既存指標と比較・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。
abstractWe developed an alternative metric for describing vacuole organization, named the Tonoplast Topology Index (TTI)
Reproduction assets foundThe paper deposits its benchmark confocal image dataset in the EMBL-EBI BioImage Archive (S-BIAD2226) and its TTI analysis software (ImageJ macro and Jupyter/Python scripts) on GitHub, both with explicit public availability statements.Dataset · publicImage data generated and analyzed in the current study are available in the EMBL-EBI BioImage Archive repository, accession number S-BIAD2226Open asset ↗EMBL-EBI BioImage Archive · S-BIAD2226lines:141-163Code · publicArchive copy, additional sample data and possible future updates of the software tool generated here are also available at [ https://github.com/GeorgeCaldarescu/TTI-Tonoplast-Topology-Index ] .Open asset ↗GitHub · GeorgeCaldarescu/TTI-Tonoplast-Topology-Indexlines:141-163Code / dataset availability confirmedOpenAlex · arXiv · checked 13 Sept 2026
Semantic reconstruction of agricultural scenes plays a vital role in tasks such as phenotyping and yield estimation. However, traditional approaches based on manual scanning or fixed camera setups remain a major bottleneck, while active-mapping methods based solely on occupancy grids are too coarse for accurate trait estimation. To address this gap, we propose an active 3D reconstruction framework for horticultural environments using a mobile manipulator. The system integrates OctoMap with 3D Gaussian Splatting to enable accurate and efficient target-aware mapping. A low-resolution OctoMap provides probabilistic occupancy information for informative viewpoint selection and collision-free planning, while 3D Gaussian Splatting leverages geometric, photometric, and semantic information to optimize 3D Gaussians for high-fidelity scene reconstruction. We further introduce a robust mapping strategy that mitigates semantic segmentation and depth noise, together with a background pruning method that reduces memory and computational cost. We validate our framework across simulated, laboratory, and real greenhouse scenes, showing consistent improvements across three state-of-the-art Gaussian Splatting backbones. In simulation, where ground-truth geometry is available, our approach outperforms occupancy-based mapping in both reconstruction accuracy and runtime efficiency: compared with a 0.01m-resolution OctoMap, it doubles the fruit-level F1 score under noisy conditions while achieving up to a threefold reduction in runtime. Beyond simulation, novel-view synthesis quality also improves consistently in laboratory and real greenhouse environments, with PSNR and mIoU improving by up to 1.5 dB and 18%, respectively. Finally, the reconstructed semantic maps enable fruit counting and volume estimation with accuracies approaching 80%.
Why it matches plant phenotyping methods園芸ロボット向けの3D再構成・能動マッピング手法を開発し、果実の計数・体積推定という植物形質の取得に適用・検証しているため、フェノタイピング手法が中心的です。
titleOctoSplat: Hybrid OctoMap-Gaussian Splatting for Active Semantic Mapping and Phenotyping with Horticultural Robots
Reproduction assets foundThe paper's supplementary material is hosted on the authors' public project page (jrcuaranv.github.io/octosplat), and the authors state that all code and data are publicly available. The SimSense repository is a third-party depth-sensor simulator tool, not a paper-specific asset.Code · publicAll code and data are publicly available to facilitate reproducibility.Open asset ↗lines:59-163Plant phenotyping relevance match · UnverifiedEurope PMC · checked 14 Sept 2026
Plants defend against pathogens such as fungi by detecting an attack and initiating both structural and chemical responses. Pathogen perception triggers rapid cytosolic calcium influx, calcium oscillations, and induces defense gene expression, yet the mechanisms by which these or other signals encode the external stressors or propagate signals plant-wide remain unclear. Here, we present a microfluidic system to examine intracellular calcium signals of the moss Physcomitrium patens upon precise and reversible exposure to fungal chitin oligosaccharides. Epifluorescent microscopy of juvenile moss cells expressing the calcium indicator GCaMP6f revealed a rapid, coordinated calcium response to chitin addition, followed by stereotyped oscillations that subsided quickly upon chitin removal. We developed an unbiased image segmentation algorithm to automatically locate regions with cell-specific oscillatory responses, using pixel-based k-means clustering, treating each time point as a separate dimension. Calcium dynamics were distinct across adjacent cells and distinguishable by cell type. Waves were dependent on time of day, adaptation time within the device, and stimulus timing. Cytosolic calcium waves, which rose and fell symmetrically within about 60 s, appeared spontaneously at night and with short adaptation time. Chitin increased wave frequency, amplitude, and duration, and repeated chitin pulses drove regular, plant-wide oscillations at a controlled frequency. This study complements prior investigations of whole plant and growth tip dynamics and provides new methods to comprehensively study calcium signaling in plants, including mechanisms of signal propagation and the role of oscillation frequency on gene expression.
Why it matches plant phenotyping methodsマイクロ流体システム、蛍光イメージング、細胞特異的画像セグメンテーションを開発し、植物の細胞内カルシウム動態という生理状態を定量化することが研究の中心である。
abstractHere, we present a microfluidic system to examine intracellular calcium signals of the moss Physcomitrium patens upon precise and reversible exposure to fungal chitin oligosaccharides.
Abstract Drought stress is a major abiotic factor limiting rice growth and productivity. Establishing scientifically screening methods at the germination stage is critical for identifying superior drought-adapted genotypes. This study aimed to develop a novel discriminant equation for evaluating drought resistance in rice at the germination stage, with field yield data used as a benchmark to calibrate laboratory-based assessments. Seventy-six rice accessions from southern China were used as experimental materials. Drought resistance during germination was assessed using nine key physiological indicators, including vigor index and root length. Three statistical methods—Membership Function Comprehensive Evaluation Value (MFSV), Principal Component Analysis Comprehensive Evaluation Value (PCASV), and Grading Coefficient (GC)—were applied to classify drought resistance levels from different analytical perspectives. Field drought treatment was conducted throughout the growth period, and final grain yield was used to represent the comprehensive drought resistance (CDR) of each accession. Results showed that the nine indicators responded variably to drought stress. Drought resistance from the three statistical methods were inconsistent, with only 45% of accessions showing consistent classification across all methods, indicating that the choice of analytical approach influences the outcome. Correlation analysis revealed that MFSV, PCASV, and GC were all positively correlated with CDR, supporting the use of CDR as a reliable reference factor for integrating laboratory-based evaluations. Finally, a Fisher discriminant function was established using MFSV, PCASV, and GC as independent variables (X) and CDR as the dependent variable (Y), providing a more comprehensive and scientifically grounded method for assessing drought resistance during rice germination.
Why it matches plant phenotyping methodsイネの発芽期の乾燥抵抗性を評価するスクリーニング手法と識別関数の開発が研究の中心であり、圃場収量を基準に検証している。
abstractEstablishing scientifically screening methods at the germination stage is critical for identifying superior drought-adapted genotypes.
Pine wilt disease (PWD), caused by the pine wood nematode (PWN) Bursaphelenchus xylophilus , is a globally destructive threat to coniferous forests, causing severe ecological and economic losses. Conventional resistance breeding is critically hampered by long life cycles of trees and field evaluation challenges. To address these limitations, we developed a three-tier biotechnology pipeline with a dual-output goal (generating both resistant germplasm and mechanistic insights) designed to bridge the in vitro-field gap. This strategy is founded upon the resolution of a longstanding pathogenesis debate, which established aseptic PWNs as a standardized research tool. The pipeline integrates high-throughput in vitro cellular screening (Tier 1), whole-plant validation via organogenesis (Tier 2), and scaled production coupled with mechanistic investigation through somatic embryogenesis (Tier 3). Tier 1 enables rapid phenotypic screening, Tier 2 validates resistance in whole plants, and Tier 3 facilitates mass production and in-depth study. It operates as a closed-loop, knowledge-driven system, simultaneously accelerating PWN-resistant germplasm development and empowering molecular mechanism discovery. Validated across Pinus massoniana and P. densiflora , this work provides a concrete, community-usable model system that directly addresses a core methodological bottleneck in forest pathology. This strategy effectively bridges the in vitro-field gap, offering a replicable model for perennial crop breeding and contributing to resilient forest management.
Why it matches plant phenotyping methodsマツ萎凋病抵抗性を評価する三層型の高速スクリーニング・全植物検証パイプライン自体が中心であり、植物の抵抗性表現型の取得と検証を技術的に扱っている。
abstractwe developed a three-tier biotechnology pipeline with a dual-output goal
This study employed an HY-6010-S hyperspectral imaging system, covering a spectral range of 400-1000 nm, combined with an RGB industrial camera to acquire multimodal data. The dataset simulates phenotypic analysis scenarios of maize seeds under controlled laboratory conditions, with the ambient temperature maintained at 20-25°C. Comprehensive testing was conducted using 12 different maize varieties. Approximately 200 seed samples were collected per variety, resulting in a total sample size of about 2400, each subjected to hyperspectral and RGB image acquisition. Preprocessing steps included noise reduction, background removal, band selection, and modality alignment. To ensure the accuracy and reliability of the experimental data, HHIT software and Python were utilized for data processing. This dataset plays a significant role in seed variety classification, phenotypic analysis, precision agriculture, and machine learning applications.
Why it matches plant phenotyping methodsトウモロコシ種子のマルチモーダル画像を収集・前処理した再利用可能なデータセットであり、種子の表現型解析を主要目的としているため、フェノタイピング手法・データセット研究に該当する。
abstractThis study employed an HY-6010-S hyperspectral imaging system, covering a spectral range of 400-1000 nm, combined with an RGB industrial camera to acquire multimodal data.
Reproduction assets foundThe paper is a Data in Brief article depositing its own multimodal maize seed hyperspectral and RGB image dataset (2400 seeds, 12 varieties) on Mendeley Data, with a direct public URL and DOI given in the article.Dataset · publicRepository name: Mendeley Data
Data identification number: doi: 10.17632/4n4xbnx8sr.1
Direct URL to data: https://data.mendeley.com/datasets/4n4xbnx8sr/1Open asset ↗Mendeley Data · 10.17632/4n4xbnx8sr.1html-lines:1-110Code / dataset availability confirmedEurope PMC · OpenAlex · Crossref · checked 15 Sept 2026
Background Root hairs play a key role in plant nutrient and water uptake. Historically, root hair traits have largely been quantified manually. As such, this process has been laborious and low-throughput. However, given their importance for plant health and development, high-throughput quantification of root hair morphology could help underpin rapid advances in the genetic understanding of these traits. With recent increases in the accessibility and availability of artificial intelligence (AI) and machine learning techniques, the development of tools to automate plant phenotyping processes has been greatly accelerated. Results We present pyRootHair, a high-throughput, AI-powered software application to automate root hair trait extraction from microscope images of plant roots grown on agar plates. pyRootHair is capable of batch processing over 600 images per hour without manual input from the end user. In this study, we deploy pyRootHair on a panel of 24 diverse wheat (Triticum aestivum and Triticum turgidum ssp. durum) cultivars and uncover a large, previously unresolved amount of variation in many root hair traits. We show that the overall root hair profile falls under 2 distinct shape categories and that different root hair traits often correlate with each other. We also demonstrate that pyRootHair can be deployed on a range of plant species, including oat (Avena sativa), rice (Oryza sativa), teff (Eragrostis tef), and tomato (Solanum lycopersicum). Conclusions The application of pyRootHair enables users to rapidly screen a large number of plant germplasm resources for variation in root hair morphology, supporting high-resolution measurements and high-throughput data analysis. This facilitates downstream investigation of the impacts of root hair genetic control and morphological variation on plant performance. pyRootHair is installable via PyPI (https://pypi.org/project/pyRootHair/) and can be accessed on GitHub at https://github.com/iantsang779/pyRootHair.
Why it matches plant phenotyping methods植物根毛形態を顕微鏡画像から自動抽出するAIソフトウェアを開発し、複数作物で適用・実証しており、表現型取得手法が研究の中心である。
abstractWe present pyRootHair, a high-throughput, AI-powered software application to automate root hair trait extraction from microscope images of plant roots grown on agar plates.
Reproduction assets foundThe paper's root hair phenotyping software (pyRootHair) is publicly available on GitHub and PyPI, the data and notebooks used to generate the manuscript figures are deposited in the repository's paper_data folder, and the software is annotated in the DOME-ML registry. The GigaDB deposit (10.5524/102771) is referenced,但Code · publicregression lines were computed using statsmodels (v0.14.4). Scikit-learn (v.1.5.2) was used for quality control of segmented images. nnU-Netv2 (v2.5.1) was used to create the image segmentation model with PyTorch (v.2.5.1) and CUDA (v.12.6).
Availability of Source Code and Requirements
Project name: pyRootHair
Project homepage: https://github.com/iantsang779/pyRootHair
Operating system(s): Linux, MacOS, Windows
Programming language: Python
License: MIT License
Supplementary Material
giaf141_Supplemental_File giaf141_Authors_Response_To_Reviewer_Comments_Original_Submission giaf141_GIGA-D-25-00279_Original_Submission giaf141_GIGA-D-25-00279_Revision_1 giaf141_Reviewer_1_Report_Original_SubmisOpen asset ↗github.com/iantsang779/pyRootHairlines:250-287Dataset · publicThe source jupyter notebook and data used to generate all figures in the manuscript have been deposited on GitHub [ 39 ].Open asset ↗lines:400-405Code · publiclarge number of plant germplasm resources for variation in root hair morphology, supporting high-resolution measurements and high-throughput data analysis. This facilitates downstream investigation of the impacts of root hair genetic control and morphological variation on plant performance. pyRootHair is installable via PyPI ( https://pypi.org/project/pyRootHair/ ) and can be accessed on GitHub at https://github.com/iantsang779/pyRootHair .
Keywords: root hairs, plant phenotyping, machine learning, computer vision, AI, U-Net, wheat, roots, software
status released display-pdf yes is-olf no is-manuscript no is-preprint no is-journal-matter no is-scanned no is-retracted no
Received 2025 JOpen asset ↗lines:1-34Plant phenotyping relevance match · UnverifiedOpenAlex · checked 14 Sept 2026
Abstract Plant phenomics is an emerging discipline that uses image analysis to extract quantitative phenotypic data to understand plant growth and development. However, phenomics tools often require a precise format of image acquisition that is set during software development or that is part of a proprietary software environment. To remove these barriers and align with NASA's Transform to Open Science initiative, we have created a web-based application to measure plant aerial phenotypes called Simple Online Automated Plant Phenomics (SOAPP). SOAPP uses two open-source Python packages, PlantCV and OpenCV, and is available either online as a web application or can be run locally from a Docker image. Users simply upload their images, select sample-specific color spaces, and specify regions of interest. Foliage size, shape characteristics, and color values are then automatically extracted. SOAPP has been successfully used to characterize plant growth in hydroponic systems, pots, and Petri plates. ArUco machine-recognizable tags further allow automated scale-finding, image plane correction, and color standardization and correction. These adjustments for variations in the distance and axis at which the images were taken greatly enhance the quantitative accuracy of data extracted from hand-held crew photography, enhancing science return from both current and future spaceflight settings.
Why it matches plant phenotyping methods植物画像から葉面積・形状・色を抽出するウェブ型フェノタイピング手法SOAPPの開発と精度向上が中心であり、明確な方法開発・プラットフォーム研究である。
abstractwe have created a web-based application to measure plant aerial phenotypes called Simple Online Automated Plant Phenomics (SOAPP).
Highlights This article proposes ISCF, a novel method for precise soybean pod and seed counting using a segmentation followed by a classification strategy. Compared to YOLO, ISCF offers faster inference, higher accuracy, and a more efficient pipeline for real-time applications. The proposed method focuses on the practical value of the lightweight design, making it deployable on edge devices for real-world use. The proposed method applies to the automated counting of seeds or fruits of various crops in controlled indoor environments, demonstrating strong generalizability and adaptability. Abstract. Accurate counting of soybean pods and seeds is essential for yield prediction, crop management, and variety improvement. However, existing automatic methods under controlled indoor conditions often exhibit limited computational efficiency, insufficient accuracy, and limited practical deployment for reducing manual workload. To address this, we propose an Indoor Soybean Counting Framework (ISCF), a lightweight deep learning framework that decouples localization and classification into two independent stages to count soybean pods and seeds. ISCF first performs precise segmentation of soybean pods using the proposed Indoor Soybean Segmentation Network (ISSN), followed by classification of the number of seeds per pod using a MobileNetV3-based architecture. Optimized for lightweight design, ISCF is well-suited to real-time deployment on edge devices. Experimental results demonstrate the superior performance of ISCF in soybean pod and seed counting tasks, achieving an AP 50 of 99.5% for pod segmentation, a mean absolute error (MAE) of merely 0.72, and an R 2 of 0.9942 for pod counting, and an MAE of 3.79 and an R 2 of 0.9573 for seed counting. Moreover, ISCF generalizes well to datasets from four additional crop species, underscoring its potential for a broad range of indoor crop counting and phenotyping applications. Keywords: Image classification, Image recognition, Instance segmentation, Lightweight network, Plant phenotyping, Soybean counting.
Why it matches plant phenotyping methods植物の莢・種子数という収量関連形質を画像から自動抽出する軽量深層学習フレームワークを開発・評価しており、表現型取得手法が中心である。
abstractwe propose an Indoor Soybean Counting Framework (ISCF), a lightweight deep learning framework that decouples localization and classification into two independent stages to count soybean pods and seeds
Highlights A screw-conveyor-based machine vision system was evaluated for online apple grading. The developed vision pipeline enabled multi-view-based size estimation and comprehensive surface defect inspection. Apple sizing accuracy exceeded 95.9% across conveyor speeds of 1–2 apples s-1 per conveyor lane. The system achieved sample-level grading accuracies of up to 95.4%, demonstrating its potential for further integration and in-field validation. ABSTRACT. Apple quality grading is a critical operation in postharvest handling; however, most existing grading systems are designed for controlled packinghouse environments rather than in-field operation at harvest, which can achieve substantial cost savings for both growers and packers and improve postharvest inventory management. To address the need for in-field apple grading technology, building on our prior work, this study developed a machine vision-based apple grading system toward in-field sorting by integrating a screw-conveyor-based fruit handling mechanism with automated defect inspection and size estimation. The system enables continuous fruit transportation and rotation, allowing multi-view image acquisition for comprehensive surface assessment. The vision module comprises an enclosed image chamber equipped with uniform LED illumination and a top-mounted RGB-D (red-green-blue-depth) camera, ensuring stable and consistent quality of acquired imagery. A computer vision-based pipeline was developed to detect, track, and segment individual apples for surface defect evaluation and sizing. Multi-view images acquired during fruit rotation were fused to achieve full surface coverage. In addition, a geometry-based diameter estimation method integrating stem/calyx-aware boundary localization was introduced to improve fruit sizing robustness and accuracy. Experimental results demonstrate diameter estimation accuracy exceeding 95.9% and maintained sample-level grading accuracies of 95.4%, 94.2%, and 92.3% at conveyor speeds of 1, 1.5, and 2 apples s -1 per lane, respectively. These results demonstrate that the proposed system can support rapid apple grading and provide a practical step toward in-field fruit sorting. Both the dataset and software programs of this study has been made publicly available. Keywords: Apple, In-field grading, Machine vision, Multi-view imaging, Online inspection.
Why it matches plant phenotyping methodsリンゴのサイズ推定と表面欠陥評価を行う画像ベースのオンライン表現型取得・選別システムを開発し、精度検証しているため、植物フェノタイピング手法が中心である。
abstractA computer vision-based pipeline was developed to detect, track, and segment individual apples for surface defect evaluation and sizing.
Sieve elements in the phloem transport carbon and small molecules, such as RNA and phytohormones, throughout the plant body. Understanding the physical dimensions of sieve elements and phloem tissue is thus crucial for predicting how much carbon can be moved at any given time. Quantification of sieve element diameters and areas has previously been performed using transmission electron microscopy, scanning electron microscopy, and light microscopy, but sieve element identification is difficult because the phloem is a heterogeneous tissue. The recently identified LM26 antibody labels a pectin in the sieve element cell wall, allowing the identification of sieve elements and the measurement of their properties, such as diameter, relatively quickly and the quantification of their number in cross sections using image analysis software. Here, we describe methods for immunolabelling sieve elements in fresh or fixed tissue embedded in polyethylene glycol or methacrylate. The protocol is broadly adaptable to various fixation and sectioning methods, provided they do not alter the structure of pectins in the cell wall.
Why it matches plant phenotyping methods師部篩要素の同定、画像解析による直径・面積・数の定量を可能にする免疫標識プロトコルが中心で、植物形態形質の取得手法を提供している。
abstractallowing the identification of sieve elements and the measurement of their properties, such as diameter, relatively quickly and the quantification of their number in cross sections using image analysis software.
Chickpea (Cicer arietinum L.), confronts substantial challenges from the emerging pathogenic fungus Macrophomina phaseolina (Tassi) Goid, causing dry root rot (DRR) disease. Chickpea plants severely affected by combined DRR and drought stress. Currently sick plot and sick pot method are utilized for germplasm screening to identify tolerant genotypes. These methods are time-consuming; therefore, we propose a novel methodology for the rapid screening of chickpea under combined DRR and osmotic stress conditions. This chapter introduces an adept high-throughput phenotyping methodology, conducted within controlled laboratory conditions, aiming to investigate the interaction between osmotic stress and DRR disease in chickpea crops. The methodology employs an innovative pouch technique for screening combined stress, providing a streamlined temporal investigation process and precise control over stress parameters. The incorporation of polyethylene glycol (PEG) enables the simultaneous imposition of osmotic stress alongside pathogen infection, making the methodology versatile for studying combined stress scenarios. This approach fills a gap in concurrent stress imposition techniques, enhancing germplasm screening by identifying genotypes with varying susceptibility and resistance levels. Thus, we suggest use of high-throughput phenotyping in combination genome-wide association study (GWAS) can take combined stress resistance breeding in chickpea at next level to combat food security and climate change.
Why it matches plant phenotyping methodsヒヨコマメの乾燥根腐病と浸透圧ストレスに対する耐性を迅速・高スループットに評価する新規ポーチ法を中心に開発しており、表現型スクリーニング手法が研究の中核である。
abstracttherefore, we propose a novel methodology for the rapid screening of chickpea under combined DRR and osmotic stress conditions.
This protocol outlines a reproducible method for assessing the virulence of Ustilago maydis strains in Zea mays seedlings through targeted syringe inoculation of the leaf whorl. Using seedlings at the V3-V4 growth stage ensures optimal susceptibility and developmental uniformity. The method can be used with both solopathogenic strains and compatible mating-type combinations, enabling comparative analyses of infection efficiency and symptom development. Fungal cultures are prepared under controlled conditions to maintain virulence, and post-infection humidity is regulated to enhance colonization success. Symptom progression is monitored over 10-12 days and quantified using an ordinal disease scoring system. This assay provides a robust tool for evaluating effector mutants, analyzing host-pathogen interactions, and comparing strain virulence under standardized conditions.
Why it matches plant phenotyping methodsトウモロコシ苗の感染症状を標準化して評価・スコア化する再現可能な表現型測定プロトコルが中心であり、単なる病原体検出ではない。
abstractThis protocol outlines a reproducible method for assessing the virulence of Ustilago maydis strains in Zea mays seedlings through targeted syringe inoculation of the leaf whorl.
RiceLaboratory / benchtopSeed / grain2D/3D reconstructionVisualization / data management
The distribution of inorganic elements in brown rice has been vigorously investigated for many years using the most advanced instruments of each era. The present study was a challenge to gain new insights into the distribution of various inorganic elements in brown rice by autoradiography using radioisotopes: 22Na, 45Ca, 54Mn, 55Fe, 60Co, 63Ni, 65 Zn, 90Sr, 203 Hg, and 210 Pb. Autoradiography of tissue sections using the Imaging Plate (IP) fully exploited its advantage of high-throughput imaging, enabling three-dimensional reconstruction that encompassed the entire brown rice grain. Consequently, characteristic distribution patterns of individual elements in the peripheral layer, endosperm, and embryo were identified following radiotracer supplementation to the culture solution. For instance, 63Ni was uniformly distributed within the endosperm during the early stages of development but progressively accumulated in the outer layers and embryo as growth advanced; such a pattern was not observed for 54Mn or 55Fe. To minimize the cost of the experiment, a direct injection method into the node was developed. This approach successfully visualized 203 Hg, demonstrating that its entry into the embryonic tissue is severely restricted irrespective of the developmental stage of the rice grain.
Why it matches plant phenotyping methods褐色米粒を対象に、オートラジオグラフィーとイメージングプレートで元素分布を高スループットに可視化し、三次元再構成する測定手法を中心に扱っているため、植物器官の状態を抽出するフェノタイピング手法として含める。
abstractThe present study was a challenge to gain new insights into the distribution of various inorganic elements in brown rice by autoradiography using radioisotopes
Fusarium wilt poses a significant threat to chickpea cultivation, causing substantial yield losses. Developing resistant chickpea varieties is a crucial strategy for managing this devastating disease. Screening a large number of germplasm and breeding lines against the pathogen is necessary to achieve this goal. In this context, the seedling root dip method has emerged as an effective technique to differentiate between resistant and susceptible chickpea genotypes. This method offers the advantages of screening a large number of lines within a short time frame and limited space. Another critical aspect of breeding for disease resistance is the rapid and accurate identification of the pathogen. Traditional pathogen detection methods are labor-intensive and time-consuming. This chapter presents a detailed protocol for the seedling root dip method, enabling the screening of chickpea genotypes against Fusarium oxysporum. Additionally, a rapid approach utilizing ITS primers for identifying the pathogen is discussed, providing a precise and expedient tool for disease resistance breeding efforts.
Why it matches plant phenotyping methods根浸漬法を用いてヒヨコマメ遺伝子型のFusarium萎凋病抵抗性を識別・スクリーニングする詳細プロトコルが主題であり、植物の病害状態を取得する表現型評価法として中心的です。ITSによる病原体同定は分子診断ですが、抵抗性表現型スクリーニング自体が主要な方法的貢献です。
abstractthe seedling root dip method has emerged as an effective technique to differentiate between resistant and susceptible chickpea genotypes
In the context of climate change, the global rise of temperature and intense heat waves affect plant development and productivity. In order to decipher the molecular and physiological mechanism established by plants to adapt to increased temperatures, we and others have designed different high-temperature regimes to mimic as much as possible temperature variations occurring in natura. This chapter outlines these thermotolerance assays employed to assess response to high temperature in Arabidopsis thaliana. We provide detailed guidelines, including plant age considerations and timing of heat application. Moreover, we introduce new findings showing that the addition of sucrose to the growth medium can artificially enhance thermotolerance, potentially masking stress-related phenotypes. These assays, which measure both basal and acquired thermotolerance, offer a framework for assessing plant heat stress responses in a reproducible and efficient manner. To illustrate some plant responses to these different regimes, we compare the response of mutants affected in the biosynthesis of the redox buffer glutathione with wild-type plants.
Why it matches plant phenotyping methods植物の高温耐性を評価する再現可能なアッセイの設計・ガイドライン・検証が中心であり、単なる生物学的実験の routine 測定ではない。
abstractThis chapter outlines these thermotolerance assays employed to assess response to high temperature in Arabidopsis thaliana.
Phytoalexins are plant secondary antimicrobial compounds that are rapidly and locally accumulated de novo upon pathogen attacks. They are strongly correlated with disease resistance; therefore, the timing and the location of their synthesis and accumulation have been explored transcriptionally and metabolically using various means separately. In this chapter, by focusing on the Arabidopsis camalexin (CA), we describe protocols for multimodal in situ detection of CA and elemental distribution, as well as the transcriptionally active region of its synthesis gene PHYTOALEXIN DEFICIENT 3 (PAD3) within the same leaf sample challenged with a pathogen.
Why it matches plant phenotyping methods病原体応答に関わる植物の防御状態を、同一葉で多モーダルに可視化・測定するプロトコルが研究の中心であり、単なる生物学実験の routine 測定ではない。
abstractwe describe protocols for multimodal in situ detection of CA and elemental distribution, as well as the transcriptionally active region of its synthesis gene PHYTOALEXIN DEFICIENT 3 (PAD3) within the same leaf sample challenged with a pathogen.
ABSTRACT Sample preparation is an important first step to obtain high quality mass spectrometry imaging (MSI) data. Preparing plant tissues is especially challenging for MSI of thin tissues along the lateral dimensions. The unique challenges involved with plant tissues, such as fragile cell walls, hydrophobic barriers, and specific tissue structures, often lead to inefficiency and difficulties in sample preparation. Imprinting plant tissues onto porous polytetrafluoroethylene (pPTFE) sheet has been widely used to extract internal metabolites in leaves and petals while keeping spatial resolution for MSI. However, pressure applications were typically made manually using a vise or pliers leading to low reproducibility and resolution in MS images. In this study, we introduce a home‐built pneumatic press (PNP) that has been designed to precisely control the pressure application parameters during imprinting. To evaluate the performance of the new device, Lemna minor fronds, Arabidopsis thaliana , and Bacopa monnieri leaves were imprinted onto the pPTFE with PNP, vise, or pliers, and matrix‐assisted laser desorption/ionization (MALDI) MSI was obtained on the imprints. The PNP showed dramatic improvements in reproducibility and image quality compared to manual pressure application tools.
Why it matches plant phenotyping methods植物組織の空間的な代謝物情報を再現性よく取得するための空気圧式インプリンティング装置を開発し、手動法と性能比較している。植物表現型取得に関わる試料調製・イメージング手法が中心である。
abstractIn this study, we introduce a home‐built pneumatic press (PNP) that has been designed to precisely control the pressure application parameters during imprinting.
Reproduction assets foundThe paper's MALDI-MSI data (imzML files of imprinted Lemna minor, Arabidopsis, and Bacopa tissues) are openly deposited in a paper-specific METASPACE project, as stated in the Data Availability Statement. No author analysis code or trained models are disclosed.Dataset · publicData Availability Statement
The data that support the findings of this study are openly available in METASPACE (https://metaspace2020.eu/project/pnp_ptfe_imprinting_plant).Open asset ↗METASPACE · pnp_ptfe_imprinting_planthtml-lines:230-307Code / dataset availability confirmedCrossref · checked 5 Sept 2026
Callus induction is a complex procedure in plant organ, cell, and tissue culture that underpins processes such as metabolite production, regeneration, and genetic transformation. It is important to monitor callus formation alongside subjective evaluations, which require labor-intensive care. In this research, the first curated lentil (Lens culinaris) callus dataset for instance segmentation was experimentally generated using three genotypes as one data set: Firat-87, Cagil, and Tigris. Leaf explants were cultured on MS medium fortified with different concentrations of gross regulators of BA and NAA to induce callus formation. Three biologically relevant stages, the leaf stage, the green callus, and the necrosis callus, were produced. During this process, 122 high-resolution images were obtained, resulting in 1185 total annotations across them. The dataset was evaluated across four successive generations (v5/7/8/11) of YOLO deep learning models under identical conditions using mAP, Dice coefficient, Precision, Recall, and IoU, together with efficiency metrics including parameter counts, FLOPs, and inference speed. The results show that anchor-based variants (YOLOv5/7) relied on predefined priors and showed limited boundary precision, whereas anchor-free designs (YOLOv8/11) used decoupled heads and direct center/boundary regression that provided clear advantages for callus structures. YOLOv8 reached the highest instance segmentation precision with mAP50@0.855, while it matched the accuracy with greater efficiency and achieved real-time inference with 166 FPS.
Why it matches plant phenotyping methods植物組織培養におけるカルスの形成段階・壊死状態を画像からインスタンスセグメンテーションする手法、データセット、モデル比較を中心に扱っており、植物状態の取得・定量化が本研究の主要な技術貢献である。
titleReal-Time Callus Instance Segmentation in Plant Tissue Culture Using Successive Generations of YOLO Architectures
Reproduction assets foundThe paper's lentil callus image dataset with annotations (122 images, 1185 annotations) is publicly available on Roboflow Universe per the Data Availability Statement. The YOLOv5 GitHub link and Ultralytics docs are generic third-party libraries, not authors' analysis code, and the FAO link is a cited reference, so allDataset · publicThe dataset used in this study, including annotated images for callus detection, is publicly available and can be accessed at Roboflow Universe: https://universe.roboflow.com/yunus-7v2b5/callus-hug7d , accessed on 13 September 2025. This repository contains all images and annotations generated and analyzed during the current study.Open asset ↗Roboflow Universe · callus-hug7dlines:314-345Plant phenotyping relevance match · UnverifiedEurope PMC · Crossref · checked 5 Sept 2026
Abstract Background Accurate assessment of plant nitrogen status is essential for optimizing fertilizer inputs, increasing productivity, and ensuring environmental quality. This study compared three nitrogen status assessment in corn ( Zea mays L.): visual assessment method, a novel real-time nutrient estimation using the Picketa LENS™ system, and the conventional laboratory tissue analysis as a reference method. To evaluate the accuracy of the Picketa LENS™ system, a field experiment with four nitrogen treatments (0% nitrogen (control), 80% nitrogen, 100% nitrogen, and 100% nitrogen + stabilizer) and four replications was conducted in York County, Nebraska. Results Visual assessment detected treatment differences, with the 0% nitrogen plots showing severe chlorosis and a progressive decline (63.4% reduction) in healthy leaves below the ear over five weeks, whereas 100% nitrogen maintained consistently higher healthy leaf counts (only a 7.4% reduction). However, visual assessment showed limited ability to distinguish between 80% nitrogen and 100% nitrogen + stabilizer treatments until weeks four and five. Both quantitative methods did not detect treatment differences due to the sampled leaf position. The Picketa system (2024 corn model) reported higher absolute nitrogen concentrations (approximately 4.4–4.6%) than laboratory analysis (2.9–3.2%) across all treatments and did not detect significant treatment effects. Conventional laboratory analysis detected only a modest increase in the 100% nitrogen treatment compared with the 0% control. For macronutrients, the Picketa system measured concentrations higher than conventional tissue sampling for phosphorus, potassium, and calcium, with potassium showing approximately 40% higher values and calcium showing 40–50% higher values, while magnesium and sulfur showed close agreement between methods. Micronutrient analysis revealed that the Picketa system consistently reported higher concentrations than conventional tissue sampling for iron (45% higher), manganese (approximately 4-fold higher), copper (90% higher), and zinc (33% higher), but reported significantly lower boron concentrations (67% lower). Despite these absolute value differences, both methods demonstrated similar patterns of detection across treatments. Conclusions Visual assessment effectively detected treatment differences, while the Picketa System and the conventional method did not, but maintained similar patterns. These findings highlight the promise of Picketa LENS and the importance of matching sample positions and timing to diagnostic objectives. Integrating real-time sensing with conventional methods can improve nitrogen management decisions.
Why it matches plant phenotyping methodsトウモロコシの窒素状態を測定するリアルタイムセンサー法を従来法と比較し、精度や処理差の検出性能を評価しているため、植物形質測定法の技術検証が中心である。
abstractThis study compared three nitrogen status assessment in corn ( Zea mays L.): visual assessment method, a novel real-time nutrient estimation using the Picketa LENS™ system, and the conventional laboratory tissue analysis as a reference method.
Introduction. Chlorophyll plays a crucial role in absorbing and transforming light energy into a chemical form that provides organic matter production in plants. Monitoring of chlorophyll content helps to assess plant-environment interactions and the degree of influence of stress factors that are essential for yield management. Traditional laboratory methods of analyzing are time-consuming, destroying samples and unsuitable for rapid field evaluations. A more reasonable solution is to use lowcost, portable devices. Aim of the Study. The study is aimed at developing and training an ANN architecture to predict the chlorophyll content in plant leaves based on their optical density within specific visible spectrum ranges. Materials and Methods. The artificial neural network dataset was compiled from experi- mental measurements using the DP-1M densitometer and the CCM-200 chlorophyll meter. Data were collected from lettuce, pepper, tomato and zucchini leaves of different ages, which were grown in different light environments. The artificial neural network training was carried out in the Google Colab environment with subsequent adaptation of the model for using in a microcontroller device – a photocolorimeter for leaves. Results. The dataset with 1,000 entries showed that the leaf optical density range isfrom 0.57 to 2.54 relative units (red), from 0.9 to 1.66 relative units (green), and from 1.09 to 3.53 relative units (blue). According to these data, the chlorophyll content variations are from 3.1 to 156.5 relative units. In the study, there were compared six artificial neural network architectures that differed by hidden-layer neurons. The structure “32:32” had the highest accuracy (MAE = 6.64 rel. units, MAPE = 16.34%, R² = 0.8886). A simplified structure “4:4” was selected to simplify the model and improve the microcontroller efficiency. This structure maintained the performance (MAE = 6.83 rel. units, MAPE = 16.86%, R² = 0.8808) with much smaller amount of resources used – 41 weight parameters and 164 bytes of memory. A comparative evaluation with classical machine learning algorithms demonstrated the superiority of the developed model across all metrics. Discussion and Conclusion. The trained artificial neural network was implemented on a microcontroller-based photocolorimeter for leaves that enabled the non-destroying optical density measurements. The developed model allows implementing non-destroying and operational monitoring of the condition of plants, which is especially important in precision farming systems. This approach has significant potential for ecological monitoring and precision agriculture. The study results demonstrate the viability of machine learning for improving plant status assessment and developing digital agrotechnology solutions.
Why it matches plant phenotyping methods葉の光学密度からクロロフィル含量を推定するANNとマイコン実装型フォトカラリメータを開発・比較評価しており、植物形質取得が研究の中心です。
abstractThe study is aimed at developing and training an ANN architecture to predict the chlorophyll content in plant leaves based on their optical density within specific visible spectrum ranges.
Plants move chloroplasts in response to light, changing the optical properties of leaves. Low irradiance induces chloroplast accumulation, while high irradiance triggers chloroplast avoidance. Chloroplast movements may be monitored through changes in leaf transmittance and reflectance, typically in red light. We present a step-by-step procedure for the detection of chloroplast positioning using reflectance hyperspectral imaging in white light. We show how to employ machine learning methods to classify leaves according to the chloroplast positioning. The convolutional network is a method of choice for the analysis of the reflectance spectra, as it allows low levels of misclassification. As a complementary approach, we propose a vegetation index, called the Chloroplast Movement Index (CMI), which is sensitive to chloroplast positioning. Our method offers a high-throughput, contactless way of chloroplast movement detection. Key features • Protocol for detached leaves handled in laboratory conditions. • Based on differential (dark-adapted versus irradiated) hyperspectral images of plant leaves. • Data analysis includes machine learning methods and the calculation of a vegetation index. • Requires irradiation equipment apart from the hyperspectral camera set.
Why it matches plant phenotyping methods葉の反射ハイパースペクトル画像から葉緑体位置を検出・分類する手法と指標を開発し、高スループット測定として提示しており、植物表現型取得が中心である。
abstractWe present a step-by-step procedure for the detection of chloroplast positioning using reflectance hyperspectral imaging in white light.
Reproduction assets foundThe protocol explicitly deposits its authors' analysis code (HyperspectralImageProcessing.m, including the pretrained CNN classifier for chloroplast positioning) on GitHub and makes the original hyperspectral images of Arabidopsis and Nicotiana leaves used in the paper's figures available on figshare. Both are paper-‐Code · publicAll code has been deposited to GitHub: https://github.com/plantPhotobiologyLab/machine-learning-for-chloroplast-movement-detection (access date, 08/18/2025)Open asset ↗plantPhotobiologyLab/machine-learning-for-chloroplast-movement-detectionhtml-lines:104-130Dataset · publicOriginal files with hyperspectral images of Nicotiana benthamiana and Arabidopsis thaliana (WT and phot2) leaves, including recordings shown in Figure 3 and Figure 4 of this protocol, can be downloaded from https://figshare.com/articles/dataset/Hyperspectral_images_of_Arabidopsis_thaliana_and_Nicotiana_benthamiana_leaves/30402409?file=58898569Open asset ↗html-lines:104-130Plant phenotyping relevance match · UnverifiedCrossref · checked 15 Sept 2026
Published18 Dec 20252025 2nd Beyond Technology Summit on Informatics International Conference (BTS-I2C)Cited by 0 · OpenAlex ↗
Leaf area is a key indicator of plant health and development. However, manual measurement is time-consuming and labor-intensive, especially when monitoring aeroponic-grown potato plants with multiple leaves over extended periods. This study applied a computer vision-based system to automate leaf growth monitoring using the YOLO (You Only Look Once) v8 framework. A dataset was collected from a controlled aeroponic system: 25 images of young leaves (4,869 individual leaf segments) and 35 of mature leaves (12,368 segments). Based on evaluation of various YOLOv8 model configurations, the best model achieved a mask mAP@50 of 0.396 and 0.250 for young leaves and mature leaves, respectively. The challenge to track the mature canopy was due to severe leaf occlusion and self-similarity in dense foliage. Despite the challenge, this study demonstrates proof of concept for tracking early leaf growth and highlights the significant computer vision challenges posed by dense, mature canopies in aeroponic systems.
Why it matches plant phenotyping methods植物の葉面積・葉成長をコンピュータビジョンで自動追跡する手法の開発と評価が中心であり、植物表現型の取得方法を直接扱っている。
abstractThis study applied a computer vision-based system to automate leaf growth monitoring using the YOLO (You Only Look Once) v8 framework.
Successful establishment and growth of constructed saltmarshes can be evaluated through consistent monitoring of plant biophysical parameters, such as aboveground biomass and leaf area index. Monitoring during the early establishment stage is vital for ensuring the long-term effectiveness of constructed saltmarshes in delivering anticipated ecosystem services, including wave energy dissipation, which strongly depends on vegetation biophysical characteristics. Efficient, low-disturbance methods are needed for the successful adoption of such monitoring plans. This study combines laboratory measurements and remote sensing observations to evaluate the performance of vegetation indices in capturing changes in aboveground biomass, leaf area index, and wave energy dissipation in a constructed saltmarsh. Allometric equations were also investigated to predict aboveground biomass from non-destructive plant traits. Results showed acceptable correlations between vegetation indices, measured biophysical parameters and wave energy dissipation characteristics. All species performed better with NIR-R-based indices for leaf area index, while aboveground biomass predictions varied, with both NIR-R- and G-R-based indices performing best depending on species. Wave energy dissipation also correlated with vegetation indices, aligning closely with the best predictors of aboveground biomass, particularly when vegetation was submerged. These findings indicate that remote sensing combined with allometric equations offers a promising method for monitoring newly established marshes and estimating their biophysical parameters, which serve as key indicators of successful establishment and initial wave energy dissipation.
Why it matches plant phenotyping methodsリモートセンシング指標とアロメトリック式によって植物のバイオマスや葉面積指数を推定し、その性能を評価することが研究の中心であるため、植物フェノタイピング手法として適格。
abstractThis study combines laboratory measurements and remote sensing observations to evaluate the performance of vegetation indices in capturing changes in aboveground biomass, leaf area index, and wave energy dissipation in a constructed saltmarsh.
Dexfruit is a robotic manipulation framework that enables gentle, autonomous handling of fragile fruit and precise evaluation of damage. Soft fruits have long faced an issue of produce loss in both the harvesting and post-harvesting processes due to their extreme fragility and susceptibility to bruising, making them one of the hardest produce type to manipulate with automation. In this work, we demonstrate by using optical tactile sensing, autonomous manipulation of fruit with minimal damage can be achieved. We show that our tactile informed diffusion policies outperform baselines in both reduced bruising and pickand- place success rate across three fruits: strawberries, tomatoes, and blackberries. In addition, we introduce FruitSplat, a novel technique to represent and quantify visual damage in a highresolution 3D representation via 3D Gaussian Splatting (3DGS). Existing metrics for measuring damage lack quantitative rigor or require expensive equipment. With FruitSplat, we distill a 2D fruit mask as well as a 2D bruise segmentation mask into the 3DGS representation from just a web-cam video. Furthermore, this representation is modular and general, compatible with any relevant 2D model. Overall, we demonstrate a 92% grasping policy success rate, up to a 15% reduction in visual bruising, and up to a 31% improvement in grasp success rate on challenging fruit compared to our baselines across our three tested fruits. We rigorously evaluate this result with over 630 trials. Please checkout our website, which contains our code and datasets athttps://dex-fruit.github.io/.
Why it matches plant phenotyping methodsFruitSplatは、果実の損傷・打撲を3D表現として定量化する画像ベースの植物状態計測手法であり、開発と厳密な評価が研究の中心です。
abstractwe introduce FruitSplat, a novel technique to represent and quantify visual damage in a highresolution 3D representation via 3D Gaussian Splatting (3DGS).
Main conclusion Antarctic plants employ distinct cold acclimation strategies: Deschampsia antarctica uses general membrane-chloroplast stabilization while Colobanthus quitensis relies on chloroplast-focused tolerance mechanisms. The two native vascular plants of Antarctica, Deschampsia antarctica and Colobanthus quitensis, persist in one of the most extreme terrestrial environments on Earth, where episodic freeze-thaw cycles are frequent even during the growing season. Survival under such conditions necessitates not only tolerance to freezing alone but also effective recovery from freeze-induced injuries-a composite trait referred to as freeze-thaw stress tolerance (FTST). Yet, estimates of FTST of Antarctic plants have remained inconsistent across studies, largely due to methodological differences in freezing regimes and injury assessment metrics. Here, we employed a standardized, ice-nucleation-controlled freeze-thaw protocol and assessed FTST using two independent physiological indicators: electrolyte leakage (membrane integrity) and chlorophyll fluorescence (Fv/Fm; PSII function). We further validated the LT 50 values-the temperature causing 50% injury-through post-thaw recovery (PTR) assays, and examined total soluble sugar dynamics as a metabolic indicator of recovery capacity. D. antarctica exhibited coordinated enhancements in both membrane and chloroplast resilience following cold acclimation, with LT 50 values from both metrics closely aligned. In contrast, C. quitensis demonstrated a chloroplast-centered acclimation strategy, characterized by pronounced improvement in Fv/Fm-based LT 50 , while electrolyte-leakage based estimates remained largely unchanged. PTR results and sugar profiling supported the biological relevance of Fv/Fm as a more reliable FTST marker in C. quitensis. Together, these findings reveal distinct, species-specific acclimation frameworks to freeze-thaw stress; a global stabilization strategy in D. antarctica and a chloroplast-focused tolerance mechanism in C. quitensis, underscoring divergent evolutionary pathways for polar plant survival.
Why it matches plant phenotyping methods凍結融解耐性の評価プロトコルと複数の生理指標を比較・検証し、LT50測定の妥当性を評価しているため、表現型取得法が中心的です。
abstractYet, estimates of FTST of Antarctic plants have remained inconsistent across studies, largely due to methodological differences in freezing regimes and injury assessment metrics.
This study aims to develop a rapid and non-destructive method for determining protein content in maize using near-infrared spectroscopy (NIRS). To mitigate the effects of surface irregularities and uneven protein distribution in whole kernels on spectral measurements, maize powder was used as the test material to enhance the uniformity and stability of spectral signals. A total of 90 maize powder samples were collected from major production regions across China, and a custom NIRS acquisition system was constructed. To optimize the spectral data, eight preprocessing methods-including Multiplicative Scatter Correction (MSC), Standard Normal Variate (SNV), First Derivative (1D), Savitzky-Golay smoothing (S-G), and their combinations-were systematically evaluated. Subsequently, traditional machine learning models (Partial Least Squares Regression, PLSR; Support Vector Machine, SVM) and deep learning models (ResNet-18, Transformer) were developed to predict protein content, and their performances were compared. Results indicated that the combined preprocessing strategy of First Derivative and Multiplicative Scatter Correction (1D + MSC) was the most effective. Among the models, PLSR demonstrated the best predictive performance, and traditional chemometric methods showed greater practical utility compared to deep learning models. To further enhance model efficiency, four feature wavelength selection methods-Partial Least Squares Regression Coefficients (PLSRC), Competitive Adaptive Reweighted Sampling (CARS), Successive Projections Algorithm (SPA), and Uninformative Variable Elimination (UVE)-were applied. It was found that the PLSR model combined with the Successive Projections Algorithm (SPA) yielded the optimal performance, achieving a validation set correlation coefficient ( R p ) of 0.927, a root mean square error of prediction (RMSE P ) of 0.301, and a residual predictive deviation (RPD) of 2.502, along with the fastest computational speed. This study provides a reliable technical solution and theoretical foundation for the rapid and non-destructive detection of protein content in maize, while also validating the advantage of using powdered samples in improving the accuracy of NIRS detection.
Why it matches plant phenotyping methodsトウモロコシ種子のタンパク質含量という種子形質を対象に、NIRS取得系、前処理、機械学習モデル、波長選択を開発・比較・検証しており、形質取得法が中心である。
abstractThis study aims to develop a rapid and non-destructive method for determining protein content in maize using near-infrared spectroscopy (NIRS).
Volume electron microscopy (vEM) provides nanometer-scale, three-dimensional imaging of cells, but applying it to plant systems remains challenging. Cell walls, large vacuoles, and tissue thickness complicate sample preparation and cryogenic imaging. Here we report a cryogenic vEM (cryo-vEM) workflow for unstained plant protoplasts that achieves volumetric imaging of whole vitrified sorghum stem protoplasts without chemical fixation, dehydration, resin embedding, or heavy-metal staining. The method integrates optimized protoplast isolation, plunge-freezing vitrification for native-state preservation, automated cryogenic focus ion beam scanning electron microscopy (cryo-FIB-SEM) slice-and-view acquisition, contrast enhancement and stack alignment, and AI-assisted human-in-the-loop 3D segmentation. Using sorghum stem protoplasts as a demonstration, the workflow captured large-volume frozen-hydrated protoplast ultrastructure, allowing visualization of major organelles, including the nucleus, mitochondria, vacuoles, ER/Golgi-like membranes, lipid bodies, and subcellular features consistent with nuclear-envelope pores. We further quantified organelle volumes and surface areas from the segmented 3D data, highlighting the potential for quantitative cellular ultrastructure analysis. This cryo-vEM workflow provides a platform for near-native structural studies of isolated plant protoplasts.
Why it matches plant phenotyping methods植物プロトプラストの三次元画像取得・セグメンテーション・オルガネラ形態量化を中核とする手法開発であり、植物の細胞形態形質を抽出するため。
abstractHere we report a cryogenic vEM (cryo-vEM) workflow for unstained plant protoplasts that achieves volumetric imaging of whole vitrified sorghum stem protoplasts
Reproduction assets found保存済みの本文根拠を更新済みルールで再検証し、公開資産1件を確認しました。Code · public369 The codes are freely available at https://github.com/xzhang0123/vEMOpen asset ↗https://github.com/xzhang0123/vEM · xzhang0123/vEMpdf-page:10 lines:1-24Plant phenotyping relevance match · UnverifiedOpenAlex · checked 6 Sept 2026
High-throughput phenotypic acquisition and analysis allow us to accurately quantify trait expressions, which is essential for developing intelligent breeding strategies. However, there is still much potential to explore in the field of high-throughput phenotyping for edible fungi. In this study, we developed a portable multi-phenotypic acquisition system for Pleurotus eryngii using RGB and RGB-D cameras. We developed an innovative Unet-based semantic segmentation model by integrating the ASPP structure with the VGG16 architecture. This allows for precise segmentation of the cap, gills and stem of the fruiting body. By leveraging depth images from RGB-D cameras, we can effectively collect phenotypic information about Pleurotus eryngii. By combining K-means clustering with Lab color space thresholds, we are able to achieve more precise automatic classification of Pleurotus eryngii cap colors. Moreover, AlexNet is utilized to classify the shapes of the fruiting bodies. The Aspp-VGGUnet network demonstrates remarkable performance with a mean Intersection over Union (mIoU) of 96.47% and a mean pixel accuracy (mPA) of 98.53%. These results reflect respective improvements of 3.03% and 2.23% compared to the standard Unet model, respectively. The average error in size phenotype measurement is just 0.15 ± 0.03 cm. The accuracy for cap color classification reaches 91.04%, while fruiting body shape classification achieves 97.90%. The proposed multi-phenotype acquisition system reduces the measurement time per sample from an average of 76 s (manual method) to about 2 s, substantially increasing data acquisition throughput and providing robust support for scalable phenotyping workflows in breeding research.
Why it matches plant phenotyping methodsRGB・深度画像による食用菌の複数形質取得システムを開発し、セグメンテーション、サイズ・色・形状の自動測定性能を検証しており、植物（菌類）の表現型取得が中心である。
abstractwe developed a portable multi-phenotypic acquisition system for Pleurotus eryngii using RGB and RGB-D cameras.
Background Protoplasts, which are plant cells devoid of cell walls, are valuable tools in plant biotechnology. However, they are highly sensitive to mechanical and osmotic stress during isolation and early culture, often leading to significant loss of viability. Reliable and efficient methods for monitoring protoplast quality are essential for downstream applications. Results We applied impedance flow cytometry to assess the viability, cell size, and early division of freshly isolated protoplasts from Arabidopsis thaliana, Brassica napus, and Beta vulgaris. This label-free technique enables fast, objective, and high-throughput assessment of individual protoplasts, allowing reliable monitoring of viability and early division in large populations. Importantly, IFC-derived viability metrics strongly correlated with microcallus formation, demonstrating their predictive value for culture competence. Conclusions Impedance flow cytometry provides a robust, efficient and reproducible method for characterizing protoplast cultures. It enables rapid assessment of viability and growth potential, supporting quality control and optimization in plant cell culture workflows.
Why it matches plant phenotyping methodsインピーダンスフローサイトメトリーを用いて、植物プロトプラストの生存性・細胞サイズ・初期分裂を高速かつ高スループットに測定し、培養能力との相関で妥当性を検証しているため、植物表現型取得法が中心です。
abstractThis label-free technique enables fast, objective, and high-throughput assessment of individual protoplasts, allowing reliable monitoring of viability and early division in large populations.
While the root architecture of potted crop seedlings directly determines subsequent crop productivity and adaptability, these root systems remain challenging to quantify using conventional methods due to their structural complexity. To investigate the microscopic characteristics of the root systems of pepper seedlings within pots, Micro-CT was employed to scan the seedling pots. After three-dimensional (3D) reconstruction was conducted on the data acquired from the pot scans, the 3D model of the root system was segmented and extracted using the watershed algorithm. Vertically, the three-dimensional root model was divided from top to bottom into four equally spaced regions (a, b, c, and d), showing the volumetric distribution characteristics of pepper seedling roots within the pots. The results showed that region a had the largest average root volume proportion (29.72%), primarily due to the substantial volume contribution of the taproot. Region d followed with an average proportion of 27.26%, resulting from root coiling and entanglement at the pot bottom caused by the spatial constraints of the seedling tray. The middle regions of the pot, b and c, showed average root volume proportions of 23.14% and 19.89%, respectively. To further investigate the influence of root system characteristics on root injury during seedling gripping, the seedlings were categorized into three types based on their taproot growth positions. A gripping experiment was conducted on these three seedling types using spatula-equipped needles. The results showed that the greatest root injury (12.67%) was observed in Type 1 seedlings, which had taproots located closest to the needle insertion point. In contrast, the least injury (4.09%) was found in Type 3 seedlings, characterized by centrally positioned taproots. Type 2 seedlings, with their taproots growing on the side (laterally away from the insertion point), sustained intermediate injury (5.45%). This was because their lateral positioning led to an uneven distribution of mechanical stress during gripping compared with Type 3 seedlings. A validation experiment conducted on an automated seedling retrieval platform confirmed the root injury analysis. The experimental results showed maximum root injury in Type 1 seedlings (14.16%), followed by Type 2 (6.03%) and Type 3 (4.82%) seedlings, with a successful retrieval rate of 95.29%. These findings were consistent with the Micro-CT analysis. This study could provide a theoretical foundation for low-injury seedling gripping in fully automated seedling transplanters.
Why it matches plant phenotyping methodsMicro-CT、3D再構成、watershed分割を用いて苗の根系形態を定量化する手法が研究の中心であり、根容積分布と根傷害の評価まで検証している。
abstractMicro-CT was employed to scan the seedling pots. After three-dimensional (3D) reconstruction was conducted on the data acquired from the pot scans, the 3D model of the root system was segmented and extracted using the watershed algorithm.
With unmanned aerial vehicles (UAVs), agricultural monitoring has developed into a new phase of innovation providing remedies to precision farming. The common traditional agricultural methods are based on manual inspection and few observations on the ground using sensors that may be inaccurate and time-consuming. New technologies such as drones and AI provide us with an opening of large scale, early detection, but most systems currently only seek pests or diseases and are usually specific to a single type of crop in controlled laboratory conditions. Drone-operated AI system, which combines RGB and, where feasible, multispectral cameras and a YOLOv8 pipeline to detect pests and crop diseases simultaneously across a variety of crops. We are developing it to be used in the real world: we load in data fields, laboratories, and the internet, perform preprocessing, transfer learning, and make the inference to be lightweight enough to execute on edge computers. The introduction of agricultural monitoring systems based on the use of UAVs builds on the peculiarities of quadcopters and fixed-wing UAVs. Quadcopters are used when conducting detailed field surveys or spot checks, allowing high-resolution imaging to be used in order to complete precise inspections, whereas fixed-wing UAVs are used when it comes to covering extensive areas and long-range capabilities. These UAVs can gather extensive data and conduct biological and chemical analyses due to sophisticated IoT devices and sensors, such as multispectral and hyperspectral cameras, GPS modules, and real-time communication tools. Our hybrid machine learning model (HMLM) has more accuracy and predictive capabilities, with an amazing score of 98.74 and hence, our machine learning model is doing the right job of 98.74 accurate classification and thereby yielding high accurate yields by predicting crop management. This research will contribute to the sustainability of agricultural practices as well as yield protection by providing timely, precise and scalable detection. The model proposed can potentially enable farmers with action-oriented insights, losses can be alleviated, and food security objectives can be achieved in areas where there are high susceptibility rates to pests and diseases.
Why it matches plant phenotyping methodsUAV画像とYOLOv8を用いて作物病害を検出する技術の開発が中心であり、植物の病害状態を直接推定するため、植物フェノタイピング手法として含める。
abstractDrone-operated AI system, which combines RGB and, where feasible, multispectral cameras and a YOLOv8 pipeline to detect pests and crop diseases simultaneously across a variety of crops.
Microbes secrete structurally diverse secondary metabolites during plant infection, some of which are detected by plant cells, which trigger stress responses. In this method, the induction of ion leakage, peroxidase activity, and callose production is measured in the same leaf disk sample. First, Arabidopsis or barley leaf disks are vacuum infiltrated in a 96-well plate. After 4-6 hours, conductivity is measured, followed by peroxidase activity and callose deposition at 24 hours. The flg22 peptide induces all three responses and is an affordable positive control. Surfactin and gramillin cyclic lipopeptides induce peroxidase activity and ion leakage, respectively, while the phytotoxic T-2 trichothecene suppresses peroxidase activity. Overall, this approach enables multiple comparisons across either plant genotypes or metabolite treatments. This approach can be applied to chemical genetics or bioprotection to identify stress-modulating compounds for further study. In plant genetics, this approach can be used to compare responses across plant populations for genetic mapping and to improve our understanding of plant-microbe interactions.
Why it matches plant phenotyping methods植物葉のストレス応答（イオン漏出、ペルオキシダーゼ活性、カロース蓄積）を同一試料で高スループット測定する方法が研究の中心であり、植物表現型取得法に該当する。
abstractIn this method, the induction of ion leakage, peroxidase activity, and callose production is measured in the same leaf disk sample.
SoybeanLaboratory / benchtopRootMorphology / geometry measurement2D/3D reconstructionSegmentationVisualization / data managementRoot system architecture
Root system analysis remains methodologically challenging in plant research: traditional soil cultivation obstructs comprehensive root observation, whereas hydroponic visualization lacks ecological relevance due to soil environment exclusion—a critical limitation for crops like soybean. This manuscript developed a cost-effective hybrid imaging system integrating transparent acrylic plates, semi-permeable membranes, and natural soil substrates with high-resolution imaging and controlled illumination, enabling non-destructive root monitoring in quasi-natural soil conditions. Complementing this hardware innovation, this manuscript proposed an unsupervised semantic segmentation algorithm that synergizes path planning with an enhanced DBSCAN framework, achieving the precise extraction of primary and lateral root architectures. Experimental validation demonstrated superior performance in soybean root analysis, with segmentation metrics reaching 0.8444 accuracy, 0.9203 recall, 0.8743 F1-score, and 0.7921 mIoU—significantly outperforming existing unsupervised methods (p 0.94) with WinRHIZO in quantifying root length, projected area, dimensional parameters, and lateral root counts confirmed system reliability. This soil-compatible phenotyping platform establishes new opportunities for root research, with future developments targeting multi-crop adaptability and complex soil condition applications through modular hardware redesign and 3D reconstruction algorithm integration.
Why it matches plant phenotyping methods根系観察用ハードウェアと画像セグメンテーション手法を開発し、根形質抽出性能を検証した、中心的な植物フェノタイピング研究である。
abstractThis manuscript developed a cost-effective hybrid imaging system
Reproduction assets foundThe paper's Data Availability Statement explicitly deposits the study's soybean root image data (the time-series NRMS dataset and scanner validation dataset used for phenotyping) in a public GitHub repository under the authors' account, matching an allowed URL. No separate analysis code availability is stated, so the资产Dataset · publicData Availability Statement: The data presented in this study are openly available in [GitHub] at
[https://github.com/xusiyue/RootPO_DBSCAN/tree/master/project_rootSystem/data (accessed
on 31 October 2025)].Open asset ↗GitHub · xusiyue/RootPO_DBSCANpdf-page:18 lines:1-58Plant phenotyping relevance match · UnverifiedOpenAlex · checked 14 Sept 2026
Published4 Dec 2025Zenodo (CERN European Organization for Nuclear Research)Cited by 0 · OpenAlex ↗
Poster presented at CellBio 2025 in Philadelphia, PA. December 2025Abstract:Adenosine deaminase (ADA) deficiency causes severe combined immunodeficiency, and current treatments include enzyme replacement therapy with immunogenic bovine proteins. To develop improved therapeutic variants, robust model systems are needed for testing rationally designed enzymes in vivo. We used Zoogle (zoogle.arcadiascience.com), a computational dataset that selects model organisms based on conserved protein characteristics rather than sequence similarity, to identify Chlamydomonas reinhardtii as an optimal system for studying human ADA1 function. This approach can identify effective models that traditional phylogenetic methods might overlook. We characterized Chlamydomonas ADA1 mutants and found clear phenotypic defects in motility and cellular metabolism, particularly altered starch accumulation under nutrient stress. We established quantitative phenotyping approaches, including high-throughput motility tracking, metabolic profiling via Raman spectroscopy, and biochemical staining to assess cellular function. These multi-modal readouts provided robust, reproducible measures of ADA1 activity in living cells. We're validating this system using wild-type human ADA1 and candidate variants designed through machine learning approaches to enhance stability and improve therapeutic properties. Initial results demonstrate that the algal system can detect functional differences in ADA1 variants, establishing a platform for screening computationally designed proteins. This approach enables systematic evaluation of engineered enzymes in a physiologically relevant cellular context. Our work establishes Chlamydomonas as an effective model for human metabolic enzymes and demonstrates the power of protein characteristic-based organism selection over traditional phylogenetic approaches. This validation platform enables rapid, cost-effective screening of designed therapeutic proteins before advancing to mammalian studies, potentially accelerating the development of next-generation enzyme replacement therapies for genetic diseases.
Why it matches plant phenotyping methodsChlamydomonasの運動性・代謝・デンプン蓄積を対象に、ハイスループット追跡、ラマン分光、染色による定量的フェノタイピング手法を確立し、治療タンパク質評価のプラットフォームとして検証しているため。
abstractWe established quantitative phenotyping approaches, including high-throughput motility tracking, metabolic profiling via Raman spectroscopy, and biochemical staining to assess cellular function.
The internal quality assessment of potato tubers is a crucial task in agro-laboratory processing. Traditional methods struggle to detect internal defects such as hollow heart, internal bruises, and insect galleries using only surface features. We present a novel, fully modular hybrid AI architecture designed for defect detection using RGB images of potato slices, suitable for integration in laboratory. Our pipeline combines high-recall multi-threshold YOLO detection, contextual patch validation using ResNet, precise segmentation via the Segment Anything Model (SAM), and skin-contact analysis using VGG16 with a Random Forest classifier. Experimental results on a labeled dataset of over 6000 annotated instances show a recall above 95% and precision near 97.2% for most defect classes. The approach offers both robustness and interpretability, outperforming previous methods that rely on costly hyperspectral or MRI techniques. This system is scalable, explainable, and compatible with existing 2D imaging hardware.
Why it matches plant phenotyping methodsジャガイモ塊茎の内部欠陥という植物器官の状態を、RGB画像と複数の画像解析モデルで検出・分割する手法の開発および性能評価が中心である。
abstractWe present a novel, fully modular hybrid AI architecture designed for defect detection using RGB images of potato slices
Plant phenotyping relevance match · UnverifiedCrossref · Europe PMC · checked 15 Sept 2026
Introduction Advances in automation and AI/ML offer new opportunities for plant science, including design, modeling, and analysis. This study aimed to develop an automated platform for researching small model plants under axenic conditions and integrate it with AI/ML tools. Methods The EcoBOT platform was developed, which consists of sterile containers (EcoFABs) for growing plants and imaging for monitoring plant growth and health. Brachypodium distachyon was grown on the EcoBOT, and its response to nutrient limitation and copper stress was evaluated. Results The results showed that Brachypodium distachyon grown in the EcoBOT maintained sterility and responded to nutrient limitation and copper stress. Analysis of over 6,500 root and shoot images revealed varying sensitivity and response rates to copper. Bayesian Optimization was used to improve model accuracies relating copper concentrations to plant biomass via sequential experiments, resulting in a >30% improvement. Discussion The findings of this study demonstrate the potential of the EcoBOT platform for researching plant responses to environmental factors. Future experiments could focus on relating other chemical stresses and microbial interactions to create generalized models of plant responses.
Why it matches plant phenotyping methodsEcoBOTという自動化プラットフォームを開発し、画像による植物成長・健康状態のモニタリングと、画像に基づくバイオマス推定を中核としているため、植物フェノタイピング手法として採用する。
abstractThis study aimed to develop an automated platform for researching small model plants under axenic conditions and integrate it with AI/ML tools.
Studying the mechanisms that promote deep rooting in crops is crucial for engineering plant varieties with enhanced drought resilience and increased carbon sequestration capacity. Soil compaction is a major constraint on rooting depth and, to overcome this, root system penetrability needs to be enhanced. However, because of the limitations of current methods, phenotyping root penetrability remains a bottleneck. Here, we developed RootXplorer, a computer vision-based 3D phenotyping platform for high-throughput quantification of root penetration-related traits/phenotypes across dicot and monocot species. RootXplorer integrates a novel Phytagel-based cylinder system, a 3D imaging unit, and an automated software pipeline to extract root penetration-related traits with high precision and at a large scale. We demonstrate that RootXplorer enables large-scale diversity screenings in conditions replicating soil compaction effects in multiple species, revealing species-specific strategies for overcoming mechanical impedance. These findings highlight the utility and promise of RootXplorer for accelerating research on root architectural plasticity under controlled compaction conditions, identifying genotypes with varying tolerance to mechanical impedance, and supporting data-driven breeding decisions for developing soil compaction-resilient crop varieties. This technology has important implications for future plant breeding strategies and supports ongoing climate change mitigation efforts.
Why it matches plant phenotyping methodsRoot penetrability関連形質を対象に、3D画像計測と自動ソフトウェアで抽出する高スループット表現型解析プラットフォームを開発しており、方法が研究の中心です。
abstractRootXplorer integrates a novel Phytagel-based cylinder system, a 3D imaging unit, and an automated software pipeline to extract root penetration-related traits with high precision and at a large scale.
Reproduction assets foundThe paper's Data Availability statement explicitly deposits the complete analysis pipeline and time-lapse video generation code in two public GitHub repositories under the authors' Salk Harnessing Plants Initiative organization. These directly support the paper's RootXplorer phenotyping analysis (image cropping, U-Net+Code · publicAll code for generating time-lapse videos is publicly available at https://github.com/Salk-Harnessing-Plants-Initiative/RootXplorer-cylinder-time-lapseOpen asset ↗Salk-Harnessing-Plants-Initiative/RootXplorer-cylinder-time-lapselines:167-180Plant phenotyping relevance match · UnverifiedEurope PMC · checked 15 Sept 2026
Published1 Dec 2025Computers and Electronics in Agriculture.
In-orchard apple size grading remains challenging under occlusions, variable illumination, and irregular fruit morphology. We present a contact–curvature strategy that integrates bending sensors into fin–ray flexible fingers to estimate fruit size during grasping, unifying grasp–and–grade operations. Neural Radiance Fields (NeRF) reconstruction provides offline ground-truth curvature for calibration, yielding strong linear agreement between sensor readings and true curvature (R2=0.9852). Using temporal curvature features across approach, grasp, and steady phases, a gradient-boosting regression model predicts fruit diameter with R2=0.9577 and RMSE = 1.19 mm on the test set. In laboratory conditions, the system achieved an overall grading accuracy of 98.0 % for 200 apples classified into four grades, with a processing capacity of approximately 6 apples·min⁻¹, meeting real-time requirements. In a small-scale orchard pilot study, the system maintainedR2=0.94 andRMSE=1.27 mm, achieving 96 % grading accuracy versus 77 % for a camera-only approach. Compared with vision-only sizing methods, contact-curvature sensing demonstrates inherent robustness to occlusion and illumination while better tolerating morphological irregularities. A methylene–blue protocol confirmed non–destructive operation. Contact–curvature sensing is robust to occlusions/illumination and can, in principle, extend to other near–spherical crops.
Why it matches plant phenotyping methods果実径という植物器官形質を、接触・曲率センサーと回帰モデルで推定する手法を開発し、校正・精度検証・圃場評価まで行っており、表現型取得法が研究の中心である。
abstractWe present a contact–curvature strategy that integrates bending sensors into fin–ray flexible fingers to estimate fruit size during grasping, unifying grasp–and–grade operations.
Why it matches plant phenotyping methods乾燥イネ組織のSEM画像取得・処理・解析プロトコル自体が中心で、植物細胞壁形態などの表現型観察を可能にする方法開発である。
abstractTo overcome the challenges surrounding SEM micrograph preparation, dried rice stems were used to develop a specific set of protocols for processing dried plant samples.
Aquatic plants are key contributors to oxygen production and ecosystem stability. This study quantifies oxygen generation capacity of Hydrilla, Vallisneria , and Potamogeton under varying concentrations of potassium bicarbonate (KHCO 3 ) using a dual-limb apparatus to measure oxygen output via water displacement. The experiment was complemented by gas chromatography-thermal conductivity detector (GC-TCD) analysis and numerical simulations to validate the results. An in silico diffusion model was developed to simulate oxygen release dynamics assuming uniform oxygen generation across plant surfaces and steady-state mass transport through the surrounding medium. The findings indicate that KHCO 3 significantly enhances photosynthetic activity and oxygen production, with Hydrilla exhibiting the highest oxygen generation rate, followed by Potamogeton and Vallisneria . The optimal concentration of KHCO 3 was determined to be 5 mg/mL, beyond which oxygen production declined due to osmotic stress and ionic imbalances. GC-TCD analysis confirmed oxygen (∼90%) as the primary gas produced, while simulated results closely aligned with the experimental data, reinforcing the robustness of the in silico analysis. This study highlights the role of bicarbonate ions in enhancing carbon availability for aquatic photosynthesis, thereby optimizing oxygen generation rate. The experimental methodology coupled with a numerical framework based on spatial diffusion model, as discussed in this endeavor, is novel in estimating oxygen generation rate from whole-plant in a closed system, enabling reproducible scaling for state-of-the-art environmental technologies. The insights gained from this in silico endeavor are expected to have broad implications for wastewater treatment (enhancing aerobic biodegradation), aquaculture (maintaining high dissolved oxygen), and carbon capture (biomass-based CO 2 sequestration). Future research could focus on the exploration of long-term physiological effects of KHCO 3 supplementation on oxygen generation and improvisation of modeling framework to incorporate biological feedback mechanisms into the underlying analysis.
Why it matches plant phenotyping methods全植物の酸素生成速度という生理形質を測定・推定する装置と拡散モデルを開発し、実験およびGC-TCDで検証しており、方法が中心的です。
abstractThe experimental methodology coupled with a numerical framework based on spatial diffusion model, as discussed in this endeavor, is novel in estimating oxygen generation rate from whole-plant in a closed system, enabling reproducible scaling for state-of-the-art environmental technologies.
Published1 Dec 2025Journal of food composition and analysis : an official publication of the United Nations University, International Network of Food Data Systems
Wheat quality detection plays a crucial role in the processing of grain storage, and moisture distribution is one of the main factors that affect wheat quality. The uniformity of moisture distribution in wheat grains significantly impacts their morphological structures, nutrient distribution, storage period, and stress resistance. This study detects the moisture distribution in wheat grains by using terahertz time-domain spectroscopy (THz-TDS) to scan wheat grains soaked for different times (0, 2, 4, 6, 8, and 10 h) and dried for different times (0, 1, 2, 3, 4, and 5 h). The scanned results are used to observe the water content changes in wheat grains on both temporal and spatial scales. This study calculates the average spectrum of wheat grains to observe the regular changes in the terahertz time-domain spectrum of wheat grains under different soaking and drying degrees. These changes exhibit opposite trends. The frequency domain spectra are obtained through Fast Fourier Transform (FFT), and comparing the imaging effects at different frequency points, it can be observed that there is a good consistency between frequency-domain imaging and time-domain imaging. The experimental results indicate that THz-TDS can be used to effectively observe the moisture distribution in wheat grains during the soaking and drying processes.
Why it matches plant phenotyping methodsTHz-TDSによる小麦粒内の水分分布という植物器官の状態を画像化・評価する手法が研究の中心であり、吸水・乾燥過程での画像化性能を検討している。
abstractThis study detects the moisture distribution in wheat grains by using terahertz time-domain spectroscopy (THz-TDS)
Published1 Dec 2025Journal of food composition and analysis : an official publication of the United Nations University, International Network of Food Data Systems
The main pathogen of sweet potato black rot, Ceratocystis fimbriata, induces the production of toxic secondary metabolites, leading to significant post-harvest economic losses. Establishing early rapid detection technologies is crucial for ensuring sweet potato food safety and reducing economic losses. This study systematically monitored the spectral image (HSI), electronic nose (E-nose) response signal, and total phenolic content (TPC) reference value of sweet potato samples after artificial inoculation with the pathogen, aiming to utilize TPC as a key biochemical indicator for the early prediction of disease progression. The experiment compared single-source and multi-source data fusion methods. Results showed that the CARS-PCA-MHA-CNN model(Parameters was reduced by 96.58%) based on a feature-level fusion strategy achieved the best predictive performance (R²=0.974, RMSEP=0.041, RPD=6.14). Compared with single-source data, the prediction accuracy was improved by 7.6% and 6.4%, respectively. Furthermore, the model's generalization ability was tested on an independent test set (unenhanced). This study proposes a reliable and non-destructive method for the early prediction of postharvest diseases in root and tuber crops, which has great application potential in the field of intelligent monitoring of agricultural products.
Why it matches plant phenotyping methods病原体接種後のサツマイモの病害進行を、HSI・電子鼻・TPCデータ融合とCNNで非破壊予測する方法が研究の中心であり、植物器官の病害状態を推定する実質的なフェノタイピング手法である。
abstractThe experiment compared single-source and multi-source data fusion methods.
Laboratory / benchtopTissueClassificationGrowth / development / phenology
The spice Ferula assa-foetida L., also known as asafoetida, is widely recognized for its medicinal and culinary applications. The non-native status of the plant and the prolonged dormancy of its seeds pose significant challenges for large-scale cultivation in India. In vitro organogenesis offers an effective solution to these obstacles. Establishing reliable in vitro regeneration protocols requires standardized statistical methods to evaluate univariate and multivariate data for optimizing specific traits. However, these methods have limitations when handling complex, nonlinear inputs, often producing large prediction errors that reduce the reliability of trait optimization. This study developed an in vitro regeneration system for F. assa-foetida L. and identified optimal PGRs for somatic embryogenesis and shoot organogenesis through image-based morphological analysis. Predictive models were created using DL and ML algorithms. Calli induced from leaf explants was cultured on the Murashige and Skoog medium supplemented with various combinations and concentrations of thidiazuron (TDZ), 6-benzylaminopurine (BAP), and α-naphthaleneacetic acid (NAA), as experimental variables. Seven ML approaches, namely random forest (RF), support vector machine (SVM), k-nearest neighbours (kNN), decision tree (DT), extreme gradient boosting (XG Boost), naïve bayes, and logistic regression, alongside five DL models-convolutional neural network (CNN), MobileNet, region-based convolutional neural network (RCNN), residual neural network (ResNet), and visual geometry group (VGG19)-were employed to predict the best PGRs for somatic embryogenesis and shoot organogenesis. Among them, the convolutional neural network (CNN) achieved the highest accuracy (87%), outperforming baseline ML models such as logistic regression and decision tree (82%). This pioneering study in F. assa-foetida L. presents an AI-driven, image-based framework for predicting optimal PGRs, offering a scalable approach to enhance micropropagation in endangered medicinal plants.
Why it matches plant phenotyping methods画像ベースの形態解析と深層学習・機械学習を中核に、植物組織の器官形成状態を予測して表現型最適化を行う手法開発であり、単なる培養実験ではない。
abstractidentified optimal PGRs for somatic embryogenesis and shoot organogenesis through image-based morphological analysis.
Seed germination is a critical phase strongly affected by abiotic stresses including drought and artificial seed ageing. Traditional indices like Germination Percentage (GP) and Mean Germination Time (MGT) often fail to capture complex stress responses and priming efficacy. This study introduces eight novel indices that quantitatively measure distinct physiological mechanisms: The Priming Efficiency Index (SPEI), Stress Performance Stability Index (SPSI), Germination Recovery Ratio (SGRR), and Combined Vigor Index (SCVI), among others. Tested on wheat under drought stress and priming treatments, the indices demonstrated 34.2% improvement in germination recovery with gibberellin priming compared to 25.8% with hydro-priming. The SCVI showed a 20.7% enhancement in integrated seedling performance, while SGRR achieved complete stress recovery (1.004) with gibberellin treatment. Validation across triticale and pumpkin revealed consistent performance, with cross-species correlations exceeding 0.89. Statistical analyses confirmed the novel indices' superior discriminatory power, requiring 37.6% smaller sample sizes than traditional metrics while maintaining 94% rank stability under data perturbations. These indices provide robust, mechanistically informed tools for precision phenotyping in breeding programs and seed technology research.
Why it matches plant phenotyping methods発芽・幼植物性能を定量化する新規指標を開発し、複数作物で性能検証しており、表現型測定法が研究の中心である。
abstractThis study introduces eight novel indices that quantitatively measure distinct physiological mechanisms
A methodology for achieving micrometer-scale 4D X-ray lab microscopy of living leaf tissue was developed to overcome challenges associated with delicate tissues, radiation damage, and motion artifacts during in vivo imaging. The study focused on optimizing laboratory based X-ray micro-computed tomography (microCT) parameters to balance high-resolution imaging with minimized physiological stress and radiation dose quantification. Assessing the dose-safe imaging window required comparing vertical and horizontal leaf mounting setups. Results demonstrated that the horizontal setup provided greater stability, preventing tissue degradation and maintaining sample viability during continuous acquisitions lasting up to 22 hours ([~]15600 Gy). MicroCT capacities were clearly able to resolve microstructures at the cellular level, achieving a pixel size down to 1 {micro}m. Furthermore, this optimized methodology confirmed the ability to track the spatiotemporal dynamics of applied compounds such as iohexol and aggregated nanoparticles within the leaf tissue. This work establishes that accessible laboratory based microCT enables the in vivo 4D monitoring of anatomical and physiological changes in living plants.
Why it matches plant phenotyping methods生葉を対象とした高解像度4D X線マイクロCTの撮像条件・線量・試料配置を開発し、生体内の解剖学的・生理学的変化を追跡する方法が研究の中心である。
abstractA methodology for achieving micrometer-scale 4D X-ray lab microscopy of living leaf tissue was developed to overcome challenges associated with delicate tissues, radiation damage, and motion artifacts during in vivo imaging.
ABSTRACT Plant microtechnique is a sequence of skill-intensive histological and microscopy procedures that often yield limited quantitative information. However, it provides the cellular context needed to uncover biomolecular functions. In this work, we developed an easier microtechnique and a novel histolomic approach for the quantitative analysis of histological features. We replaced paraffin with resin as the embedding medium, developed an adhesive treatment for glass slides, and developed a trichrome staining. These improvements provided superior tissue stability and greatly facilitated the skill-dependent steps. Unlike current stainings, our trichrome staining produced a broader color palette and sharply contrasted numerous organelles and ultrastructures in light microscopy. We leveraged these microtechnique advances through image segmentation and quantitative analysis in MATLAB and Adobe Photoshop to measure a wide range of morphometric and compositional features, thereby generating the histolome. To validate this workflow, we applied it comprehensively and systematically to several model plants and calculated their C 4 Kranz-anatomy level using a combination of characteristic histological features. The histolomes provided new insights into cellular functions and quantitative anatomical differentiation among species. The resin-based microtechnique and histolomic approach will help facilitate, standardize, and make plant histology research quantitative. GRAPHICAL ABSTRACT
Why it matches plant phenotyping methods樹脂包埋・染色・画像セグメンテーション・定量解析を統合し、植物組織の形態・構成特徴を抽出する新規ヒストロミクス手法を開発・検証しており、植物表現型取得が中心です。
abstractIn this work, we developed an easier microtechnique and a novel histolomic approach for the quantitative analysis of histological features.
Accurate fruit shape reconstruction under real-world field conditions is essential for high-throughput phenotyping, sensor-based yield estimation, and orchard management. Existing approaches based on 2D imaging or explicit 3D reconstruction often suffer from occlusions, sparse views, and complex scene dynamics as a result of the plant geometries. This paper presents a novel UAV-based monocular 3D panoptic mapping framework for robust and scalable fruit shape completion in orchards. The proposed method integrates (1) Grounded-SAM2 for multi-object tracking and segmentation (MOTS), (2) photogrammetric structure-from-motion for 3D scene reconstruction, and (3) DeepSDF, an implicit neural representation, for completing occluded fruit geometries with a neural network. We furthermore propose a new MOTS evaluation protocol to assess tracking performance without requiring ground truth annotations. Experiments conducted in both controlled laboratory conditions and an operational apple orchard demonstrate the accuracy of our 3D fruit reconstruction at the centimeter level. The Chamfer distance error of the proposed shape completion method using the DeepSDF shape prior reduces this to the millimeter level, and outperforms the traditional method, while Grounded-SAM2 enables robust fruit tracking across challenging viewpoints. The approach is highly scalable and applicable to real-world agricultural scenarios, offering a promising solution to reconstruct complete fruits with visibility higher than 10% for precise 3D fruit phenotyping at a large scale under occluded conditions.
Why it matches plant phenotyping methods果実形状を対象とするUAV画像・3D再構成・形状補完法を開発し、実験で精度評価しており、植物表現型取得が研究の中心である。
abstractThis paper presents a novel UAV-based monocular 3D panoptic mapping framework for robust and scalable fruit shape completion in orchards.
Reproduction assets foundThe paper's authors publicly release their UAV orchard video data, lab 3D apple scans, and analysis code via a GitHub repository explicitly stated in the text. A Zenodo deposit (10.5281/zenodo.15635994) is also mentioned for the data, but its URL is not among the allowed URLs, so only the GitHub asset is reported.Code · publicing in orchard environments,(2) to propose a novel method to evaluate
MOTS without any annotations, and (3) to provide a highly accurate
3D apple dataset collected in a laboratory environment, along with
UAV-captured high-resolution videos in the field. The dataset and codes
for this research are publicly available at: https://github.com/Kaiwen-Robotics/Mono3DOrchard.2. Study area and materials
This study contains two data collection areas: field data collection
and laboratory data collection.
2.1. Field data collection
2.1.1. Study area
The field data collection was conducted within an apple orchard
located in Randwijk, Overbetuwe, the Netherlands (51.9376, 5.703057
in WGS84 UTM 31U), as shOpen asset ↗Kaiwen-Robotics/Mono3DOrchard.2pdf-raw-page:2 lines:75-128Plant phenotyping relevance match · UnverifiedCrossref · checked 15 Sept 2026
In facility horticultural production, intelligent disease recognition and precise intervention are vital for crop health and economic efficiency. We construct a multi-source dataset from Bayan Nur, Weifang, and Honghe that integrates handheld camera photos, drone field images, and laboratory-controlled samples. Handheld images capture fine lesion texture for close-up diagnosis common in greenhouses; drone images provide canopy-scale patterns and spatial context suited to open-field management; laboratory images offer controlled illumination and background for stable supervision and cross-crop feature learning. Our objective is robust cross-crop, cross-regional diagnosis and economically rational control. To this end, a model named CCGD-Net is proposed. It is designed as a multi-task framework. The framework incorporates a multi-scale perception module (MSFE) to produce hierarchical representations. It includes a cross-domain alignment module (CDAM) that reduces distribution shifts between greenhouse and open-field environments. The training follows an unsupervised domain adaptation setting that uses unlabeled target-region images. When such images are not available, the model functions in a pure generalization mode. The framework also integrates a regional economic strategy module (RESM) that transforms recognition outputs and local cost information into optimized intervention intensity. Experiments show an accuracy of 91.6%, an F1-score of 89.8%, and an mAP of 88.9%, outperforming Swin Transformer and ConvNeXt; removing RESM reduces F1 to 87.2%. In cross-regional testing (Weifang training → Honghe testing), the model attains an F1 of 88.0% and mAP of 86.5%. These results indicate that integrating complementary imaging modalities with domain alignment and economic optimization provides an effective solution for disease diagnosis across greenhouse and field systems.
Why it matches plant phenotyping methods植物病斑・冠層画像から病害状態を推定するマルチモーダル深層学習法を開発し、データセット、ドメイン適応、交差地域検証を含むため、植物フェノタイピング手法が中心である。
abstractWe construct a multi-source dataset from Bayan Nur, Weifang, and Honghe that integrates handheld camera photos, drone field images, and laboratory-controlled samples.
Laboratory / benchtopMicroscopyRootMorphology / geometry measurementRoot system architecture
The behavior study of plant roots under physical obstacles is of significant importance for comprehending how plants adapt to the changes in the soil environment. Currently, there is no satisfactory method to simulate the soil obstacle environment and track the dynamic change of the root system under physical obstacles. In this work, based on the soil compaction and mechanical obstacles encountered by the root system in the soil, an obstacle microfluidic chip with seven different channels was designed. The obstacle microfluidic chip was fully utilized to take advantage of the variable structure of microfluidic chips to design chip architectures, making it convenient to study the plasticity of root systems under various barriers. The results demonstrated that the microfluidic system's high-resolution imaging capabilities enabled the visualization and quantification of the plant root system's growth behavior in the presence of mechanical obstacles. In addition, to account for the growth resistance or pressure experienced by the roots in the soil, the models were simulated by the fluid flow within the chip. Overall, the obstacle microfluidic chips designed in this study can be used for imaging and quantifying the plasticity of plant roots, which can be an effective tool for tracking the root system's response to mechanical stress.
Why it matches plant phenotyping methods根系の障害物応答を高解像度画像で可視化・定量化するマイクロ流体チップを設計した研究であり、根系形態・成長の取得手法が中心的です。
abstractthere is no satisfactory method to simulate the soil obstacle environment and track the dynamic change of the root system under physical obstacles.
Plant 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.
Premise Analyzing structural changes along the length of an organ provides insight into its development. However, traditional histological methods are limited by intensive procedures and size restrictions. Micro-computed tomography (microCT) enables non-destructive internal imaging along the length of an organ, but high cost, technical complexity, and limited accessibility hinder widespread application. Here, we describe serial section videography (SSV), a new low-cost technique for generating three-dimensional (3D) reconstructions of internal plant anatomy using serial sectioning and open-source software. Methods and results SSV was applied to four fern rhizomes with varied gross morphology and diverse vascular architectures. Specimens were sectioned using a sliding microtome or a handheld blade, and imaged using either a digital camera or smartphone setup. Images were aligned using Fiji and segmented using 3D Slicer. The SSV method enabled continuous visualization of internal stem anatomy over several centimeters and is adaptable to both laboratory and field settings. Conclusions This protocol offers an alternative to microCT for generating 3D anatomical reconstructions, enabling researchers to examine development and structural variation across organs with minimal equipment and software. This accessible protocol reduces technical and financial barriers and is particularly well-suited for comparative studies of vascular tissues, advancing the study of plant anatomy and development.
Why it matches plant phenotyping methods植物器官内部構造を連続撮像・画像処理して3D形態を再構成する低コスト手法の開発と適用が中心であり、植物形態・解剖状態の取得法として収載対象。
abstractHere, we describe serial section videography (SSV), a new low-cost technique for generating three-dimensional (3D) reconstructions of internal plant anatomy using serial sectioning and open-source software.
Stomata regulate gas and water exchange in plants and are crucial for plant productivity and survival, making their trait analysis essential for advancing plant biology research. While current machine learning methods enable automated stomatal trait extraction, existing approaches face significant limitations that require extensive manual labeling for training and additional human annotation when applied to new species. This study presents an automated system for extracting stomatal traits from Pisum sativum (pea) leaves that addresses these challenges through generative artificial intelligence. Our pipeline integrates imaging, detection, segmentation, and synthetic data generation processes. A nail polish impression technique was employed to prepare leaf microscopic images, followed by the application of deep learning networks to identify and segment stomata in these images. By including generative AI-produced synthetic data, our system achieves high segmentation accuracy across species, reducing manual relabeling requirements. This approach enables seamless cross-species model adaptation for many cases, alleviating the annotation bottleneck that often limits machine learning applications in plant biology. Our results demonstrate the pipeline's effectiveness for automated stomatal trait extraction and highlight generative AI's transformative potential in advancing stomatal detection methodologies, offering a scalable solution for broad-scale comparative stomatal analysis.
Why it matches plant phenotyping methods気孔形質を画像取得・検出・セグメンテーションで抽出する手法と、生成AIによる合成データを用いた種間適応を中心に開発しているため、植物フェノタイピング手法に該当する。
abstractThis study presents an automated system for extracting stomatal traits from Pisum sativum (pea) leaves that addresses these challenges through generative artificial intelligence.
The bigleaf hydrangea (Hydrangea macrophylla) is an emerging fashionable flower. However, leaf spot disease caused by Corynespora cassiicola is a major fungal disease in this species, seriously limiting the high-quality development of the industry. Since potential C. cassiicola induced genes have been identified via RNA-seq data, we aimed to develop an efficient method to screen C. cassiicola resistant genes in hydrangea. Firstly, we established a C. cassiicola inoculation system for detached leaf discs (CISDLD) and applied it to assess leaf spot resistance in hydrangea cultivators. Then, HmPDS1 and HmPDS2 were selected as the reporter genes to test the silencing effect in both cuttings and detached leaf discs, through observing a significant reduction in gene expression and chlorophyll content. Furthermore, ethylene was identified as a positive regulator in hydrangea defense responding to C. cassiicola, as well as jasmonic acid. The results showed that ethylene synthesis genes (HmACS1 and HmACO3) and signaling transduction genes (HmEIN3 and HmERF001) were validated as the C. cassiicola resistant genes in hydrangea. The VIGS screen of C. cassiicola-induced ethylene related genes demonstrated the potential benefits of this method for the high-throughput identification of gene function. This study offers a rapid approach for characterizing C. cassiicola-related gene functions in hydrangea and provides a theoretical framework for high-throughput gene screening in other plant species. KEY MESSAGE: A rapid and effective method for gene function screening based on VIGS with detached leaf discs reveals the crucial role of ethylene in hydrangea leaf spot resistance.
Why it matches plant phenotyping methods切除葉ディスクへの接種系とVIGSを組み合わせ、植物の葉斑病抵抗性を評価・スクリーニングする方法の確立が研究の中心であるため。
abstractA rapid and effective method for gene function screening based on VIGS with detached leaf discs reveals the crucial role of ethylene in hydrangea leaf spot resistance.
Why it matches plant phenotyping methods深層学習と画像処理により、感染植物細胞内のハウストリアを自動検出・セグメンテーションする公開パイプラインを開発し、手動計数および種間移植性を検証しているため、植物病害表現型の取得手法が中心である。
abstractWe report an openly available pipeline that automates the detection of β-glucuronidase (GUS)-stained epidermal cells and the intracellular haustoria formed by powdery mildew on barley and wheat leaves.
The internal quality assessment of potato tubers is a crucial task in agro-industrial processing. Traditional methods struggle to detect internal defects such as hollow heart, internal bruises, and insect galleries using only surface features. We present a novel, fully modular hybrid AI architecture designed for defect detection using RGB images of potato slices, suitable for integration in industrial sorting lines. Our pipeline combines high-recall multi-threshold YOLO detection, contextual patch validation using ResNet, precise segmentation via the Segment Anything Model (SAM), and skin-contact analysis using VGG16 with a Random Forest classifier. Experimental results on a labeled dataset of over 6000 annotated instances show a recall above 90\% and precision near 100\% for most defect classes. The approach offers both robustness and interpretability, outperforming previous methods that rely on costly hyperspectral or MRI techniques. This system is scalable, explainable, and compatible with existing 2D imaging hardware.
Why it matches plant phenotyping methodsジャガイモ塊茎の内部欠陥をRGB画像から検出・分割する画像解析パイプラインの開発と性能評価が中心であり、植物器官の状態を直接推定するため、植物フェノタイピング手法に該当する。
abstractWe present a novel, fully modular hybrid AI architecture designed for defect detection using RGB images of potato slices
Background Seed quality analysis using X-rays is increasingly explored due to its non-invasive and rapid nature. Yet, the current absence of reliable and standardised imaging protocols has led to contradictory effects of X-ray exposure in previous studies. Our work systematically investigated the effect of low-energy X-rays (peak energy ≲25 keV) with limited doses ( Results The baseline of three germination categories was established across seven species before the application of low-dose X-ray exposure under controlled standard germination conditions. The high inter-varietal and inter-lot variabilities, in addition to the strong interaction between X-ray exposure with both variety and lot, reinforced the need to consider genetic and seed quality aspects while evaluating the impacts of low-dose, low-energy X-rays ( 2 = 0.82) and their germination outcomes after exposure (p 2 = 0.88). Among all species, fennel with notably low density (0.7 g/cm 3 ) demonstrated the most pronounced gains in germination after exposure (4.6 ± 6.3%) due to the stimulative effect. Conclusion Low-dose X-ray exposure is non-destructive with a beneficial effect on germination, but can be strongly influenced by underlying genetics and the physical quality of the tested seeds. This work addressed important gaps in evaluating X-ray impacts and proposed a robust design and well-examined radiography protocol for a proven non-destructive seed quality analysis.
Why it matches plant phenotyping methods種子品質を非破壊に評価するX線ラジオグラフィーのプロトコルを検討・検証し、種子品質および発芽状態の測定法として方法論的貢献が中心に含まれる。
abstractSeed quality analysis using X-rays is increasingly explored due to its non-invasive and rapid nature.
Plant phenotyping relevance match · UnverifiedCrossref · Europe PMC · checked 6 Sept 2026
Introduction Diseases of plants remain one of the greatest threats to sustainable agriculture, with a direct adverse effect on crop productivity and threatening food security worldwide. Conventional detection methods rely heavily on manual detection and laboratory analysis, which are time-consuming, subjective, and unsuitable for large-scale monitoring. The use of the most recent progress in computer vision and artificial intelligence has opened up a prospect of automated, scalable, and precise disease diagnosis. Methods This paper introduces a feature-efficient hybrid model that trains classical Machie Learning (ML) classifiers with Deep Neural Network (DNN) using ResNet-based feature extraction and Principal Component Analysis (PCA). The PlantVillage dataset with mixed crop-disease pairs is used to implement and thoroughly test five hybrid models. Results Wide-ranging experiments proved that the Logistic Regression (LR)+DNN hybrid resulted in the best classification accuracy of 96.22% as compared to other models and available benchmarks. Besides being able to outperform other techniques in terms of predictive power, the framework displayed good training stability and robustness to class imbalance as well as a higher degree of interpretability based on LIME-based analysis. Discussion The obtained results confirm the hybrid ML+DNN paradigm as a safe, transparent, scalable disease recognition framework when applied to plant diseases. Providing opportunities for timely and accurate disease detection, the proposed framework can help with precision agriculture, where pesticide use can be reduced, consequently, and a significant contribution to sustainable farming can be achieved.
Why it matches plant phenotyping methods植物病害を対象に、ResNet特徴抽出・PCA・ML/DNNを組み合わせた画像ベースの病害認識手法を開発・評価しており、植物状態の推定方法が研究の中心である。
abstractThis paper introduces a feature-efficient hybrid model that trains classical Machie Learning (ML) classifiers with Deep Neural Network (DNN) using ResNet-based feature extraction and Principal Component Analysis (PCA).
When plants undergo senescence or experience carbon starvation, leaf cells degrade proteins in the chloroplasts on a massive scale via autophagy, an evolutionarily conserved process in which intracellular components are transported to the vacuole for degradation to facilitate nutrient recycling. Nonetheless, how portions of chloroplasts are released from the main chloroplast body and mobilized to the vacuole remains unclear. Here, we developed a method to observe the autophagic transport of chloroplast proteins in real time using confocal laser-scanning microscopy on transgenic plants expressing fluorescently labeled chloroplast components and autophagy-associated membranes. This protocol enabled us to track changes in chloroplast morphology during chloroplast-targeted autophagy on a timescale of seconds, and it could be adapted to monitor the dynamics of other intracellular processes in plant leaves. Key features • This protocol enables real-time monitoring of chloroplast morphology in living Arabidopsis leaves. • The method is based on confocal microscopy of transgenic plants that express fluorescent protein markers for specific organelles or suborganellar compartments. • We used this protocol to monitor the piecemeal autophagic degradation of chloroplasts, but it could also be extended to other intracellular phenomena.
Why it matches plant phenotyping methods生きた植物葉の葉緑体形態を共焦点画像でリアルタイム取得・追跡するプロトコルが中心であり、植物の形態状態を測定するフェノタイピング手法に該当する。
abstractHere, we developed a method to observe the autophagic transport of chloroplast proteins in real time using confocal laser-scanning microscopy on transgenic plants expressing fluorescently labeled chloroplast components and autophagy-associated membranes.
Introduction Cyclic loads caused by natural factors such as strong winds are common in plant growth environments. Prolonged exposure to such loads can compromise the anchorage performance of plants. This study examines how cyclic loading influences the root anchorage of Betula platyphylla, a prominent tree species in northern China. Methods A series of pull-out tests were performed on soil-embedded roots, including monotonic pull-out tests and 100 cycles of loading and unloading. Results The research results show that under different cyclic load amplitudes, the peak bearing capacity is negatively correlated with the load amplitude. Energy dissipation in the root system increases with higher load amplitudes but decreases as the number of cycles increases. From the initial cycle to the 25th cycle, energy dissipation decreased substantially, with no further significant reduction observed between the 25th and 100th cycles. To more effectively capture the nonlinear hysteretic behavior of roots, an enhanced Bouc-Wen model was developed and successfully fitted to the force-displacement curves. The model accurately replicated the hysteresis loops and characterized the damage progression in root anchorage under cyclic loading. Discussion These findings offer valuable insights into the mechanical stability of plant roots under repeated environmental stresses and provide a robust framework for modeling root anchorage performance in natural settings.
Why it matches plant phenotyping methods根系引抜試験によるアンカレッジ性能の測定と、力–変位曲線を用いた拡張Bouc-Wenモデルの開発・検証が研究の中心であり、植物の機械的形質を抽出・モデル化している。
abstractTo more effectively capture the nonlinear hysteretic behavior of roots, an enhanced Bouc-Wen model was developed and successfully fitted to the force-displacement curves.
In this study, we present a pilot investigation using a single Purple Heart plant (Tradescantia pallida) to explore whether bioelectrical signals for dual-purpose classification tasks: environmental state detection and human emotion recognition. Using an AD8232 ECG sensor at 400 Hz sampling rate, we recorded 3 s bioelectrical signal segments with 1 s overlap, converting them to mel-spectrograms for ResNet18 CNN (Convolutional Neural Network) classification. For lamp on/off detection, we achieved 85.4% accuracy with balanced precision (0.85–0.86) and recall (0.84–0.86) metrics across 2767 spectrogram samples. For human emotion classification, our system achieved optimal performance at 73% accuracy with 1 s lag, distinguishing between happy and sad emotional states across 1619 samples. These results should be viewed as preliminary and exploratory, demonstrating feasibility rather than definitive evidence of plant-based emotion sensing. Replication across plants, days, and experimental sites will be essential to establish robustness. The current study is limited by a single-plant setup, modest sample size, and reliance on human face-tracking labels, which together preclude strong claims about generalizability.
Why it matches plant phenotyping methods植物の生体電気信号をセンサーで取得し、スペクトログラムとCNNで環境状態を分類する手法を開発・評価しており、植物の生理状態に基づく表現型取得が中心です。ただし、人間の感情分類は植物表現型ではありません。
abstractUsing an AD8232 ECG sensor at 400 Hz sampling rate, we recorded 3 s bioelectrical signal segments with 1 s overlap, converting them to mel-spectrograms for ResNet18 CNN (Convolutional Neural Network) classification.
Reproduction assets foundThe paper's Data Availability Statement provides explicit public URLs for both the phenotype/bioelectrical signal dataset (figshare project) and the authors' analysis code (GitHub), directly reproducing this paper's plant-phenotyping measurements and computational analysis.Dataset · publicThe data is available at https://figshare.com/projects/Plant_Bioelectrical_Signals_for_Environmental_and_Emotional_State_Classification/265783 (accessed on 25 October 2025).Open asset ↗figshare · Plant_Bioelectrical_Signals_for_Environmental_and_Emotional_State_Classification/265783lines:129-206Code · publicCode is available at https://github.com/pgloor/hiddenbiosignals (accessed on 25 October 2025).Open asset ↗github · pgloor/hiddenbiosignalslines:129-206Plant phenotyping relevance match · UnverifiedEurope PMC · checked 15 Sept 2026
Published1 Nov 2025Journal of food composition and analysis : an official publication of the United Nations University, International Network of Food Data Systems
Maize seed vigor significantly impacts seedling emergence and overall yield. Thus, accurately assessing seed viability is crucial for ensuring crop quality. This study employs multispectral imaging to capture spectral images of maize seeds during the swelling absorption process. We analyzed and compared the spectral characteristics and their trends between viability and non-viability seeds across various absorption times, specifically at 12-h intervals. To improve the identification of seed viability, we integrated spectral data collected at multiple water absorption times with spectral difference data at 12-h intervals, forming a comprehensive time-series dataset. A classification model for seed viability was developed using stochastic subspace screening in conjunction with support vector machine (SVM) techniques. The results indicate that the stochastic subspace integrated learning approach effectively classifies maize seed viability, achieving classification accuracy exceeding 90 % after 36 h of water absorption. This method enables viability detection prior to seed germination. In conclusion, the integration of stochastic subspace learning and time-series spectral data significantly improves the identification of maize seed viability, offering new insights for seed viability detection.
Why it matches plant phenotyping methodsトウモロコシ種子の生存性という植物状態を、時系列マルチスペクトル画像と機械学習で非破壊推定する手法の開発が中心である。
abstractThis study employs multispectral imaging to capture spectral images of maize seeds during the swelling absorption process.
Amid growing challenges to global food security, high-throughput crop phenotyping has become an essential tool, playing a critical role in genetic improvement, biomass estimation, and disease prevention. Unlike controlled laboratory environments, field-based phenotypic data collection is highly vulnerable to unpredictable factors, significantly complicating the data acquisition process. As a result, the choice of appropriate data collection equipment and processing methods has become a central focus of research. Currently, three key technologies for extracting crop phenotypic parameters are Light Detection and Ranging (LiDAR), Multi-View Stereo (MVS), and depth camera systems. LiDAR is valued for its rapid data acquisition and high-quality point cloud output, despite its substantial cost. MVS offers the potential to combine low-cost deployment with high-resolution point cloud generation, though challenges remain in the complexity and efficiency of point cloud processing. Depth cameras strike a favorable balance between processing speed, accuracy, and cost-effectiveness, yet their performance can be influenced by ambient conditions such as lighting. Data processing techniques primarily involve point cloud denoising, registration, segmentation, and reconstruction. This review summarizes advances over the past five years in 3D reconstruction technologies—focusing on both hardware and point cloud processing methods—with the aim of supporting efficient and accurate 3D phenotype acquisition in high-throughput crop research.
Why it matches plant phenotyping methods作物キャノピーの3D形質取得に用いるLiDAR、MVS、深度カメラと点群処理を中心に扱うレビューであり、植物フェノタイピング手法が主題。
titleApplications of 3D Reconstruction Techniques in Crop Canopy Phenotyping: A Review
Plant diseases cause approximately 220 billion USD in annual agricultural losses, driving demand for automated detection systems. This systematic review analyzes deep learning approaches for plant disease detection using RGB and hyperspectral imaging, examining their evolution from classical image processing to modern neural architectures. We evaluate state-of-the-art models across 11 benchmark datasets, revealing significant performance gaps between laboratory conditions (95-99% accuracy) and field deployment (70-85% accuracy). Transformer-based architectures demonstrate superior robustness, with SWIN achieving 88% accuracy on real-world datasets compared to 53% for traditional CNNs. Our analysis identifies three critical deployment constraints: environmental variability sensitivity, economic barriers (500-2000 USD for RGB vs. 20,000-50,000 USD for hyperspectral systems), and interpretability requirements for farmer adoption. Case studies of successful platforms (Plantix with 10+ million users) highlight the importance of offline functionality and multilingual support. We establish evidence-based guidelines prioritizing deployment viability over laboratory optimization and identify key research directions including lightweight model design, cross-geographic generalization, and explainable multimodal fusion. This review provides a comprehensive framework for advancing plant disease detection from research prototypes to practical agricultural tools that can improve global food security.
Why it matches plant phenotyping methods植物病害をRGB・ハイパースペクトル画像から検出する手法を体系的にレビューし、ベンチマーク比較、性能評価、展開上の制約、今後の方法論を扱うため、植物フェノタイピング手法レビューとして中心的です。
abstractThis systematic review analyzes deep learning approaches for plant disease detection using RGB and hyperspectral imaging
We address the challenge that occlusions in on-branch soybean images impede accurate pod-level phenotyping. We propose a lab on-branch pipeline that couples a prior-guided synthetic data generator (producing synchronized visible and amodal labels) with an amodal instance segmentation framework based on an improved Swin Transformer backbone with a Simple Attention Module (SimAM) and dual heads, trained via three-stage transfer (synthetic excised → synthetic on-branch → few-shot real). Guided by complete (amodal) masks, a morphology-driven module performs pose normalization, axial geometric modeling, multi-scale fused density mapping, marker-controlled watershed, and topological consistency refinement to extract seed per pod (SPP) and geometric traits. On real on-branch data, the model attains Visible Average Precision (AP) 50/75 of 91.6/77.6 and amodal AP50/75 of 90.1/74.7, and incorporating synthetic data yields consistent gains across models, indicating effective occlusion reasoning. On excised pod tests, SPP achieves a mean absolute error (MAE) of 0.07 and a root mean square error (RMSE) of 0.26; pod length/width achieves an MAE of 2.87/3.18 px with high agreement (R 2 up to 0.94). Overall, the co-designed data-model-task pipeline recovers complete pod geometry under heavy occlusion and enables non-destructive, high-precision, and low-annotation-cost extraction of key traits, providing a practical basis for standardized laboratory phenotyping and downstream breeding applications.
Why it matches plant phenotyping methods大豆莢の遮蔽下形状を画像から復元し、種子数や幾何形質を抽出する画像解析パイプラインを開発・評価しており、植物表現型取得が中心である。
abstractWe propose a lab on-branch pipeline that couples a prior-guided synthetic data generator (producing synchronized visible and amodal labels) with an amodal instance segmentation framework
Abstract. Carbonyl sulfide (COS) has been proposed as a proxy for gross primary production (GPP), as it is taken up by plants through a pathway comparable to that of CO2. COS diffuses into the leaf, where it undergoes an essentially one-way reaction in the mesophyll cells, irreversibly catalyzed by the enzyme carbonic anhydrase (CA), and is likely not respired by the leaf. In order to use COS as a proxy for GPP, the mechanisms of COS uptake and its coupling to photosynthesis need to be well understood. Characterizing the isotopic discrimination of COS during plant uptake could provide valuable information on the physiological COS uptake process and may help to constrain the COS budget. This study presents joint measurements of isotope discrimination during plant uptake for COS (CO34S) and CO2 (13CO2 and C18O16O). A C3 plant, sunflower (Helianthus annuus), and a C4 plant, papyrus (Cyperus papyrus), were enclosed in a flow-through plant chamber and exposed to varying light levels. The incoming and outgoing gas compositions were measured online, and discrete air samples were taken for isotope analysis. Simultaneously measuring fluxes and isotope discrimination of both COS and CO2 yielded a unique dataset that includes information on the plant's behavior and allowed for the estimation of stomatal- and mesophyll conductances. The average COS uptake fluxes were 73.3 ± 1.5 pmol m−2 s−1 for sunflower and 107.3 ± 1.5 pmol m−2 s−1 for papyrus (PAR > 0) and displayed virtually no trend with increasing PAR from 200 to 600 µmol m−2 s−1. The mean observed 34Δ for COS was 3.4 ± 1.0 ‰ for sunflower and 2.6 ± 1.0 ‰ for papyrus. 34Δ was stable across all light intensities, which could be explained by a sufficient stomatal opening and low variability in the ratio of mesophyll vs. ambient COS mole fraction, CmS/CaS. For both C3 and C4 plants, for CO2, a negative relationship was observed between the uptake flux and the isotopic discriminations 13Δ and 18Δ. The CO2 uptake and 13CO2 and C16O18O discriminations of sunflower have expected values for a C3 plant, while the low CO2 flux and high 13Δ and 18Δ values observed for papyrus were not in the typical C4 range, which was perhaps due to the relatively low light conditions during our experiments.
Why it matches plant phenotyping methods植物のCOS・CO2取り込み、同位体識別、気孔・葉肉コンダクタンスをフロースルー植物チャンバーで定量する生理的表現型測定が研究の中心であり、再利用可能な測定データセットと推定手法を提示している。
abstractThis study presents joint measurements of isotope discrimination during plant uptake for COS (CO34S) and CO2 (13CO2 and C18O16O).
Reproduction assets foundThe paper's isotope discrimination and gas-exchange dataset from the flow-through chamber experiments is publicly deposited on Zenodo by the authors.Dataset · publicynthetically available radiation at the top of the chamber, 34 Δ is the discrimination against CO 34 S and LRU is the leaf relative uptake ratio.
* n =1 , error states is the single measurement precision instead of the repeatability precision.
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Data availability
The dataset is available at: https://doi.org/10.5281/zenodo.14677494 (Baartman et al., 2025).
Author contributions
Conceptualization: SLB, MCK, MEP, LW. Data curation: SLB. Formal analysis: SLB, NUL. Funding acquisition: MCK. Investigation: SLB, SMD, MW, LMJK, LM, AC, SH. Methodology: SLB, SMD, MW, LMJK, MEP. Resources: SMD, MW, LM, SH. Supervision: MEP, TR, MCK. Visualization: SLB, NUL. WritingOpen asset ↗Zenodo · 10.5281/zenodo.14677494lines:652-942Plant phenotyping relevance match · UnverifiedCrossref · Europe PMC · checked 15 Sept 2026
Laboratory / benchtopMicroscopyMultimodalCell / cellular structureRootTissueTrackingVisualization / data management
Abstract Root biology is pivotal in addressing global challenges including sustainable agriculture and climate change. However, roots have been relatively understudied among plant organs, partly due to the difficulties in imaging root structures in their natural environment. Here we used microfabricated ecosystems (EcoFABs) to establish growing environments with optical access and employed nonlinear multimodal microscopy of third-harmonic generation (THG) and three-photon fluorescence (3PF) to achieve label-free, in situ imaging of live roots and microbes at high spatiotemporal resolution. THG enabled us to observe key plant root structures including the vasculature, Casparian strips, dividing meristematic cells, and root cap cells, as well as subcellular features including nuclear envelopes, nucleoli, starch granules, and putative stress granules. THG from the cell walls of bacteria and fungi also provides label-free contrast for visualizing these microbes in the root rhizosphere. With simultaneously recorded 3PF signal, we demonstrated our ability to investigate root-microbe interactions by achieving single-bacterium tracking and subcellular imaging of fungal spores and hyphae in the rhizosphere.
Why it matches plant phenotyping methodsTHG/3PFによる根の構造を高時空間分解能でラベルフリー取得するイメージング手法を開発・実証しており、植物表現型取得が中心である。
abstractemployed nonlinear multimodal microscopy of third-harmonic generation (THG) and three-photon fluorescence (3PF) to achieve label-free, in situ imaging of live roots and microbes at high spatiotemporal resolution
Common beanLaboratory / benchtopRaman / spectroscopySeed / grainPhysiological trait estimation
Common bean ( Phaseolus vulgaris L.) is the world's most important legume crop and a vital staple food for millions of people in Latin America and Africa. Given the increasing trend in bean consumption and its importance for nutrition and food security in these regions, there is an urgent need to enhance common bean seeds' nutritional value through breeding. This requires rapidly assessing large and diverse germplasm collections to uncover key nutritional traits in the available genetic diversity. To address this challenge, Near-Infrared Spectroscopy (NIRS) offers a large-scale, cost-effective and non-destructive approach for accurately predicting nutrient content in intact common bean seeds. This study describes the development of predictive models based on NIRS to predict nitrogen (N), iron (Fe) and zinc (Zn) content, using whole common bean seeds from a germplasm core collection held at the International Center for Tropical Agriculture. Spectra were captured for 1754 accessions (wild and domesticated), and reference values for N, Fe, and Zn content were measured with conventional destructive methods in a panel of 401 accessions. Prediction models of N content achieved a concordance correlation coefficient (CCC) of 0.84, while for Fe and Zn, CCC was 0.4. NIRS quantification detected higher N content in wild accessions than in domesticated accessions. These results demonstrate that NIRS can effectively estimate the N content of common bean seeds in a non-destructive manner, while providing valuable nutritional information to enhance access to large genebank collections for bean breeding.
Why it matches plant phenotyping methodsNIRSによるインタクトなインゲン種子の栄養形質推定モデルを開発・検証しており、方法が研究の中心である。
abstractThis study describes the development of predictive models based on NIRS to predict nitrogen (N), iron (Fe) and zinc (Zn) content
Abstract Insertion of nanoparticles (NPs) in plants induce various biophysical changes as well as modulate ion channels and transporters, resulting in improved water and nutrient uptake. High concentration of NPs has toxic effect like excessive production of reactive oxygen species, hormonal imbalances and impaired cellular processes. These biophysical changes also change the complex impedance of plant leaves. Here, we use impedance spectroscopy to probe, for the first time, the electrochemical response of the succulent Crassula ovata leaves following exposure to water-soluble carbon nanodots (CNDs) and zinc oxide (ZnO) nanoparticles. Nanoparticles were introduced through static root immersion in aqueous suspensions at varying concentrations (1, 5, and 10 mg L-1). Quantitative analysis revealed strikingly different dielectric signatures. CND treatment caused grain boundary (gb) resistance to rise from ~256 Ω in the control sample to ~27.6 kΩ at 10 mg L-1 accompanied by a consistent suppression of permittivity, reflecting progressive obstruction of ionic pathways and space-charge accumulation, on NP insertion. ZnO NPs, in contrast, showed a saturation effect: gb resistance peaked at ~14.6 kΩ at 5 mg L-1 but declined to ~7.3 kΩ at 10 mg L-1, where conductivity and dielectric relaxation partially recovered through Zn2+-mediated defect pathways. Equivalent-circuit modelling and Jonscher analysis corroborated these concentration-dependent shifts, revealing nanomaterial-specific modulation of ionic mobility and capacitive behaviour. Together, these findings establish a mechanistic contrast between carbon-based and metal-oxide nanomaterials in plant systems, underscoring nanoparticle chemistry as a key determinant of electrochemical response. This comparative framework advances plant nanobionics by linking material composition to bioelectrical function, with implications for bioelectronics, sensing, and sustainable energy interfaces.
Why it matches plant phenotyping methods植物葉の電気化学的・生理状態をインピーダンス分光で定量抽出し、等価回路モデル等で検証する測定法が研究の中心であるため、植物フェノタイピング手法として採用。
abstractHere, we use impedance spectroscopy to probe, for the first time, the electrochemical response of the succulent Crassula ovata leaves following exposure to water-soluble carbon nanodots (CNDs) and zinc oxide (ZnO) nanoparticles.
ArabidopsisLaboratory / benchtopMicroscopyCell / cellular structureRootPhysiological trait estimationGrowth / development / phenologyWater status / transpirationYield / yield components
Tip growth is a tightly regulated process that enables root hairs to explore their surroundings, enhancing plant development, particularly by improving nutrient uptake. While Lockhart's viscoplastic framework is widely used to describe this process, it has received limited experimental validation. By integrating optical microscopy with a custom microplate-based rheometer, we created a novel protocol to simultaneously measure, for individual growing root hairs, both the reduction in growth rate and the instantaneous compression in response to a step in applied axial force. The observed growth rate reduction aligns remarkably with a 1D Lockhart viscoplastic model, experimentally validating this framework in tip-growing cells. Additionally, the instantaneous compression upon force application provided an in situ estimate of turgor pressure. Together, these measurements allowed us to determine, for the first time in Arabidopsis root hairs, two critical parameters: the yield turgor pressure and cell wall viscosity. Our approach, including the technique, protocol, and analytical framework, can be readily adapted to other tip-growing species and diverse experimental conditions (e.g., varying nutrient availability or osmotic stress). This opens new opportunities to explore cell wall mechanosensitivity and its role in adapting tip growth to environmental signals.
Why it matches plant phenotyping methods個々の根毛の成長速度・圧縮・膨圧を測定する新規手法と解析枠組みが研究の中心であり、植物形質の取得とモデル検証を実施している。
abstractBy integrating optical microscopy with a custom microplate-based rheometer, we created a novel protocol to simultaneously measure, for individual growing root hairs, both the reduction in growth rate and the instantaneous compression in response to a step in applied axial force.
With the global population expected to increase substantially, it raises a concern about feeding these populations, and it becomes essential to protect crops from diseases for food security. According to several studies, plant diseases and pests cause about 20–40% of the world's crop yield to be lost each year. Current plant disease detection methods include visual inspections, microscopy, culture-based procedures, molecular techniques, etc. These techniques are time-consuming, require specialized equipment and expertise, and are prone to human error. To address this problem, this study employs a customized Convolution Neural Network (CNN), which provides a more effective and scalable substitute for manual inspection and lab-based diagnostic techniques. The model uses CNN's sequential architecture along with softmax and ReLU activation functions. While ReLU introduces non-linearity in the model, which is essential for complex feature extraction, softmax helps in the normalization of vectors and multiclass classification. It has 3 blocks, each consisting of a convolution layer, a pooling layer, and a dropout layer. The model operates on a publicly available hybrid dataset taken from PlantVillage and DoctorP datasets, with a combined total of 5,721 images organized into sub-directories representing different diseases belonging to major groups like fungi, bacteria, virus, non-infectious conditions, nematodes and pests/insects. Images of each category were fed to the model, to identify diseases which are complex to be detected through images. Our model achieved an overall accuracy of 96.54%, illustrating the potential of CNN-based approaches for automated plant disease detection.
Why it matches plant phenotyping methods植物画像から病害状態を推定するCNNを開発し、公開画像データセットで性能を評価しており、植物フェノタイピング手法が研究の中心です。
abstractthis study employs a customized Convolution Neural Network (CNN), which provides a more effective and scalable substitute for manual inspection and lab-based diagnostic techniques.
Phytoremediation, the use of plants to mitigate environmental contaminants, offers a sustainable and cost-effective approach to cleaning contaminated sites. Developing methods that aid in elucidating the mechanisms behind plant uptake and metabolism of pollutants is crucial for improving phytoremediation practices. This article describes a method to assess the uptake and transformation of organic contaminants by plants using radiolabeled compounds. 14 C-labelled organic compounds, such as model 14 C-naphthenic acids, are used to trace their absorption, translocation, localization, and metabolism in plant tissues. We have previously used this method with multiple plant species, including Elymus trachycaulus and Salix interior. These observations are corroborated here with the model plant, Arabidopsis thaliana, grown hydroponically in modified Hoagland solutions. Radiolabel uptake was monitored via liquid scintillation counting and phosphor-imaging, which allows for visualization and quantification of radiolabeled compounds within plant tissues. This method details the preparation of plant materials, the use of radiolabeled compounds, and the process of analyzing the distribution and fate of contaminants within plants. The method also includes strategies for assessing compound exudation and allows for the evaluation of both plant uptake and translocation of environmental contaminants. This approach provides insight into plant-mediated remediation processes and can be applied to the study of a wide range of environmental contaminants and plant species.
Why it matches plant phenotyping methods植物組織における汚染物質の吸収・移行・局在を定量・可視化する放射標識およびホスファーイメージング法が中心で、植物の生理状態を測定する再利用可能な手法を詳述している。
abstractThis article describes a method to assess the uptake and transformation of organic contaminants by plants using radiolabeled compounds.
Plant phenotyping relevance match · UnverifiedOpenAlex · Europe PMC · Crossref · checked 6 Sept 2026
In recent years, non-destructive and non-invasive methods for 3D plant reconstruction have gained increasing importance in plant phenotyping. Morphological traits reflect the physiological status of a plant and serve as key indicators for precision agriculture, crop protection, and food quality assessment. Accurate and efficient 3D modelling enables objective and repeatable monitoring of plant development and health, thus supporting data-driven decision-making in agricultural and food research. This study presents a novel, cost-effective, and flexible photogrammetric apparatus for the routine analysis of plant morphological traits under controlled laboratory conditions. Existing systems often rely on expensive instrumentation and provide limited adaptability, whereas the platform described here combines affordability with high precision and robustness. A key innovation is the use of a robotic arm to control an industrial RGB camera, providing substantial flexibility in image acquisition. This mobility ensures comprehensive coverage of plants of different sizes and architectures while minimising occlusions. Another distinctive feature is the implementation of an optimised parameter tweak in the photogrammetric pipeline, which markedly improves the reconstruction of thin and delicate plant parts such as leaves, petioles, and fine stems. In combination with optimised acquisition parameters, including an exposure time of 50 milliseconds, a tweak value of 0.9, and a camera-to-object distance of 16 centimetres, the system achieves consistent model fidelity across diverse plant structures. Efficiency was further enhanced through automation and an optimised scanning procedure. Comparative testing showed that using a larger number of camera positions with fewer frames per position improved throughput, with the best configuration consisting of three height levels and 40 frames each. These improvements reduced the processing time by 75%, decreasing the average scan duration from 8 min to only 2.7 min per plant, while maintaining accuracy and reliability. Overall, the developed apparatus constitutes a reliable and low-cost solution that integrates robotic-assisted flexibility, improved reconstruction through the parameter tweak, and markedly reduced scanning time. The combination of precision, affordability, and efficiency makes the system competitive with existing approaches and, due to its accessibility and detailed methodological description, provides a distinctive contribution to the phenotyping community.
Why it matches plant phenotyping methods植物形態形質の3D取得を目的とするSfM-MVS撮像・再構成プラットフォームを開発し、精度、処理時間、撮像条件を比較検証しており、フェノタイピング手法が中心である。
abstractThis study presents a novel, cost-effective, and flexible photogrammetric apparatus for the routine analysis of plant morphological traits under controlled laboratory conditions.
Abstract Background and Aims Rare Earth Elements (REE) are essential for the development of clean technologies. Hyperaccumulator plants are metal-loving organisms that can be used to remove metals from contaminated soils. This study aimed to discover new REE hyperaccumulators in the Australasian region among the Blechnaceae and Gleicheniaceae families using specimens stored at the Queensland Herbarium. Methods A handheld X-ray fluorescence (XRF) instrument was harnessed to scan herbarium specimens, and this data was analysed with Dynamic Analysis in GeoPIXE. Selected specimens were further analysed to validate the XRF results: elemental analysis was conducted with inductively coupled plasma optical emission spectroscopy (ICP-OES), an elemental distribution map through micro-X-ray fluorescence (µXRF) and scanning electron microscopy (SEM) to rule out airborne contamination of plant samples. Results From the 3256 specimens analysed with the portable XRF, 73 specimens met the criteria to be considered REE hyperaccumulators (yttrium >50 µg g-1 on XRF analysis). Among this group, 11 new hyperaccumulator taxa were discovered, and the elemental analysis reported a total REE concentration around 1000 µg g-1, i.e. Diploblechnum neglectum (978 µg g-1), Sticherus flabellatus (1130 µg g-1), Sticheropsis milnei (1290 µg g-1). We validated the strong REE hyperaccumulating capacity of the previously reported ferns Blechnopsis orientalis (3850 µg g-1 total REEs) and Dicranopteris linearis (1280 µg g-1 total REEs). Conclusions The use of non-destructive portable XRF to scan herbaria collections is a tool to discover hyperaccumulator plants and this information could also be used as a bioprospecting tool to find REE deposits for potential REE phytomining.
Why it matches plant phenotyping methods携帯型XRFによる植物標本の非破壊スキャンを用いてREE蓄積形質を抽出し、ICP-OES等で検証しており、植物形質の取得・検証法が研究の中心である。
abstractA handheld X-ray fluorescence (XRF) instrument was harnessed to scan herbarium specimens, and this data was analysed with Dynamic Analysis in GeoPIXE.
Accurate 3D phenotyping of agricultural produce remains challenging due to the trade-off between reconstruction quality and acquisition throughput in existing sensing technologies. While RGB-D cameras enable high-throughput scanning in operational settings like harvesting conveyors, they produce incomplete, low-quality 3D models. Conversely, close-range Structure-from-Motion (SfM) produces high-quality reconstructions but is not suitable for high-throughput field application. This study bridges this gap through 3DPotatoTwin , a paired dataset containing 339 tuber samples across three cultivars collected in Hokkaido, Japan. Our dataset uniquely combines: (1) conveyor-acquired RGB-D point clouds, (2) ground measurement, (3) SfM reconstructions under indoor controlled environment, and (4) aligned model pairs with transformation matrices. The multi-sensory alignment employs an semi-supervised pin-guided pipeline incorporating single-pin extraction and referencing, cross-strip matching, and binary-color-enhanced ICP, achieving 0.59 ± 0.11 mm registration accuracy. Beyond serving as a benchmark for 3D phenotyping algorithms, the dataset enables training of 3D completion networks to reconstruct high-quality 3D models from partial RGB-D point clouds. Meanwhile, the proposed semi-automated annotation pipeline has the potential to accelerate 3D dataset generation for similar studies. The presented methodology demonstrates broader applicability for multi-sensor data fusion across crop phenotyping applications. The dataset and pipeline source code are publicly available at HuggingFace and GitHub, respectively.
Why it matches plant phenotyping methodsジャガイモ塊茎の3D表現型計測を対象に、RGB-D・SfM・地上計測を統合したデータセット、位置合わせパイプライン、ベンチマークを開発しており、表現型取得手法が中心である。
abstractAccurate 3D phenotyping of agricultural produce remains challenging due to the trade-off between reconstruction quality and acquisition throughput in existing sensing technologies.
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 batch processing scripts mentioned in this section were provided in the 3dscan folder at Github (https://github.com/UTokyo-FieldPhenomics-Lab/PotatoScan/).Open asset ↗UTokyo-FieldPhenomics-Lab/PotatoScanhtml-lines:119-131Plant phenotyping relevance match · UnverifiedEurope PMC · Crossref · checked 6 Sept 2026
Abstract Background Arbuscular mycorrhizal fungi (AMF) are ancient soil symbionts that form mutualistic associations with approximately 80% of terrestrial plant species. They enhance host nutrient and water acquisition in exchange for photosynthetic carbon. Current AMF research relies on field trials, compartmented cultivation and pot cultures‒methods that are time-consuming (months to years) and unable to monitor dynamic nutrient transport, thus limiting efficient strain screening. Results We developed a real-time fluorescence imaging platform integrating sterile symbiotic microchambers with photodiode array detection. This system enables non-invasive, quantitative tracking of nutrient flux at plant-fungal interface. Distinct AMF strains exhibit significant differences in fluorescence kinetics—such as accumulation rate and peak intensity—providing measurable indicators of transport efficiency. The platform allows high-throughput functional screening of AMF strains, dramatically accelerating the identification of high-performance symbionts. Conclusion Our method overcome the temporal and technical limitations of conventional AMF screening approaches. By enabling simultaneous real-time monitoring and high-throughput analysis, it shortens screening cycles and establishes a standardized framework for (1) precision breeding of efficiency AMF strains, (2) mechanistic study of nutrient exchange, and (3) development of sustainable microbial inoculants.
Why it matches plant phenotyping methods植物と菌根菌の界面における栄養輸送をリアルタイム定量する蛍光イメージング基盤を開発しており、植物関連の生理状態を取得する方法が研究の中心である。
abstractWe developed a real-time fluorescence imaging platform integrating sterile symbiotic microchambers with photodiode array detection.
Introduction: spp.) is a perennial legume traditionally cultivated as a forage crop and is now emerging as a promising candidate for development as a perennial grain legume. Despite its potential, no research has addressed the breeding of sainfoin varieties with superior grain processing properties. Methods: We conducted a multifactorial experiment to evaluate the depodding and dehulling efficiency of five commercially available sainfoin varieties. Seeds were processed using two different methods (belt thresher and impact dehuller) across five sample sizes. A pre-trained Faster R-CNN (Region-based Convolutional Neural Network) object detection model was fine-tuned to identify intact pods, whole seeds, and split seeds from images of the processed mixtures. These predictions were used to calculate processing efficiency (PE) for each variety. A comprehensive power analysis was performed to determine the minimum sample size of sainfoin pods required to detect differences in PE with high statistical power. Results: We observed strong varietal differences in PE, as well as clear effects of the processing method. Belt threshing produced mixtures with more intact pods, while the impact dehuller generated a higher proportion of split seeds. Increasing sample size led to more intact pods across all varieties and methods, and notably decreased seed proportion in belt-threshed samples. Statistical modeling combined with object detection outputs revealed that a minimum of 2 g of pods is required to reliably detect an absolute proportional difference of 0.25 in PE between two breeding lines with 80% power. Discussion: Our findings demonstrate that sainfoin varieties differ significantly in processing efficiency and that processing outcomes depend strongly on both method and sample size. Integrating deep learning-based phenotyping with robust statistical design enables efficient evaluation of processing traits and provides actionable guidelines for breeding programs. While deep learning models offer powerful, cost-effective tools for plant phenotyping, their outputs must be paired with rigorous statistical design to yield reliable and actionable insights for crop improvement.
Why it matches plant phenotyping methods画像からポッド・種子を検出し、処理効率という植物由来形質を算出する深層学習ベースの表現型解析が研究の中心であるため。
titleDeep learning driven, image-based phenotyping of seed processing efficiency in sainfoin
Reproduction assets foundThe paper's data availability statement explicitly deposits the seed image dataset and Faster R-CNN model weights in two public Zenodo repositories and all Python/R analysis code in a public GitHub repository, all with direct URLs.Dataset · publicThe image dataset and FasterRCNN model weights presented in the study are deposited in publicly available Zenodo repositories under accession numbers https://doi.org/10.5281/zenodo.8346923Open asset ↗Zenodo · 10.5281/zenodo.8346923lines:501-517Code · publicAll Python and R code used in this study are deposited in a public GitHub repository at https://github.com/BoMeyering/sainfoin_seed_RCNNOpen asset ↗GitHub · BoMeyering/sainfoin_seed_RCNNlines:501-517Plant phenotyping relevance match · UnverifiedEurope PMC · checked 6 Sept 2026
Abstract This proof-of-concept study explores the potential of hyperspectral imaging (HSI) for nondestructive characterization of feeding damage by two invasive stink bug species on bean pods under controlled laboratory conditions. We compared spectral signatures of feeding sites caused by Halyomorpha halys and Nezara viridula on Phaseolus vulgaris seven days postinfestation. Using a limited dataset of 45 observations from 20 pods (12 individual plants), we identified distinct spectral modifications induced by both species. H. halys feeding increased visible spectrum reflectance (450-700 nm) with reduced near-infrared (NIR) reflectance, while N. viridula caused more severe spectral changes with substantial NIR decreases (up to 31% at 800 nm). Difference spectra revealed species-specific sensitivity patterns with maximum responses at 540 nm (green) and 740 nm (NIR) for both species, though N. viridula showed consistently larger deviations from healthy tissue. Key wavelengths for damage characterization were identified at 660-680 nm and 730-780 nm for both species. While these initial findings suggest potential spectral discrimination between stink bug feeding damage and healthy tissue, we emphasize that this preliminary study requires extensive validation with larger sample sizes, multiple cultivars, comparison with other herbivores, and field conditions before any practical application. The results provide first insights into spectral responses to stink bug feeding that warrant comprehensive follow-up investigation practical hyperspectralbased pest symptom monitoring tools that could enable more targeted management strategies against these economically significant pests.
Why it matches plant phenotyping methods豆莢の吸汁被害という植物状態をハイパースペクトル画像で非破壊評価する方法の概念実証であり、スペクトル特徴と識別波長の抽出が研究の中心です。
abstractThis proof-of-concept study explores the potential of hyperspectral imaging (HSI) for nondestructive characterization of feeding damage by two invasive stink bug species on bean pods under controlled laboratory conditions.
The objective of this study is to use synchrotron-based X-ray fluorescence imaging (XFI) and bulk analysis to investigate elements (Mn, Fe, Cu, Zn, P, S, K, Ca) distributions and relative concentrations in four cool-season oat varieties (CDC Arborg, CDC Nasser, CDC Haymaker, and Summit) obtained from the same growing location, soil conditions and harvest time at the University of Saskatchewan. XFI at the Canadian Light Source's BioXAS-Imaging beamline (5 μm resolution, 15 keV) revealed that P, K, Mn, and Zn were concentrated in the aleurone layer, scutellum, and embryo, while Ca was only localized in the aleurone layer and scutellum in the four oat varieties. Notably, S and Cu were distributed in all parts of the seed across four varieties, but the intensity was low in the endosperm. Bulk analysis results show that there were significant differences in the relative concentrations of K, Fe and Zn among four oat varieties harvested for three consecutive years (2018, 2019, 2020) at the completely mature stage. CDC Nasser oat had the lowest K and Zn, while CDC Haymaker had the highest Fe among the oat varieties. These findings highlight the impact of variety on nutritional quality and could help inform future biofortification strategies to enhance the micronutrient content for human and animal diets. This work is the first to map the oat mineral distributions across cool-season varieties using high-resolution XFI. Unlike rice, oats showed minimal mineral accumulation in the hull, ensuring nutritional retention post-milling. Overall, this study shows XFI's potential as a non-destructive tool for cereal grain analysis and supports breeding nutrient-dense oat varieties to address global micronutrient deficiencies.
Why it matches plant phenotyping methodsX線蛍光イメージングを用いてオート麦種子の元素分布・相対濃度を高解像度かつ非破壊で取得し、その方法の有用性を主要な成果として示しているため、植物器官の化学的形質測定法として採用。
abstractThis work is the first to map the oat mineral distributions across cool-season varieties using high-resolution XFI.
A rigid body can have 3 degrees of rotational freedom. Of these, the yaw or in-plane rotation is well known and studied while the pitch or the first in-plane rotation is somewhat studied. However the roll or the second in-plane rotation has not been so well studied. It is here that we show how to detect roll rotation for a 4-fold symmetric Calcium Oxalate crystal inside a plant leaf cell by using the anisotropy of the scatter pattern while trapped in optical tweezers, behind a set of crossed polarizers. The difference in halves signal in the appropriate sense gives roll rotation while that in another sense gives pitch rotation of the crystal. We show that this can be used to perform nano-tribology of the interior surface of the leaf cell with a contact radius of about 500nm, without relying upon Atomic Force Microscopes, thus enabling soft probing.
Why it matches plant phenotyping methods植物葉細胞内の結晶回転を光トラップと散乱異方性で検出し、葉細胞内面の接着性を定量する新規測定法が中心であり、植物細胞の生理的状態を取得する方法開発に該当する。
abstractwe show how to detect roll rotation for a 4-fold symmetric Calcium Oxalate crystal inside a plant leaf cell by using the anisotropy of the scatter pattern while trapped in optical tweezers
Plant diseases cause approximately 220 billion USD in annual agricultural losses, driving demand for automated detection systems. This systematic review analyzes deep learning approaches for plant disease detection using RGB and hyperspectral imaging, examining their evolution from classical image processing to modern neural architectures. We evaluate state-of-the-art models across 11 benchmark datasets, revealing significant performance gaps between laboratory conditions (95-99% accuracy) and field deployment (70-85% accuracy). Transformer-based architectures demonstrate superior robustness, with SWIN achieving 88% accuracy on real-world datasets compared to 53% for traditional CNNs. Our analysis identifies three critical deployment constraints: environmental variability sensitivity, economic barriers (500-2000 USD for RGB vs. 20,000-50,000 USD for hyperspectral systems), and interpretability requirements for farmer adoption. Case studies of successful platforms (Plantix with 10+ million users) highlight the importance of offline functionality and multilingual support. We establish evidence-based guidelines prioritizing deployment viability over laboratory optimization and identify key research directions including lightweight model design, cross-geographic generalization, and explainable multimodal fusion. This review provides a comprehensive framework for advancing plant disease detection from research prototypes to practical agricultural tools that can improve global food security.
Why it matches plant phenotyping methods植物病害をRGB・ハイパースペクトル画像から検出する手法を体系的にレビューし、ベンチマーク、性能比較、実運用上の技術課題を扱っており、植物の病害状態推定法が中心である。
abstractThis systematic review analyzes deep learning approaches for plant disease detection using RGB and hyperspectral imaging, examining their evolution from classical image processing to modern neural architectures.
Anthropogenic climate change is causing the decline of seaweed forests in many parts of the world. Despite successful preservation efforts, their immense biodiversity is still severely underrepresented in germplasm biobanks throughout the world. These culture libraries can preserve genetic diversity and provide inoculum for marine forest restoration and mariculture ventures, and potentially accelerate the selection and breeding of climate-resilient and high-yielding strains. However, the complex life cycles and body plans of seaweeds pose a huge challenge for the development of standardized phenotyping and isolating protocols for microscopic stages, especially with the efficiency necessary to deal with the current pace of global climatic changes. Here, we present SAMMBA (Seaweed Automatable Microplate Microscopy for Breeding Approaches), an end-to-end pipeline for the high-throughput isolation, phenotyping and storage of macroalgal cells in 384-well plates (384WP). By optimizing fluorescence microscopy imaging and analysis, along with a novel fragmentation method and dilution-to-extinction isolation, different unialgal seaweed tissues could be regrown after thousand-fold dilutions. In a single plate, we successfully isolated 68 singlet gametophyte fragments of Laminaria ochroleuca (39 males, 29 females; 17.7% efficiency) and 60 spores of Phyllariopsis purpurascens (31.25% efficiency). Furthermore, the taxonomic versatility of SAMMBA was demonstrated through the successful isolation of 60 unialgal cultures of red algae ( Halymenia sp., Hydrolithon sp., Erythrotrichia sp. ) and 10 strains of the green alga Ulva sp, without cross-contamination. The viability and unialgal nature of the isolated strains were verified by distributing a single L. ochroleuca strain across an entire 384-well plate and imaging each well over 30 days. We found that the average specific daily growth rates (daily SGR) per well were 0.130 ± 0.006 and 0.117 ± 0.01 day -1 for males and females, respectively, showing a significant difference between sexes (n = 768; p = 1.27e -53 ), while edge effects significantly reduced daily SGR in males but not in females. This approach dramatically increases experimental reproducibility and statistical power compared to conventional methods. Due to its modular design and cost-effectiveness, SAMMBA is readily adaptable to macroalgal repositories globally. It supports high-throughput, selective recovery of unialgal strains without reliance on robotic platforms, while remaining fully compatible with automation. This system significantly expands the experimental and operational capacity in macroalgal hatcheries, providing a scalable foundation for phenomics, domestication programs, and standardized, verifiable biobanking of unialgal strains. Ultimately, SAMMBA could provide critical support for breeding strategies required to ensure the resilience of marine forests and aquaculture in a rapidly changing ocean.
Why it matches plant phenotyping methodsマクロ藻類の高スループットな単離・表現型取得を目的としたSAMMBAパイプラインを開発し、蛍光顕微鏡画像解析と増殖測定を技術的に検証しているため、植物表現型手法が研究の中心である。
abstractHere, we present SAMMBA (Seaweed Automatable Microplate Microscopy for Breeding Approaches), an end-to-end pipeline for the high-throughput isolation, phenotyping and storage of macroalgal cells in 384-well plates (384WP).
The seed coat serves as a protective barrier between seeds and their environment. This structure plays fundamental roles in protection, environmental sensing, and germination regulation. Current phenotypic characterization methods typically measure the seed coat together with adjacent structures, including the aleurone layer and endosperm. Such combined measurements hinder accurate assessment of seed coat-specific traits. This study presents an integrated analytical approach for phenotyping isolated maize seed coats. The method combines microscopic hyperspectral imaging with atomic force microscopy (AFM), enabling quantitative assessment of 24 phenotypic indicators spanning roughness, light transmittance, color, and texture parameters. The investigation of phenotypic diversity focused on inbred lines from natural association populations. The analytical workflow involved kernel contour extraction from RGB images followed by detailed phenotypic mapping. Population-wide analysis revealed substantial phenotypic variation. Coefficients of variation ranged from 30 % to 45 % for light transmittance and color texture phenotypes, while exceeding 60 % for roughness parameters. A phenotypic interaction network was constructed to elucidate trait relationships, identifying VLD as key characteristic phenotypes in seed coat morphology. Dimensional reduction analysis highlighted 12 critical indicators: Rp, Ra, Rv, Rz, LAQ, VLI, LAD, TRGSD, TSGSH, TRGSE, CBAve, and SCAve. Germination studies demonstrated significant correlations between seed emergence rate (SER) and multiple seed coat traits, including light transmittance, color, and texture characteristics (R: −0.204 to −0.194, P < 0.05). Notable inbred lines, including Ry737, Dong46, CML486, and CML426, exhibited superior germination rates characterized by low seed coat roughness, high light transmittance, enhanced texture roughness, and increased color saturation and brightness. The methodological advances presented here provide novel insights into maize seed coat characteristics. These findings have significant implications for precise germplasm identification and the development of high-quality, high-vigor maize varieties.
Why it matches plant phenotyping methods分離したトウモロコシ種皮を対象に、顕微鏡ハイパースペクトル画像とAFMを統合し、形態・色・透過性・テクスチャなど24指標を定量化するフェノタイピング手法が研究の中心である。
abstractThis study presents an integrated analytical approach for phenotyping isolated maize seed coats.
The present critical literature review describes the state-of-the-art innovative proximal (ground-based) solutions for plant disease diagnosis, suitable for promoting more precise and efficient phytosanitary measures. Research and development of new sensors for this purpose are currently a challenge. Present procedures and diagnosis techniques depend on visual characteristics and symptoms to be initiated and applied, compromising an early intervention. Also, these methods were designed to confirm the presence of pathogens, which did not have the required high throughput and speed to support real-time agronomic decisions in field extensions. Proximal sensor-based systems are a reasonable tool for an efficient and economic disease assessment. This work focused on identifying the application of optical and spectroscopic sensors as a tool for disease diagnosis. Biophoton emission, fluorescence spectroscopy, laser-induced breakdown spectroscopy, multi- and hyperspectral spectroscopy (HS), nuclear magnetic resonance spectroscopy, Raman spectroscopy, RGB imaging, thermography, volatile organic compounds assessment, and X-ray fluorescence were described due to their relevant potential. Nevertheless, some techniques revealed a low technology readiness level (TRL). The main conclusions identify HS, single and multi-spatial point observation, as the most applied methods for early plant disease diagnosis studies (88%), combined with distinct feature selection (FeS), dimensionality reduction (DR), and modeling techniques. Vegetation indices (28%) and principal component analysis (19%) were the most popular FeS and DR approaches, highlighting the most relevant wavelengths contributing to disease diagnosis. In modeling, classification was the most applied technique (80%), used mainly for binary and multi-class health status identification. Regression was used in the remaining (21%) scientific works screened. The data was collected primarily in laboratory conditions (62%), and a few works were performed in field conditions (21%). Regarding the study’s etiological agent responsible for causing the disease, fungi (53%) and viruses (23%) were the most analyzed group of pathogens found in the literature. Overall, proximal sensors are suitable for early plant disease diagnosis before and after symptom appearance, presenting classification accuracies mostly superior to 71% and regression coefficients superior to 61%. Nevertheless, additional research regarding the study of specific host-pathogen interactions is necessary.
Why it matches plant phenotyping methods植物病害の早期診断に用いる光学・分光センシング技術を体系的にレビューしており、病害状態という植物表現型の取得・推定手法が中心である。
abstractThe present critical literature review describes the state-of-the-art innovative proximal (ground-based) solutions for plant disease diagnosis, suitable for promoting more precise and efficient phytosanitary measures.
Plant diseases remain one of the most significant threats to crop yields, farmer livelihoods and economic stability worldwide. Conventional methods of predicting these diseases, especially through manual inspections and laboratory tests, are slow, costly, and prone to inaccuracies, particularly in rural areas with limited resources. Recent advances in artificial intelligence (AI), computer vision, and agricultural data analytics have created a new opportunity to monitor plant health in real-time. With the incorporation of deep learning algorithms and precision agriculture datasets, it is now possible to predict plant diseases before they occur by analyzing leaf images, weather data, and soil health characteristics. In this paper, we proposed an integrated framework that bridges agricultural data analytics with machine learning (ML) models for a decision support system to provide timely interventions, minimize pesticide usage and reduce crop loss. A model that is flexible to weather, cost-effective for smallholder farmers, and provided in platforms that are easy to use as a technology. The framework includes cloud-based dashboards, predictive alerts and market-driven analytics to ensure that disease management strategies are both environmentally sustainable and economically viable. The evaluation of the results suggests that there is significant improvement in diagnosis times, detection accuracy and increased overall efficiency of decision-making. It also sheds some light on various socio-economic benefits flowing from the integration of AI in plant pathology and secures it as a transformative tool against sustainable, climate-resilient agriculture.
Why it matches plant phenotyping methods葉画像から植物病害状態を推定するAI手法と、その診断精度・検出時間を評価する枠組みが中心であり、植物の病害表現型を対象とするため採用。
abstractanalyzing leaf images, weather data, and soil health characteristics
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.
Abstract The timely and precise identification of diseases in plants is essential for efficient disease control and safeguarding of crops. Manual identification of diseases requires expert knowledge in the field, and finding people with domain knowledge is challenging. To overcome the challenge, computer vision-based machine learning techniques have been proposed by the researchers in recent years. Most of these solutions with the standard convolutional neural network (CNN) approaches use uniform background laboratory setup leaf images to identify the diseases. However, only a few works considered real-field images in their work. Therefore, there is a need for a robust CNN architecture that can identify the diseases in plants in both laboratory and real-field conditioned images. In this paper, we have proposed an Inception-Enhanced Vision Transformer (IEViT) architecture to identify diseases in plants. The proposed IEViT architecture extracts local as well as global features, which improves feature learning. The use of multiple filters with different kernel sizes efficiently uses computing resources to extract relevant features without the need for deeper networks. The robustness of the proposed architecture is established by hyper-parameter tuning and comparison with state-of-the-art. In the experiment, we consider five datasets with both laboratory-conditioned and real-field conditioned images. From the experimental results, we see that the proposed model outperforms state-of-the-art deep learning models with fewer parameters. The proposed model achieves an accuracy rate of 99.23% for the apple leaf dataset, 99.70% for the rice dataset, 97.02% for the ibean dataset, 76.51% for the cassava leaf dataset, and 99.41% for the plantvillage dataset.
Why it matches plant phenotyping methods植物葉の病害状態を画像から認識する新規深層学習手法を提案・比較評価しており、植物フェノタイピング手法が中心である。
abstractIn this paper, we have proposed an Inception-Enhanced Vision Transformer (IEViT) architecture to identify diseases in plants.
ArabidopsisRiceLaboratory / benchtopRootMorphology / geometry measurementRoot system architecture
Despite its significant relevance to drought adaptation, optimization of nutrient acquisition, and carbon sequestration in soil, genetic factors determining root depth remain poorly explored, mostly due to the limitations of the methods currently available to estimate it. Although several such methods have been developed for crops, their applicability to large-scale studies and those involving smaller, more fragile root systems is severely limited. To address this, we have developed ClearDepth, a simple, non-destructive, low-cost method. In ClearDepth, the root system develops naturally inside the soil in clear pots. As it expands, secondary roots reach the transparent walls of the pot ("wall roots"), becoming visible. The shallowness of each wall root is then measured (wall root shallowness, WRS), and the depth of the root system is expressed as the average of all single WRS measurements. We demonstrated the suitability of ClearDepth for root depth studies using Arabidopsis thaliana and Oryza sativa (rice), two species with contrasting root system architecture (RSA) and root size. The robustness and sensitivity of the WRS trait allow us not only to reproducibly discriminate between shallow and deep root systems but also to detect smaller yet significant differences in depth determined by the influence of environmental factors, such as light. Here, we present a comprehensive protocol for utilizing this method. Key features • ClearDepth measures the depth of a minimum number of secondary roots, set by the user, to estimate the depth of the root system. • The method captures differences of root depth at a spatio-developmental stage rather than at one specific time point after planting. • ClearDepth captures differences in root depth independently of differences in total root biomass.
Why it matches plant phenotyping methods根系深度という植物形質を非破壊的に測定するClearDepth法を開発し、複数植物種で頑健性・感度・再現性を実証した方法論論文である。
abstractTo address this, we have developed ClearDepth, a simple, non-destructive, low-cost method.
Deep understanding of the structural composition and growth of biological specimens is becoming increasingly important for the development of bio-based and sustainable material systems. Full-field nano-computed tomography is particularly suitable for this purpose as it allows for non-destructive 3D imaging at high spatial resolution. However, most biological samples are functionalized by water and respond sensitively to any changes in climate conditions, specifically relative humidity, by adjusting their material moisture content. To date, only a limited number of tomography instruments offer an in situ climate control option to users. These, however, are limited either by the range of relative humidity states, the long times required to change the climate state, or obstruction or attenuation of the beam. Here, the first fully automatized climate cell for in situ full-field nanotomography is presented. It has been designed, built and integrated at the nanotomography station at the P05 imaging beamline, operated by Hereon at the DESY storage ring PETRA III, Germany. The highly flexible and windowless design allows the humidity dependent swelling and shrinking of lignified plant cell walls to be studied in situ, using phase contrast nanotomography. The concept of this climate chamber can easily be integrated into other setups. It operates in the relative humidity range of 0-90% and can be controlled in a temperature range of 10-50°C. Climate conditions can be adjusted at any time, remotely from the control hutch by using a humidity generator. Results show that the developed setup maintains a stable climate during the entire duration of a tomographic scan at different humidities and does not obstruct the sample or hinder the imaging conditions. During the tomographic investigation the sample remains stable in the flow of the air stream and shows typical cell wall swelling and shrinking behaviour depending on the equilibrium moisture content. This new climate cell is now available to all users of the P05 nanotomography instrument for conditioning samples, serving a wide range of scientific applications.
Why it matches plant phenotyping methods植物細胞壁の膨潤・収縮を非破壊に取得するナノトモグラフィー用の自動環境制御セルを開発・統合し、安定性と撮像性能を検証しているため、植物表現型取得法が中心である。
abstractHere, the first fully automatized climate cell for in situ full-field nanotomography is presented.
BACKGROUND: Efficient and dependable techniques for determining seed viability are essential in agronomy, forestry, and safeguarding endangered plant species, as seeds represent the most effective way to preserve plant germplasm. Certain seeds can endure preservation for thousands of years, while others may survive only a few weeks. However, all seeds ultimately deteriorate over time during storage. Reactive oxygen species (ROS) and their imbalance with intracellular antioxidants are the primary causes of seed aging and deterioration. Consequently, developing highly effective analytical methods to measure the antioxidant capacity of stored seeds is becoming increasingly critical. This study examines the application of cyclic voltammetry (CV) using a glassy carbon electrode to characterize the antioxidant milieu in aging recalcitrant seeds of Acer saccharinum L. This is a versatile electrochemical method that can be easily applied to investigate a broad range of biological matrices because it does not require redox-active reagents to determine the total antioxidant capacity. Instead, it is explicitly based on the electrochemical behavior of antioxidants in samples and their physicochemical properties. RESULTS: Seed deterioration occurred when A. saccharinum seeds with a high moisture content (MC of 45%) were subjected to accelerated aging at 35 °C for up to 14 days. Oxidative stress and antioxidant depletion were monitored by measuring ROS levels, quantifying antioxidants through the Cu2+ reduction reaction (CUPRAC-BCA) and CV, and evaluating the glutathione half-cell reduction potential (EGSSG/2GSH). Compared with Cu2+ reduction measurements, which yielded misleading results, CV appeared to be a more reliable technique for differentiating seeds based on their total antioxidant capacity. CV measurements of 80% methanolic and 1x PBS extracts were highly correlated with seed viability, observed as total germination (R = 0.92 and 0.86, respectively, p ≤ 0.01 for both solvents). CONCLUSIONS: For the first time, we demonstrated a strong correlation between the CV results on the total antioxidant capacity and viability of seeds. This finding suggests that electrochemical techniques can be a quick and efficient method for evaluating seeds prior to germination, potentially from various species. This method enhances seed viability monitoring, achieving 92% accuracy and showing species-agnostic potential, pending validation in lipid-rich seeds.
Why it matches plant phenotyping methods種子の生存性を評価するサイクリックボルタンメトリー法の適用・比較検証が研究の中心であり、抗酸化能と発芽率との相関および精度を評価している。
titleCyclic voltammetry as a method for determining the viability of seeds
Abstract Leaf Chlorophyll Concentration (LCC) is a vital biochemical parameter for assessing plant status due to its essential role in physiological activities, photosynthesis, and overall plant health. In order to illustrate the development of potato crops and offer advice for precision agriculture management, research was conducted on non-invasive testing methods for chlorophyll levels and methods for mapping crop yield in potatoes. The objective of this study is to examine the spatial distribution of chlorophyll content and yield of potato crops using Sentinel 2 data, SPAD chlorophyll measurements, and laboratory analyses. Artificial intelligence (AI) using the Random forest (RF) classification method was used to study the spatial distribution of crop type and discriminate the potato crop. The overall accuracy and kappa statistics for the spatial distribution derived from Sentinel 2 satellite imagery for potato crops in the study area were 0.79 and 82.5%, respectively. Stepwise Multilinear regression model (SWMLR) between Spectral vegetation indices (Normalized Difference Vegetation Indexed NDVI, Modified Chlorophyll Absorption Ratio Index (MCARI), Leaf Chlorophyll Index (LCI), derived from spectral vegetation indices (SVI), (SPAD chlorophyll and chemical analysis through potato crop growth stages (S1, S2 and S3) were correlated to estimate chlorophyll content and crop yield map. The model accuracy between vegetation indices and Total chlorophyll showed that models based on VIS and selected spectral bands derived from ASD to predict total chlorophyll(chlt) and SPAD chlorophyll values achieved a high coefficient of determination (R 2 ) at the different growth stages, which were 0.983 and 0.986. The produced map for the potato crop, total chlorophyll derived from Sentinel 2, showed high accuracy at 0.966 and 0.974 based on SPAD, VIS, and selected spectral bands, respectively. The study showed that the estimation and mapping of Chlt and SPAD values of a potato crop under an irrigation system pivot can be done with the help of RS and AI techniques.
Why it matches plant phenotyping methodsSentinel-2、近接センサー、SPAD、分光データとAI・回帰モデルを用いて、ジャガイモのクロロフィル量と収量を推定・マッピングする技術的評価が研究の中心であり、植物形質の取得・推定方法を扱っている。
abstractresearch was conducted on non-invasive testing methods for chlorophyll levels and methods for mapping crop yield in potatoes
Laboratory / benchtopRGB / grayscaleFruitClassificationGrowth / development / phenology
Okra is a highly nutritious farming product that combats malnutrition issues while supporting sustainable agricultural methods. In order to keep its quality and versatility in preparation applications, it is important to classify its maturity stages into under-mature, mature, and over-mature categories. Classification is vital to identify the best time to harvest, satisfy market demands, minimize post-harvest losses, and optimize cooking uses. This data in brief uses a non-destructive approach to okra maturity classification based on a dataset of images taken under controlled illumination using a standard RGB camera. The dataset contains okra samples that were sourced from various farms and vegetable markets, ensuring that it encapsulates the natural variability found in real-world farm and market environments. The availability of such a large dataset enables the creation of precise classification models that can assist farmers in optimizing the time of harvest, fulfilling consumers' requirements, and improving market results. The research has great relevance to promoting agricultural quality evaluation and boosting market readiness using non-invasive techniques.
Why it matches plant phenotyping methodsRGB画像データセットによるオクラ果実の成熟段階分類が研究の中心であり、植物器官の状態を画像から推定する再利用可能なフェノタイピング資源に該当する。
titleRGB image dataset for okra maturity classification to enhance agricultural quality and market readiness.
Reproduction assets foundThe paper is a Data in Brief article whose core contribution is a public RGB okra image dataset (364 images across three maturity classes) deposited on Mendeley Data, directly serving as the paper's phenotyping image asset. No separate analysis code repository is described.Dataset · publicVellore Institute of Technology - Chennai Campus.
City/Country: Chennai, India.
Latitude and longitude for collected samples/data: (12.8406° N, 80.1534° E), Vellore Institute of Technology - Chennai.
Data accessibility
Repository name: Okra Image Dataset
Data identification number: DOI: 10.17632/jmhz4826f2.1
Direct URL to data: https://data.mendeley.com/datasets/jmhz4826f2/1
Related research article
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Agricultural quality assessment is advancing through non-invasive methods that include the use of RGB image analysis for effective determination of okra maturity stages.
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Utilization of ML and DL methods in recognizing visual characteristics, i.e., color, texture, andOpen asset ↗10.17632/jmhz4826f2.1lines:1-56Plant phenotyping relevance match · UnverifiedEurope PMC · checked 6 Sept 2026
ArabidopsisLaboratory / benchtopRootPhysiological trait estimationGrowth / development / phenologyWater status / transpiration
Root hairs are outgrowths of the epidermal cells of plant roots. They increase the root’s exchange surface with the soil and provide it with good anchorage in the soil. Root hairs are an emblematic model of apical growth, a process also used by yeasts and hyphae to invade their environment. From a mechanical perspective, the root hair is considered as an elastic cylinder under pressure, closed by a dome that behaves like a yield fluid. We introduce here two innovative mechanical setups and protocols to characterize the mechanical properties of single growing root hairs in Arabidopsis thaliana. In the first setup, root hairs grow against an elastic obstacle until buckling. By measuring the critical buckling force, we determine the surface modulus and estimate the Young’s modulus of the cell wall, which aligns with previous measurements. Using a 1D elasto-viscoplastic model of root hair growth, we assess the excess pressure beyond the yield threshold (the driver of tip growth) and estimate the axial stiffness of the root hair, reflecting its elastic resistance to compression. For the second protocol, we designed a setup where a single root hair grows against a cantilever with variable stiffness, a technique adapted from our earlier work on rigidity sensing by animal cells. This method provides an independent estimate of the root hair’s axial stiffness, confirming our initial findings and suggesting that this stiffness primarily involves tip compression and depends mainly on turgor pressure, at least within the low deformation regime explored.
Why it matches plant phenotyping methods単一根毛の力学特性を定量する革新的な実験セットアップとプロトコルを開発・検証しており、植物表現型の取得手法が研究の中心です。
abstractWe introduce here two innovative mechanical setups and protocols to characterize the mechanical properties of single growing root hairs in Arabidopsis thaliana.
Significant concerns regarding the impact of copper (Cu) and copper oxide (CuO) nanoparticles (NPs) and microparticles (MPs) on plant systems have been brought to light through the growing use of these materials in industry and agriculture. The properties of NPs are critical in determining their uptake by plant cells and the ensuing effects on plant physiology. This emphasizes the need for accurate monitoring techniques to determine the impact caused by NPs on seed development and plant growth. This study uses foliar exposure at 0 and 100 mg/L, as well as seed exposure at 0, 25, and 100 mg/L, to explore the effects of Cu ( Lens culinaris ). Biospeckle optical coherence tomography (bOCT) was employed to monitor internal physiological activity in real time, non-invasively-capabilities that static imaging methods, such as OCT, are unable to provide. Results showed that exposure to Cu and CuO NPs led to significant reductions in biospeckle contrast, indicating heightened physiological stress, while MPs generally produced minimal or even positive effects. These early changes detected by bOCT within just 6 h of exposure were consistent with traditional morphological and biochemical assessments-such as germination rate, growth, biomass, and catalase activity-that typically require several days to detect. The study demonstrates that bOCT enables the rapid, functional assessment of nanomaterial effects, including those resulting from foliar exposure, thereby offering a powerful tool for early and non-destructive evaluation of plant responses to engineered particles in agricultural contexts.
Why it matches plant phenotyping methodsbOCTによる植物内部の生理活性をリアルタイム・非侵襲的に測定し、ナノ粒子ストレスを早期評価する手法が研究の中心であるため。
abstractBiospeckle optical coherence tomography (bOCT) was employed to monitor internal physiological activity in real time, non-invasively
Background Phenotypic characterization of onion germplasm is requisite for designing breeding programs, and for meeting industrial processing, and marketing demands. Onion bulb morphology, and geometrical properties, which are the physical and spatial dimensions and shape characteristics influence consumer and market demand, as well as suitability for processing and mechanizing post-harvest handling. Many previous studies employed manual tools such as Vernier calipers for measurement of onion bulb parameters, which is time-consuming. The emergence and application of phenomics tools such as digital cameras are more convenient for rapid phenotypic characterization. Aim This study aimed to investigate the phenotypic variability of 29 onion accessions based on ten qualitative and twelve quantitative bulb characteristics. Methodology Freshly harvested onion bulbs ( n = 10/accession) were obtained from the Allium Vegetable Research Institute (AVRI), at Muan-Gun, Republic of Korea. A digital camera was used to capture images of the bulbs. The images were saved in JPEG file format, and uploaded into ImageJ software for measurement of linear dimensions, including polar diameter, equatorial diameter, transverse diameter or thickness. To ensure accurate measurement, images were first calibrated, using the straight line tool and the "Set scale" function in the software. Results of the linear dimensions were then used for estimating other geometrical properties, such as aspect ratio, sphericity, and geometric and arithmetic mean diameters. Results Our findings revealed a broad range of phenotypic variation within the germplasm. Polar and equatorial diameters ranged from 4.731 to 11.998 cm, and from 4.54 to 10.196 cm, with mean values of 9.213 and 7.472 cm, respectively. Also, geometric and arithmetic mean diameters ranged from 4.224 to 10.484 cm, and from 4.257 to 10.569 cm, with corresponding mean of 7.901 and 7.980 cm, respectively. Principal component analysis grouped the accessions into three distinct clusters, with cluster three composing the highest number of accessions. Strong significant positive associations were observed among several traits. For instance, polar diameter correlated strongly with polar diameter and transverse diameter ( r > 0.97), geometric and arithmetic mean diameters ( r > 0.98), surface area ( r > 0.96), frontal surface area ( r > 0.94), cross sectional area ( r > 0.96), equatorial diameter ( r > 0.83), and thickness of neck ( r > 0.84). High to moderate broad sense heritability and genetic gain were estimated for several traits. Conclusion Overall, the significant variability within the onion germplasm provides a potential for breeding new cultivars to meet consumer and industrial requirements. The results also provide information vital for future genomic and metabolite studies.
Why it matches plant phenotyping methodsタマネギ球の形態・幾何形質をデジタル画像とImageJで取得・算出するワークフローが、遺伝資源の表現型評価の中心であるため。
abstractThe emergence and application of phenomics tools such as digital cameras are more convenient for rapid phenotypic characterization.
Background The plant vacuole arises by orchestrated interplay of membrane trafficking, cytoskeletal rearrangements and a variety of signalling pathways. In the root, the characteristic large central vacuole develops by endomembrane reorganization occurring mainly in the transition zone. The vacuole’s bounding membrane - the tonoplast - can be visualized in vivo using fluorescent protein markers, allowing for quantitative analysis of confocal microscopy images. Tonoplast organization can thus serve as a sensitive indicator of changes to any of the processes involved in vacuole biogenesis. The Vacuolar Morphology Index (VMI) is widely accepted as a quantitative measure of vacuole structure. However, this metric has two drawbacks - it only reflects the size of the largest vacuolar compartment (missing therefore possible differences in the organization of smaller compartments), and its determination is labor intensive, limiting its use on large datasets. Results We developed an alternative metric for describing vacuole organization, named the Tonoplast Topology Index (TTI), which overcomes the above-mentioned shortcomings of the VMI. We compared the performance of our protocol with VMI on a simulated dataset and on real data. To validate the methods’ performance, we used it to confirm the previously reported differences in vacuole shape and size between Arabidopsis thaliana roots grown on the surface of an agar medium compared to those embedded inside the agar. Both VMI and TTI could efficiently detect the relatively subtle changes in vacuole organization depending on the position of the root in the agar, and provided correlated results. However, only TTI produced data with close to normal value distribution, simplifying subsequent statistical evaluation. Conclusions We present the protocol for TTI determination as a two-stage semi-automated procedure involving microscopic image analysis employing an ImageJ macro and subsequent processing of numeric data in the Jupyter Notebook environment, together with benchmarking image data. Since this implementation is freeware-based, platform-independent and (relatively) user-friendly, we hope it will find its use as a high throughput, added value alternative to the VMI metric.
Why it matches plant phenotyping methods植物の液胞構造を定量化する新規指標と半自動画像解析プロトコルを開発し、既存指標との比較・実データおよびシミュレーションによる検証、ベンチマークデータを提示しており、表現型取得・抽出法が中心である。
abstractWe developed an alternative metric for describing vacuole organization, named the Tonoplast Topology Index (TTI), which overcomes the above-mentioned shortcomings of the VMI.
Reproduction assets found保存済みの本文根拠を更新済みルールで再検証し、公開資産1件を確認しました。Code · publicthe software tool generated here are also available at https://github.com/GeorgeCaldarescu/TTI-Open asset ↗GeorgeCaldarescu/TTI-pdf-page:9 lines:1-52Plant phenotyping relevance match · UnverifiedOpenAlex · checked 14 Sept 2026
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.
Plant phenotyping relevance match · UnverifiedOpenAlex · Europe PMC · Crossref · checked 6 Sept 2026
Abstract In recent years, non-destructive and non-invasive methods for 3D plant reconstruction have gained importance in plant phenotyping. Morphological traits reflect a plant’s physiological status and serve as key indicators for precision agriculture, crop protection, and food quality assessment. Accurate and efficient 3D modelling enables objective, repeatable monitoring of plant development and health, supporting data-driven decision-making in agricultural and food research. This study presents a cost-effective and flexible photogrammetric methodology for analysing plant morphological traits under controlled laboratory conditions. The system includes an industrial RGB camera mounted on a robotic arm, a rotating platform with an adjustable plant holder, and stable illumination. The key steps involved camera calibration, exposure optimisation, fine-tuning of evaluation algorithm parameters (tweaks), setting the optimal camera-to-object distance, and reducing computational load for 3D model evaluation. Comparative testing revealed that the most effective calibration strategy integrated simultaneous calibration, pre-calibrated parameters, and adaptive fitting, ensuring high reconstruction accuracy and consistent model quality. The optimal acquisition parameters were a 50 milliseconds exposure time, a tweak value of 0.9, and a 16 cm camera-to-object distance. Using more camera positions with fewer frames per position proved more efficient than the reverse. The optimal configuration consisted of three height levels with 40 frames each. Automation and data reduction led to a 75% decrease in processing time, reducing the scan time from 8 minutes to 2.7 minutes per plant. The developed method proved to be a reliable, reproducible, and affordable tool for routine 3D analysis of plant morphology via close-range photogrammetry.
Why it matches plant phenotyping methods植物形態を取得するSfM-MVSフォトグラメトリ法と撮像・校正・処理条件を開発、比較検証しており、植物フェノタイピング手法が研究の中心である。
abstractThis study presents a cost-effective and flexible photogrammetric methodology for analysing plant morphological traits under controlled laboratory conditions.
In response to environmental changes, chloroplasts, the cellular organelles responsible for photosynthesis, undergo intracellular repositioning, a phenomenon known as chloroplast movement. Observing chloroplast movement within leaf tissues remains technically challenging in leaves consisting of multiple cell layers, where light scattering and absorption hinder deep tissue visualization. This limitation has been particularly problematic when analyzing chloroplast movement in the mesophyll cells of C 4 plants, which possess two distinct types of concentrically arranged photosynthetic cells. In response to stress stimuli, mesophyll chloroplasts aggregate toward the inner bundle sheath cells. However, conventional methods have not been able to observe these chloroplast dynamics over time in living cells, making it difficult to assess the influence of adjacent bundle sheath cells on this movement. Here, we present a protocol for live leaf section imaging that enables long-term and detailed observation of chloroplast movement in internal leaf tissues without chemical fixation. In this method, a leaf blade section prepared either using a vibratome or by hand was placed in a groove made of a silicone rubber sheet attached to a glass slide for microscopic observation. This technique allows for the quantitative tracking of chloroplast movement relative to the surrounding cells. In addition, by adjusting the sectioning angle and thickness of the unfixed leaf sections, it is possible to selectively inactivate specific cell types based on their size and shape differences. This protocol enables the investigation of the intercellular interactions involved in chloroplast dynamics in leaf tissues. Key features • Thin leaf sections prepared while still alive enable prolonged microscopic observation of chloroplast movement within the leaf tissue. • Selective cell inactivation can be achieved by adjusting the slice thickness and angle. • This method is applicable to a wide range of plant species.
Why it matches plant phenotyping methods生葉切片のライブイメージングにより、葉内部の葉緑体運動を長時間観察・定量追跡する手法を開発しており、植物表現型の取得が研究の中心である。
abstractHere, we present a protocol for live leaf section imaging that enables long-term and detailed observation of chloroplast movement in internal leaf tissues without chemical fixation.
The redox state of the plastoquinone pool (PQ-redox) acts as a central element in a variety of intracellular signal pathways. Several methods for determining PQ-redox have been established. Although some of these methods may be quantitative, such as those based on liquid chromatography, they are typically sensitive to sample preparation. Here, we critically evaluate the use of fast chlorophyll a fluorescence induction kinetics (the so-called OJIP transient) for semi-quantitative PQ-redox estimation in green algae (Chlorella vulgaris) and cyanobacteria (Synechocystis sp. PCC 6803). The method, based on the evaluation of relative fluorescence yield at the J-step of the OJIP transient (VJ, VJ), has already been reported; however, thus far, it has been used mostly for studying dark-acclimated leaves, which limits its range of application. Here, we show that the OJIP transient can be used for semi-quantitative estimation of PQ-redox in algal and cyanobacterial cell cultures, in addition to plants. We further show that it can reflect PQ-redox in both dark-acclimated and light-acclimated samples. Our systematic comparison of Multi-Color PAM, AquaPen, and FL 6000 fluorometers demonstrates that accurate measurement of VJ and VJ parameters in suspension cultures requires low culture density and a high-intensity saturation pulse. We further show that with increasing light intensity to which the cells are exposed, the state of photosystem II (PSII) changes due to light-induced reduction of quinone A (QA-) and conformational changes, which in turn influence both the sensitivity and dynamic range of the VJ parameter towards PQ-redox estimation. A comparison of fluorescence transients in Chlorella and Synechocystis revealed high homeostatic control over PQ-redox in Synechocystis, maintained by terminal oxidases present at the thylakoid membrane. While we discuss certain limitations, our systematic assessment suggests that the OJIP method has great potential to become a routine tool for semi-quantitative PQ-redox estimation under a wide range of experimental conditions in green algae and cyanobacteria.
Why it matches plant phenotyping methodsOJIP蛍光法による植物・藻類・シアノバクテリアのPQ-redox推定を中心に、複数蛍光計の系統比較、測定条件、感度・適用範囲を評価しており、植物の生理状態を取得する方法の検証研究である。
abstractHere, we critically evaluate the use of fast chlorophyll a fluorescence induction kinetics (the so-called OJIP transient) for semi-quantitative PQ-redox estimation in green algae (Chlorella vulgaris) and cyanobacteria (Synechocystis sp. PCC 6803).
• A high-precision digital platform for plant phenotyping and cultivation was developed. • LED illumination based multispectral imaging system to study crop plant responses. • Different multispectral signatures were induced by plant abiotic and biotic factors. Important plant stresses are drought, but also biotic stresses caused by pathogens have economically important losses to crops worldwide. Advancements in our ability to fast, sensitive and cost efficient detect stress responses by sensor based imaging are important to improve crop management practices. As a step towards this, we introduce a fully automated, high-throughput plant phenotyping platform called “PhenoLab”. It automatically ensures precise and automatic irrigation of plants and non-destructively, fast and quantitatively measure biomass, abiotic and biotic stresses via multispectral imaging. A user friendly software for supervised machine learning based spectral image analysis is used for image processing and water consumption of individual plants can be extracted from an integrated database. As a proof of concept, we used two important crop plants for phenotyping and detecting abiotic and biotic stresses. Individual multi-spectral measurements (within 365–970 nm) and vegetation index were considered in the image processing to detect drought symptoms of maize plants. Powdery mildew of barley plants was sufficiently detected and quantified via multi-reflectance and multi-fluorescence image system during disease progression. The integrated settings for multispectral image recording, computer vision and image processing platform with customized settings and protocols are expected as practical importance for academic and translational high-throughput research. It will be notably relevant for more complex systems with additional multiple factors e.g. , multiple plant genotypes and their resistance and susceptibility to abiotic and biotic stresses, or treatments of beneficial microbes for sustainable improvement of general stress resiliency.
Why it matches plant phenotyping methods植物の画像取得・解析、ストレス定量、ソフトウェアを統合した高スループット表現型解析プラットフォームの開発が中心である。
abstractA high-precision digital platform for plant phenotyping and cultivation was developed.
Plants biosynthesize a wide range of antioxidants capable of attenuating ROS-induced oxidative damage. There exist several in vitro methods to analyze antioxidants and total antioxidant capacity from different tissues and of various plant species. We have established a single, fast and cost-efficient extraction protocol combined with a semihigh throughput 96-well plate assay methods for determination of the level of the key antioxidants phenolics, anthocyanins and flavonoids in combination with the determination of total antioxidant capacity using ferric reducing antioxidant power (FRAP) and trolox equivalent antioxidant capacity (TEAC). The method was optimized and verified with samples from different strawberry species and cultivars with known differences in the parameters measured. This method proved to be suitable for analyses of eight model and crop plants, and distinct antioxidant signatures were determined for the different tissues and organs analyzed, including leaf, root, fruit, spike, and tuber samples. The method was robust and was shown in two case studies to be a resource-efficient and fast experimental platform also to assess biotic and abiotic stress responses, notably including fungal infection and the impact of a progressive drought regime. Since method was adapted for a semi-high throughput 96-well assay format it is well-suited for integration of cell physiological phenotyping into a holistic phenomics approach for germplasm assessment and plant breeding screening. This analytical platform uses microplate spectrophotometer which proved to be suitable to determine the antioxidant contents and total antioxidant capacity signatures of various plant species and tissues with similar findings as reported in literature.
Why it matches plant phenotyping methods植物組織の抗酸化物質と抗酸化能を測定する、最適化・検証済みの半ハイスループット分析法および表現型解析プラットフォームが研究の中心である。
abstractWe have established a single, fast and cost-efficient extraction protocol combined with a semihigh throughput 96-well plate assay methods
Mechanical properties of plant cells and tissues change dynamically, influencing plant growth, development, and interactions with the environment. Despite their central roles in plant life, current knowledge of how these properties change in vivo is very limited. Here we have combined Brillouin microscopy and molecular rotors to investigate stiffness, viscosity and porosity in living Arabidopsis thaliana seedling roots during differentiation and in response to stress and genetic manipulation. We found that mechanical properties change in a cell- and tissue-specific manner. The properties change dynamically during differentiation to support directional cell expansion. Cell-type-specific adaptations are induced within hours in response to stress or changes in cell wall metabolism. The findings form the foundation for future studies to characterize regulatory mechanisms linking biochemical signaling and mechanical properties.
Why it matches plant phenotyping methods生きた植物体での硬さ・粘性・多孔性をBrillouin顕微鏡と分子ローターにより測定する手法適用が研究の中核であり、植物の生理状態・組織特性を定量化している。
abstractHere we have combined Brillouin microscopy and molecular rotors to investigate stiffness, viscosity and porosity in living Arabidopsis thaliana seedling roots during differentiation and in response to stress and genetic manipulation.
This paper provides a comprehensive review of advancements in plant disease detection, moving from traditional to modern and AI-driven approaches. It highlights that traditional methods, such as visual inspection, microbiological isolation, culturing, and molecular and serological techniques, are often limited by being time-consuming, subjective, or requiring specialized expertise and lab processing. These limitations can lead to significant crop yield losses, economic setbacks, and threats to food security. The review then discusses modern, non-destructive sensor technologies, which are crucial for detecting diseases in their early stages, often before visible symptoms appear. These technologies include: * Hyperspectral Imaging (HSI): Captures detailed "spectral fingerprints" of plants to detect subtle physiological changes. * Multispectral Imaging (MSI): Uses a limited number of spectral bands, often including near-infrared (NIR), to identify abnormal plant conditions more cost-effectively than HSI. * Thermal Imaging: Detects temperature fluctuations in plants caused by physiological changes during infection. * Chlorophyll Fluorescence Imaging (CFI): A non-invasive technique that detects early stress responses by analyzing chlorophyll emissions. * LiDAR and Drones: Used for aerial analysis of crop health, enabling early diagnosis and monitoring of large agricultural areas. Finally, the paper details how Artificial Intelligence (AI) and Deep Learning (DL) have revolutionized this field through automated, highly accurate diagnostic capabilities. The document covers various deep learning architectures, including Convolutional Neural Networks (CNNs) like AlexNet, VGG, ResNet, and YOLO, which are used for image classification, feature extraction, and real-time disease localization. It also mentions the use of semantic segmentation models like U-Net for pixel-level disease mapping, and the role of transfer learning and explainable AI (XAI) in improving model performance and transparency. The review concludes with an emerging paradigm of federated learning for decentralized, privacy-preserving model training.
Why it matches plant phenotyping methods植物病害の症状・生理状態を画像およびセンサーで検出する手法を中心に扱う包括的レビューであり、植物フェノタイピング手法のレビューに該当する。
abstractThis paper provides a comprehensive review of advancements in plant disease detection, moving from traditional to modern and AI-driven approaches.
Plants release volatiles, specifically volatile organic compounds (VOCs), that play a key role in communication, defense mechanisms, and responses to environmental stress. One of those significant abiotic stresses is temperature, which has a variety of negative effects like reduced growth, impaired photosynthesis, and ultimately threatening plant survival. Understanding how these volatiles function under heat stress can provide insight into plant resilience mechanisms and pinpoint key signaling pathways that can be targeted to enhance stress tolerance. In this study, we employ a novel technique, temperature programming secondary electrospray ionization (TP-SESI) to investigate how temperature variations influence the signal of VOC emissions from Ocimum basilicum or basil leaves. With our sample stage's ability to systematically adjust temperature by applying a voltage, TP-SESI enables real-time, non-destructive monitoring of VOCs with enhanced sensitivity and thermal control. Our findings demonstrate that TP-SESI reliably detects temperature-dependent changes in VOC abundance and composition, confirming its utility as an orthogonal technique for investigating plant metabolic responses to heat.
Why it matches plant phenotyping methods植物の熱ストレス応答をVOCとして非破壊・リアルタイム測定する新規質量分析法を開発し、その検出性能を示しており、植物状態の取得方法が中心である。
abstractIn this study, we employ a novel technique, temperature programming secondary electrospray ionization (TP-SESI) to investigate how temperature variations influence the signal of VOC emissions from Ocimum basilicum or basil leaves.
Elucidating the spatial and temporal regulation of gene expression during plant organogenesis is crucial for enabling precise crop improvement strategies that incorporate beneficial traits into crops while avoiding adverse effects. Root nodules, specialised organs formed in symbiosis with nitrogen-fixing bacteria, provide a valuable system to study cell-type-specific gene networks in a symbiosis-induced developmental context. However, capturing these dynamics at cellular resolution in intact plant tissues remains technically challenging. Spatial transcriptomics technologies developed for animal systems are often not directly transferable to plant tissues due to fundamental differences in tissue composition between plants and animals, including rigid and heterogeneous plant cell walls, high cell wall autofluorescence, and large vacuoles in plant cells that complicate probe access and signal detection. To address these challenges, we present an optimised protocol for applying the Xenium in situ sequencing platform to formalin-fixed paraffin-embedded (FFPE) sections of plant tissues, including Medicago truncatula roots and nodules. Key technical adaptations include customised tissue preparation, optimised section thickness, hybridisation conditions, post-Xenium staining, imaging, and downstream image analysis, all tailored specifically for plant samples. To mitigate autofluorescence and enhance detection sensitivity, we employed a strategic approach to codeword selection during probe design. Furthermore, we developed a modular probe design approach combining a custom 380-gene standalone panel with a 100-gene add-on panel. This design allows flexibility for addressing diverse research questions and includes orthologous gene sequences from two Medicago ecotypes, ensuring compatibility for downstream functional validation using mutant lines available in both genetic backgrounds. We validated the protocol across nodules at multiple developmental stages using both the 50-gene panel targeting mature nodule cell identity and the extended 480-gene panel, which includes markers across different cell types and developmental stages, as well as genes of interest identified from prior single-cell and bulk RNA-seq analyses. This optimised workflow provides a reproducible and scalable method for high-resolution spatial transcriptomics in plant tissues, establishing a robust foundation for adaptation to other plant species and developmental systems.
Why it matches plant phenotyping methods植物組織向け空間トランスクリプトミクスの技術適応・最適化と検証が研究の中心であり、植物器官の細胞状態を高解像度で取得する再現可能なワークフローを開発している。
abstractwe present an optimised protocol for applying the Xenium in situ sequencing platform to formalin-fixed paraffin-embedded (FFPE) sections of plant tissues, including Medicago truncatula roots and nodules.
Abstract To watch the growth of 1200 P. sylvestris cv. Negorelskaya trees from seeds to young or even old stage is a big grant project. We want to make a «seed–culture» passport. Each individual seed (N = 1200) was weighed, and image acquisition via a flatbed scanner in the VIS wavelength region and seeded into an individual 120 cm 3 cell of a 40-cell container. On day 30, container-grown germination was evaluated according to the following dichotomous criterion: 1 – germinated (n 1 = 942), 0 – did not germinate (n 0 = 258), and 0-group and 1-group datasets were formed. The RGB space color of the individual seed epidermis between the 0- and the 1-group were compared via the Kolmogorov‒Smirnov criterion D. The lower individual weight of the seed in the 0-group compared with the 1-group was not accidental (p = 0.0045). Additionally, in the 0 group, the median values of R, G, and B brightness of pixels from individual seeds are not accidental (p = 0.0000381) compared with those of the 1 group. Therefore, in this experiment, seeds that reflected most of the light from the epidermis showed a lower germination when placed in the container.
Why it matches plant phenotyping methods個体種子を対象にスキャナ画像からRGB形質を抽出し、発芽との関連を評価する画像ベースの表現型取得が研究の中心である。
abstractimage acquisition via a flatbed scanner in the VIS wavelength region
Reproduction assets foundThe paper's data availability statement openly deposits all three paper-specific phenotyping assets in Mendeley Data: Dataset 1 (individual seed morphometric/weight data, N=1200), Dataset 2 (original VIS flatbed-scanner seed images, N=1200), and Dataset 3 (individual container germination data, N=1200). These directly供Dataset · publicThe original morphometric data—Dataset 1—of Pinus sylvestris L. сv. The individual Negorelskaya seeds (N = 1200) presented in the study are openly available in Mendeley Data at DOI : https://doi.org/10.17632/8g258nbgmf.1.Open asset ↗Mendeley Data · 10.17632/8g258nbgmf.1lines:152-174Dataset · publicThe original VIS image data of Pinus sylvestris L. сv. The individual Negorelskaya seeds (N = 1200) presented in the study are openly available in Mendeley Data at DOI : https://doi.org/10.17632/dt78jhyw2j.2.Open asset ↗Mendeley Data · 10.17632/dt78jhyw2j.2lines:152-174Dataset · publicThe original germination data—Dataset 3—of Pinus sylvestris L. cv. The individual Negorelskaya seeds (N = 1200) presented in the study are openly available in Mendeley Data at DOI : https://doi.org/10.17632/hrs3fgc8tt.1.Open asset ↗Mendeley Data · 10.17632/hrs3fgc8tt.1lines:152-174Plant phenotyping relevance match · UnverifiedEurope PMC · checked 6 Sept 2026
Abstract Background Apple scab (AS), caused by the fungal pathogen Venturia inaequalis , is a major disease of apple that manifests as lesions on leaves and fruits. It significantly reduces fruit quality and yield, leading to substantial economic losses. Traditional AS assessment relies on visual scoring, which is labor-intensive, subjective, and poorly reproducible. This study proposes a deep learning-based framework to overcome these limitations and to enable an accurate, scalable AS phenotyping approach. Results Deep learning techniques were employed for the object detection and segmentation of AS symptoms in apple fruits. A two-stage fine-tuning process using the YOLO foundation model (YOLOv11) was applied to color images collected under orchard and laboratory conditions. The first model achieved over 90% precision in detecting apples, while the second achieved 78% precision in identifying and quantifying AS lesions. The YOLO-based architecture supports the real-time processing of both images and video streams, enabling rapid in situ evaluation. Despite challenges such as variable lighting, shading, and symptom heterogeneity across developmental stages, the model’s performance was enhanced through extensive data augmentation, a diverse image dataset, and the use of high-resolution (840 × 840 pixels) training images, which improved detection of fine-scale features by 40%. Compared to manual scoring, this method is significantly faster, more objective, and more reproducible. Conclusion These results demonstrate the strong potential of the proposed deep learning-based approach as a robust and scalable tool for automated AS phenotyping. By improving the precision and efficiency of disease assessments in both controlled and field environments, this framework effectively supports apple grading assessments and accelerates breeding efforts aimed at identifying AS-resistant genotypes. Moreover, it establishes a solid foundation for broader applications in real-time plant disease monitoring and the future integration of additional apple diseases.
Why it matches plant phenotyping methodsリンゴ果実上の黒星病病斑を画像から検出・セグメンテーションし、病斑を定量化する深層学習法の開発であり、植物病害表現型の取得が研究の中心である。
abstractThis study proposes a deep learning-based framework to overcome these limitations and to enable an accurate, scalable AS phenotyping approach.
Ultrasonic sensing technology can contribute significantly to improving smart agricultural practices by recognizing plants and land features. Accurate detection of these field features is essential for the development of unmanned vehicles, which require precision navigation, obstacle avoidance, and successful field operation. Therefore, the objectives of the study were to employ ultrasonic sensors to detect key parameters of pepper plants and land features, specifically plant height, canopy volume, row spacing, and ridge spacing. Row spacing is the space between rows of plants, and ridge features are the raised soil beds that are often made for planting in upland farming systems. A data collection device was developed and tested in both laboratory and open-field environments. Initially, laboratory tests were conducted to evaluate the sensor accuracy of pepper plant height and canopy volume detection. Following successful validation, field trials were carried out in a pepper cultivation area using a remote-controlled vehicle platform to measure plant height, canopy volume, and row and ridge spacing. An open-source application was used to collect data and visualize the outcomes in real-time. The algorithm presented in the study effectively estimated the height, canopy volume, row spacing, and ridge spacing for pepper plants and associated land features. The results showed plant height of 61.34 and 61.49 cm, canopy volume of 0.29 and 0.31 m³, ridge spacing of 28.88 and 28.94 cm, and row spacing of 44.42 and 43.88 cm, respectively. No significant differences (p>0.05) were found between the measured and estimated plant and land features. Estimation values were strongly correlated with the measured values, with simple linear coefficients of determination (r2) of 0.95, 0.93, 0.88, and 0.81 for height, canopy volume, row spacing, and ridge spacing, respectively. The RMSE of these measurements ranged from 0.93 to 2.08 cm, highlighting relatively high accuracy of the proposed methods. The developed system shows the potential of ultrasonic sensors to develop automatic crop monitoring systems and support smart crop production and be adaptable to greenhouses, open fields or on-farm vehicles to identify different types of plants and land features.
Why it matches plant phenotyping methods超音波センサーと車載データ収集システムを開発し、コショウ植物の草丈・樹冠体積を検出・推定して実測値と検証しており、植物形質取得法が研究の中心である。
abstractA data collection device was developed and tested in both laboratory and open-field environments.
The root meristem navigates the highly variable soil environment where water availability limits water absorption, slowing or halting growth. Traditional studies use uniform high osmotic potentials, poorly representing natural conditions where roots gradually encounter increasing osmotic potentials. Uniform high osmotic potentials reduce root growth by inhibiting cell division and shortening mature cell length. This protocol describes a simple and effective in vitro system using a gradient mixer that generates a vertical gradient in an agar gel based on the principle of communicating vessels, exploiting gravity to generate a continuous mannitol concentration gradient (from 0 to 400 mM mannitol) reaching osmotic potentials of -1,2 MPa. It enables long-term Arabidopsis root growth analysis under progressive water deficit, improving phenotyping and molecular studies in soil-like conditions. Key features • Novel approach: Unique method to evaluate primary root growth in Arabidopsis under increasing osmotic potentials. • Osmotic gradient system: Simulating a gradual osmotic gradient in the root growth zone while maintaining aerial tissues under control conditions. • Sustained growth: Arabidopsis Col-0 and ttl1 mutant seedlings maintain proper root growth for 25 days, even at osmotic potentials as low as -1.2 MPa. • Enhanced growth rates: Roots grown in the osmotic gradient exhibit higher growth rates than those in homogeneous high osmotic potential conditions. • Phenotypic observation: ttl1 seedlings grown in the osmotic gradient do not show the typical swelling phenotype observed at extreme osmotic potentials (-1.2 MPa).
Why it matches plant phenotyping methods根の成長表現型を測定するための浸透圧勾配培養システム自体を開発・提示したプロトコルであり、表現型取得法が中心である。
abstractThis protocol describes a simple and effective in vitro system using a gradient mixer that generates a vertical gradient in an agar gel
Plant phenotyping relevance match · UnverifiedOpenAlex · Europe PMC · bioRxiv · Crossref · checked 15 Sept 2026
1 Abstract Root hairs play a key role in plant nutrient and water uptake. Historically, root hair traits have been largely quantified manually. As such, this process has been laborious and low-throughput. However, given their importance for plant health and development, high-throughput quantification of root hair morphology could help underpin rapid advances in the genetic understanding of these traits. With recent increases in the accessibility and availability of artificial intelligence (AI) and machine learning techniques, the development of tools to automate plant phenotyping processes has been greatly accelerated. Here, we present pyRootHair, a high-throughput, AI-powered software application to automate root hair trait extraction from images of plant roots grown on agar plates. pyRootHair is capable of batch processing over 600 images per hour without manual input from the end user. In this study, we deploy pyRootHair on a panel of 24 diverse wheat cultivars and uncover a large, previously unresolved amount of variation in many root hair traits. We show that the overall root hair profile falls under two distinct shape categories, and that different root hair traits often correlate with each other. We also demonstrate that pyRootHair can be deployed on a range of plant species, including arabidopsis ( Arabidopsis thaliana) , brachypodium ( Brachypodium distachyon ), medicago ( Medicago truncatula ), oat ( Avena sativa ), rice ( Oryza sativa ), teff ( Eragostis tef ) and tomato ( Solanum lycopersicum ). The application of pyRootHair enables users to rapidly screen large numbers of plant germplasm resources for variation in root hair morphology, supporting high-resolution measurements and high-throughput data analysis. This facilitates downstream investigation of the impacts of root hair genetic control and morphological variaton on plant performance.
Why it matches plant phenotyping methods根毛形態という植物形質を画像から自動抽出するAIソフトウェアを開発し、複数作物で適用・検証しており、表現型取得手法が研究の中心である。
abstractHere, we present pyRootHair, a high-throughput, AI-powered software application to automate root hair trait extraction from images of plant roots grown on agar plates.
Accurate, quantitative phenotyping aids in the discovery of quantitative trait loci, particularly those with minor effects. Previously, we optimized replicated precision phenotyping of mapping families after inoculation of leaf discs with the grapevine powdery mildew pathogen ( Erysiphe necator ). Pathogen colonies were stained, and hyphal density was estimated using hyphal transects. This approach outperformed field evaluations and other controlled phenotyping methods but required one or two person-months of microscopy per experiment to evaluate resistance across 300 host genotypes. More recently, we combined advanced macrophotography, robotic sample positioning, and convolutional neural networks to produce a high-throughput phenotyping device, which was modified and commercialized as "Blackbird." Here, that device was tested for nondestructive image collection and computer vision quantification of foliar grapevine powdery mildew. Blackbird outpaced manual microscopy up to 60-fold and nondestructively generated time-series segregating phenotypes from 2 to 9 days postinoculation (dpi). Paired analysis of these phenotypes with RNase H2-amplicon sequencing haplotype markers targeting the Vitis core genome detected REN13 on chromosome 8. Genetic analysis of Blackbird convolutional neural network data explained a greater proportion of the phenotypic variance via hyphae at 4 dpi (24.5%) and conidia at 9 dpi (24.0%) than manual microscopy at 8 dpi (15.8%). As a moderate-effect resistance locus in the widely planted resistant variety 'Norton', which already produces commercial wine quality, REN13 could significantly delay epidemics and could be useful in grape breeding programs to increase the durability of stronger resistance loci (e.g., RUN1 , REN4 , or REN12 ) in resistance gene stacks while maintaining fruit quality.
Why it matches plant phenotyping methods画像取得、ロボット試料位置決め、CNNによる病徴定量を統合した装置を開発・改変し、手動顕微鏡法と比較検証しているため、植物病害表現型の取得・抽出法が中心である。
abstractwe combined advanced macrophotography, robotic sample positioning, and convolutional neural networks to produce a high-throughput phenotyping device
The AI revolution, advanced Graphics Processing Units (GPUs), and open-source platforms have enabled Machine Learning (ML) and Deep Learning (DL) algorithms to rapidly and accurately extract phenotypic features from Uncrewed Aerial System (UAS)-derived imagery. Such advancement leads to phenotypic digitization and sorghum yield forecasting. Yield analytics are critical for breeding programs to assess the genetics and breeding potential of genotypes to enhance cultivar development. This trial followed a three-replicated Randomized Complete Block Design (RCBD) with 36 diverse sorghum genotypes in 2023 at Ashland Bottoms, Kansas. The field images were captured 6 meters above using a DJI M300 drone equipped with the P1 sensor at nadir (90 degrees) and oblique (45 degrees) angles. This research trained YOLO and the Faster R-CNN (Detectron2) models to harness yield attributes from UAS field and lab images. The YOLO models outperformed the Faster R-CNN model in detecting sorghum panicles, achieving a mean average precision at 50% Intersection over Union (IoU) ranging from 0.92 to 0.98, compared to 0.61 to 0.89. Panicle detection from field imagery correlated at 0.86 with ground truth. Lab imagery analyses measured panicle size, seed counts, and seed area with correlation coefficients of 0.71, 0.95, and 0.25, respectively. Three machine learning models: Support Vector Regression (SVR), Decision Tree Regression (DTR), and Random Forest Regression (RFR) are used to predict yield with correlation coefficients of 0.58, 0.76, and 0.70, respectively. We observed that YOLO models are well-suited for extracting yield-attributing traits from images, which are then incorporated into ML regression models to improve yield prediction performance.
Why it matches plant phenotyping methodsUAS・実験室画像からソルガム穂の検出、サイズ・種子数・面積などの形質抽出と収量予測を行い、複数の物体検出モデルを比較検証しているため、表現型取得手法が中心である。
abstractThis research trained YOLO and the Faster R-CNN (Detectron2) models to harness yield attributes from UAS field and lab images.
Plant disease classification plays a vital role in advancing modern agriculture, transitioning from traditional manual diagnosis to intelligent, automated systems powered by machine learning. Historically, identification of plant diseases relied on visual inspections, expert advice, and lab tests— methods that were accurate for small-scale use but often subjective, slow, and inconsistent. These limitations resulted in delayed treatment and substantial crop losses, highlighting the inefficiency and high cost of conventional approaches, especially at scale. To address this, the proposed system introduces an innovative machine learning-based solution capable of accurately classifying plant diseases using sparse and categorical IoT data. It incorporates comprehensive data preprocessing techniques, including handling missing values, label encoding, and class imbalance correction using the Synthetic Minority Oversampling Technique (SMOTE), ensuring a high-quality dataset for model training. The classification pipeline integrates multiple models—Gaussian Naive Bayes, Support Vector Machines, K-Nearest Neighbors, and a novel Decision Tree Classifier. Among these, the Decision Tree model demonstrated superior performance, achieving an accuracy of 99.07% with precision, recall, and F1-scores consistently exceeding 98%, confirming its robustness and reliability. This research is significant in offering real-time, data-driven diagnostics that enable early disease detection and precise pesticide recommendations. It not only improves crop yield and reduces financial losses but also promotes environmentally sustainable agriculture by limiting excessive chemical usage. By overcoming the limitations of traditional methods—such as subjectivity, delay, and lack of scalability—this system presents a transformative approach to plant disease management through advanced machine learning, marking a pivotal shift toward precision agriculture.
Why it matches plant phenotyping methods植物病害をIoTデータから機械学習で自動分類する手法が研究の中心であり、感染植物の病害状態を推定する植物フェノタイピング手法に該当する。
abstractthe proposed system introduces an innovative machine learning-based solution capable of accurately classifying plant diseases using sparse and categorical IoT data.
AppleLaboratory / benchtopMultispectral / hyperspectralFruitClassificationGrowth / development / phenology
The study created a detailed database for apple quality inspection using a cost-effective, self-designed multi-spectral imaging system. The system was optimized to allow spectral information to be obtained in 8 discrete wavebands, which enabled non-destructive determination of such key fruit components as ripeness, sugar type and cultivar. Stringent environmental conditions were maintained during image acquisition for optimal measurement consistency and experimental repeatability. The detailed dataset encompasses 32,463 multi-spectral images across three distinct classification categories. For sweetness evaluation, 1620 images spanning Brix values from 10 % to 15 % were collected from five apple varieties. Ripeness evaluation includes 29,160 images documenting the complete maturation cycle over 18 days, while variety classification contains 1683 images from three distinct cultivars. Each image was captured under controlled lighting conditions using eight specific wavelengths, ensuring spectral consistency crucial for machine learning applications. These multi-spectral images were concatenated for grading by sweetness, ripeness, and variety, creating a processed dataset of concatenated images optimized for AppleNet processing. The concatenation process combines the eight wavelength channels into unified image representations suitable for deep learning applications. Sample collection included the picking of different apple cultivars at different physiological development phases of fruit from local orchards. Single specimens were imaged sequentially using a multi-spectral technique. Information on sugar content concentration ( % Brix), maturation phase classification and varietal identification was recorded according to standard laboratory procedure. The resulting annotated database includes such quantitative reference points, which can be used to train supervised learning classifiers in computational classification systems. The reuse value of the dataset covers a wide range of applications such as machine learning-based fruit quality evaluation, agricultural automation and food industry examination. This dataset of ours can be used by researchers to develop and test algorithms to classify apples and estimate their ripeness and the presence of diseases. Furthermore, the proposed multi-spectral imaging can be generalized to cover other fruits and agricultural products, extending the application of the method in smart agriculture. This dataset serves as a valuable resource for researchers in computer vision, machine learning, and agricultural technology, fostering advancements in non-destructive fruit quality evaluation methodologies.
Why it matches plant phenotyping methodsリンゴの甘度・成熟度・品種を推定するマルチスペクトル撮像システムと、注釈付き大規模画像データセットを中心に構築しており、植物器官の品質・状態を定量化する再利用可能なフェノタイピング手法に該当する。
abstractThe study created a detailed database for apple quality inspection using a cost-effective, self-designed multi-spectral imaging system.
Reproduction assets foundThe article is a Data in Brief describing a public multi-spectral apple image dataset (sweetness/Brix, ripeness over 18 days, variety) deposited on Mendeley Data with DOI 10.17632/y5h6v8w6ms.2 and a direct URL, explicitly stated as publicly accessible. This is the paper's own phenotyping image dataset. The MATLAB code,Dataset · publicme environment using a custom-built multi-spectral imaging chamber . The imaging conditions were carefully maintained to ensure consistency. The dataset is securely stored for research and study purposes.
Data accessibility
Repository name: Mendeley Data
Data identification number: DOI: 10.17632/y5h6v8w6ms.2
Direct URL to data: https://data.mendeley.com/datasets/y5h6v8w6ms/2
Instructions for accessing these data:
Dataset Title: Dataset of Apples for Grading by Sweetness, Ripeness, and Variety
Public Access: The dataset titled ``Dataset of Apples for Grading by Sweetness, Ripeness, and Variety'' is publicly available on Mendeley Data and can be accessed via the following DOI:
https://doi.org/Open asset ↗Mendeley Data · 10.17632/y5h6v8w6ms.2lines:40-82Plant phenotyping relevance match · UnverifiedbioRxiv · Crossref · checked 13 Sept 2026
AO_SCPLOWBSTRACTC_SCPLOWThis study evaluates a practical, low-cost solution for image-based leaf SPAD (Soil and Plant Analysis Development) value and chlorophyll content monitoring using a mobile phone. We compare laboratory assay and SPAD-502+ measurements with image-based estimates from a mobile phone app (PhotoFolia). Performance is tested for four commercial rice varieties grown in Thailand. Results show that the image-based method can predict SPAD values within {+/-} 1.2 units Mean Absolute Error (MAE) and- chlorophyll concentrations within 7.2% Mean Absolute Percentage Error (MAPE) of laboratory results. Achieving a SPAD value error close to the industry standard of {+/-}1 unit and a relative error of less than 10% in chlorophyll concentration estimation (compared to a laboratory method) demonstrates that an image-based approach using standard mobile phones can serve as an accessible, low-cost tool for on-farm chlorophyll monitoring, without the need for specialised equipment. Key Points / HighlightsNovel low-cost approach for chlorophyll assay and SPAD-value measurement using standard mobile phone. Achieves accuracy comparable to commercial tools. Eliminates need for specialised sensors or laboratory equipment. ImpactThis study demonstrates that mobile phone-based image analysis can accurately estimate leaf SPAD and chlorophyll levels in rice under ambient lighting conditions, offering a low-cost, accessible tool for monitoring plant health.
Why it matches plant phenotyping methods携帯電話画像から葉のSPAD値とクロロフィル濃度を推定する手法を開発・検証しており、植物形質取得が研究の中心である。
abstractThis study evaluates a practical, low-cost solution for image-based leaf SPAD (Soil and Plant Analysis Development) value and chlorophyll content monitoring using a mobile phone.
Plant phenotyping relevance match · UnverifiedCrossref · Europe PMC · checked 6 Sept 2026
Summary Reflectance spectroscopy is a rapid method for estimating traits and discriminating species. Spectral libraries from herbarium specimens represent an untapped resource for generating broad phenomic datasets across space, time, and taxa. We conducted a proof‐of‐concept study using trait data and spectra from herbarium specimens up to 179 yr old, alongside data from recently dried and pressed leaves. We validated model accuracy and transferability for trait prediction and taxonomic discrimination. Trait models from herbarium spectra predicted leaf mass per area (LMA) with R 2 = 0.94 and %RMSE = 4.86%. Models for LMA prediction were transferable between herbarium and pressed spectra, achieving R 2 = 0.88, %RMSE = 8.76% for herbarium to pressed spectra, and R 2 = 0.76, %RMSE = 10.5% for the reverse transfer. Discriminant models classified leaf spectra from 25 species with 74% accuracy, and classification probabilities were significantly associated with several herbarium specimen quality metrics. The results validate herbarium spectral data for trait prediction and taxonomic discrimination, and demonstrate that trait modeling can benefit from the complementary use of pressed‐leaf and herbarium‐leaf spectral datasets. These promising advancements help to justify the spectral digitization of plant biodiversity collections and support their application in broad ecological and evolutionary investigations.
Why it matches plant phenotyping methods葉の反射スペクトルからLMAなどの植物形質を推定する方法を開発・検証し、標本間のモデル移 transferability と精度を評価しており、表現型取得・推定が中心である。
abstractReflectance spectroscopy is a rapid method for estimating traits and discriminating species.
Laboratory / benchtopX-ray / CTRootMorphology / geometry measurementRoot system architecture
Soil compaction and escalating global drought increase soil strength and stiffness. It remains unclear which plant root biomechanical mechanisms/traits enable growth in these harsh conditions. Here, we combine synchrotron X-ray computed tomography with spatially resolved X-ray diffraction to characterize the biomechanics of a replica root-soil system. We map the strain field around the root tip analog, finding strong agreement with finite element simulations, thereby demonstrating a promising new in vivo measurement protocol.
Why it matches plant phenotyping methods根の生育に関わる土壌内の力学的状態・ひずみを、X線CTと回折で可視化・定量する新しいin vivo測定プロトコルを開発・検証しており、植物表現型取得法が中心です。
abstractWe map the strain field around the root tip analog, finding strong agreement with finite element simulations, thereby demonstrating a promising new in vivo measurement protocol.
Reproduction assets foundThe paper's Data availability and Code availability statements both deposit the study's XCT/XRD imaging and diffraction data and the processing scripts in the University of Southampton Pure repository (DOI 10.5258/SOTON/D3309), which is an allowed URL. These are paper-specific, publicly declared assets directly reproduCode · publicAll scripts used to process the data can be found in the Pure repository: https://doi.org/10.5258/SOTON/D3309 .Open asset ↗Pure · 10.5258/SOTON/D3309lines:209-251Plant phenotyping relevance match · UnverifiedEurope PMC · checked 15 Sept 2026
Published1 Jul 2025Computers and Electronics in Agriculture.
Accurate canopy characterisation is crucial for the targeted application of plant protection products following the variable rate application (VRA) concept. In this study, two different canopy measurement systems were compared: ultrasonic (US) sensors and UAV-based photogrammetry. A specific device was developed to host a series of US sensors that could conduct a fully automatic canopy characterisation of two vine rows in a single pass. The results of canopy characterisation (canopy width, canopy height, leaf wall area, and tree row volume) were compared with those obtained after complete data processing of the images obtained using a multispectral camera embedded on a UAV. Results indicated that no significant differences have been obtained in the definition of main canopy parameters. Field tests indicated that US sensors offered stable canopy height readings but exhibited variability in width measurements due to factors like ground conditions and sensor placement. Compared to with UAV photogrammetry, US sensors provided comparable results for canopy height and width at a lower cost and with less precision. Therefore, the choice between US sensors and UAVs should consider the resolution requirements, cost, and field conditions. Field data were collected from two commercial vineyards in the Penedès region close to Barcelona (Spain). Before this, laboratory tests were performed using an artificial target to achieve an accurate evaluation of the US sensors. Overall, this study highlighted the potential of ground-based sensing systems for precise and repeatable canopy measurements, contributing to improved vineyard management practices and advanced technological integration for agricultural monitoring.
Why it matches plant phenotyping methodsブドウ樹冠の形態形質を取得する超音波センサーとUAV画像法を開発・比較検証しており、フェノタイピング手法が研究の中心である。
abstractA specific device was developed to host a series of US sensors that could conduct a fully automatic canopy characterisation of two vine rows in a single pass.
Nitrogen is an important nutrient with respect to crop growth, development and yield. Hence, site specific and optimal nitrogen fertilization requires knowledge of spatial nitrogen distribution and deficiencies in the field. Optical methods to determine leaf nitrogen concentration (LNC) have advantages over laboratory methods because of lower costs and faster performance. Together with e.g. unmanned aerial vehicles (UAV), optical methods can also be used to acquire LNC information with high spatial resolution. The main goal of this research was therefore to determine the most suitable vegetation indices for the detection and classification of nitrogen differences and deficiencies in maize (Zea mays L.). Hyperspectral images from 450 nm to 998 nm of fully expanded maize leaves from four different nitrogen treatments (0.72–2.88 g N/plant) were acquired under controlled light conditions and the corresponding LNC were determined. Then optimal wavelength-pairs for two predefined vegetation index formulas, the normalized difference spectral index (NDSI) and the ratio spectral index (RSI), were identified. Finally, the performances of the identified vegetation indices and selected vegetation indices from the literature to predict and classify LNC were assessed by means of a simulated pattern map that reflects spatially varying LNC classes. It was found that a wavelength from the red edge region (718 nm) was the most significant for LNC (r = 0.92). The vegetation index formulas considered (NDSI and RSI) showed the best performances when wavelength-pairs from the red-edge and NIR region (722 nm, 950 nm) were combined. Both vegetation indices showed a strong relationship with LNC (R²=0.90 for NDSI, R²=0.86 for RSI) and performed best at predicting LNC classes and their distribution in the simulated pattern map (accuracy=91.7 %, kappa=0.87).
Why it matches plant phenotyping methodsトウモロコシ葉の窒素濃度という植物生理形質を、ハイパースペクトル画像と植生指数で推定・分類する手法の開発および性能評価が研究の中心である。
abstractThe main goal of this research was therefore to determine the most suitable vegetation indices for the detection and classification of nitrogen differences and deficiencies in maize (Zea mays L.).
Plant phenotyping relevance match · UnverifiedCrossref · Europe PMC · checked 6 Sept 2026
ABSTRACT Arabidopsis thaliana is a model species for uncovering genetic adaptation to alkaline calcareous soils (ACS). This species thrives in ACS, often occurring in dry marginal and urban environments. Existing research largely focused on vegetatively grown seedlings, with a notable lack of studies examining phenotypic variations across the life cycle. A valuable tool for understanding stress resilience is machine‐aided phenotyping, as it is non‐invasive, rapid, and accurate, but often unavailable to small plant labs. Here, we established and validated an affordable multispectral machine‐aided phenotyping approach implementable by individual labs. We collected and correlated quantitative growth data across the entire plant life cycle in response to ACS. We used an A. thaliana wildtype and the coumarin‐deficient mutant f6'h1‐1 , exhibiting chlorosis under alkaline conditions, to assess weekly morphological and leaf color data, both manually and using a multispectral 3D phenotyping scanner. Through correlation analysis, we selected machine parameters to differentiate size and leaf chlorosis phenotypes. The correlation analysis indicated a close connection between rosette size and multiple spectral parameters, highlighting the importance of rosette size for growth of A. thaliana in ACS. The most reliable phenotyping was at the beginning of the bolting stage. This methodology is further validated to detect novel leaf chlorosis phenotypes of known iron deficiency mutants across growth stages. Hence, our affordable machine‐aided phenotyping procedure is suitable for high‐throughput, accurate screening of small‐grown rosette plants, including A. thaliana , and enables the discovery of novel genetic and phenotypic variations during the plant's life cycle for understanding plant resilience in challenging soil environments.
Why it matches plant phenotyping methods安価なマルチスペクトル3Dスキャナーを用いた植物表現型取得法を確立・検証し、形態と葉色の定量化およびストレス表現型識別を技術的に評価しているため。
abstractHere, we established and validated an affordable multispectral machine‐aided phenotyping approach implementable by individual labs.
Background Global warming is currently occurring at a rapid rate and is having a particularly severe impact on plants, which, as sessile organisms, have a limited ability to escape high temperatures. This requires a better understanding of the thermal limits for different plant species and a better understanding of the processes involved in the development of heat injury in plant leaves. Heat injury results from multiple processes and occurs at the molecular level, involving increased membrane fluidity, lipid peroxidation, and protein aggregation and denaturation. Results We have tested whether the DSC method allows the detection of heat-induced denaturation and aggregation of molecules in intact leaves. During controlled heating a consistent and repeatable pattern was observed in the DSC plot, from which critical heat thresholds could be derived. These critical temperatures were in good agreement with the temperatures determined using classical methods and also clearly mark the thermal limits of molecular structures. The advantage of the DCS method is the precise, rapid and easy detection of heat thresholds. Finally, taken all thresholds together, we can draw a better image of the sequence of events associated with heat injury in plant leaves: heat injury begins with membrane leakage and continues with protein denaturation and aggregation at high (sublethal, lethal) temperatures. Conclusion Since heat injury results from multiple processes, a holistic understanding requires the acquisition of parameters indicative of different processes. The presented DSC method, which allows the detection of denaturation and aggregation of cellular compounds, therefore complements well the classical methods that reflect photosynthetic impairment and whole leaf tissue damage. The new simple and rapid method requires only a minimal amount of leaf material and allows rapid collection of data on damaging temperatures for different plants, which is particularly important in the face of rapidly progressing climatic changes.
Why it matches plant phenotyping methods葉の熱傷害・熱閾値を測定するDSC法の開発と古典的方法との検証が中心であり、植物の生理状態を定量化する方法論研究に該当する。
titleA novel method for measuring heat injury in leaves provides insights into the sequence of processes of heat injury development.
Phenotype observations are common methodologies in plant biology studies, ranging from recording growth parameters to flowering dates. Identifying mutants or varieties with different phenotypes greatly advances our understanding of regulatory mechanisms in plant growth and development. Over the past 2 decades, naked-eye-based observations and manual measurements using ImageJ software have been leading approaches for recording phenotypes. However, these low-efficiency and error-prone methods have met difficulties in large-scale pipelines. Although some high-throughput imaging platforms have been commercialized, it remains challenging to efficiently, conveniently, accurately, and automatically analyze data generated by these platforms. To address this issue, we designed an automatic phenotype analysis tool. We trained a YOLOv11 (You Only Look Once version 11) model to locate Arabidopsis thaliana seedlings grown on petri dishes and developed a high-accuracy semantic segmentation model based on Swin Transformer and kernel update head, achieving a segmentation accuracy of 83.56% mIoU. By postprocessing the segmentation masks, we automated the analysis of 5 representative seedling phenotypes: hypocotyl length, root length, root gravitropic bending angle, petiole length, and cotyledon opening rate. Compared with manual recording, our tool demonstrated high accuracy across all 5 phenotypes, offering a reliable and efficient solution for phenotypic analysis in plant research. Our automatic tool enables high-throughput phenotyping and will shift the traditional paradigm of phenotype recording.
Why it matches plant phenotyping methods植物表現型を自動取得・抽出する画像解析ツールの開発が研究の中心であり、複数の実測形質を手動記録と比較検証しているため。
abstractTo address this issue, we designed an automatic phenotype analysis tool.
Accurate, quantitative phenotyping aids in the discovery of quantitative trait loci, particularly those with minor effects. Previously, we optimized replicated precision phenotyping of mapping families after inoculation of leaf discs with the grapevine powdery mildew pathogen (Erysiphe necator). Pathogen colonies were stained, and hyphal density was estimated using hyphal transects. This approach outperformed field evaluations and other controlled phenotyping methods but required one or two person-months of microscopy per experiment to evaluate resistance across 300 host genotypes. More recently, we combined advanced macrophotography, robotic sample positioning, and convolutional neural networks to produce a high-throughput phenotyping device, which was modified and commercialized as "Blackbird." Here, that device was tested for nondestructive image collection and computer vision quantification of foliar grapevine powdery mildew. Blackbird outpaced manual microscopy up to 60-fold and nondestructively generated time-series segregating phenotypes from 2 to 9 days postinoculation (dpi). Paired analysis of these phenotypes with RNase H2-amplicon sequencing haplotype markers targeting the Vitis core genome detected REN13 on chromosome 8. Genetic analysis of Blackbird convolutional neural network data explained a greater proportion of the phenotypic variance via hyphae at 4 dpi (24.5%) and conidia at 9 dpi (24.0%) than manual microscopy at 8 dpi (15.8%). As a moderate-effect resistance locus in the widely planted resistant variety 'Norton', which already produces commercial wine quality, REN13 could significantly delay epidemics and could be useful in grape breeding programs to increase the durability of stronger resistance loci (e.g., RUN1, REN4, or REN12) in resistance gene stacks while maintaining fruit quality.
Why it matches plant phenotyping methods高スループット画像取得とコンピュータビジョンによるブドウうどんこ病表現型の定量化が研究の中心で、手動顕微鏡との性能比較・検証も行っている。
abstractwe combined advanced macrophotography, robotic sample positioning, and convolutional neural networks to produce a high-throughput phenotyping device
Why it matches plant phenotyping methods干ばつ耐性を評価するための高スループット水耕アッセイを開発・提示し、植物表現型（バイオマス、光合成、回復）を再現可能なプラットフォームで取得することが中心である。
abstractwe present a rapid, high-throughput hydroponic assay designed as an efficient pre-screening tool for evaluating potato cultivars and CRISPR-edited lines.
Early-stage plant stress detection is a key measure for sustainable agriculture management. Mineral dust as an abiotic stressor affects the physical, chemical, and physiological characteristics of plants, which are linked to the plant's visible and near-infrared (VNIR) reflectance. However, considering the intensity of plant exposure to dust and associated spectral feedback remain unclear. This study investigates the effects of dust particles on the spectral properties of 11 plant species over the growing season by conducting an in-vitro experiment based on VNIR spectroscopy. The capabilities of machine learning algorithms based on VNIR data, including partial least-squares regression (PLSR) and support vector machine (SVM), were also evaluated for dust stress detection. Analyses show that increases in dust concentration lead to (i) reduction of leaf chlorophyll and water contents; (ii) increase of spectral reflectance at 450–490, 640–660, 1370–1450, and 1820–1940 nm; (iii) decrease of spectral reflectance at 530–590, 740–1200 nm; (iv) decrease the slope and height of the red edge; (v) red absorption feature (AF) became smaller and shifted towards shorter wavelength; (vi) reduction of area, width, and depth of AFs at 400–740, 1350–1450, and 1800–1900 nm; and (vii) shift of AF position at 400–740 nm towards shorter wavelength. The results show that, PLSR estimates dust concentration with an R² ranging from 0.83 to 0.95. Additionally, the SVM successfully distinguishes between dust-exposed and non-dust-exposed samples, achieving an overall accuracy of 80–96 %. The research reveals how mineral dust affects the spectral behavior of plants, providing a basis for early-stage dust stress detection through the combination of VNIR spectroscopy and machine learning. Leveraging the research findings, transition from laboratory spectroscopy to hyperspectral remote sensing imagery enables cost-effective and extensive spatiotemporal monitoring, facilitating timely protective measures to mitigate dust-induced damage to plants.
Why it matches plant phenotyping methodsVNIR分光と機械学習により、植物のダストストレス状態をスペクトルから検出・推定する方法を評価しており、植物表現型取得が研究の中心である。
abstractThe capabilities of machine learning algorithms based on VNIR data, including partial least-squares regression (PLSR) and support vector machine (SVM), were also evaluated for dust stress detection.
Arbuscular mycorrhizal (AM) fungi, ubiquitously distributed across diverse terrestrial ecosystems, establish symbiotic associations with the majority of vascular plants, fulfilling essential physiological and ecological functions. Mycorrhizal development represents the initiation of host-fungus interactions and serves as a metric for assessing mutualistic efficacy. However, mycorrhizal detection underscores the urgent need to develop cost-effective, efficient, and environmentally benign dyestuff. Therefore, wild-collected and laboratory-grown roots of Medicago sativa were selected. Six reagents including black ink, red ink, acid fuchsin, trypan blue, Sudan IV, and aniline blue were evaluated in conjunction with computer vision techniques to identify optimal one. Concurrently, root characteristics were quantified, and interrelationships among root traits, image quality, and colonization indices were analyzed to unravel the mechanism of their interactions. The findings demonstrated that wild roots exhibited pronounced lignification, achieving a mycorrhizal colonization rate of 100 %, which was better than the two laboratory groups. And the fungal community displayed a markedly greater colonization intensity compared to the Claroideoglomus etunicatum. Evaluation of the six reagents revealed distinct staining efficacy, with significant variations in image clarity, gray-level co-occurrence matrix (GLCM) indices, and colonization parameters across treatments. Specifically, aniline blue proved ineffective, while Sudan IV showed selective binding. Notably, black ink in glacial acetic acid achieved optimal mycorrhizal detection efficacy. Moreover, correlation matrix identified microscopic image quality as critical determinant of quantification accuracy, influenced by both reagent types and root properties, and AvgDiam exerted the most substantial impact (|R| > 0.75).
Why it matches plant phenotyping methods植物根のAM菌根菌感染状態を染色とコンピュータビジョンで定量する手法の開発・比較評価が中心であり、単なる生物学的測定ではない。
abstractSix reagents including black ink, red ink, acid fuchsin, trypan blue, Sudan IV, and aniline blue were evaluated in conjunction with computer vision techniques to identify optimal one.
Plant phenotyping relevance match · UnverifiedCrossref · Europe PMC · checked 15 Sept 2026
Lignin, a major component of plant cell walls, plays a critical role in structural support and stress resistance. Despite its importance, the transport and deposition dynamics of the glycosylated lignin monomer during lignification in living cells remain poorly understood, hindering advances in biomass utilization. To address this challenge, alkyne-labeled glycosylated lignin precursors (pGCA ALK and CF ALK ) were synthesized by introducing propargyl groups at the ortho position of aromatic rings. These precursors were successfully incorporated into lignin polymers in flax (a herbaceous plant) and ginkgo (a gymnosperm), enabling the real-time tracking of lignification via fluorescent click chemistry. Quantitative imaging revealed that lignification initiates at cell corners and the middle lamella and then progressively extends into secondary cell walls. Distinct deposition patterns were observed: parenchyma cells exhibited continuous lignin accumulation, whereas fiber tracheids underwent rapid lignification, followed by cell death. Specialized pit structures displayed "tunnel-like" lignin deposition in longitudinal pits and unilateral patterns in transverse pits. In vitro synthesis of dehydrogenation polymers (DHP) and extraction of the cellulolytic enzyme lignin (CEL) from ginkgo confirmed the biocompatibility of labeled monomers. LC-MS analysis further demonstrated that alkynyl groups formed oxygen-containing cyclic structures without disrupting natural β-O-4 and β-5 lignin linkages. Application of this labeling method in biomass utilization indicated that lower overall fluorescence intensity correlates with more efficient lignin removal during pretreatment. These results provide new insights into the spatiotemporal dynamics of lignification and establish a bioorthogonal platform for lignin research, offering promising strategies for optimizing plant biomass in industrial applications.
Why it matches plant phenotyping methods蛍光クリック化学による生細胞内リグニン形成の時空間追跡と定量イメージング手法を開発・適用しており、植物状態の取得方法が研究の中心である。
abstractenabling the real-time tracking of lignification via fluorescent click chemistry.
The integration of nanotechnology in agriculture allows for more precise nutrient delivery through nanoparticles (NPs), particularly via foliar application. To mature this technology for enhancing fertilizer efficiency, it is essential to shed new light on the transport and dissolution of NPs in plants. Available analytical methods struggle to address this challenge in a direct manner. We introduce correlative X-ray imaging as a novel analytical tool capable of tracking NP pathways, dissolution and hence nutrient release in plants. By utilizing three complementary X-ray techniques, we offer a unique insight into the plant processes associated with foliar fertilization. We demonstrate that small-angle X-ray scattering enables the characterization of NP size and concentration, while X-ray fluorescence imaging, maps the distribution of elements within the sample. Finally, micro-computed tomography integrates these findings into a complete three-dimensional digital representation of the plant’s microstructure, revealing regions of apparent densification associated with NP accumulation. Using freeze-dried barley plants infiltrated with nano-hydroxyapatite (nHAP), we observed rapid dissolution of NPs, and we are able to associate time and space attributes to the translocation process of nutrients up to three days following foliar application of NPs. With the first pilot study of applying correlative X-ray imaging to live plants, we sought to indicate the potential of this new analytical approach for future nano-enabled agricultural research.
Why it matches plant phenotyping methods植物内のナノ粒子経路・溶解・栄養輸送を可視化する相関X線イメージング手法の導入と実証が中心であり、植物の状態・生理過程を測定する方法論的研究である。
abstractWe introduce correlative X-ray imaging as a novel analytical tool capable of tracking NP pathways, dissolution and hence nutrient release in plants.
Reproduction assets foundThe article's data availability statement points to a public figshare repository hosting the study's datasets (X-ray imaging/phenotyping measurements). No author analysis code with explicit public deposit language was identified; Dragonfly is a commercial visualization tool, not a paper-specific asset.Dataset · publicng Wan , Hainan University, China
Zhansheng Li , Chinese Academy of Agricultural Sciences, China
Firozeh Solimani , Politecnico di Bari, Italy
Data availability statement
The datasets presented in this study can be found in online repositories. The names of the repository/repositories and accession number(s) can be found below: https://figshare.com/s/33ee8388a36600fe98d5 .
Author contributionsOpen asset ↗figsharelines:191-206Plant phenotyping relevance match · UnverifiedCrossref · checked 14 Sept 2026
Abstract Recent advancements in plant sensing technologies have significantly improved agricultural productivity while reducing resource inputs, resulting in higher yields by enabling early disease detection, precise diagnostics, and optimized fertilizer and pesticide applications. Each adopted technology offers unique advantages suitable for various farm operations, breeding programs, and laboratory research. This review article first summarizes key target traits, endogenous structures, and metabolites that serve as focal points for plant diagnostic and sensing technologies. Next, conventional plant sensing technologies based on light reflectance and fluorescence, which rely on foliar phytopigments and fluorophores such as chlorophylls are discussed. These methods, along with advanced analytical strategies incorporating machine learning, enable accurate stress detection and classification beyond general assessments of plant health and stress status. Advanced optical techniques such as Fourier transform infrared spectroscopy (FT‐IR) and Raman spectroscopy, which allow specific measurements of various plant metabolites and structural components are then highlighted. Furthermore, the design and applications of nanotechnology chemical sensors capable of highly sensitive and selective detection of specific phytochemicals, including phytohormones and signaling second messengers, which regulate physiological and developmental processes at micro‐ to sub‐micromolar concentrations are introduced. By selecting appropriate sensing methodologies, agricultural production, and relevant research activities can be significantly improved.
Why it matches plant phenotyping methods植物の診断・センシング技術を体系的にレビューし、形質・構造・代謝物の測定、ストレス検出、光学・分光・化学センサーを中心に扱うため、方法論レビューとして対象範囲に該当する。
abstractThis review article first summarizes key target traits, endogenous structures, and metabolites that serve as focal points for plant diagnostic and sensing technologies.
Photosynthesis provides energy and organic substrates to most life. In plants, photosynthesis dominates chloroplast physiology but represents only a fraction of the tightly interconnected metabolic network that spans the entire cell. Here, we explore how photosynthetic activity affects energy physiology within and beyond the chloroplast. We developed a new standard for the live-monitoring of subcellular energy physiology by combining confocal imaging of genetically encoded fluorescent protein biosensors with advanced on-stage illumination technology to investigate pH, MgATP2- and NADH/NAD+ dynamics at dark-light transitions in Arabidopsis mesophyll cells. Our findings reveal a stromal alkalinization signature induced by photosynthetic proton pumping, extending to the cytosol and mitochondria as an alkalinization wave. Photosynthesis leads to increased MgATP2- levels in both the stroma and cytosol. Additionally, we observed reduction of the NAD pool driven by photosynthesis-derived electron export. Arabidopsis lines defective in chloroplast NADP- and mitochondrial NAD-dependent malate dehydrogenases show more reduced cytosolic NAD redox status even in darkness, highlighting the involvement of chloroplasts and mitochondria in shaping cytosolic redox metabolism via malate metabolism. Our study sets a novel methodological standard for precision live-monitoring of photosynthetic cell physiology. Applying this technology reveals signatures of photosynthetic physiology within and beyond the chloroplast with unprecedented resolution. Those signatures link photosynthetic activity and the fundamental biochemical functions of phototrophic cells. Significance statementBy applying novel live microscopy monitoring using fluorescent protein biosensors in plant cells, we reveal that dark-light transitions trigger profound re-orchestration of subcellular pH, ATP and NAD redox physiology not limited to chloroplasts but extending into the cytosol and the mitochondria.
Why it matches plant phenotyping methods植物細胞内のpH、ATP、NAD酸化還元状態を測定するライブイメージング手法を開発し、技術標準として提示・適用しており、表現型取得法が研究の中心である。
abstractWe developed a new standard for the live-monitoring of subcellular energy physiology by combining confocal imaging of genetically encoded fluorescent protein biosensors with advanced on-stage illumination technology
The global carbon cycle depends heavily on the carbon sequestration rates of aquatic ecosystems. Sinking of phytoplankton is a rapid mediator of carbon sequestration, because phytoplankton are globally abundant photoautotrophs that grow rapidly. Pico- and nano-phytoplankton sinking velocities vary depending on their growth state, viability, clumping, and distribution in the water column. We introduced high throughput fluorescence microscopy of well-plates, to measure sinking velocities of three diatom strains, and three cyanobacteria strains, with cell radii spanning an order of magnitude, all grown under three different light levels. Cultures were measured for sinking velocities repeatedly across their growth trajectories. Tracking multiple fluorescence wavebands allowed us to simultaneously determine sinking velocities for living vs. dead cells. Sinking velocities varied strongly across growth light levels, and across growth stages. These monoclonal cultures furthermore show distinct sub-populations of slow- and fast-sinking cells. Our results departed widely from simple Stokes Law estimates of sinking based upon radii and mass density of cells. Complex, heterogeneous phytoplankton communities likely show more complicated sinking patterns than are currently expressed in biogeochemical ocean models. Our well-plate microscopy approach using parallel imaging of many samples generates high-throughput measures of cell sinking at population- or community-scales, to in turn improve modelling of carbon export to deeper layers.
Why it matches plant phenotyping methods植物プランクトンの沈降速度を高スループット蛍光顕微鏡で測定する手法を導入し、生活状態や集団スケールの生理・機能形質を定量化しているため、測定法が研究の中心です。
abstractWe introduced high throughput fluorescence microscopy of well-plates, to measure sinking velocities of three diatom strains, and three cyanobacteria strains
Reproduction assets foundThe paper's sinking-velocity analysis code is explicitly stated to be openly available on the authors' GitHub repository. The raw phenotype data is promised for Dryad only upon acceptance, so it is not yet publicly actionable.Code · publicfunctional groups of cyanobacteria, diatoms strains with 156
diameter less than 10µm and diatoms strains with diameter larger than 10µm based on 157
growth light, viability state (living vs. dead and dying) and slow vs. fast sinking 158
velocity clustering groups. 159
The code used to analyse the data is public available at 160
https://github.com/maxberthold/PhytoplanktonSinkVelocities. 161
Sinking according to Stokes’ law 162
Sinking velocities of spherical objects falling under the case of Reynolds numbers 163
smaller than 1 can be described by Stokes’ law. Several studies have used Stokes law or 164
a modified version of Stokes’ law to estimate sinking velocities of plankton and marine 16Open asset ↗maxberthold/PhytoplanktonSinkVelocitiespdf-raw-page:8 lines:1-44Plant phenotyping relevance match · UnverifiedEurope PMC · checked 6 Sept 2026
Precision spraying technology has attracted increasing attention in orchard production management. Traditional chemical pesticide application relies on subjective judgment, leading to fluctuations in pesticide usage, low application efficiency, and environmental pollution. This study proposes a machine vision-based precision spraying control system for orchards. First, a canopy leaf wall area calculation method was developed based on a multi-iteration GrabCut image segmentation algorithm, and a spray volume calculation model was established. Next, a fuzzy adaptive control algorithm based on an extended state observer (ESO) was proposed, along with the design of flow and pressure controllers. Finally, the precision spraying system's performance tests were conducted in laboratory and field environments. The indoor experiments consisted of three test sets, each involving six citrus trees, totaling eighteen trees arranged in two staggered rows, with an interrow spacing of 3.4 m and an intra-row spacing of 2.5 m; the nozzle was positioned approximately 1.3 m from the canopy surface. Similarly, the field experiments included three test sets, each selecting eight citrus trees, totaling twenty-four trees, with an average height of approximately 1.5 m and a row spacing of 3 m, representing a typical orchard environment for performance validation. Experimental results demonstrated that the system reduced spray volume by 59.73% compared to continuous spraying, by 30.24% compared to PID control, and by 19.19% compared to traditional fuzzy control; meanwhile, the pesticide utilization efficiency increased by 61.42%, 26.8%, and 19.54%, respectively. The findings of this study provide a novel technical approach to improving agricultural production efficiency, enhancing fruit quality, reducing pesticide use, and promoting environmental protection, demonstrating significant application value.
Why it matches plant phenotyping methods画像分割により果樹の樹冠葉壁面積という植物形態形質を抽出し、その値に基づく散布量制御システムを開発・検証しており、形質取得手法が中心的です。
abstracta canopy leaf wall area calculation method was developed based on a multi-iteration GrabCut image segmentation algorithm
Daylily ( Hemerocallis spp.) are perennial herbaceous flowers with high ornamental and medicinal value. Currently, the breeding of new daylily cultivars was mainly achieved through hybrid breeding, but issues such as self-incompatibility, hybridization barriers, and asynchronous reproductive phenology severely hinder the breeding process. Understanding pollen viability was essential for daylily breeding and cultivar improvement. In this study, we systematically investigated the effects of pollen viability determination methods, collection time, medium combinations, culture temperature and storage conditions on the pollen germination characteristics of daylily, using five daylily cultivars introduced in the Zhanjiang region of China as materials. Comparing the Iodine-potassium iodide (I 2 -KI) staining and Acetocarmine staining, the results of 2,3,5-Triphenyltetrazolium Chloride (TTC) staining showed a significant positive correlation ( p -1 sucrose + 0.1 g/L -1 H 3 BO 3 + 0.06 g/L -1 KNO 3 + 0.2 g/L -1 Ca(NO 3 ) 2 . The temperature experiment showed that the optimum temperature for pollen germination was 24.1-26.7 °C, and the optimum range for pollen tube growth was 24.1-25.7 °C, and the high temperature significantly inhibited the elongation rate of pollen tube. Storage experiments showed that low temperature (-40 °C) combined with drying treatment could significantly prolong pollen life, and the "Water Dragon" variety still maintained 41.29% vigor after 60 days of dry storage. This study provides theoretical basis and technical support for the introduction and domestication of daylily in South China, hybridization and garden application.
Why it matches plant phenotyping methods花粉の生存性・発芽性という植物形質の測定法を比較・最適化し、培養条件や保存条件による測定性能を体系的に評価しているため、測定法開発・検証が研究の中心です。
abstractIn this study, we systematically investigated the effects of pollen viability determination methods, collection time, medium combinations, culture temperature and storage conditions on the pollen germination characteristics of daylily
Forestry industry requires high-quantity and quality seeds for afforestation and assisted migration programs. Finding reliable non-destructive methods to characterize seeds would significantly enhance efforts to identify climate-adapted populations. This study presents near-infrared (NIR) spectroscopy models to classify seed origin and predict germination characteristics at different temperatures non-destructively. We focus on Abies alba Mill., a key European forest tree with genetic variation along climatic gradients and seeds with shallow physiological dormancy. Seeds from six populations were analyzed using NIR spectroscopy, and germination was tested at 15°C, 20°C, and 25°C after stratification treatments at 4°C (0 or 3 weeks). Population classification accuracy using Partial Least Squares Discriminant Analysis was 69%, with significant NIR peaks at 1712, 1929, and 2111 nm, linked to moisture content and storage compounds. NIR spectra explained 51% and 65% of the variation in germination probability and timing using Partial Least Squares Regression, with significant peaks at 1712, 1929, 2111, 1632, and 2073 nm. General Linear Mixed-Effects Models showed that a NIR predictor contributed to 39% of the germination probability variance explained by fixed-effects, and the stratification treatment was the most important driver explaining germination time. Our results proved the utility of NIR-based tools to effectively classify bulked seeds and predict germination, opening new perspectives to nursery and forestry sectors and populations’ adaptation and adjustments to warming climate. This study will facilitate further investigations on the physiological processes that occur during dormancy, a critical process for forest regeneration given the expected impact of shorter and warmer winters on seed behavior.
Why it matches plant phenotyping methodsNIR分光法を用いて種子由来と発芽特性を非破壊的に推定するモデルを開発・評価しており、植物形質の取得・予測手法が研究の中心である。
abstractThis study presents near-infrared (NIR) spectroscopy models to classify seed origin and predict germination characteristics at different temperatures non-destructively.
Amyloplasts, non-photosynthetic plastids specialized for starch synthesis and storage, proliferate in storage tissue cells of plants. To date, studies of amyloplast replication in roots and the ovule nucelli from various plant species have been performed using electron and fluorescence microscopy. However, a complete understanding of amyloplast replication remains unclear due to the absence of experimental systems capable of tracking their morphology and behavior in living cells. Recently, we demonstrated that Arabidopsis ovule integument could provide a platform for live-cell imaging of amyloplast replication. This system enables precise analysis of amyloplast number and shape, including the behavior of stroma-filled tubules (stromules), during proplastid-to-amyloplast development in post-mitotic cells. Here, we provide technical guidelines for observing and quantifying amyloplasts using conventional fluorescence microscopy in wild-type and several plastid-division mutants of Arabidopsis . Key features • Novel approach for investigating amyloplast differentiation and replication in plant cells. • Detection of stroma-labeled amyloplasts in whole-mount ovules using conventional fluorescence microscopy. • Facilitates quantitative and comparative analysis of amyloplast proliferation using various Arabidopsis resources. • Enables high-resolution analysis of changing amyloplast and stromule morphologies in living cells.
Why it matches plant phenotyping methods生細胞蛍光イメージングによりアミロプラストの数・形状・増殖を定量する技術の技術指針を提示しており、植物表現型の取得・解析方法が研究の中心である。
abstractHere, we provide technical guidelines for observing and quantifying amyloplasts using conventional fluorescence microscopy in wild-type and several plastid-division mutants of Arabidopsis .
Why it matches plant phenotyping methods成長中の植物根の突出力や見かけのヤング率を定量するマイクロ流体・カンチレバー型計測法を開発しており、根の機械的表現型取得が研究の中心である。
abstractwe developed a polydimethylsiloxane (PDMS) microfluidic device integrated with a cantilevered sensing pillar for measuring the protrusive force generated by the growing roots.
Abstract Background Seed quality analysis using X-rays is increasingly explored due to its invasive and rapid nature. Yet, the current absence of reliable and standardised imaging protocols has led to contradictory effects of X-ray exposure in previous studies. Our work systematically investigated the effect of soft X-rays on a wide range of plant materials. Results The baseline of three germination categories was established across seven species before the application of soft X-ray exposure under controlled standard germination conditions. The high inter-varietal and inter-lot variabilities, in addition to the strong interaction between X-ray exposure with variety and lot, reinforced the need to consider genetic and seed quality aspects while evaluating the impacts of X-rays. A slight stimulative effect was observed on most of the species (bean, carrot, fennel, maize, radish, and ryegrass), notably, with a repeated reduction in ungerminated seeds. Intrinsic physical quality holds a crucial value where the minor negative impact observed in soybean originated from its degraded physical quality and not from X-ray exposure, hence, no destructive effects were detected. To understand whether seed size plays a significant role in a seed's response to exposure, linear regression models were built to predict 3D seed traits (volume) from 2D X-ray images. Yet, seed size did not explain the variation in responses to soft X-rays. However, the average density of the seven species explained both their natural germination ( p p Conclusion Soft X-ray exposure is non-destructive with a beneficial effect on germination but can be strongly influenced by underlying genetics and the physical quality of the tested seeds. This study adopted internationally-standardised germination procedures and tested the effect of soft X-rays across diverse botanical, genetic and seed quality profiles. This work addressed important gaps in evaluating X-ray impacts and proposed a robust design and well-examined radiography protocol for a proven non-destructive seed quality analysis.
Why it matches plant phenotyping methods軟X線ラジオグラフィーによる非破壊的な種子品質・3D形質推定プロトコルの検討と検証が中心的に含まれており、単なる発芽試験ではない。
abstractSeed quality analysis using X-rays is increasingly explored due to its invasive and rapid nature.
Main conclusion A model predicting the level of resistance of basil to downy mildew was developed. The model integrates plant age, genetic background, sporulation, disease intensity, pathogen races, and environmental data at an early stage of disease. These results can be used to select and develop new basil cultivars and accelerate the time needed in breeding for basil downy mildew resistance. Basil downy mildew (BDM) caused by the oomycete Peronospora belbahrii emerged as a global threat, rapidly becoming the most devastating disease of sweet basil (Ocimum basilicum) and other Ocimum spp. worldwide. Despite advancements in understanding its biology and epidemiology, and the availability of approved fungicides and management strategies, BDM remains economically destructive and an ongoing risk to basil production worldwide. Recently, the development and introduction of resistant cultivars have emerged as crucial tools in BDM management and the emergence of new BDM races creates new challenges to controlling this disease. The present study aimed to provide growers and breeders with insights into the survival capabilities of resistant basil cultivars under varying genetic backgrounds, pathogen races, growth stages, and various environmental conditions. Through a series of lab and field experiments, we evaluated the response of multiple resistant sources and their lineages to various isolates of P. belbahrii across different locations, using multiple indices to assess their resistance. Entries carrying the R genes Pb1/Pb2 exhibited complete resistance across all races, growth stages, and environmental conditions. Those harboring the R-gene Pb2 showed similar resistance levels, with minor variability due to growth stage. Responses of Pb1 plants varied with pathogen race, displaying full resistance to race 0 at all growth stages but displaying susceptibility to race 1. Plant cultivars possessing MRI resistance genes and their recombinant inbred lines (RIL's) exhibited variable responses to pathogen attacks, ranging from high tolerance to complete susceptibility. Some MRI RIL's showed high resistance similar to Pb2 entries. Pb0 cultivars and 'Eleonora' (unknown background) were susceptible to all races and growth stages in all experiments. Comprehensive analysis across all genetic backgrounds revealed a significant correlation (R = 0.73) between disease intensity (D.I) at the seedling stage under controlled conditions and D.I in adult plants under field conditions. Principal Component Analysis (PCA) across six experiments indicated that the primary components influencing disease outcomes were the accession, race, and growth stage, explaining 65%, 22%, and 7% of the variability, respectively. A prediction model based on the statistical parameters residual (%) and root-mean-square error (RMSE) demonstrated strong predictability, particularly regarding pathogen sporulation and daily disease development rates. The model predicted resistance probabilities with R 2 values of 0.81, 0.91, and 0.93 at the second, third, and final disease score readings, respectively, significantly earlier (~ 14-21 days post-infection) than traditional assessments (~ 42 days). These findings demonstrate that resistance in basil entries against current pathogen races can be effectively assessed within weeks of disease onset, facilitating more timely and informed management decisions for growers and providing an important tool for plant breeders in search of improved BDM resistance.
Why it matches plant phenotyping methodsバジルべと病の植物病害抵抗性を早期に推定する予測モデルを開発し、従来評価より早い病害評価性能を検証しているため、病害表現型の取得・推定が中心的である。
abstractA model predicting the level of resistance of basil to downy mildew was developed.
There is growing interest in hyperspectral imaging to complement observation needs and techniques required to capture the critical zone dynamics. It is already widely used in remote sensing satellite imagery, for regional-scale monitoring of canopies (Asner et al. 2004), or suspended sediment transport (Yepez et al. 2017). Spectral imaging offers dense, remote and non-intrusive measurement coverage. Its implementation at fixed-station for fine temporal monitoring would ensure maximum temporal coverage to study the phenology and functioning of ecosystems (vegetation-water-soil interactions) and watersheds (sediment dynamics), at integrative scales (e.g. watershed outlets), or over experimental plots. On-site hyperspectral data also enable links to the regional scale through cross-comparison with data from space (de Moura et al. 2017). It would then enables to better control measurement biases, offering in that way better opportunity for standardizing observables, as required by international research infrastructures. In recent years, both technological progresses and applications for commercial uses made these kind of cameras more reliable, compact, and affordable, making feasible on-site hand-held or UAV-based experiments (Stuart et al. 2019). Nevertheless, deployment for continuous monitoring remains uncommon, and limited to specific applications (de Moura et al. 2017, Woodgate et al. 2020), due to a still high instrumental complexity and costs. Furthermore, correct data exploitation requires a complete mastery of the calibration, acquisition, normalization and processing chain, that can be complex with “black-box” commercial systems. The development of a dedicated spectral camera is thus preferred. Such a camera is developed within the program TERRA FORMA from the French Agency for Research (Longuevergne et al. 2022). This program aims to implement integrated socio-ecosystem observatories, in support of the French RZA and OZCAR infrastructures, by developing and deploying a dozen types of state-of-the-art sensors dedicated to environmental monitoring at national-scale until 2029. A part of this project is dedicated to the deployment up to 20 spectral cameras, within two scientific topics: monitoring of plant canopies, monitoring of suspended sediment dynamics in rivers. monitoring of plant canopies, monitoring of suspended sediment dynamics in rivers. The instrumental solution we are implementing is based on developments carried out at IPAG since 2016 in compact spectral imaging for spaceborne Earth Observation (Gousset et al. 2019, Le Coarer et al. 2021). In addition to its compactness and optical simplicity, the main advantage of this kind of camera lies in its ability to acquire all spectral and spatial information in a single acquisition (“snapshot”) of a fraction of a second. By opposite to pushbroom or linescanner concepts, which require tens of seconds of exposure under stable illumination conditions. The TERRA FORMA camera complements these instruments with a frugal, less expensive solution, suitable for deployment as a stand-alone fixed station or for handle-held/UAV acquisitions on the field. Since May 2024, we integrated and tested in laboratory an operational camera (Fig. 1), with the following specifications: Field of view 22 by 12°, for 365 by 200 pixels 1 cm / pixel at 9 m distance 42 spectral channels between 400 and 780 nm (up to 850) Spectral resolution 10 nm (up to 6 nm) 10 x 10 x 6 cm, 0.6 kg, powered by LiPo battery Field of view 22 by 12°, for 365 by 200 pixels 1 cm / pixel at 9 m distance 42 spectral channels between 400 and 780 nm (up to 850) Spectral resolution 10 nm (up to 6 nm) 10 x 10 x 6 cm, 0.6 kg, powered by LiPo battery We carried out a first field test in August 2024 at the eLTER site Lautaret / Roche Noire (French Alps). During this single day of acquisition, we acquired data over the landscape jointly to a reference commercial non-imaging spectrometer. This last is shown on Fig. 2, demonstrating a good adequacy between hyperspectral data from the camera and reference spectra. The next steps for 2025 are on site campaigns, lasting 3 to 6 months at fixed stations on pilot sites. On the biodiversity topic: acquisition during a full growing season in a snow-covered mountain grassland equipped with a flux tower should enable: To compare the series of data from hyperspectral imagery with the installed multi-spectral NDVI sensor (only two channels in red and near infrared). To compare spectral measurements with balances of radiative fluxes, and with CO 2 and H 2 O exchanges in the soil-plant-atmosphere continuum. To identify the best optical proxies for inferring vegetation water status and CO 2 fixation capacity during a season. To compare the series of data from hyperspectral imagery with the installed multi-spectral NDVI sensor (only two channels in red and near infrared). To compare spectral measurements with balances of radiative fluxes, and with CO 2 and H 2 O exchanges in the soil-plant-atmosphere continuum. To identify the best optical proxies for inferring vegetation water status and CO 2 fixation capacity during a season. Mid-term objective is to be able to increase the effective footprint of the tower, then to be able to infer canopy function and structure using imagery, through integrated and continuous measurement of several biodiversity parameters at the same time, complementary to data collected as part of the eLTER and ICOS infrastructures. On the hydrology topic: another camera will be deployed on hydrological stations (campus of Grenoble, then Galabre river (Legout et al. 2021)). The aggregation of data should enable: To identify optical proxies for quantifying suspended solids concentrations. To evaluate the robustness of this approach in a concentration range from 0 to a few tens of g/l, currently well measured by the combined turbidimetry and sampling approach (Navratil et al. 2011). To identify optical proxies capable of discriminating between the different types of suspended solids transported in rivers during floods. To apply an approach based on these optical proxies to trace the sources of suspended solids using mixture models, and compare these results with those obtained using the spectro-colorimetric manual suspended solids tracing method implemented on the Galabre site since 2013 (Legout et al. 2013). To identify optical proxies for quantifying suspended solids concentrations. To evaluate the robustness of this approach in a concentration range from 0 to a few tens of g/l, currently well measured by the combined turbidimetry and sampling approach (Navratil et al. 2011). To identify optical proxies capable of discriminating between the different types of suspended solids transported in rivers during floods. To apply an approach based on these optical proxies to trace the sources of suspended solids using mixture models, and compare these results with those obtained using the spectro-colorimetric manual suspended solids tracing method implemented on the Galabre site since 2013 (Legout et al. 2013). The final objective is to be able to complement in situ techniques (turbidimetry) and river sampling with a remote, robotized measurement method, providing better temporal coverage of flood episodes, more reliable than submerged sensors.
Why it matches plant phenotyping methods植物キャノピーの状態・機能を測定する専用ハイパースペクトルカメラを開発・試験しており、植物フェノタイピング用の取得基盤が研究の中心である。
abstractThe development of a dedicated spectral camera is thus preferred.
Various high-throughput screening methods have been developed to explore plant phenotypes, primarily at the organ and whole plant levels. There is a need to develop phenomics methods at the cellular level to narrow down the genotype to phenotype gap. This study used double-resonator piezoelectric cytometry biosensors to capture the dynamic changes in mechanical phenotypes of living cells of two rice species, drought-resistant Lvhan No. 1 and drought-sensitive 6527, under PEG6000 drought stress. In rice cells of Lvhan No. 1 and 6527, mechanomics parameters, including cell-generated surface stress (ΔS) and viscoelastic parameters (G', G″, G″/G'), were measured and compared under 5-25% PEG6000. Lvhan No. 1 showed larger viscoelastic but smaller surface stress changes with the same concentration of PEG6000. Moreover, Lvhan No. 1 cells showed better wall-plasma membrane-cytoskeleton continuum structure maintaining ability under drought stress, as proven by transient tension stress (ΔS > 0) and linear G'~ΔS, G″~ΔS relations at higher 15-25% PEG6000, but not for 6527 cells. Additionally, two distinct defense and drought resistance mechanisms were identified through dynamic G″/G' responses: (i) transient hardening followed by softening recovery under weak drought, and (ii) transient softening followed by hardening recovery under strong drought. The abilities of Lvhan No. 1 cells to both recover from transient hardening to softening and to recover from transient softening to hardening are better than those of 6527 cells. Overall, the dynamic mechanomics phenotypic patterns (ΔS, G', G″, G″/G', G'~ΔS, G″~ΔS) verified that Lvhan No. 1 has better drought resistance than that of 6527, which is consistent with the field data.
Why it matches plant phenotyping methods植物細胞の機械的表現型を取得する高スループットなバイオセンサー手法を用い、乾燥ストレス応答を定量化しており、表現型取得法が研究の中心である。
abstractThere is a need to develop phenomics methods at the cellular level to narrow down the genotype to phenotype gap.
Reproduction assets foundThe paper's DRPC phenotyping measurements (frequency and motional resistance traces underlying the ΔS, G′, G″ analyses) are provided as downloadable supplementary figures at the MDPI supplementary URL. No standalone public dataset or author analysis code repository is stated; the Data Availability Statement only offersSupplement · publicansient softening under strong drought. The results presented in this work demonstrated the potential to develop a new cellular mechanical phenotype platform to screen for biotic and abiotic stress-resistant crop varieties, as shown in Figure 11 .
Supplementary Materials
The following supporting information can be downloaded at https://www.mdpi.com/article/10.3390/bios15060334/s1 , Figure S1: Changes in frequency and motional resistance of 9 MHz AT and BT cut chips during the adhesions of Lvhan No.1 rice cells followed by the treatments of different concentrations of PEG6000 stresses. (A, B, C, D, E): AT cut, (A1, B1, C1, D1, E1): BT cut, (A, A1): 5% PEG6000, (B, B1): 10%PEG6000, (C, C1) 15%Open asset ↗lines:136-159Code / dataset availability confirmedOpenAlex · Crossref · Europe PMC · checked 6 Sept 2026
Introduction Recent advancements in sensor technologies have enabled collection of many large, high-resolution plant images datasets that could be used to non-destructively explore the relationships between genetics, environment and management factors on phenotype or the physical traits exhibited by plants. The phenotype data captured in these datasets could then be integrated into models of plant development and crop yield to more accurately predict how plants may grow as a result of changing management practices and climate conditions, better ensuring future food security. However, automated methods capable of reliably and efficiently extracting meaningful measurements of individual plant components (e.g. leaves, flowers, pods) from imagery of whole plants are currently lacking. In this study, we explore interdisciplinary application of MapReader, a computer vision pipeline for annotating and classifying patches of larger images that was originally developed for semantic exploration of historical maps, to time-series images of whole oilseed rape (Brassica napus) plants. Methods Models were trained to classify five plant structures in patches derived from whole plant images (branches, leaves, pods, flower buds and flowers), as well as background patches. Three modelling methods are compared: (i) 6-label multi-class classification, (ii) a chain of binary classifiers approach, and (iii) an approach combining binary classification of plant and background patches, followed by 5-label multi-class classification of plant structures. Results A combined plant/background binarization and 5-label multi-class modelling approach using a ‘resnext50d_4s2x40d’ model architecture for both the binary classification and multi-class classification components was found to produce the most accurate patch classification for whole B. napus plant images (macro-averaged F1-score = 88.50, weighted average F1-score = 97.71). This combined binary and 5-label multi-class classification approach demonstrate similar performance to the top-performing MapReader ‘railspace’ classification model. Discussion This highlights the potential applicability of the MapReader model framework to images data from across scientific and humanities domains, and the flexibility it provides in creating pipelines with different modelling approaches. The pipeline for dynamic plant phenotyping from whole plant images developed in this study could potentially be applied to imagery from varied laboratory conditions, and to images datasets of other plants of both agricultural and conservation concern.
Why it matches plant phenotyping methods植物全体画像から葉・花・莢などの構造を自動抽出・分類する動的フェノタイピング手法を開発・評価しており、方法が研究の中心である。
abstractautomated methods capable of reliably and efficiently extracting meaningful measurements of individual plant components (e.g. leaves, flowers, pods) from imagery of whole plants are currently lacking.
Reproduction assets foundThe paper's phenotyping analysis is based on a public RGB image dataset of Brassica napus plants, explicitly deposited by the authors with a public URL. MapReader is a generic pre-existing library and the HuggingFace railspace models are cited prior work, not paper-specific assets.Dataset · publicThe datasets presented in this study can be found in online repositories. The names of the repository/repositories and accession number(s) can be found below: https://research.aber.ac.uk/en/datasets/collection-of-side-view-and-top-view-rgb-images-of-brassica-napus .Open asset ↗research.aber.ac.uklines:982-1027Plant phenotyping relevance match · UnverifiedCrossref · checked 14 Sept 2026
Abstract The Organization for the Prohibition of Chemical Weapons (OPCW) enforces strict regulations on the production, storage, and use of chemical warfare agents (CWAs). However, in recent decades, terrorist incidents involving the use of CWAs have remained frequent, posing a persistent and serious threat to global security. Plants have emerged as a promising medium for detecting CWAs exposure or toxic chemical leaks due to their wide availability, straightforward sample preparation, and the absence of ethical concerns associated with animal testing. When plants are exposed to CWAs or toxic chemicals, various components within their tissues can react with these agents, generating specific biomarkers of exposure; the detection of these biomarkers enables accurate traceability of CWAs exposure. Furthermore, when plants are subjected to stress from external toxic substances, their phenotypic characteristics undergo significant changes. These changes can be non‐destructively analyzed using hyperspectral imaging (HSI) technology, offering rapid monitoring and early warning for laboratory analysis of chemical exposure or toxic chemical leaks. This article presents a comprehensive review of the detection of metabolites and adducts produced in plants exposed to sulfur mustards, nitrogen mustards, sarin (GB), soman (GD), VX, Russian VX (RVX), and chlorine, as well as the monitoring of plant phenotypic changes using HSI technology over the past decade. The review aims to inspire further discoveries of novel plant biomarkers and inform research related to plant exposure to CWAs.
Why it matches plant phenotyping methods植物曝露モニタリング手法のレビューであり、HSIによる植物表現型変化の非破壊モニタリングを明示的に扱うため、フェノタイピング手法レビューとして中心的です。
abstractThis article presents a comprehensive review of the detection of metabolites and adducts produced in plants exposed to sulfur mustards, nitrogen mustards, sarin (GB), soman (GD), VX, Russian VX (RVX), and chlorine, as well as the monitoring of plant phenotypic changes using HSI technology over the past decade.