This study presents a systematic quantitative multi-elemental investigation of four major plant organs (roots, stems, leaves, and flowers) of Nerium oleander using calibration-based Laser-Induced Breakdown Spectroscopy (LIBS), with the results validated by Atomic Absorption Spectroscopy (AAS). Plasma characterization was carried out using Boltzmann plot and Stark broadening analyses, while negligible self-absorption observed through the Hα emission line confirmed optically thin plasma conditions and reliable quantitative measurements. A total of nine elements were detected, including Fe, Zn, Mn, Ca, Mg, K, Na, Cu, and Ni, each exhibiting different concentration levels across the analyzed tissues. Compositional analysis using standard calibration curve-based LIBS demonstrated that elemental concentrations were non-uniform, showing marked variations between the different plant tissues. Among the detected elements, calcium emerged as the most prevalent across all tissues. The highest calcium concentration was observed in leaves (16,385 mg L-1), followed by roots (12,092 mg L-1) and flowers (11,185 mg L-1). Root tissues exhibited elevated concentrations of Fe and Mn, reaching 1918 and 513 mg L-1, respectively. In contrast, flowers showed the highest Mn concentration (1888 mg L-1), while leaves were enriched in Mg (4445 mg L-1) and K (3700 mg L-1). The highest Na concentration was observed in stems (8025 mg L-1). Trace metals, including Cu, Zn, and Ni, were detected at comparatively low concentrations, while Pb remained undetected in all samples. The strong agreement between LIBS and AAS measurements confirms the reliability of the proposed methodology and demonstrates the potential of LIBS as a rapid and non-destructive tool for elemental assessment of medicinal plants.
Why it matches plant phenotyping methods植物組織の元素濃度を取得するLIBS測定法を校正し、AASで検証しており、元素組成という植物形質の測定方法が研究の中心である。
abstractusing calibration-based Laser-Induced Breakdown Spectroscopy (LIBS), with the results validated by Atomic Absorption Spectroscopy (AAS).
RiceMultimodalX-ray / CTRootMorphology / geometry measurementPhysiological trait estimationGrowth / time-series analysisGrowth / development / phenologyRoot system architecture
Rhizosphere oxidation is a key adaptive mechanism in reductive soil environments, in which oxygen released from roots alters rhizosphere redox conditions and regulates biogeochemical processes. Rice plants possess an internal oxygen transport system, and radial oxygen loss (ROL) from roots is closely associated with root development. However, the spatial patterns of ROL in soil and their relationships with root traits remain poorly characterized. In this study, we developed a multimodal imaging system that integrates planar oxygen optodes with X-ray computed tomography to simultaneously visualize rhizosphere oxidation and root development in rice. Daily time-course tracking of individual crown roots revealed dynamic changes in the spatial distribution and magnitude of rhizosphere oxygen in relation to root elongation and aging. Root thickness was positively correlated with dissolved oxygen levels near root tips. Genotypic comparisons further identified a cultivar with reduced rhizosphere oxidation despite possessing thicker roots among the tested genotypes, thereby indicating the involvement of additional physiological processes. Overall, these findings demonstrate that rhizosphere oxidation is regulated by root growth stage and thickness and dynamically modulated during root development.
Why it matches plant phenotyping methods平面酸素オプトードとX線CTを統合したマルチモーダル画像システムを開発し、根の発達と根圏酸化を時系列・個体別に定量化しており、表現型取得手法が研究の中心である。
abstractwe developed a multimodal imaging system that integrates planar oxygen optodes with X-ray computed tomography to simultaneously visualize rhizosphere oxidation and root development in rice.
Reproduction assets foundThe paper's Data availability statement explicitly deposits the authors' RG2DO-Root analysis program together with sample optode and CT images (the paper's phenotyping inputs) in a public GitHub repository, matching the allowed URL.Code · publicing
8
This work was supported by project JPNP18016, commissioned by the New Energy and
9
Industrial Technology Development Organization (NEDO), JST CREST (JPMJCR17O1),
10
and JST ALCA-Next (JPMJAN23D3).
11
12
Data availability
13
The source code and sample data (optode and CT images) are available from the
14
GitHub repository (https://github.com/tsubasa-kawai28/RG2DO-Root).15
16
References
17
Aguilar EA et al. 2003. Oxygen distribution and movement, respiration and nutrient
18
loading in banana roots (Musa spp. L.) subjected to aerated and oxygen-depleted
19
environments. Plant Soil. 253:91–102. https://doi.org/10.1023/A:1024598319404.20
Armstrong W, Wright EJ. 1975. Radial oxygen loss fromOpen asset ↗https://github.com/tsubasa-kawai28/RG2DO-Root · RG2DO-Rootpdf-raw-page:19 lines:1-82Code / dataset availability confirmedCrossref · checked 15 Sept 2026
Abstract Background and Aims Drought reduces wheat yields, yet field-scale quantification of root water uptake (RWU) remains challenging because below-ground processes are difficult to monitor. This study developed a non-invasive hydrogeophysical framework integrating Electrical Resistivity Tomography (ERT), TDR-based soil monitoring, and depth-aware Random Forest calibration to quantify depth-resolved RWU and evaluate genotype-specific water-use strategies under terminal drought. Methods Time-lapse ERT (44 surveys, ≥ 3 week⁻ 1 ) was combined with TDR sensor measurements of soil water content (n = 278 paired ρ–θ observations) to convert resistivity measurements into depth-resolved RWU estimates across 0.1–1.0 m depth. Five petrophysical models were evaluated using date-grouped fivefold cross-validation, with the depth-aware Random Forest performing best. Three wheat genotypes with contrasting root architectures were monitored under terminal drought (142 mm available water). ERT-derived RWU were analysed alongside stomatal conductance, chlorophyll fluorescence, and grain yield. Results ERT resolved RWU strategies among genotypes. WM-203 exhibited aggressive, coordinated multi-layer water extraction across the soil profile (r = 0.80–0.98), whereas WM-140 showed a delayed uptake strategy characterized by early deep-layer dominance followed by mid- and deep-profile engagement, and IPLR-760 displayed inconsistent uptake with mid-profile hydraulic decoupling. Genotypic RWU rankings were consistent with stomatal conductance and grain yield, spanning from 7.0 t ha⁻ 1 in WM-203 to 1.5 t ha⁻ 1 in IPLR-760 despite comparable total water extraction. Conclusion ERT-based quantification of RWU provides a robust, non-invasive approach for resolving genotype-specific water-use strategies under field conditions. The framework enables characterization of water-use coordination patterns and offers a tool for phenotyping drought-resilient wheat genotypes.
Why it matches plant phenotyping methodsERT・TDR・Random Forestを統合し、圃場コムギの根系水吸収を定量化する方法を開発・検証し、乾燥耐性遺伝子型の表現型評価に用いているため、フェノタイピング手法が中心である。
abstractThis study developed a non-invasive hydrogeophysical framework integrating Electrical Resistivity Tomography (ERT), TDR-based soil monitoring, and depth-aware Random Forest calibration to quantify depth-resolved RWU and evaluate genotype-specific water-use strategies under terminal drought.
Reproduction assets foundThe paper's Data availability statement explicitly states that the code and supporting data for this ERT-based root water uptake study are publicly available on the authors' GitHub repository, which is listed in allowed_urls. This qualifies as a paper-specific public code/data asset for the phenotyping analysis.Code · publicsity of Jerusalem. This research was supported by the Chief
Scientist of the Israeli Ministry of Agriculture and Food Secu-
rity (grant no. 12–01-0056) and the Israeli Council for Higher
Education (Project: Future Crops for Carbon Farming).
Data availability The code and supporting data for this study
are publicly available at:
https://github.com/emmaiyke/ERT_RWU_Wheat_Project
Additional datasets are available from the corresponding
author upon reasonable request.
Declarations
Competing interests The authors declare that they have no
known competing financial interests or personal relationships
that could have appeared to influence the work reported in this
paper.
Open Access This article isOpen asset ↗ERT_RWU_Wheat_Project · emmaiyke/ERT_RWU_Wheat_Projectpdf-raw-page:22 lines:1-95Code / dataset availability confirmedOpenAlex · Crossref · checked 15 Sept 2026
ArabidopsisRootMorphology / geometry measurementSegmentationRoot system architecture
Introduction Plant parasitism by sedentary plant-parasitic nematodes is a dynamic and continuously evolving process, accompanied by profound remodelling of host root system architecture across distinct infection stages. However, the physiology and anisotropic growth of Arabidopsis thaliana roots under Heterodera schachtii infection, together with complex lateral root proliferation and increasingly dense, overlapping morphology, pose substantial challenges for accurate image segmentation. Methods Here, we introduce Root-TransUNet, an optimised segmentation architecture that extends TransUNet by incorporating a composite loss function to improve boundary precision and structural continuity, as well as dual-stage strip-pooling (SP) modules to enhance elongated and directional root features. These adaptations address the unique morphological complexity of the infected root system. Additionally, we integrated Root-TransUNet into a high-throughput phenotyping pipeline and applied it to an existing dataset of ~120,000 images of 362 A. thaliana MAGIC recombinant inbred lines collected over several months of infection. By extracting root system architecture traits, including root surface area and estimated root volume across infection stages, we enabled stage-specific association analyses between host root growth and nematode performance across these genotypes. Results Root-TransUNet achieved strong segmentation performance, demonstrating improved structural continuity and boundary precision compared with widely used CNN- and Transformer-based baselines, including UNet++. Stage-specific analyses revealed that the relationship between host root traits and nematode performance changed as infection progressed. During establishment, nematode number was largely independent of initial root size and varied strongly among genotypes, whereas during the reproductive phase (10-30 dpi), greater root expansion coincided with reduced estimated nematode volume accumulation. Notably, nematode burden was largely independent of host root size before infection, indicating that root quantity was generally not a limiting factor for infection in this experiment. Discussion These results demonstrate that Root-TransUNet can robustly segment infected root systems across a wide range of nematode infection densities, providing a scalable image-analysis framework for studying plant-parasitic nematode parasitism in combination with host root phenotyping.
Why it matches plant phenotyping methods感染根系の画像セグメンテーション手法を開発し、高スループット表現型解析パイプラインに統合して根系形態形質を抽出しており、表現型取得・抽出法が中心的である。
abstractHere, we introduce Root-TransUNet, an optimised segmentation architecture that extends TransUNet by incorporating a composite loss function to improve boundary precision and structural continuity, as well as dual-stage strip-pooling (SP) modules to enhance elongated and directional root features.
Reproduction assets foundThe paper analyzes a public BioImages dataset (S-BIAD2402) of ~400,000 RGB root/nematode infection images and provides authors' analysis code on GitHub; both are paper-specific, public, and actionable.Code · publicng molecular signatures, deepening our understanding of host-parasite resource allocation strategies, and establishing a foundation for the discovery of novel resistance mechanisms.
Code and data availability
Python-based source code for automating root analysis using the datasets above is accessible via our GitHub repository ( https://github.com/JieZhou1025/Root-nematode-interaction ).
Statements
Data availability statement
Publicly available datasets were analyzed in this study. This data can be found here: https://www.ebi.ac.uk/biostudies/bioimages/studies/S-BIAD2402 .
Ethics statement
The manuscript presents research on animals that do not require ethical approval for their study.
AuthorOpen asset ↗JieZhou1025/Root-nematode-interactionlines:412-424Code / dataset availability confirmedEurope PMC · Crossref · checked 15 Sept 2026
Published3 Sept 2026Methods in ecology and evolution
Growth chamberMultimodalStereoRootStem / branchTrackingGrowth / development / phenology
Understanding plant behaviour requires the integration of multiple phenotypic and physiological signals measured over time under controlled conditions. However, different plant signals are typically studied using separate experimental setups, limiting temporal alignment and integrative analyses.We present Mind(the)Plant, a modular experimental facility designed for the synchronized, long-term acquisition of multimodal plant data, including three-dimensional shoot kinematics, above- and below-ground volatile organic compounds (VOCs) and root imaging. Its modular architecture is designed to accommodate additional acquisition modules, such as electrophysiological signalling, as future extensions. The platform integrates a controlled growth environment with stereovision imaging, high-resolution time-of-flight mass spectrometry and custom rhizocameras. These components are connected through a unified network infrastructure that ensures synchronized acquisition and centralized data handling.We validate the performance of each acquisition module through multi-week recordings, demonstrating high-temporal stability, reliable stereovision synchronization, effective isolation of VOCs signals and robust operation of below-ground imaging. We further illustrate the analytical potential of the platform using a one-day continuous multimodal acquisition combining shoot kinematics, above-ground VOC emissions, rhizocameras observations and environmental data.Mind(the)Plant provides a novel methodological framework for studying plant behaviour, signalling and phenotypic plasticity in ecological and evolutionary research. By enabling coordinated measurements of multiple plant response modalities, the platform supports investigations of dynamic plant-environment and plant-plant interactions from a behavioural perspective.
Why it matches plant phenotyping methods植物の複数の表現型・生理シグナルを同期取得する施設を開発し、各取得モジュールの性能を検証しているため、表現型計測プラットフォームが研究の中心です。
abstractWe present Mind(the)Plant, a modular experimental facility designed for the synchronized, long-term acquisition of multimodal plant data, including three-dimensional shoot kinematics, above- and below-ground volatile organic compounds (VOCs) and root imaging.
Reproduction assets foundThe paper's data availability statement explicitly deposits data, code and processing pipelines (supporting the multimodal plant phenotyping measurements and analysis) in a public Zenodo archive with an authors' URL matching an allowed URL.Code · publicf Interest Statement
The authors have no conflicts of interest to declare.
Peer Review
The peer review history for this article is available at https://www.webofscience.com/api/gateway/wos/peer-review/10.1111/2041-210x.70411 .
Data availability Statement
Data, code and processing pipelines supporting this study are available at https://doi.org/10.5281/zenodo.22095454 ( Simonetti & Castiello, 2026 ).
References
Avesani S, Bonato B, Simonetti V, Guerra S, Ravazzolo L, Gjinaj G, Dadda M, Castiello U. Comparing proton transfer reaction (PTR) and adduct ionization mechanism (AIM) for the study of volatile organic compounds. Molecules. 2026;31(3):402. doi: 10.3390/molecules31030402.
Baluška F, LeOpen asset ↗zenodo · 10.5281/zenodo.22095454lines:482-508Plant phenotyping relevance match · UnverifiedEurope PMC · Crossref · checked 5 Sept 2026
All living organisms rely on the movement of ions across cell membranes as the fundamental physical basis of their internal energy and signaling, and plants are no exception. Plants perceive, integrate, and respond to environmental stimuli through electrical signals, classified as action, variation, and system potentials, that are coupled with calcium waves, reactive oxygen species, and hydraulic and hormonal changes to coordinate whole-organism responses despite the absence of a nervous system. Yet most studies characterize these signals using a single feature, such as amplitude or spike duration, in a single tissue, an approach that cannot establish how such signals correspond to the underlying ionic activity, mobility, and structural complexity of the signaling environment, or how this correspondence varies across organs. Here, we correlate plant bioelectrical signals with potential ionic energy flow using a multi-domain framework, combining discrete spike events, continuous waveform properties, spectral composition, and signal complexity applied to leaf, stem, and root recordings from tomato ( Solanum lycopersicum ) exposed to different stimulus. Electrical activity with increased stimulus strength, likely reflecting increased ionic flow, with the root showing the largest response. This suggests plant electrical signaling works as a distributed, ion-based information system, useful for stress monitoring and bio-inspired sensor design.
Why it matches plant phenotyping methods植物の電気生理シグナルを多面的に取得・解析する枠組みを中心に扱い、ストレスモニタリングへの応用可能性を示しているため、植物状態の測定方法として含める。
abstractHere, we correlate plant bioelectrical signals with potential ionic energy flow using a multi-domain framework, combining discrete spike events, continuous waveform properties, spectral composition, and signal complexity applied to leaf, stem, and root recordings from tomato
Alfalfa / lucerneX-ray / CTRoot2D/3D reconstructionVisualization / data managementGrowth / development / phenology
The symbiotic relationship between the legume Medicago sativa and the soil bacteria Sinorhizobium meliloti results in the formation of nitrogen-fixing root nodules. Traditional destructive methods, including paraffin sectioning, vibratome sectioning, and cryosectioning, have been applied to visualize how bacteria occupy the nodule, making it extremely difficult to obtain reliable three-dimensional information. These approaches are often combined with fluorescent labeling or staining, which can introduce additional stress affecting plant growth and nodule formation. MicroCT has emerged as a relatively quick, easy, and robust tool for plant biology that can non-destructively visualize plant histological features in three dimensions (3D), thereby avoiding destructive artifacts during sample preparation and ensuring high-fidelity 3D reconstruction. While microCT has been applied to legume root nodules, a detailed established protocol that documents the process from plant harvest and sample preparation to scanning and software visualization is lacking. In this study, we show a step-by-step microCT workflow using Medicago sativa as a model. The protocol includes nodule excision from roots, fixation, contrast enhancement, mounting, scanning, and three-dimensional reconstruction. Critical parameters affecting elements such as image quality, tissue preservation, and contrast are highlighted. Using this approach, it is possible to visualize the overall tissue organization, bacteroid-infected cells, and vascular bundles in three dimensions without physically sectioning the nodules. The pipeline described here provides a reproducible method for non-destructive, high-resolution imaging of native root nodules and is likely adaptable to other legume species, offering researchers a practical tool for studying nodule structure and bacterial organization within nodules in 3D.
Why it matches plant phenotyping methods根粒の組織構造と感染細胞を3Dで取得するMicroCT撮像・再構成プロトコルが研究の中心であり、植物器官の形態状態を測定する実質的なフェノタイピング手法である。
abstractIn this study, we show a step-by-step microCT workflow using Medicago sativa as a model.
Abstract Technological advances have expanded the adoption of digital technologies in agriculture, helping to reduce labour effort, increase profitability, improve crop efficiency and productivity, enhance product quality, mitigate environmental impacts, and promote human health. This context also extends to soybean farming, a sector of major economic importance in Brazil. Most importantly, Brazil has the global leadership in soybean production with biological nitrogen fixation (BNF) replacing chemical fertilisers. The research and evaluation of BNF is limited by manual counting of nodules, a time-consuming procedure. This study presents SoyNodules, designed for the automatic identification of soybean nodules, consisting of a dataset of images. The dataset includes 1,701 images acquired under controlled conditions: 1,662 images of soybean roots with nodules and 39 images of isolated nodules without roots. A total of 49,210 nodule instances are manually annotated with bounding boxes. SoyNodules was designed to promote reuse and interoperability in alignment with the FAIR principles (Findable, Accessible, Interoperable, Reusable) and to support the development, training, and evaluation of computer vision and deep learning methods for precision agriculture.
Why it matches plant phenotyping methods大豆根粒を自動識別する画像データセットであり、手作業計数の代替となる植物器官形質の抽出・評価を支援する方法論的データセット。
abstractThis study presents SoyNodules, designed for the automatic identification of soybean nodules, consisting of a dataset of images.
Reproduction assets foundThe paper is a data descriptor for SoyNodules, an annotated dataset of 1,701 soybean root/nodule images with 49,210 bounding-box annotations, publicly deposited on Zenodo with a DOI. The same repository also hosts the authors' annotation-format conversion script (AnyLabeling to Pascal VOC/COCO), per the Code AvailabilDataset · publicThe SoyNodules dataset, released as version 1.0, is publicly available on Zenodo [28]
at https://doi.org/10.5281/zenodo.22081914.Open asset ↗Zenodo · 10.5281/zenodo.22081914pdf-page:9 lines:1-43Plant phenotyping relevance match · UnverifiedEurope PMC · checked 14 Sept 2026
Adventitious root formation is critical for the cost-effective propagation of grapevine rootstocks, and poor rooting limits the adoption of new rootstocks derived from underutilized Vitis L. species. We evaluated 308 accessions representing 18 Vitis species over three growing seasons, scoring rooting at two developmental stages, callus-stage and post-transplant, together with root biomass, cutting weight, and a derived transplant- response index. Phenotypic variation was extensive within and among species, and species rankings depended on the trait considered: V. riparia , V. rupestris and V. californica ranked among the top five species for all four rooting traits, whereas V. arizonica and V. acerifolia rooted well at the callus stage but only intermediately after transplanting. Repeatability was moderate to high for root weight and callusstage rooting and lower for post-transplant rooting. Between-species differences accounted for most of the genetic variance in callus-stage rooting but little of that in cutting weight. After removing differences among species, accessions originating from wild sites with lower dry-season precipitation rooted better and produced more root biomass. Genome-wide association analysis of 3.4 million single-nucleotide polymorphisms identified 54 significant markers resolving into 18 independent loci across four traits. Candidate genes implicate auxin-linked cell proliferation, cell wall and lignin remodeling, and solute transport. Genomic and phenomic prediction achieved moderate accuracies across traits and seasons, including for previously unevaluated accessions, and combining spectral with genotypic data improved performance; accuracy was essentially flat between 5,000 and 50,000 markers. These results provide a framework for broadening the germplasm base of grapevine rootstock breeding. Plain Language Summary Grapevines are almost always grown as two plants joined together: a fruiting variety grafted onto a rootstock that supplies the root system. Nurseries build these plants from dormant cuttings, so a rootstock is only useful in practice if its cuttings root easily. Almost all commercial rootstocks descend from just three wild North American grape species, in part because cuttings of other species are believed to root poorly. We grew cuttings from 308 wild and cultivated grapevines representing 18 species over three years and measured how well each one rooted, first in the callusing room and again after the young plants were transplanted. Rooting ability varied a great deal, and several species outside the usual three rooted as well as the standards. Vines originally collected from places with drier summers tended to root best. We also located regions of the grape genome linked to rooting, and showed that the rooting ability of a vine can be predicted from its DNA or from light reflected by its leaves. Together, these results give breeders a way to screen a much wider range of wild grapevines for rootstock development before testing them in a nursery. Core Ideas Rooting ability varies widely across 18 Vitis species, well beyond the three used in rootstock breeding. Callus-stage and post-transplant rooting behave as genetically distinct stages of root formation. Accessions from collection sites with drier summers rooted better and produced more root biomass. GWAS resolved 18 loci implicating auxin signaling, cell wall remodeling, and solute transport. Genomic and phenomic prediction reached moderate accuracy and was insensitive to marker density.
Why it matches plant phenotyping methods発根形質を対象に、スペクトル情報を用いたフェノミック予測を実施し、遺伝子型情報との統合や予測精度を評価しているため、形質取得・推定手法が研究の主要な技術的要素です。
abstractGenomic and phenomic prediction achieved moderate accuracies across traits and seasons, including for previously unevaluated accessions, and combining spectral with genotypic data improved performance
Plant phenotyping relevance match · UnverifiedbioRxiv · Europe PMC · OpenAlex · checked 13 Sept 2026
Lodging in sorghum presents a significant challenge for plant breeders due to the trade-off between lodging resistance and grain yield. Manually measuring lodging across thousands of plots is time-consuming, expensive, and error-prone, making selection for lodging resistance challenging in breeding programs. Unmanned aerial vehicle (UAV)-derived metrics provide a potential high-throughput alternative; however, it remains unclear whether photogrammetric heights derived from UAV imagery can estimate plot-level lodging severity in large sorghum breeding trials. This study developed a framework for predicting plot-level lodging from UAV imagery across 2,675 sorghum breeding plots. Multi-temporal canopy height data were collected at two critical time points: maximum crop height and at manual lodging assessment. Height percentiles were extracted from UAV-derived point clouds generated using photogrammetric algorithms. These data were used to develop parametric, non-parametric, and ensemble prediction models, which were evaluated using three statistical metrics. The ensemble model, averaging predictions from all models, achieved the highest accuracy with Pearson correlations of r = 0.80-0.84 and lowest root mean square error (RMSE=16-18%), explaining 64-70% of variation in manual lodging counts. Model diagnostics and iterative refinement, including inspection of UAV imagery and dataset curation, had minimal impact on model performance, demonstrating the robustness of the approach. Model performance was consistent across sites, with minimal effects of stratified sampling on accuracy, confirming the ensemble approach as optimal for plot-level lodging assessment. This study demonstrates that integrated multi-temporal UAV imagery offers a practical alternative to labor-intensive manual evaluation methods by enabling high-throughput lodging assessment suitable for implementation in sorghum breeding programs.
Why it matches plant phenotyping methodsUAV画像と写真測量点群からソルガム区画の倒伏程度を推定する取得・解析フレームワークを開発し、実データで精度評価しており、植物表現型測定法が研究の中心である。
abstractThis study developed a framework for predicting plot-level lodging from UAV imagery across 2,675 sorghum breeding plots.
Field / plotLiDAR / point cloudRootMorphology / geometry measurementRoot system architecture
Coarse root systems govern tree anchorage, yet remain among the least documented components of tree architecture: excavation is irreversible, and established 3D methods rely on specialist scanners and lengthy post-processing. We evaluated whether a consumer smartphone records exposed coarse root architecture metrically, and which traits agree most closely with manual measurement. Four fully exposed Scots pine (Pinus sylvestris L.) root systems in northwestern Poland were scanned with an iPhone 17 Pro running Scaniverse, at about 30 min of acquisition and 5 h of processing per tree. Clouds were registered, cleaned and oriented to magnetic north in CloudCompare; of eight architectural metrics, four were validated against manual references at 95 cross-sections on 44 roots, and four were exploratory. Visible root length (root-mean-square error, RMSE, 22.2 cm, 8.4%), azimuth (RMSE 3.58°, mean absolute error 2.47°) and depth (RMSE 3.18 cm, 14.9%) agreed most closely with the reference; 70 of 77 first-order roots were detected with no false positives. Diameter was the weakest metric and the only one dependent on the operator (RMSE 0.46 and 0.29 cm for two operators on the same clouds). Smartphone LiDAR thus turns an irreversible excavation into a permanent, measurable record of the traits relevant to anchorage, provided that centimetre-level diameters are not required.
Why it matches plant phenotyping methodsスマートフォンLiDARによる露出根系の3D形態計測ワークフローを開発・検証し、手動測定と複数の根系形質を比較しているため、植物フェノタイピング手法が中心である。
abstractWe evaluated whether a consumer smartphone records exposed coarse root architecture metrically, and which traits agree most closely with manual measurement.
Accurate and non-destructive assessment of drought stress is important for improving lettuce production and supporting timely crop management. This study presents a detection-guided deep learning framework for plant-level drought-stress assessment in hydroponically grown lettuce using bird’s-eye-view RGB images. The study further investigates whether canopy segmentation can improve classification performance by reducing irrelevant background information. The framework was evaluated using 2190 images collected across three independent cultivation cycles in which drought stress was induced by isolating the plant root zones from the nutrient solution. In the first stage, YOLO-based object detection was used to localize individual plants, with YOLO26m achieving the highest detection performance of 99.4% mAP@0.5. The detected regions were subsequently used as spatial prompts for zero-shot canopy segmentation using the Segment Anything Model (SAM), with SAM ViT-B achieving a mean IoU of 0.9864. Six convolutional, transformer-based, and hybrid classification architectures were then evaluated independently using YOLO-cropped and SAM-segmented plant images. Segmented inputs consistently improved classification performance, with MaxViT-S achieving the highest binary test accuracy of 96.3%. The framework further distinguished time-defined pre-stress, early-stress, and late-stress periods with an accuracy of 92.4%. Plant-level generalization was further assessed using six-fold leave-one-plant-out cross-validation, resulting in a mean test accuracy of 90.25 ± 1.78% on unseen plants. These findings demonstrate that RGB-based plant-level analysis can support non-destructive drought-stress assessment and that canopy segmentation improves classification by reducing background influence.
Why it matches plant phenotyping methodsRGB画像からレタス個体の乾燥ストレス状態を推定する検出・セグメンテーション・分類フレームワークを開発し、複数サイクル、未見個体、性能指標で検証しており、表現型取得手法が中心である。
abstractThis study presents a detection-guided deep learning framework for plant-level drought-stress assessment in hydroponically grown lettuce using bird’s-eye-view RGB images.
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.
Live imaging of plant subcellular structures is key to deciphering the spatiotemporal bases of cellular processes, and their functional impact on growth and morphogenesis at various biological scales. Live imaging of plant cells essentially relies on expression of fluorescent markers labeling cells or subcellular structures of interest. Simultaneous multi-channel imaging of several markers is still not routine practice in plant cell biology, owing to issues linked to genetic or spectral compatibility of markers, differences in expression levels, silencing, toxicity, etc. Here we designed a three-color marker in Arabidopsis thaliana and Capsella rubella , enabling high-resolution live imaging of plant morphogenesis, including labeling of the cell membrane, the nucleus and the microtubule cytoskeleton. Detection of MT arrays involved the development of a MAP4-MBD-based microtubule marker optimized for plant cells. The three- color marker allows visualization of the three-dimensional organization and dynamics of plant microtubules within the intracellular space with unprecedented precision, in various organs including the root and shoot meristems, the leaf, anther, and gynoecium. Our results demonstrate the potential of such single-construct strategy for cell biology studies in plants.
Why it matches plant phenotyping methods植物細胞の形態形成を可視化する三色ライブイメージング法と、植物細胞用に最適化した微小管マーカーの開発が研究の中心である。
abstractDetection of MT arrays involved the development of a MAP4-MBD-based microtubule marker optimized for plant cells.
Plant secondary metabolites are mainly synthesized and stored in secretory tissues. Secretory canal development has been mainly characterized in Apiaceae. The secretory canals of Peucedanum praeruptorum contain pharmacologically active coumarins, but their organ-specific distribution and developmental dynamics remain poorly understood. This study integrated light microscopy (LM), transmission electron microscopy (TEM), X-ray microcomputed tomography (µ-CT), and high-performance liquid chromatography (HPLC) to investigate canal development, distribution, ultrastructure, 3D architecture, and coumarin accumulation in P. praeruptorum roots. Histological analysis showed that canals adjacent to the periderm originate from pericycle cells, whereas those in secondary phloem arise from parenchyma differentiation; both develop schizogenously. Canal quantity and dimensions varied temporally. Canals located in phloem showed density increasing toward the cambial zone, where cross-sectional areas were smaller. The canal density index increased from September to November, peaking on November 15, then declined. HPLC revealed dynamic accumulation of five major coumarins: content increased from September, peaked on November 15, then gradually decreased. TEM showed that epithelial cells surrounding the canal lumen were rich in Golgi, ER, mitochondria, plastids, starch grains, and osmiophilic droplets. µ-CT volumetric analysis and segmentation generated detailed 3D models, revealing spatial organization and enabling size-based grouping of canals (1000-3000 μm). These dimensional characteristics aligned with developmental progression. This study characterizes the ontogeny, distribution, ultrastructure, and 3D architecture of secretory canals, providing a structural foundation for investigating correlations between secretory tissues and compound synthesis.
Why it matches plant phenotyping methods根の分泌道の密度・寸法・3D構造という植物器官形質を、µ-CTの体積解析・セグメンテーションと顕微鏡法で取得・抽出しており、画像計測が研究の中心的要素である。
abstractµ-CT volumetric analysis and segmentation generated detailed 3D models, revealing spatial organization and enabling size-based grouping of canals (1000-3000 μm).
Plant phenotyping relevance match · UnverifiedbioRxiv · Europe PMC · checked 5 Sept 2026
Red crown rot of soybean (RCR), caused by Calonectria ilicicola, is an emerging soilborne disease whose quantification is challenging due to its complex symptom development across root and foliage levels. This study developed and evaluated a multi-scale framework to improve the assessment of RCR severity from controlled environments to field conditions using root imaging and standardized visual scales. Under controlled conditions, a standard area diagram (SAD) for root necrosis was developed and validated, and SAD-assisted evaluations significantly improved accuracy, precision, and inter-rater agreement compared with unaided assessments. In field conditions, a diagrammatic symptom scale (DSS) was developed using consensus-rated images from experts and showed high reliability, repeatability, and reproducibility across 18 raters, with strong intra- and inter-rater agreement. This study developed and evaluated complementary methods to improve the assessment of RCR severity from controlled environments to field conditions using root imaging and standardized visual scales.
Why it matches plant phenotyping methods根の壊死と地上部症状という植物病害状態の定量評価法を開発・検証しており、画像化と標準視覚尺度が研究の中心であるため。
abstractThis study developed and evaluated a multi-scale framework to improve the assessment of RCR severity from controlled environments to field conditions using root imaging and standardized visual scales.
ArabidopsisCell / cellular structureRootPhysiological trait estimationGrowth / development / phenology
Decoding how plants integrate multiple hormone signals to coordinate growth requires tools capable of resolving pathway interactions at cellular resolution in living tissue. Here we present ACE (Auxin–Cytokinin–Ethylene) and ACE2 , proof-of-concept single-locus reporters to simultaneously capture activity of multiple hormones. Deploying ACE alongside well-established reporters, exogenous hormone treatments, and reverse-genetic perturbations of hormone biosynthesis, signaling, and transport in three-day-old etiolated Arabidopsis seedlings, we dissect the spatiotemporal hierarchy governing primary root elongation and root apical meristem (RAM) size. We demonstrate that both ethylene- and cytokinin-triggered root growth inhibition involve a boost of TRYPTOPHAN AMINOTRANSFERASE OF ARABIDOPSIS1 (TAA1)-mediated auxin biosynthesis and AUXIN RESISTANT1 (AUX1)-dependent auxin redistribution. Two spatially distinct auxin responses underlie the respective root growth effects: ethylene expands TAA1-dependent auxin biosynthesis from the root vasculature into the epidermis and promotes AUX1-mediated auxin import into the transition and elongation zones to inhibit cell elongation, while cytokinin confines ethylene-dependent TAA1-boosted activity to the vasculature and drives auxin accumulation in lateral root cap cells to reduce RAM size. Together, these data establish a reciprocal regulatory loop between these hormones, positioning ethylene as a convergence node in auxin–cytokinin crosstalk, and cytokinin as a modulator of the ethylene–auxin interaction. Critically, the changes in cross-activated reporter patterns described for different genetic backgrounds, alongside quantitative assessment of hormone-specific inhibition of the mutants’ growth, were consistent with the multi-hormone network established over two decades of research, and added cell-type-resolved spatial detail and a proposed hierarchy for the etiolated seedling root. Finally, a second-generation reporter, ACE2 , overcomes key technical limitations of ACE , expanding the platform’s capacity toward a higher-order multi-hormone monitoring system. These resources expand the Arabidopsis genetic toolkit and provide a generalizable framework instrumental for dissecting multi-hormone signaling hierarchies at the cellular level.
Why it matches plant phenotyping methods多ホルモン活性を生体組織で同時可視化するACE/ACE2レポーターを開発・改良し、遺伝背景や根成長阻害との整合性を検証しているため、植物フェノタイピング手法が中心である。
abstractHere we present ACE (Auxin–Cytokinin–Ethylene) and ACE2 , proof-of-concept single-locus reporters to simultaneously capture activity of multiple hormones.
Calibrating the mutagenic dose is the first practical step of any radiation mutation-breeding programme, and it is usually summarised by the median lethal dose (LD50) or the median growth-reduction dose (GR50). We asked whether an accessible, image-based phenotyping pipeline can quantify the early radiation response of cowpea (Vigna unguiculata L. Walp.) seedlings finely enough to estimate GR50 and to rank organ- and pigment-level sensitivities. Seeds of the traditional Paraguayan landrace kumandá pyta’i were exposed to Cobalt-60 gamma rays at 0, 100, 200, 300, 400, 500, 600, and 700 Gy, grown in a greenhouse, and photographed at the early seedling stage. A single calibrated photograph (5.1 px mm−1) of 83 seedlings was segmented in Fiji/ImageJ 1.54p and analysed with Python to extract morphometric traits (total, root, and shoot length, root:shoot ratio, tortuosity, and a two-dimensional biomass proxy) and colorimetric traits (CIE L*a*b*, a normalised greenness index, and colour-class pixel fractions). Because the data departed from normality, dose effects were tested with Kruskal–Wallis, Spearman rank correlation, and Dunn post hoc tests, and GR50 was estimated by regression of each trait expressed as a percentage of the control. Total length, shoot length, and the biomass proxy declined significantly with dose (Spearman ρ = −0.40, −0.51, and −0.47; all p < 0.001), preceded by a low-dose stimulation at 100 Gy. Estimated GR50 values were ≈390 Gy for shoot length, ≈510 Gy for total length, and ≈550 Gy for the biomass proxy, within the range reported for other cowpea genotypes. Shoot elongation was more radiosensitive than root elongation, so the root:shoot ratio did not decline; tortuosity showed no dose response. Among pigment traits, the loss of greenness was the most robust signal (a* increased, ρ = +0.62, p = 5 × 10−10; green pixel fraction fell from 0.32 to near zero by 500 Gy). These results show that single-photograph phenotyping resolves a coherent, statistically supported dose response and yields a GR50 estimate usable for dose calibration. For kumandá pyta’i, doses of roughly 300–400 Gy (below GR50) are the most defensible starting window for mutation induction. The framework is reproducible and low-cost, but it is based on one greenhouse experiment and a single genotype, and should be validated across independent trials and cultivars.
Why it matches plant phenotyping methods画像取得・セグメンテーション・解析による形態および色彩形質の抽出を中心に、放射線応答とGR50を推定する低コスト画像ベース表現型解析法を提示しているため。
abstractA single calibrated photograph (5.1 px mm−1) of 83 seedlings was segmented in Fiji/ImageJ 1.54p and analysed with Python to extract morphometric traits
Plant phenotyping relevance match · UnverifiedCrossref · Europe PMC · checked 15 Sept 2026
Abstract Probe-based spatial transcriptomics platforms use predefined oligonucleotide panels to detect selected RNAs in tissue sections while preserving transcript spatial coordinates. Accurate cell segmentation is required for reliable transcript-to-cell assignments. This analytical process is affected in plant tissues by cell walls, large vacuoles, and strong autofluorescence, which often reduce boundary contrast and elevate background. Nucleus-only segmentation with fixed-distance expansion can be an alternative approach, but it underestimates cellular area and morphology and reduces the number of assignable transcripts per cell. Here, we present a practical workflow for segmentation and downstream processing in plant probe-based spatial transcriptomics. Using the soybean nodule, soybean seed, rice root, and wheat inflorescence, we demonstrate the applicability of our workflow across species, tissues, and technological platforms. In brief, candidate cell masks are generated from available fluorescence signals and then selected and corrected using two napari plugins. Transcript-informed refinement with Baysor is included as an optional step. Upon benchmarking our approach using a collection of metrics (assignment yield, background/negative controls, and per-cell transcript/gene distributions) and linked segmentation choices to expression-matrix quality and downstream clustering, we demonstrate the potential of our workflow to support the analysis of plant probe-based spatial transcriptomics.
Why it matches plant phenotyping methods植物組織の細胞セグメンテーションとトランスクリプト割当てを改善する実用ワークフローを開発し、複数種・組織でベンチマークしている。植物形態そのものの測定ではないが、細胞レベルの空間状態を抽出する解析手法が中心である。
abstractHere, we present a practical workflow for segmentation and downstream processing in plant probe-based spatial transcriptomics.
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.
Starch, a key biological macromolecule accounting for 50-80% of dry weight in sweetpotato (Ipomoea batatas [L.] Lam.) storage roots, underpins food and industrial applications. However, sweetpotato starch characterization is limited by local-sectioning approaches that fail to capture the whole-root granule dynamics. Here, we established a new morphological observation system covering three key root regions based on two representative cultivars: Okinawa 100 (V100), and Yanshu25 (Y25). It was effective and convenient for in situ starch observation and analysis in sweetpotato roots. The whole-root in situ microscopy, starch physicochemical profiling, and transcriptomic correlation were integrated to resolve starch dynamics in Y25 and V100. We identified widespread simple starch granules (SSGs)-compound starch granule (CSG) coexistence across the whole root tissues, with Y25 exhibiting programmed CSG fragmentation driven by ARCs/FtsZ-mediated amyloplast envelope destabilization and concomitant AMY/BMY upregulation. Y25 had a higher amylose content and a higher proportion of medium/long chains, but the average degree of polymerization was slightly lower. Transcriptomic analyses revealed that the differentially expressed genes were annotated in pathways of carbohydrate metabolism, and the differentially expressed genes in the starch metabolism pathway were analyzed. Weighted gene co-expression network analysis further identified the hub genes from different modules and analyzed the co-expression networks. This work will not only advance the understanding of starch granule assembly and remodeling in sweetpotato, but also provide a robust methodological and transcriptome-guided framework for starch-focused germplasm screening and quality improvement.
Why it matches plant phenotyping methodsサツマイモ根全体のデンプン粒形態・動態を観察する新しい形態観察システムを構築し、その有効性を示しており、表現型取得法が研究の中心である。
abstractHere, we established a new morphological observation system covering three key root regions based on two representative cultivars: Okinawa 100 (V100), and Yanshu25 (Y25).
Sorghum (Sorghum bicolor L. Moench) is a major cereal in water-limited environments. Its C4 carbon-concentrating pathway suppresses photorespiration and supports comparatively high photosynthetic and water-use efficiency at high temperature, although yield remains sensitive to the timing and intensity of drought. This systematic review critically evaluates how coordinated variation in phenology, canopy development, transpiration regulation, photosynthetic resilience and root-mediated water capture can be phenotyped for sorghum improvement. The review was conducted and reported in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) 2020 statement. Eligible primary studies examined sorghum drought physiology, sensing-based phenotyping, trait retrieval, root-associated water capture, or breeding applications. Following duplicate removal and title, abstract and full-text screening, 45 sorghum-specific studies were included. Owing to substantial heterogeneity in experimental design, drought treatment, sensing platform, target trait, and validation metric, evidence was synthesised narratively rather than by meta-analysis. We compare sorghum studies across Light Detection and Ranging (LiDAR), multi-spectral, hyperspectral, thermal, structural, and fluorescence sensing, with emphasis on reported accuracy, transferability and physiological interpretation. We then examine how PROSAIL (PROSPECT coupled with Scattering by Arbitrarily Inclined Leaves) and SCOPE (Soil Canopy Observation, Photochemistry and Energy Fluxes) can be constrained for sorghum canopies and combined with machine learning. The central contribution is a sorghum-specific framework that distinguishes directly observed or model-retrieved canopy traits from indirect root-function predictions requiring ground validation. The synthesis identifies practical routes for measuring functional stay-green, high-vapour-pressure-deficit responses and post-anthesis water capture, while defining priorities for cross-environment validation and breeding deployment.
Why it matches plant phenotyping methodsソルガムの干ばつ適応に関するセンシング型フェノタイピング手法を体系的にレビューし、形質推定の精度・移植性・検証、およびモデルと機械学習の統合を扱うため、方法論が中心である。
abstractThis systematic review critically evaluates how coordinated variation in phenology, canopy development, transpiration regulation, photosynthetic resilience and root-mediated water capture can be phenotyped for sorghum improvement.
ArabidopsisMicroscopyCell / cellular structureRootMorphology / geometry measurementVisualization / data management
Super-resolution microscopy has transformed our ability to visualize subcellular structures, but its application in plant biology remains challenging due to the optical complexity of plant tissues. Here, we present a detailed protocol for tau-STED microscopy (Leica Microsystems), which combines stimulated emission depletion (STED) with fluorescence lifetime imaging (FLIM) to achieve nanoscale resolution while minimizing phototoxicity. This method leverages time-correlated single-photon counting (TCSPC) to separate fluorescence signals based on their lifetimes, enhancing signal specificity and enabling the visualization of elusive subcellular compartments in Arabidopsis thaliana root tips. The protocol covers sample preparation, fluorophore selection, microscope configuration, image acquisition, and data analysis, providing a step-by-step guide to optimize tau-STED imaging for plant cell biology. By addressing the unique challenges of plant tissue imaging, such as autofluorescence, refractive index mismatches, and light scattering, this approach facilitates super-resolution imaging of intracellular structures, including the plant endoplasmic reticulum-Golgi intermediate compartment (ERGIC). This protocol is designed to be accessible to researchers with basic microscopy experience and offers a robust framework for exploring subcellular dynamics in plants with unprecedented detail. Key features • tau-STED integrates STED signals with fluorescence lifetime via phasor analysis at confocal speeds, enabling low-noise super-resolution imaging. • Morphometry analysis workflow at super resolution.
Why it matches plant phenotyping methods植物組織の細胞内構造を超解像で取得・解析する顕微鏡プロトコルであり、植物表現型の画像取得法が中心。超解像下の形態計測ワークフローも含む。
abstractHere, we present a detailed protocol for tau-STED microscopy (Leica Microsystems), which combines stimulated emission depletion (STED) with fluorescence lifetime imaging (FLIM) to achieve nanoscale resolution while minimizing phototoxicity.
Ammonium (NH 4 + ) is the primary inorganic nitrogen source for rice ( Oryza sativa L.). Substantial progress has been made in characterizing the functions of ammonium transporters (AMTs) in roots; however, the regulatory dynamics governing subcellular ammonium compartmentation after its entry into cells, particularly its vacuolar sequestration and efflux back to the external environment, remain poorly understood. This knowledge gap stems mainly from two factors: the difficulty of applying conventional detection methods at the organellar scale and interference caused by nonspecific ion adsorption to the cell wall of intact roots. To address these challenges, we present a detailed and reproducible protocol for real-time measurement of net NH 4 + fluxes in rice roots, root protoplasts, and isolated vacuoles using non-invasive micro-test technology (NMT). The protocol covers the preparation of protoplasts and vacuoles from rice roots, the configuration and calibration of the NMT system, and the step-by-step measurement of net NH 4 + fluxes at three distinct biological levels (intact roots, protoplasts, and vacuoles). By employing a unified sample preparation and measurement strategy, this protocol enables quantification of net uptake fluxes across the plasma membrane, characterization of net efflux dynamics under specific conditions, and indirect estimation of vacuolar sequestration capacity using the isolated vacuole system. Overall, this protocol provides a flexible and robust framework for studying NH 4 + homeostasis in plants and is readily adaptable to different crop species, treatment conditions, and experimental objectives. Owing to its modular design and compatibility with standard NMT equipment, it can be readily adopted by laboratories seeking to investigate nitrogen transport mechanisms in plants. Key features • Allows for testing of NH 4 + fluxes in roots, protoplasts, and vacuoles. • Applicable to plants grown under different culture systems, including Arabidopsis thaliana grown in dishes and rice grown in hydroponic systems. • Supports both long-term and transient stress treatments. • Real-time monitoring.
Why it matches plant phenotyping methods植物根・プロトプラスト・液胞のNH4+フラックスをリアルタイム定量するNMT測定プロトコルが研究の中心であり、植物の生理状態を取得する方法を詳細に開発・標準化している。
abstractwe present a detailed and reproducible protocol for real-time measurement of net NH 4 + fluxes in rice roots, root protoplasts, and isolated vacuoles using non-invasive micro-test technology (NMT).
Nano- and microplastics (NMPs) are now widely detected across agroecosystems and can act as physiological stressors in plants. Exposure occurs through contaminated soil, irrigation water, or airborne deposition, bringing particles into direct contact with roots and above-ground tissues. Reported entry routes include apoplastic transport, cracks formed at lateral root emergence, leaf stomata, and endocytosis once particles have crossed the cell wall. Once internalized, particles may translocate through the xylem and, in some cases, the phloem, accumulating in roots, stems, and leaves depending on particle size, surface charge, and plant structural characteristics. NMPs have been associated with oxidative stress, disrupted photosynthesis, and altered metabolic pathways. Detecting NMPs within heterogeneous, hydrated plant tissues remains challenging, as particles often show low contrast against biological structures and can be mistaken for cellular components. This review examines how microscopy techniques reveal NMPs size, surface attachment, tissue distribution, and cellular-level interactions, while noting that these approaches primarily provide morphological or localization information rather than confirming polymer identity. Complementary spectroscopic and mass-based analytical methods are discussed for their role in chemical confirmation and quantification. This review supports informed selection among imaging, spectroscopic, and quantitative techniques for studying plant-plastic interactions, while highlighting current analytical challenges facing the field.
Why it matches plant phenotyping methods植物組織内の粒子サイズ・付着・分布・細胞相互作用を測定する顕微鏡、分光、質量分析手法を中心にレビューしており、植物状態の観測手法が主題である。
abstractThis review examines how microscopy techniques reveal NMPs size, surface attachment, tissue distribution, and cellular-level interactions
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.
Plant phenotyping relevance match · UnverifiedOpenAlex · Crossref · Europe PMC · checked 5 Sept 2026
Accurate identification and effective prediction of the maize tasseling stage are of great significance for guiding precision field management and ensuring stable crop yields. Conventional manual observation methods suffer from high labor intensity, poor timeliness, and strong subjectivity. In this study, based on an unmanned aerial vehicle (UAV) remote sensing platform, LiDAR point cloud data and RGB imagery were simultaneously acquired to construct digital surface models (DSMs) and digital terrain models (DTMs). Multi-dimensional statistical features were extracted to establish a high-precision plant height estimation method applicable to the entire growth cycle of maize. On this basis, the Logistic growth curve function was introduced to fit the dynamic changes in plant height, enabling the identification and early prediction of the maize tasseling stage based on the plant height growth curve. The research results indicate the following: (1) For maize plant height estimation, the LiDAR sensor outperforms RGB. The optimal accuracy is achieved by combining the 99th percentile of DSM with the minimum DTM, yielding a root mean square error (RMSE) of 0.17 m. (2) Based on the high-accuracy plant height time series, the point of inflection (POI) achieves the highest accuracy in tasseling stage identification, with an RMSE of 2.586 d under the reconstructed time series. (3) Prediction accuracy of the tasseling stage improves with increasing plant height threshold, and optimal performance is observed when the threshold is ≥1.6 m with a growth rate between 0.11 and 0.13. This study establishes a technical framework of "time-series perception-dynamic simulation-feature identification-early prediction", providing a scientific basis for automated monitoring and precision management of the maize tasseling stage. It holds significant theoretical and practical value for the advancement of smart agriculture and crop phenotyping research.
Why it matches plant phenotyping methodsUAV LiDAR/RGBによる草丈推定と時系列成長曲線から、トウモロコシの抽だい期を識別・予測する技術フレームワークが研究の中心であり、植物形質取得と検証を伴うため採用。
abstractMulti-dimensional statistical features were extracted to establish a high-precision plant height estimation method applicable to the entire growth cycle of maize.
As a critical structural component that connects almost all other types of plant organs, the stem system not only supports the weight of the total plant, but also serves as a vital channel for nutriment transportation. Accurate phenotypic measurement of stem instances is of practical significance for assessing crop growth dynamics and predicting yield. To address current 3D phenotyping challenges of crops such as the difficulty in separating stem segments from the stem system and the low accuracy in stem length measurement, we propose a Hierarchical-annotation-guided Phenotypic Framework (HagPF) for Stem Instance Segmentation and Length Measurement in plant point clouds. Specifically, a hierarchical leaf-stem organ instance annotation strategy is devised to effectively train a PSegNet network for leaf and stem instance segmentation. The segmentation is then followed by a shape-adaptive measurement algorithm to automatically measure the length of stem segments that are morphologically diverse in space. On a 3D dataset comprising four crop species, the proposed framework achieved an Intersection over Union (IoU) of 95.45% for organ semantic segmentation and a Mean Weighted Coverage (mWCov) of 87.87% for instance segmentation (both stem and leaf). Regarding to the stem length measurement, the method obtained an average Root Mean Square Error (RMSE) of 1.044 cm and a relative error of 11.907%, outperforming 7 mainstream methods. The relevant dataset and source code can be found at: https://github.com/Jinx00/stem-length-measurement .
Why it matches plant phenotyping methods植物点群から茎のインスタンスを分割し、茎長を自動測定する3D表現型解析フレームワークの開発・比較検証が中心である。
abstractwe propose a Hierarchical-annotation-guided Phenotypic Framework (HagPF) for Stem Instance Segmentation and Length Measurement in plant point clouds.
Why it matches plant phenotyping methods感染根のカルロース沈着という植物の病態・生理状態を、染色・蛍光画像・Fiji/TWSで検出および定量する方法を最適化した手法論文であり、表現型取得が中心です。
abstractHere, we have optimized a robust and reliable method for detecting callose deposition in soybean lateral roots during Macrophomina phaseolina infections.
Field / plotMRI / PETMultispectral / hyperspectralX-ray / CTRoot2D/3D reconstruction
Mycorrhizal fungi form essential symbiotic relationships with plant roots, facilitating nutrient exchange and promoting plant health. Understanding their interactions can benefit from advanced imaging techniques capable of visualizing nutrient exchange and structural colonization at subcellular resolution across large sample sizes. This review explores novel imaging approaches that are revolutionizing our understanding of root-mycorrhizal fungal symbioses. Several techniques can now visualize and characterize mycorrhizal fungi and associated root structures non-destructively and in three dimensions, for example X-ray computed tomography (micro-CT), X-ray fluorescence (XRF), and X-ray absorption near edge structure (XANES) spectroscopy. Metabolic processes and nutrient exchange can be tracked through positron emission tomography (PET), fluorescent nanoparticles (FNPs), and the monitoring of electrical signalling. Artificial intelligence (AI)-powered image processing software is enabling high-throughput analysis of complex images generated from a range of sources. Mycorrhiza systems are also able to be tracked in-field at multiple scales: hyperspectral imaging can detect mycorrhizal associations at the kilometre scale, while portable MRI imagers can detect changes at the tissue scale. These converging technologies enable the direct, continuous measurement of structural and metabolic root-mycorrhizal fungi interactions, paving the way for a mechanistic understanding of these vital symbiotic partnerships and their impact on plant health and ecosystem functioning.
Why it matches plant phenotyping methods植物根と菌根の構造・代謝・栄養交換を画像およびセンサーで直接測定する手法を扱うレビューであり、植物状態の取得技術が中心である。
abstractThis review explores novel imaging approaches that are revolutionizing our understanding of root-mycorrhizal fungal symbioses.
Field / plotLiDAR / point cloudRGB / grayscaleRootStem / branchWhole plant / canopy / plot / fieldMorphology / geometry measurement2D/3D reconstructionVisualization / data managementArchitecture / morphology / geometry
Diameter at breast height (DBH) is a crucial indicator for obtaining tree phenotypes in orchard management, plantation monitoring, and agroforestry systems. LiDAR technology has high measurement accuracy, but it is costly and difficult to deploy flexibly in outdoor scenarios, while smartphones have emerged as a viable alternative due to their portability and low cost. In this paper, we propose a DBH estimation method based on monocular depth estimation, supported by a mobile application for algorithm deployment and result visualization. To address the limited computing resources on mobile devices, we design HR-DiffusionDepth, a lightweight diffusion-based monocular depth estimation network for smartphones, which generates pixel-wise 3D coordinates from a single image using camera intrinsics, thereby replacing LiDAR for DBH calculation. Experiments on the KITTI and SPREAD datasets show that HR-DiffusionDepth achieves the best depth estimation accuracy among similar lightweight models, reducing Abs Rel by up to 25.3% relative to the state-of-the-art (SoTA) lightweight baseline, with only 6.26 M parameters. The validation results show that the root mean square error (RMSE) of DBH estimation is 3.10 cm and the mean absolute error (MAE) is 2.25 cm, demonstrating the potential of this approach for agricultural scenarios such as orchards and plantations.
Why it matches plant phenotyping methodsスマートフォン画像と軽量深度推定ネットワークにより樹木DBH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。
abstractwe propose a DBH estimation method based on monocular depth estimation, supported by a mobile application for algorithm deployment and result visualization.
Accurate estimation of crop plant height using unmanned aerial vehicles (UAVs) is essential for field-scale crop monitoring and phenotyping. Most previous studies using UAV-based structure-from-motion (SfM) photogrammetry have relied on raster-based crop surface models (CSMs) and have evaluated their performance using accuracy metrics such as the coefficient of determination ( R 2 ) and root mean square error (RMSE). However, such evaluations provide limited insight into how estimation behavior varies across space and time, particularly during dynamic crop growth stages. To address this gap, this study conducted a time-series comparison of rice plant height estimates derived from UAV-SfM-generated dense point clouds (DPCs) and raster-based CSMs in farmer-managed paddy fields in Cambodia, which are characterized by heterogeneous micro-environmental conditions. Rice plant height was measured throughout the growing season and UAV-derived estimates were evaluated using regression analysis, analysis of covariance, and canopy cover dynamics. In the pooled analysis, both approaches achieved high overall accuracy, with R 2 = 0.92 and RMSE = 7.2 cm for the CSM-based approach and R 2 = 0.90 and RMSE = 8.8 cm for the DPC-based approach. However, time-series analyses revealed that CSM-derived plant height estimates exhibited strong location-dependent variability and sensitivity to early-stage canopy development, whereas DPC-based estimates showed more consistent performance across locations and growth stages. Regression coefficients derived from CSM-based estimates varied significantly among locations, whereas those from DPC-based estimates did not, suggesting that point-based representations may provide more spatially consistent estimation behavior under heterogeneous field conditions. By explicitly considering temporal dynamics, canopy development, and data representation, this study highlights the limitations of current raster-based UAV-SfM workflows for structurally complex crop canopies and suggests that DPC-based approaches may offer a useful complementary representation for crop monitoring and phenotyping, particularly when spatial consistency across heterogeneous field conditions is important.
Why it matches plant phenotyping methodsUAV-SfMによるイネの草丈推定手法を、3D点群と作物表面モデルで時系列比較・検証しており、表現形式と技術性能の評価が研究の中心です。
titleA comparative analysis of 3D point clouds and crop surface models for rice plant height estimation using UAV-SfM
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.
Hydrogen peroxide (H 2 O 2 ) is an important signaling molecule in plants under stress, and its level can be stimulated by abiotic stress and oxidative stress, which will seriously affect plant growth and development. Additionally, the presence of excessive residual H 2 O 2 in food can pose significant health risks to humans, because intake of H 2 O 2 can lead to serious pathological conditions. Therefore, it is necessary to develop a simple and efficient method to detect H 2 O 2 in both plants and food. In this paper, we designed a fluorescence probe NBP, which has the advantages of high selectivity, low detection limit (80 nM) and long emission wavelength (648 nm). The imaging effect of exogenous H 2 O 2 was realized in the roots of Platycodon grandiflorum . By exploring the interplay between H 2 O 2 , plant metals, and drought stress, we can observe the up-regulation of H 2 O 2 in the roots of Platycodon grandiflorum under adverse conditions, and the root 3D imaging study could be realized. Then we combined the fluorescence probe with a smartphone, which enables on-site detection of residual H 2 O 2 in various milk samples. In addition, we investigated the fluorescence imaging of endogenous and exogenous H 2 O 2 in living cells using NBP. Therefore, this study provides a new way to assess the oxidative stress risk of Platycodon grandiflorum roots under abiotic stress, which is expected to improve plant production and has broad application prospects in food sample detection.
Why it matches plant phenotyping methods植物根におけるH2O2の蛍光イメージング手法を開発し、乾燥ストレス下の酸化ストレス状態を評価しているため、植物フェノタイピング手法が中心です。
abstractTherefore, it is necessary to develop a simple and efficient method to detect H 2 O 2 in both plants and food.
Remote sensing has become an important tool for crop monitoring and precision agriculture, yet its applications in sugar beet production remain fragmented across sensing platforms, target traits and modelling strategies. This review synthesises the development, current applications and future directions of remote sensing in sugar beet production, with particular attention to the transition from crop monitoring to precision management. A structured search was conducted in Scopus and the Web of Science Core Collection for publications from 2003 to 2025, and 181 relevant peer-reviewed articles were retained for thematic analysis. The literature shows a clear increase in sugar beet remote sensing studies, particularly after 2015, coinciding with the availability of Sentinel-2 imagery and, from 2016 onwards, the growing use of unmanned aerial vehicle-based sensing. It also indicates a gradual shift from crop mapping and canopy monitoring towards disease detection, weed mapping, yield prediction and management-oriented applications. Current studies demonstrate the value of satellite, unmanned aerial vehicle and proximal sensing for retrieving canopy traits, assessing biotic stresses, estimating root yield and supporting field-scale management. However, sugar beet presents specific challenges because its economic value depends not only on canopy development or root biomass, but also on sucrose concentration, recoverable sugar yield, and processing quality. These quality-related traits remain less studied and are difficult to infer directly from canopy observations. Modelling approaches have evolved from vegetation-index-based empirical models towards machine learning, deep learning, multi-temporal analysis, data fusion and crop model assimilation, but issues of model transferability, ground-truth availability and operational decision support remain unresolved. Future research should strengthen multi-source observations, external validation, quality-oriented prediction and decision-support workflows to promote robust, scalable and economically meaningful remote sensing applications in sugar beet production.
Why it matches plant phenotyping methodsサトウダイコンのリモートセンシングによるキャノピー形質、ストレス、根収量などの推定手法を体系的にレビューしており、センシング基盤とモデル化・検証課題が中心的に扱われている。
abstractThis review synthesises the development, current applications and future directions of remote sensing in sugar beet production, with particular attention to the transition from crop monitoring to precision management.
WheatGrowth chamberRootMorphology / geometry measurementRoot system architectureStress response / toleranceWater status / transpiration
Description: Background: Water scarcity severely threatens wheat (Triticum aestivum L.) production in arid and semi-arid regions like Pakistan, requiring the development of climate-resilient crop varieties. Root System Architecture (RSA) is critical for drought tolerance, yet evaluating these "hidden half" traits has traditionally been limited by destructive, time-consuming field methods. Objective: This study comparatively evaluates the RSA and drought adaptability of four prominent wheat cultivars—Akbar-19, Fakhar-e-Bhakkar-19, Dilkash-19, and CN3—under varying water stress conditions. Methodology: The experiment was conducted in a controlled rhizobox setup at the speed breeding facility of CSI-NARC, Islamabad. Four cultivars were exposed to three irrigation levels ($100\%$, $75\%$, and $50\%$ field capacity). Digital root phenotyping was performed using the open-source software Rhizo Vision Explorer to evaluate 2D scanned images for traits including total root length, root diameter, volume, surface area, and branching frequency. Key Findings: The analysis revealed distinct genotypic strategies for drought adaptation. CN-3 (G2) emerged as highly promising for water-limited environments, demonstrating the highest relative water content (RWC) stability ($71.27\%$), thickest root profiles ($7.95\text{ mm}$), and largest root volume ($1,773.2\text{ mm}^3$). Dilkash-19 (G3) showed strong structural stability with a consistent root length ($160.33\text{ mm}$), while Fakhar-E-Bhakkar-19 (G4) excelled in root surface area ($2,145.8\text{ mm}^2$) and root tip density ($958.99\text{ tips}$), signaling high potential for nutrient foraging. Conversely, Akbar-19 (G1) displayed lower adaptability due to lower RWC and limited root volume. Significance: This research bridges the gap between digital phenotyping platforms and traditional breeding practices. It identifies vital genetic donors like CN3 and Dilkash-19 for breeding programs targeting drought tolerance, offering practical pathways to sustain wheat productivity and strengthen food security under changing climatic conditions.
Why it matches plant phenotyping methodsRhizo Vision Explorerを用いた根系画像解析が研究の中心で、根長・径・体積・表面積・分枝などの植物形質をデジタル抽出しているため、実質的な植物フェノタイピング応用研究である。
abstractDigital root phenotyping was performed using the open-source software Rhizo Vision Explorer to evaluate 2D scanned images for traits including total root length, root diameter, volume, surface area, and branching frequency.
WheatGrowth chamberRootMorphology / geometry measurementRoot system architectureStress response / toleranceWater status / transpiration
Description: Background: Water scarcity severely threatens wheat (Triticum aestivum L.) production in arid and semi-arid regions like Pakistan, requiring the development of climate-resilient crop varieties. Root System Architecture (RSA) is critical for drought tolerance, yet evaluating these "hidden half" traits has traditionally been limited by destructive, time-consuming field methods. Objective: This study comparatively evaluates the RSA and drought adaptability of four prominent wheat cultivars—Akbar-19, Fakhar-e-Bhakkar-19, Dilkash-19, and CN3—under varying water stress conditions. Methodology: The experiment was conducted in a controlled rhizobox setup at the speed breeding facility of CSI-NARC, Islamabad. Four cultivars were exposed to three irrigation levels ($100\%$, $75\%$, and $50\%$ field capacity). Digital root phenotyping was performed using the open-source software Rhizo Vision Explorer to evaluate 2D scanned images for traits including total root length, root diameter, volume, surface area, and branching frequency. Key Findings: The analysis revealed distinct genotypic strategies for drought adaptation. CN-3 (G2) emerged as highly promising for water-limited environments, demonstrating the highest relative water content (RWC) stability ($71.27\%$), thickest root profiles ($7.95\text{ mm}$), and largest root volume ($1,773.2\text{ mm}^3$). Dilkash-19 (G3) showed strong structural stability with a consistent root length ($160.33\text{ mm}$), while Fakhar-E-Bhakkar-19 (G4) excelled in root surface area ($2,145.8\text{ mm}^2$) and root tip density ($958.99\text{ tips}$), signaling high potential for nutrient foraging. Conversely, Akbar-19 (G1) displayed lower adaptability due to lower RWC and limited root volume. Significance: This research bridges the gap between digital phenotyping platforms and traditional breeding practices. It identifies vital genetic donors like CN3 and Dilkash-19 for breeding programs targeting drought tolerance, offering practical pathways to sustain wheat productivity and strengthen food security under changing climatic conditions.
Why it matches plant phenotyping methodsRhizo Vision Explorerによるデジタル根系表現型計測が研究の主要な方法として明示され、根長・径・体積・表面積・分枝などの植物形質を抽出しているため。
abstractDigital root phenotyping was performed using the open-source software Rhizo Vision Explorer to evaluate 2D scanned images for traits including total root length, root diameter, volume, surface area, and branching frequency.
Early detection of heavy metal stress in plants is essential for effective environmental monitoring, particularly in contaminated urban areas. This study evaluated whether remote sensing combined with simplified anatomical diagnostics can provide a rapid and reliable method for detecting chromium (Cr) and nickel (Ni) stress in common urban weed species. Five species were selected: Trifolium pratense, Rumex acetosa, Alcea rosea, Amaranthus retroflexus, and Plantago lanceolata. Visible plant injuries were assessed using Evans Blue staining and image-based anatomical analysis, which enabled distinguishing between living, partially damaged, and dead cells. Multispectral observations using a MicaSense RedEdge-M camera allowed calculation of the Normalized Difference Vegetation Index (NDVI) to detect stress-related changes in photosynthetic apparatus. The studied species differed in their capacity to accumulate and translocate Cr and Ni. Metal bioaccumulation was low in all species (bioconcentration factor < 1), with the highest Ni accumulation observed in Plantago lanceolata. Translocation of both metals was the greatest in Trifolium pratense and Amaranthus retroflexus. Hydrogen peroxide levels increased in roots and leaves of all species, particularly in Alcea rosea. Despite the absence of visible injuries, microscopic anatomical changes were detected in T. pratense and R. acetosa, while NDVI values differed between sites. In summary, this study indicates that no simple relationship was found between physiological stress parameter values and NDVI. It is important to emphasize the need for continued research under controlled conditions with specific doses of PTEs salts. This should clearly demonstrate the relationship between plant physiological responses to stress and the results of multispectral observations.
Why it matches plant phenotyping methodsリモートセンシング、画像ベースの解剖診断、NDVIを用いた植物ストレス検出法の評価が研究目的として明示されており、植物状態の取得・推定が中心的です。
abstractThis study evaluated whether remote sensing combined with simplified anatomical diagnostics can provide a rapid and reliable method for detecting chromium (Cr) and nickel (Ni) stress in common urban weed species.
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
Sweet potato is a nutritionally valuable crop that contributes to food security, owing to its storage roots rich in starch, sugars, and antioxidants, while requiring minimal cultivation inputs. Estimating its yield based on visible above-ground traits remains challenging due to weak and inconsistent correlations between shoot biomass and storage root development. Therefore, direct assessment of underground biomass is essential. In this study, we demonstrate the field application of ground penetrating radar (GPR) for non-destructive detection and yield estimation of sweet potato. GPR is a geophysical technique that typically transmits ultra high frequency radio waves into the soil and records reflections from subsurface objects. Electromagnetic wave simulations within the soil-root system revealed GPR signals that strongly correlate with root length, forming the basis for yield quantification. We developed an image-processing pipeline comprising static correction, gain adjustment, noise filtering, and hyperbola segmentation via the Hough transform to enable semi-automated storage root detection from GPR data. By integrating detection and quantification approaches, a linear regression model predicting sweet potato yield from GPR signals achieved moderate accuracy ( R 2 = 0.567, normalized RMSE 0.190). We established a non-destructive and low-labor approach for monitoring root systems, providing a foundation for rapid, scalable, and field-ready yield estimation in sweet potato and other root and tuber crops.
Why it matches plant phenotyping methodsGPRによる地下貯蔵根の検出・定量化と収量推定を中心に、信号処理および画像処理パイプラインを開発・評価しているため、植物フェノタイピング手法として収載する。
abstractWe developed an image-processing pipeline comprising static correction, gain adjustment, noise filtering, and hyperbola segmentation via the Hough transform to enable semi-automated storage root detection from GPR data.
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 datasets analyzed during the current study consist of GPR line scans of the field at ALRC and list of sweet potato storage root weights. These data are available together with the analysis scripts on GitHub under open access. All data and scripts are the property of NARO and are distributed under the Creative Commons Attribution-NonCommercial 4.0 International License (CC BY-NC 4.0). The repository can be accessed at: https://github.com/mtei1/GPRScript.Open asset ↗mtei1/GPRScripthtml-lines:240-264Plant phenotyping relevance match · UnverifiedEurope PMC · bioRxiv · checked 11 Sept 2026
A bstract Quantifying root traits such as root length (RL) and root surface area (RSA) from minirhizotron imagery is a valuable approach for overcoming the phenotyping bottleneck that limits understanding and improvement of crop productivity, resource use efficiency and resilience in field experiments. However, current approaches remain labor-intensive, and deep learning (DL) methods suffer from limited generalization ability. We present RootQuant, an end-to-end DL model that simultaneously predicts RL and RSA directly from minirhizotron images using only whole-image trait values as supervision, thereby eliminating the need for pixel-level annotations. The model’s generalization ability was evaluated across species and fine-tuning configurations. The practical applicability of the model was further assessed under field conditions by converting image-derived RL estimates into volumetric root length density (vRLD). Using 118,191 maize and soybean images collected between 2009 and 2020, RootQuant trained on both species achieved an R 2 of 0.90 and an RMSE of 2.9 mm for RL, and an R 2 of 0.88 and an RMSE of 4.2 mm 2 for RSA. The same mixed-species model generalized strongly across species, yielding an 8% relative improvement in R 2 and a 30% lower RMSE on maize compared with the same architecture trained on a single species and applied zero-shot. Image-derived RL predictions converted to vRLD showed the expected depth-dependent decline in vRLD, as was also found by coincident destructive quantification of roots washed out of soil cores. By providing a generalist backbone model trained on a large dataset from two major crop species, RootQuant enables high-throughput simultaneous estimation of two relevant root traits directly from raw imagery without task-specific fine-tuning, thereby accelerating in situ root system analysis and phenotyping applications.
Why it matches plant phenotyping methodsミニライゾトロン画像から根長・根表面積を推定する深層学習手法を開発し、種間一般化と圃場適用性を評価しており、植物フェノタイピング手法が研究の中心である。
abstractWe present RootQuant, an end-to-end DL model that simultaneously predicts RL and RSA directly from minirhizotron images using only whole-image trait values as supervision
The endodermis plays a critical role in root function by regulating the movement of water and nutrients. Because endodermal function emerges from coordinated interactions among neighboring cells, the three-dimensional (3D) organization of cellular networks may influence how transport pathways are spatially arranged within root tissues. However, the 3D cellular network topology of the endodermis and its potential functional significance in woody plants remain poorly understood. Here, we combined light-sheet fluorescence microscopy (LSFM), 3D reconstruction, and network topology analysis to compare the endodermal cellular networks of two tree species, balsa (Ochroma pyramidale) and Eucalyptus robusta. We found that the balsa endodermis exhibits a distinct network topology characterized by higher local connectivity, lower closeness centrality, and lower edge betweenness centrality than that of Eucalyptus. Confocal Raman spectroscopy revealed broadly similar lignin and suberin signatures in the Casparian strip of the two species. Physiological measurements further showed that balsa roots exhibited significantly higher K + influx than Eucalyptus roots. Together, these observations indicate an association between variation in endodermal network organization and differences in root ion transport characteristics. This study highlights the value of integrating three-dimensional cellular reconstruction with network analysis to investigate structure-function relationships in plant tissues.
Why it matches plant phenotyping methodsLSFMによる3D細胞再構築とネットワーク解析が、根内皮の形態・構造特性を定量化する中心的手法として用いられているため、植物フェノタイピング手法の実質的応用に該当する。
abstractHere, we combined light-sheet fluorescence microscopy (LSFM), 3D reconstruction, and network topology analysis to compare the endodermal cellular networks of two tree species, balsa (Ochroma pyramidale) and Eucalyptus robusta.
Rice seedling salt-tolerance evaluation commonly relies on visual scoring or destructive assays, which are subjective, labor-intensive, and difficult to standardize for population-level analysis. This study developed a new deep-learning-assisted hydroponic RGB phenotyping framework for standardized salt-stress evaluation and genetic mapping in rice seedlings. The framework integrates controlled hydroponic cultivation, RGB imaging, RicePhenoSeg-assisted annotation and trait extraction, ELMERF-based semantic segmentation, and image-derived quantification of salt-induced shoot injury. Using this framework, we constructed the Rice Seedling-Salt RGB Dataset (RSSD), which contains green shoot tissues, yellow shoot tissues, roots, and background from hydroponically grown rice seedlings. Based on RSSD, ELMERF achieved a mean Intersection over Union of 51.4% and a mean Accuracy of 89.5%, outperforming nine representative segmentation models. We further defined shoot yellowing rate (SYR) as an image-derived quantitative trait describing visible salt-induced shoot injury. The framework was applied to 261 re-sequenced rice accessions for population-level phenotyping and genome-wide association analysis. Compared with standard evaluation score and seedling death rate, SYR showed a more continuous phenotypic distribution and detected 36 significant SNPs, including a major signal near the Saltol/OsHKT1; 5 region. Notably, 34 SYR-associated SNPs were not detected by conventional visual scores. Overall, this study provides a targeted hydroponic RGB phenotyping framework for standardized rice seedling salt-stress evaluation and genetic analysis.
Why it matches plant phenotyping methods深層学習によるRGB画像セグメンテーション、形質抽出、データセット構築、性能比較を中核とし、画像由来の塩ストレス傷害形質を定量化する植物フェノタイピング手法である。
abstractThis study developed a new deep-learning-assisted hydroponic RGB phenotyping framework for standardized salt-stress evaluation and genetic mapping in rice seedlings.
Reproduction assets foundThe paper's Data Availability statement explicitly deposits datasets, source code, and supporting data in a public GitHub repository (ELMERF), which covers the RSSD RGB image dataset, segmentation code, and phenotyping/GWAS analysis assets. RiceVarMap is a cited external SNP database, not a paper-specific asset.Code · publicThe datasets, source code, and other supporting data are openly available on the ELMERF repository (https://github.com/PhenoCodexh/ELMERF).Open asset ↗PhenoCodexh/ELMERFhtml-lines:446-478Plant phenotyping relevance match · UnverifiedbioRxiv · checked 15 Sept 2026
Auxin is a key phytohormone that regulates all aspects of plant growth, development, and environmental responses, making the precise analysis of its distribution and signaling essential for understanding plant adaptation and physiological processes. However, despite the agricultural importance of oilseed rape (Brassica napus), the lack of robust, species-specific molecular tools limits detailed studies of hormone signaling in this crop. Here, we developed and characterized reporter systems for the sensitive visualization and quantification of auxin distribution and signaling in B. napus. The DR5cc auxin signaling reporter and a novel synthetic auxin-responsive reporter, BIP3, assembled from promoter fragments of three oilseed rape IAA genes, were generated to drive GUS expression. In hairy roots, both reporters showed auxin-responsive expression in the root apical meristem that became broader after auxin treatment. In transgenic seedlings, flowers at anthesis, and 12-day-old embryos, DR5cc exhibited a more defined expression pattern than BIP3. To monitor real-time auxin dynamics under abiotic stress, DR5cc fluorescent reporters were employed in hairy roots. Mannitol and NaCl treatments induced a time-dependent increase in fluorescence, peaking at 6-12 h before returning to basal levels after 24 h. Furthermore, dual-reporter assays enabled simultaneous monitoring of auxin and cytokinin signaling, revealing distinct hormone-specific spatial responses in hairy roots. Finally, we established a quantitative DII (qDII) reporter system using degron domains from B. napus Aux/IAA proteins, providing a high-resolution quantitative readout of auxin depletion. Together, these reporter systems enable spatial, temporal, and quantitative analyses of auxin dynamics during development and stress adaptation in oilseed rape.
Why it matches plant phenotyping methodsナタネにおけるオーキシン分布・シグナルを可視化および定量するレポーター系を開発・評価しており、植物の生理状態を取得する方法が研究の中心である。
abstractHere, we developed and characterized reporter systems for the sensitive visualization and quantification of auxin distribution and signaling in B. napus.
Plant phenotyping relevance match · UnverifiedOpenAlex · Crossref · Europe PMC · checked 15 Sept 2026
X-ray / CTRootMorphology / geometry measurement2D/3D reconstructionRoot system architecture
A dual-substrate X-ray CT platform using high-contrast media and modular scanning overcomes traditional resolution and size limits to enable large-scale, high-resolution 3D in situ root phenotyping.
Why it matches plant phenotyping methods作物根系のin situ表現型測定を目的とするX線CTプラットフォームの開発であり、取得・解析手法が研究の中心である。
abstractA dual-substrate X-ray CT platform using high-contrast media and modular scanning overcomes traditional resolution and size limits to enable large-scale, high-resolution 3D in situ root phenotyping.
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].
ThermalRootGrowth / time-series analysisPlant / canopy temperature
Plants rely on the circadian clock to anticipate daily environmental fluctuations and to coordinate key physiological, metabolic, and developmental processes. Most if not all plant cells have semi-autonomous circadian oscillators. Roots possess a modified yet robust circadian oscillator that is entrained by external cues such as light and temperature to synchronize nutrient uptake, water transport, and metabolic activity. It has been shown that the root and shoot oscillators can communicate through long-distance signals including mobile proteins and carbon assimilates such as sucrose. Moreover, recent studies indicate root-microbe interactions; root-associated microbial communities exhibit diurnal oscillations structured by the host circadian system, while microbes can in turn modulate the circadian period and rhythmic outputs of the plant. However, in general, while the shoot circadian oscillator has been extensively characterized, much less is known about the root circadian system. Progress has been hampered by a lack of high-throughput, non-invasive methods to study root rhythmicity. Existing methods including luciferase reporters, quantitative RT-PCR, and microscopy remain limited by cost, destructive sampling, or require transgenic lines with reporter genes. We have developed a thermal infrared imaging platform that enables non-invasive, high resolution of circadian rhythms in roots across plant species and growth conditions. We show that our system can be used to analyse metabolite and microbial effects on root circadian regulation. This platform provides new opportunities to investigate below-ground circadian regulation and the possibilities of harnessing the root clock to enhance plant performance and resilience.
Why it matches plant phenotyping methods根の概日リズムという植物状態を、非破壊・高解像度の熱赤外画像で測定するプラットフォームを開発しており、フェノタイピング手法が研究の中心である。
abstractProgress has been hampered by a lack of high-throughput, non-invasive methods to study root rhythmicity.
The co-evolutionary arms race between crops and their parasites requires continuous identification of new resistance mechanisms. Broomrape (Orobanche cumana), a root parasitic plant, poses a severe threat to sunflower (Helianthus annuus) production, yet the genetic architecture underlying host resistance remains poorly understood. To address this, we established a high-throughput phenotyping platform to quantify root infestation across a diverse sunflower association mapping (SAM) population. Combining this phenotypic resource with a dual genome-wide association study (GWAS) strategy based on both single nucleotide polymorphisms (SNPs) and k-mers, we highlight the genetic basis of broomrape resistance at unprecedented resolution. Our analyses revealed quantitative trait loci (QTLs) and identified novel candidate genes, including putative leucine-rich repeat receptor kinases potentially involved in parasite recognition and defense activation. Importantly, the k-mer approach circumvented reference genome bias and uncovered key genomic introgressions from wild Helianthus relatives that contribute substantially to resistance. These findings demonstrate the utility of integrating high-resolution phenotyping with advanced association mapping to dissect complex host-parasite interactions. Moreover, they emphasize the enduring value of wild germplasm as a reservoir of adaptive variation, providing crop breeders with crucial tools to counter the rapid evolutionary dynamics of parasitic plants.
Why it matches plant phenotyping methods根部の寄生程度を定量する高スループット表現型解析プラットフォームの確立が明示され、遺伝解析の基盤として方法が実質的に扱われている。
abstractwe established a high-throughput phenotyping platform to quantify root infestation across a diverse sunflower association mapping (SAM) population.
Reproduction assets foundThe paper's Data availability statement explicitly deposits the paper-specific raw phenotyping images on Zenodo, the k-mer genotype data on the sunflower genome database, and the authors' analysis code on the Hübner lab GitHub repository, all with public URLs.Dataset · publicAll phenotypes raw images for Gadot and Yavor are available through the Zenodo repository ( https://doi.org/10.5281/zenodo.18961268 ).Open asset ↗Zenodo · 10.5281/zenodo.18961268lines:238-238Code · publicCode is accessible through the Hübner lab github: https://github.com/hubner-lab/Sunflower-Broomrape-paper .Open asset ↗Hübner lab github · hubner-lab/Sunflower-Broomrape-paperlines:238-238Code / dataset availability confirmedCrossref · Europe PMC · checked 6 Sept 2026
RiceRootTissueMorphology / geometry measurementSegmentationRoot system architecture
Abstract Background Quantification of rice root anatomical traits such as cortical aerenchyma lacunae is key to understanding rice adaptation to diverse water regimes and to support climate-smart breeding. Aerenchyma lacunae contributes to rice internal gas transport and influences methane emissions from flooded systems and can also limit rice water conductivity. It could be an interesting anatomical trait for breeding, however, large-scale anatomical phenotyping remains limited because manual analysis of root cross-sections is labor-intensive, subjective, and difficult to scale across heterogeneous imaging conditions. Existing pipelines often require parameter tuning and do not generalize well across environments. Results We developed a deep learning pipeline based on a vision transformer architecture to automatically segment rice root cross-sections and quantify cortical aerenchyma lacunae. The model was trained on 1,760 annotated images collected across multiple countries, growth stages, cultivation systems, and experimental contexts, using a collaboratively defined annotation protocol. The final model achieved high segmentation accuracy, with mean intersection over union values exceeding 0.92 for cortical tissues and lacunae. Quantification of the lacuna-to-cortex ratio showed strong agreement with manual annotations, with a coefficient of determination of 0.98 on an independent test set. An independent expert review indicated that model predictions were at least as consistent as manual annotations and reduced large annotation inconsistencies. The pipeline is released as open-source software and includes an interactive online demonstrator, and is accompanied by an online test dataset to support testing and reproducibility. Application across six experimental use cases revealed reproducible differences in aerenchyma lacunae across genotypes, water regimes, environments, and developmental stages. Conclusions This work provides a robust, scalable, and transferable tool for automated root anatomical phenotyping under heterogeneous experimental conditions. Transformer-based segmentation enables consistent and high-throughput quantification of lacunae, facilitating integration of these anatomical traits into breeding, physiological studies, and climate-smart crop improvement programs.
Why it matches plant phenotyping methodsイネ根の通気組織空隙を画像から自動セグメンテーション・定量するTransformerベースの表現型解析パイプラインを開発し、独立データで精度検証、ソフトウェアとテストデータセットを公開しているため、植物フェノタイピング手法が中心である。
abstractWe developed a deep learning pipeline based on a vision transformer architecture to automatically segment rice root cross-sections and quantify cortical aerenchyma lacunae.
Reproduction assets foundThe paper releases its authors' phenotyping pipeline (preprocessing/training code archived on Zenodo and an interactive Hugging Face Space demonstrator with a test dataset subset) as public assets. The full multi-environment training image dataset is only available upon reasonable request, so it is not a public asset.Code · publicall code used for preprocessing and
training is released under an open-source licence on GitHub, tagged v1.0.2, and
archived with a Zenodo DOI (Atef, 2025).Open asset ↗Zenodopdf-page:46 lines:1-65Code / dataset availability confirmedEurope PMC · checked 5 Sept 2026
Root phenotypic analysis is closely related to crop yield and stress resistance. Although deep learning can improve the efficiency of root phenotype recognition, existing methods suffer from insufficient segmentation accuracy under complex soil backgrounds and focus on a single target. To address the issues of limited accuracy and operational complexity in existing root segmentation models, this paper proposes a novel wavelet-enhanced full-scale segmentation network. The WaveUNet+ model is based on U-Net3plus, replaces traditional downsampling with the Haar wavelet transform, and introduces the EMA module. The impact of the wavelet transform is validated using Grad-CAM, and HD95 is employed to evaluate the improvement in segmentation quality brought by the attention mechanism from the perspective of boundary accuracy. Transfer learning is used to improve model generalization, and the test results on diverse roots and various soils are compared. A Docker containerized root image segmentation method is designed to achieve convenient and practical operation, and the deployment feasibility of the model on edge devices is also verified. Our model effectively enhances the recognition of fine roots in soil backgrounds, leading to improvements across various metrics, achieving an Accuracy of 99.2%, while improving model accuracy with relatively low parameter count and model size. Compared with the original U-Net model, mIoU is increased by 1.52% and Recall by 2.93%. The results show that the model not only performs excellently on the original dataset but also maintains good generalization ability across different imaging modalities, crop species, and soil conditions. With Docker, users can achieve root image segmentation on their own computers without tedious program installation and environment configuration. In the future, we will attempt methods such as pruning and quantization to reduce model size, so as to better adapt to the deployment requirements of edge devices.
Why it matches plant phenotyping methods根系画像から根系形態を抽出するセグメンテーション手法を開発し、複数条件で精度・汎化性・境界性能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。
abstractTo address the issues of limited accuracy and operational complexity in existing root segmentation models, this paper proposes a novel wavelet-enhanced full-scale segmentation network.
Reproduction assets foundThe paper's Data Availability Statement explicitly states the analysis code is publicly available at the authors' GitHub repository (WaveUNet-), which implements the WaveUNet+ root segmentation and phenotyping analysis. The supplementary materials only contain Grad-CAM figures and parameter tables, not datasets or codeCode · publicData Availability Statement
The data are available in a publicly accessible repository. The code can be obtained from https://github.com/WLL-cyber/WaveUNet-.git (accessed on 16 June 2026).Open asset ↗WLL-cyber/WaveUNet-lines:182-213Code / dataset availability confirmedCrossref · checked 14 Sept 2026
Wild-relative introgression broadens wheat diversity, as exemplified by the Multiple Synthetic Derivatives (MSD) population, a unique hexaploid wheat resource capturing extensive genetic diversity from Aegilops tauschii. However, root system architecture (RSA), a key determinant of resource acquisition and stress adaptation, remains poorly characterized in this population. Here, we established a practical two-dimensional root phenotyping framework that enables continuous imaging to track RSA traits and their responses to heat stress. Using this framework we evaluated MSD417 as a representative genotype against its recurrent parent, Norin 61 (N61). Under control conditions, MSD417 displayed greater total root length, root system width, and convex hull area than N61 (p < 0.001), indicating enhanced early root vigor. MSD417 also exhibited larger second pair seminal root angle (p < 0.001) and length (p < 0.01) across both conditions, suggesting enhanced horizontal root exploration while maintaining similar rooting depth to N61 (p = 0.981). Heat stress reduced overall root growth and narrowed genotypic differences, limiting RSA expression. Microscopic observations revealed a lower coleorhiza height-to-width ratio in MSD417. These findings demonstrate the effectiveness of the two-dimensional platform for early-stage RSA phenotyping and highlight Aegilops tauschii-derived germplasm as a source of favorable root traits in wheat breeding.
Why it matches plant phenotyping methods二次元画像による根系構造フェノタイピング基盤を構築し、連続撮像で根形質を追跡する方法が研究の中心であるため含める。
abstractHere, we established a practical two-dimensional root phenotyping framework that enables continuous imaging to track RSA traits and their responses to heat stress.
Reproduction assets foundThe paper's data availability statement deposits the paper-specific phenotyping inputs publicly on Zenodo: root images of wheat N61 and MSD417 (the two genotypes measured for RSA traits) and microscopic coleorhiza images. These are public, paper-specific image datasets directly underlying the study's measurements. No作者Dataset · publical development in arid regions.
ORCID
Sultan Md Monwarul Islam http://orcid.org/0009-0002-7219-2104
Izzat Sidahmed Ali Tahir http://orcid.org/0000-0002-1711-6961
Kinya Akashi http://orcid.org/0000-0002-9991-5766
Data availability statement
The root images of wheat N61 and MSD417 are deposited in the Zenodo data repository under https://doi.org/10.5281/zenodo.18080159 and https://doi.org/10.5281/zenodo.18079748, respectively. The microscopic images of
coleorhiza are deposited under https://doi.org/10.5281/zenodo.18091131. The other original contributions presented
in the study are included in the article and/or supplementary material.
References
Alahmad, S., El Hassouni, K., Bassi, F. M., DiOpen asset ↗Zenodo · 10.5281/zenodo.18080159pdf-raw-page:14 lines:1-49Dataset · publicMd Monwarul Islam http://orcid.org/0009-0002-7219-2104
Izzat Sidahmed Ali Tahir http://orcid.org/0000-0002-1711-6961
Kinya Akashi http://orcid.org/0000-0002-9991-5766
Data availability statement
The root images of wheat N61 and MSD417 are deposited in the Zenodo data repository under https://doi.org/10.5281/zenodo.18080159 and https://doi.org/10.5281/zenodo.18079748, respectively. The microscopic images of
coleorhiza are deposited under https://doi.org/10.5281/zenodo.18091131. The other original contributions presented
in the study are included in the article and/or supplementary material.
References
Alahmad, S., El Hassouni, K., Bassi, F. M., Dinglasan, E., Youssef, C., Quarry, G., Aksoy,Open asset ↗Zenodo · 10.5281/zenodo.18079748pdf-raw-page:14 lines:1-49Dataset · public-6961
Kinya Akashi http://orcid.org/0000-0002-9991-5766
Data availability statement
The root images of wheat N61 and MSD417 are deposited in the Zenodo data repository under https://doi.org/10.5281/zenodo.18080159 and https://doi.org/10.5281/zenodo.18079748, respectively. The microscopic images of
coleorhiza are deposited under https://doi.org/10.5281/zenodo.18091131. The other original contributions presented
in the study are included in the article and/or supplementary material.
References
Alahmad, S., El Hassouni, K., Bassi, F. M., Dinglasan, E., Youssef, C., Quarry, G., Aksoy, A., Mazzucotelli, E., Juhász, A.,
Able, J. A., Christopher, J., Voss-Fels, K. P., & Hickey, L. T. (2019). A majOpen asset ↗Zenodo · 10.5281/zenodo.18091131pdf-raw-page:14 lines:1-49Plant phenotyping relevance match · UnverifiedEurope PMC · checked 14 Sept 2026
Understanding root system architecture (RSA) is critical for improving crop productivity and resilience, yet phenotyping root traits such as root growth angle and rooting depth remains technically challenging, especially at high throughput. Here, we present ClearDepthIAS, a high-throughput imaging and analysis platform that enables non-destructive, automated quantification of root architecture traits in taproot system crops. By capturing and stitching 360° images of roots growing along the transparent walls of pots and applying deep learning-based segmentation (ClearDepth-WRT), we measured wall root shallowness (WRS)-a proxy for root growth angle-with high precision. We demonstrated for the tap root systems of soybean and canola that the system accurately detects root tips, quantifies their vertical distribution, and extracts biologically meaningful traits such as root area, distribution indices, and growth angles. Validation experiments in canola and soybean demonstrated that WRS can correlate with root crown architecture in mature plants, both in greenhouse and field settings. Furthermore, WRS and root distribution indices derived from ClearDepthIAS are predictors of early root architecture and can be correlated with root biomass distribution across soil depths under field conditions; however, environmental interactions may influence these relationships and weaken or even negate such correlations, as observed when comparing field to field variation in root system architecture. Our system enables efficient phenotyping of genetically diverse populations, with medium to high trait heritability, supporting its utility for genome-wide association studies and breeding. ClearDepthIAS accelerates the development of root ideotypes for improved resource acquisition and carbon sequestration, offering a scalable tool for supporting climate-resilient agriculture.
Why it matches plant phenotyping methods植物根系形態を自動取得・定量化する画像解析プラットフォームを開発し、精度と圃場での妥当性を検証しており、フェノタイピング手法が研究の中心である。
abstractwe present ClearDepthIAS, a high-throughput imaging and analysis platform that enables non-destructive, automated quantification of root architecture traits
Introduction eaf area index (LAI) and leaf nitrogen accumulation (LNA) are key indicators of wheat growth and nitrogen nutritional status. However, existing prediction methods predominantly rely on single-modal information and single-output models, limiting their ability to characterize the complex structural and physiological traits of crops. This study aimed to develop a multimodal learning framework for the simultaneous and accurate prediction of wheat LAI and LNA. Methods Spectral, image, and canopy structural features were extracted from wheat canopies across different cultivars, nitrogen treatments, and growth stages. A canopy height correction-based preprocessing method was developed to improve the extraction of structural features. A Dual-Output Bayesian Neural Network (DO-BNN) was then constructed to simultaneously predict LAI and LNA. In addition, an Extreme Sample Mining (ESM) strategy and a joint loss function were introduced to strengthen the learning of complementary information across modalities and the intrinsic correlation between the two target variables. Results The DO-BNN achieved its best predictive performance when all feature modalities were fused. The coefficients of determination (R²) for LAI and LNA were 0.89 and 0.77, respectively, while the corresponding relative root mean square errors (RRMSEs) were 0.15 and 0.35. Compared with single-modal and conventional single-output approaches, the proposed method provided more accurate and robust predictions of both wheat growth parameters. Discussion The results demonstrate that integrating spectral, image, and structural information can improve the characterization of wheat canopy traits. By jointly modeling LAI and LNA, the DO-BNN effectively exploited the physiological relationship between crop growth and nitrogen accumulation. The proposed framework provides a promising approach for the high-accuracy, collaborative monitoring of wheat growth and nitrogen nutritional status.
Why it matches plant phenotyping methods小麦キャノピーのスペクトル・画像・構造情報からLAIと葉窒素蓄積を推定するマルチモーダル手法を開発し、前処理、ニューラルネットワーク、性能比較まで中心的に扱っているため。
abstractThis study aimed to develop a multimodal learning framework for the simultaneous and accurate prediction of wheat LAI and LNA.
The architecture of the root system is a primary factor in determining rootstock performance, affecting water and nutrient uptake, biomass accumulation, and overall vigor. However, direct root phenotyping is destructive, labor-intensive, and difficult to do routinely in breeding programs. The present study investigated early root morphological variation among developed interspecific tomato rootstock candidates (Solanum lycopersicum x S. habrochaites). The ability of linear regression and machine learning models to predict root traits from easily measured plant growth parameters was assessed. Nineteen interspecific hybrid rootstock candidates, two commercial rootstocks, and one scion were grown under optimal greenhouse conditions and evaluated at 0, 10, 20, and 30 days after planting. Root length, root surface area, root diameter, and root volume were determined by digital image analysis. In contrast, genotype, plant length, and stem diameter were used as input variables. Significant genotype x sampling date effects were observed for most morphological and biomass traits, indicating dynamic changes in root and shoot development during the first 30 days of growth. The rootstock candidates RSH-17 and RSH-6 generally showed relatively higher root length, surface area, root volume, and biomass accumulation than the commercial rootstocks and scion. XGBoost and OLR were the best predictive models, with R 2 values as high as 0.95 for root length, surface area, and volume. Root diameter was predicted less accurately than root length, surface area, and volume, suggesting that it might be a more independent or less variable root trait during early development. Overall, results suggest that vigor-related traits can serve as useful proxies for estimating major root architectural traits in early-stage tomato rootstock selection. Both XGBoost and OLR performed well, suggesting that root and shoot development were highly coordinated under optimal (non-stress) conditions. Hence, predictive modeling may help prioritize promising rootstock candidates before destructive root analysis. However, more validation under stress conditions and for longer periods of development is needed to determine the greater applicability of these models.
Why it matches plant phenotyping methods根系形態形質をデジタル画像解析で取得し、線形回帰・機械学習による非破壊予測モデルを評価・比較しており、植物フェノタイピング手法が研究の中心である。
abstractThe ability of linear regression and machine learning models to predict root traits from easily measured plant growth parameters was assessed.
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:108-129Plant phenotyping relevance match · UnverifiedEurope PMC · checked 15 Sept 2026
Strawberry greenhouse cultivation is increasingly supported by sensing technologies, artificial intelligence (AI), and decision-support infrastructure, but their horticultural value depends on whether heterogeneous measurements can be translated into biologically meaningful crop states and practical management decisions. This review synthesizes strawberry phenotyping, multimodal sensing, AI-based crop-state interpretation, and supervised agentic coordination as a phenotyping-to-action framework for greenhouse strawberry cultivation. The reviewed studies show substantial progress in measuring and interpreting vegetative, reproductive, fruit-quality, stress-related, and environmental crop states through imaging, spectral, environmental, root-zone, and modeling approaches. However, much of the literature still emphasizes measurement accuracy, model performance, or infrastructure capability, whereas fewer studies validate whether AI-derived outputs improve crop response, management decisions, workflow, resource use, or production outcomes. The review therefore distinguishes sensing technologies for data acquisition and measurement from AI-based methods for interpretation and prediction, and examines how crop-state information can be connected to practical greenhouse decision making. It also compares established decision technologies, including expert systems, model predictive control, digital twins, and closed-loop coordination, with supervised agentic coordination as bounded decision-support concepts rather than as evidence of unrestricted autonomous control. Future work should emphasize phenotype-to-action validation, domain-aware benchmarking, and supervised deployment studies that connect model outputs with decision rules, crop outcomes, operational constraints, and grower oversight. By grounding sensing technologies and AI-based interpretation methods in crop-response validation, strawberry greenhouse systems can progress toward supervised, crop-state-driven decision support.
Why it matches plant phenotyping methods温室イチゴのフェノタイピング、マルチモーダルセンシング、AIによる作物状態解釈を中心に整理する方法論レビューであり、植物状態の取得・推定手法が主題。
abstractThis review synthesizes strawberry phenotyping, multimodal sensing, AI-based crop-state interpretation, and supervised agentic coordination as a phenotyping-to-action framework for greenhouse strawberry cultivation.
Global food security requires crop improvement strategies that can respond to population growth, climate variability and increasing constraints on agricultural resources. Conventional plant breeding has contributed substantially to crop productivity, yet long selection cycles and dependence on extensive field evaluation can limit the rate of genetic gain. This review synthesises advances in genomics, phenomics and machine learning for next-generation crop breeding, with emphasis on their combined contribution to selection accuracy and breeding efficiency. Key genomic approaches discussed include whole-genome sequencing, reference and pan-genome resources, genome-wide association studies, genomic selection and CRISPR-Cas-based genome editing. The review also examines high-throughput phenotyping platforms, including controlled-environment systems, ground-based robots, UAV-based remote sensing and root phenotyping tools. Machine learning approaches, ranging from random forest and support vector machines to convolutional neural networks, recurrent networks, transformers and explainable artificial intelligence, are considered in relation to genomic prediction, image analysis and breeding decision support. Multi-omics integration, data management, FAIR principles and an integrated genomics-phenomics-ML breeding pipeline are reviewed as enabling components for practical deployment. Crop-specific examples from wheat, rice, maize, soybean and legumes illustrate the potential and constraints of these technologies. The review further identifies key challenges, including phenotyping bottlenecks, genotype-environment interaction, data governance, model interpretability and regulatory uncertainty.
Why it matches plant phenotyping methods植物フェノタイピング手法を中心に、ハイスループット計測プラットフォーム、画像解析、機械学習、UAV・ロボット・根系計測などをレビューしているため。
abstractThis review synthesises advances in genomics, phenomics and machine learning for next-generation crop breeding
Field / plotLeafRootWhole plant / canopy / plot / fieldAnnotation / quality control
Abstract Background Trait-based ecology has become central for understanding plant form, function and ecosystem processes, but progress has been hampered by biased representation in trait databases. As such, global trait syntheses remain strongly biased towards temperate forest biomes. Tropical savannas are the most extensive, biodiverse and disturbance-driven ecosystems worldwide, yet are poorly represented in functional trait databases, limiting ecological inference and applied decision-making. Scope Here, we introduce the Cerrado Plant Traits (CPT), an open-access initiative compiling and standardising plant functional trait data for the Brazilian Cerrado, the world’s most biodiverse tropical savanna. CPT integrates trait information for all major plant organs (whole-plant, root, shoot, leaf, flower, fruit and seed) across vegetation types in the Cerrado, drawing on a collaborative and inclusive research network. The current version of CPT compiles data from 148 datasets, totalling 113,859 curated trait records for 2,134 taxonomically verified species across 150 families. Trait records span pristine, degraded and restored environments and capture both interspecific and intraspecific variation. Whole-plant and leaf traits dominate the current dataset, while belowground and reproductive traits remain comparatively underrepresented, highlighting key priorities for future research. Conclusions By substantially increasing the representation of savanna species in global trait repositories, CPT enables tests of ecological hypotheses across multiple levels of organization, analyses of trait–environment relationships across fire, soil and climatic gradients, and robust comparisons across forest–savanna transitions. Beyond its scientific value, CPT provides a practical, standardised resource to support conservation planning, restoration programs and evidence-based policy in a biodiversity hotspot facing accelerating land-use and climate pressures.
Why it matches plant phenotyping methods植物の機能形質を標準化して統合した大規模データセットであり、再利用可能な形質リソースの構築が中心です。
abstractHere, we introduce the Cerrado Plant Traits (CPT), an open-access initiative compiling and standardising plant functional trait data for the Brazilian Cerrado
The development of Low-Cost Phenotyping Platforms Supported by Generative Artificial Intelligence (AI) is part of a recently launched research initiative at Embrapa Vegetables in Brasília, Federal District, aimed at creating a National Platform for Adaptation to Climate Change Applied to Family Farming (Clima AF). Through Prompt Engineering and Command Chaining, this stage was designed for the visual assessment of physiological disorders in sweet potato (Ipomoea batatas) tuberous roots in the context of the Climate Emergency. The pipeline consists of four stages: 1 - Definition of an expert persona; 2 - Phenological contextualization and critical root filling period; 3 - Visual anatomical phenotyping; and 4 - Synthesis of the physiological disorders found, with a focus on heat stress. The methodology is available as open access following FAIR principles. The analysis is conducted using minimal information, such as photos that can be taken with everyday devices like smartphones and information about the harvest season. Because it is available as open access, it democratizes information and contributes to achieving climate justice for a socioeconomically vulnerable audience (family farmers).
Why it matches plant phenotyping methods生成AIとプロンプト連鎖を用い、スマートフォン画像からサツマイモ塊根の生理障害を視覚的に評価する低コスト表現型解析プラットフォームの開発であり、植物状態の取得・抽出法が中心である。
abstractThe development of Low-Cost Phenotyping Platforms Supported by Generative Artificial Intelligence (AI)
Doubled Haploid (DH) technology significantly accelerates the development of homozygous lines in maize breeding; however, its scalability is constrained by the reliable discrimination of haploid and diploid individuals. The widely used R1-nj anthocyanin marker at the seed stage is susceptible to genetic suppression and environmental variability, leading to high misclassification rates. This limitation has driven a shift toward seedling root morphology as a more robust phenotypic marker, yet it introduces major challenges, including complex image noise and severe class imbalance. In this study, we systematically evaluate the limitations of standard computer vision pipelines and baseline deep learning models for root-based classification. Automated background removal methods (HSV, Rembg) are shown to misinterpret fine root hairs as noise, resulting in significant morphological data loss. Additionally, experiments conducted under a realistic class imbalance (1:5.4) demonstrate that widely used CNN architectures (ResNet50, VGG16, EfficientNetB0, DenseNet121) exhibit strong majority class bias, with haploid recall dropping to 0.00% and 27.7%. These findings reveal a critical limitation in existing approaches and highlight the need for domain-informed datasets and imbalance-aware learning strategies for robust and scalable AI-based maize breeding systems.
Why it matches plant phenotyping methodsトウモロコシの根形態画像を用いた倍加半数体・二倍体分類について、画像前処理と深層学習モデルの限界を体系的に評価しており、表現型取得・抽出手法が中心である。
abstractIn this study, we systematically evaluate the limitations of standard computer vision pipelines and baseline deep learning models for root-based classification.
We evaluated a multi-output neural network framework for jointly analyzing maize grain yield (GY) and root lodging percentage (LP) using above-ground morphological traits measured under defined environmental conditions. To address model robustness, the multi-output neural network was compared with linear regression, elastic net, random forest, and XGBoost using repeated five-fold cross-validation, an 80/20 holdout split, and independent year-wise validation. Under repeated cross-validation, XGBoost provided the strongest average predictive performance for both traits, with R2 values of 0.57 for GY and 0.67 for LP. The multi-output neural network showed moderate performance, with R2 values of 0.49 for GY and 0.57 for LP. Final holdout performance for the neural network for GY and LP was R2 = 0.64 and R2 = 0.92, respectively. Year-wise validation showed weak temporal transferability because the two seasons differed not only in environmental conditions, but also in lodging mechanism. Repeated permutation importance identified ear width (EW), kernel row number (RNE), thousand kernel mass (KM1000), and kernel number per ear (KNE) as important predictors of GY, while LP prediction was most strongly associated with internode major diameter (IDmajor), ear length (EL), and the number of green leaves (NGL). Across both permutation importance and SHAP, only RNE and NGL were consistently shared between GY and LP. Supplementary ALE diagnostics indicated that RNE showed increasing model-estimated effects for both predicted GY and LP, whereas NGL showed a positive association with predicted GY but a decreasing or nonlinear association with predicted LP. These results show that joint modeling can support exploratory trait interpretation, but the predictive relationships remain environment-specific and should not be interpreted as causal or broadly transferable without further multi-environment validation.
Why it matches plant phenotyping methods穀粒収量と倒伏率という植物形質を推定する多出力ニューラルネットワーク等のモデルを開発・比較し、交差検証、ホールドアウト、年次外部検証で性能評価しており、計算的形質推定が中心である。
abstractWe evaluated a multi-output neural network framework for jointly analyzing maize grain yield (GY) and root lodging percentage (LP) using above-ground morphological traits measured under defined environmental conditions.
MaizeRoot2D/3D reconstructionGrowth / development / phenologyRoot system architecture
Maize (Zea mays L.) is a major cereal crop whose productivity across diverse agro-ecological environments is strongly influenced by belowground traits. The root system functions as the primary plant-soil interface, regulating water and nutrient uptake, providing mechanical support and enabling adaptive responses to abiotic and biotic stresses. Despite its central importance, maize root biology has historically received less attention than aboveground characteristics. The maize root system comprises primary, seminal, nodal and lateral roots differing in developmental origin, growth behaviour and physiological role. Key architectural traits-such as rooting depth, root growth angle and branching density-play a crucial role in root system development. These traits, along with anatomical features like cortical aerenchyma and root hair development, are governed by complex genetic networks involving regulatory genes, quantitative trait loci and hormone-mediated signalling pathways. Root growth and spatial distribution are further shaped by soil properties and agronomic practices, including irrigation and nutrient management. Recent advances in high-throughput phenotyping, three-dimensional (3D) reconstruction and artificial intelligence (AI) based image analysis technologies have enhanced quantitative assessment of root traits. This review consolidates recent progress in maize root research with emphasis on root system architecture (RSA), developmental regulation and their functional relevance to crop productivity. Uniquely, it integrates structural, genetic and phenotyping advances in maize root research into a unified framework, while explicitly linking RSA with its functional significance, an aspect often treated separately in earlier reviews. Optimising maize root systems is therefore essential for improving productivity, resource-use efficiency and agricultural sustainability under changing climatic conditions.
Why it matches plant phenotyping methodsトウモロコシ根系研究のレビューであり、根系形態形質の定量評価に用いるハイスループット表現型解析、3D再構成、AI画像解析を明示的に扱うため、表現型解析手法レビューとして中心的です。
abstractRecent advances in high-throughput phenotyping, three-dimensional (3D) reconstruction and artificial intelligence (AI) based image analysis technologies have enhanced quantitative assessment of root traits.
Plant phenotyping relevance match · UnverifiedCrossref · Europe PMC · checked 13 Sept 2026
Modern agriculture operates at an unprecedented crossroads, it must simultaneously accelerate crop yields to feed an expanding global population and adapt to the severe, fluctuating pressures of climate change, structural soil degradation, abiotic water deficits, and evolving biological threats. Historically, selecting resilient crop varieties and implementing field-scale management strategies relied extensively on destructive, labor-intensive, and fundamentally subjective visual metrics. This manual processing approach has long been recognized as the primary operational bottleneck in agricultural advancement.To bridge the gap between rapidly expanding genomic data and actual field performance, the systematic, non-destructive quantification of structural and functional plant traits, plant phenotyping, has emerged as a transformative frontier. By integrating high-throughput engineering, multi-scale remote sensing, deep learning, and advanced molecular biology, modern phenotyping transitions crop science away from qualitative estimation toward highly reproducible, multidimensional data frameworks. This Research Topic presents new advances in advanced 3D reconstruction and deep semantic segmentation at the seedling stage; amodal fruit segmentation, morphological extraction, and early water-stress diagnostics; high-throughput in-field seedling counting and dynamic density modeling; multimodal foundation models, network pruning, and intelligent phytoprotection; aerial and spaceborne remote sensing for canopy analysis and weed monitoring; plant physiology, functional spectroscopy, and functional genomics under abiotic stress; and automated diagnostics for real-time orchard scouting and vineyard management.Automating the characterization of complex spatial layouts under controlled or greenhouse environments is essential for early variety selection and early-stage structural evaluation. Several contributions within this volume provide key breakthroughs in navigating overlapping tissues, severe occlusions, and low-contrast edge regions. showcases how substituting standard convolutions with deformable convolutions enables deep neural networks to accurately isolate the main stem of mature, high-density crops like soybeans. This architecture overcomes the traditional challenges of color mimicry and severe occlusion by pods and leaves, achieving an outstanding mIoU of 90.58% and providing reliable indices for lodging resistance and structural yield modeling (R 2 = 0.9746).Accurately extracting fruit morphology under commercial greenhouse conditions remains heavily constrained by overlapping crop structures, foliage cover, and variable shadows. Simple semantic masks typically fail when a target fruit is partially blocked, leading to a loss of key volumetric data.To resolve the challenge of hidden boundaries, Li, Yin, et al. (2025) developed CGA-ASNet, a specialized RGB-D amodal segmentation network driven by a Contextual and Global Attention (CGA) module designed to restore occluded tomato regions. Trained on a high-fidelity synthetic greenhouse dataset (Tomato-sim) generated via NVIDIA Isaac Sim's Replicator Composer and optimized with a mean coordinate fusion algorithm for real-world validation, this architecture expands the network's receptive field to predict the complete, hidden circular forms of occluded tomatoes, achieving an F@0.75 score of 94.2 and an amodal mIoU of 82.4%. This proves that simulation-to-real (Sim2Real) domain pathways can successfully decode full physical volumes under dense commercial canopies.Complementing this structural restoration, Yang, Li, et al. (2025) designed an integrated diagnostic framework to identify early water stress dynamics in greenhouse tomatoes. Built upon an optimized YOLOv11n core, their system integrates adaptive kernel convolutions (AKConv) into the network backbone's C3k2 modules and implements a recalibration feature pyramid detection head based on the specialized P2 small-target layer. This combination achieved a 5.4% increase in mAP50-95 for identifying fine phenotypic parts. By applying automated geometric analysis to the extracted bounding boxes, the system extracts plant heights and petiole count with low relative errors, feeding these phenotypic parameters into a Random Forest classification routine that flags water-stressed plants with 98% accuracy to guide targeted, automated drip irrigation.Accurate plant stands during early vegetative stages represent the foundational metric required to establish true field emergence rates, validate seed vigor across diverse breeding blocks, and perform early yield predictions.To solve the challenges of small targets, extreme spatial density, and adjacent leaf overlap, Zang et al. (2025) designed DM_IOC_fpn, a wheat seedling counting framework that balances local and global contextual features. By structuring a point-annotated dataset and embedding a densityenhanced encoder module, their network balances micro-scale spatial limits with macro-scale canopy structures. Optimized through a combined loss function tracking counting, classification, and regression parameters, this architecture achieved low error scores (RMSE = 2.91; MAE = 2.23), outperforming standard object-detection benchmarks in complex field environments.At the same time, scaling up to real-time aerial monitoring required major reductions in model complexity to support resource-constrained edge computers on autonomous aerial platforms. Feng, Nie, and Li (2025) engineered an ultra-lightweight YOLOv8n variant tailored for real-time maize seedling counting from high-speed UAV RGB overflights. By reparametrizing RepConv with HGNetV2, they constructed a lean Rep_HGNetV2 backbone, integrated a Bidirectional Feature Pyramid Network (BiFPN) for multi-scale feature alignment, and implemented a Task Dynamically Aligned Detection Head (TDADH). This architecture compressed total model parameters by 47% and reduced weight sizes to 3.5 MB while maintaining a 96.5% detection accuracy and an ultra-fast processing speed of 146.3 FPS, paving the way for low-cost, real-time field scouting.Automated phytoprotection requires machine-vision architectures capable of generalizing across highly diverse species, complex field conditions, and varying computational boundaries. A significant subset of the published papers addresses these challenges through foundation model adaptation, multi-modal alignment, and efficient network compression.A major paradigm shift presented in this collection involves moving away from task-specific training and toward foundation model adaptation. Chen, Ruan, et al. (2026) introduce a novel architecture integrating the DinoV3 foundation model with a Unet framework to achieve robust leaf lesion segmentation across diverse species (such as coffee and black gram). By incorporating a Spatial Prior Module (SPM), their approach surpassed standard benchmark networks by over 10.5% in IoU while reducing inference times by approximately 93.6%, demonstrating that highparameter foundation models can be highly optimized for resource-constrained edge devices in real-time scouting.To solve the perennial problem of limited training data for rare or emerging crop diseases, Cooper et al. ( 2026) developed an ingenious synthetic data generation pipeline. Combining 3D procedural leaf modeling in Blender with diffusion-based disease synthesis (Stable Diffusion fine-tuned with LoRA and ControlNet), they synthesized highly accurate plant disease images with perfect groundtruth annotation masks. When deployed in low-resource data settings, combining these synthetic pipelines with restricted real-world datasets consistently drives significant improvements in downstream segmentation tasks. To tackle specific, complex pathologies, Xu, Chang, et al. (2025) developed the TSSC deep learning model, which embeds three-neighbor channel attention paired with a complementary squeeze-and-excitation mechanism. This specific architecture minimizes structural degradation risks while pushing classification accuracy to 99.61% for highly complex pea leaf pathologies. Similarly, Feng, Liu, et al. (2025) tackled overlapping leaf occlusions and small lesion footprints in citrus groves with YOLO-Citrus, an optimized framework integrating C3K2-STA, ADown modules, and a Wise-Inner-MPDIoU loss function to strike a balance between edge computational constraints and field deployment.UAVs and high-resolution satellite imagery have expanded the operational scale of phenotyping from individual pots to vast breeding blocks and commercial fields, allowing researchers to capture macro-dynamic parameters over time.In complex canopy systems that defy standard top-down aerial sensing, such as single-staked white Guinea yams, Iseki et al. (2026) demonstrated the distinct advantage of utilizing multi-angle (combined nadir and oblique) UAV imaging configurations. When coupled with support vector regression, this method captures complementary canopy-structure information to model shoot biomass trajectories (R 2 = 0.79) across multiple years and management zones. These nondestructive, time-series datasets enabled the fitting of genotype-specific Richard's growth curves using Bayesian inference, isolating valuable genetic variations in early growth allocation.To capture full-season vertical physiological changes over large scales, Li, Yue, and Luo (2025) developed a hybrid CNN-LSTM-Attention (CLA) model designed to estimate the full-period Leaf Area Index (LAI) in rice using multi-temporal UAV multispectral imagery. By using the CNN layer to extract instantaneous spatial features, the LSTM block to process seasonal time-series intervals, and a self-attention mechanism to weight critical growth transitions, their platform achieved a high coefficient of determination (R 2 = 0.92) and kept relative root mean square errors (RRMSE) below 9%. This network minimized soil background noise during early vegetative stages (LAI values 1-
Why it matches plant phenotyping methods植物フェノタイピングの技術動向を扱うEditorialであり、画像解析、UAVセンシング、深層学習、形質抽出などの方法が中心的に整理されている。
titleEditorial: Plant phenotyping for agriculture
Plant phenotyping relevance match · UnverifiedOpenAlex · Crossref · Europe PMC · checked 5 Sept 2026
MaizeGrowth chamberRootMorphology / geometry measurementSegmentationSkeletonization / topologyRoot system architecture
monitoring capabilities, and existing models have limited accuracy in root segmentation. To address these issues, we developed a crop root phenotyping system integrating crop cultivation and data collection. We also proposed a DB-UNet model for hydroponic maize root segmentation. DB-UNet builds a CNN-ViT dual-branch parallel structure during encoder downsampling level. The lightweight ViT branch uses sequential downsampling to achieve global topological dependency modeling while reducing computational costs. An attention fusion module dynamically calibrate dual-branch features weights, achieving complementary fusion of local root edge details and global context information. we constructed a mixed loss function combining Dice loss, Focal loss, and structural consistency KL loss to solve class imbalance, hard sample segmentation, and semantic divergence of dual-branch features. On our custom hydroponic maize root dataset, DB-UNet achieved an mIoU of 91.02%, an FG IoU of 82.78%, and a Centerline-Dice of 97.72%.Compared to classic UNet, mIoU, FG IoU, and Centerline-Dice increased by 0.92%, 1.84%, and 1.99%, respectively. Plant-level five-fold cross-validation further showed that DB-UNet maintained stable segmentation performance across different plant-level partitions. Based on DB-UNet segmentation results, we propose a custom skeleton-based algorithm for multi-trait root phenotyping, enabling the extraction of total root length and root branch points. Root area is calculated from binary mask pixel statistics. Compared to the traditional Zhang-Suen algorithm, the average relative error of root length measurement is reduced to 3.14%, which is 8.42 percentage points lower than the traditional method. Furthermore, we analyzed relationships between segmentation accuracy metrics and phenotypic relative errors. Higher segmentation quality generally led to lower phenotypic relative errors and more reliable trait measurements. In particular, Centerline-Dice was closely associated with root length estimation, whereas pixel-level segmentation consistency was more closely related to root area measurement. Pearson and Spearman correlation analyses showed a strong positive correlation between maize plant height and total root length, with coefficients of 0.8466 and 0.8634, respectively.
Why it matches plant phenotyping methods画像ベースの根セグメンテーションと骨格解析を開発・検証し、根長や分枝点などの形質を抽出するシステムが研究の中心であるため。
abstractwe developed a crop root phenotyping system integrating crop cultivation and data collection.
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.
TomatoRootMorphology / geometry measurementSegmentationRoot system architectureStress response / tolerance
Automated phenotyping of crops is a vital component of precision agriculture. As a representative economic crop, the radicle length of tomato seeds is a key phenotypic indicator for assessing seed vigor and seedling health. To reduce labor costs and improve measurement efficiency, we developed an integrated seed germination phenotype acquisition system that combines cultivation and imaging, enabling continuous image acquisition throughout the tomato seed germination process. To achieve automatic measurement of radicle length, we propose a deep learning-based segmentation framework, termed RootNet. The framework incorporates a custom-designed module, MambaNextBlock (MNB), within skip connections to enhance long-range feature modelling, and integrates an Atrous Spatial Pyramid Pooling (ASPP) module at the bottleneck to capture multi-scale contextual information. Post-segmentation, Canny edge detection is employed to extract radicle contours, from which actual lengths are computed based on contour arc length. Experimental results show that RootNet achieved 77.75% Intersection over Union (IOU), 86.03% Precision, 88.72% Recall, and 87.46% F1-Score on the root class. Compared with manual measurements conducted using ImageJ, our method showed high agreement across 1170 radicle measurements, with an R² of 0.9785, an MAE of 0.274 mm, an RMSE of 0.344 mm, and a Bias of −0.008 mm. Bland–Altman analysis further confirmed the absence of systematic bias, with 95% limits of agreement ranging from −0.682 mm to +0.666 mm. Meanwhile, measurement efficiency was improved by approximately 680-fold. Furthermore, the method was applied to evaluate the effects of drought, salinity stress, and different concentrations of Streptomyces albidoflavus (HL4) and Streptomyces virginiae (GZ2) on radicle growth. The results indicated that drought stress, salinity stress, and undiluted HL4 inhibited radicle elongation, whereas diluted HL4, as well as both undiluted and diluted GZ2, significantly promoted radicle growth. This study provides an efficient and cost-effective solution for non-destructive crop phenotyping and intelligent agricultural management in precision farming.
Why it matches plant phenotyping methodsトマト幼根長を画像から自動抽出・測定する深層学習フレームワークを開発し、手動測定との定量的検証も行っており、植物フェノタイピング手法が研究の中心である。
abstractwe developed an integrated seed germination phenotype acquisition system that combines cultivation and imaging, enabling continuous image acquisition throughout the tomato seed germination process.
Abstract This study investigates the accuracy of Multiple Linear Regression (MLR) and Artificial Neural Networks (ANN), specifically a hybrid Genetic Algorithm-ANN (GA-ANN), for predicting wheat yield in plant breeding. This research, conducted using 782 wheat genotypes in Rafsanjan, Iran, compares MLR and ANN methodologies. MLR, using seven traits selected via stepwise regression, achieved an R² of 0.90, the root of mean square of error (RMSE) of 14, Average absolute percentage error (MAPE) of 13.3, and Average deviation of prediction from the actual value (MAE) of 10. Key traits identified were biological weight (weight of total plant per line, WPP) and harvest index (HI). Conversely, the GA-ANN model, employing six selected traits, demonstrated superior performance with R² values of 0.94, 0.96, and 0.94 for training, testing, and combined datasets respectively. Validation metrics for the ANN model were MSE of 144.3, RMSE of 12, and MAE of 5.7. GA-ANN selected height, peduncle length, days to flowering, spike length, biological weight, and harvest index as significant predictors. The results underscore that ANN models, particularly when combined with genetic algorithms for feature selection and optimization, can improve prediction accuracy by modeling complex, non-linear relationships in agricultural data, therefore providing more precise yield prediction tools for plant breeders. This study emphasizes the need for advanced predictive techniques in achieving more accurate assessments of crop yield for sustainable agriculture.
Why it matches plant phenotyping methods小麦遺伝子型レベルの収量という植物形質を対象に、MLRとGA-ANNの予測精度を比較・検証しており、計算による形質推定手法が研究の中心です。
abstractThis study investigates the accuracy of Multiple Linear Regression (MLR) and Artificial Neural Networks (ANN), specifically a hybrid Genetic Algorithm-ANN (GA-ANN), for predicting wheat yield in plant breeding.
Clubroot disease, caused by Plasmodiophora brassicae , is one of the major constraints in rapeseed production. Breeding disease-resistant cultivars is the best way to control this devastating disease. However, breeding reliable resistant germplasm and genes is limited. Inactivation of susceptible genes has been shown to be a new and effective strategy for developing resistant crops. Therefore, we aimed to screen key candidate susceptible genes in this study. Firstly, we established a stable, high-throughput visualization method for identifying gall formation at the early stage of P.brassicae infection. At 14 days post-inoculation (dpi), the earliest time point with a clear record of scorable root swelling, remarkable variations in the speed of gall formation were observed among 85 genotypes. Secondly, genome-wide association studies (GWAS) were performed to identify genes involved in gall development. Three and two consecutive significant peaks were detected at 14 and 21 dpi, respectively. Thirdly, comparative transcriptomic analysis was conducted between 2AF195 and 2AF058 at 7 and 14 dpi; these two materials exhibit contrasting speeds of gall development. Gene clustering analysis revealed two opposite expression patterns at 14 dpi. One pattern comprised 1,383 genes downregulated in 2AF195 but upregulated in 2AF058, which were significantly enriched in 10 KEGG pathways, including Environmental Information Processing and Plant-pathogen interaction, and involved core repressors JAZ8/10 in the jasmonic acid (JA) signaling pathway, as well as nucleotide-binding site (NBS) protein-encoding genes. The opposite pattern consisted of 79 genes upregulated in 2AF195 but downregulated in 2AF058, which were enriched in an additional 10 KEGG pathways, predominantly related to Carbohydrate Metabolism and the Ubiquitin System. These genes were functionally annotated mainly as pectin methylesterases, xyloglucan endotransglucosylase/hydrolases (XTHs), and lignin biosynthesis-related enzymes. These findings demonstrated that distinct regulatory networks exist in different susceptible rapeseed genotypes. Finally, through the combined analysis of haplotype and transcriptome data, we co-localized and identified the candidate gene BnaC08g46100D , a nodulin-related gene belonging to the MtN21 transporter family. These results provide a theoretical basis for developing novel disease-resistant materials by editing the key susceptibility genes involved in root gall formation. The candidate genes identified in this study are the most promising targets for this purpose.
Why it matches plant phenotyping methods根こぶ形成を高スループットに可視化・判定する方法の確立が明示され、感染植物の病徴を測定する手法として研究の主要な技術要素になっている。
abstractwe established a stable, high-throughput visualization method for identifying gall formation at the early stage of P.brassicae infection.
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 2
Disease incidence data of 85 rapeseed accessions at various time points following inoculation with the Xinmin strain.Open asset ↗lines:502-594Plant 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
The nucleus is the characteristic organelle of eukaryotic organisms. Unlike the classic textbook view of static nuclei, nuclear shape is dynamic in live cells. Altered or deformed nuclear shape is a hallmark of cancer in animal cells and environmental stress in plants. Nuclear envelope proteins interact with chromatin to regulate gene expression. Unfortunately, little is known about the impact of abiotic stress on nuclear shape, movement, and chromatin dynamics. To confront this issue, we developed a pipeline using confocal microscopy and particle tracking software to quantify nuclear and chromatin dynamics in Arabidopsis roots under control and abiotic stress condition. This confocal imaging method utilizes a dual fluorescently tagged marker line - nuclear envelope protein and chromatin - to perform live cell imaging of the root in model plant Arabidopsis thaliana under control and salt-stressed conditions. These captured movies are analyzed to quantify nuclear and chromatin dynamics using open-source image processing software Fiji/ImageJ with the help of the TrackMate plugin. To validate this method, we imaged and quantified chromatin movement in control and salt-stressed roots, revealing a decrease in chromatin speed under salt-stressed conditions. This method allows for quantitative live cell imaging of root nuclear shape and chromatin dynamics during plant development and environmental stress, thus enabling analysis of changes in nuclear and chromatin dynamics caused by abiotic stressors.
Why it matches plant phenotyping methodsシロイヌナズナ根の核形状・クロマチン動態を定量する共焦点ライブイメージングと画像解析パイプラインを開発し、塩ストレス条件で検証しており、植物表現型取得が中心です。
abstractwe developed a pipeline using confocal microscopy and particle tracking software to quantify nuclear and chromatin dynamics in Arabidopsis roots under control and abiotic stress condition.
ArabidopsisMicroscopyCell / cellular structureRootVisualization / data management
Conventional light microscopy is limited in resolution by the diffraction limit of light, restricting the visualization of the nanoscale organization of biomolecules. Expansion microscopy (ExM) has emerged as a powerful technique to overcome this barrier by physically expanding the specimen embedded in a swellable hydrogel without requiring specialized or high-cost imaging hardware. ExM is widely used in animal models, whereas its application to plant tissues has been challenging due to their multicellularity, in which each cell is encompassed by the rigid cell wall, which resists the expansion forces and prevents isotropic swelling. Here, we describe a robust and optimized ExM protocol specifically designed for Arabidopsis thaliana root tissues. This protocol details critical steps, including immunostaining, anchoring, gelation, denaturation, cell wall digestion, and expansion. Our method achieves an expansion factor of approximately 4.3×, enabling an effective lateral resolution of ~60 nm using a standard confocal microscope. We demonstrate the visualization of microtubules with preserved ultrastructure. This accessible protocol allows plant researchers to perform super-resolution imaging without specialized optical equipment, facilitating detailed structural analysis of plant cells. Key features • Expansion microscopy to break the diffraction barrier by increasing the physical distances between proteins while preserving relative spatial relationships and fluorescence signals. • 4-fold expansion of Arabidopsis root tissues. • 3D super-resolution imaging. • Deep-tissue imaging thanks to optical clearing associated with expansion of hydrogel-embedded specimens.
Why it matches plant phenotyping methods植物組織向けに最適化した超解像イメージングプロトコルを開発し、根の微細構造を定量・可視化する方法が中心である。
abstractHere, we describe a robust and optimized ExM protocol specifically designed for Arabidopsis thaliana root tissues.
Plant phenotyping relevance match · UnverifiedOpenAlex · Europe PMC · bioRxiv · Crossref · checked 5 Sept 2026
1 Abstract Non-structural carbohydrates (NSCs) are central to plant carbon allocation and physiological regulation, yet their quantification typically relies on destructive biochemical assays that lack spatial resolution. Here, we developed a shortwave infrared (SWIR) hyperspectral imaging workflow for non-destructive estimation and spatial reconstruction of starch-associated variation in strawberry leaves. The workflow combined automated hyperspectral segmentation, spectral preprocessing, Partial Least Squares Regression (PLSR), and constrained wavelength selection. Sample-level spectra extracted from 114 strawberry leaf samples grown across three different metabolic conditions were paired with destructive starch measurements and used to train models across the 900–1750 nm spectral range. A constrained greedy band-selection strategy revealed that predictive performance approached a plateau at approximately 12 wavelengths, indicating substantial spectral redundancy within the full hyperspectral dataset. The final reduced-band model achieved a cross-validated coefficient of determination (R 2 ) of 0.771 ± 0.066 and a root mean squared error (RMSE) of 0.743 ± 0.098 mg g −1 fresh weight using repeated stratified 5-fold cross-validation. Pixel-wise application of the final model generated spatial starch-associated maps that preserved pronounced intra-leaf heterogeneity, including vein-associated spatial structure. These results demonstrate that starch-associated spectral information can be reconstructed from a constrained reduced-band SWIR framework while retaining sufficient predictive performance for spatial mapping. The identified wavelength reduction supports the feasibility of deployable multispectral systems for non-destructive carbohydrate sensing in plant phenotyping applications.
Why it matches plant phenotyping methods植物葉のデンプン状態を非破壊推定・空間再構成するSWIR画像計測とケモメトリック解析ワークフローを開発・検証しており、フェノタイピング手法が中心です。
abstractHere, we developed a shortwave infrared (SWIR) hyperspectral imaging workflow for non-destructive estimation and spatial reconstruction of starch-associated variation in strawberry leaves.
Soil salinization poses a severe threat to global food security. However, deciphering the spatiotemporal dynamics of key metabolites and ions in living plants remains a formidable challenge due to the lack of robust in vivo sensing tools. In this study, we developed a nonmetallic MoO x @CuPc core-shell nanoprobe anchored to the plant cell wall, which serves as the cornerstone of an "in vivo-in situ-long term-multitargeted" (VSLM) surface-enhanced Raman spectroscopy (SERS) platform. This design overcomes critical limitations of conventional metallic probes, such as rapid corrosion in saline microenvironments and inability to achieve stable multitarget detection, by synergizing a corrosion-resistant MoO x core with a protective CuPc shell. The optimized interface electronic coupling enables simultaneous tracking of adenosine triphosphate (ATP), salicylic acid (SA), Na + , and K + at nanomolar detection limits, with signal stability maintained over 48 h ( Suaeda salsa ( S. salsa ) under salt stress, revealing a shift from "growth-priority" to "defense-priority" resource allocation alongside coordinated ion partitioning across roots, stems, and leaves. This work presents a novel in situ and multitargeted monitoring methodology, which substantially expands the capability of SERS for complex biological systems and opens a new avenue in analytical chemistry for dynamic, multiparameter life science research.
Why it matches plant phenotyping methods植物体内の代謝物・イオンを長期・多標的に測定するSERSナノプローブ／プラットフォームの開発が中心で、塩ストレス下の植物の生理状態を直接評価しているため。
abstractwe developed a nonmetallic MoO x @CuPc core-shell nanoprobe anchored to the plant cell wall, which serves as the cornerstone of an "in vivo-in situ-long term-multitargeted" (VSLM) surface-enhanced Raman spectroscopy (SERS) platform.
Unmanned aerial vehicle (UAV) based remote sensing has emerged as a disruptive technology for detecting crop water stress (CWS) in real time, precisely and at low cost offering significant advancements over conventional approaches. The study examined the red green blue (RGB), multispectral (MSP), hyperspectral (HSP), thermal image sensors integrated with UAVs, which offers a high-spatial and temporal resolution of physiological indicators such as chlorophyll content and canopy cover, canopy temperature, stomatal conductance. The study highlights that in spring maize, random forest (RF) models using UAV-derived MSP and thermal indices with leaf area index (LAI) performed well (R² > 0.575, root mean square error (RMSE)
Why it matches plant phenotyping methodsUAV搭載センサーによる作物の水ストレスや生理形質のモニタリング技術をレビューしており、表現型取得法が中心である。
titleRecent trends in crop water stress monitoring using remote sensing technologies: A review
This study developed an integrated diagnostic system for tebuthiuron-induced soil ecotoxicity based on morphophysiological indicators of Mucuna pruriens, using the germination index (GI) of Lactuca sativa as a sensitive ecotoxicological validation endpoint. The experiment was conducted under greenhouse conditions using a completely randomized design with 12 treatments and 360 individual pots (independent samples evaluated via destructive sampling), which were distributed across five evaluation periods at 14, 28, 42, 56, and 70 days after sowing. Morphophysiological variables, including plant height, root length, shoot and root dry mass, chlorophyll content, nodule number, and visual phytotoxicity, were quantified and integrated with multivariate and probabilistic modeling approaches. Given the multifactorial nature of the germination index, Principal Component Analysis (PCA) was applied to identify ecological and physiological gradients associated with plant vigor, stress, and symbiotic functioning. The PCA outputs were subsequently used as inputs for Probabilistic Neural Networks (PNNs), enabling the classification and prediction of bioindicator-based ecotoxicological levels using mathematically defined low, medium, and high GI classes. Model performance was internally assessed using training and validation datasets, confusion matrices, overall accuracy, sensitivity, specificity, and ROC curves. Because no independent external dataset was available, the predictive performance should be interpreted as evidence of internal consistency rather than definitive generalizability across different soils, climates, herbicide doses, or field conditions. Multivariate analyses revealed that ecotoxicological attenuation trajectories in tebuthiuron-contaminated soils are inherently nonlinear, being structured by coordinated shifts in morphophysiological traits rather than isolated responses of individual variables. The integrated PCA-PNN framework demonstrated that aboveground traits. Particularly plant height, chlorophyll content, and shoot dry mass, were more sensitive indicators of tebuthiuron-induced stress than root traits alone. Higher GI values were associated with PCA regions characterized by increased shoot biomass, greater plant height, reduced phytotoxicity, and improved physiological performance, whereas lower GI classes corresponded to suppressed growth and multidimensional stress signatures. The progressive convergence between plant vigor and GI across evaluation periods suggests a gradual mitigation of ecotoxicological stress signals on the indicator plants, indicating transitions from acute injury to physiological adaptation states. These findings confirm that M. pruriens functions as an effective bioindicator for diagnosing soil ecotoxicological status and monitoring tebuthiuron-induced impacts. However, as tebuthiuron residues were not chemically quantified, these responses should not be interpreted as direct evidence of herbicide degradation, dissipation, or removal. These findings confirm that M. pruriens functions as an effective bioindicator for diagnosing soil ecotoxicological status and monitoring tebuthiuron-induced impacts. However, as tebuthiuron residues were not chemically quantified, the observed improvements should be interpreted as evidence of physiological adaptation and/or ecological attenuation rather than definitive proof of herbicide degradation or removal. Overall, this approach provides a robust framework for early detection of soil contamination and supports its application in monitoring and guiding soil rehabilitation processes, with potential for future validation under field conditions.
Why it matches plant phenotyping methods植物の形態・生理形質を統合し、PCA-PNNで植物ストレスおよび土壌生態毒性レベルを診断する手法の開発・内部検証が中心であり、単なる生物学的測定ではない。
abstractThis study developed an integrated diagnostic system for tebuthiuron-induced soil ecotoxicity based on morphophysiological indicators of Mucuna pruriens
Abstract Macromolecular crowding is a fundamental physical property of the cytoplasm that governs intracellular diffusion and biochemical reactions. However, in situ quantitative characterization of intracellular dynamics and associated biophysical states in intact plant tissues remains challenging. Using 40-nm genetically encoded multimeric nanoparticles (GEMs) and single-particle tracking in Arabidopsis roots, we quantitatively map the regional heterogeneity of cytoplasmic diffusion dynamics and crowding along the root developmental axis: elongation zone cells exhibit a dense, low-mobility baseline, whereas maturation zone and root hair cells display higher mobility. These regions exhibit different sensitivities to osmotic stress. Notably, under severe ionic stress, both the diffusion coefficients and non-Gaussian parameters of the maturation zone and root hair cells converge toward the levels of the elongation zone cells, suggesting an intrinsic physical baseline for cytoplasmic crowding. This kinetic convergence in these cells is accompanied by vacuolar retraction and an increase in cytoplasmic thickness. Together, our study establishes a GEMs-based platform for in situ biophysical analysis in plant cells and uncovers a spatially-resolved physical landscape of cytoplasmic crowding and its dynamic reorganization under osmotic stress.
Why it matches plant phenotyping methods植物細胞内の拡散動態・細胞質クラウディングを定量するGEMs単粒子追跡法を構築し、植物根で実証した研究であり、表現型取得基盤が中心である。
abstractUsing 40-nm genetically encoded multimeric nanoparticles (GEMs) and single-particle tracking in Arabidopsis roots, we quantitatively map the regional heterogeneity of cytoplasmic diffusion dynamics and crowding along the root developmental axis
Improving crop productivity while maintaining low environmental impact is essential for sustainable food production under increasing population pressure and irreversible climate changes. Dynamic biomass prediction is critical for effective crop growth monitoring and management, yet existing approaches struggle to provide consistent and reasonable predictions across diverse environments in a rapid, economic, and practical manner. Here, we propose SpecWeaNet, a biophysics-informed neural network framework that integrates explicit biophysical principles governing biomass accumulation with implicit mechanisms learned from representative training data. The framework enables dynamic prediction of wheat biomass from sowing to harvest using daily weather data and limited in-season spectral observations, without requiring model recalibration. From a systematic perspective, SpecWeaNet is designed as a flexible framework, from which we further developed three ready-to-use pre-trained variants with different input configurations tailored to commonly used sensors. Our comprehensive evaluation demonstrates the robustness and generalizability of pre-trained models for seasonal prediction of biomass dynamics from non-daily spectral observations (with random interval between two consecutive observations) and daily weather data, with coefficient of determination (R 2 ) higher than 0.99, relative mean absolute error (RMAE) within 26% and relative root mean square error (RRMSE) within 35% on more than 250,000 in-silico simulation scenarios across diverse environmental conditions, including different years, geographical locations, and crop varieties. Furthermore, validation on multiple field experiments showcases the capability of pre-trained models to provide reliable predictions at both trial (R 2 = 0.77–0.88, RMAE = 20–26%, RRMSE = 29–40%) and plot (R 2 = 0.83–0.93, RMAE = 12–19%, RRMSE = 15–26%) scales, utilizing daily weather observations and available satellite or drone-based imagery. This work demonstrates how integrating crop modelling with artificial intelligence can enable scalable estimation of crop biomass dynamics, advancing remote sensing–based crop phenotyping and monitoring for sustainable agricultural systems.
Why it matches plant phenotyping methodsSpecWeaNetは気象・スペクトル観測からコムギのバイオマス動態を推定する計算フェノタイピング手法であり、モデル開発、シミュレーション評価、複数圃場での検証が中心である。
abstractwe propose SpecWeaNet, a biophysics-informed neural network framework that integrates explicit biophysical principles governing biomass accumulation with implicit mechanisms learned from representative training data.
Abstract Warmer temperatures, permafrost thaw, and increased wildfire activity are driving rapid ecological change across the Arctic, significantly altering plant productivity and aboveground biomass (AGB). These rapid changes highlight the urgent need to improve monitoring of vegetation dynamics in the Earth’s northern ecosystems, where high spatiotemporal heterogeneity occurs at scales finer than those captured by traditional satellite observations. The growing use of unoccupied aerial systems (UASs) presents an opportunity to overcome this limitation. Yet, the diversity of UAS platforms, sensors, and data collection and processing workflows presents challenges for developing standardized, generalizable approaches. To address this challenge, we compiled 672 AGB plots co-located with 183 UAS-based structure-from-motion (SfM) or light detection and ranging (LiDAR) surveys collected across the Arctic. Here, we: (1) evaluated the generalizability of UAS-derived canopy structure derived from high-resolution SfM and LiDAR for estimating AGB, (2) assessed scaling errors and their sources in two recent satellite-based AGB products derived from Landsat and moderate resolution imaging spectroradiometer, and (3) demonstrated the use of high-resolution AGB maps to quantify biomass variation across tundra plant functional types (PFTs) and to monitor post-fire recovery. Our results show that both SfM and LiDAR accurately captured AGB and its variability across tundra PFTs using a random forest model (overall root mean squared error: 0.332 kg m –2 ), with mapping performance varying slightly by region and data source. Using UAS-derived AGB maps as a benchmark, we identified systematic biases in satellite-derived AGB products, largely attributable to the magnitude of AGB and structural heterogeneity within coarse-resolution pixels. Applying our model to repeat UAS surveys following a tundra fire on Seward Peninsula, we observed rapid AGB recovery in non-shrub patches, with biomass recovering to pre-fire levels within two years. In contrast, shrub patches recovered more slowly, with AGB gains continuing over 2–4 years through both in-patch growth and lateral expansion (via dispersal) into remaining burned areas. Overall, these findings support the generalizability of UAS-based SfM and LiDAR data for estimating tundra AGB and highlight the need for broader collection and synthesis of such data to improve ecological monitoring and model benchmarking in the Arctic.
Why it matches plant phenotyping methodsUASのSfMおよびLiDARから植物群落の地上部 biomass (AGB) を推定する手法の一般化性能を評価し、衛星推定値のベンチマークにも用いており、植物形質取得が研究の中心である。
abstractevaluated the generalizability of UAS-derived canopy structure derived from high-resolution SfM and LiDAR for estimating AGB
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 codes and training data is available on GitHub: https://github.com/Daryl-Open asset ↗pdf-page:20 lines:1-30Plant phenotyping relevance match · UnverifiedCrossref · checked 14 Sept 2026
Abstract Proteolysis is a universal process, as proteases play a pivotal role in modulating numerous signaling pathways. Proteases control the fate and function of their target proteins by hydrolyzing peptide bonds within these proteins. Understanding the temporal and spatial dynamics of proteolytic events, including the proteases that execute them, is crucial for elucidating their particular roles across diverse biological processes. In this study, we developed and characterized a set of genetically encoded Förster resonance energy transfer (FRET)-based reporters for the detection of various proteolytic activities in plants. Our sensors reliably reported the activity of specific proteases, exhibiting a performance comparable to previously established detection systems. In addition, we engineered variants capable of detecting the spatial dynamics of metacaspase-triggered proteolysis after wounding and during programmed cell death in roots. We demonstrated the feasibility of these FRET-based sensors for detecting various activities in vivo with high spatiotemporal resolution. The implementation of these tools in plant research opens opportunities to explore proteolytic mechanisms with enhanced precision. Overall, these biosensors constitute a versatile toolbox for probing protease function within its native cellular context, paving the way for deeper insights into plant biology and signaling.
Why it matches plant phenotyping methods植物内のプロテアーゼ活性という生理状態を高い時空間分解能で測定するFRETセンサーを開発・検証しており、表現型取得法が研究の中心です。
abstractwe developed and characterized a set of genetically encoded Förster resonance energy transfer (FRET)-based reporters for the detection of various proteolytic activities in plants.
MaizeAerial / UAVField / plotRootRoot system architectureYield / yield components
Understanding and predicting complex traits in plants remains a fundamental challenge due to the emergent nature of most phenotypes and their dependence on genetic, regulatory, and environmental interactions. Accurate prediction of traits and identification of underlying genetic elements have broad applications for plant breeding, systems biology, and biotechnology. Here, we tested if multi-omic datasets could improve predictive accuracy of 129 diverse maize phenotypes across 9 environments using genomic markers, field-based transcriptomic data from 2 locations, and drone-derived phenomic data of vegetative indices. We trained and compared linear (rrBLUP) and nonlinear (support vector regression) models using single- and multi-omics inputs. Multi-omics models consistently outperformed single-omics models for most traits, with genomic and transcriptomic inputs contributing distinct biological features. Phenomic features alone yielded the lowest predictive power but improved predictions for specific trait categories like root architecture. Transcriptomic datasets enabled cross-environment prediction, demonstrating that gene expression patterns from one field site could accurately predict traits measured in another. Environment-specific expression of benchmark flowering time genes highlighted the value of transcriptomics in capturing genotype-by-environment (G × E) interactions not detectable through genomic data alone. Analysis of model feature weights further indicated that predictive signal is distributed across many genes, consistent with complex traits such as yield arising from coordinated, network-level processes rather than a small number of dominant loci. These findings demonstrate that integrating transcriptomic and phenomic data with genotypes enhances trait prediction, improves model generalizability across environments, and provides deeper insight into the genetic and regulatory architecture of agriculturally important traits in maize.
Why it matches plant phenotyping methods複数オミクスとドローン由来フェノミックデータを統合し、植物形質を予測するモデルを比較・評価しており、形質推定ワークフローが研究の中心である。
abstractWe trained and compared linear (rrBLUP) and nonlinear (support vector regression) models using single- and multi-omics inputs.
Urban forest carbon sequestration is vital for environmental health, climate change mitigation, and enhancing the quality of life in urban areas. This paper presents a framework for high density point clouds production by integrating point data from aerial photogrammetry, UAV-based LiDAR and terrestrial LiDAR for individual tree measurements and carbon storage estimation. The aerial photos, UAV-based LiDAR and terrestrial LiDAR were observed based on common ground control points and combined using Iterative Closest Point (ICP) algorithm. The combined point cloud was iltered to separate ground points and normalized based on Digital Terrain Model (DTM). The normalized point cloud was used for individual tree segmentation from which individual tree measurements such as, tree height, Diameter at Breast Height (DBH) and crown diameter were estimated. The estimated tree parameters were used for individual carbon estimation. The results show that the individual tree segmentation method signi icantly underestimated the number of trees. The estimation of DBH, tree height, and crown diameter achieved the Root Mean Square Error (RMSE) value of 0.107m, 1.385m and 2.650m respectively. However, in general the estimates experience underestimation as shown by Mean Bias Error (MBE) with 0.003m, -0.636m and 0.001m for DBH, tree height and crown diameter respectively. The estimated values for each individual tree were used for individual tree biomass and carbon storage recording the Root Mean Square Error (RMSE) at 1970.236 kg and 886.606 kgC respectively while attaining the Mean Bias Error (MBE) measure of 140.019 kg and 63.009 kgC each. The proposed framework showed promising results for individual tree carbon estimation. Nonetheless, further attention should be given on individual tree delineation process.
Why it matches plant phenotyping methods航空写真、UAV・地上LiDARを統合し、個体樹木の分離と樹高・DBH・樹冠径を推定して精度評価する手法が中心であり、植物形質計測の技術的検証に該当する。
abstractThis paper presents a framework for high density point clouds production by integrating point data from aerial photogrammetry, UAV-based LiDAR and terrestrial LiDAR for individual tree measurements and carbon storage estimation.
Plant phenotyping relevance match · UnverifiedCrossref · OpenAlex · Europe PMC · checked 5 Sept 2026
Abstract Climate change increasingly threatens global agriculture by intensifying abiotic stresses and destabilizing crop productivity, necessitating a deeper understanding of root-mediated traits governing resource acquisition and stress resilience. Here, we synthesize recent advances in root-centred plant phenomics, emphasizing how high-throughput phenotyping enables high-resolution, scalable characterization of complex root traits and robust comparative analysis across diverse genotypes and environments. Innovations in multimodal imaging, notably X-ray computed tomography, MRI, and machine learning-integrated rhizotrons, facilitate detailed reconstruction of root system architecture and its temporal dynamics under both controlled and semi-field conditions. Furthermore, root phenotyping is increasingly interpreted within an integrated whole-plant framework. The integration of organ-specific assessments with physiological phenomics leveraging spectral and thermal data enables the characterization of developmental plasticity and root-mediated processes, including water-use dynamics, nutrient acquisition, and canopy stress responses under heterogeneous field conditions. These approaches link root traits such as rooting depth and spatial distribution to canopy-level physiological responses under stress. Despite these advances, significant bottlenecks persist in data interoperability, analytical scalability, and protocol standardization. Future progress will require integration of root phenomics with genomics, predictive modelling, and digital twin frameworks to improve resource-use efficiency, yield stability, and climate resilience in global cropping systems.
Why it matches plant phenotyping methods根系フェノタイピングの高スループット画像化、計算ツール、機械学習統合、データ標準化を中心に扱う方法論レビューであり、植物形質の取得・解析手法が主題である。
titleAdvances in root phenotyping: high-throughput imaging, computational tools, and integrative approaches for crop improvement.
Understanding below-ground biomass dynamics is essential for improving crop performance in water-limited regions. Yet field-scale root monitoring remains constrained by destructive and labor-intensive sampling. This study presents explainable machine learning models to estimate root biomass of maize, millet, and sorghum using UAV multispectral imagery and key canopy phenotypic traits. Across 405 samples collected during the 2024 growing season, eight algorithms were evaluated, among which Random Forest and XGBoost achieved the highest predictive accuracy (R² = 0.763 for millet, 0.688 for maize, and 0.659 for sorghum). SHAP analysis revealed that leaf area was the dominant predictor across all crops, with 2-3 times greater influence than other traits, while leaf water content and chlorophyll-related parameters exhibited species-specific effects associated with drought adaptation. Under the conditions tested, these results suggest that UAV-based multispectral phenotyping, combined with interpretable machine learning, can enable non-destructive estimation of root biomass at the field scale. Within the limits of this single-site, single-season study, the approach demonstrates potential for large-scale root phenotyping and for supporting crop improvement in semi-arid regions. We quantify a 15-25% reduction in R² relative to above-ground trait prediction, which we term the 'cost of indirect inference'-highlighting the inherent challenge of estimating below-ground biomass from canopy-level data. These findings offer insights for precision agriculture, subject to broader validation.
Why it matches plant phenotyping methodsUAVマルチスペクトル画像と説明可能な機械学習を用いて根 biomass という植物形質を非破壊推定する手法が研究の中心であり、実証・比較評価も行っている。
abstractThis study presents explainable machine learning models to estimate root biomass of maize, millet, and sorghum using UAV multispectral imagery and key canopy phenotypic traits.
Nodule color and morphology are key readouts of legume symbiotic performance. However, long-term preservation of post-excavation nodules with intact morphology, color, and microbial cleanliness remains a major challenge. This study developed a two-stage aqueous-phase preservation method (TAPP) that enables rapid structural fixation and long-term chemical stabilization. A comprehensive evaluation was subsequently established, incorporating composite morphological score (0-5), color difference (ΔE) and its piecewise slope over time ([Formula: see text]), and visible contamination grade (0-3). Peanut and soybean nodules from multiple regions and cultivars were tracked for 24 months under five preservation methods: TAPP, FormalinCu, TAPP-Resin, Resin, and AirDry. TAPP showed the best overall preservation, with composite morphological scores of 4.65 ± 0.14 for peanut and 4.63 ± 0.22 for soybean at 24 months, and no visible mold. Color change slowed over time: [Formula: see text] decreased from 1.83 to 1.10 ΔE·month - 1 during 0-1 month to 0.16 ΔE·month - 1 during 12-24 months, yielding final ΔE values of 10.53 ± 1.88 and 10.32 ± 1.93, respectively. Notably, TAPP pretreatment markedly improved resin-embedded samples, demonstrating scalability and flexible deployment. In addition, this study further proposes a stage-wise workflow that integrates on-site pre-fixation, long-distance transport, and long-term storage to enable cross-regional circulation and collaborative phenomics of oxidation-prone, dehydration-sensitive nodules. Together, this work establishes a standardized, traceable workflow to preserve and benchmark legume root nodule phenotypes, supporting cross-laboratory comparability and longitudinal cross-source analyses.
Why it matches plant phenotyping methodsマメ科根粒の形態・色・汚染状態という植物表現型を長期保存し、定量評価・比較する手法と標準化ワークフローが研究の中心であるため。
abstractThis study developed a two-stage aqueous-phase preservation method (TAPP) that enables rapid structural fixation and long-term chemical stabilization.
Plant phenotyping relevance match · UnverifiedOpenAlex · Crossref · Europe PMC · checked 5 Sept 2026
Climate change is increasing the frequency of compound drought and heat events, threatening forest stability worldwide. While genomics has helped identify resilient genotypes, our ability to characterize adaptive traits - phenotyping - has not kept pace. This creates a bottleneck: we can sequence trees faster than we can understand how they physically respond to stress. Moving away from single-sensor monitoring, the field is now embracing multi-sensor data fusion, in which thermal imaging, Solar-Induced Fluorescence (SIF), hyperspectral remote sensing, and LiDAR are combined on platforms ranging from Unmanned Aerial Vehicles (UAVs) to ground-based robotic systems. These integrated approaches are proving effective for detecting physiological stress - such as changes in stomatal conductance - before visible damage appears. Deep learning models, meanwhile, are beginning to outperform traditional vegetation indices for specific tasks such as tree-crown segmentation and stress classification, although their performance remains constrained by overfitting, limited transferability, and domain shift across forest types in analyzing complex forest canopies. A major limitation remains, however: most high-throughput phenotyping (HTP) focuses on the canopy, largely ignoring the root system and the soil-plant-atmosphere continuum (SPAC), which are critical for drought resilience. In this review, we argue that developing climate-resilient forests requires looking below the canopy. We propose a constraint-based framework that couples aerial sensor data with eco-hydrological approaches and process-based modeling to narrow the range of plausible root functional strategies-rather than to directly identify root phenotypes, while critically evaluating the assumptions and validation challenges inherent in this approach. Future research should focus on standardized protocols, open benchmark datasets, and Explainable AI (XAI) to strengthen the link between above-ground signals and below-ground traits.
Why it matches plant phenotyping methods植物フェノタイピング手法を中心に、マルチセンサー融合、深層学習、検証課題、標準化・ベンチマークをレビューしているため。
abstractIn this review, we argue that developing climate-resilient forests requires looking below the canopy.
Foundation models pre-trained on massive datasets have demonstrated impressive performance, but in some specialised domains have been found to have lower accuracy. Domain-specific foundation models target a particular domain such as retinal or plant images. These domain-specific models have shown inconsistent results and the benefit to root segmentation is unknown. We train and evaluate the first domainspecific foundation model for root segmentation. Evaluation uses a leave-one-dataset-out design across nine diverse root datasets with two architectures. Applied zero-shot to unseen datasets, the root foundation model achieves 92% of fine-tuned Dice on average (0.636 versus 0.698), with 5 of 9 datasets above 90%. With 10 patches of few-shot fine-tuning, the root foundation model recovers 95% of its full-data Dice on average, versus 69% for a general pre-trained model. At low patch counts the general pre-trained model often failed to converge, with 5 of 9 datasets giving Dice below 0.05 at 3 patches, while the root foundation model produced Dice above 0.47 on every dataset and patch count. With full target-data fine-tuning, the two perform comparably, with mean improvements of +0.011 Dice for MobileSAM and +0.022 for M2F Swin-S, neither significant (Wilcoxon p = 0.150 and 0.064). We release our pre-trained MobileSAM root foundation model for use with RootPainter, enabling fully automatic root segmentation on new datasets with an ordinary laptop or desktop computer, with no need for annotation or training.
Why it matches plant phenotyping methods根の画像セグメンテーションを行う基盤モデルを開発し、9データセットでゼロショット性能を評価する研究であり、植物形質取得手法が中心です。
abstractWe train and evaluate the first domainspecific foundation model for root segmentation.
Rhizosphere oxidation is a key adaptive mechanism in reductive soil environments, in which oxygen released from roots alters rhizosphere redox conditions and regulates biogeochemical processes. Rice plants possess an internal oxygen transport system, and radial oxygen loss (ROL) from roots is closely associated with root development. However, the spatial patterns of ROL in soil and their relationships with root traits remain poorly characterized. In this study, we developed a multimodal imaging system that integrates planar oxygen optodes with X-ray computed tomography to simultaneously visualize rhizosphere oxidation and root development in rice. Daily time-course tracking of individual crown roots revealed dynamic changes in the spatial distribution and magnitude of rhizosphere oxygen in relation to root elongation and aging. Root thickness was positively correlated with dissolved oxygen levels near root tips. Genotypic comparisons further identified a cultivar with reduced rhizosphere oxidation despite possessing thicker roots among the tested genotypes, thereby indicating the involvement of additional physiological processes. Overall, these findings demonstrate that rhizosphere oxidation is regulated by root growth stage and thickness and dynamically modulated during root development.
Why it matches plant phenotyping methods平面酸素オプトードとX線CTを統合したマルチモーダル画像システムを開発し、イネ根の発達と根圏酸化を時系列・空間的に測定しているため、植物フェノタイピング手法が研究の中心である。
abstractwe developed a multimodal imaging system that integrates planar oxygen optodes with X-ray computed tomography to simultaneously visualize rhizosphere oxidation and root development in rice.
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.
Plant phenotyping relevance match · UnverifiedCrossref · Europe PMC · OpenAlex · checked 5 Sept 2026
Introduction With the continuous advancement of smart agriculture, multi-modal remote sensing based on unmanned aerial vehicles (UAVs) offers new technical approaches for monitoring and managing crop moisture in fields. However, significant challenges remain in developing high-precision field-scale crop Plant Moisture Content (PMC) prediction models and translating them into actionable irrigation strategies. Methods This study focuses on winter wheat, employing field experiments with PMC and water use efficiency (WUE) as indicators of crop water status. Vegetation indices (VIs) derived from UAV data were used to construct a leaf area index (LAI) inversion model. Crop Height was extracted from oblique photogrammetry point cloud data. By combining the Penman-Monteith equation with dual crop coefficients, an improved evapotranspiration (ET) model was developed, utilizing multispectral data from UAVs, thermal infrared data, point cloud-derived plant height, and LAI inversion results. Further utilizing VIs, temperature indices (TIs), and machine learning algorithms (Random Forest Regression (RFR), Back Propagation Neural Network (BPNN), Partial Least Squares Regression (PLSR), and Support Vector Regression (SVR), we established PMC prediction models for winter wheat at different growth stages. These models, integrated with WUE, form the basis for an irrigation scheduling optimization framework at the field scale. Results Results indicate that VIs, the difference between canopy temperature and air temperature (ΔT), Crop Water Stress Index (CWSI), and ET exhibit varying correlations with PMC during three critical growth stages of winter wheat, with ET showing the highest correlation during the jointing and heading stages (absolute correlation coefficient |r| ≥ 0.639). Compared to PMC prediction models constructed with different combinations of VIs, ET, VIs+ET, and VIs+TIs, the model employing the RFR algorithm with multimodal inputs (VIS+TIs+ET) demonstrated the best performance. The model’s predictive accuracy gradually improved across all growth stages, peaking during the grain-filling stage, with the coefficient of determination(R 2 ) of 0.900 and a normalized root mean square error (nRMSE) of 2.688%. Optimal WUE varied across growth stages under different irrigation treatments. The highest values were achieved at the jointing stage under treatment W3 (PMC = 81.8%), and at the heading and grain-filling stages under treatment W1 (PMC = 76.8% and 64.0%, respectively). Discussion The study suggests that stage-specific irrigation scheduling based on PMC thresholds can improve overall water use efficiency. This study shows that integrating multi-modal UAV data with machine learning and an improved ET model enables high-precision PMC monitoring, supporting data-driven irrigation scheduling in precision agriculture.
Why it matches plant phenotyping methodsUAVマルチモーダルデータと機械学習により、作物水分状態（PMC）、LAI、草高、蒸発散量を推定する手法を開発・評価しており、フェノタイピング手法が研究の中心である。
abstractCrop Height was extracted from oblique photogrammetry point cloud data.
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 · UnverifiedCrossref · checked 15 Sept 2026
This study proposes the Hydroponic Plant Growth Analysis System (HPGAS), a public-data-based preliminary framework for multimodal plant growth state analysis toward future filter-free aquaponic validation. The HPGAS integrates plant images, water quality signals, and environmental signals to estimate an image-centered growth index, growth stage, and proxy abnormal state probability. Because no public dataset jointly provides plant images, direct growth labels, fish metabolic variables, suspended solids, and nitrification-related measurements from a real filter-free aquaponic system, this study is not a direct operational validation. A two-stage evaluation was conducted using the Autonomous Greenhouse Challenge (AGC), HydroGrowNet, and two aquaponic Internet of Things (IoT) water quality datasets. Stage 1 implemented dataset loaders, image–sensor alignment, proxy label generation, and unimodal and fusion baselines. Stage 2 expanded handcrafted image and sensor-context features and adopted month-wise hold-out evaluation. The image-only model achieved the best growth index regression performance, with a root mean square error (RMSE) of 0.0492 ± 0.0187, whereas the fusion model showed a RMSE of 0.0837 ± 0.0196. Conversely, the fusion model achieved the best proxy abnormal state classification performance, with a F1 score of 0.9695 ± 0.0057 under the clean condition, decreasing to 0.9232 ± 0.0263 under sensor dropout and 0.9132 ± 0.0169 under image noise. Under sensor dropout, the fusion model was more stable than the sensor-only model, whereas under image noise it degraded more than the image-only model. These results indicate that multimodal fusion is most useful for proxy abnormal state classification and robust state interpretation, rather than universally superior scalar growth regression. The HPGAS provides a reproducible baseline for future real filter-free aquaponic experiments, while its operational validity remains to be tested using real filter-free aquaponic data.
Why it matches plant phenotyping methods植物画像とセンサーデータを統合し、成長指数・成長段階・異常状態確率を推定する再現可能な解析フレームワークを開発・評価しており、植物表現型の取得・推定手法が中心である。
abstractThis study proposes the Hydroponic Plant Growth Analysis System (HPGAS), a public-data-based preliminary framework for multimodal plant growth state analysis
Plant phenotyping relevance match · UnverifiedCrossref · Europe PMC · checked 5 Sept 2026
X-ray / CTRootMorphology / geometry measurement2D/3D reconstructionRoot system architecture
Summary Plant–microbe interactions are inherently spatial, yet the physical structure of the soil and rhizosphere is rarely treated as a mechanistic variable in experimental design. X‐ray computed tomography (X‐ray CT) enables nondestructive, three‐dimensional, and time‐resolved imaging of intact root–soil systems, providing direct access to the structural context in which plant–microbe interactions occur. Rather than a secondary imaging technique, X‐ray CT can offer a wealth of data as a primary experimental platform for future plant–microbe research. Here, we highlight key structural traits that X‐ray CT can quantify and discuss how they may shape microbial behaviour, plant immune responses, and disease outcomes. We expand on how X‐ray CT could be employed in future to provide a framework to disentangle direct microbial effects from indirect, structure‐mediated feedbacks. For breeding and management, it could enable selection for root traits and soil practices that engineer favourable microhabitats rather than targeting organisms in isolation. Despite this potential, broader adoption will require overcoming current limitations related to access to instrumentation, analytical expertise, and the integration of structural data with biological measurements. Overall, we suggest that resolving these issues will enable the integration of X‐ray CT‐derived structure with molecular, microbiome, and modelling approaches to enable the development of digital rhizospheres, offering a pathway from descriptive observations to predictive, structure‐aware in silico frameworks in plant–microbe research.
Why it matches plant phenotyping methodsX線CTを用いて根・土壌系の構造形質を定量する方法を、植物・微生物相互作用研究の主要な実験プラットフォームとして論じる方法論レビューであり、植物フェノタイピング手法が中心です。
abstractX‐ray computed tomography (X‐ray CT) enables nondestructive, three‐dimensional, and time‐resolved imaging of intact root–soil systems, providing direct access to the structural context in which plant–microbe interactions occur.
RootPhysiological trait estimationPlant / canopy temperature
Wireless temperature monitoring of plant roots remains challenging due to signal dependence on variable sensor-reader distance. To overcome this limitation, this paper proposes a wireless, battery-free RLC-based temperature sensing system incorporating a data-driven distance compensation framework based on a two-stage polynomial regression strategy. A multi-model is proposed to estimate temperature from resistance data, while a fourth-degree polynomial model is used to estimate the sensor-reader distance from self-inductance measurements. The final predicted temperature is obtained by linear interpolation. The model approach is developed using data collected from an inductanceto- digital converter (LDC1101) reader of an RLC sensor with a PT1000. Experimental results show that at a sensor-reader distance of 2 mm, the system achieves a root mean square error (RMSE) of 0.788 °C, with errors normally distributed near zero (σ = 0.705 °C), and an RMSE of 2.163 °C with an error distribution (σ = 1.24 °C) at a distance of 6 mm. This performance corresponds to a reduction in prediction error of up to 94% at short distances and over 80% at larger separations compared to a single global model.
Why it matches plant phenotyping methods植物根の温度という生理状態を測定する無線センサーと距離補償・推定手法を開発し、誤差で性能検証しているため、植物フェノタイピング手法が中心です。
abstractthis paper proposes a wireless, battery-free RLC-based temperature sensing system incorporating a data-driven distance compensation framework based on a two-stage polynomial regression strategy.
Climate change threatens global Chinese cabbage ( Brassica rapa L. ssp. pekinensis ) production, a cool-season crop essential for Asian markets. With optimal growth at 18-20°C and severe disruption above 25°C, developing heat-resilient varieties is critical. This study integrated high-throughput 3D multispectral phenotyping with multivariate analysis to characterize temporal heat stress responses in 18 Chinese cabbage genotypes. Seedlings were subjected to heat stress (setpoint 40/35°C day/night; measured 35.7/31.5°C day/night air temperature) or controls (setpoint 25/20°C day/night; measured 25.0/17.7°C day/night air temperature) for 14 days, with continuous non-destructive monitoring of 14 morphological and spectral parameters using PlantEye F600 multispectral 3D scanner. Principal component analysis of temporal phenotyping data explained 62-68% of variance, enabling quantitative assessment of phenotypic stability through Euclidean distance measurements in PC space. Temporal analysis revealed crop-specific response patterns with maximum treatment separation at 3 days after treatment (DAT) (ΔC=3.27), reflecting Chinese cabbage’s rapid heat sensitivity as a cool-season crop, followed by progressive acclimation by 14 DAT (ΔC=1.41). Early responses (3-5 DAT) were dominated by morphological parameters, transitioning to physiological adjustments (10-14 DAT) characterized by spectral indices. Under heat stress, plants prioritized evaporative cooling through increased transpiration (four-fold increase) over carbon assimilation. A critical finding was the disproportionately greater reduction in root biomass relative to shoot biomass under to heat stress, with root biomass declining 38-47% versus 20% in shoots. Strong correlations (r>0.8) between 3D imaging parameters and destructive biomass measurements validated the non-destructive approach’s reliability. Notably, image-based root surface area analysis correlated strongly with actual root biomass (R 2 =0.698, p<0.001), enabling practical assessment of root area without conventional destructive processing. Based on integration of phenotypic stability (Euclidean distances in PC space) and biomass production under heat stress, this approach identified four distinct heat tolerance strategies: stable-productive genotypes (ideal breeding targets combining phenotypic stability with high heat-stress biomass production), stable-conservative genotypes (phenotypic stability with lower production), plastic-productive genotypes (substantial phenotypic changes yet high biomass production), and plastic-sensitive genotypes (phenotypically unstable and poor biomass production). This validated framework accelerates heat-tolerant Chinese cabbage breeding through efficient high-throughput phenotyping, enabling targeted genotype selection for diverse production environments facing climate warming.
Why it matches plant phenotyping methods3Dマルチスペクトルスキャナによる非破壊・時系列表現型取得と、その解析・検証が研究の中心であり、熱ストレス下の形態・生理形質を定量化する実質的なハイスループット表現型解析研究である。
abstractThis study integrated high-throughput 3D multispectral phenotyping with multivariate analysis to characterize temporal heat stress responses in 18 Chinese cabbage genotypes.
Reproduction assets foundThe paper states its collected phenotyping data are available in the supplementary material hosted with the article (open access under CC BY-NC-ND), making the paper-specific phenotype dataset publicly actionable via the article DOI. The analysis code, however, is only available from the corresponding author uponReasonDataset · publichrough field phenotyping.) between RDA and the World Vegetable Center (WorldVeg)” and by the long-term strategic donors to the WorldVeg: Taiwan, the United States, Australia, the United Kingdom, Germany, Thailand, South Korea, Philippines, and Japan.
Footnotes
Appendix A
Supplementary data to this article can be found online at https://doi.org/10.1016/j.plaphe.2026.100221 .
Appendix A.
Supplementary data
The following is the Supplementary data to this article.
Multimedia component 1
Data availability
The data collected and used in this study are available in the supplementary material. The code used for analysis can be obtained from the corresponding author upon reasonable request.
ReferenceOpen asset ↗lines:486-514Plant phenotyping relevance match · UnverifiedEurope PMC · checked 5 Sept 2026
Soils exhibit remarkable spatial heterogeneity in environmental conditions, which plants perceive at the levels of the whole root system, individual roots, and root tissues. Cropping practices aimed at reducing the environmental footprint of agriculture are likely to intensify this heterogeneity, highlighting the urgent need to adapt crops to heterogeneous soil environments. Recent advances in soil imaging and spatial omics offer unprecedented opportunities to decipher the molecular, physiological, and ecological processes that underpin plant-soil interactions. In this review, we explore the substantial yet largely untapped potential of integrating soil imaging with spatial omics to uncover the fundamental mechanisms that control root foraging in heterogeneous soils. We present an overview of key imaging and molecular approaches that have particular potential for revealing root foraging behavior. To demonstrate their capabilities for generating spatially explicit insights into root-soil interactions, we highlight selected case studies covering both biotic (beneficial and detrimental soil organisms) and abiotic (physical and chemical soil properties) factors. Finally, we outline a workflow for integrating spatial omics with soil imaging through vertical integration of experimental studies across levels of environmental complexity, coupled with predictive modeling. Unlocking the full potential of these approaches will require linking molecular, physiological, and ecological mechanisms at the root-soil interface to whole-plant growth and crop productivity. These fundamental insights into the edaphic drivers of root foraging will be essential for guiding crop adaptation to future, more heterogeneous soil environments.
Why it matches plant phenotyping methods根の探索行動や根系・根組織の状態を可視化・解析する土壌イメージング手法を空間オミクスと統合するレビューであり、植物表現型取得・解析の方法論が中心です。
abstractRecent advances in soil imaging and spatial omics offer unprecedented opportunities to decipher the molecular, physiological, and ecological processes that underpin plant-soil interactions.
Soil-borne vascular pathogens pose serious threats to agriculture with complex invasion strategies that remain poorly characterized compared to foliar pathogens. While foliar pathogens like Magnaporthe oryzae employ specialized appressoria to penetrate plant surfaces through a combination of mechanical force and enzymatic degradation, the invasion mechanisms of vascular pathogens that lack classical appressoria have remained largely theoretical. The nanoscale processes governing root penetration and colonization by these pathogens are particularly challenging to visualize due to technical limitations of conventional microscopy. Here we show, using phase-contrast X-ray computed microtomography and advanced microscopy, that Fusarium oxysporum (Fo) employs distinct mitogen-activated protein kinase (MAPK) cascades to orchestrate root invasion through unprecedented morphological plasticity. We identify previously undocumented appressoria-like structures that facilitate physical penetration, while demonstrating that Fo exhibits remarkable cellular adaptability, reducing hyphal diameter by more than 20-fold (from 5 μm to 220 nm) to navigate confined plant spaces, a dramatic morphological transition previously thought impossible. By using cellulase-deficient mutants, we demonstrate that cellulolytic activity is dispensable for surface breach and submicrometric hyphal colonization, establishing that mechanical force generation rather than enzymatic degradation is the primary determinant of successful host penetration. Three-dimensional reconstruction reveals a quantitative correlation between fungal proliferation and progressive embolism formation, with distinct MAPK pathways differentially regulating penetration force generation (Fmk1), osmotic adaptation during apoplastic colonization (Hog1), and directional growth toward vascular tissues (Mpk1). These findings provide a mechanistic framework for vascular wilt pathogenesis and reveal potential targets for controlling these economically devastating plant diseases.
Why it matches plant phenotyping methods位相コントラストX線マイクロトモグラフィーと3次元再構成を中核に、根の侵入・菌糸形態・塞栓形成という植物の病態と形態を定量化しており、病理学的機構研究であるものの表現型取得法の適用が実質的です。
titlePhase-contrast microtomography unveils mechanisms of root colonization by a vascular fungal pathogen
Abstract Premise There is a knowledge gap regarding how foliar injury and restricted water uptake can be detected by measuring root dielectric response. This pot study nondestructively evaluated the efficiency of real‐time dielectric measurement to monitor the effects of glyphosate spraying. Methods Root dielectric properties were recorded on a minute scale in control and glyphosate‐treated maize, cucumber, and pea. Chlorophyll, stomatal conductance, and biomass measurements were taken to interpret the dielectric changes. Results Electrical capacitance and conductance varied diurnally due to the circadian regulation of water uptake and hydraulic conductance. Glyphosate application reduced capacitance, indicating the impeded root growth and activity caused by impaired amino acid synthesis, foliar damage, and restricted transpiration. The dissipation factor decreased in response to glyphosate due to impeded apoplastic water flow, suppressed root lignification, and hampered water absorption. The enhanced leaf and root hydraulic resistance caused by glyphosate was manifested in sharply reduced electrical conductance. Changes in the species’ dielectric response were consistent with physiological symptoms and biomass loss. Discussion Real‐time dielectric measurement proved suitable for the nondestructive monitoring of plant responses to foliar stress through altered root traits. This method could be employed to evaluate herbicide tolerance in crops and to develop and determine dosage of herbicide ingredients.
Why it matches plant phenotyping methods植物の根の誘電特性をリアルタイム・非破壊で測定し、ストレス応答や根形質を評価する方法が研究の中心であるため。
abstractnondestructively evaluated the efficiency of real‐time dielectric measurement to monitor the effects of glyphosate spraying
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.
Multispectral / hyperspectralLeafRootMorphology / geometry measurementLeaf traitsRoot system architecture
Premise Selective breeding over thousands of years has prioritized aboveground yield, with little regard for changes belowground. Roots underpin plant growth and resilience, but our knowledge of these critical structures lags behind that of aboveground structures. Accurately phenotyping root traits is labor-intensive, expensive, and often destructive. High-throughput, nondestructive methods are required to advance understanding of the fundamental biology of root systems and to integrate hard-to-measure root traits into breeding programs. Methods We used American licorice (Glycyrrhiza lepidota Pursh.), a perennial legume with a rich ethnobotanical history, as a model to investigate root system phenotypes. We assessed root traits across multiple populations, analyzed relationships between above- and belowground phenotypes, and tested the use of multidimensional leaf traits, including spectral reflectance, in predicting root traits. Results Root traits of American licorice varied significantly across source populations. Root traits were strongly intercorrelated and each root trait correlated with an aboveground phenotype. Leaf spectral reflectance and elemental composition predicted belowground traits; however, interpretation of some trait-specific signals were complicated by isometric scaling between plant size and root traits. Conclusions These findings demonstrate the use of high-dimensional leaf traits as a proxy for root traits, with potential applications for understanding foundational questions in plant biology and in breeding programs targeting belowground structures of perennial herbaceous species. Further optimization and larger studies are needed to improve predictive models.
Why it matches plant phenotyping methods葉の高次元形質とスペクトル反射を用いて、測定困難な根形質を非破壊・高スループットに推定する方法が研究の中心である。
abstractHigh-throughput, nondestructive methods are required to advance understanding of the fundamental biology of root systems and to integrate hard-to-measure root traits into breeding programs.
Reproduction assets foundThe paper's data availability statement points to two public, paper-specific assets: raw root scans on Zenodo and a Figshare deposit containing RhizoVision Explorer output features, CropReporter data and metadata, spectral reflectance data, elemental composition data, and all R code needed to reproduce the analyses. NoDataset · publich Center Bioanalytical Chemistry Facility (RRID:SCR_001047). Finally, we thank the reviewers for their careful evaluation of our manuscript and constructive comments, which helped us clarify the conceptual framing and strengthen the overall quality of the work.
DATA AVAILABILITY STATEMENT
Raw root scans can be found on Zenodo ( https://zenodo.org/records/18852041 ). RhizoVision Explorer output features, CropReporter and associated metadata, spectral reflectance data, elemental composition data, and all R code needed to reproduce the analyses presented in this manuscript can be found on Figshare ( https://doi.org/10.6084/m9.figshare.28742870 ).
REFERENCES
Alahmad , S.
,
D.
Smith
,
C.
KatOpen asset ↗Zenodo · 18852041lines:173-419Dataset · publicILITY STATEMENT
Raw root scans can be found on Zenodo ( https://zenodo.org/records/18852041 ). RhizoVision Explorer output features, CropReporter and associated metadata, spectral reflectance data, elemental composition data, and all R code needed to reproduce the analyses presented in this manuscript can be found on Figshare ( https://doi.org/10.6084/m9.figshare.28742870 ).
REFERENCES
Alahmad , S.
,
D.
Smith
,
C.
Katsikis
,
Z.
Aldiss
,
S. M.
Brunner
,
S. V.
Meer
,
L.
Meijer
, et al. 2025 .
Phenotyping the hidden half: combining UAV phenotyping and machine learning to predict barley root traits in the field
. Journal of Experimental Botany
76 : 5161 ‐ 5178 .
40580084
10.1093/jxb/eraf268
PMC1Open asset ↗Figshare · 10.6084/m9.figshare.28742870lines:173-419Plant phenotyping relevance match · UnverifiedEurope PMC · checked 5 Sept 2026
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
To address the issue of 3D reconstruction information loss caused by occlusion during single-view camera acquisition of crop phenotypic parameters, this study proposes a detection method for tomato plant phenotypic parameters based on multi-view 3D point cloud reconstruction. The Kinect 2.0 sensor was employed to acquire point cloud data of tomato plants from three different viewpoints. Background noise was effectively removed using a combination of Conditional Filtering and Statistical Outlier Removal methods. By extracting surface normal features and calculating Fast Point Feature Histograms (FPFH), the Sample Consensus Initial Alignment (SAC-IA) and Iterative Closest Point (ICP) algorithms were utilized to accomplish coarse and accurate registration of the point clouds, respectively, ultimately achieving 3D reconstruction. Experimental results demonstrated that the reconstructed 3D model of the tomato plant was clear in outline and complete in structure. For the phenotypic parameters of plant height, canopy width, and leaf angle, the coefficients of determination (R²) between the calculated and manually measured values were 0.98, 0.94, and 0.89, respectively, with Root Mean Square Errors (RMSE) of 0.75 cm, 1.10 cm, and 4.43 °. Compared to single-view measurements, the accuracy of plant height and maximum canopy width derived from multi-view reconstruction increased by 15.31% and 13.12%, respectively. This method provides technical support for the rapid and accurate extraction of phenotypic parameters in tomato plants.
Why it matches plant phenotyping methodsトマトの草丈、キャノピー幅、葉角を抽出するマルチビュー3D点群再構成法を開発し、手動測定との精度検証も行っており、植物表現型取得が研究の中心である。
abstractthis study proposes a detection method for tomato plant phenotypic parameters based on multi-view 3D point cloud reconstruction.
BarleyRyeWheatField / plotLeafRootGrowth / time-series analysisGrowth / development / phenologyRoot system architecture
Summary Cereal architecture is underpinned by the coordinated development of modular phytomer units. While above-ground phenology is well characterized by metrics such as the phyllochron, an equivalent framework for root system development is lacking. Because each phytomer node initiates both leaves and adventitious roots, root and shoot development are inherently linked. Here, we quantified this coordination in wheat, barley, and rye across contrasting temperature regimes and validated the results under field conditions. We introduce the rhizochron, defined as the thermal time (growing degree-days, °C d) period between the emergence of nodal roots on successive stem nodes, and the root appearance interval, describing the emergence rate of individual root axes. Root development followed a highly conserved thermal sequence synchronized with shoot phenology. Across species and environments, the rhizochron averaged 146.1°C d, closely matching the phyllochron (126.6°C d). We also identified a consistent thermal offset, with nodal roots emerging approximately 185.3°C d after the corresponding leaf on the same phytomer node. The root appearance interval averaged 45.3°C d, reflecting continuous root deployment across active nodes. By integrating root phenology into a node-based framework, the rhizochron provides a predictive tool for crop modeling, trait-based breeding, and more target phenotyping aimed at improving resource acquisition and climate resilience.
Why it matches plant phenotyping methods根系と地上部の発達を定量化する新しい熱時間指標（rhizochron等）を導入し、複数種・環境および圃場条件で検証しており、表現型測定法が研究の中心である。
abstractWe introduce the rhizochron, defined as the thermal time (growing degree-days, °C d) period between the emergence of nodal roots on successive stem nodes, and the root appearance interval, describing the emergence rate of individual root axes.
Abstract 3D models are used in plant phenotyping for non-destructive quantification and analysis of morphological characteristics. Analyzing plant structure allows breeders to select for desirable traits, associated with e.g. drought tolerance or increased productivity. In sugar beet, morphological parameters depict an essential element of the variety approval for distinguishing between genotypes. However, only a limited number of measured or scored parameters are considered at a single time point. In contrast, 4D data adds a temporal component and can depict the dynamic development of 3D parameters. To explore the potential of spatio-temporal 4D phenotyping for automated crop genotype differentiation, a greenhouse experiment was conducted by us covering twelve sugar beet genotypes. High-resolution 3D models were generated twice a week over the course of two months and both common and novel 3D morphological parameters were extracted. The importance of these parameters was assessed by us, and the dataset was analyzed using unsupervised pointwise clustering and time series clustering. Varying importance of parameters depending on the time point and significantly higher importance of plant parameters compared to leaf parameters are demonstrated by our results. Moreover, increased and more stable genotype differentiation is archived using time series clustering compared to pointwise clustering. Furthermore, taproot formation of sugar beet was found to have a crucial impact on morphological development. Substantial variations in the dynamic development of 3D morphological parameters underline the importance of 4D data for plant genotype differentiation. Thus, a novel foundation for genotype differentiation in plant phenotyping is provided by our findings.
Why it matches plant phenotyping methods3Dモデルから植物形態形質を抽出し、時系列クラスタリングで遺伝型識別を評価する4Dフェノタイピング手法が研究の中心である。
titleSpatio-temporal 4D phenotyping for automated morphological genotype differentiation of sugar beet
Reproduction assets foundThe paper publicly deposits its generated sugar beet point cloud dataset under CC BY 4.0 at a Dataverse DOI, directly reproducing the paper's phenotyping measurements. Supplementary Python codes and extracted parameter values are stated to be included with the article, but no authors' public URL for the code is presentDataset · publicThe generated point cloud dataset is available at https://doi.org/10.60507/FK2/IS8YBZ under CC BY 4.0 license.Open asset ↗10.60507/FK2/IS8YBZlines:277-363Plant phenotyping relevance match · UnverifiedEurope PMC · checked 15 Sept 2026
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
Plant phenotyping relevance match · UnverifiedOpenAlex · Europe PMC · checked 15 Sept 2026
Roots play a pivotal role for plant performance, but they are difficult to access, which hampers quantitative measurements. Repeated imaging of rhizotrons, flat growth containers with a transparent side, has proven suitable to assess dynamics of root traits in indoor experiments. However, measuring hundreds of soil-grown plants with high temporal resolution remains a laborious challenge. We introduce a novel whole-plant phenotyping platform with a capacity of almost 900 rhizotrons, which we named GrowScreen-Rhizo 3. This platform was designed to image shoots and roots of individual plants simultaneously and derive digital proxy traits for biomass and growth. In addition, built-in weighing and watering stations deliver water use data for each rhizotron. To achieve the desired throughput (image all 896 plants once a day) a high degree of automatization and standardization was required. We realized a modular plant-to-sensor solution, using a fleet of automated guided vehicles (AGVs) to transport large rhizotrons (80x40x5 cm) to four measurement chambers for daily imaging, weighing, and watering. Simultaneous imaging of the root system with a high-resolution camera (116 μm per px) and the shoot from six different viewing angles allows to monitor plant growth with high spatial and temporal accuracy. First, we verified that moving plants to the measurement chambers did not significantly affect above- or belowground plant growth. Next, we measured phenotypic variation in root and shoot traits of 24 barley genotypes, parents of a nested association mapping population. Our analysis revealed that heritability of root traits such as root system depth and seminal root length was moderate to high (r 2 =0.52 and r 2 =0.93, respectively), enabling further assessment of increasing numbers of recombinant genotypes. The results demonstrate the suitability of GrowScreen-Rhizo 3 to phenotype a range of plant species characterized by various growth habits, including crop, niche, and wild plant species. We conclude that GrowScreen-Rhizo 3 will contribute significantly to the development of phenotyping pipelines for the identification of candidate genotypes with improved resource use efficiency and to pre-breeding processes of climate-resilient crops.
Why it matches plant phenotyping methods根とシュートを自動撮像し、バイオマス・成長などの形質を抽出する大規模フェノタイピング platform の開発・検証が中心である。
abstractWe introduce a novel whole-plant phenotyping platform with a capacity of almost 900 rhizotrons, which we named GrowScreen-Rhizo 3.
Field / plotRootGrowth / development / phenologyRoot system architecture
Understanding root growth and phenology is essential for improving the productivity, resilience, and sustainability of pasture-based systems. However, roots remain one of the most difficult components of plant systems to measure and monitor, particularly in managed, high-turnover pastures, such as those in New Zealand (NZ) dairy systems. As a result, root processes are often underrepresented in both experimental studies and pasture system models. This perspective paper identifies critical, but underdeveloped areas in root research, with particular focus on root phenology. Current studies are limited by insufficient temporal resolution, a lack of species- and cultivar-specific trait data in mixed swards, and weak integration of root dynamics into breeding programmes and farm system models. These constraints limit our ability to link root processes to pasture persistence, nutrient cycling, and climate resilience. To address this gap, we propose that root phenology should be treated as a dynamic functional trait that links plant responses to environmental and management drivers with ecosystem-level outcomes. This framing provides a conceptual foundation for integrating root dynamics into pasture research and modelling, particularly in systems subject to frequent defoliation and environmental variability. We further highlight opportunities arising from rapid advances in sensing technologies, automation, and data analytics, which enable continuous, high-resolution root monitoring systems at multiple scales. However, realising this potential requires integration of complementary measurement approaches and alignment with system-level research questions. In this context, NZ provides a unique platform for developing scalable, pasture-based root monitoring framework that integrates science, management and policy. We argue for a coordinated effort that bridges fundamental root biology with applied pasture management, supported by long-term datasets, methodological integration, and engagement with end users. Embedding root traits and phenological dynamics into the next generation of pasture models and decision-support tools will be critical for improving system performance and environmental outcomes. This perspective aims to stimulate a shift towards more integrated, temporally explicit approaches for studying root systems in pasture environments, with relevance to grazing system beyond NZ and across temperate regions.
Why it matches plant phenotyping methods根の成長・フェノロジーという植物形質の測定課題と、センシング・自動化・データ解析を統合した高頻度モニタリング手法を中心に論じる方法論的パースペクティブである。
abstractroots remain one of the most difficult components of plant systems to measure and monitor
Plant phenotyping relevance match · UnverifiedOpenAlex · Crossref · Europe PMC · checked 15 Sept 2026
SoybeanGrowth chamberRootMorphology / geometry measurementObject detectionRoot system architecture
). Accurate quantification of nodule traits is essential for understanding host-microbe interactions and genetic determinants of nodulation. However, traditional manual or semi-quantitative approaches are labor-intensive, subjective, and unsuitable for large-scale studies. Here, we present a high-throughput phenotyping pipeline based on the YOLO deep learning architecture for the automated detection and extraction of soybean root traits. The pipeline quantifies nodule count, dimensions, and spatial distribution, enabling measurement of 24 distinct nodulation-related traits. Using root images from 21-day-old hydroponically grown soybean plants, the model achieved a precision of 0.94, a recall of 0.95, and an F1 score of 0.94 for nodule detection, maintaining accuracy across count ranges. It processes 50 root images in 37 seconds on a single GPU (45 GB memory), representing a ~227-fold improvement in efficiency compared to manual scoring (~2 h 20 min). As proof of concept, we applied this pipeline in a genome-wide association study (GWAS) using the FarmCPU approach and identified 50 significant SNPs associated with multiple nodulation traits, including novel ones. Several candidate genes linked to these loci suggest potential new regulators of nodulation. This YOLO-based phenotyping framework provides a robust, scalable, and reproducible tool for trait discovery and genetic analysis, advancing research in legume genomics and crop improvement. To promote the adoption of this user-friendly nodulation phenotyping pipeline and to support its further development, we have made all essential resources publicly available at: https://github.com/Salk-Harnessing-Plants-Initiative/soybean-nodule-detection.
Why it matches plant phenotyping methodsYOLOを用いた根粒形質の自動取得パイプラインを開発し、検出精度・処理速度を検証した方法中心の研究である。
abstractHere, we present a high-throughput phenotyping pipeline based on the YOLO deep learning architecture for the automated detection and extraction of soybean root traits.
Conventional approaches to measuring stomatal conductance (gs) and transpiration often rely on instruments that interfere with plant physiology. Porometers, for example, restrict natural leaf movement, apply pressure, and introduce dry airflow that can alter stomatal behaviour, thereby reducing the relevance of such measurements. Prior studies report discrepancies among devices attributable to such interferences (Toro et al. 2019). To minimise artefacts, transpiration should be estimated remotely without physical contact, which theoretically can be achieved via a thermal leaf energy-balance approach that infers gs from leaf temperature, radiative load, and boundary-layer terms. In this study, we combine 3D plant models, light interception models, and thermal imaging to solve the energy-balance equation of individual leaves, estimating transpiration entirely remotely. Approaches to estimate stomatal conductance based on the energy-balance equation were developed recently to aid phenotyping of plantss. Most methods either imposed rapid changes in air humidity to perturb transpiration and, consequently, leaf temperature (Driever et al. 2023), or relied on ‘dry’ and ‘wet’ reference surfaces (as in Leinonen et al. 2006) to compute stress indices (Vialet-Chabrand and Lawson 2020). These methods require reference materials to assess surface temperatures under maximum and zero transpiration, showing the effect of longwave radiation. However, reference-material methods were constrained by heterogeneity in light interception caused by variation in leaf angle and orientation, because reference surfaces could not reorient like real leaves (Zhang et al. 2025). In this study, we addressed this challenge by using thermal imaging and 3D photogrammetry to capture leaf temperature and geometry noninvasively, allowing parameter estimation for each leaf individually. Here, ρ is the density of air (kg m−3), cp is the specific heat capacity of air (J kg−1 K−1) and rHR is the parallel resistance to heat and radiative transfer on the leaf surface (s m−1), s is the slope of the curve relating saturating water vapour pressure to temperature (Pa °C−1). TL and TA are leaf and air temperatures (°C), respectively, δe is air vapour pressure deficit (Pa), γ is the psychrometric constant (Pa K−1) and rva is the boundary layer resistance to water vapour (s m−1) (Supporting Information S2: Equation S1). The net radiative energy Rn in the energy-balance term was obtained from the same 3D light interception model, which integrates measured direct and lateral scattered irradiance (W m−2) (Supplement Material and Methods, File S2). Stomatal conductance gs (m s−1) is the inverse of stomatal resistance rs (s m−1). To experimentally obtain a wide range of gs values, we grew eggplant (Solanum melongena L.) plants in hydroponic units in growth chambers under four sets of environmental conditions (Table 1). Thirty-day-old plants (4–5-leaf stage) were placed on balances (Supplementary Materials and Methods, File S2). Units were sealed with plastic film to minimise evaporation. Mass loss attributable to transpiration was logged automatically every 30 s. To induce short-term changes in stomatal conductance, we imposed an acute osmotic stress by delivering a saline NaCl solution with high electrical conductivity (60 mS cm−1) to the root zone, producing a steep drop in root osmotic potential. This created rapid physiological and morphological responses that altered incident irradiance at the leaves, leaf temperature, and consequently energy balance, stomatal conductance and transpiration. We chose this stressor for operational simplicity. Any perturbation that modifies transpiration dynamics and thus gas exchange could have served our purpose. The total transpiration of a leaf, Et (kg s−1), is the product of the total conductance to water vapour from the mesophyll to the atmosphere, gv (m s−1), calculated from the estimated stomatal resistance rs (s m−1) and the boundary layer conductance gva (m s−1), the difference between water vapour concentration inside the leaf Cvs (dimensionless), and in surrounding air Cva (dimensionless), the leaf area A (m2), and the density of water ρw (kg/m3) (Jones 1992). Estimated stomatal conductance was obtained from leaf energy balance calculation (Equation 1). Boundary-layer conductance was computed from measured wind speed and leaf dimensions (leaf area, length, width) extracted from structure-from-motion 3D reconstructions (Supporting Information S1: Equation S6; Grace et al. 1980). Transpiration was then calculated for each leaf at each thermal 3D imaging time point, and whole-plant transpiration for comparison with gravimetric logs was the sum of all per-leaf estimates. As a non-invasive approach, we evaluated the plausibility or our model derived stomatal conductance (Equation 2) indirectly by comparing calculated and measured whole plant transpiration. We emphasise that this is not a direct validation of gs. Rather, the close agreement between modelled and measured transpiration across the wide range of environmental treatments, both stressed and non-stressed, provides confidence that the inferred gs is realistic. RGB and thermal images acquired before, during, and after stress application enabled dynamic tracking of leaf position and temperature (Supplementary Material and Methods, File S2). As expected, osmotic stress application had immediate effects on morphology and physiology. While control leaves maintained an angle of around 110° throughout, osmotic shock induced immediate turgor loss and drooping in all environments except one (Figure 1A,B). Leaf angles recovered to pre-stress positions within 1 h, indicating adaptation to the osmotic shock and restoration of turgor. Only environment 4 (high light, low air temperature and low humidity) maintained turgor during stress. Angle shifts were most pronounced in older leaves, which drooped and reduced light interception; younger leaves better preserved structure and turgor (Supporting Information S1: Figure S2). These angle changes also altered incident irradiance at the leaf surface (Supporting Information S1: Figure S3). These morphological responses coincided with increases in leaf temperature, consistent with altered water fluxes and stomatal regulation after stress. Across environments, plants showed a uniform rise in leaf temperature following osmotic stress, regardless of initial temperature (Supporting Information S1: Figure S4). This response held across leaf ages, encompassing older (Figure 1C) and younger (Figure 1D) leaves. Stomatal conductance estimated with our method followed the same pattern, dropping rapidly after osmotic shock in both older (Figure 1E) and younger (Figure 1F) leaves (Supporting Information S1: Figure S5). We estimated no stomatal conductance recovery to pre-stress conditions over the time course of stress exposure. Model-estimated and gravimetrically measured transpiration showed identical time courses across all four environmental conditions (Figure 1G–J). Transpiration rates did not recover to the same extent as leaf turgor, indicating long-term effects of the osmotic shock. Across environments and time points, correlation between model estimated and measured whole-plant transpiration was high (Figure 1K). In this study, stomatal conductance (gs) is a model-derived quantity inferred from the same physically constrained framework and model (leaf temperature, boundary-layer conductance and vapour pressure deficit). Since we did not measure gs directly, we cannot validate gs directly. Instead, we used a non-invasive check via transpiration. Model predictions closely tracked measured transpiration across the four controlled environments. This agreement increases confidence that the inferred gs is realistic, while we acknowledge that transpiration agreement alone is not a rigorous validation and cannot fully rule out compensating errors. Our study demonstrated the potential of our approach to estimate transpiration accurately by combining 3D imaging and thermography with physiological modelling without the use of reference materials that imitate real leaves. This remote approach enables simultaneous assessment of morphological and physiological responses to stress, yielding a more integrated view on plant transpiration and gas exchange. In contrast to chamber and porometer measurements or IR methods requiring wet and dry references or calibration plates, our workflow is reference-free. Absorbed shortwave radiation is derived from measured irradiance and a 3D reconstruction of leaf geometry, with no external reference materials. Moreover, remote measurements avoid continuous pressure from clamp-on porometers, permitting long-term observation and capture of rapid stress responses without sustained damage or microclimate artifacts. Further, the approach is not limited by any clamp on sensors and as such enables multi-leaf tracking. Applied to crop canopies, this approach could improve understanding of canopy processes that influence productivity and enable remote estimation of canopy transpiration. Future research could further improve by replacing our strong saline solution stress by gradual soil drying to depict a more realistic and natural stress while testing the approach under long-term conditions. Recent studies indicate that, with rising atmospheric CO2 concentrations, breeding for reduced stomatal conductance could increases WUE without affecting photosynthetic capacity (Srivastava et al. 2024). As such, remote systems for high-throughput plant phenotyping (HTP) are required to scan vast quantities of plants. We see a potential use of our system for such purposes to quickly estimated whole plant and individual leaf transpiration, as initial image capturing is very fast. A large bottleneck in our work was 3D model generation speed and manual extraction of leaf parameters from these 3D models. Both could be streamlined with more automated software, possibly including neural network solutions. The authors have nothing to report. The authors declare no conflict of interest. The data that support the findings of this study are available from the corresponding author upon reasonable request. Please note: The publisher is not responsible for the content or functionality of any supporting information supplied by the authors. Any queries (other than missing content) should be directed to the corresponding author for the article.
Why it matches plant phenotyping methods3D画像、熱画像、光遮断モデル、エネルギーバランスモデルを統合し、葉ごとの蒸散と気孔コンダクタンスを非侵襲的に推定する手法を開発・評価しており、植物表現型取得が研究の中心である。
abstractIn this study, we combine 3D plant models, light interception models, and thermal imaging to solve the energy-balance equation of individual leaves, estimating transpiration entirely remotely.
Reactive oxygen species (ROS) are central regulators of plant development and stress responses, with hydrogen peroxide (H 2 O 2 ) acting as a key signaling molecule whose spatial distribution determines adaptive versus damaging outcomes. Accurate detection of H 2 O 2 at tissue and cellular resolution is therefore essential for understanding redox-dependent regulation of plant growth. A variety of techniques have been used to monitor H 2 O 2 , including bulk spectrophotometric and fluorometric assays, genetically encoded sensors for real-time measurements, and chemical probes for in situ detection. While these approaches differ in sensitivity, specificity, and temporal resolution, many are limited by a lack of spatial information, technical complexity, or dependence on transgenic material. Here, we present a detailed protocol for 3,3'-diaminobenzidine (DAB)-based histochemical detection of H 2 O 2 in seedling roots, covering staining, imaging, and semi-quantitative image analysis using open-source software (FIJI/ImageJ). The method relies on peroxidase-mediated oxidation of DAB, resulting in a stable, light-resistant, and insoluble precipitate that enables visualization of H 2 O 2 accumulation with high spatial resolution. This protocol provides a robust, accessible, and genetically independent approach for spatial analysis of H 2 O 2 in plant tissues. Its simplicity, compatibility with diverse genotypes and treatments, and suitability for semi-quantitative analysis make it a valuable tool for examining the spatial distribution of H 2 O 2 , thereby providing spatial insight into redox-related regulatory processes during plant development and stress responses. Key features • Built upon methods developed by Thordal-Christensen et al. [1] and Daudi and O'Brien [2], with a specific focus on root staining. • Includes a downstream image analysis pipeline for semi-quantitative H 2 O 2 measurement in DAB-stained roots using the open-source software FIJI/ImageJ. • Provides detailed, step-by-step video tutorials for image analysis in FIJI/ImageJ. • Includes a Fiji/ImageJ script (Macro 1) for automating the application of fixed-intensity scaling using Spectrum LUT.
Why it matches plant phenotyping methods植物組織内の過酸化水素を画像取得・画像解析で空間的かつ半定量的に測定する実験プロトコルが中心であり、植物の生理状態を抽出するフェノタイピング手法に該当する。
abstractHere, we present a detailed protocol for 3,3'-diaminobenzidine (DAB)-based histochemical detection of H 2 O 2 in seedling roots, covering staining, imaging, and semi-quantitative image analysis using open-source software (FIJI/ImageJ).
RootMorphology / geometry measurementSegmentationRoot system architecture
BACKGROUND: Root segmentation is a fundamental yet challenging task in image-based plant phenotyping. Accurate segmentation is a prerequisite for extracting root traits relevant to plant physiology, breeding, and agronomy. While U-Net and other convolutional neural network (ConvNet) architectures have been applied to root segmentation, no systematic comparison of multiple Transformer and ConvNet architectures has been conducted across diverse root imaging conditions. RESULTS: We evaluated 21 segmentation architectures across nine diverse root image datasets, training 1511 models to assess all combinations of architecture, dataset, pre-training strategy, and learning rate, producing over 3 million segmentations for evaluation. Transformer-based models significantly outperformed ConvNets for Dice (mean Dice 0.679 vs 0.659; [Formula: see text]). Root-diameter and root-length correlation were also higher for Transformers, but the differences were not statistically significant ([Formula: see text] and [Formula: see text] respectively). Pre-training significantly improved mean Dice from 0.623 to 0.666 ([Formula: see text]), with Transformers benefiting more from pre-training than ConvNets (Dice improvement + 0.072 vs + 0.021; [Formula: see text]), supporting the hypothesis that fine-tuned Transformers transfer more effectively across large domain gaps. MobileSAM achieved the highest Dice score (0.693) while maintaining computational efficiency. Both architecture families underestimated thin root length compared to manual annotations. Dataset choice explained 70.9% of performance variance, far exceeding model architecture (6.7%). PURPOSE: Transformer architectures significantly outperform ConvNets for root segmentation accuracy, and pre-training significantly improves performance, particularly for Transformers. Pre-trained MobileSAM offers the best accuracy at competitive computational cost. Dataset choice dominates performance variance, suggesting practitioners should prioritize data curation over architecture selection.
Why it matches plant phenotyping methods根の画像セグメンテーション手法を複数データセットで体系的に比較・検証し、根長・根径などの形質抽出性能も評価しているため、植物フェノタイピング手法が中心である。
abstractRoot segmentation is a fundamental yet challenging task in image-based plant phenotyping.
Reproduction assets foundThe paper's root image datasets (DeepRootLab, Grassland, Chicory, PRMI) are publicly available, and the authors' training code and modified RhizoVision Explorer trait-extraction fork are on GitHub with explicit availability statements.Dataset · publicImages are available from https://zenodo.org/records/15213661 .Open asset ↗Zenodo · 15213661lines:872-982Dataset · publicImages are available from https://figshare.com/ndownloader/articles/20440497/versions/2 .Open asset ↗Figshare · 20440497lines:872-982Dataset · publicImages are available from https://zenodo.org/records/3527713 .Open asset ↗Zenodo · 3527713lines:872-982Dataset · publicImages are available from https://gatorsense.github.io/PRMI/ .Open asset ↗lines:872-982Code · publicTraining code is available at https://github.com/sotlampr/seg .Open asset ↗GitHub · sotlampr/seglines:1183-1225Code · publicAll nine root image datasets used in this study are publicly available. DeepRootLab images are available from Zenodo (https://zenodo.org/records/15213661). Grassland images are available from Figshare (https://figshare.com/ndownloader/articles/20440497/versions/2). Chicory images are available from Zenodo (https://zenodo.org/records/3527713). The six PRMI datasets (Papaya, Peanut, Sesame, Sunflower, Cotton, Switchgrass) are available from https://gatorsense.github.io/PRMI/. Training code is available at https://github.com/sotlampr/seg. The modified RhizoVision Explorer fork used for trait extraction is available at https://github.com/sotlampr/RhizoVisionExplorer.Open asset ↗GitHub · sotlampr/RhizoVisionExplorerlines:1294-1347Plant phenotyping relevance match · UnverifiedOpenAlex · Crossref · checked 5 Sept 2026
Published20 Apr 2026Chemical and Biological Technologies in AgricultureCited by 1 · OpenAlex ↗
Biological control represents a valuable tool for the sustainable management of soil-borne diseases in tomato cultivation and relies on the availability of effective microbial solutions. Digital technologies can support to the ecodesign stage by accelerating the screening and selection of high-performing microbial biocontrol agents. In this study, a collection of eleven endophytic bacteria strains recruited from the tomato root endosphere and proved to be compatible with Trichoderma spp. (non-target effect), was characterized for antagonistic and biofertilization/biostimulant traits, and evaluated in planta against two major tomato pathogens: Fusarium oxysporum f. sp. lycopersici and Sclerotium rolfsii. Plant phenomics realized using the PlantEye 500 multispectral dual scanner, was used to screen the effectiveness of microbial agents determining plant performances under both infection and healthy conditions. Multivariate analysis of 20 digitally computed phenotypic traits helped the detection of Peribacillus sp. C5NA and Neobacillus sp. TR12 as highly effective against wilt, and capable of counteracting the reduction in leaf angle surface and chlorophyll: typical tracheofusariosis symptoms. On the other hand, Peribacillus strains TR2 and C6 treatments caused partial phenotypic recovery in plants affected by Sclerotium rot. Interestingly, Microbacterium sp. TR9, appeared to be multifaceted. It showed mild multisuppressivity against both pathogens coherently with the exhibited N-acetyl-b-glucosaminidase, polysaccharide breaking and in vitro antifungal activities. In addition, it also acted as a putative biostimulant in the absence of pathogens, increasing digital biomass, plant height, and NDVI, in line with its proven strong ability to produce ammonia, fix nitrogen, solubilize phosphates, and release indoleacetic acid. Overall, the integration of phenomics supported the high-resolution detection of plant responses and supported the identification of multifunctional microbial strains with biocontrol and biofertilization potential for sustainable tomato production.
Why it matches plant phenotyping methodsPlantEye 500によるマルチスペクトル表現型取得と20形質のデジタル解析が、微生物資材のスクリーニングおよび植物応答評価の中心的手法として用いられているため。
abstractPlant phenomics realized using the PlantEye 500 multispectral dual scanner, was used to screen the effectiveness of microbial agents determining plant performances under both infection and healthy conditions.
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.
Root exudation mediates the delivery of plant primary and secondary metabolites into soil, where they regulate plant–microbe interactions and terrestrial carbon cycling. Conventional exudate analyses quantify total root-released carbon yet obscure the spatial origin and rhizosphere influence of individual compounds. Here, we develop a rhizobacterial biosensor platform, named Suc-MAPP, to map local exudate profiles along the surface of colonized root tissues. Focusing on sucrose, we engineered sfGFP-based, sucrose-responsive gene circuits in Pseudomonas putida KT2440 for live imaging of exudate concentrations in the micromolar range. These biosensors reveal spatially structured sucrose exudation patterns across eudicots and monocots and implicate photoassimilated source–sink dynamics as a major determinant. We further apply this platform to phenotype exudation modulated by synthetic gene circuitry in Arabidopsis thaliana , identifying genetic design rules for graded sucrose release and quantifying how engineered export sculpts rhizosphere assembly of a defined bacterial community. Together, these results establish programmable rhizobacterial biosensors as tools to spatially resolve plant–environment carbon exchange in situ and provide a framework for extending this approach to diverse exudate targets.
Why it matches plant phenotyping methods植物根からのスクロース滲出を空間的・定量的に測定する生体センサープラットフォームを開発し、植物表現型として適用しているため、方法が中心的である。
abstractHere, we develop a rhizobacterial biosensor platform, named Suc-MAPP, to map local exudate profiles along the surface of colonized root tissues.
WheatField / plotRootMorphology / geometry measurementPhysiological trait estimationRoot system architectureWater status / transpiration
Root hydraulic properties affect water uptake in wheat (Triticum aestivum L.) and are strongly influenced by root anatomy, yet how they vary along root axes and differ among cultivars remains underexplored. We investigated crown roots of six German winter wheat cultivars spanning one century of release, sampled from a field experiment. Roots were imaged at different positions along their axis using a high-throughput system (Rapid Anatomics Tool), and the resulting anatomical traits were coupled to the GRANAR-MECHA model to estimate radial and axial hydraulic conductance. Longitudinal anatomical gradients were pronounced: tissue dimensions, metaxylem number, and apoplastic barriers decreased from the base onwards, resulting in radial conductance increasing and axial conductance decreasing with distance from the base. Cultivar differences were also apparent: modern cultivars had smaller tissues and fewer metaxylem vessels, reducing both axial and radial conductance and lowering whole-root water uptake capacity (∼20-30%). By integrating field sampling with high-throughput image analysis and modeling, this study establishes an integrated phenotyping approach linking root anatomy to hydraulic function and uncovering anatomical traits relevant for water uptake. The results show that longitudinal gradients and cultivar-associated anatomical differences contribute to variation in hydraulic properties and persist along fully mature root segments.
Why it matches plant phenotyping methods根の高スループット画像解析とモデル推定を統合した表現型解析手法が研究の中心であり、解剖学的形質と水理機能を定量化している。
abstractRoots were imaged at different positions along their axis using a high-throughput system (Rapid Anatomics Tool), and the resulting anatomical traits were coupled to the GRANAR-MECHA model to estimate radial and axial hydraulic conductance.
Plant phenotyping relevance match · UnverifiedCrossref · Europe PMC · checked 5 Sept 2026
As a core resource of traditional Chinese medicine (TCM), medicinal plants are conventionally monitored and assessed using high-cost, low-efficiency methods. Remote sensing offers an efficient technical alternative for large-scale and dynamic evaluation. This study systematically reviewed the literature from 2005 to 2025, summarized remote sensing platforms, sensors, and data analytical methods, and specifically analyzed their applications in medicinal plant resource investigation, planting monitoring, stress monitoring, and TCM quality assessment. These studies mainly focus on resource surveys and quality analysis, targeting root and rhizome herbs. Integrated satellite-, UAV-, and ground-based remote sensing enables distribution mapping, growth retrieval, stress monitoring, and non-destructive quality evaluation in medicinal plants, achieving overall accuracies ranging from 80% to 100%. Currently, remote sensing applications in medicinal plants are evolving toward space–air–ground integration, multi-source data fusion, artificial intelligence empowerment, and multi-omics integration. However, they are constrained by complex wild habitats, difficulties in monitoring root herbs, spectral confusion, and limited model generalization. Future efforts should focus on establishing an integrated monitoring network, developing full-chain quality inversion models for geo-authentic herbs, building climate-adaptive cultivation systems, creating early pest–disease warning technologies, and deepening the integration of remote sensing and multi-omics to support the sustainable utilization and high-quality development of medicinal plant resources.
Why it matches plant phenotyping methods植物の成長・ストレス・品質などの状態を対象に、リモートセンシングのプラットフォーム、センサー、解析手法を体系的にレビューしており、植物フェノタイピング手法が中心です。
abstractThis study systematically reviewed the literature from 2005 to 2025, summarized remote sensing platforms, sensors, and data analytical methods
Growth chamberRootMorphology / geometry measurementSegmentationGrowth / time-series analysisGrowth / development / phenologyRoot system architecture
Abstract 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 cereal root system architecture and 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 hair area from single images. Validation of the image analysis algorithm showed good segmentation performance, with average Matthews Correlation Coefficient values of 0.68 for root area and 0.65 for 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 rapid, scalable, and training-free method for integrated analysis of root architecture 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根系画像解析システムとRベースの画像分析ワークフローを開発し、根系構造と根毛面積の定量化およびアルゴリズム性能検証を中心に扱っているため、植物フェノタイピング手法として適格です。
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, that enables repeated, in situ quantification of cereal root system architecture and root hair area over three weeks of growth.
Reproduction assets foundThe paper's authors state that the RootHairFinder C++ file, R script, and example rhizotron data will be available via a GitHub repository and Zenodo upload, and the preprint's supplementary files already include RootHairFinder.cpp and example rhizotron images (Supplimentaryfile4.tif, Supplimentaryfile5.tif). This is aCode · publicThe RootHairFinder cpp file, R script and example data files for rhizotron analysis will be made available through github https://github.com/TracyValentine/RootHairFinder and https://zenodo.org/uploads/19288922Open asset ↗TracyValentine/RootHairFinderlines:236-263Plant phenotyping relevance match · UnverifiedCrossref · checked 14 Sept 2026
RootMorphology / geometry measurementGrowth / development / phenologyRoot system architecture
All plants rely on their roots for survival. Due to the natural plasticity of roots in response to different stimuli, breeders can investigate natural adaptation and uncover advantageous root features to increase plant yield in agricultural system. The plant's physiology, development, and ability to respond to different pressures are all influenced by the root system. Root system architecture (RSA)-related factors are very important for breeding selection. However, quantifying these traits is difficult, requires a lot of resources, and frequently produces a lot of variability. With the development of computer vision and machine learning (ML) technologies, which allow for effective trait extraction and evaluation, the use of RSA traits for genetic improvement to create more robust and resilient crop cultivars has attracted greater attention. Root phenotype is a crucial component of yield improvement which is regulated by the interaction of internal genetic factors and external environmental conditions. To meet the demands of population growth and climate change, significant increases in agricultural productivity are required. Enhancing crop root architecture has the potential to improve water and nutrient use efficiency; however, a major challenge remains in accurately characterizing the structure and function of the root phenome. Numerous advances have been made in recent years in the measurement and analysis of root system, including the development of 2D and 3D root phenotyping platforms. These platforms are high-throughput and non-invasive techniques for root phenotype characterization. These approaches involve the use of advanced imaging and analytical tools to collect data on root structure, growth, and function across a large number of plants, while enabling automated evaluation of multiple root traits. To phenotype root systems numerous imaging tools, software, and platforms have been developed. This study focuses on recent advancements in in-situ root phenotyping techniques that allow researchers and breeders to efficiently assess root characteristics and apply them to different breeding initiatives. In-situ root phenotyping techniques encompass a variety of 2D and 3D platforms for thorough and efficient root analysis. This review highlights current developments in in-situ root phenotyping and stresses their expanding potential to aid in the development of stress-resistant, high-yielding crops for sustainable agriculture.
Why it matches plant phenotyping methods根系表現型取得に関する2D・3D画像化、解析ツール、ソフトウェア、プラットフォームの進展を扱うレビューであり、植物フェノタイピング手法が中心である。
titleAdvances in In-situ Root Phenotyping: A Review
Carbon monoxide (CO) has long been viewed as an environmental pollutant, but recent work points to its role as an endogenous signaling molecule in infection and stress. Here, we present RDM-CO, a near-infrared fluorescent probe built by attaching an allyl formate recognition unit to a rhodamine scaffold. When CO and Pd2+ are both present, the probe undergoes a Tsuji-Trost deallylation that turns on fluorescence at 653 nm within 13 min. RDM-CO shows a 63 nm Stokes shift, good selectivity over other biologically relevant species, a detection limit of 1.35 µM, and no obvious toxicity to cells. Using this probe, we visualized endogenous CO production in LPS-stimulated macrophages and found it localized to mitochondria. We also used RDM-CO to follow CO dynamics in a mouse model of bacterial pneumonia-allowing us to monitor anti-inflammatory treatment effects-and in plant roots under cadmium stress. These experiments demonstrated that RDM-CO can be used to study CO signaling across different biological systems.
Why it matches plant phenotyping methods植物根のストレス下における内因性CO動態を可視化する蛍光プローブを開発し、植物の生理状態の測定に適用しているため、植物フェノタイピング手法が中心的である。
abstractHere, we present RDM-CO, a near-infrared fluorescent probe built by attaching an allyl formate recognition unit to a rhodamine scaffold.
Drought stress poses a significant challenge to food security in sub-Saharan Africa, particularly for smallholder farmers in dryland systems. Bambara groundnut ( Vigna subterranea (L.) Verdc.), an underutilised legume with inherent drought tolerance, remains underexplored in terms of its root system traits. This greenhouse study investigated the early root and shoot responses of six Bambara groundnut genotypes under well-watered (100% field capacity) and water-stressed (50% field capacity) conditions using rhizotron-based phenotyping. Significant genotypic differences ( p < 0.01) were observed in root traits such as root system depth (RSD: 11.0-19.9 cm), root system width (RSW: 6.96-12.2 cm), and root dry mass (RDM: 0.42-1.27 g). The ARC genotype exhibited a strong drought-avoidance strategy, increasing RSD from 12.2 to 19.9 cm and RDM from 0.42 to 1.16 g under stress. The Tiga Nicuru DIP-C-F7471 genotype showed adaptive plasticity, maintaining deeper roots (11.0-14.5 cm), high convex hull area (CHA), and root-shoot ratio (RSR) values, despite a reduction in RDM, suggesting a resource-conserving strategy. Principal Component Analysis (PCA) captured 93.6% of the total variability among genotypes. Root traits, particularly total root length (TRL), convex hull area (CHA), root system width (RSW), and root dry mass (RDM), were the main contributors to genotype differentiation. Strong positive correlations (r = 0.88-0.97) between root and shoot traits suggest that genotypes with more developed root systems also supported greater shoot growth, highlighting the coordinated response of above- and below-ground traits under drought stress. These findings provide valuable targets for breeding and highlight the value of rhizotron-based screening for root trait selection. Future field validation and full-season studies are recommended to confirm their relevance for improving yield stability in dryland agriculture.
Why it matches plant phenotyping methods根系・地上部形質を取得するrhizotron-based phenotypingを用い、そのスクリーニング価値を主要な貢献として扱っているため、植物フェノタイピング手法の実質的応用に該当する。
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
Abstract Meloidogyne incognita (root-knot nematode) is one of the most damaging soilborne pathogens affecting the common bean. Control relies primarily on resistant cultivars, making accurate resistance phenotyping a key component of breeding programs. Here, we developed an integrated phenotyping approach to identify resistant genotypes in a recombinant inbred line (RIL) population. For initial screening, 361 RILs were evaluated with three replications for galling index (GI), number of galls (NG), and egg masses (EM) at 60 days after inoculation (DAI). A subset of 24 segregating RILs was further assessed in a second trial for GI, NG, EM, and reproduction factor (RF), with seven replications at 30 and 60 DAI. A multi-trait factor analytic mixed model was used to derive an overall resistance index (ORI) for genotype classification into moderately resistant (MR), intermediate (I) and susceptible (S) classes. We also assessed the potential of a qPCR-based phenotyping protocol using two contrasting RILs from the segregating subset. High heritability (> 0.8) and strong genotypic correlations among resistance components were observed in the RIL segregants, indicating a robust genetic basis for selection. MR genotypes consistently exhibited reduced GI, NG, EM, and RF, and transgressive segregants were identified within the MR group, confirming that the ORI framework effectively distinguished resistance levels. Moreover, later evaluation improved genotype classification and revealed resistance shifts. qPCR-based phenotyping consistently discriminated MR and S lines in agreement with classical phenotyping, supporting its use as a complementary evaluation tool. Overall, our results validate an integrative multi-trait strategy for more precise resistance phenotyping and genotype selection.
Why it matches plant phenotyping methodsマメの線虫抵抗性を対象に、複数の表現型指標、統合モデル、qPCRプロトコルを組み合わせた抵抗性フェノタイピング手法を開発・検証しており、手法が研究の中心である。
abstractwe developed an integrated phenotyping approach to identify resistant genotypes in a recombinant inbred line (RIL) population.
Plant phenotyping relevance match · UnverifiedOpenAlex · Crossref · Europe PMC · checked 5 Sept 2026
Field / plotPhotogrammetry / SfM / MVSRootWhole plant / canopy / plot / fieldCalibration / preprocessing2D/3D reconstructionRoot system architecture
Accurate 3D reconstruction is essential for high-throughput plant phenotyping, particularly for studying complex structures such as root systems. While photogrammetry and Structure from Motion (SfM) techniques have become widely used for 3D root imaging, the camera settings used are often underreported in studies, and the impact of camera calibration on model accuracyccu remains largely underexplored in plant science. In this study, we systematically evaluate the effects of focus, aperture, exposure time, and gain settings on the quality of 3D root models made with a multi-camera scanning system. We show through a series of experiments that calibration significantly improves model quality, with focus misalignment and shallow depth of field (DoF) being the most important factors affecting reconstruction accuracy. Our results further show that proper calibration has a greater effect on reducing noise than filtering it during post-processing, emphasizing the importance of optimizing image acquisition rather than relying solely on computational corrections. This work improves the repeatability and accuracy of 3D root imaging for phenotyping pipelines by giving useful calibration guidelines. This leads to better trait quantification for use in crop research and plant breeding in downstream analysis.
Why it matches plant phenotyping methods根の3D画像フェノタイピングにおけるカメラ校正・撮像条件を体系的に評価し、再構成精度と再現性を改善する技術指針を提示しており、フェノタイプ取得手法が研究の中心である。
abstractThis work improves the repeatability and accuracy of 3D root imaging for phenotyping pipelines by giving useful calibration guidelines.
MaizeRiceSoybeanWheatRootMorphology / geometry measurementSegmentationGrowth / time-series analysisGrowth / development / phenologyRoot system architecture
Root phenotyping is crucial for advancing our understanding of plant development and adaptation. However, existing platforms often face challenges in balancing high-throughput capacity with long-term, high-frequency monitoring. To overcome this limitation, we present HTPRootSlides, an integrated root phenotyping platform designed for dynamic and scalable trait analysis. Its design features a circulating zone that accommodates 141 specialized root boxes for high-throughput operation synchronously. Root boxes follow a continuous S-shaped trajectory step by step, facilitating repetitive imaging for high-throughput, time-series data acquisition. To address challenges such as water vapor condensation and fine root entanglement, we developed a dedicated segmentation algorithm, achieving 89.56 % accuracy in root isolation. Combining morphological and skeleton-based feature extraction techniques, the platform ensures comprehensive and efficient phenotypic trait quantification. We validated HTPRootSlides by dynamically monitoring root development in four staple crops (soybean, maize, wheat, and rice) during early-stage germination (<14 d). The results demonstrate the capability of HTPRootSlides for high-frequency, high-precision and large-scale root phenotyping (< 1h with 141 root boxes per run), offering researchers a powerful tool to investigate root dynamics and optimize crop performance through trait selection.
Why it matches plant phenotyping methods根の動態を高スループットで撮像・分割・特徴抽出し、形態・骨格形質を定量するプラットフォームの開発と検証が中心である。
abstractwe present HTPRootSlides, an integrated root phenotyping platform designed for dynamic and scalable trait analysis
Metabolic processes are essential for regulating and maintaining developmental transitions. However, the distinct metabolite-driven mechanisms that are crucial for development remain poorly characterized due to inherent challenges in measuring their localization and function in situ. We applied desorption electrospray ionization mass spectrometry imaging (DESI-MSI) to generate near single-cell resolution (50-80 µm) images of metabolites in the maize root tip, which has a well-characterized longitudinal developmental gradient. We developed a new computational tool, called Developmental Imaging Mass Spectrometry Pipeline for Linear Evaluation (DIMPLE), which processes mass signatures along linear gradients and clusters metabolites based on their developmental enrichment patterns. We employed this method to compare developmental enrichment of metabolites in Oaxacan Green, a salt-resilient maize variety, to B73, which is salt sensitive. DIMPLE uncovers specific differences in individual mass signatures and overall enrichment patterns between these varieties. Further characterization of these differences revealed meristem enrichment of D-erythrose, a metabolite that can improve stress tolerance in maize. Overall, DIMPLE enables comprehensive and rapid analysis of metabolite patterns along a linear gradient, informing biological hypotheses related to plant growth and stress response.
Why it matches plant phenotyping methods植物根端の発達勾配に沿った代謝物分布を画像化・解析する計算ツールを開発しており、植物の発達状態やストレス応答に関わる表現型抽出が研究の中心である。
abstractWe developed a new computational tool, called Developmental Imaging Mass Spectrometry Pipeline for Linear Evaluation (DIMPLE), which processes mass signatures along linear gradients and clusters metabolites based on their developmental enrichment patterns.
Reproduction assets foundThe paper's authors publicly deposited the DIMPLE analysis code and raw DESI-MSI data on the Dickinson Lab GitHub and Zenodo, as stated in the Technical aspects and Data availability sections.Code · publicThe full R code analysis can be found in the Dickinson Lab Github at https://github.com/dickinsonlab.Open asset ↗dickinsonlabhtml-lines:198-204Code · publicSource code and raw data for DIMPLE are available on the Dickinson Lab GitHub (https://github.com/dickinsonlab) and at https://zenodo.org/records/17187822.Open asset ↗17187822html-lines:198-204Plant phenotyping relevance match · UnverifiedOpenAlex · Europe PMC · Crossref · checked 5 Sept 2026
3D plant phenotyping has garnered significant interest for its ability to quantify key structural traits such as plant volume and canopy architecture. However, standard monocular 3D reconstruction techniques suffer from inherent scale ambiguity, requiring an additional step to recover the true metric scale of the plants. Existing scale recovery methods, whether based on precisely fabricated 3D objects or planar patterns such as checkerboards, have been successfully applied in controlled environments but face practical constraints in certain real-world scenarios: some require costly fabrication or pre-reconstruction calibration, which can limit throughput in dynamic field environments. Here, we present MagicRing, a novel, affordable, and physically reliable post-reconstruction scale recovery approach that addresses these specific constraints and provides a complementary solution for high-throughput, mobile, and field-based phenotyping. MagicRing features a simple red ring printed on A4 paper with a known diameter. By leveraging color-based segmentation and geometric curve fitting, our approach automatically detects the ring within 3D point clouds, recovers the metric scale, and establishes a standardized world coordinate system without the need for pre-calibration. Its planar, isotropic design ensures robustness even under significant occlusion. We demonstrate the utility of MagicRing through MobilePheno3D, an integrated smartphone-based pipeline that performs fully automated 3D reconstruction, scale recovery, and phenotypic extraction from video sequences. This system, which was validated across multiple plant species, including vegetables, wheat, rice, and maize in both indoor and field settings, reliably reconstructs aboveground and root structures and supports continuous growth monitoring. MagicRing decouples data collection from data analysis, enabling a workflow transition from conventional step-by-step, scene-specific calibration toward more scalable, high-throughput 3D plant phenotyping.
Why it matches plant phenotyping methods植物の3D形態形質を抽出するためのスケール復元法とスマートフォン型フェノタイピング・パイプラインを開発し、複数植物種・環境で検証しており、手法が研究の中心である。
abstractHere, we present MagicRing, a novel, affordable, and physically reliable post-reconstruction scale recovery approach
Plant phenotyping relevance match · UnverifiedOpenAlex · Europe PMC · checked 5 Sept 2026
Soybean ( Glycine max ) root nodules, formed through symbiosis with nitrogen-fixing rhizobia, are essential for biological nitrogen fixation. While quantifying key nodulation traits, nodule number and weight, is critical for assessing symbiotic efficiency and yield potential, current methods are destructive and labor-intensive, unsuitable for longitudinal monitoring and high-throughput phenotyping. Here, we established hyperspectral leaf reflectance as a non-destructive, high-resolution tool capable of monitoring root nodule development. Using Partial Least Squares Regression models, we connected spectral data with nodule metrics from 528 unique soybean plants across 18 genotypes, inoculated with different rhizobium strains, and under different abiotic stresses. These models achieved high accuracy for predicting nodule number (R 2 = 0.75, nRMSE = 6.02%) and moderate accuracy for nodule weight (R 2 = 0.53, nRMSE = 12.38%). Crucially, spectral analyses revealed distinct hyperspectral signatures sensitive to nodule traits. While different rhizobium strains induced comparable changes in both nodule traits, and therefore produced highly overlapped spectral domains, diagnostically distinct spectral patterns were generated under drought versus salt stress, with the former suppressing nodulation more significantly than the latter. Furthermore, we demonstrated the effectiveness of our models for real-time in-situ monitoring of nodule development for individual plants. Spectral-nodule trait covariation analyses further revealed leaf signatures correlated with nodule traits primarily through systemic physiological coupling governed by carbon-nitrogen exchange dynamics and plant water status. This study showcased hyperspectral sensing as a transformative methodology, enabling the unprecedented non-destructive quantification of nodulation dynamics, revealing novel physiological insights into plant-microbe-environment interactions, facilitating breeding and management strategies for sustainable soybean production.
Why it matches plant phenotyping methods葉のハイパースペクトル反射を用いて根粒数・重量を非破壊推定するセンシング手法を開発・評価しており、植物表現型取得が研究の中心です。
abstractcurrent methods are destructive and labor-intensive, unsuitable for longitudinal monitoring and high-throughput phenotyping.
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.
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
ArabidopsisMaizeMilletRootPhysiological trait estimationRoot system architecture
Drought is a significant factor in agricultural losses, making it imperative to understand how root system architecture (RSA) adapts to environmental condition like water deficit. HydroRoot is a functional-structural plant model (FSPM) aimed at analyzing and simulating hydraulic and solute transport of RSA. The model integrates a static hydraulic solver, a coupled water-solute transport solver, a statistical generator of RSA based on Markov model, and a dynamic hydraulic model accounting for root growth. This paper presents the model, the mathematical description of the formalism of solvers, and use cases with their associated tutorials. Five use cases illustrate capabilities of HydroRoot, which has been successfully used for phenotyping root hydraulics across various species, including Arabidopsis, maize, and millet. The model-driven phenotyping method “cut and flow” is presented to characterize axial and radial conductivities on a given root genotype. Finally, three step-by-step tutorials provide a structured way to learn how to use HydroRoot 1) to simulate hydraulic on a given architecture, 2) to simulate water and solute transport on a maize root, and 3) to simulate hydraulic on two pearl millet genotypes with varying soil conditions. Hydroroot is an open-source package of the OpenAlea platform, with the code publicly available on Github. A comprehensive documentation is available with a reproducible gallery of examples.
Why it matches plant phenotyping methods根系の水理特性を解析・予測し、表現型化するモデルとオープンソースソフトウェアを開発・提示しており、植物フェノタイピング手法が中心である。
abstractHydroRoot is a functional-structural plant model (FSPM) aimed at analyzing and simulating hydraulic and solute transport of RSA.
1 Summary Zinc (Zn) deficiency limits rice productivity and poses a risk to human health, particularly in populations reliant on rice-based diets. Although rice germplasm exhibits wide variation in Zn-deficiency tolerance, the underlying physiological mechanisms remain poorly resolved. Evidence across the literature for Zn-deficiency–induced secretion of 2′-deoxymugineic acid (DMA) is inconsistent. This study clarifies the role of DMA secretion as a Zn-deficiency stress response. We developed and validated a sensitive LC–ESI–Q–TOF–MS method for selective detection of DMA in rice root exudates. Five rice genotypes with contrasting Zn-deficiency tolerance were grown hydroponically and DMA secretion measured. Zn-deficiency increased DMA exudation across all genotypes, with sensitive genotypes also showing higher secretion compared with control, supporting DMA’s role as a general response to Zn stress rather than being restricted to efficient genotypes. Fold-change responses exceeded previous studies, likely due to more severe stress exposure. Our results confirm that DMA secretion is induced under Zn-deficiency in rice as part of the micronutrient stress response. However, the lack of increased Zn uptake indicates that additional tolerance mechanisms are involved. These findings reconcile inconsistencies in the literature and position DMA secretion as an important, but not exclusive, component of Zn-deficiency adaptation in rice.
Why it matches plant phenotyping methodsイネ根滲出液中のDMAを選択的に検出するLC–MS法を開発・検証し、亜鉛欠乏応答という植物生理状態を測定しているため、化学分析が単なる付随測定ではなく中心的な方法貢献である。
abstractWe developed and validated a sensitive LC–ESI–Q–TOF–MS method for selective detection of DMA in rice root exudates.
Reproduction assets foundThe paper's Data availability statement points to a public Zenodo deposit containing the datasets generated and analysed in this study (DMA exudation and Zn uptake measurements in rice).Dataset · publicthe experiments, developed the
525
methods and analysed the results. The experimental data were collected by C.R. assisted by
526
G.L.M., C.T. and D.J.W. Data analysis and writing of paper by all authors.
527
528
Data availability
529
The data sets generated and/or analysed during the current study are available on Zenodo,
530
https://zenodo.org/uploads/18184803
531
532
533
.
CC-BY 4.0 International license
perpetuity. It is made available under a
preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in
The copyright holder for this
this version posted March 18, 2026.
;
https://doi.org/10.64898/2026.03.16.71158Open asset ↗Zenodo · 18184803pdf-raw-page:21 lines:1-47Code / dataset availability confirmedOpenAlex · Europe PMC · checked 14 Sept 2026
A detailed characterization of root system architecture (RSA) and growth dynamics is key to develop stress-resilient maize varieties. We evaluated sixty-five Mediterranean maize inbred lines using automated high-throughput phenotyping under controlled conditions. Shoot and root traits were extracted from imaging data during early vegetative development, revealing significant genotype-specific variation in root biomass-related traits (total root length, total root volume), root architecture (root angle, root system depth, root system width), and relative growth rates. Notably, lines previously classified as heat and drought stress-resilient or stress-sensitive based on above-ground development did not group according to particular root traits, indicating that multiple strategies may underlie tolerance to combined stress. We identified lines with contrasting RSA, including deeper roots, shallower roots, or overall larger root systems, that offer new opportunities for resilience breeding. Our results underscore root traits as critical yet underexploited targets for improving stress resilience and resource efficiency.
Why it matches plant phenotyping methods自動化ハイスループット画像解析により根系形態・成長形質を抽出する表現型取得が研究の主要手段であり、根系構造の実質的な応用解析に該当する。
abstractWe evaluated sixty-five Mediterranean maize inbred lines using automated high-throughput phenotyping under controlled conditions.
Reproduction assets found保存済みの本文根拠を更新済みルールで再検証し、公開資産1件を確認しました。Supplement · publicSupplementary Materials
The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/plants15060935/s1 , Figure S1: Repeatability of image-derived shoot (a) and root traits (b) of the tested 65 maize inbred lines over time.Open asset ↗lines:68-215Plant phenotyping relevance match · UnverifiedOpenAlex · Europe PMC · checked 5 Sept 2026
ArabidopsisRootGrowth / time-series analysisGrowth / development / phenologyRoot system architecture
Predicting plant phenotypes from genomic data requires models that bridge molecular regulation and organ-scale morphogenesis. We introduce BioOS, a computational runtime in which plant behavior - cell division, differentiation, and elongation - emerges from the execution of a gene regulatory network rather than from hardcoded rules. The system is built on the Formal Cell abstraction: a minimal signal-processing unit analogous to the McCulloch-Pitts formal neuron, whose transfer function is gene expression. Each Formal Cell evaluates promoters, transcribes mRNA, translates proteins, and derives its entire behavioral repertoire from the resulting protein concentrations - without a single hardcoded rule in the simulator code. A multi-scale architecture with level-of-detail switching enables real-time simulation of Arabidopsis thaliana primary root development. On the current official five-case primary-root auxin benchmark, BioOS achieves 75.4% mean score, 5/5 qualitative matches, 5/5 cases passing all current gates, and Spearman severity correlation ρ = 0.70. The current root-auxin runtime is driven by a curated 35-gene registry with explicit promoter logic, kinetic parameters, and epigenetic state; for readability, this manuscript details a core 18-gene subnetwork that carries the main auxin benchmark logic. We describe the architecture, the gene expression runtime, the epigenetic memory model, the completed transition to post-hoc (non-causal) zone classification, and candidate benchmark extensions for persistent plasmodesmata and intracellular auxin compartmentalization within a broader six-suite, 63-case benchmark framework. Beyond the root-auxin slice, the current codebase also closes the official flowering (5/5), photosynthesis (7/7), and cytokinin (5/5) gates, while root-patterning remains a passing candidate panel.
Why it matches plant phenotyping methods植物の発生表現型を遺伝子制御モデルから予測する計算ランタイムの開発と、根の発生ベンチマークによる評価が中心であり、単なる生物学的測定ではない。
abstractPredicting plant phenotypes from genomic data requires models that bridge molecular regulation and organ-scale morphogenesis.
SoybeanRootSeed / grainMorphology / geometry measurementObject detectionGrowth / development / phenologyRoot system architectureStress response / tolerance
Introduction Nanoparticle-induced treatments can promote seed germination and improve germination potential under environmental stresses such as drought and salinity. This study aimed to investigate the effects of Zinc oxide nanoparticles (ZnONPs) on soybean seed germination and to develop a precise evaluation method. Methods We developed a full-time sequence crop growth vitality monitoring system. Using germination rate and root length as primary evaluation indicators, we conducted full-time sequence germination vitality monitoring experiments on soybean seeds treated with ZnONPs. A dataset was constructed from images documenting embryonic root growth. The developed detection model was used to evaluate image detection accuracy during germination. Germination index and embryonic root length were also calculated. Further tests were performed on seeds exposed to 600 mg/L ZnONPs dispersion, followed by treatment with different concentrations of NaCl and PEG6000 solutions. Results At a concentration of 600 mg/L ZnONPs dispersion, soybean seeds showed the highest germination rate (an increase of 28%) and the longest radicle length (an increase of 42%). Compared with deionized water, the 600 mg/L ZnONPs dispersion accelerated initial germination time, increased germination rate, and enhanced radicle length under low-concentration stress. Discussion The results indicate that, at certain concentrations, ZnONPs dispersion positively influences soybean seed germination under varying salinity and drought conditions. We examined morphological and physiological changes in ZnONPs-treated seeds under stress, establishing a preliminary foundation for evaluating crop and variety vitality. These findings provide new insights that may contribute to improving soybean germination under simulated stress conditions, serving as a preliminary theoretical reference for potential applications in arid and saline environments.
Why it matches plant phenotyping methods発芽中の画像から発芽率・幼根長を抽出する連続モニタリングシステムと検出モデルを開発し、精度評価とデータセット構築を行っており、表現型取得手法が中心である。
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.
Field / plotLeafRootMorphology / geometry measurement
Upscaling traits from plant-level measurements to grid-scale predictions is crucial for accounting for biodiversity when simulating and predicting the impacts of climate change and human activities on ecosystems at large scales. However, current trait upscaling frameworks face limitations, particularly the scarcity of trait observations. Based on a dense sampling strategy on the Tibetan Plateau, this study aims to develop a robust upscaling framework that (1) provides reliable trait predictions for this region and (2) enables analysis of how sampling density affects trait prediction.The Tibetan Plateau, known as the Roof of the World with an average elevation above 4,000 m, supports diverse zonal vegetation, both horizontally and vertically. This significant environmental and vegetation heterogeneity, combined with sparse in situ trait measurements, currently leads to high prediction uncertainty in existing global and Chinese trait maps for this region, limiting their ecological accuracy for spatial scaling on the Tibetan Plateau.Our approach toward a more robust trait upscaling includes: 1) performing standardized trait measurements on 3,961 species-level leaf samples and 504 site-level fine root samples collected from 650 sites between 2018 and 2024, covering 12 morphological and chemical traits; 2) constructing predictor sets that include bioclimate, soil, topography, and vegetation indices; 3) training machine learning models (such as random forest, boosted regression trees, and generalized additive models), using cross-validation to evaluate performance and select optimal parameters for each trait; 4) refining plant functional type (PFT) based on regional vegetation characteristics and aligning them with a detailed 10 m resolution land cover map of the Tibetan Plateau; 5) predicting traits for each PFT and aggregating them into grid-level values using PFT abundance weighting; and 6) generating a suite of 1 km resolution trait maps. We expect this work to establish a reproducible methodological framework for trait upscaling in heterogeneous landscapes, yielding more reliable trait maps for the Tibetan Plateau and providing further insight into how sampling density influences trait upscaling.
Why it matches plant phenotyping methods植物の形態・化学形質を測定し、機械学習とPFT加重により広域推定する再現可能な形質アップスケーリング手法の開発が中心であり、単なる生態学的測定ではない。
abstracttraining machine learning models (such as random forest, boosted regression trees, and generalized additive models), using cross-validation to evaluate performance and select optimal parameters for each trait
Quantitative studies of plant growth and environmental responses increasingly rely on time-series imaging, yet automated segmentation remains challenging due to continuous growth, large non-rigid morphological change, and frequent self-occlusion. Traditional image-processing pipelines and task-specific deep learning models often require extensive annotated datasets and retraining, limiting portability across species, developmental stages, and imaging conditions. Here we present SAP (Segment Any Plant), a plant-focused framework that leverages the pretrained Segment Anything Model 2 (SAM2) to enable few-shot, training-free segmentation of plant time-series imagery. SAP integrates interactive prompting, automated temporal mask propagation, and centerline extraction within a web-based interface, allowing users to move from raw images to quantitative descriptors of organ shape and dynamics without programming expertise. Across multiple systems, including Arabidopsis thaliana rosette development, root growth, sunflower gravitropism, and confocal root microscopy, SAP achieves high segmentation accuracy (mean IoU 0.89–0.93) and sub-pixel centerline precision from single-frame prompting. By reducing the need for task-specific retraining, SAP provides a transferable framework for reproducible time-series phenotyping across diverse experimental contexts.
Why it matches plant phenotyping methods植物の時系列画像から器官形状・動態を抽出するセグメンテーション手法とWeb基盤を開発し、複数系で精度検証しているため、植物フェノタイピング手法が中心である。
abstractHere we present SAP (Segment Any Plant), a plant-focused framework that leverages the pretrained Segment Anything Model 2 (SAM2) to enable few-shot, training-free segmentation of plant time-series imagery.
Reproduction assets foundThe paper's authors publicly release both the SAP analysis code (GitHub repository) and the datasets generated/analyzed in the study (Zenodo), including raw images, ground-truth and SAP-generated segmentation masks, centerline validation data, and supplementary videos. Both are paper-specific, public, and directly cit.Dataset · publicCode Availability. The code is available at
https://github.com/merozlab/plant-segmentation-app.Data Availability. The datasets generated and an-
alyzed during this study are available on Zenodo at
https://doi.org/10.5281/zenodo.18732705. This includes
raw images and segmentation masks for the sunflower
gravitropism and Arabidopsis root growth experiments,
SAP-generated masks for the Lee et al. (9) and Strauss
et al. (13) datasets, centerline validation data, and supple-
mentary videos.
Funding. Y.M. acknowledges support from the Israel Sci-
ence Foundation ResOpen asset ↗zenodo · 10.5281/zenodo.18732705pdf-raw-page:9 lines:1-74Plant phenotyping relevance match · UnverifiedEurope PMC · checked 5 Sept 2026
Field / plotRootMorphology / geometry measurementPhysiological trait estimationGrowth / time-series analysisGrowth / development / phenologyRoot system architectureWater status / transpiration
Deep-rooted crops accessing water and nutrients from deep soil layers enhance the resource base for crop production. However, studying these roots in field conditions is labour-intensive, limiting research scope. We established a field root research facility with 48 plots for replicated experiments. The facility includes 144 6-metre-long minirhizotron tubes and an AI-based pipeline for rapid root trait analysis. We also attempted to install access tubes and customized ingrowth core production for less-invasive root activity determination. Our study revealed significant differences in deep root density among species, particularly at depths of 2.5 to 4.5 m, over 5 years. The less-invasive studies using ingrowth cores reached depths of 4.2 m. Nutrient tracer 15 N analysis showed marked differences in deep root activity among crop species. Time domain reflectometry sensors indicated varying water depletion in deeper soil layers, influenced by crop species and root growth patterns. We established a field facility for studying deep root growth and function, demonstrating its effectiveness in analysing diverse deep-rooted plant species. This facility provides an ideal platform for conducting meaningful research in deep soil layers, yielding statistically and biologically significant results for agricultural applications.
Why it matches plant phenotyping methods深根研究施設とAIによる根形質解析パイプラインの構築・有効性評価が中心的に記述されており、根密度などの植物形質を取得するフェノタイピング基盤に該当する。
abstractThe facility includes 144 6-metre-long minirhizotron tubes and an AI-based pipeline for rapid root trait analysis.
The regulation of apoplastic pH is critical for plant growth and development, affecting processes such as nutrient uptake, cell wall expansion and intercellular signaling. Conventional methods for measuring apoplastic pH, including pH indicators in growth media and ion-selective electrodes, often fall short of providing the spatial resolution and accuracy needed for detailed studies. Here we present a protocol for the quantitative imaging of apoplastic pH in Arabidopsis thaliana roots using confocal microscopy combined with the fluorescent pH probe 8-hydroxy-pyrene-1,3,6-trisulfonic acid trisodium salt, also called pyranine. This approach addresses the limitations of genetic sensors and traditional pH measurement techniques by offering a nontoxic, cost-effective and precise method for pH assessment at cellular resolution via ratiometric confocal imaging. In addition, we introduce an updated Fiji plugin for ratiometric image conversion. The new plugin enhances workflow efficiency by automating image processing while offering several options for customization, thereby ensuring reliable and reproducible results. The full procedure, from staining to image analysis, can be completed within ~2-4 h, depending on the number of samples and imaging depth. This protocol provides a robust tool for plant physiologists to investigate apoplastic pH dynamics with high spatial resolution and accuracy in plant tissues.
Why it matches plant phenotyping methods植物根のアポプラストpHを細胞解像度で定量画像化する手法と、画像解析プラグインを開発・提示しており、植物状態の取得法が中心である。
abstractHere we present a protocol for the quantitative imaging of apoplastic pH in Arabidopsis thaliana roots using confocal microscopy combined with the fluorescent pH probe 8-hydroxy-pyrene-1,3,6-trisulfonic acid trisodium salt, also called pyranine.
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 · UnverifiedEurope PMC · OpenAlex · checked 5 Sept 2026
ArabidopsisChlorophyll fluorescenceMicroscopyLiDAR / point cloudCell / cellular structureRootMorphology / geometry measurement2D/3D reconstructionGrowth / time-series analysisGrowth / development / phenology
Embryogenesis in the model plant Arabidopsis thaliana provides a framework for understanding how cell polarity and patterning coordinate with hormonal signalling to establish the plant body plan. Following fertilisation, the zygote divides asymmetrically to generate apical and basal lineages, establishing the apical-basal axis that defines future shoot and root poles. Genetic and molecular analyses of classical mutants including gnom, monopteros (mp), bodenlos (bdl) and topless revealed that localised auxin biosynthesis, directional transport and downstream transcriptional responses are central to apical-basal axis establishment and organ initiation. The main components of this regulation are polarly localised PIN auxin transporters and downstream modules involving MONOPTEROS and WUSCHEL-RELATED HOMEOBOX transcription factors. Advances in microscopy have transformed the study of Arabidopsis embryogenesis: fluorescence-compatible clearing reagents and three-dimensional reconstructions now permit quantitative analyses of cell geometry, division orientation, and cytoskeletal dynamics. Live ovule imaging setups with confocal laser scanning and multiphoton microscopes enable real-time observation of embryo development, while laser-assisted cell ablation can be used to probe cell-to-cell communication and fate plasticity. Together, these methodological breakthroughs position Arabidopsis embryos as a prime model for dissecting the chemical and biophysical cues that shape plant development.
Why it matches plant phenotyping methods胚発生を解析するための蛍光イメージング、三次元再構成、ライブ撮像などの方法論と定量的な細胞形態・分裂方向測定が中心であり、植物フェノタイピング手法に該当する。
abstractAdvances in microscopy have transformed the study of Arabidopsis embryogenesis: fluorescence-compatible clearing reagents and three-dimensional reconstructions now permit quantitative analyses of cell geometry, division orientation, and cytoskeletal dynamics.
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.
BACKGROUND: Root biomass serves as a critical indicator of plant eco-physiological status and crop productivity, yet its non-destructive monitoring remains challenging because of its underground location. The use of transparent nutrient film technique (NFT) systems enables direct observation of entire root systems, rendering image-based phenotyping feasible. In this study, we investigated and compared the performance of RGB and hyperspectral imaging for predicting root dry weight in hydroponically grown spinach (Spinacia oleracea L.). RESULTS: Using 430 root segments divided from 60 plants, three models were developed: (1) an area-based regression based on root coverage, (2) a convolutional neural network (CNN) using RGB images, and (3) a partial least squares regression (PLSR) model using hyperspectral data (450-950 nm). The area-based regression exhibited limited accuracy (R² = 0.446) because of saturation at high root coverage. The CNN model improved predictive performance (R² = 0.739) but tended to overestimate sparse roots as a result of resolution constraints. The PLSR model achieved the highest accuracy (R² = 0.822, RMSE = 0.019 g/segment), with significantly lower error than RGB-based approaches (P < 0.01). Variable importance in projection analysis indicated that PLSR effectively exploited spectral signatures at 450 nm (background contrast) and 750 nm (tissue scattering), thereby maintaining stable accuracy across the full biomass range. When validated using 104 independent plants, the PLSR model achieved high predictive accuracy. Furthermore, as a proof of concept, this model successfully visualized the spatiotemporal dynamics of root biomass accumulation over 50 days, with only a 7.70% relative error at harvest. CONCLUSIONS: To our knowledge, this study is among the first to demonstrate the non-destructive monitoring of biomass distribution within entire root systems under production conditions. Hyperspectral imaging combined with PLSR outperforms RGB-based approaches by capturing spectral signatures that reflect internal tissue properties of roots, thereby overcoming limitations caused by morphological occlusion. This approach provides a robust tool for precision agriculture and high-throughput phenotyping, enabling continuous assessment of root growth through simple modifications to the existing hydroponic systems.
Why it matches plant phenotyping methodsRGB・ハイパースペクトル画像と機械学習／PLSRを用いて根乾物重を非破壊推定・検証する方法研究であり、植物表現型の取得と定量化が中心である。
abstractThe use of transparent nutrient film technique (NFT) systems enables direct observation of entire root systems, rendering image-based phenotyping feasible.
Reproduction assets foundThe paper's Data availability statement deposits the paper-specific phenotyping assets (raw hyperspectral images, RGB images, and root dry weight measurements) in a Zenodo record. The provided URL includes a token and 'preview=1', suggesting the record may not yet be fully open, but it is the authors' stated public URLDataset · publicThe datasets generated and analyzed during the model construction of the current study are available in the Zenodo repository: [https://zenodo.org/records/18072801?preview=1&token=eyJhbGciOiJIUzUxMiJ9.eyJpZCI6ImFiYTkzMzY2LTIzZjktNDlkMy1iZTBjLTk3M2E5YTUyOTFmZCIsImRhdGEiOnt9LCJyYW5kb20iOiIzNGE1ZjMxNDZhYjhiYjlhZWRiOWFjNzBkNzcwY2I3NyJ9.uR4HfosoSaVWhtSblMOS1v9bJFA5MvHwXvcW9uoNbcTWRDU4RNxZpVHjXTC3ulBM1JTlBbeHp_4T5EcILawxdg].The dataset includes:
- Raw hyperspectral images and data- RGB images
- Root dry weight measurementsOpen asset ↗Zenodo · 18072801lines:176-248Plant phenotyping relevance match · UnverifiedCrossref · checked 15 Sept 2026
Field / plotLeafRootMorphology / geometry measurement
Upscaling traits from plant-level measurements to grid-scale predictions is crucial for accounting for biodiversity when simulating and predicting the impacts of climate change and human activities on ecosystems at large scales. However, current trait upscaling frameworks face limitations, particularly the scarcity of trait observations. Based on a dense sampling strategy on the Tibetan Plateau, this study aims to develop a robust upscaling framework that (1) provides reliable trait predictions for this region and (2) enables analysis of how sampling density affects trait prediction.The Tibetan Plateau, known as the Roof of the World with an average elevation above 4,000 m, supports diverse zonal vegetation, both horizontally and vertically. This significant environmental and vegetation heterogeneity, combined with sparse in situ trait measurements, currently leads to high prediction uncertainty in existing global and Chinese trait maps for this region, limiting their ecological accuracy for spatial scaling on the Tibetan Plateau.Our approach toward a more robust trait upscaling includes: 1) performing standardized trait measurements on 3,961 species-level leaf samples and 504 site-level fine root samples collected from 650 sites between 2018 and 2024, covering 12 morphological and chemical traits; 2) constructing predictor sets that include bioclimate, soil, topography, and vegetation indices; 3) training machine learning models (such as random forest, boosted regression trees, and generalized additive models), using cross-validation to evaluate performance and select optimal parameters for each trait; 4) refining plant functional type (PFT) based on regional vegetation characteristics and aligning them with a detailed 10 m resolution land cover map of the Tibetan Plateau; 5) predicting traits for each PFT and aggregating them into grid-level values using PFT abundance weighting; and 6) generating a suite of 1 km resolution trait maps. We expect this work to establish a reproducible methodological framework for trait upscaling in heterogeneous landscapes, yielding more reliable trait maps for the Tibetan Plateau and providing further insight into how sampling density influences trait upscaling.
Why it matches plant phenotyping methods植物形質の標準化測定から機械学習による推定・空間集約・形質マップ作成までを含む、再現可能な形質アップスケーリング手法の開発が中心である。
abstractOur approach toward a more robust trait upscaling includes: 1) performing standardized trait measurements on 3,961 species-level leaf samples and 504 site-level fine root samples collected from 650 sites between 2018 and 2024, covering 12 morphological and chemical traits;
Charcoal rot, caused by Macrophomina phaseolina, is a destructive soil-borne disease that threatens several oilseed crops. Its persistence in the soil through microsclerotia, wide host range, and strong association with drought and heat stress makes it a formidable challenge for sustainable production. Breeding for resistance is widely recognized as the most effective and environmentally sound management strategy. This review synthesizes current knowledge on the biology and economic impact of charcoal rot in oilseed crops, with a focus on screening methodologies, genetic resistance, and breeding interventions. Field-based techniques such as root and stem severity scoring and colony-forming unit indices remain central to resistance evaluation, although challenges of standardization and reproducibility persist. Advances in molecular and genomic tools, including QTL mapping, genome-wide association studies (GWASs), marker-assisted selection (MAS), and genomic selection (GS), have begun to strengthen the identification and deployment of resistance loci. In addition, speed breeding, high-throughput phenotyping, and gene-editing platforms such as CRISPR/Cas offer novel opportunities to accelerate cultivar development. Integration of these approaches, along with the exploration of wild relatives and pre-breeding materials, is essential for broadening the genetic base of resistance and achieving durable, climate-resilient oilseed production. By linking pathogen biology, screening methods, and advanced genetic strategies, this review provides a comprehensive framework for future breeding programs aimed at mitigating the impact of charcoal rot in oilseed crops.
Why it matches plant phenotyping methods耐病性評価に用いる根・茎の病徴重症度スコアリングやCFU指標などのスクリーニング方法を明示的に扱い、標準化・再現性の課題も論じるレビューであり、植物病害表現型の取得法が主要な内容に含まれる。
abstractThis review synthesizes current knowledge on the biology and economic impact of charcoal rot in oilseed crops, with a focus on screening methodologies, genetic resistance, and breeding interventions.
Charcoal rot, caused by Macrophomina phaseolina, is a destructive soil-borne disease that threatens several oilseed crops. Its persistence in the soil through microsclerotia, wide host range, and strong association with drought and heat stress makes it a formidable challenge for sustainable production. Breeding for resistance is widely recognized as the most effective and environmentally sound management strategy. This review synthesizes current knowledge on the biology and economic impact of charcoal rot in oilseed crops, with a focus on screening methodologies, genetic resistance, and breeding interventions. Field-based techniques such as root and stem severity scoring and colony-forming unit indices remain central to resistance evaluation, although challenges of standardization and reproducibility persist. Advances in molecular and genomic tools, including QTL mapping, genome-wide association studies (GWASs), marker-assisted selection (MAS), and genomic selection (GS), have begun to strengthen the identification and deployment of resistance loci. In addition, speed breeding, high-throughput phenotyping, and gene-editing platforms such as CRISPR/Cas offer novel opportunities to accelerate cultivar development. Integration of these approaches, along with the exploration of wild relatives and pre-breeding materials, is essential for broadening the genetic base of resistance and achieving durable, climate-resilient oilseed production. By linking pathogen biology, screening methods, and advanced genetic strategies, this review provides a comprehensive framework for future breeding programs aimed at mitigating the impact of charcoal rot in oilseed crops.
Why it matches plant phenotyping methods油糧作物の炭腐病抵抗性評価に用いる症状重症度スコアやCFU指標などのスクリーニング方法を中心に整理したレビューであり、植物病害状態の表現型測定法が実質的な主題である。
abstractThis review synthesizes current knowledge on the biology and economic impact of charcoal rot in oilseed crops, with a focus on screening methodologies, genetic resistance, and breeding interventions.
Volume is an important shape descriptor in postharvest quality evaluation and breeding programs of sweetpotatoes and is also valuable for other agricultural engineering applications. Traditional volume measurement methods based on water displacement are, however, laborious, destructive, and unsuitable for high-throughput online scenarios. To address this gap, this dataset was developed to support the advancement of non-destructive, automated online volume estimation using a LiDAR (light detection and ranging)-based three-dimensional (3-D) machine vision system. A total of 200 sweetpotato storage roots of the cultivar "Beauregard" were collected for constructing a 3-D multi-view imagery dataset. Each sample was imaged online using a short-range LiDAR camera (Intel RealSense™ L515) while traveling on a custom-built roller conveyor system that enables simultaneous translation and rotation for full-surface coverage. The curated dataset comprises raw color images (1280 × 720 pixels, .png format) and corresponding raw and segmented point clouds (1280 × 720 pixels, .laz format) for individual samples, alongside the reference volume measurements obtained using the standard water displacement method. In addition, to illustrate the modeling pipeline for volume prediction, the dataset provides the extracted geometric features derived from the segmented two-dimensional (2-D) masks and point clouds, and volume prediction results obtained through regression modeling. As the first publicly available LiDAR-based dataset for sweetpotato volume estimation, this dataset provides a valuable resource for developing and validating image processing pipelines, optimizing machine learning models, and advancing 3-D vision technologies for non-destructive, rapid measurement of the volume of irregularly shaped agricultural products.
Why it matches plant phenotyping methodsサツマイモ貯蔵根の体積という植物器官形質をLiDAR 3D画像から推定する公開データセットであり、取得系・参照測定・特徴抽出・予測結果を含むため、フェノタイピング手法とデータセットが中心です。
abstractthis dataset was developed to support the advancement of non-destructive, automated online volume estimation using a LiDAR (light detection and ranging)-based three-dimensional (3-D) machine vision system.
Reproduction assets foundThe paper's own LiDAR sweetpotato dataset (images, point clouds, ground-truth volumes, feature data, and Python modeling scripts) is publicly deposited on Zenodo with an explicit DOI. The librealsense GitHub link is a generic camera SDK, not a paper-specific asset.Dataset · publicDirect URL to data: https://doi.org/10.5281/zenodo.18378019Open asset ↗Zenodo · 10.5281/zenodo.18378019html-lines:90-113Code · publicThe complete Python modeling script and the associated feature datasets have been included in the public dataset repository [13] to facilitate reproducibility and provide a benchmark for future algorithm development.Open asset ↗html-lines:168-182Plant phenotyping relevance match · UnverifiedEurope PMC · bioRxiv · checked 15 Sept 2026
The nucleus is the characteristic organelle for eukaryotic organisms. Unlike the classic textbook view of static two-dimensional nuclei, nuclear shape is dynamic inside the live cell. The alteration or deformed nuclear shape is the hallmark of cancer in animal cells and environmental stress in plants. The nuclear envelope proteins interact with chromatin to regulate gene expression. Unfortunately, we have limited knowledge about the impact of abiotic stress on nuclear shape, movement, and chromatin dynamics. To circumvent this issue, we are utilizing a dual fluorescently tagged marker lines – nuclear envelope protein and chromatin – to perform live cell imaging in the model plant Arabidopsis thaliana root. The live cell imaging was performed in control and salt-stressed conditions. We utilized these captured movies to analyze through open-source image processing software Fiji/ImageJ with the help of the TrackMate plugin. Using this method, we have demonstrated that chromatin velocity is decreased in salt-treated conditions. This method will be widely applied to quantitative live cell imaging of nuclear shape and chromatin dynamics during plant development and environmental stress. Summary This process aims to simultaneously record nucleus and chromatin dynamics in Arabidopsis thaliana roots and investigate changes in these dynamics in response to developmental and environmental cues.
Why it matches plant phenotyping methods植物の核形状・クロマチン動態をライブイメージングと画像解析で定量化する手法が中心であり、環境ストレス下の植物状態を測定する再利用可能なワークフローを提示している。
abstractwe are utilizing a dual fluorescently tagged marker lines – nuclear envelope protein and chromatin – to perform live cell imaging in the model plant Arabidopsis thaliana root.
BarleyRootMorphology / geometry measurementSegmentationSkeletonization / topologyRoot system architecture
• A fully automated pipeline for barley root extraction and characterization. • DeepRoot-3H : multi-head network for segmenting roots, tips, and sources. • Post-processing stage handling overlaps and dense root clusters. • Graph-based path analysis for RSML generation and trait extraction • High accuracy and robustness on challenging barley root image datasets Understanding plant root architecture under diverse environmental conditions is crucial for improving crop resilience and ensuring global food security. We present a fully automated method for segmenting barley root systems from high-resolution images and detecting keypoints such as tips and sources with high precision. At the core of our approach is DeepRoot-3H , a novel multi-head deep network built upon the DeepLabv3+ backbone, designed to jointly handle root segmentation and keypoint detection within a unified architecture. This integrated design enhances both the consistency and robustness of the outputs. A dedicated post-processing stage further refines keypoint localization, effectively handling challenges such as dense root clusters and variability in image quality. The resulting predictions are then structured into a graph representation, on which a path-walking algorithm identifies biologically meaningful connections between tips and sources. This enables the generation of RSML files and the extraction of critical morphological traits. To evaluate the system, we employ IoU and Dice scores for segmentation quality, alongside Euclidean and weighted distance metrics for tip and source detection. We also assess the biological consistency of the extracted traits—such as total root length, tortuosity, covered area, and outer angles—through correlation and discrepancy measures. Experimental results on a challenging benchmark dataset demonstrate significant improvements over existing techniques, confirming the effectiveness and reliability of our method for high-fidelity root system analysis.
Why it matches plant phenotyping methods根系画像から分割・キーポイント検出・形態形質抽出を行う自動フェノタイピング手法の開発と評価が中心である。
abstractA fully automated pipeline for barley root extraction and characterization.
MaizeRootMorphology / geometry measurementPhysiological trait estimationRoot system architectureWater status / transpiration
Crop adaptation to the mixture of environments that defines the target population of environments is the result of balanced resource allocation between roots, shoots, and reproductive organs. Root growth plays a critical role in the determination of this delicate balance. The responses of root growth and function to temperature can determine the strength of roots as sinks but also influence a crop's ability to uptake water and nutrients. Surprisingly, this behavior has not been studied in maize (Zea mays) since the middle of the last century, and the genetic determinants are unknown. Low temperatures recorded frequently in deep soil layers limit root growth and soil exploration and may constitute a bottleneck for increasing drought tolerance, nitrogen recovery, sequestration of carbon, and productivity in maize. We developed high-throughput phenotyping systems to investigate these responses and to examine genetic variability therein across diverse maize germplasm. Here, we show that there is (i) genetic variation in root growth under low temperature below a previously set threshold of 10 °C and (ii) genotypic variation in water transport under low temperature. The trait set examined herein and the high-throughput phenotyping platform developed for its characterization provide a unique opportunity for removing a major bottleneck for crop improvement and adaptation to climate change.
Why it matches plant phenotyping methods根の成長と水輸送という植物形質を評価するためのハイスループット表現型解析システムの開発が中心的に記述されており、遺伝的変異の評価にも用いられているため。
abstractWe developed high-throughput phenotyping systems to investigate these responses and to examine genetic variability therein across diverse maize germplasm.
To enable sensitive in vivo monitoring of the glucose transport and metabolism, we developed a series of ultrasensitive and ratiometric genetically encoded sensors (MGlucoMeter) by inserting a Matryoshka dual fluorophore cassette consisting of cpsfGFP (circularly permuted superfolder GFP) and LSSmApple (Large Stokes Shift mApple) into the glucose-binding protein ttGBP (Thermus thermophilus glucose-binding protein) from Thermus thermophilus. The initial MGlucoMeter1.0 version was subjected to an alanine scan of the hinge region producing the more sensitive MGlucoMeter2.6 with a glucose-induced ΔF/F 0 change of 3.0, an affinity for glucose of 15 μm, and an approximate detection range of 1-215 μm. To generate variants suitable for in vivo measurements, a series of affinity mutants was generated by mutating two histidines predicted to be involved in substrate binding. MGlucoMeter2.6-353n (affinity 353 nm), MGlucoMeter2.6-15 μ (affinity 15 μm), MGlucoMeter2.6-700 μ (affinity 700 μm), MGlucoMeter2.6-1 m (affinity 1 mm), and MGlucoMeter2.6-7 m (affinity 7 mm) cover a combined detection range between ∼40 nm-55 mm. When expressed from a ubiquitous promoter in the cytosol of the Arabidopsis gene-silencing mutant rdr6 (RNA-dependent RNA polymerase 6), MGlucoMeter2.6-1 m reports time- and concentration-dependent accumulation of glucose in seedling roots after external addition of glucose. The sensor also detected rapid release of sugars in the root tip and rapid hydrolysis of the shoot-derived sucrose.
Why it matches plant phenotyping methods植物体内のグルコースを定量する遺伝子コード型蛍光センサーを開発・最適化し、シロイヌナズナ根で検証しているため、植物生理状態の取得法が中心です。
abstractwe developed a series of ultrasensitive and ratiometric genetically encoded sensors (MGlucoMeter)
MilletSorghumRootTissueSegmentationRoot system architecture
Root anatomical features are critical for plant performance characterization, yet phenotyping at the anatomical scale remains limited by the extreme annotation burden of cellular segmentation. We present a two-stage segmentation framework that greatly reduces annotation requirements while maintaining high accuracy across diverse plant species and imaging conditions. Our approach decomposes multi-class segmentation into species-agnostic tissue identification followed by tissue type classification. By designing robust input representations invariant to imaging artifacts and morphological variations, our framework enables rapid adaptation to new species with fewer than 40 labeled images. Additionally, the first stage automatically generates tissue boundaries, transforming tedious manual tracing into simple tissue labeling. We validate our method on pearl millet, and sorghum root cross-sections from different imaging protocols, achieving state-of-the-art performance while dramatically reducing deployment time. This efficiency breakthrough enables scalable root phenotyping across diverse crop species, accelerating the development of climate-resilient varieties for global food security.
Why it matches plant phenotyping methods植物根の解剖学的形質を対象とする画像セグメンテーション手法を開発し、複数種・撮像条件で検証しているため、方法が研究の中心である。
abstractWe present a two-stage segmentation framework that greatly reduces annotation requirements while maintaining high accuracy across diverse plant species and imaging conditions.
Reproduction assets foundThe paper's Data availability statement explicitly deposits the annotated root image dataset (Zenodo 17726414), trained segmentation models (Zenodo 17737703), and the authors' source code (GitHub janetkok/Root-Segmentation-Beyond-Species-Boundaries), all directly reproducing this paper's root anatomical phenotyping andDataset · publicThe dataset and models are available at https://doi.org/10.5281/zenodo.17726414 and https://doi.org/10.5281/zenodo.17737703 , respectively.Open asset ↗Zenodo · 10.5281/zenodo.17726414lines:242-251Code · publicThe source code is hosted at https://github.com/janetkok/Root-Segmentation-Beyond-Species-Boundaries .Open asset ↗GitHub · janetkok/Root-Segmentation-Beyond-Species-Boundarieslines:242-251Plant phenotyping relevance match · UnverifiedEurope PMC · checked 15 Sept 2026
Published1 Mar 2026Artificial Intelligence in Agriculture
Nodule formation and their involvement in biological nitrogen fixation are critical features of leguminous plants, with phenotypic characteristics closely linked to plant growth and nitrogen fixation efficiency. However, the phenotypic analysis of root nodules remains technically challenging due to their small size, weak texture, dense clustering, and occlusion. To address these challenges, this study constructed a scanner-based imaging platform and optimized data acquisition conditions for high-resolution, high-consistency root nodule images under field conditions. In addition, A hybrid small-object detection method, SCO-YOLOv8s, was proposed, integrating Swin Transformer and CBAM attention mechanisms into the YOLOv8s framework to enhance global and local feature representation. Furthermore, an Otsu segmentation-based post-processing module was incorporated to validate and refine detection results based on geometric features, boundary sharpness, and image entropy, effectively reducing false positives and enhancing robustness in complex scenes. Using this integrated approach, over 3375 nodules were identified from a single plant sample in under 1 min, with extracted phenotypic features such as diameter, color, and texture. A total of 10,879 high-quality annotated images were collected from 39 peanut varieties across 14 provinces and 31 soybean varieties across 12 provinces in China, addressing the current lack of large-scale datasets for legume root nodules. The SCO-YOLOv8s model achieved a precision of 97.29 %, a mAP of 98.23 %, and an overall identification accuracy of 95.83 %. This integrated approach provides a practical and scalable solution for high-throughput nodule phenotyping, and may contribute to a deeper understanding of nitrogen fixation mechanisms.
Why it matches plant phenotyping methods根粒の画像取得・検出・セグメンテーション・形質抽出を統合した高スループット表現型解析手法を開発し、精度評価と大規模データセット構築も行っているため、方法が研究の中心である。
abstractthis study constructed a scanner-based imaging platform and optimized data acquisition conditions for high-resolution, high-consistency root nodule images under field conditions.
Sweetpotato ( Ipomoea batatas (L.) Lam.) is a crucial crop for global food security. However, its sustainable production is hindered by low nutrient use efficiency. Reliable screening protocols that accurately identify nutrient-efficient germplasm of this crop across developmental stages are still lacking. To bridge this gap, we established a novel two-phase evaluation system integrating hydroponic seedling screening with multi-nutrient field validation. We conducted principal component and regression analyses of 35 germplasms lines under controlled deficiencies of nitrogen (N), phosphorus (P), and potassium (K). Five conserved seedling traits were identified, including leaf number per plant, shoot fresh weight, root fresh weight, shoot dry weight, and net photosynthetic rate (Pn). These traits consistently correlated with tolerance to N, P, or K deficiency, thereby supporting their utility as reliable early indicators of nutrient stress. Field validation further confirmed that storage root fresh and dry weight, nutrient content, accumulation, and use efficiency varied significantly among nutrient treatments and genotypes, serving as key indicators of field performance. This integrated approach successfully identified elite germplasm with specific nutrient use efficiency: XN1985-7 as a low-N-tolerant and N-efficient utilization genotype, XN17104-132 as low-K-tolerant and K-efficient utilization, XN2141-3 as low-P-tolerant and P-efficient utilization, and notably XN2153-5, which exhibited concurrent tolerance to low N, P, and K with broad-spectrum efficiency. Our integrated two-phase framework provides a scalable model for screening nutrient-efficient germplasm in root crops, thereby contributing to sustainable breeding programs.
Why it matches plant phenotyping methods栄養効率遺伝資源を評価するための二段階スクリーニング系を構築し、複数の形態・生理形質を初期指標として検証しているため、植物フェノタイピング手法が中心的です。
abstractReliable screening protocols that accurately identify nutrient-efficient germplasm of this crop across developmental stages are still lacking.
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
WheatGrowth chamberRootMorphology / geometry measurementRoot system architectureStress response / tolerance
The Multiple Synthetic Derivatives (MSD) population is a unique hexaploid wheat resource that captures extensive genetic diversity from Aegilops tauschii and exhibits wide variation in agronomic traits. However, root system architecture (RSA), a key determinant of resource acquisition and stress adaptation, remains poorly characterized in this population. Here, we established a practical phenotyping framework for RSA analysis and evaluated MSD417 as a representative genotype. A two-dimensional cultivation platform enabling continuous imaging of seedling root growth under controlled conditions was established to quantify RSA traits and their responses to high temperatures. MSD417 was compared with its recurrent parent, Norin 61 (N61). Under controlled conditions, MSD417 displayed greater total root length, root system width, and convex hull area than N61, indicating enhanced early root vigor. This genotype also exhibited a wider seminal root angle, suggesting improved horizontal soil exploration while maintaining root depth. High-temperature treatment reduced overall root growth and minimized genotypic differences, indicating that temperature stress constrains RSA expression. Microscopic observations further revealed a lower height-to-width ratio of coleorhiza tissue of MSD417, suggesting restricted downward expansion. Collectively, this study establishes a practical framework for RSA phenotyping and demonstrates the potential of Aegilops tauschii-derived germplasm to enhance wheat root-related adaptive traits.
Why it matches plant phenotyping methods根系構造を連続画像化して定量する2次元表現型解析プラットフォームを構築し、RSA形質の測定に実質的に適用しているため、方法が中心的である。
abstractHere, we established a practical phenotyping framework for RSA analysis and evaluated MSD417 as a representative genotype.
Reproduction assets foundThe paper deposits its paper-specific root images (N61 and MSD417) and coleorhiza microscopic images in Zenodo with explicit DOIs. The R analysis scripts are only in Supplementary Document S1 with no public URL, so they do not qualify as a public code asset.Dataset · publicThe microscopic images of coleorhiza are deposited under https://doi.org/10.5281/zenodo.18091131.Open asset ↗Zenodo · 10.5281/zenodo.18091131pdf-page:14 lines:1-71Code / dataset availability confirmedOpenAlex · Europe PMC · bioRxiv · checked 15 Sept 2026
Abstract Root segmentation is a fundamental yet challenging task in image-based plant phenotyping. We present the first systematic comparison of Transformer and Convolutional Neural Network (ConvNet) architectures for root segmentation, evaluating 21 architectures across nine diverse datasets and comparing pre-trained models to training from scratch. Transformer-based models significantly outperform ConvNets for segmentation accuracy and root-diameter agreement. Pre-training significantly improves mean Dice from 0.623 to 0.666 ( p = 3.3 × 10 −10 ). We also find that Transformers benefit more from pre-training than ConvNets, with Dice improvements of +0.072 versus +0.022 ( p = 3.7 × 10 −4 ), supporting the hypothesis that fine-tuned Transformers transfer more effectively across large domain gaps. Among evaluated models, MobileSAM achieved the highest Dice score while maintaining computational efficiency. Dataset choice explained far more performance variance (70.9%) than model architecture (6.7%), suggesting that data curation matters more than model selection.
Why it matches plant phenotyping methods根の画像セグメンテーション手法を21種類・9データセットで体系比較し、植物フェノタイピングにおける精度と根径推定を検証しているため、方法評価が中心である。
abstractRoot segmentation is a fundamental yet challenging task in image-based plant phenotyping.
Reproduction assets foundThe paper's authors explicitly state that their training/segmentation analysis code is publicly available on GitHub. The nine root image datasets evaluated are cited prior public datasets (DeepRootLab, Grassland, Chicory, PRMI), not paper-specific assets of this study, so the authors' own code repository is the only inCode · publicr of parameters, as these affect
hardware requirements, running costs, and environmental
impact. To jointly compare efficiency and accuracy, we
ranked models by the mean of their Dice, parameter count,
and FLOPs ranks, providing a simple combined metric for
practitioners balancing these trade-offs. Training code is
available at https://github.com/sotlampr/seg.Configuration selection
To prevent overfitting to the test set, model selection used
a two-stage procedure based on validation performance:
Replicate selection: For each combination of model,
dataset, learning rate, and pre-training, the replicate with
the highest validation Dice was retained, along with its
paired test result.
HyperparOpen asset ↗sotlampr/segpdf-raw-page:4 lines:1-95Plant phenotyping relevance match · UnverifiedEurope PMC · checked 5 Sept 2026
Soil salinity is a major constraint for cultivating the economically important medicinal and ornamental shrub henna (Lawsonia inermis L.) in arid regions like Kerman Province, Iran. To address the lack of systematic germplasm evaluation for salinity tolerance, this study quantified the responses of ten geographically distinct henna populations to salt stress (0, 50, and 100 mM NaCl) during the critical germination and early seedling stages. A Fuzzy Comprehensive Evaluation (FCE) framework, based on the membership function values (MFV) derived from trait-specific Salt Tolerance Indices (STI), was used to integrate data from multiple germination parameters, e.g., germination percentage (GP), mean germination time (MGT), germination index (GI), germination vigor index (GVI) and seedling growth traits, e.g., radicle length (RL), plumule length (PL), total seedling length (TSL), seedling fresh weight (SFW). Results identified germplasm J-02 as the most salt-tolerant genotype (mean MFV = 0.536), demonstrating exceptional stability in RL (STI = 1.01) and SFW (STI = 0.90) under severe stress (100 mM NaCl). In contrast, K-01 was highly sensitive (mean MFV = 0.322), suffering severe GVI loss (STI = 0.56) and TSL reduction (34.1%) despite superior control performance. Regression analysis identified SFW as the optimal single-trait predictor for overall tolerance (R² = 0.714 at 50 mM; R² = 0.549 at 100 mM). The FCE model effectively resolved genotype-specific trait conflicts, a finding corroborated by principal component analysis (PCA) and hierarchical cluster analysis (HCA) which provided mechanistic insights: elite performers (e.g., J-02) prioritized seedling elongation, while others (e.g., F-01) excelled in germination under moderate stress. This study establishes J-02 as prime germplasm for saline zones and validates the integration of FCE with multivariate analysis for precision phenotyping in henna breeding programs.
Why it matches plant phenotyping methodsFCEモデルと多変量解析による複数形質の統合・塩耐性評価が研究の中心であり、単なる生物学的処理実験を超えた計算的フェノタイピング手法として扱われている。
abstractA Fuzzy Comprehensive Evaluation (FCE) framework, based on the membership function values (MFV) derived from trait-specific Salt Tolerance Indices (STI), was used to integrate data from multiple germination parameters
Summary Anatomical and histochemical imaging of grass root systems relies on tissue sectioning and cell wall staining dyes because molecular reporter lines are limited for most organisms. Distinct staining dyes require variable incubation time and concentration across different tissues and organisms. As a result, staining with multiple dyes becomes time consuming or challenging. Here, we report a rapid method to perform simultaneous triple staining on a glass slide. The entire protocol requires ∼4 hours and a smaller volume of stain than traditional methods. We tested this method using the roots of two economically important crops, Triticum aestivum (wheat) and Zea mays (maize), as proof of concept. We have also demonstrated the presence of exodermis in wheat roots. Additionally, we identified the formation of polar lignin caps in maize exodermis using our simultaneous triple staining method. This method empowers a quantitative approach to cell biology by elucidating cell-type specific spatio-temporal distribution of cell wall materials in monocot root systems.
Why it matches plant phenotyping methods単子葉植物の根における細胞壁物質の細胞型別・時空間分布を定量的に可視化する同時三重染色法の開発が中心であり、植物状態の取得・解析手法に該当する。
abstractHere, we report a rapid method to perform simultaneous triple staining on a glass slide.
Accurate and in-situ monitoring of frost growth on plant leaves is crucial for disaster prevention in smart agriculture. To address the limitations of traditional methods in quantification and continuity, this study proposes a novel monitoring paradigm integrating dynamic dielectric spectrum analysis with hybrid intelligent algorithms. A mesh-electrode-based capacitive sensor was designed to capture in-situ and continuous dielectric spectrum changes on leaf surfaces. Subsequently, a hybrid SWT-SSA-LSTM model was constructed for high-fidelity denoising and prediction of the original signals. Field experiments demonstrated that this system could quantify frost layer mass and thickness with high precision. The established nonlinear regression models achieved coefficients of determination of 0.924 and 0.975, respectively. The prediction model exhibited outstanding performance, with a root mean square error as low as 1.475. This study establishes a complete technical closed-loop from physical perception to intelligent prediction, providing an innovative solution for precise frost monitoring in agriculture.
Why it matches plant phenotyping methods植物葉面の霜の質量・厚さという状態を、誘電スペクトルセンサーと予測モデルで連続的に定量する手法を開発・検証しており、フェノタイピング手法が中心である。
abstractA mesh-electrode-based capacitive sensor was designed to capture in-situ and continuous dielectric spectrum changes on leaf surfaces.
Plant 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
Abstract Aims This study evaluated the suitability of root electrical capacitance measurements for nondestructive plant phenotyping in a free-air CO 2 enrichment (FACE) experiment. Methods A two-year FACE study was conducted with maize grown under ambient and elevated [CO 2 ], and low and high nitrogen supply in three replicate plots. The saturation root electrical capacitance (C R *) was monitored during the plant growth cycle. Aboveground plant parameters were measured in situ at flowering. Results Capacitance measurements revealed a seasonal pattern in root development with a peak at flowering, and the positive effect of higher nitrogen dose and [CO 2 ] enrichment on plant growth. At anthesis, C R * was significantly ( p < 0.001) and linearly correlated with stem basal area (R 2 : 0.51–0.68), aboveground biomass index (basal area × plant height; R 2 : 0.47–0.62) and leaf chlorophyll concentration (R 2 : 0.40–0.56). However, the best correlation (R 2 : 0.73 and 0.74) was found for plant leaf area, which is closely related to root water uptake, suggesting that the applied current signal penetrated the roots, and that the capacitance method directly measured root status in the field. In addition, C R * at flowering was a reasonable early predictor of maize grain yield (R 2 : 0.58 and 0.64) under our experimental conditions. Conclusions The electrical capacitance method proved to be a practical high-throughput tool for phenotyping not only the root but the whole plant in the field. Being noninvasive, it is particularly beneficial in FACE systems, where destructive sampling and soil disturbance should be minimized. It would also provide cost-effective support for breeding stress-tolerant and climate-resilient crops. Graphical Abstract
Why it matches plant phenotyping methods根の電気容量測定を非破壊・高スループットな植物フェノタイピング手法として評価し、圃場での相関および予測性能を検証しているため、方法が研究の中心である。
abstractThis study evaluated the suitability of root electrical capacitance measurements for nondestructive plant phenotyping in a free-air CO 2 enrichment (FACE) experiment.
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.
Roots are major contributors to nutrient acquisition, water absorption, and plant anchoring and stability. However, little is known about the root system of industrial hemp (Cannabis sativa L.), an increasingly important crop worth $16 billion annually. Hemp is commonly cultivated for grain as an oilseed, stalk biomass for fiber and industrial materials, but has also had growing interest for its carbon sequestration potential due to its reported deep rooting profile. The objectives of this research were to (1) phenotype a panel of 46 industrially-relevant hemp genotypes, (2) quantify the phenotypic differences of shoot and root traits through 2D image analysis, (3) and to investigate genotype grouping strategies and gene targets that could be useful for crop improvement. To phenotype the root system architecture of multiple hemp genotypes representative of production hemp, a large format raised-bed was developed in a greenhouse in which hemp was planted in rows. Root and shoot traits varied across genotypes, with a difference of 175% in total root length between the largest and smallest genotype, and heritability values ranging from 0.51 to 0.88 for key root traits. A strong positive correlation was found between root and shoot biomass (R = 0.93) suggests coordinated resource allocation strategies across genotypes. Of the 46 genotypes studied, two genotypes consistently showed the greatest differences across most of the traits analyzed in the panel. A root-to-shoot quadrant framework was applied to classify hemp ideotypes based on biomass allocation and architectural traits. In addition, comparative genomic analysis identified 74 candidate root architecture genes in hemp that are orthologous to known regulators in maize, rice, and Arabidopsis. These findings highlight substantial phenotypic diversity in hemp root systems and provide a foundation for developing genotype grouping strategies and selecting breeding targets for mapping populations.
Why it matches plant phenotyping methods複数遺伝子型の根系形態を2D画像解析で定量し、温室内の大規模 raised-bed フェノタイピング基盤も開発しているため、植物形質取得が研究の中心である。
abstractTo phenotype the root system architecture of multiple hemp genotypes representative of production hemp, a large format raised-bed was developed in a greenhouse
Abstract Unmanned aerial systems (UAS) equipped with multispectral and RGB sensors offer valuable data for monitoring crop health and assessing disease severity. However, the wide range of available photogrammetric software complicates software selection for high‐throughput plant phenotyping. This study compares the consistency of three widely used software packages, OpenDroneMap (ODM), Agisoft Metashape, and Pix4Dmapper in processing UAS‐acquired imagery for wheat ( Triticum aestivum L.) phenotyping. Over two seasons at Oklahoma State University research sites, imagery from a DJI Phantom 4 Pro Multispectral was used to generate eight vegetation indices (VIs), which were evaluated using correlation () and root mean square error (RMSE). Normalized VIs showed strong consistency across software, with values between 0.85 and 0.99 and RMSEs ranging from 0.004 to 0.07. Non‐normalized indices exhibited greater variability but retained high correlations ( > 0.76). Ground‐truth validation used single‐view imagery and disease severity ratings. Bayesian models quantified spectral measurement differences, their distributions (mean, standard deviation, skewness, and excess kurtosis) across processing approaches, and evaluated disease classification performance using ordinal logistic regression. Normalized VIs were highly consistent across software (posterior median differences <0.01 units, overlapping 95% highest density intervals), while single‐view imagery showed 15%–25% higher pixel‐level variability than software outputs. Non‐normalized indices showed greater processing sensitivity. RGB indices demonstrated near to perfect consistency. Disease classification accuracy ranged from 35% to 48% with minimal software differences (<2%). All three software produce biologically consistent results, ensuring stable genotype rankings regardless of processing choice. ODM performed comparably to proprietary alternatives while offering cost‐effectiveness, transparency, and reproducibility advantages.
Why it matches plant phenotyping methodsUAS画像から抽出する小麦表現型について、複数の写真測量ソフトウェアの一貫性・誤差・再現性を比較検証しており、フェノタイピング手法の技術評価が中心です。
abstractThis study compares the consistency of three widely used software packages, OpenDroneMap (ODM), Agisoft Metashape, and Pix4Dmapper in processing UAS‐acquired imagery for wheat ( Triticum aestivum L.) phenotyping.
RiceRootMorphology / geometry measurementSegmentationRoot system architecture
Root phenotypic traits such as length and number are critical indicators of plant growth and productivity. However, accurate extraction of these traits remains challenging due to the slender morphology, dense overlap, and frequent occlusion within root systems. Traditional digital image processing methods suffer from low throughput and limited robustness, while most deep learning-based approaches rely on semantic segmentation, which fails to distinguish individual roots and therefore limits their applicability in instance-level phenotypic analysis.To address these limitations, we propose Dual-Guided Asymmetric MP-Former (DGA-MP-Former), a novel instance segmentation model tailored for root phenotyping, with rice roots as a representative case. Building upon the MP-Former framework, our model introduces two key components: the Guided-Enhancement Pixel Decoder (GEPD) and the Asymmetric Dual-Query Decoder (ADQD). The GEPD enhances multi-scale feature representations via Hybrid Convolution Aggregator, Semantic-Guided Fusion Module and Frequency-Guided Feature Enhancement Module, effectively capturing fine root structures and low-contrast regions. ADQD employs asymmetric interaction between semantic and instance queries to improve long-range dependency modeling and instance separation in occluded scenarios.Additionally, we present the Rice Root Segmentation Dataset (RRSD), comprising of 343 high-resolution images with instance-level annotations. Experimental results show that DGA-MP-Former achieves state-of-the-art performance on RRSD, with 57.2% AP 0.5:0.95 and 87.4% AP 0.5 . Importantly, the accurate instance segmentation results enable reliable computation of instance-level geometric traits, such as root perimeter and area. To quantitatively assess phenotypic measurement accuracy, Relative Area Error (RAE) and Relative Perimeter Error (RPE) are further introduced, achieving 26.4% and 20.2%, respectively. These results demonstrate that the proposed method effectively bridges instance segmentation accuracy and phenotypic quantification reliability, supporting high-throughput and precise root phenotyping.
Why it matches plant phenotyping methodsイネ根の個体別セグメンテーションモデルを開発し、データセット提供、性能評価、および根の形態形質推定まで行っており、植物フェノタイピング手法が研究の中心である。
abstractwe propose Dual-Guided Asymmetric MP-Former (DGA-MP-Former), a novel instance segmentation model tailored for root phenotyping
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 Rice Root Segmentation Dataset is open sourced for the research community at ”https://github.com/Run-19/DGA-mpformer”.Open asset ↗Run-19/DGA-mpformerhtml-lines:442-469Plant phenotyping relevance match · UnverifiedEurope PMC · checked 5 Sept 2026
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.
MaizeField / plotChlorophyll fluorescenceRootStem / branchPhysiological trait estimationGrowth / time-series analysisGrowth / development / phenologyPhotosynthesis / fluorescenceWater status / transpiration
The soil-plant-atmosphere continuum ( SPAC ) plays a critical role in the distribution of water and nutrients in terrestrial ecosystems. To understand the complex and rapid dynamics within the SPAC , it is necessary to observe its components with sub-daily resolution. While measurements of above-ground processes are frequently employed, monitoring of the below-ground part remains scarce due to its inaccessibility. In this study, we monitored water and nutrient transport processes in a maize field over several months. The rhizosphere was monitored with spectral electrical impedance tomography ( sEIT ) to capture soil water content ( SWC ) dynamics, root structure, and activity. Stem water transport and photosynthetic activity were measured with sapflow sensors and a fluorescence sensor, respectively, while atmospheric conditions were measured with a weather station. Timeseries were analyzed using cross and coherence wavelet analysis. Electrical imaging results revealed spatially and temporally resolved daily variations in subsurface conductivity and polarization properties, suggesting a sensitivity to water and ion uptake processes. Conductivity development was strongly correlated with SWC dynamics controlled by evaporation and water uptake of plants. Wavelet power showed that belowground polarization diurnality was consistent with a typical growth pattern of maize, and disappeared shortly after harvest. Cross wavelet analysis of sun-induced fluorescence, sapflow density, photosynthetically active radiation, and vapor pressure deficit revealed lags caused by environmental conditions, highlighting the coupling of plant activity to the atmosphere. Our results show that sEIT is a valuable tool to study rhizosphere processes and may aid in the holistic modeling of the SPAC .
Why it matches plant phenotyping methodssEITを用いて根圏の水分動態・根構造・根の活動を時空間的に取得し、他センサーとの時系列解析で植物の水輸送・生理状態を評価しており、植物状態のセンシング手法の実質的な適用が中心です。
abstractThe rhizosphere was monitored with spectral electrical impedance tomography ( sEIT ) to capture soil water content ( SWC ) dynamics, root structure, and activity.
RiceRootAnnotation / quality controlMorphology / geometry measurementSegmentationRoot system architecture
ABSTRACT Quantification of root anatomical traits such as cortical aerenchyma is key to understanding rice adaptation to diverse water regimes. Recently, the role of aerenchyma in regulating methane emissions has been demonstrated, making it a target for climate change mitigation. Despite its importance, breeding for root anatomical traits remains limited because manual analysis of root cross-sections is labor-intensive, inconsistent, and poorly scalable, and analysis pipelines do not generalize across heterogeneous imaging conditions. We present a deep learning pipeline based on a recent vision transformer architecture to automatically segment rice root anatomical structures and quantify aerenchyma. The model was trained on a multi-environment dataset of 1,760 annotated rice root cross-sections acquired across growth stages, cultivation systems, and countries, using a collaboratively defined annotation protocol. The model achieved high segmentation performance (mean Intersection-over-Union > 0.92) and near-perfect aerenchyma ratio quantification (R 2 = 0.98), and was evaluated by two experts as performing on par with, and in some cases better than, expert annotators. Delivered as open-source software with an online interactive demonstrator, the pipeline revealed differences in aerenchyma across genotypes, water regimes, environments, and developmental stages. Overall, this work demonstrates that transformer-based segmentation enables high-throughput anatomical phenotyping, supporting scalable and climate-smart rice breeding. HIGHLIGHTS Transformer-based segmentation enables robust aerenchyma phenotyping across environments A SegFormer model achieves expert-level accuracy on diverse rice root cross-sections Automated analysis delivers near-perfect lacuna-to-cortex ratio quantification (R 2 ≈ 0.98) Our online demonstrator supports scalable, climate-smart rice breeding applications
Why it matches plant phenotyping methodsイネ根の画像から通気組織を自動分割・定量する深層学習パイプラインを開発し、異なる環境で性能検証した、中心的な植物フェノタイピング研究である。
abstractWe present a deep learning pipeline based on a recent vision transformer architecture to automatically segment rice root anatomical structures and quantify aerenchyma.
In recent years, excessive tillering caused by high temperatures during early growth has contributed to rice quality deterioration in warm regions of Japan. Accurate determination of midseason drainage timing is essential but remains difficult due to year- and cultivar-dependent variability. In this study, we developed a smartphone-based web application that estimates rice tiller number from canopy images and diagnoses the optimal timing of midseason drainage by comparing estimated tiller numbers with cultivar-specific target values. The system operates entirely on a smartphone using HTML5 canvas-based pixel extraction, JavaScript computation, and Google Apps Script-based backend processing. Field experiments conducted in Chiba Prefecture using three rice cultivars showed a strong linear relationship between estimated and observed tiller numbers (R 2 = 0.9439). The root mean square error (RMSE) was 42.6 tillers m -2 , with a consistent negative bias (-34.6 tillers m -2 ), indicating systematic underestimation. Considering typical tiller increase rates near midseason drainage (12.0-24.3 tillers m -2 day -1 ), these errors correspond to approximately 1-3 days of growth progression, which is acceptable for timing-based decision-making. Although the system does not aim to provide precise absolute tiller counts, it reliably captures relative growth-stage dynamics and supports threshold-based diagnosis. The proposed approach enables rapid, on-site decision support using only a smartphone, contributing to labor-saving and improved water management in rice production.
Why it matches plant phenotyping methodsスマートフォン画像からイネの分げつ数を推定する手法とWebアプリを開発し、圃場で精度検証しているため、植物表現型取得が研究の中心である。
abstractwe developed a smartphone-based web application that estimates rice tiller number from canopy images
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.
Plant phenotyping relevance match · UnverifiedCrossref · Europe PMC · OpenAlex · checked 14 Sept 2026
Why it matches plant phenotyping methodsSBR耐性品種のスクリーニングを目的に、ハイパースペクトル画像、2D画像、構造化光3Dスキャン、機械学習を用いた植物形態・スペクトル形質の取得と評価が研究の中心である。
abstractDigital plant phenotyping can support the screening process for tolerant varieties by characterizing traits of interest and quantifying tolerance.
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.
Field / plotLeafRootWhole plant / canopy / plot / fieldMorphology / geometry measurementLeaf traitsPlant / canopy heightRoot system architecture
To explore the distinctness, uniformity, and stability(DUS) testing technology for Angelica dahurica and develop its DUS testing guidelines, this study conducted systematic research on the morphological and growth characteristics of A. dahurica throughout its whole growth period. The research was based on 428 A. dahurica lines from 48 sampling sites across China, with unified standardized field planting, regular and fixed-point observation, classified statistics of phenotypic characteristics, and comprehensive data analysis. Ultimately, 49 test characteristics were identified, including 36 basic characteristics and 13 optional characteristics. Classified by attribute, they consisted of 4 qualitative characteristics, 3 pseudo-qualitative characteristics, and 42 quantitative characteristics. Classified by organ and growth stage, the characteristics covered 2 cotyledon characteristics, 19 leaf characteristics(including basal leaves and cauline leaves), 2 plant characteristics, 3 saccate leaf sheath characteristics, 5 fruit characteristics, 9 root characteristics, 5 stem characteristics, 3 flower characteristics, and 1 growth period characteristic. Through the evaluation of characteristic discriminability and stability, five grouping characteristics were screened out, namely "basal leaf: anthocyanin coloration on the back of the leaf sheath" "basal leaf: anthocyanin coloration at the attachment site of the petiolule" "flowering period" "plant: height" "main root: arrangement pattern of lenticel-like protuberances". These can serve as important bases for the preliminary screening and classification of A. dahurica varieties. Meanwhile, 20 standard varieties with typical phenotypes were identified to provide a unified reference for characteristic observation. In addition, the guidelines also specify the scope of application, requirements for propagation materials, growth stages, observation periods, observation methods, DUS judgment criteria, and other content. This study fills the gap in the field of DUS testing technology for A. dahurica, and provides a scientific basis and technical support for DUS testing, resource identification and description, variety breeding of A. dahurica varieties, as well as management and protection of new A. dahurica arieties.
Why it matches plant phenotyping methodsアンジェリカ・ダフリカ品種のDUS試験に向け、標準化された形質観察法、判定基準、識別性・安定性評価を開発しており、植物表現型取得手法が研究の中心である。
abstractTo explore the distinctness, uniformity, and stability(DUS) testing technology for Angelica dahurica and develop its DUS testing guidelines
Rooting systems of plants perceive environmental stimuli and flexibly regulate their growth. Therefore, understanding stimulus perception and response mechanisms is essential for optimizing cultivation. During the transition from aquatic to terrestrial environments, land plants have acquired mechanisms to adapt to gravitational force on land. Thus, elucidating gravity responses of rhizoids in bryophytes, early diverging land plants, provides important insights into how gravity-response mechanisms were established during land plant evolution. Analyzing rhizoid morphology under microgravity, where gravitational effects are largely eliminated, provides an effective approach to examine the gravity-response mechanisms that evolved after terrestrialization. In this study, to elucidate microgravity effects on rhizoid growth of Physcomitrium patens , we analyzed 3D datasets obtained by refraction-contrast micro-CT using synchrotron radiation after fixation and embedding of samples from the Space Moss experiment conducted on the International Space Station. Because each CT volume contains numerous rhizoids, we optimized a WEKA-based machine-learning segmentation approach by improving preprocessing, training, and postprocessing steps, resulting in a significantly improved segmentation accuracy. Comparison of 3D morphological indices between manually segmented rhizoids and predicted results supported the validity of the proposed method for morphological analysis. Morphological analyses revealed that, compared with both ground and artificial 1 × g conditions, rhizoid elongation and gravitropic responses were suppressed under microgravity, leading to reduced vertical growth. These findings indicate that gravity plays a fundamental role in rhizoid morphogenesis, and their absence affects growth orientation and elongation. This study provides foundational data for research on the rooting systems of bryophytes in space.
Why it matches plant phenotyping methodsマイクロCT画像からコケ植物の根茎の3D形態を抽出する機械学習セグメンテーション法を開発・最適化し、手動セグメンテーションとの比較で妥当性を検証しているため、植物フェノタイピング手法が中心です。
abstractwe optimized a WEKA-based machine-learning segmentation approach by improving preprocessing, training, and postprocessing steps, resulting in a significantly improved segmentation accuracy.
Plant phenotyping relevance match · UnverifiedOpenAlex · Europe PMC · checked 7 Sept 2026
MRI / PETX-ray / CTRootMorphology / geometry measurementRoot system architectureStress response / toleranceWater status / transpiration
Strategic optimisation of Root System Architecture (RSA) represents a critical frontier for stabilising crop productivity amid increasingly unpredictable moisture-deficit regimes. Understanding key root traits underlying effective drought response is necessary to harness the genetic diversity associated with root growth patterns and environmental adaptations. Many functionally significant root architectural traits have been reported, and the mechanistic importance of some of the anatomical ideotypes, such as the increased metaxylem vessel diameter to reduce axial hydraulic resistance to maintain leaf water potential and change in root growth angle to promote geotropic deep-soil moisture foraging, are discussed in this review. Despite the identification of these characteristics, the knowledge gap in their integration into predictive breeding frameworks remains. This review addresses this fragmentation by critically evaluating how the bottleneck of the ‘phenotyping’ process is being broken down through non-invasive high-throughput phenotyping modalities. Dynamic root-soil interfaces can be spatio-temporally quantified in situ using non-destructive technologies such as X-ray computed tomography and MRI, which can detect developmental plasticity masked by destructive sampling. Artificial Intelligence (AI), especially Convolutional Neural Networks, enables automated extraction of high-dimensional topological parameters from complex digital rhizograms. Present review integrates recent advances in phenotyping with molecular regulatory mechanisms, bridging two traditionally disparate fields. By focusing on the DRO1/qSOR1 loci and ABA-auxin crosstalk, we establish critical connections between molecular regulation and field-scale architectural performance. The resulting multi-scale roadmap may help in targeted selection of climate-resilient cultivars to maximize resource use efficiency.
Why it matches plant phenotyping methods根系構造の非破壊・ハイスループット表現型解析技術を中心に、X線CT、MRI、AIによる根系形質抽出をレビューしており、植物フェノタイピング手法が中核です。
abstractThis review addresses this fragmentation by critically evaluating how the bottleneck of the ‘phenotyping’ process is being broken down through non-invasive high-throughput phenotyping modalities.
Background Hydrogen sulfide (H 2 S) is a key endogenous gasotransmitter involved in plant physiological regulation and stress responses. Monitoring its dynamic changes in plants is essential for understanding related signaling mechanisms. However, due to its chemical instability and the complexity of plant tissues, developing a reliable method for accurate quantification and real-time visualization of H 2 S in living plants remains challenging. Although fluorescent probes have been developed for H 2 S imaging, most still face critical limitations, such as emission in the visible region being susceptible to background fluorescence interference, and insufficient quantitative reliability of single-wavelength-based signal output modes in complex samples. Therefore, developing novel probes with near-infrared emission and rationetric response characteristics is of great significance. Results We constructed a near-infrared rationetric fluorescent probe, NIR-Cou-H 2 S, for H 2 S detection. The probe itself emits at 716 nm, and after specific reaction with H 2 S, a new emission peak appears at 552 nm, resulting in a distinct dual-emission rationetric response (716 nm/552 nm) accompanied by a visible color change. Using chemometrics-based fluorescence analysis, the probe successfully enabled direct quantitative detection of H 2 S in river and lake water samples. Its near-infrared emission effectively reduced interference from plant autofluorescence, thereby achieving high-contrast dual-channel fluorescence imaging. The probe was successfully applied for high-quality in situ visualization of H 2 S in living cells and tobacco seedling roots. More importantly, using NIR-Cou-H 2 S, we observed and recorded in real time the dynamic upregulation trend of endogenous H 2 S levels in tobacco roots under both drought and flooding stress conditions. Significance As a novel near-infrared rationetric probe, NIR-Cou-H 2 S provides a powerful tool for monitoring endogenous H 2 S dynamics in plants. Its characteristics significantly improve the reliability of imaging and quantification in complex plant samples, offering a key methodological approach for further elucidating the regulatory mechanisms of H 2 S in plant stress resistance.
Why it matches plant phenotyping methods植物体内のH₂S動態を可視化・定量する近赤外蛍光プローブを開発し、植物根でのストレス応答測定に適用しており、表現型・生理状態の取得法が中心である。
abstractWe constructed a near-infrared rationetric fluorescent probe, NIR-Cou-H 2 S, for H 2 S detection.
To investigate how different training modes of salt stress priming affect the dynamic variation of the salt tolerance threshold (STT) in summer maize, and to enable the accurate quantification and prediction of STT, a micro-plot experiment utilizing diverse regimes of brackish water irrigation was conducted. Utilizing physiological, shoot, and root indicators, a comprehensive evaluation framework was developed to define a dynamic salt tolerance coefficient (αSTT), enabling the precise quantification of STT across growth stages. Building on this, the study established a unified predictive framework to systematically evaluate the performance of diverse modeling pathways and machine learning algorithms. The results revealed a distinct two-stage stress response pattern of summer maize to salt stress, characterized by an initial physiological adaptation phase dominated by regulatory adjustments, followed by a phenotypic adaptation phase associated primarily with improvements in growth performance. Different training modes led to distinct salt stress memory effects by modulating the coordination between these two adaptive stages. Among all modes, the S1-2-3 training regime exhibited the most favorable adaptive outcome, with the αSTT gradually recovering to 1.0 during later growth stages, indicating full adaptation to saline stress, and concomitantly exhibiting a relatively high STT. Regarding predictive performance, the PCR-STP modeling pathway incorporating process constraints outperformed purely data-driven pathways, and its combination with CatBoost achieved the highest accuracy (R² = 0.910, RMSE = 0.241). Overall, our study elucidates the dynamic nature of salt tolerance in summer maize, while the proposed STT quantification and prediction method provides a scientific basis for refining salt stress modules in crop models, optimizing brackish water irrigation regimes, and improving precision water resource management in arid and semi-arid regions.
Why it matches plant phenotyping methods塩耐性閾値（STT）の動的定量・予測フレームワークと機械学習モデルの構築が研究の中心であり、植物の生理・生育状態を抽出する方法論的貢献が明確である。
abstracta comprehensive evaluation framework was developed to define a dynamic salt tolerance coefficient (αSTT), enabling the precise quantification of STT across growth stages.
Field / plotRootWhole plant / canopy / plot / fieldClassificationGrowth / time-series analysisGrowth / development / phenology
Current mainstream remote sensing approaches to forest age estimation frequently neglect interspecific differences in functional traits, which may limit the accurate representation of species-specific tree growth strategies. This study develops and validates a technical framework that incorporates multi-source remote sensing and tree species functional trait heterogeneity to systematically improve the accuracy of plantation age mapping. We constructed a processing chain—“multi-source feature fusion–species identification–heterogeneity modeling”—for a typical karst plantation landscape in southeastern Yunnan. Using the Google Earth Engine (GEE) platform, we integrated Sentinel-1/2 and Landsat time-series data, implemented a Gradient Boosting Decision Tree (GBDT) algorithm for species classification, and built age estimation models that incorporate species identity as a proxy for the growth strategy heterogeneity delineated by the Plant Economic Spectrum (PES) theory. Key results indicate: (1) Species classification reached an overall accuracy of 89.34% under spatial block cross-validation, establishing a reliable basis for subsequent modeling. (2) The operational model incorporating species information achieved an R2 (coefficient of determination) of 0.84 (RMSE (Root Mean Square Error) = 6.52 years) on the test set, demonstrating a substantial improvement over the baseline model that ignored species heterogeneity (R2 = 0.62). This demonstrates that species identity serves as an effective proxy for capturing the growth strategy heterogeneity described by the Plant Economic Spectrum (PES) theory, which is both distinguishable and valuable for modeling within the remote sensing feature space. (3) Error propagation analysis demonstrated strong robustness to classification uncertainties (γ = 0.23). (4) Plantation structure in the region was predominantly young-aged, with forests aged 0–20 years covering over 70% of the area. Despite inherent uncertainties in ground-reference age data, the integrated framework exhibited clear relative superiority, improving R2 from 0.62 to 0.84. Both error propagation analysis (γ = 0.23) and Monte Carlo simulations affirmed the robustness of the tandem workflow and the stability of the findings, providing a reliable methodology for improved-accuracy plantation carbon sink quantification.
Why it matches plant phenotyping methodsマルチソースリモートセンシングと種分類を統合し、植林地の林齢という植物・林分状態を推定する技術的枠組みを開発・検証しており、フェノタイピング手法が中心である。
abstractThis study develops and validates a technical framework that incorporates multi-source remote sensing and tree species functional trait heterogeneity to systematically improve the accuracy of plantation age mapping.
Understanding how plants perceive and respond to environmental and developmental cues requires tools capable of monitoring molecular signals in vivo, across whole tissues, and in real time. Genetically encoded fluorescent indicators, coupled with fluorescence microscopy, have transformed plant biology, but their application remains largely confined to small model organisms and specialized microscopy instrumentation. Here, we present MAcro Plant Projection Imaging (MAPPI), an open-source, low-cost, and modular fluorescence imaging platform for soil-grown plants beyond the model organism or seedling stage. MAPPI enables wide field-of-view, dual-projection imaging of fluorescent reporters, supporting real-time visualization of systemic signals under near-physiological conditions. We validate MAPPI by tracking calcium and l -glutamate dynamics in adult Nicotiana benthamiana plants, revealing developmentally regulated long-distance calcium waves triggered by wounding, burning, or submergence, including bidirectional shoot-to-root and root-to-shoot signaling. By democratizing access to whole-plant functional imaging, MAPPI provides a scalable tool for dissecting signal propagation, stress adaptation, and systemic communication in both model and nonmodel species.
Why it matches plant phenotyping methods植物全体の蛍光シグナルをリアルタイム取得する低コスト・オープンな画像プラットフォームを開発し、成体植物で検証しているため、植物表現型取得法が研究の中心です。
abstractHere, we present MAcro Plant Projection Imaging (MAPPI), an open-source, low-cost, and modular fluorescence imaging platform for soil-grown plants beyond the model organism or seedling stage.
Reproduction assets foundThe authors publicly deposit raw imaging data and MAPPI analysis code on Zenodo, host the MAPPI acquisition/analysis code on GitHub, and release the napari-roi-registration image registration plugin on GitHub. All are paper-specific, public, and actionable.Dataset · publicThe raw data for the images presented in the manuscript and the code to run the MAPPI system are available on Zenodo ( https://doi.org/10.5281/zenodo.15845576 ).Open asset ↗Zenodo · 10.5281/zenodo.15845576lines:170-466Code · publicThe code to run the MAPPI system is also available on the dedicated GitHub repository ( https://github.com/micropolimi/MAPPI ) along with the code used to analyze the data.Open asset ↗GitHub · micropolimi/MAPPIlines:170-466Code · publicThe software is open-source and available on GitHub ( https://github.com/GiorgiaTortora/napari-roi-registration ) and the napari-hub ( www.napari-hub.org/plugins/napari-roi-registration ).Open asset ↗GitHub · GiorgiaTortora/napari-roi-registrationlines:156-169Code / 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:141-163Plant phenotyping relevance match · UnverifiedEurope PMC · checked 15 Sept 2026
BACKGROUND: Durum wheat (Triticum durum L.) productivity is strongly limited by salinity stress, particularly during early growth stages, due to disruptions in growth, water relations, and nutrient uptake. Seaweed extracts (SWEs), especially those derived from Ascophyllum nodosum, are widely used as biostimulants to enhance stress tolerance; however, their effects on durum wheat under salinity remain insufficiently characterized. In parallel, artificial neural networks (ANNs) provide effective tools for modeling complex plant responses to environmental stress. RESULTS: Salinity significantly reduced growth and physiological parameters, including biomass, chlorophyll content, and relative water content. SWE applications (2 and 4 g L⁻¹) effectively mitigated these negative effects. Biochemical traits such as proline accumulation, total phenolic content, and total antioxidant capacity were markedly enhanced under salinity. SWE treatments also improved macro- and micronutrient uptake in roots and shoots. ANN models successfully predicted multiple plant traits with high accuracy (R² > 0.90 for several key parameters). These models were implemented in a web-based R Shiny application to enable real-time prediction of plant responses. CONCLUSIONS : SWE application alleviates salinity-induced stress in durum wheat by improving growth, antioxidant capacity, and nutrient acquisition. The integration of ANN modeling with experimental data provides a reliable and practical approach for predicting plant responses, supporting artificial intelligence-assisted strategies for sustainable wheat production under saline conditions.
Why it matches plant phenotyping methods塩ストレス・生物刺激剤実験を背景とするが、ANNによる複数の植物生理・生化学・栄養形質の予測とWebアプリ実装が題名および結果の中心であり、再利用可能な計算的形質推定ワークフローに該当する。
titleArtificial neural network-based estimation of physiological, biochemical, and nutrient parameters in durum wheat under NaCl and biostimulant treatments.
The legume-rhizobium symbiosis is a cornerstone of sustainable agriculture due to its ability to facilitate biological nitrogen fixation. Still, real-time visualization and quantification of this interaction remain technically challenging, especially across different host backgrounds. In this study, we systematically evaluate the efficacy of the nitrogenase system nifH promoter (P nifH ) in driving expression of distinct fluorescent reporters; superfolder yellow fluorescent protein (sfYFP), superfolder cyan fluorescent protein (sfCFP), and various red fluorescent proteins (RFPs) within root nodules of determinate ( Lotus japonicus-Mesorhizobium japonicum ) and indeterminate ( Pisum sativum-Rhizobium leguminosarum ) systems. We show that P nifH -driven sfYFP and sfCFP yield strong, uniform, and reproducible fluorescence in nodules of both systems, facilitating reliable quantification of nodulation traits and strain occupancy. In contrast, RFPs including monomeric (mScarlet-I, mRFP1, mARs1) and multimeric (AzamiRed1.0) variants exhibited weak or inconsistent signals in pea. Notably, fluorescent labeling did not impair rhizobial competitiveness for root nodule occupancy, and P nifH -driven sfYFP and sfCFP reporters enabled robust multiplexed imaging in single-root and split-root assays. In the lotus, mScarlet-I worked robustly and facilitated a tripartite strain labeling system. Complementing our molecular toolkit, we established a deep learning-based analytical pipeline for high-throughput, automated quantification of nodulation traits, validated against standard ImageJ analysis. Altogether, our results identify P nifH -driven sfYFP and sfCFP as robust, broadly applicable reporters for legume-rhizobium symbiosis studies, while highlighting the need for optimized red fluorophores in some contexts. The integration of validated promoter-reporter constructs with state-of-the-art computational approaches provides a scalable framework for dissecting the spatial and competitive dynamics of plant-microbe mutualisms. Importance The legume-rhizobium symbiosis is central to sustainable agriculture through its capacity for biological nitrogen fixation, yet tools for real-time, quantitative visualization of this interaction remain limited. Here, we demonstrate that the nifH promoter (P nifH ) effectively drives expression of superfolder yellow (sfYFP) and cyan (sfCFP) fluorescent proteins in both determinate ( Lotus japonicus-Mesorhizobium japonicum ) and indeterminate ( Pisum sativum-Rhizobium leguminosarum ) nodules. These reporters enable robust, reproducible fluorescence without impairing rhizobial competitiveness, supporting multiplexed imaging and quantitative nodulation analyses. By contrast, red fluorescent proteins exhibited host-dependent variability, underscoring the need for improved red fluorophores. Integration of validated promoter-reporter constructs with a deep learning-based image analysis pipeline establishes a scalable framework for high-throughput assessment of nodule occupancy and symbiotic dynamics. This work provides a practical molecular and computational toolkit for dissecting plant-microbe mutualisms across diverse host systems.
Why it matches plant phenotyping methods植物根粒の蛍光可視化・定量法を複数宿主で評価し、深層学習画像解析パイプラインを標準法と比較検証しており、植物形質（根粒形成・占有）の取得手法が中心的です。
abstractwe established a deep learning-based analytical pipeline for high-throughput, automated quantification of nodulation traits, validated against standard ImageJ analysis
ABSTRACT Plant growth is a dynamic process affected by genes and growing environment, with all kinds of interactions between them. These complex relationships make the prediction of plant growth challenging. We propose a hybrid modelling framework that combines a logistic ordinary differential equation model with a Long Short-Term Memory (LSTM) neural network, resulting in a Physics Informed Neural Network (PINN). While PINNs have been widely applied to physical dynamical systems, their use in modelling the dynamics of plant growth systems is still largely unexplored. We illustrate the construction of a PINN on plant height data in wheat and compare its performance with alternative models for longitudinal plant data. All temporal prediction models only require time and temperature as input. Among a set of competing models, our PINN had the lowest average root mean squared error (RMSE) of prediction and the smallest standard deviation across multiple random initialisations. Therefore, we conclude that incorporating biological growth constraints into data-driven growth models can enhance prediction accuracy of longitudinal plant traits. Highlights Integrating plant growth equations into a temporal neural network improves plant height growth prediction over ordinary differential equations and machine learning models, especially when training data are limited.
Why it matches plant phenotyping methods植物高の時系列形質を予測するPINNを開発し、代替モデルと精度比較しているため、植物表現型の計算手法が中心である。
abstractWe propose a hybrid modelling framework that combines a logistic ordinary differential equation model with a Long Short-Term Memory (LSTM) neural network, resulting in a Physics Informed Neural Network (PINN).
MaizeRoot2D/3D reconstructionSegmentationRoot system architecture
Abstract Background and aims Root system architecture (RSA) shapes biogeochemical concentration patterns in the rhizosphere. Root-soil studies are often conducted on plants cultivated in rectangular rhizotrons, including when using 2D hydrochemical analysis methods. However, roots naturally expand in three dimensions, with the rhizosphere extending accordingly. Three-dimensional neutron imaging can enhance interpretation of such studies, yet imaging flat, slab-shaped rhizotrons is technically challenging. This study presents a methodological comparison between conventional neutron tomography (NT) and neutron computed laminography (NCL) to assess whether NT under high-flux conditions can achieve image quality sufficient for 3D root segmentation, comparable to NCL, without requiring tilting of the rotation axis. Methods NT and NCL were applied to maize plants grown in rectangular rhizotrons. Imaging artifacts and their impact on root segmentation were assessed for two plants representing low and high soil moisture conditions suitable for neutron imaging. Results Both methods produced 3D tomograms of comparable quality across the tested moisture range, enabling effective segmentation of primary and seminal roots. Lateral root detection was more challenging and depended on soil moisture. NCL captured a greater number of horizontally oriented lateral roots while NT was more effective in resolving vertically oriented roots. Conclusions NCL is not required to resolve 3D RSA of maize plants in flat rhizotrons. Under high-flux neutron beam conditions, NT is preferable as it simplifies sample handling, reduces plant stress, avoids soil water redistribution and enables direct integration with timeseries of 2D chemical and neutron radiographic imaging.
Why it matches plant phenotyping methods3D中性子画像法を用いた根系構造の抽出を中心に、NTとNCLを比較検証しており、植物表現型取得手法が研究の主題である。
abstractThis study presents a methodological comparison between conventional neutron tomography (NT) and neutron computed laminography (NCL) to assess whether NT under high-flux conditions can achieve image quality sufficient for 3D root segmentation, comparable to NCL
Reproduction assets foundThe paper's neutron imaging datasets (NT and NCL scans of maize in slab rhizotrons) are stated to be publicly available on the ILL Data Portal under DOI 10.5291/ILL-DATA.UGA-111. No author analysis code or trained models are explicitly deposited.Dataset · publicacknowledge funding of the
research presented here by the German Research Foundation
(DFG project numbers 396368046 and 516672636).
Data availability The datasets used in this study were gener-
ated as part of a measurement campaign on the neutron imag-
ing instrument NeXT at the ILL and are available on the ILL
Data Portal at https://doi.org/10.5291/ILL-DATA.UGA-111.Declarations
Competing interests The authors have no relevant financial
or non-financial interests to disclose.
Open Access This article is licensed under a Creative Com-
mons Attribution 4.0 International License, which permits
use, sharing, adaptation, distribution and reproduction in any
medium or format, as long as you give Open asset ↗10.5291/ILL-DATA.UGA-111pdf-raw-page:16 lines:1-92Plant phenotyping relevance match · UnverifiedCrossref · checked 13 Sept 2026
PoplarMicroscopyCell / cellular structureRootVisualization / data management
Abstract Background Cortical microtubules (CMTs), one of the components of cytoskeleton, control the orientation and localization of newly deposited cellulose microfibrils in cell walls, and thereby determine the shape, size, and structure of plant cells. Imaging of CMTs in plant tissues is generally performed using fluorescently labeled specimens under an optical fluorescence or confocal laser scanning microscope. However, optical microscopy has insufficient resolution to visualize individual CMTs, and its observation range is limited to superficial tissue layers that light can penetrate. In contrast, transmission electron microscopy offers high-resolution visualization of CMTs in plant cells but is restricted to slightly oblique ultrathin sections with an approximate thickness of 70–100 nm. Results Herein, we introduce a technique for visualizing CMTs within unstained plant tissues by combining cryofracture techniques with field emission scanning electron microscopy (FE-SEM). We successfully observed the arrangement of CMTs in several plant specimens, including young branches of ginkgo ( Ginkgo biloba ), calli from the leaves of hybrid poplar ( Populus sieboldii × P. grandidentata ), and root tips of the adzuki bean ( Vigna angularis ). CMTs were visualized on the protoplasmic fracture face using both cryo-FE-SEM and conventional room-temperature FE-SEM. Conclusions The combination of freeze-fracture techniques with FE-SEM enables the visualization of CMT arrangement in plant tissues at a high resolution and across a broad area without the need for staining or extraction of cellular components. This technique is applicable to various plant tissues and allows for detailed observation of CMTs within these tissues, providing valuable insights into the role of microtubules in the division and differentiation of plant cells.
Why it matches plant phenotyping methods植物組織内の微小管配列を高解像度で可視化するFE-SEMと凍結割断の新規画像取得法を開発しており、植物細胞状態の観察手法が研究の中心です。
abstractHerein, we introduce a technique for visualizing CMTs within unstained plant tissues by combining cryofracture techniques with field emission scanning electron microscopy (FE-SEM).
BACKGROUND: Plant developmental plasticity, particularly in root system architecture, is fundamental to understanding adaptability and agricultural sustainability. Existing automated phenotyping solutions face limitations, including binary segmentation approaches, restricted structural analysis capabilities, and text-based interfaces that limit accessibility, with most focusing solely on root structures while overlooking valuable information from simultaneous analysis of multiple plant organs. FINDINGS: ChronoRoot 2.0 builds upon established low-cost hardware while significantly enhancing software capabilities and usability. The system employs nnUNet architecture for multi-class segmentation, demonstrating significant accuracy improvements while simultaneously tracking 6 distinct plant structures encompassing root, shoot, and seed components: main root, lateral roots, seed, hypocotyl, leaves, and petiole. This architecture enables easy retraining and incorporation of additional training data without requiring machine learning expertise. The platform introduces dual specialized graphical interfaces: a Standard Interface for detailed architectural analysis with novel gravitropic response parameters and a Screening Interface enabling high-throughput analysis of multiple plants through automated tracking. Functional principal component analysis integration enables discovery of novel phenotypic parameters through temporal pattern comparison. We demonstrate multi-species analysis, with Arabidopsis thaliana and Solanum lycopersicum, both morphologically distinct plant species. Three use cases in Arabidopsis thaliana and validation with tomato seedlings demonstrate enhanced capabilities: circadian growth pattern characterization, gravitropic response analysis in transgenic plants, and high-throughput etiolation screening across multiple genotypes. CONCLUSIONS: ChronoRoot 2.0 maintains the low-cost, modular hardware advantages of its predecessor while dramatically improving accessibility through intuitive graphical interfaces and expanded analytical capabilities. The open-source platform makes sophisticated temporal plant phenotyping more accessible to researchers without computational expertise. SOFTWARE AVAILABILITY: https://chronoroot.github.io.
Why it matches plant phenotyping methods根・シュート・種子を時系列追跡し、植物形態・成長・重力応答などの表現型を抽出するオープンプラットフォームの開発と検証が中心である。
titleChronoRoot 2.0: An Open AI-Powered Platform for 2D Temporal Plant Phenotyping
Reproduction assets foundThe paper publicly releases its authors' analysis code (GitHub), the annotated plant image dataset used for segmentation training/validation (HuggingFace), a pre-configured Docker image, and a project home page, all with explicit availability statements and URLs matching allowed entries.Code · publicapproach to open science will not only ensure transparency and reproducibility but also allow the system to evolve alongside the changing needs of the plant biology community.
Availability of source code and requirements
Project name: ChronoRoot 2.0.
Project home page: https://chronoroot.github.io .
Main Source Code repository: https://github.com/ChronoRoot/ChronoRoot2 .
Operating system(s): Platform independent.
Programming language: Python.
Other requirements: Conda, Apptainer, or Docker.
License: GNU GPL 3.0.
Additional files
Supplementary Text S1 : Functional PCA. Provides an intuitive explanation of functional principal component analysis (FPCA) for readers without a quantitative backgrOpen asset ↗https://github.com/ChronoRoot/ChronoRoot2lines:439-479Dataset · publicgulates LAZY genes. Plant J. 2025;121:e70016. 10.1111/tpj.70016.
19.
Gaggion N, Boccardo N A, Bonazzola R, et al. ChronoRoot 2.0 Main Source Code Repository. 2026. https://github.com/ChronoRoot/ChronoRoot2 . Accessed 25 February 2026.
20.
Gaggion N, Boccardo N A, Bonazzola R, et al. ChronoRoot 2.0 Annotated Image Dataset. 2026. https://huggingface.co/datasets/ngaggion/ChronoRoot2 . Accessed 25 February 2026.
21.
Gaggion N, Boccardo N A, Bonazzola R, et al. ChronoRoot 2.0 Docker Image. 2026. https://hub.docker.com/r/ngaggion/chronoroot . Accessed 25 February 2026.
22.
Gaggion N, Boccardo N A, Bonazzola R, et al. ChronoRoot 2.0 Project Home Page. 2026. https://chronoroot.github.io . Accessed 2Open asset ↗https://huggingface.co/datasets/ngaggion/ChronoRoot2lines:568-618Code · publicical modules, and experimental protocols. We hope that this approach to open science will not only ensure transparency and reproducibility but also allow the system to evolve alongside the changing needs of the plant biology community.
Availability of source code and requirements
Project name: ChronoRoot 2.0.
Project home page: https://chronoroot.github.io .
Main Source Code repository: https://github.com/ChronoRoot/ChronoRoot2 .
Operating system(s): Platform independent.
Programming language: Python.
Other requirements: Conda, Apptainer, or Docker.
License: GNU GPL 3.0.
Additional files
Supplementary Text S1 : Functional PCA. Provides an intuitive explanation of functional princOpen asset ↗lines:439-479Code / 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 · UnverifiedCrossref · OpenAlex · checked 15 Sept 2026
Published1 Jan 2026ISPRS Journal of Photogrammetry and Remote SensingCited by 6 · OpenAlex ↗
Three-dimensional (3D) plant modeling is fundamental for precise phenotyping analysis. In this study, a high-throughput, multi-stage 3D reconstruction pipeline is proposed to efficiently generate point clouds with real-world physical scales. The pipeline comprises five core components: data acquisition, semantic segmentation, sparse reconstruction, dense reconstruction, and phenotypic trait extraction. To enhance the accuracy of plant structure identification, the SegFormer semantic segmentation model is employed for pixel-level segmentation, thereby guiding the subsequent reconstruction stages to focus specifically on plant regions. In feature-based sparse reconstruction, the scarcity of texture information often results in an insufficient number of matching point pairs, leading to failures in camera parameter estimation. Furthermore, the reconstructed point clouds frequently lack consistency with real-world scale. To address these challenges, a calibration-constrained sparse reconstruction method, Sparse Reconstruction from Calibrated Images (SRCI) was proposed. By integrating precise calibration results computed in a custom world coordinate system, SRCI circumvents the limitations of traditional feature matching in scenarios with scarce features, thereby resolving camera pose estimation failures caused by insufficient matching pairs and generating sparse point clouds with true physical scale. Subsequently, CL-MVSNet was employed to generate dense point clouds. The validation experiments are conducted from three perspectives: visual comparison, phenotypic accuracy assessment, and reconstruction accuracy evaluation. First, five groups of rapeseed plants are selected to perform visual comparisons between the proposed reconstruction pipeline and other advanced reconstruction software. The results demonstrate that the proposed reconstruction pipeline achieves superior performance in terms of visual quality. Additionally, three phenotypic parameters of rapeseed plants—plant height, leaf width, and chord length are manually measured and compared with the corresponding phenotypic parameters extracted from the reconstructed point clouds. The analysis revealed mean absolute errors of 4.93 mm, 3.16 mm, and 6.02 mm; root mean square errors of 6.38 mm, 4.56 mm, and 8.35 mm; and coefficients of determination of 0.98, 0.94, and 0.93, respectively. To further validate the generalization performance and accuracy of the proposed method, four additional plant categories with progressively increasing complexity were selected for accuracy evaluation. For the first three plant categories, the Chamfer distances between the reconstructed point clouds and ground truth point clouds were all within 2.4 mm. In the most complex rice reconstruction experiments, the Chamfer distances between the reconstructed point clouds and ground truth point clouds were all within 9 mm, while other methods failed to achieve effective reconstruction. The proposed high-throughput, automated, and high-quality 3D reconstruction framework provides reliable technical support and data resources for genetic research applications, including gene localization, quantitative trait locus analysis, and genome-wide association studies. • We propose a Plant-to-Camera system for high-quality 3D plant reconstruction within 6 minutes, showing strong generalizability. • Our Sparse Reconstruction from Calibrated Images (SRCI) method prevents failures in feature-scarce scenes. • We develop SFNet, a feature descriptor module that fuses multi-frequency to enhance plant feature representation.
Why it matches plant phenotyping methods植物の3D再構成と形質抽出パイプラインを開発し、植物形質および再構成精度を検証しており、フェノタイピング手法が中心である。
abstracta high-throughput, multi-stage 3D reconstruction pipeline is proposed to efficiently generate point clouds with real-world physical scales
Background High-throughput phenotyping is addressing the current bottleneck in phenotyping within breeding programs. Imaging tools are becoming the primary resource for improving the efficiency of phenotyping processes and providing large datasets for genomic selection approaches. The advent of artificial intelligence (AI) brings new advantages by enhancing phenotyping methods using imaging, making them more accessible to breeding programs. In this context, we have developed an open Python workflow for analyzing morphology, color, and morphometric traits using AI, which can be applied to fruits and other plant organs. Results The workflow was implemented in almond (Prunus dulcis (Mill.) D. A. Webb), a species where breeding efficiency is critical due to its long breeding cycle. Over 25,000 kernels, more than 20,000 nuts, and over 600 individuals were phenotyped, making this the largest morphological study conducted in almond so far. The best segmentation and reconstruction approaches achieved error rates below 1%. Weight and area variables enabled accurate estimation of kernel thickness, with a root mean squared error of 0.47. Fifty-five heritable morphological, morphometric, and color traits were identified, highlighting their potential as target traits in breeding programs. Conclusion The proposed workflow demonstrated robust performance across diverse datasets and was effective with limited training data for fine-tuning. Its compatibility with the output of AI-based labeling tools allows users to fully leverage the advantages of these technologies-reducing manual effort, accelerating dataset preparation, and streamlining the fine-tuning process of segmentation models. This flexibility enhances the scalability and practical applicability of the workflow in real-world phenotyping scenarios, especially in the context of breeding programs.
Why it matches plant phenotyping methods植物器官の形態・色・形状特性を抽出するオープンなRGB画像解析ワークフローを開発し、分割・再構成精度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す。
abstractwe have developed an open Python workflow for analyzing morphology, color, and morphometric traits using AI, which can be applied to fruits and other plant organs.
Reproduction assets foundThe authors publicly release their almond phenotyping workflow (AlmondCV) as Python/R notebooks on GitHub and as a registered WorkflowHub workflow, covering preprocessing, segmentation model development/deployment, morphology, and morphometric analyses used for this paper's measurements.Code · publiche manual process, which is challenging to automate because of variability in shell hardness and size. This extensive dataset will facilitate future studies aimed at dissecting quantitative traits and implementing genomic selection approaches.
Availability of Source Code and Requirements
Project name: AlmondCV
Project homepage: https://github.com/jorgemasgomez/almondcv2
Operating system(s): Platform independent
Programming language: Python, R
Other requirements: see public environment file released under GNU GPL v3
RRID: SCR_027064
WorkflowHub: https://workflowhub.eu/workflows/1731
Bio.tools: https://bio.tools/almondcv2
Additional Files
Supplementary Table S1 . Article metrics studied relateOpen asset ↗https://github.com/jorgemasgomez/almondcv2lines:222-243Code · publicselection approaches.
Availability of Source Code and Requirements
Project name: AlmondCV
Project homepage: https://github.com/jorgemasgomez/almondcv2
Operating system(s): Platform independent
Programming language: Python, R
Other requirements: see public environment file released under GNU GPL v3
RRID: SCR_027064
WorkflowHub: https://workflowhub.eu/workflows/1731
Bio.tools: https://bio.tools/almondcv2
Additional Files
Supplementary Table S1 . Article metrics studied related to quantitative almond morphological traits.
Supplementary Fig. S1 . Workflow description outlining the steps involved in developing the segmentation model (green) and deploying it (purple).
Supplementary Fig. S2 . YOpen asset ↗https://workflowhub.eu/workflows/1731lines:222-243Plant phenotyping relevance match · UnverifiedEurope PMC · checked 15 Sept 2026
Plant responses to environmental stimuli are often shaped by a history of previous interactions, forming the foundation for stress memory and adaptive plasticity. Arbuscular mycorrhizal (AM) fungi establish a mutualistic relationship with most land plants, enhancing nutrient uptake and stress resilience, and are increasingly recognized as biological agents contributing to plant stress memory. However, quantifying AM colonization, especially in large-scale or time-course experiments investigating priming or memory effects, remains a technical bottleneck. Conventional staining methods are time-consuming, destructive, and incompatible with live imaging. This chapter presents a robust, nondestructive, and quantitative protocol to assess AM colonization in Medicago truncatula roots using a visible anthocyanin pigmentation marker. The method employs a synthetic construct expressing the R2R3 MYB transcription factor MtLAP1, driven by the AM-inducible Kunitz Protease Inhibitor 106 (KPI106) promoter, enabling visualization of arbuscule-containing root cells through purple/red pigmentation. The protocol encompasses Agrobacterium rhizogenes-mediated hairy root transformation, standardized mycorrhization assays, and anthocyanin pigment extraction and quantification. Anthocyanin accumulation correlates strongly with conventional staining-based colonization estimates, and the system enables early detection, live imaging, and high-throughput screening of mutants with altered AM phenotypes. This method offers a powerful tool for dissecting the functional role of mycorrhizal symbiosis in plant stress memory and is especially suited for forward genetic screens, stress priming experiments, and live-tracking of root-fungus interactions over time.
Why it matches plant phenotyping methodsAM菌根菌の植物根内コロニー形成を、非破壊のアントシアニン可視化・定量法で測定するプロトコルを開発し、従来染色法との相関検証も行っているため、植物フェノタイピング手法が中心である。
abstractThis chapter presents a robust, nondestructive, and quantitative protocol to assess AM colonization in Medicago truncatula roots using a visible anthocyanin pigmentation marker.
Accurate segmentation and analysis of root images from soil-grown plants are critical for advancing our understanding of root growth and plasticity under varying environmental conditions. Most approaches typically rely on binary segmentation of the entire root system architecture (RSA), which limits their ability to capture the hierarchical complexity of root structures, including axial and lateral roots. To address this, our study evaluated five convolutional neural network (CNN) architectures for multi-class (i.e. axial/lateral) segmentation of 2D root images from barley plants grown in rhizoboxes: (i) U-Net, (ii) U-Net with Atrous Spatial Pyramid Pooling (UnetASPP), (iii) U-Net with Attention Block (UnetAtt), (iv) DeepLabV3+ with MobileNetV2 (DLMB), and (v) DeepLabV3+ with ResNet-50 (DLR50). Among these, the DLR50 model achieved the highest segmentation accuracy, particularly for distinguishing lateral roots within complex RSA structures. Furthermore, analysis of root traits derived from the segmented images confirmed that DLR50 produced the most reliable estimations of phenotypic traits compared to ground truth measurements. These findings highlight the strong potential of advanced multi-class CNN models—especially DLR50—for detailed and quantitative analysis of soil-root systems, providing new insights into root responses to environmental conditions.
Why it matches plant phenotyping methods根画像から軸根・側根を分割し、分割画像に基づく表現型形質推定のCNN手法を開発・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す。
abstractour study evaluated five convolutional neural network (CNN) architectures for multi-class (i.e. axial/lateral) segmentation of 2D root images from barley plants grown in rhizoboxes
Three-dimensional (3D) reconstructions of orchards offer richer data for digital phenotyping and underpin smart-agriculture applications. However, achieving high-level reconstruction quality and robustness is challenging due to the complex structure of the orchard. This study presents a novel framework that provides centimeter-level 3D realtime reconstructions and phenotyping for apple orchards. A multi-RGBD camera array was adopted, creating a wide overlapping view and robust features. The hybrid odometry front-end and the dual loop-closure strategy ensured low-drift pose estimation. The generated pointcloud was input into the ellipsoid-fitting routine to extract fruit diameter and volume. We validated this framework through reconstruction and phenotypic errors in four rows of an apple orchard with different tree spacings. The root mean square error of the absolute trajectory error in global reconstruction was less than 16 mm. The mean absolute percentage error (MAPE) of the local fiducial distance of approximately 5 m was less than 0.12%. The system was implemented at higher than 12.5 frames per second in an embedded system. The MAPEs of the fruit’s diameter were 2–2.17%, and those of its volume were 5.3–5.6%. Additionally, ablation experiments were carried out on multi-camera and loop-closed elements, and comparisons were made with existing methods to further demonstrate their effectiveness. In conclusion, this research provides an efficient and stable deployable solution for 3D reconstruction of orchards, which is conducive to the development of more advanced and multilayer modern orchard models and promotes the practice of smart agriculture.
Why it matches plant phenotyping methodsリンゴ園向けのマルチRGB-Dによる3D再構成と、点群から果実径・体積を抽出するフェノタイピング手法を開発・検証しており、方法が研究の中心である。
abstractThis study presents a novel framework that provides centimeter-level 3D realtime reconstructions and phenotyping for apple orchards.
Histochemical staining and microscopy-based techniques have been widely used to detect, quantify, and analyze the morphology of arbuscular mycorrhizal fungi (AMF) in roots. Here, we describe a traditional standardized method for staining of AMF in colonized roots using trypan blue, along with possible modifications to adapt the protocol to specific needs, such as root type or reducing the use of toxic reagents. We also summarize common approaches for quantifying arbuscular mycorrhizal colonization. In addition, we present a simple fluorescent staining protocol, using wheat germ agglutinin-Alexa Fluor conjugates, for high-resolution imaging of fungal colonization patterns and arbuscule morphology in roots. Finally, we describe a GUS staining method for localizing the promoter activity of plant genes potentially involved in mycorrhization, using transformed mycorrhizal hairy roots carrying promoter-GUS fusions.
Why it matches plant phenotyping methodsAMF感染根の菌根菌コロニー形成量・形態を染色と顕微鏡で取得・定量する標準化プロトコルおよび高解像度画像法を中心に扱っており、植物状態の表現型計測法として中心的です。
abstractHere, we describe a traditional standardized method for staining of AMF in colonized roots using trypan blue, along with possible modifications to adapt the protocol to specific needs, such as root type or reducing the use of toxic reagents.
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.
Dry root rot (DRR) disease is a major threat to chickpea production across the world. This disease is caused by a soil-borne necrotrophic fungal pathogen, Macrophomina phaseolina. The use of disease-resistant varieties paves the way to conquer the disease spread. Though chickpea germplasm with rich genetic diversity is available around the world, its response to DRR is still unexplored. In turn, this demands screening and identification of resistant genotypes for crop protection against the disease. Here we describe an improved blotting paper technique for the large-scale screening of chickpea genotypes for DRR resistance. The method is quick, cost-effective, less labour-intensive, and thus optimized for high-throughput screening and can be efficiently used to screen a large number of chickpea genotypes for resistance against DRR.
Why it matches plant phenotyping methodsヒヨコマメの乾燥根腐病抵抗性を評価するための改良ブロッティングペーパー法を開発・最適化しており、植物病害状態の表現型取得が研究の中心です。
abstractHere we describe an improved blotting paper technique for the large-scale screening of chickpea genotypes for DRR resistance.
This systematic literature review investigates the development of a Ground-Penetrating Radar (GPR)-based object detection system tailored for under-ground garlic crop monitoring. While garlic-specific GPR applications re-main limited, studies on structurally similar root crops such as potatoes and carrots provide a valuable reference framework. Using a PRISMA-guided methodology, 16 relevant studies were analysed and synthesized, highlighting advancements in GPR signal processing, object reconstruction, and machine learning integration. Results show that mid- frequency GPR (500–800 MHz), especially when paired with deep learning models such as 3D Convolutional Neural Networks (CNNs), offers high accuracy in detecting root structures. Key challenges such as signal attenuation in clay-rich and tropical soils are addressed through electromagnetic induction (EMI) hybridization and antenna optimization. A comparative matrix summarizes the most relevant findings, and actionable recommendations are proposed to guide future research. These include the development of garlic-specific datasets, localized field testing, and AI- enhanced signal classification. GPR, when effectively configured and paired with machine learning, presents a viable solution for real-time, non-invasive garlic crop monitoring in tropical agriculture.
Why it matches plant phenotyping methods地下作物の成長・根構造をGPRで検出する手法の開発に焦点を当てた系統的レビューであり、植物形態の非破壊取得・抽出方法が中心です。
titleA Systematic Literature Review for the Development of an Object Detection System for Monitoring Underground Crop (Garlic) Growth Using GPR
ChickpeaRootMorphology / geometry measurementRoot system architectureStress response / tolerance
Mechanical impedance in agricultural land is a significant constraint in modern agriculture. It dramatically affects seed germination, plant growth, development, and grain yield. Soil compaction hinders root growth and the ability to access deeper nutrients and water resources, impacting climate resilience, crop productivity, and global food security. Crops display variations in root system architecture (RSA) traits when grown in compacted soils. We can better understand the mechanisms behind soil compaction by examining root-related traits and their associated genes. Our recently published study investigated RSA traits across different soil compaction levels and identified significant genomic associations in chickpeas. We developed reliable methods for creating soils with varying bulk densities (i.e., compaction levels), growing chickpea seedlings, and harvesting the roots. We also conducted high-throughput phenotyping and screening of root-related traits using winRHIZO software. By integrating these phenotypic data with available genotypic data through Genome-Wide Association Studies (GWAS), we could identify genetic loci influencing root penetration in response to increasing soil compaction. These methods will help us identify key architectural traits of roots that can be targeted in crop breeding efforts to enhance resilience and productivity in compacted soils. By improving the root system and understanding the genes involved, we aim to develop plants more responsive to root penetration.
Why it matches plant phenotyping methods根系形態形質のハイスループット取得とwinRHIZOによる解析手法を開発・適用し、土壌圧密下の根系表現型をGWASに利用することが中心である。
abstractWe developed reliable methods for creating soils with varying bulk densities (i.e., compaction levels), growing chickpea seedlings, and harvesting the roots.
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
Soybean-rhizobia symbiotic nitrogen fixation, a process in which rhizobia mediate biological nitrogen fixation by converting inert atmospheric nitrogen (N 2 ) into biologically available forms (e.g., ammonium, NH 4 + ), has been extensively investigated. However, non-invasive, in situ monitoring methods for this process remain lacking. Herein, we report a solid-state membrane potentiometric ammonium ion-selective microelectrode (NH 4 + -ISμE) for the in situ detection of NH 4 + in soybean root nodules. A Prussian blue analogue with ion channels, which enables the specific insertion/extraction of NH 4 + ions while excluding interfering cations, was electrodeposited on a carbon fiber to fabricate the microelectrode. The cation sorption capability and ion selectivity of the thin film were explored by modulating the intercalation/deintercalation process and reducing the interfering cations within the framework. The NH 4 + -ISμE exhibits a Nernstian response to NH 4 + over the concentration range of 1.0 × 10 -6 to 1.0 × 10 -3 M, with a detection limit of 6.2 × 10 -7 M. This sensor enables in situ, real-time detection of NH 4 + -the direct product of biological nitrogen fixation in the legume plant-rhizobium symbiotic system. The release of NH 4 + ions in soybean root nodules during nitrogen fixation was successfully monitored. Overall, this work provides a simple and versatile tool for studying and monitoring biological symbiotic nitrogen fixation processes.
Why it matches plant phenotyping methodsダイズ根粒内のアンモニウムと窒素固定状態を非侵襲・リアルタイムに測定する新規マイクロセンサーを開発し、性能評価と植物体内での実証を行っており、植物生理状態の取得法が中心である。
abstractHerein, we report a solid-state membrane potentiometric ammonium ion-selective microelectrode (NH 4 + -ISμE) for the in situ detection of NH 4 + in soybean root nodules.
WheatRootMorphology / geometry measurementRoot system architecture
Root anatomical phenotyping has become a demonstrably essential part of investigating root physiology and in acquiring a holistic understanding of plant development. However, accessible high throughput methods for root anatomical analysis are still lacking. Here, we present the Rapid Anatomics Tool (RAT), a novel, low-cost platform for high throughput root anatomical imaging with a shallow learning curve for obtaining high quality images suitable for comparative analysis across a number of plant species. Its efficiency comes from combining blockface-like imaging and stain-free imaging using near-ultraviolet (nUV) autofluorescence utilising a combination of low-cost commercial equipment, readily available mechanical components, and custom designed and 3D printed tools. Using this platform, we investigated the anatomy of mature tissue along the axis of wheat crown roots, revealing a tendency of reduction in vascular complexity (expressed through a reduction in metaxylem number, area, and mean area per metaxylem file) from the basal to the distal region of the root. This study highlights the importance of thorough sampling strategies for investigating root anatomy in relation to organ function and introduces an accessible, relatively high-throughput platform to support such research.
Why it matches plant phenotyping methods根の解剖学的形質を高スループットに画像取得する低コスト基盤を開発しており、植物フェノタイピング手法が研究の中心です。
abstractHere, we present the Rapid Anatomics Tool (RAT), a novel, low-cost platform for high throughput root anatomical imaging
Reproduction assets foundThe paper's supplementary materials (hosted at the publisher DOI page) explicitly include the 3D design files (STL) for the RAT platform and the Python script used to control image acquisition, which are paper-specific phenotyping hardware/analysis assets. The phenotype datasets generated and analysed are only 'on the'Code · public3D design files (STL) are provided in the supplementary material. The Python script used to control image acquisition using the specific USB microscope used in this study is available in the supplementary materialsOpen asset ↗lines:229-267Plant phenotyping relevance match · UnverifiedCrossref · checked 15 Sept 2026
Published19 Dec 20252025 1st International Conference on Smart and Intelligent Systems (SISCON)Cited by 0 · OpenAlex ↗
Detecting crop diseases is critical but labor-intensive task in agriculture, often requiring expert knowledge and manual inspection. This paper describes an efficient technique for automated disease using computer vision and Machine learning. The system analyzes images of plant leaves, stems, and roots to identify symptoms with high accuracy using Otsu's thresh-olding. A structured data acquisition process ensures quality input, while convolutional neural networks (CNNs) enable robust classification. This approach reduces reliance on skilled labor, supports early disease intervention, and improves overall crop health monitoring. The solution is designed for scalability and real-time use, including mobile-based applications for on-field diagnosis.
Why it matches plant phenotyping methods植物の葉・茎・根の画像から病徴を自動検出する画像解析・CNN手法が研究の中心であり、植物病害状態の表現型推定に該当する。
abstractThis paper describes an efficient technique for automated disease using computer vision and Machine learning.
Limited annotated data often constrain accurate yield prediction in underrepresented crops. To address this challenge, we developed a cross-crop deep transfer learning (TL) framework that leverages potato (Solanum tuberosum L.) as the source domain to predict sweet potato (Ipomoea batatas L.) yield using multi-temporal uncrewed aerial vehicle (UAV)-based multispectral imagery. A hybrid convolutional–recurrent neural network (CNN–RNN–Attention) architecture was implemented with a robust parameter-based transfer strategy to ensure temporal alignment and feature-space consistency across crops. Cross-crop feature migration analysis showed that predictors capturing canopy vigor, structure, and soil–vegetation contrast exhibited the highest distributional similarity between potato and sweet potato. In comparison, pigment-sensitive and agronomic predictors were less transferable. These robustness patterns were reflected in model performance, as all architectures showed substantial improvement when moving from the minimal 3 predictor subset to the 5–7 predictor subsets, where the most transferable indices were introduced. The hybrid CNN–RNN–Attention model achieved peak accuracy (R2≈0.64 and RMSE ≈ 18%) using time-series data up to the tuberization stage with only 7 predictors. In contrast, convolutional neural network (CNN), bidirectional gated recurrent unit (BiGRU), and bidirectional long short-term memory (BiLSTM) baseline models required 11–13 predictors to achieve comparable performance and often showed reduced or unstable accuracy at higher dimensionality due to redundancy and domain-shift amplification. Two-way ANOVA further revealed that cover crop type significantly influenced yield, whereas nitrogen rate and the interaction term were not significant. Overall, this study demonstrates that combining robustness-aware feature design with hybrid deep TL model enables accurate, data-efficient, and physiologically interpretable yield prediction in sweet potato, offering a scalable pathway for applying TL in other underrepresented root and tuber crops.
Why it matches plant phenotyping methodsUAVマルチスペクトル画像から作物収量を推定する深層転移学習法を開発し、複数モデル・予測子構成で性能を比較検証しているため、植物表現型推定法が中心である。
abstractwe developed a cross-crop deep transfer learning (TL) framework that leverages potato (Solanum tuberosum L.) as the source domain to predict sweet potato (Ipomoea batatas L.) yield using multi-temporal uncrewed aerial vehicle (UAV)-based multispectral imagery.
The conversion of glucosinolates (GSLs) into chemopreventive isothiocyanates (ITCs) primarily relies on plant myrosinase (MYR) or specific bacteria. MYR dynamics are deeply involved in plant defense systems, gut microbiota metabolism, and complex interactions and regulation across species. A set of activity-based probes was developed to track MYR in vivo by biomimicking natural GSL with robust sensitivity and selectivity. The dynamics and heterogeneous distribution of MYR in distinct sections and species were captured via fluorescence imaging of live plants. Specifically, under herbivore challenge to leaves, a systemic, long-distance upregulation of MYR activity in root tissues has confirmed cross-species MYR regulation in plant defense. Furthermore, for the first time, quantitative visualization of the dynamic metabolic competition of GSL and sugar has confirmed the metabolic priority of sugar in gut microbiota and colonized zebrafish in vivo. The competitive metabolism is involved in the crosstalk during cross-species microbes and host-microbe interactions. Tracking MYR regulation across species by the designed probes has offered rich insights into the dynamic interplay among diet, microbiota, and host health.
Why it matches plant phenotyping methods植物体内のミロシナーゼ活性を蛍光プローブとライブ植物イメージングで可視化・追跡する手法開発が中心であり、植物防御応答という生理状態を定量的に測定しているため。
abstractA set of activity-based probes was developed to track MYR in vivo by biomimicking natural GSL with robust sensitivity and selectivity.
Coffee has long promoted international trade and prosperity, employing millions of small-scale producers. The high demand for this crop has resulted in global supply networks. Young coffee seedlings are vulnerable to fungal diseases such as damping-off and root rot, which cause significant damage and substantially reduce plant productivity. Signs include wilting, root rot, and seedling death both before and after sprouting. Deep learning could allow automatic and scalable prediction of plant diseases. This study aims to enhance early detection of coffee seedling diseases, ensure model adaptability across samples, and optimize computational efficiency for practical implementation. The proposed Vision-based Heterogeneous Graph Neural Network (Vi-HGNN) model, which combines computer vision and graph neural networks (GNNs), provides information about disease transmission patterns over time and space. After training, the model can accurately detect early signs of infection, allowing farmers to intervene before the damage spreads. Experimental results show that Vi-HGNN achieves a 97.77 % detection accuracy, outperforming existing methods in precision, F1-score, and pathogen coverage. Future developments will aim to expand detection capabilities to include additional diseases, pests, and weeds, improving overall crop health monitoring.
Why it matches plant phenotyping methodsコーヒー苗の病徴を画像と深層学習で検出する手法が研究の中心であり、植物の病害状態を直接推定・評価しているため。
abstractDeep learning could allow automatic and scalable prediction of plant diseases.
Early-season prediction of yield and nitrogen‐related performance is essential for enabling timely agronomic interventions yet remains challenging in crops with limited prior digital phenotyping research, such as Tritordeum . Root traits, although fundamental to early nutrient uptake and crop establishment, remain largely absent in ML prediction frameworks. This study evaluated how root morphological traits, in combination with UAV-derived multispectral indices and proximal agronomic measurements, contribute to predicting yield and nitrogen efficiency indices under Mediterranean field conditions. Measurements were collected during the first three phenological stages, and a diverse set of machine-learning algorithms, including Random Forest, ExtraTrees, AdaBoost, Support Vector Regression, regularised linear models and the foundation model TabPFN, were benchmarked using a rigorous 10×5 cross-validation scheme. Yield emerged as the most reliably predictable trait, reaching an R² of 0.90 in the best multivariate configuration, while nitrogen-efficiency indices (NUE, NHI, NUtE) showed substantially higher variability and limited early-season predictability. Root diameter at the tillering stage consistently ranked among the most informative predictors, and its combination with SPAD at stem elongation, MCARI at tillering, or plant height at tillering produced the strongest yield models. These findings highlight the importance of integrating early-season below-ground information with spectral and agronomic traits to enhance prediction accuracy. Overall, the study demonstrates that accurate early-season yield forecasting in Tritordeum can be achieved using a minimal set of measurements, supporting cost-efficient monitoring and enabling actionable in-season adjustments to nitrogen management. The results also show the potential of foundation models such as TabPFN for limited agronomic datasets, providing a basis for developing scalable, data-driven decision-support tools for sustainable cereal production.
Why it matches plant phenotyping methodsUAVマルチスペクトル、根形態・農学測定を統合した機械学習による収量・窒素関連形質の推定を中心に、複数モデルを厳密にベンチマークしており、形質推定ワークフローが実質的な方法貢献である。
abstracta diverse set of machine-learning algorithms, including Random Forest, ExtraTrees, AdaBoost, Support Vector Regression, regularised linear models and the foundation model TabPFN, were benchmarked using a rigorous 10×5 cross-validation scheme
Plant phenotyping relevance match · UnverifiedCrossref · Europe PMC · checked 6 Sept 2026
A new rhodamine-based fluorescent probe (RDC, rhodamine-based derivative) was rationally designed and synthesized for the highly selective, sensitive, and quantitative detection of Cu2+. The probe demonstrated outstanding specificity toward Cu2+, even in the presence of competing metal ions (e.g., Al3+, Fe3+, Cr3+, Na+, and K+), exhibiting negligible interference and confirming its robust anti-interference capability. A spectroscopic analysis revealed that Cu2+ induced spirocyclic ring cleavage, resulting in a colorless-to-pink colorimetric transition and enhancement of the yellow–green fluorescence at 590 nm. Upon addition of Cu2+, the fluorescence spectrum showed a linear response in the concentration range of 0.4–20 μM, with a correlation coefficient (R2) of 0.9907 and the limit of detection (LOD) calculated to be 0.12 μM. Meanwhile, Job’s plot analysis verified that the binding stoichiometry between RDC and Cu2+ was 1:1. The probe exhibits rapid response kinetics ( maturation zone epidermis > xylem vessels > cortical cell walls. In conclusion, RDC is a well-characterized, high-performance tool with high accuracy, excellent selectivity, and superior sensitivity for plant Cu2+ studies, and this work opens new technical avenues for rhodamine-based probes in plant physiology, environmental toxicity monitoring, and rational design of phytoremediation strategies.
Why it matches plant phenotyping methods植物根内のCu2+を可視化・定量する蛍光プローブを開発し、選択性、感度、応答性、植物組織内での検出性能を検証しているため、植物状態の取得方法が中心である。
abstractA new rhodamine-based fluorescent probe (RDC, rhodamine-based derivative) was rationally designed and synthesized for the highly selective, sensitive, and quantitative detection of Cu2+.
Phalaenopsis orchids are one of Taiwan's key floral export products, and maintaining consistent quality is crucial for international competitiveness. To improve production efficiency, many orchid farms outsource the early flask seedling stage to contract growers, who raise the plants to the 2.5-inch potted seedling stage before returning them for further greenhouse cultivation. Traditionally, the quality of these outsourced seedlings is evaluated manually by inspectors who visually detect defects and assign quality grades based on experience, a process that is time-consuming and subjective. This study introduces a smart image-based deep learning system for automatic quality grading of Phalaenopsis potted seedlings, combining computer vision, deep learning, and machine learning techniques to replace manual inspection. The system uses YOLOv8 and YOLOv10 models for defect and root detection, along with SVM and Random Forest classifiers for defect counting and grading. It employs a dual-view imaging approach, utilizing top-view RGB-D images to capture spatial leaf structures and multi-angle side-view RGB images to assess leaf and root conditions. Two grading strategies are developed: a three-stage hierarchical method that offers interpretable diagnostic results and a direct grading method for fast, end-to-end quality prediction. Performance comparisons and ablation studies show that using RGB-D top-view images and optimal viewing-angle combinations significantly improve grading accuracy. The system achieves F1-scores of 84.44% (three-stage) and 90.44% (direct), demonstrating high reliability and strong potential for automated quality assessment and export inspection in the orchid industry.
Why it matches plant phenotyping methods胡蝶蘭苗の画像から欠陥・根・葉の状態を抽出し、品質を自動評価する画像ベースの表現型推定手法を開発・比較しており、手法が研究の中心である。
abstractThis study introduces a smart image-based deep learning system for automatic quality grading of Phalaenopsis potted seedlings
Abstract Malnutrition and food insecurity have remained critical issues faced in Africa. According to the 2023 statistical data in Nigeria, for instance, approximately 87 million out of the country’s 220 million people (39.5 per cent) still live below the poverty line. From this trajectory, it is safe to say that the continent of Africa is not yet on the path to Zero Hunger (SDG 2) by 2030. While several successive government administrations have proposed various intervention programs such as Operation Feed the Nation, the Green Revolution, and Lower Niger River Basin Development Authority, fertiliser support, among others, in Nigeria, little attention has been given to plant diseases, one of the root causes of low productivity among smallholder farmers. Therefore, this research leveraged the inherent characteristics of deep learning models and developed an improved deep learning Plant Doctor based on MobileNetV3-Small architecture with a user-friendly interface that enables the drag and drop of plant images or direct upload. The developed system was tailored towards the computational demands of smallholder farmers’ low computing devices. The developed MobileNetV3-Small architecture uses a smart patch-based scanning process that focuses on the leaf regions, resizes the image to 224 × 224 as input size, and unfreezes 20 layers for feature learning from the patches. The patches are augmented using brightness, contrast, and slight rotation to ease the detection of tiny symptoms. This allows for detailed symptom analysis without overwhelming memory or processing power. The developed, improved MobileNetV3-based plant doctor easily detects plant diseases through their visual symptoms on their leaves, prescribes treatments, and broadcasts detected diseases to farmers within the same region for preventive control measures. The evaluation of the developed MobileNetV3-small showed that the system can detect plant disease with an accuracy of 99.85% on the merged PlantVillageDoc dataset, with the added advantage of broadcasting detected plant diseases to other farmers within the same cluster through their registered email. This system offers a paradigm shift in educating smallholder farmers by providing timely disease detection and expert guidance, thereby reducing crop losses, improving yields, and strengthening national food security.
Why it matches plant phenotyping methods葉の視覚症状から植物病害を検出する深層学習システムの開発と評価が中心であり、植物の病害状態を直接推定するフェノタイピング手法に該当する。
abstractdeveloped an improved deep learning Plant Doctor based on MobileNetV3-Small architecture with a user-friendly interface that enables the drag and drop of plant images or direct upload
PeaRootMorphology / geometry measurementSegmentationGrowth / time-series analysisRoot system architecture
Background With the intensification of global climate change, extreme weather events have become increasingly frequent, severely impacting the growth cycles and yield stability of crops. Against this backdrop, cultivating new crop varieties with high stress resistance has become a core task for achieving sustainable agriculture and ensuring food security. Root length, as a critical phenotypic trait that reflects a plant's ability to absorb water and nutrients, is closely related to the crop's capacity to withstand adversities, such as drought, high temperatures and salinisation. However, root length measurement technology remains a significant bottleneck in plant science research. Traditional manual methods are inefficient and prone to human-induced variability (e.g. subjective standard discrepancies, operational errors, and potential contamination or damage to seeds). Meanwhile, existing automated measurement models face challenges in large-scale practical applications due to their high deployment costs. Results This study developed a seed germination image acquisition system and constructed a pea root dataset. Based on the YOLOv8-Seg-n instance segmentation model, a lightweight automatic root measurement (ARM) model was then developed using feature distillation, structured pruning techniques, and a series of post-processing procedures for root length calculation. Experimental results demonstrated that the ARM model had only 1.81 M parameters, with 8.3 GFLOPs and a weight file size of 4.2 MB, and achieved 70.4 FPS. It realised outstanding performance with mAP@0.5 and AP root scores of 90.3% and 81.2%, respectively, showing a high consistency with manual measurement results (R² = 0.993). Compared to existing models, the ARM model significantly reduces parameter scale and computational complexity, making it more accommodating to device performance and computational requirements while also decreasing the workload associated with root sample processing. Furthermore, the application of the ARM model in a 72-hour full time-series analysis of pea root length under drought conditions validated its potential for practical use in real-world scenarios. Conclusions The ARM model offers an efficient and cost-effective technological solution for high-throughput root length measurement in peas. It achieves a favorable balance between accuracy, speed, and computational resource requirements, demonstrating broad application potential in agricultural production and breeding research. The model offers critical technical support for ensuring food security and enhancing crop stress resistance.
Why it matches plant phenotyping methodsエンドツーエンドの画像取得・セグメンテーション・根長算出モデルを開発し、手動測定との整合性および実利用を検証しており、植物表現型測定法が中心である。
abstractThis study developed a seed germination image acquisition system and constructed a pea root dataset.
PoplarField / plotRoot2D/3D reconstructionRoot system architecture
Root system architecture (RSA) is pivotal for comprehending the ecological adaptation strategies and resource acquisition mechanisms of urban flora, playing a vital role in soil stability, carbon sequestration, and ecosystem sustainability. However, the non-destructive detection and precise three-dimensional (3D) reconstruction of RSA within urban environments remain challenging. In this study, a non-destructive reconstruction method utilizing ground-penetrating radar (GPR) technology was developed to achieve 3D reconstruction and visualization of RSA, with the goal of advancing the intelligent construction and precise ecological management of urban forest parks. Field-based GPR surveys of a 9-year-old triploid poplar were conducted using a square grid and concentric circular scanning scheme. A 3D data volume (C-scan) was constructed from two-dimensional (2D) profiles, and the spatial distribution of RSA was reconstructed using instantaneous amplitude analysis. The method was validated by comparing the results with actual root structures in sandy loam environments. The research results of the 1600 MHz GPR under the square grid scanning scheme show that extracting the instantaneous amplitude isosurface of GPR can effectively reflect the spatial distribution of roots with diameters greater than 1 cm within a depth of 0.4 m subsurface. The accuracy of RSA reconstruction can reach 89 %. The results demonstrate the applicability of the proposed method for non-destructive environmental monitoring in urban forest parks, showing significant potential for the large-scale detection and reconstruction of subsurface root systems. This research provides a novel approach for RSA reconstruction with significant implications for urban ecosystem management, soil conservation, and climate resilience research. The method enhances our capability to monitor the growth and adaptation of urban roots, laying the groundwork for the large-scale, non-destructive analysis of RSA.
Why it matches plant phenotyping methodsGPRと瞬時振幅解析を用いて樹木根系構造を3D再構成する方法を開発し、実際の根構造との比較で検証しており、根系形態の取得が中心的な研究目的である。
abstracta non-destructive reconstruction method utilizing ground-penetrating radar (GPR) technology was developed to achieve 3D reconstruction and visualization of RSA
Reproduction assets foundThe paper's Data Availability statement explicitly releases the GPR root scanning data on Zenodo and the RSA reconstruction analysis code on GitHub, both with public URLs matching allowed entries.Code · publicCode is available at https://github.com/Niceguoqiu/RSA-Reconstruction-Code.git .Open asset ↗GitHub · Niceguoqiu/RSA-Reconstruction-Codelines:268-286Plant phenotyping relevance match · UnverifiedEurope PMC · bioRxiv · checked 6 Sept 2026
Climate change is increasing environmental stress, particularly rising temperatures and water scarcity, in both natural and human-managed systems such as agroecosystems and urban environments. Traditional methods for monitoring plant health in human-managed systems remain limited, underscoring the need for novel approaches. This study explores the potential of plant electrophysiological signals (EPS) and derived statistical features for the early detection of drought stress. The two main objectives of this research are: i) to identify EPS features that are both ecologically relevant and statistically robust for detecting drought stress, and ii) to develop statistical models that integrate these features. EPS data was collected from two drought-stress experiments, one on tomato plants and one on apricot trees. Sixteen features from both time and frequency domains were selected and evaluated. Two models, a logistic and a machine learning classifier, were developed and compared using accuracy, precision, and recall metrics. In apricots, ten time-domain features (Frequency Center, Generalized Hurst Exponent, Hjorth Complexity, Hjorth Mobility, Kurtosis, Root Mean Squared Frequency, Root Variance Frequency, Shape Factor, Skewness and Standard Deviation) showed significant differences between stressed and control groups. In tomatoes, four frequency-domain features (Frequency Centre, Root Variance Frequency, Root Mean Squared Frequency, and Power Law Distribution Exponent) were significantly different. Model accuracy was approximately 50% for apricots and 66% for tomatoes, insufficient for practical deployment but indicative of potential. This study illustrates the potential value of plant EPS data, its derived statistical features, and models for developing early drought stress detection systems in both agricultural and urban plant management contexts.
Why it matches plant phenotyping methods植物の電気生理信号から干ばつストレス状態を検出する特徴量と分類モデルを開発・評価しており、表現型取得・抽出手法が研究の中心です。
abstractThis study explores the potential of plant electrophysiological signals (EPS) and derived statistical features for the early detection of drought stress.
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:1-58Code / dataset availability confirmedOpenAlex · Europe PMC · checked 6 Sept 2026
Cassava (Manihot esculenta Crantz) is a staple food and a key industrial crop across tropical regions, but traditional phenotyping for critical quality traits like dry matter content (DMC) and starch content (StC) is a laborious and low-throughput process. This study investigates the efficacy of a handheld near-infrared spectrometer device (NIRS) for the non-destructive, rapid prediction of these traits. The research methodology involved collecting spectral data from 2,236 cassava clones from 19 field trials in Brazil, using two sample types: fresh roots and mashed roots. Six spectral pre-processing methods and three machine learning algorithms-Partial Least Squares (PLS), Support Vector Machines (SVM), and Extreme Gradient Boosting (XGB)-were evaluated to optimize predictive models. Model performance was assessed using the coefficient of determination in calibration ([Formula: see text]), the root mean squared error of calibration ([Formula: see text]), and the Kappa index to quantify the consistency of clone selection. Results show that mashed samples consistently yielded superior predictive performance across all models. Specific preprocessing methods, such as Savitzky-Golay filtering combined with Standard Normal Variate (SG + SNV) and first-derivative transformations, significantly enhanced model accuracy. Among the algorithms, PLS demonstrated the best overall performance, with high predictive accuracy ([Formula: see text] >0.96) and low prediction errors ([Formula: see text]<1.3 for DMCo), especially with mashed samples. High Kappa index values, consistently approaching 1.0, confirmed a good alignment between NIRS-based selection and traditional phenotypic methods. This study validates a portable spectrometer as a reliable and efficient tool for high-throughput phenotyping in cassava breeding programs. The findings confirm that portable NIRS devices, when used with optimal sample preparation (mashed roots) and robust modeling (PLS), can effectively yield good predictions for plant selection. This approach can significantly accelerate breeding cycles by enabling rapid, early-stage selection decisions, thereby overcoming a major bottleneck and contributing to a more efficient and sustainable genetic improvement of cassava.
Why it matches plant phenotyping methods携帯型NIRSによるキャッサバ根の品質形質予測モデルを開発・比較・検証し、育種選抜への適用性能を評価しており、フェノタイピング手法が中心である。
abstractThis study investigates the efficacy of a handheld near-infrared spectrometer device (NIRS) for the non-destructive, rapid prediction of these traits.
Reproduction assets foundThe paper's spectral and phenotypic data (NIRS spectra from 2,236 cassava clones, DMC/StC trait measurements) are openly deposited on Figshare per the Data Availability statement. No author analysis code or trained models are explicitly shared.Dataset · publicData Availability: The data that support the findings of this study are openly available in Figshare at https://figshare.com/s/d2e947f467bd8f655ede .Open asset ↗Figsharelines:142-152Plant phenotyping relevance match · UnverifiedOpenAlex · Europe PMC · bioRxiv · checked 6 Sept 2026
Abstract Protein-rich leguminous plants, such as faba bean and white clover are prospectively interesting crops in the North-European countries for reducing dependence on soybean import. Significant expansion of the production area of leguminous crops is challenged by the sub-optimal climatic conditions in this region, especially by the increasing probability of year-to-year fluctuation of extreme weather conditions due to global climate change. To overcome these challenges, development of new climate-resilient varieties suitable for growing under Northern-European conditions are needed. Root architecture and early root development, as well as the availability of efficient root phenotyping technologies are crucial factors of advancing in breeding of adequate varieties. We report a study of a simple and affordable screening technology of early root development using rhizoboxes in connection with semi-automated image analysis and provide a conceptual pipeline for estimation of Genomic Estimated Breeding Values (GEBVs) and correlating greenhouse and field phenotype data. Based on bivariate models, high genetic correlation (r=0.83) could be detected between total root length values recorded in greenhouse rhizobox experiments and field grain yield in faba bean. In white clover, moderately positive genetic correlation (r=0.17) between estimated breeding values of rhizobox-detected total root length and field yield could be identified. Our results suggest that phenotyping and selection of early root development components could potentially be useful in breeding programs to increase the genetic gain for field yield.
Why it matches plant phenotyping methods根系形態を対象に、rhizoboxと半自動画像解析による早期根発達の表現型取得技術を提示し、育種価推定へのパイプラインも示しているため、フェノタイピング手法が中心的である。
abstractWe report a study of a simple and affordable screening technology of early root development using rhizoboxes in connection with semi-automated image analysis and provide a conceptual pipeline for estimation of Genomic Estimated Breeding Values (GEBVs) and correlating greenhouse and field phenotype data.
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.
Introduction Microplastics (MPs), ubiquitous and insidious pollutants pervading agricultural systems, pose an escalating threat to global food security. This makes the development of nondestructive methods for the early detection of MPs stress in rice seedling an urgent scientific imperative. Method Rice seedlings were cultivated under exposure to polyethylene terephthalate (PET), polystyrene (PS), and polyvinyl chloride (PVC) MPs at concentrations of 0 (control), 10, and 100 mg/L. Based on the stress-induced alterations in root exudates composition, a novel detection method for MPs stress in rice seedlings was developed using excitation-emission matrix fluorescence (EEMF) spectra combined with deep learning. Results Analysis of the original EEMF spectra revealed discernible differences. Feature extraction was performed using both the peak method and the PARAFAC method. Spectral changes in seedlings exposed to the low MP concentration (10 mg/L) were relatively minor compared to the control group. In contrast, exposure to the high concentration (100 mg/L) induced significant alterations in humic acid-like and amino acid-like substances. Subsequently, enhanced Vision Transformer (VIT) models were developed utilizing three distinct data representations: full EEMF spectra, emission spectra at specific excitation wavelengths, and extracted characteristic fluorescence values. The optimal model achieved 100% classification accuracy. Furthermore, SHapley Additive exPlanations (SHAP) analysis was employed to evaluate feature importance, identifying both humic acid-like and marine humic acid-like components as major contributors to the model's predictions. Conclusion In summary, this study establishes a novel, non-destructive, and interpretable framework for the early detection of MPs stress in rice seedlings based on EEMF spectra of root exudates combined with deep learning.
Why it matches plant phenotyping methodsイネ幼苗のマイクロプラスチックストレス状態を、根圏滲出物の蛍光スペクトルと深層学習から非破壊推定する手法の開発が中心であり、単なる生物学的測定ではない。
abstractthe development of nondestructive methods for the early detection of MPs stress in rice seedling
CottonRiceRootMorphology / geometry measurementSegmentationRoot system architecture
Beyond its fundamental roles in nutrient uptake and plant anchorage, the root system critically influences crop development and stress tolerance. Rhizobox enables in situ and nondestructive phenotypic detection of roots in soil, serving as a cost-effective root imaging method. However, the opacity of the soil often results in intermittent gaps in the root images, which reduces the accuracy of the root phenotype calculations. We present a root inpainting method built upon Generative Adversarial Networks (GANs) architecture In addition, we built a hybrid root inpainting dataset (HRID) that contains 1206 cotton root images with real gaps and 7716 rice root images with generated gaps. Compared with computer simulation root images, our dataset provides real root system architecture (RSA) and root texture information. Our method avoids cropping during training by instead utilizing downsampled images to provide the overall root morphology. The model is trained using binary cross-entropy loss to distinguish between root and non-root pixels. Additionally, Dice loss is employed to mitigate the challenge of imbalanced data distribution Additionally, we remove the skip connections in U-Net and introduce an edge attention module (EAM) to capture more detailed information. Compared with other methods, our approach significantly improves the recall rate from 17.35 % to 35.75 % on the test dataset of 122 cotton root images, revealing improved inpainting capabilities. The trait error reduction rates (TERRs) for the root area, root length, convex hull area, and root depth are 76.07 %, 68.63 %, 48.64 %, and 88.28 %, respectively, enabling a substantial improvement in the accuracy of root phenotyping. The codes for the EU-GAN and the 8922 labeled images are open-access, which could be reused by researchers in other AI-related work. This method establishes a robust solution for root phenotyping, thereby increasing breeding program efficiency and advancing our understanding of root system dynamics.
Why it matches plant phenotyping methods根画像の欠損を補完するGAN手法と再利用可能なデータセットを開発・評価し、根形態形質の推定誤差改善を実証しており、植物フェノタイピング手法が中心である。
abstractWe present a root inpainting method built upon Generative Adversarial Networks (GANs) architecture
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:167-180Code / dataset availability confirmedCrossref · OpenAlex · Europe PMC · checked 6 Sept 2026
RiceWheatField / plotMesh / voxelNeRF / 3D Gaussian SplattingLiDAR / point cloudPanicle / ear / spikeLeafRootSeed / grain
Advanced plant phenotyping technologies are vital for trait improvement and accelerating intelligent breeding. Due to the species diversity of plants, existing methods heavily rely on large-scale high-precision manually annotated data. For self-occluded objects at the grain level, unsupervised methods often prove ineffective. This study proposes IPENS, an interactive unsupervised multi-target point cloud extraction method. It utilizes radiance field information to lift 2D masks, segmented by SAM2 (Segment Anything Model 2), into 3D space for target point cloud extraction. A multi-target collaborative optimization strategy addresses the challenge of segmenting multiple targets from a single interaction. On a rice dataset, IPENS achieves a grain-level segmentation mean Intersection over Union (mIoU) of 63.72%. For phenotypic trait estimation, it achieves a grain voxel volume coefficient of determination R 2 = 0.7697 (Root Mean Square Error, RMSE = 0.0025), leaf surface area R 2 = 0.84 (RMSE = 18.93), and leaf length and width prediction accuracies of R 2 = 0.97 and R 2 = 0.87 (RMSE = 1.49 and 0.21). On a wheat dataset, IPENS further improves segmentation performance to a mIoU of 89.68%, with exceptional phenotypic estimation results: panicle voxel volume R 2 = 0.9956 (RMSE = 0.0055), leaf surface area R 2 = 1.00 (RMSE = 0.67), and leaf length and width predictions reaching R 2 = 0.99 and R 2 = 0.92 (RMSE = 0.23 and 0.15). Without requiring annotated data, IPENS rapidly extracts grain-level point clouds for multiple targets within three minutes using single-round image interactions. These features make IPENS a high-quality, non-invasive phenotypic extraction solution for rice and wheat, offering significant potential to enhance intelligent breeding.
Why it matches plant phenotyping methods植物形質抽出のためのNeRF-SAM2融合手法を開発し、作物データセットで分割性能と形質推定精度を検証しているため、方法開発・検証が中心である。
abstractThis study proposes IPENS, an interactive unsupervised multi-target point cloud extraction method.
Reproduction assets foundThe paper's analysis code is publicly available on GitHub. The rice/wheat MMR/MMW phenotype datasets (multi-view images, point clouds, annotations) are only available upon reasonable request, so they are not public.Code · publicCode is available at https://github.com/Vincent-Songwentao/IPENS-Code.git .Open asset ↗https://github.com/Vincent-Songwentao/IPENS-Code.gitlines:472-496Plant phenotyping relevance match · UnverifiedCrossref · OpenAlex · checked 15 Sept 2026
Field / plotMultimodalRootWhole plant / canopy / plot / fieldCountingObject detectionStress / disease detectionGrowth / time-series analysisGrowth / development / phenologyRoot system architecture
• Presents a full-process review of image-based high-throughput plant phenotyping (HTPP). • Covers recent advances in platforms, sensors, deep learning, and field-level applications. • Highlights emerging methods like Promptable models, Digital Twins, and weak supervision. • Discusses deployment challenges including data scarcity and model generalization. • Proposes future directions: multimodal fusion, uncertainty modeling, and lightweight design. With the rapid global population growth and increasing challenges in sustainable agriculture, high-throughput plant phenotyping (HTPP) has become a vital tool for advancing crop breeding and precision agriculture. This review provides a comprehensive overview of recent technological trends in image-based HTPP, focusing on the integration of advanced sensors, automated phenotyping platforms, and deep learning techniques. We summarize the evolution of imaging modalities, including 2D, 2.5D, and 3D sensors, and their respective applications in phenotype acquisition. We then examine the progress of deep learning-based models in core phenotyping tasks such as stress and disease detection, growth monitoring, organ counting, root system analysis, and postharvest quality assessment. Special attention is given to the emergence of Transformer architectures, multimodal fusion strategies, weakly supervised learning, and prompt-based foundation models. Despite significant advancements, current HTPP systems still face several challenges, including high costs, limited generalization in open-field conditions, and the need for large-scale annotated datasets. To address these, we discuss potential solutions such as transfer learning, synthetic data generation via digital twins, lightweight deployment for edge devices, and uncertainty estimation for model interpretability. By highlighting key developments and open problems, this review aims to guide future research toward scalable, robust, and intelligent plant phenotyping systems that can operate reliably in real-world agricultural environments.
Why it matches plant phenotyping methods画像ベース高スループット植物フェノタイピングのセンサー、プラットフォーム、画像解析技術を包括的にレビューしており、方法論が中心です。
abstractPresents a full-process review of image-based high-throughput plant phenotyping (HTPP).
SoybeanLiDAR / point cloudRootMorphology / geometry measurement2D/3D reconstructionRoot system architecture
Abstract Accurate phenotyping of root traits is essential for understanding how plants respond to varying soil water treatment conditions, yet traditional phenotyping methods are often destructive and limited in capturing the full three‐dimensional (3D) complexity of root systems. Existing two‐dimensional imaging techniques and advanced 3D methods for performing root phenotyping, like magnetic resonance imaging or computed tomography, either compromise on resolution, are cost‐prohibitive, or lack scalability. To address these limitations, this study proposes fringe projection profilometry (FPP), a rapid, nondestructive 3D imaging method, for root phenotyping. Using FPP, two architectural root traits were extracted: the number of root tips and the volumetric occupancy of the root system. These traits, difficult to obtain through manual phenotyping or conventional imaging, were automatically derived from the FPP 3D point clouds and validated against expert‐assigned fibrosity scores serving as the biological reference. The study involved 36 soybean ( Glycine max (L.) Merr.) plants from six genotypes, pre‐classified as either stress‐treated or grown under rain‐fed conditions. Results showed strong alignment between FPP‐derived traits and expert evaluations. Stress‐ treated plants consistently exhibited more root tips and greater volumetric occupancy, confirming the biological relevance of these metrics. While this study does not attempt to classify drought tolerance directly, the structural variations observed under drought stress may serve as a foundation for identifying stress‐responsive phenotypes in future work. Overall, the findings demonstrate that FPP provides a fast, scalable, and accurate tool for 3D root phenotyping under variable water conditions.
Why it matches plant phenotyping methodsFPPによる根系の3次元形質取得・自動抽出を開発し、専門家評価と検証した研究であり、フェノタイピング手法が中心です。
abstractthis study proposes fringe projection profilometry (FPP), a rapid, nondestructive 3D imaging method, for root phenotyping.
Reproduction assets foundThe paper's Data Availability Statement provides a public Google Drive link to the datasets generated and/or analyzed in this soybean root FPP phenotyping study, which is an allowed URL. No author analysis code is explicitly deposited.Dataset · publicying and Overcoming Weaknesses via Breed-
ing, Genomics, Phenomics and Physiology).
C O N F L I C T O F I N T E R E S T S TAT E M E N T
The authors declare no conflicts of interest.
DATA AVA I L A B I L I T Y S TAT E M E N T
The datasets generated and/or analyzed dur-
ing the current research are available at Google
Drive link: https://drive.google.com/file/d/1BJ4yq8QEWY3E5qQIQmYcOXHhEYn1zTE-
/view?usp=sharing
O RC I D
JiaqiongLi https://orcid.org/0009-0006-2247-425X
ZengluLi https://orcid.org/0000-0003-4114-9509
BeiwenLi https://orcid.org/0000-0001-8130-7730
R E F E R E N C E S
Balasubramaniam, B., Li, J., Liu, L., & Li, B. (2023). 3D imaging with
fringe projection for food and agriculturalOpen asset ↗pdf-raw-page:17 lines:1-91Plant phenotyping relevance match · UnverifiedEurope PMC · checked 15 Sept 2026
Black shank disease and root rot disease represent the most destructive diseases of tobacco. Once it occurs, it will spread rapidly, endangering the health of tobacco plants, and even killing them. The stem near the root of tobacco plant is the first part that can exhibit observable signs of root disease. Monitoring the dynamic variations of in-situ stem water content (SₜWC) near the root is beneficial for the early detection of tobacco root diseases. Therefore, we developed a wearable plant sensor with a flexible interdigitated-electrodes (IE) probe design for in-situ monitoring of StWC and early identification and warning of tobacco root diseases. The IE probe of wearable sensor was securely affixed to the stem, and the soil moisture (SM) sensors were buried in the corresponding root area. The results demonstrated a clear inconsistency in the observed trend between the SₜWC near roots of diseased and healthy tobacco plants. About 60 h before the blackening of the stems near roots, the SM of diseased tobacco plants (0.007 cm³/cm³) indicated a slower decrease compared to healthy tobacco plants (0.021 cm³/cm³). In accordance with this phenomenon, the daily variation of SₜWC near roots of diseased tobacco plants (0.023 cm³/cm³) was significantly less than that of healthy tobacco plants (0.048 cm³/cm³). Moreover, the abnormal changes of SₜWC near roots of diseased tobacco plants after blackening further validated the availability of the wearable sensor in the early detection and warning of tobacco root diseases. The tobacco plant may have been in early diseased stage when the daily change of SₜWC was continuously less than 0.037 cm³/cm³. Future research will focus on the mechanism of water conduction between soil and stem near the root of tobacco plants, and the potential application of the wearable sensor in early disease detection.
Why it matches plant phenotyping methods根域付近の茎水分量という植物の生理状態を測定し、根病害の早期検出に用いるウェアラブルセンサーを開発・検証しており、表現型取得手法が中心である。
abstractwe developed a wearable plant sensor with a flexible interdigitated-electrodes (IE) probe design for in-situ monitoring of StWC and early identification and warning of tobacco root diseases.
SoybeanLiDAR / point cloudRoot2D/3D reconstructionSegmentationSkeletonization / topologyRoot system architecture
Characterizing root system architecture (RSA) is essential for understanding plant acclimatization and guiding breeding strategies to enhance stress tolerance and optimize resource uptake. Although 3D root analysis provides significantly more detailed and structurally informative insights than conventional 2D methods, the development of robust and quantitative tools for 3D root phenotyping has been hindered by challenges such as data complexity, noise, and root overlap. In this study, we present a biologically inspired skeletonization framework that segments root architectures by tracing root growth trajectories. The primary objective is to enable anatomically accurate extraction of RSA traits from 3D point clouds. Our method begins by segmenting the primary root through shortest-path extraction and tangent-plane-based clustering. Lateral root initiation points are then detected, and candidate paths are grown using a bionic pathfinding strategy with adaptive parameters; an optimal, non-overlapping skeleton is selected through clustering and combination sorting, and finally refined via an inward back-tracing procedure to improve junction connectivity. To support downstream phenotyping, we compute root length and angle from the segmented skeletons, and reconstruct anatomically faithful tubular meshes for each lateral root to analytically estimate surface area and volume. Our method achieved high accuracy across multiple traits, including an F1 score of 0.88 for lateral root numeration, R2 values of 0.992 and 0.987 for primary and lateral root length estimation, respectively, and strong agreement in surface area (R2=0.953) and volume (R2=0.912) validation against reference methods. Overall, our method offers a robust and biologically meaningful solution for 3D root phenotyping. The extracted traits provide plant breeders with critical insights for genotype selection and offer plant scientists a powerful tool to evaluate the effects of agronomic treatments and environmental interventions.
Why it matches plant phenotyping methods3D根系骨架化と形態形質抽出法の開発・検証が研究の中心であり、根長・角度・表面積・体積などの表現型を定量化している。
abstractwe present a biologically inspired skeletonization framework that segments root architectures by tracing root growth trajectories
Black shank disease and root rot disease represent the most destructive diseases of tobacco. Once it occurs, it will spread rapidly, endangering the health of tobacco plants, and even killing them. The stem near the root of tobacco plant is the first part that can exhibit observable signs of root disease. Monitoring the dynamic variations of in-situ stem water content (S t WC) near the root is beneficial for the early detection of tobacco root diseases. Therefore, we developed a wearable plant sensor with a flexible interdigitated-electrodes (IE) probe design for in-situ monitoring of StWC and early identification and warning of tobacco root diseases. The IE probe of wearable sensor was securely affixed to the stem, and the soil moisture (SM) sensors were buried in the corresponding root area. The results demonstrated a clear inconsistency in the observed trend between the S t WC near roots of diseased and healthy tobacco plants. About 60 h before the blackening of the stems near roots, the SM of diseased tobacco plants (0.007 cm 3 /cm 3 ) indicated a slower decrease compared to healthy tobacco plants (0.021 cm 3 /cm 3 ). In accordance with this phenomenon, the daily variation of S t WC near roots of diseased tobacco plants (0.023 cm 3 /cm 3 ) was significantly less than that of healthy tobacco plants (0.048 cm 3 /cm 3 ). Moreover, the abnormal changes of S t WC near roots of diseased tobacco plants after blackening further validated the availability of the wearable sensor in the early detection and warning of tobacco root diseases. The tobacco plant may have been in early diseased stage when the daily change of S t WC was continuously less than 0.037 cm 3 /cm 3 . Future research will focus on the mechanism of water conduction between soil and stem near the root of tobacco plants, and the potential application of the wearable sensor in early disease detection. • A wearable plant sensor is developed for early warning of tobacco root diseases. • The sensors were used to monitor diseased and healthy tobacco plants in the field. • The sensor can achieve early in-situ detection of tobacco root diseases.
Why it matches plant phenotyping methods植物茎内水分状態を測定し、根部病害の早期検出へ用いるウェアラブルセンサーを開発・検証しており、植物状態の取得法が中心である。
abstractTherefore, we developed a wearable plant sensor with a flexible interdigitated-electrodes (IE) probe design for in-situ monitoring of StWC and early identification and warning of tobacco root diseases.
In the context of precision agriculture, high-throughput phenotyping (HTP) aims to rapidly and effectively identify factors that affect crop yield, enabling timely and appropriate interventions. However, interpreting data from HTP remains challenging. We performed a proximal red-green-blue (RGB)-based HTP on several tomato genotypes exposed to abiotic stress (drought) or biotic stress induced by tomato spotted wilt virus (TSWV), Pseudopyrenochaeta lycopersici (corky root rot; CRR), or Meloidogyne incognita (root-knot nematode; RKN). We aimed to determine if RGB-based HTP is effectively able to: a) distinguish the effects of biotic from abiotic stress; b) differentiate resistant/tolerant from susceptible genotypes. Our HTP data analysis produced 12 morphometric and eight colorimetric indices. Principal Component Analysis (PCA; P < 0.0001; 83 % variation explained by three PCs) showed that factors such as shoot area solidity and certain color-based indices, including the senescence index and green area, effectively differentiated biotic from abiotic stress. Morphometric parameters, including plant height, projected shoot area, and convex hull area, proved to be applicable for identifying the stress status regardless of the type of stress. HTP effectively distinguished the genotype resistant to TSWV from the susceptible ones. This task was more challenging for below-ground stresses like CRR and RKN. Different profiles of HTP indices were observed among the genotypes assayed for drought tolerance, indicating variability in their ability to withstand drought conditions. In conclusion, our findings highlight the value of RGB-based HTP as a tool for precision farming of tomatoes, enabling the identification of both biotic and abiotic stressors.
Why it matches plant phenotyping methodsトマトのRGBベース高スループット表現型解析を用い、形態・色彩指標の抽出と、ストレス識別および遺伝子型判別への有効性を評価しており、フェノタイピング手法が研究の中心である。
abstractWe performed a proximal red-green-blue (RGB)-based HTP on several tomato genotypes exposed to abiotic stress (drought) or biotic stress induced by tomato spotted wilt virus (TSWV), Pseudopyrenochaeta lycopersici (corky root rot; CRR), or Meloidogyne incognita (root-knot nematode; RKN).
Reproduction assets foundThe paper's HTP dataset (20 indices from five stress experiments) is stated to be available in the supplementary material hosted at the article DOI, which qualifies as a paper-specific public phenotype dataset. However, the analysis code has no public deposit: it is only available from the corresponding author upon 'a'Dataset · publicThe data collected and used in this study are available in the supplementary material. The code used for analysis is available from the corresponding author, GBu, upon reasonable request.Open asset ↗lines:400-518Plant phenotyping relevance match · UnverifiedEurope PMC · checked 14 Sept 2026
LentilRootClassificationMorphology / geometry measurementRoot system architecture
Root System Architecture (RSA) plays a central role in plant performance by regulating water and nutrient uptake. As agriculture faces increasing challenges from environmental variability, nutrient limitation and water scarcity, identifying adaptive root traits in wild relatives is critical for developing resilient crop varieties. We screened a diverse panel of cultivated and wild lentil (Lens spp.) accessions using the Rhizoscope, a high-throughput root phenotyping system developed by CIRAD. In total, 42 wild accessions and eight advanced breeding lines were evaluated for RSA traits and quantified at 30 days after sowing using a rhizobox-based phenotyping platform. Our objectives were to assess RSA variation within wild species and compare RSA traits between cultivated and wild genotypes. Cultivated lentil showed higher values for traits such as root mass, diameter, root volume, root angle (RA) and maximum root depth (MRD), suggesting greater resource acquisition efficiency. In contrast, wild accessions exhibited higher root:shoot ratios and Collar-First Ramification length (CRL), consistent with adaptation to resource-limited environments. To understand the drivers of RSA variation, we incorporated environmental variables from the center of origin of each accession, including Aridity Index, soil type and bedrock depth, into multivariate analyses using Linear Discriminant Analysis and Classification and Regression Trees. Results showed that variation in traits such as MRD, RA and CRL was more strongly linked to environmental conditions than species classification. Deeper roots were associated with arid regions and deep bedrock, while wider RAs and shorter CRL lengths were typical of genotypes from compacted or shallow soils. These findings suggest that RSA traits in wild lentil species are shaped primarily by local environmental selection rather than taxonomic identity. This highlights the importance of integrating ecological provenance with phenotypic assessments when evaluating wild germplasm. Relying solely on species classification may overlook key adaptive traits. Incorporating environmental data can improve the identification of genotypes with root traits conferring tolerance to drought and edaphic stress, thereby supporting the development of more resilient lentil cultivars.
Why it matches plant phenotyping methodsRhizoscopeを用いた高スループット根系表現型解析プラットフォームによるRSA形質の取得が研究の中心であり、単なる生物学的評価ではない。
abstractWe screened a diverse panel of cultivated and wild lentil (Lens spp.) accessions using the Rhizoscope, a high-throughput root phenotyping system developed by CIRAD.
Field / plotMicroscopyRootRoot system architecture
Background and aims The mechanical properties of plant roots are crucial for soil stabilization and vegetation restoration. To effectively employ bioengineering methods, understanding the tensile properties of plant roots is essential. In most studies, root diameter is used as a predictor of tensile strength but this fails to accurately describe root mechanical behaviour. The stele and cortex are two anatomical parts of the root whose actual mechanical behaviour and specific contributions to root biomechanisms remain unclear. Methods Tensile tests and scanning electron micrography were performed on roots of four typical species (Robinia pseudoacacia, Pinus tabuliformis, Vitex negundo and Syzygium aromaticum) in the Loess Plateau of China to investigate the roles of the stele and cortex in explaining the root's tensile strength. Then, based on the 'same strain' principle, a tensile strength prediction model was developed and validated using experimental data from plant root. Key results The stele and cortex of roots exhibited distinct mechanical behaviours: elastic plasticity and linear elasticity, respectively. Tensile strength was negatively correlated with diameter and stelar diameter and cortical thickness were positively correlated with diameter. The cortex had lower tensile strength, strain at maximum stress and thickness compared with the stele. The observed increase in scatter of tensile strength with decreasing root diameter was attributed to the higher coefficient of variation in cortical tensile strength compared with the stele. Notably, predicted results of intact root tensile strength fell within the 95 % prediction interval of the measured intact root tensile strength and could be enhanced 30-80 % by strengthening dataset quality. Conclusions Our results demonstrated the actual mechanical behaviour characteristics of cortex and stele, and provide a new perspective for addressing the mechanical properties of roots using composite materials mechanics. The findings of this study will provide a theoretical foundation for implementing plant-based ecological restoration and disaster prevention measures.
Why it matches plant phenotyping methods根の引張強度という植物形質を対象に、予測モデルを開発し実験データで検証しており、形質推定法が研究の中心である。
abstractbased on the 'same strain' principle, a tensile strength prediction model was developed and validated using experimental data from plant root.
Background and aims Increasing C storage in cultivated soils requires a better understanding of C dynamics, particularly at depth, where root litter decomposition dynamics is expected to be slower than in ploughed layers. Methods We assessed the effect of barley root diameter on root decomposition in situ using a non-invasive method at different depths. Temporal decreases in root diameter and length were measured using images acquired by optical scanners buried at depths of 20, 50 and 90 cm from seeding and for 1.5 years. A parallel root litterbag experiment was performed to measure root mass loss. Results Root decomposition was observed on the scanned images before the flowering stage, with up to 85 % of the maximum root volume achieved being lost at harvest. Thinner roots ( Conclusions Optical scanner-based image analysis complements litterbags by enabling individual root tracking and in situ decomposition assessment without root manipulation. This method offers the opportunity to measure root decomposition at various soil depths over long periods, and could improve the estimation of root-derived soil C inputs.
Why it matches plant phenotyping methods埋設光学スキャナーと画像解析による根径・根長・根体積の非侵襲的経時測定が研究の中心で、根の分解を個別追跡する再利用可能な植物表現型取得法を実証している。
abstractTemporal decreases in root diameter and length were measured using images acquired by optical scanners buried at depths of 20, 50 and 90 cm from seeding and for 1.5 years.
Field / plotMicroscopyCell / cellular structureRootTissueClassificationMorphology / geometry measurementGrowth / development / phenology
Growth rings in woody plants form in response to seasonal variation in the environment and are fundamental to dendrochronological studies, but estimating plant ages—especially in underexplored growth forms such as forbs, shrubs and subshrubs from grasslands—remains challenging. Here, we address a knowledge gap in the anatomy and histochemistry of subshrubs from natural Cerrado grasslands and evaluate their potential for dendrochronological applications. We studied underground woody organs of Jacaranda decurrens, Lippia lupulina, and Mandevilla longiflora, collected at the Santa Bárbara Ecological Station (Brazil). We used autofluorescence microscopy and a suite of histochemical tests targeting structural and non-structural compounds. Autofluorescence allowed spatial assessment of wood tissues without staining, and improved growth-ring visualization. FASGA staining increased contrast between fibers and parenchyma, facilitating tissue discrimination and growth-ring delimitation, while Mäule staining highlighted differences in cell-wall composition and guaiacyl/syringyl (G/S) ratios throughout growth-ring formation. Starch was consistently detected in parenchymatic cells of all species (lowest in J. decurrens, intermediate in L. lupulina, highest in M. longiflora), and its spatial association with parenchyma aided growth-ring identification. Combining fluorescence and histochemical approaches provides complementary insights into the anatomy and chemistry of underground organs and advances dendrochronological studies in grassland ecosystems.
Why it matches plant phenotyping methods自家蛍光顕微鏡と組織化学染色を用いて地下木質器官の成長輪を可視化・判別する方法が研究の中心であり、植物の年齢・成長状態という形質の取得に直接関与する。
abstractAutofluorescence allowed spatial assessment of wood tissues without staining, and improved growth-ring visualization.
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.
Abstract Cassava ( Manihot esculenta Crantz) is an important food security crop in sub-Saharan Africa and other tropical regions, but its genetic improvement is hindered by long breeding cycles and labour-intensive phenotyping procedures. This study aimed to develop a rapid phenotyping protocol and assess its predictive capacity for yield and plant architecture traits in cassava using Normalized Difference Vegetation Index (NDVI) data obtained with affordable handheld sensor (Trimble GreenSeeker). A diverse panel of 453 cassava accessions was evaluated across two contrasting agroecological zones in Nigeria; Mokwa (Southern Guinea Savannah) and Onne (Humid Forest) during the 2021/2022 planting season. NDVI data collected at 3, 6, and 9 months after planting (MAP) were integrated with ground truth phenotypic measurements of 26 agronomic traits.Genetic parameters including broad-sense heritability and genotype-by-environment interactions were estimated. Results showed moderate to high heritability for important traits such as fresh root yield (FYLD), dry matter content (DM), and harvest index (HI). NDVI data, especially at 6 months after planting, demonstrated strong predictive power (R² up to 0.9) for yield components, with prediction accuracy varying across locations. Significant negative correlations between lodging (LODG) and yield traits highlighted the influence of plant architecture on productivity in cassava. These findings affirm the applicability of handheld NDVI sensors as cost-effective tools for enhanced phenotyping and selection in cassava breeding programs for rapid genetic gains and varietal development under diverse field conditions.
Why it matches plant phenotyping methodsキャッサバの収量・草型形質を推定するNDVIベースの迅速な表現型取得プロトコルを開発し、ハンドヘルドセンサーの予測性能を評価しているため、方法が中心的である。
abstractThis study aimed to develop a rapid phenotyping protocol and assess its predictive capacity for yield and plant architecture traits in cassava using Normalized Difference Vegetation Index (NDVI) data obtained with affordable handheld sensor (Trimble GreenSeeker).
WheatField / plotRootWhole plant / canopy / plot / fieldMorphology / geometry measurementPhysiological trait estimationRoot system architectureWater status / transpiration
Root hydraulic properties affect water uptake in wheat ( Triticum aestivum L.) and are strongly influenced by root anatomy, yet how they vary along root axes and interact with cultivar differences remains underexplored. We investigated crown roots of six German winter wheat cultivars spanning one century of release, sampled from a field experiment. Roots were imaged at different positions along their axis using a high-throughput system (Rapid Anatomics Tool), and the resulting anatomical traits were coupled to GRANAR–MECHA to model radial ( K r ) and axial conductance ( k x ). Longitudinal anatomical gradients were pronounced: tissue dimensions, metaxylem number, and apoplastic barriers decreased from the base onwards, resulting in K r increasing and k x decreasing with distance from the base. Cultivar differences were also apparent: modern cultivars had smaller tissues and fewer metaxylem vessels, reducing both axial and radial conductance and lowering whole-root water uptake capacity (∼20–30%). By integrating field sampling with high-throughput image analysis and mechanistic modeling, this study establishes an integrated phenotyping approach that links root anatomy to water uptake and uncovers anatomical traits relevant to hydraulic function. The results show that longitudinal gradients and cultivar-associated anatomical differences contribute to variation in hydraulic properties and persist along fully mature root segments. Highlight High-throughput imaging–modeling shows that longitudinal gradients and cultivar-associated anatomical differences along crown roots shape radial and axial conductance, leading to reduced whole-root water uptake capacity in modern winter wheat
Why it matches plant phenotyping methods根の高スループット画像解析と機械論的モデリングを統合し、解剖形質から水理特性を推定するフェノタイピング手法が研究の中心であるため。
abstractRoots were imaged at different positions along their axis using a high-throughput system (Rapid Anatomics Tool), and the resulting anatomical traits were coupled to GRANAR–MECHA to model radial ( K r ) and axial conductance ( k x ).
Abstract The use of unmanned aerial vehicles for remote sensing is an effective method for monitoring crop growth, particularly for tall crops such as maize. High‐resolution imagery obtained from unmanned aerial vehicles enables the measurement of plant height, which is a critical indicator of crop growth. However, a reference plant height is required to assess growth. This study aimed to develop a model to predict the reference height for growth assessment using temperature data. Furthermore, a methodology was proposed to estimate model parameters from the relative maturity, thus enabling adaptation to a range of maize varieties. In 2022 and 2023, maize plant height was measured using an unmanned aerial vehicle at two flying altitudes (40 and 100 m) several times for 12 varieties with varying relative maturity. Moreover, a regression model was developed to predict the silking stage and identify the optimal sensing time 1 week before the silking stage. The results showed that the growth rate was not statistically different among the varieties, indicating that maximum plant height was determined by the duration of the growth period. A growth model was developed based on these results. The root mean square error (RMSE) for the model was 0.16 and 0.15 m for data sets from 40‐ and 100‐m altitudes, respectively. In estimating plant height, this growth model performed marginally better than the logistic curves used in existing studies. Additionally, a linear relationship was observed between relative maturity and the parameters of the developed growth model. Consequently, the newly developed growth model can predict the plant height for new varieties because the parameters of the model can be inferred from the relative maturity.
Why it matches plant phenotyping methodsUAV画像によるトウモロコシ草高推定のための成長モデルと成熟度に基づく適応手法を開発・評価しており、植物形質取得が中心である。
abstractThis study aimed to develop a model to predict the reference height for growth assessment using temperature data.
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 development depends on coordinated growth at cellular and organ scales, yet comparative analyses are hindered by inconsistent reporting of growth across studies. We conducted a meta-analysis of Arabidopsis thaliana growth dynamics, integrating data from 176 studies to create the first multiscale atlas of plant growth. We developed a unified mathematical framework to harmonise growth data from diverse organs (shoot apical meristem, root, hypocotyl, and leaf), methodologies, and experimental setups, allowing the conversion and direct comparison of expansion rates at cellular and organ levels. Analyses revealed both organ-specific and general growth strategies linked to size control. In the meristem, a conserved offset in cell expansion between central and peripheral zones was observed. Root elongation was driven mainly by cell expansion and differentiation in the elongation zone, rather than meristem activity. Hypocotyl and leaf growth showed unexpected parallels: early exponential elongation resembled primary morphogenesis, while later linear growth matched secondary morphogenesis. Comparing dark- vs light-grown hypocotyls and juvenile vs transition leaves showed that organ size was modulated by a trade-off between growth rate and duration of the scaling phase. Cellular-scale growth during early development was shown to influence final organ size, underscoring the need for early-stage measurements. This growth atlas provides benchmark values and a reference framework for interpreting mutant phenotypes, guiding experimental design, and advancing our understanding of growth regulation across plant organs.
Why it matches plant phenotyping methods植物の成長形質を統合・比較する数学的フレームワークと成長アトラスを構築しており、再利用可能な形質標準化・ベンチマークが研究の中心である。
abstractWe developed a unified mathematical framework to harmonise growth data from diverse organs (shoot apical meristem, root, hypocotyl, and leaf), methodologies, and experimental setups, allowing the conversion and direct comparison of expansion rates at cellular and organ levels.
Abstract Purpose Dormant pruning is critical for fruit tree management and maintaining fruit quality. Traditional manual pruning is labor-intensive, driving interest in automated robotic dormant pruning. However, automated robotic dormant pruning meets significant challenges in trunk and branches detection, due to the complexity of the orchard environment and the interlacing of the branches. This paper proposes an automatic method for real-time detection and pruning of pear tree trunks and upright branches using an RGB-D camera. Methods Pear trunk detection was conducted by enhancing the You Only Look Once version 5 Nano (YOLOv5n) model with Squeeze-and-Excitation Networks (SENet) and optimizing the anchor boxes. For branch segmentation, YOLOv8n-seg integrated with Dynamic Snake Convolution (DSConv) and Focal Scale Intersection over Union (Focal_SIoU) was employed. The length and angle of the branches were calculated from the generated mask images, and the pruning position was determined. A PRUNING_ROS package was developed for real-time orchard applications. Results The evaluation demonstrated 96.7% mean average precision (mAP) for trunk detection and 82.6% mAP for branch segmentation in test dataset. Field test results showed that the mean absolute error (MAE) of trunk distance localization compared to manual measurements was 3.71 cm, with the root mean square error (RMSE) of 3.84 cm, and the frames per second (FPS) of 31.6. The MAE was 2.3 cm (RMSE: 2.6 cm) for pruning points in depth direction and 2.34° (RMSE: 2.71°) for upright branches angle, with the FPS of 36.2. The field pruning experiment showed a pruning success rate of 47.6%. Conclusion This method provides technical support for the operation of fruit tree pruning robots, representing a step toward the full automation of fruit tree management.
Why it matches plant phenotyping methodsRGB-D画像からナシ樹の幹・枝を検出・分割し、枝の長さと角度を算出する手法が中心で、単なる対象位置検出を超えた植物器官形態の計測と技術評価を行っている。
abstractFor branch segmentation, YOLOv8n-seg integrated with Dynamic Snake Convolution (DSConv) and Focal Scale Intersection over Union (Focal_SIoU) was employed. The length and angle of the branches were calculated from the generated mask images, and the pruning position was determined.
Field / plotFlowerLeafRootStem / branchClassificationDisease symptoms / severity
This dataset comprises 5452 images of durian plant parts-including leaves, flowers, branches, stems, and roots-affected by ten common disease classes. The images were captured from one family-owned durian orchard and four nearby orchards in Vinh Long Province, Vietnam. Each class contains approximately 405-427 raw images, photographed using an iPhone 14 under natural field conditions. These conditions simulate typical farmer photography practices, featuring varied angles, inconsistent lighting, and complex environmental backgrounds, resulting in significant visual noise. All raw JPEG images were manually reviewed and cropped on macOS systems using MacBook devices equipped with Apple M4 chips to focus on disease-affected regions, reduce file size, and minimize background noise. The processed, cropped images are provided in PNG format with variable dimensions. Images were resized to 224×224 pixels only during model training for machine learning experiments. Disease symptoms were verified in collaboration with plant pathologists to ensure accurate classification. This dataset is publicly available on Mendeley Data and is suitable for developing and evaluating machine learning models in plant disease classification. It is particularly valuable for testing model performance under real-world, noisy conditions and for supporting the creation of mobile or edge-based diagnostic tools in agriculture.
Why it matches plant phenotyping methods植物病徴を画像で直接捉えた公開データセットで、植物病害状態の分類モデル開発・評価を主目的とするため、表現型計測データセットとして中心的です。
abstractThis dataset comprises 5452 images of durian plant parts-including leaves, flowers, branches, stems, and roots-affected by ten common disease classes.
Reproduction assets foundThe paper is a Data in Brief describing a public durian disease image dataset (5452 field images, ten classes) deposited on Mendeley Data with an explicit DOI and direct URL, matching an allowed URL. This is a paper-specific, publicly available image dataset directly reproducing the paper's phenotyping measurements. NoDataset · publicRepository name: Mendeley Data
Data identification number: 10.17632/mhjwyb5p48
Direct URL to data: https://data.mendeley.com/datasets/mhjwyb5p48/1Open asset ↗Mendeley Data · 10.17632/mhjwyb5p48lines:47-125Plant phenotyping relevance match · UnverifiedEurope PMC · checked 6 Sept 2026
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.
Plant phenotyping relevance match · UnverifiedbioRxiv · Europe PMC · checked 6 Sept 2026
The legume-rhizobia symbiosis is a cornerstone of sustainable agriculture due to its ability to facilitate biological nitrogen fixation. Still, real-time visualization and quantification of this interaction remain technically challenging, especially across different host backgrounds. In this study, we systematically evaluate the efficacy of the nitrogenase system nifH promoter (PnifH) in driving expression of distinct fluorescent reporters; superfolder yellow fluorescent protein (sfYFP), superfolder cyan fluorescent protein (sfCFP), and various red fluorescent proteins (RFPs) within root nodules of determinate (Lotus japonicus-Mesorhizobium japonicum) and indeterminate (Pisum sativum-Rhizobium leguminosarum) systems. We show that PnifH-driven sfYFP and sfCFP yield strong, uniform, and reproducible fluorescence in nodules of both systems, facilitating reliable quantification of nodulation traits and strain occupancy. In contrast, RFPs including monomeric (mScarlet-I, mRFP1, mARs1) and multimeric (AzamiRed1.0) variants exhibited weak or inconsistent signals in pea. Notably, fluorescent labeling did not impair rhizobial competitiveness for root nodule occupancy, and PnifH-driven sfYFP and sfCFP reporters enabled robust multiplexed imaging in single-root and split-root assays. In the lotus, mScarlet-I worked robustly and facilitated a tripartite strain labeling system. Complementing our molecular toolkit, we established a deep learning-based analytical pipeline for high-throughput, automated quantification of nodulation traits, validated against standard ImageJ analysis. Altogether, our results identify PnifH-driven sfYFP and sfCFP as robust, broadly applicable reporters for legume-rhizobia symbiosis studies, while highlighting the need for optimized red fluorophores in some contexts. The integration of validated promoter-reporter constructs with state-of-the-art computational approaches provides a scalable framework for dissecting the spatial and competitive dynamics of plant-microbe mutualisms. IMPORTANCEThe legume-rhizobia symbiosis is central to sustainable agriculture through its capacity for biological nitrogen fixation, yet tools for real-time, quantitative visualization of this interaction remain limited. Here, we demonstrate that the nifH promoter (PnifH) effectively drives expression of superfolder yellow (sfYFP) and cyan (sfCFP) fluorescent proteins in both determinate (Lotus japonicus-Mesorhizobium japonicum) and indeterminate (Pisum sativum-Rhizobium leguminosarum) nodules. These reporters enable robust, reproducible fluorescence without impairing rhizobial competitiveness, supporting multiplexed imaging and quantitative nodulation analyses. By contrast, red fluorescent proteins exhibited host-dependent variability, underscoring the need for improved red fluorophores. Integration of validated promoter-reporter constructs with a deep learning-based image analysis pipeline establishes a scalable framework for high-throughput assessment of nodule occupancy and symbiotic dynamics. This work provides a practical molecular and computational toolkit for dissecting plant-microbe mutualisms across diverse host systems.
Why it matches plant phenotyping methods植物根粒の蛍光可視化・定量手法と、深層学習による結節形質の自動画像解析パイプラインを開発・検証しており、植物フェノタイピング手法が中心です。
abstractwe established a deep learning-based analytical pipeline for high-throughput, automated quantification of nodulation traits, validated against standard ImageJ analysis.
ArabidopsisMicroscopyCell / cellular structureRootTrackingGrowth / development / phenology
Arabidopsis is a widely used model plant to study physiology and development. Live imaging is an important technique to visualize and quantify processes in plant growth and cell division, where accurate cell tracking is essential. The commonly used software TrackMate adopts a tracking-by-detection approach, applying Laplacian of Gaussian (LoG) for blob detection and a Linear Assignment Problem (LAP) tracker for tracking. However, its performance declines when cells are densely arranged. To overcome this limitation, we propose an accurate tracking method based on a Genetic Algorithm (GA) that incorporates knowledge of Arabidopsis root cellular patterns and spatial relationships among volumes. Our method follows a coarse-to-fine strategy: first performing relatively simple line-level tracking of nuclei, then refining associations based on the linear arrangement of cell files and their spatial relationships. We evaluated the method on long-term live imaging datasets of Arabidopsis root tips, and with minor manual correction, it achieved accurate nuclear tracking. To the best of our knowledge, this represents the first successful attempt to address a long-standing problem in time-lapse microscopy of the root meristem by providing an accurate tracking method for Arabidopsis root nuclei.
Why it matches plant phenotyping methods植物の3Dタイムラプス画像から根の細胞核を追跡する計算手法を開発・評価しており、植物の成長・細胞分裂の定量化を支える画像ベースの表現型取得法が中心です。
abstractTo overcome this limitation, we propose an accurate tracking method based on a Genetic Algorithm (GA)
Improving crop root systems for enhanced adaptation and productivity remains challenging due to limitations in scalable non-destructive phenotyping approaches, inconsistent translation of root phenotypes from controlled environments to the field, and a lack of understanding of genetic controls. This study serves as a proof of concept, evaluating a panel of Australian barley breeding lines and cultivars in field experiments conducted across two contrasting environments. A diverse subset of 20 genotypes was subjected to ground-based root and shoot phenotyping at key growth stages, and this dataset was used in combination with unmanned aerial vehicle (UAV)-captured vegetation indices (VIs) to train machine learning models to predict root distribution and above-ground biomass for the untested panel comprising 544 genotypes across the two seasons. Unlike previous root studies that have focused on above-ground traits or indirect proxies, this approach predicts root traits in the field using machine learning. Haplotype-based mapping using predicted root and shoot traits in the broader panel revealed key genomic regions. These include novel regions, previously reported root quantitative trait loci, and EGT2-a recently cloned gene that regulates root gravitropism in barley. This scalable phenotyping approach offers opportunities to advance root research across crops and support the development of future varieties adapted to changing climates.
Why it matches plant phenotyping methodsUAV画像由来の植生指数と機械学習を組み合わせ、圃場で根系形質と地上部バイオマスを推定するスケーラブルな表現型解析手法が研究の中心である。
titlecombining UAV phenotyping and machine learning to predict barley root traits in the field
Plant phenotyping relevance match · UnverifiedOpenAlex · Europe PMC · checked 14 Sept 2026
SorghumRootRoot system architectureStress response / tolerance
Climate-induced challenges, such as drought and nutrient depletion, are increasingly constraining global crop production, threatening food and nutritional security. Sorghum bicolor (L.), a climate-resilient cereal, demonstrates strong adaptive potential under resource-limited conditions due to its robust root system architecture (RSA). While above-ground improvements have received significant attention, the role of RSA in enhancing resource-use efficiency (RUE), particularly water use efficiency (WUE) and nitrogen use efficiency (NUE), remains underexploited in breeding programs. This review explores the physiological and molecular roles of sorghum RSA traits (e.g., root depth, density, branching pattern, and root angle) in improving RUE under abiotic stress. It highlights advances in multi-omics approaches, including transcriptomics, proteomics, and genome-wide association studies (GWAS), which provide insights into the genetic regulation of root development. High-throughput phenotyping platforms, including 2D, 3D, and emerging 4D imaging techniques, are evaluated for their effectiveness in capturing dynamic root traits and informing selection strategies. Sorghum's RSA offers a functional model for developing climate-resilient cultivars with improved WUE and NUE. The integration of modern phenotyping techniques with molecular insights and multi-omics strategies will expedite the identification of critical genetic and physiological determinants of RSA characteristics. This synthesis underscores the potential of RSA-targeted breeding strategies to enhance crop productivity and sustainability in water-and nutrient -constrained environments, aiding sustainable intensification and global food security in the face of climate change challenges.
Why it matches plant phenotyping methodsソルガム根系形態の表現型計測を扱うレビューであり、2D・3D・4D画像による高スループット表現型解析手法を評価しているため、方法レビューとして中心的です。
abstractHigh-throughput phenotyping platforms, including 2D, 3D, and emerging 4D imaging techniques, are evaluated for their effectiveness in capturing dynamic root traits and informing selection strategies.
WheatRootRoot system architectureYield / yield components
ABSTRACT Global wheat production is extending to dryland and tropical environments prone to drought and heat stress due to breeding and deploying new‐generation ideotypes with desirable product profiles. However, yield gains are low and stagnant under these environments, attributable to abiotic stresses, primarily drought. Genotypes with drought‐adaptive root traits will enhance grain yield and productivity under dryland and drought‐stress conditions. Root traits are valued and related to high biomass production, nutrient and water extraction, ultimately boosting yield and yield components, notably in dryland agro‐ecologies. Hence, the objective of the current review is to explore and document the opportunities, challenges and progress in wheat breeding targeting novel root traits to enhance drought adaptation and improve productivity under dryland agro‐ecologies. The review presents a detailed account of the available genetic resources of wheat possessing desirable root traits for breeding programs. This is followed by outlines on the genetic gains for breeding for wheat root system architecture traits and the potential of high‐throughput phenotyping techniques. Challenges and limitations on root phenotyping methods are presented. Lastly, the paper discusses the potential utilities of molecular breeding approaches, including marker‐assisted selection, genomic‐assisted breeding, and next‐generation sequencing for accelerated breeding targeting root system architecture traits. The review can guide wheat breeders and agronomists in developing drought‐tolerant varieties by exploiting the root system and climate‐smart wheat varieties for moisture‐deficient production environments.
Why it matches plant phenotyping methodsコムギの根形態形質を対象とし、根のハイスループット表現型解析技術と根形質計測法の課題・限界をレビューしているため、表現型解析手法レビューが中心である。
abstractThe review presents a detailed account of the available genetic resources of wheat possessing desirable root traits for breeding programs.
RootMorphology / geometry measurement2D/3D reconstructionGrowth / time-series analysisRoot system architecture
BACKGROUND AND AIMS: Isoetales is a clade of lycopsids that evolved colossal arborescent forms during their Palaeozoic prime but today are represented solely by the small, herbaceous monogeneric Isoetes. Despite the differences in scale of taxa in the clade, the rooting system of all members consists of two parts; rootlets develop from a rhizomorph in a regular pattern termed rhizotaxy. Rhizomorphs are highly diverse in morphology, leading to different terms being used to describe aspects of rhizotaxy in contrasting lineages. Here we set out to investigate the degree to which rhizotaxy was conserved among taxa, aiming to provide a standard geometric definition and developmental interpretation of rhizotaxy. METHODS: We developed a pipeline to quantitatively describe rhizotaxy. This pipeline allowed rootlet arrangement to be captured in 3D, before being visualized on a 2D lattice to which Delaunay triangulation could be applied. This approach offers a standard quantitative method of comparing rhizotaxy across disparate rhizomorphs. Next, to investigate the evolution and development of rhizotaxy we applied our pipeline to 3D reconstructions we generated of the rooting system of the extinct Carboniferous lycopsid, Oxroadia. Finally, we made direct observations of rootlet development in Isoetes using time-course imaging. KEY RESULTS: We demonstrate that rhizotaxy can be described as an equilateral triangular lattice for all members of the Isoetales, including Oxroadia. By combining evidence from direct observation of rootlet development in Isoetes with inferences of rootlet development and the early stages of sporophyte ontogeny of Oxroadia, we conclude that the conserved rhizotaxy developed through the process of rootlet intercalation. CONCLUSIONS: We provide a single geometric definition and predicted developmental mechanism for rhizotaxy that applies to all Isoetales. Our findings call into question the literal interpretation that the rhizomorph is a modified shoot.
Why it matches plant phenotyping methods植物の根器官配置（rhizotaxy）を3Dで定量化し、2D格子化・Delaunay三角測量によって比較する解析パイプラインを開発した研究であり、形態表現型の取得・抽出法が中心です。
abstractWe developed a pipeline to quantitatively describe rhizotaxy.
Global crop losses of 20–40% continue because traditional plant assessment methods are either invasive, damaging plant tissues, or reactive, detecting stress only after visible symptoms. Recent developments have remained fragmented, focusing on single modalities, individual organs, or limited frequency ranges. This study developed a unified bioelectrical sensor system capable of non-invasive, multimodal, multiscale, and integrative assessment by integrating capabilities that existing methods address only separately. The system combines spectroscopy and tomography within a single platform, enabling simultaneous evaluation of multiple organs. Unlike approaches confined to narrow frequencies, it captures complete physiological responses across scales. Validation on strawberry (Fragaria × ananassa ‘Sweet Charlie’) demonstrated comprehensive multi-organ assessment: 98.3% accuracy for fruit categorization, 95.8% for leaf water status, and 88.2% for stem productivity. Tomographic performance reached 2.6–2.8 mm resolution for 3D root mapping and 2.8–3.0 mm for 2D postharvest fruit sorting. Correlations with reference metrics were used exclusively for validation, confirming that the extracted features reflect genuine physiological variations. Importantly, the system detects stress before visible symptoms, enabling intervention within the reversible window. By unifying spectroscopy and tomography with complete frequency coverage and multi-organ capability, this platform overcomes existing fragmentation and establishes a foundation for proactive, comprehensive plant monitoring essential for sustainable agriculture.
Why it matches plant phenotyping methods植物の生理状態を非侵襲的に取得するマルチモーダル・マルチスケール生体電気センサー基盤を開発し、果実・葉・茎・根の評価と基準指標による検証を行っており、表現型取得法が研究の中心です。
abstractThis study developed a unified bioelectrical sensor system capable of non-invasive, multimodal, multiscale, and integrative assessment
RootObject detection2D/3D reconstructionSkeletonization / topologyRoot system architecture
Plant roots typically exhibit a highly complex and dense architecture, incorporating numerous slender lateral roots and branches, which significantly hinders the precise capture and modeling of the entire root system. Additionally, roots often lack sufficient texture and color information, making it difficult to identify and track root traits using visual methods. Previous research on roots has been largely confined to 2D studies; however, exploring the 3D architecture of roots is crucial in botany. Since roots grow in real 3D space, 3D phenotypic information is more critical for studying genetic traits and their impact on root development. We have introduced a 3D root skeleton extraction method that efficiently derives the 3D architecture of plant roots from a few images. This method includes the detection and matching of lateral roots, triangulation to extract the skeletal structure of lateral roots, and the integration of lateral and primary roots. We developed a highly complex root dataset and tested our method on it. The extracted 3D root skeletons showed considerable similarity to the ground truth, validating the effectiveness of the model. This method can play a significant role in automated breeding robots. Through precise 3D root structure analysis, breeding robots can better identify plant phenotypic traits, especially root structure and growth patterns, helping practitioners select seeds with superior root systems. This automated approach not only improves breeding efficiency but also reduces manual intervention, making the breeding process more intelligent and efficient, thus advancing modern agriculture.
Why it matches plant phenotyping methods3D画像から植物根系の骨格・構造を抽出する手法を開発し、データセット上で正解値と比較検証しており、根形態フェノタイピングが研究の中心である。
abstractWe have introduced a 3D root skeleton extraction method that efficiently derives the 3D architecture of plant roots from a few images.
Climate change and evolving land management practices are reshaping soil–plant interactions critical for sustainable viticulture. These interactions are driven by soil texture, hydrogeochemical gradients, and climatic conditions, influencing grapevine traits like nutrient and water content. Integrating innovative methods, this study explores the relationship between soil variability and grapevine characteristics in the Médoc wine region, France. The research combines hyperspectral imaging, electromagnetic induction (EMI), and electrical resistivity tomography (ERT) with traditional soil and leaf sampling. Hyperspectral data, using visible-near infrared (VNIR) wavelengths, reliably estimated leaf traits such as nitrogen and water content, yielding strong predictive relationships (R2 up to 0.8). These findings suggest VNIR-based indices are cost-effective for monitoring grapevine physiology. Geophysical data revealed significant soil textural gradients, delineating sand, transitional (loam, sandy loam), and clay textural soil classes. Apparent electrical conductivity (ECa) and inverted electrical conductivity (EC) correlated with soil texture and grapevine traits, particularly at depths around 50 cm, aligning with primary root zones. However, interannual variability in correlations emphasised the influence of weather conditions and phenological stages, highlighting the need to align data acquisition with vine growth phases. The integration of hyperspectral imaging and geophysical methods provides a novel framework for linking soil and plant parameters. This interdisciplinary approach enhances the spatial resolution and scalability of vineyard monitoring, offering actionable insights for precision viticulture. Future work should expand datasets and refine predictive models to improve the understanding of soil–plant dynamics under changing environmental conditions. These findings underscore the potential of combining hyperspectral and geophysical data to develop climate-resilient vineyard management strategies, advancing precision agriculture, and sustainable viticulture practices.
Why it matches plant phenotyping methodsハイパースペクトル画像によりブドウ葉の窒素・水分などの植物形質を推定し、予測性能を評価している。土壌調査も含むが、植物形質の取得・推定手法が主要な技術的貢献である。
abstractHyperspectral data, using visible-near infrared (VNIR) wavelengths, reliably estimated leaf traits such as nitrogen and water content, yielding strong predictive relationships (R2 up to 0.8).
Water deficit during the early development of cowpea (Vigna unguiculata (L.) Walp.) can compromise seedling establishment and reduce crop uniformity. This study aimed to evaluate morphological responses and biomass allocation in eight cowpea genotypes, including four commercial cultivars and four landraces, under two water conditions (control and deficit). A randomized block design was applied in a 2 × 8 factorial scheme. Morphological traits of roots and shoots, including length, surface area, volume, and diameter, were measured using image-based analysis. Dry biomass and root-to-shoot ratio were determined through gravimetric methods. Significant genotype-by-environment interactions were observed. Commercial cultivars tended to maintain structural attributes such as stem and root diameter, while landraces, particularly “Marronzinha” and “Verdinha”, exhibited greater plasticity in root morphology and biomass accumulation under water restriction. Although the methodology allowed efficient early phenotyping, limitations such as the short stress duration and use of two-dimensional imaging may restrict broader inferences. Future studies should incorporate extended drought periods, field validation, and physiological assessments to enhance the identification of drought-resilient genotypes.
Why it matches plant phenotyping methods画像解析による根・シュート形態形質の抽出を中心に、乾燥耐性フェノタイピングへ適用した研究であり、単なる生物学的測定にとどまらない。
titleImage-based assessment of morphological responses and biomass allocation in cowpea seedlings: A methodological approach to drought resilience phenotyping
Plant 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
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.
Brassica vegetablesLettuceRadishMicroscopyMultimodalRootVisualization / data management
Microfibers (MFs), primarily originating from sewage sludge and laundry effluents, are the most prevalent form of microplastics (MPs) in agricultural soils. While their ecological effects have been explored, the visualization, crop-level accumulation, and potential transport mechanisms of MFs within soil-plant systems remain poorly understood. This study combines 1,3,6,8-pyrene tetrasulfonic acid (PTSA) fluorescent staining with a sequential multimodal microscopy workflow to effectively track the distribution, adsorption, accumulation, and uptake of MFs under realistic soil cultivation conditions. Three edible vegetables-lettuce, Chinese cabbage, and cherry radish-were used to evaluate species-specific response patterns. The results revealed clear differences in MF interactions across species: lettuce exhibited strong MF adsorption on root surfaces and subsequent penetration via crack-entry and apoplastic pathways without entering cells. In contrast, Chinese cabbage and cherry radish showed limited MF adsorption and no uptake. These patterns were associated with root permeability and antioxidative capacities, indicating that plant functional traits play a critical role in determining the transport capacity of MPs. Beyond introducing a novel method for MF visualization in complex terrestrial matrices, this study provides new insights into the risks posed by MFs to soil-plant systems. The findings also highlight potential threats to food safety and underscore the need to establish plant-specific thresholds and pollution mitigation strategies to support sustainable agriculture and protect public health.
Why it matches plant phenotyping methods植物体内のマイクロファイバー分布・吸着・蓄積・取り込みを可視化する新規蛍光染色・マルチモーダル顕微鏡ワークフローが研究の中心であり、植物状態の測定法として該当する。
abstractThis study combines 1,3,6,8-pyrene tetrasulfonic acid (PTSA) fluorescent staining with a sequential multimodal microscopy workflow to effectively track the distribution, adsorption, accumulation, and uptake of MFs under realistic soil cultivation conditions.
Abstract Background Drought stress can significantly impede plant productivity, adversely impacting crop yields. The root system is an important plant organ contributing to drought resistance mechanisms. Therefore, assessing root systems under drought stress conditions can provide insights to identify root traits associated with enhanced drought resistance. When seeking dense and high-quality root data, root phenotyping can be complex, costly, and time-consuming. The objectives of this study were to establish a method to grow chili pepper plants in a soil-based rhizobox container under water deficit conditions and compare two methods for collecting two-dimensional root trait data from their roots. Method We grew two chile peppers ( Capsicum annuum ) accessions in soil-based rhizobox containers to analyze the responses of root architecture traits under well-watered and water-deficit conditions during the vegetative stage. The root traits were phenotyped using two different methods. The first method involved non-destructive in-box imaging of roots in situ through acrylic glass while the plant grew. The second method involved scanning destructively harvested and washed roots—the gold standard for root measurements. For the first method, we developed a pipeline for rhizobox studies to demonstrate the response of root system architecture to water deficit over time and assessed the quality of non-destructive in-box imaging methods as compared to scans of destructively harvested and washed roots. We used a relatively large rhizobox (53.34 cm in width x 78.73 cm in height) into which we established and maintained well-watered and water deficit conditions based on the field capacity and permanent wilting point of the soil (Bodner et al. 2017; Cassel & Nielsen 1986).Our in-box root imaging pipeline captures high-resolution root images with an affordable camera that can achieve a maximum resolution of 9152 x 6944 pixels, as well as high-quality root segmentation using a robust graphical user interface-based software called RootPainter (Smith et al. 2022). Results Root growth decreased under water deficit compared to well-watered conditions. There were strong positive relationships between total root length using the washed scanned method and the in-box imaging method. The same was observed for root perimeter and most of the total root length distinct root diameter classes, but not for average root diameter. Some of these relationships weakened under water deficit conditions. In addition, we also found a strong relationship between root biomass and total root length using both phenotyping methods. Conclusion Overall, we developed a rhizobox pipeline for phenotyping the root system architecture of chile pepper plants under both well-watered and water-deficit conditions. We showed that measurements taken via non-destructive in-box imaging strongly predict those taken directly on washed scanned roots, with the added benefit of allowing repeated measurements over time.
Why it matches plant phenotyping methods根系表現型取得のためのrhizobox画像化パイプラインを開発し、非破壊画像法を洗浄根スキャン法と比較検証しており、フェノタイピング手法が研究の中心である。
abstractThe objectives of this study were to establish a method to grow chili pepper plants in a soil-based rhizobox container under water deficit conditions and compare two methods for collecting two-dimensional root trait data from their roots.
WheatMicroscopyCell / cellular structureRootTissueMorphology / geometry measurementRoot system architecture
The anatomy or the arrangement of cells often determines the organization and function of plant tissues. However, current methods in large-scale imaging and accurate quantification of anatomical traits face major limitations. To address these challenges, we introduce the AnatomyArray system, an integrated platform for multiplexed tissue sectioning and anatomical phenotyping in plants. This system includes a highly adaptable device for high-throughput paraffin sectioning and multichannel slide imaging of various plant tissues, along with AnatomyNet, a deep learning tool for analyzing tissue-scale patterns of cell arrangement and morphology. AnatomyNet delivers accurate, automated quantification of anatomical traits at both the tissue and cellular levels, outperforming existing tools in image analysis. Using the AnatomyArray system, we dissected the genetic basis of root anatomy in a diverse wheat (Triticum aestivum L.) population through anatomics-based genome-wide association studies. Among the candidate genes identified, SQUAMOSA PROMOTER BINDING PROTEIN-LIKE 14 (TaSPL14) was associated with stele and pericycle size in roots. Analysis of Taspl14 mutants confirmed that TaSPL14 plays a critical role in regulating root growth and tissue size by influencing phytohormone pathways. The AnatomyArray platform enables high-throughput characterization of cellular-level features and provides insights into the mechanisms shaping anatomical structure in plants.
Why it matches plant phenotyping methods植物組織の高スループット画像取得と、細胞・組織形態の自動定量を中核とするプラットフォームおよび解析ツールを開発しているため。
abstractwe introduce the AnatomyArray system, an integrated platform for multiplexed tissue sectioning and anatomical phenotyping in plants.
Summary To enable sensitive in vivo monitoring of the glucose transport and metabolism, we developed a series of ultrasensitive and ratiometric genetically encoded nanosensors by inserting a Matryoshka dual fluorophore cassette consisting of cpsfGFP and LSSmApple into the glucose binding protein ttGBP from Thermus thermophilus . The initial MGlucoMeter1.0 was subjected to an alanine scan of the hinge region producing more sensitive MGlucoMeter2.6 with a glucose-induced ΔF/F 0 change of 3.0, an affinity for glucose of 15 µM, and an approximate detection range of 1.1-216 µM. To generate variants suitable for in vivo measurements, a series of affinity mutants was generated by mutating two histidines predicted to be involved in substrate binding. MGlucoMeter2.6-353n, MGlucoMeter2.6-15µ, MGlucoMeter2.6-700µ, MGlucoMeter2.6-1m, and MGlucoMeter2.6-7m cover a detection range between ∼40 nM - 55 mM. When expressed from a ubiquitous promoter in the cytosol of the Arabidopsis gene silencing mutant rdr6 , MGlucoMeter2.6-1m reports time- and concentration-dependent accumulation of glucose in seedling roots. The sensor also detects rapid hydrolysis of shoot-derived sucrose in the root tip.
Why it matches plant phenotyping methods植物内グルコース動態を測定する遺伝子 encoded センサーの開発・感度評価・シロイヌナズナ根での実証が中心であり、植物の生理状態を取得するフェノタイピング手法に該当する。
titleEvidence for rapid hydrolysis of shoot-derived sucrose using an ultrasensitive ratiometric Matryoshka-type MGlucoMeter sensor
Cell / cellular structureRootMorphology / geometry measurementPhysiological trait estimationGrowth / development / phenologyRoot system architecture
Sustainable phosphorus fertilization is a growing challenge in agriculture. Phosphorus is necessary for plant growth, but it is typically only bioavailable in its orthophosphate form. Phosphate fertilizers contribute to environmental damage as they leach into aquatic ecosystems. Therefore, it is imperative to develop new fertilization techniques such as controlled-release small-scale phosphate fertilizers. However, iteratively optimizing various new fertilizers using a comparable method is difficult. Here, we use three-dimensional bioprinting as a high-throughput screening platform to evaluate cellular phosphate uptake of various phosphate sources, including triple super phosphate, diammonium phosphate and struvite, which are composed of different chemistries and scales. As a result, we identified ideal phosphate fertilizer sources for the development of controlled-release phosphate fertilizers. Then, we evaluated whether plant growth and root architecture responded differently to the ideal controlled-release fertilizers. This study demonstrates the utility of this screening platform in developing a controlled-release phosphate fertilizer that effectively provides phosphate to plants at the microparticle scale.
Why it matches plant phenotyping methods3Dバイオプリンティングを用いた高スループットの植物リン吸収評価プラットフォームが研究の中心であり、植物のリン吸収および根系構造を測定しているため、単なる肥料試験を超える方法適用に該当する。
abstractHere, we use three-dimensional bioprinting as a high-throughput screening platform to evaluate cellular phosphate uptake of various phosphate sources
RiceGrowth chamberRootStress / disease detectionRoot system architectureStress response / tolerance
Background Drought is a global challenge that severely restricts crop yields and threatens food security. Plants respond to drought stress by modulating gene expression before visible phenotypic changes occur. However, most studies of drought resistance have examined phenotypes after drought treatment, with little emphasis on how severely the plants were perceiving drought-stress conditions before the appearance of stress symptoms. We therefore developed drought-stress biomarkers (DSBMs) to detect drought-stress perception levels based on gene expression profiles by performing time-series transcriptome analysis and phenotypic analysis of rice (Oryza sativa) under drought conditions in the growth chamber. Results Time-series RNA-seq of the drought-susceptible rice cultivar IR64 revealed drastic changes in the transcriptome after 4-6 days of drought treatment in plants grown in pot culture mimicking drought conditions in the field, particularly for genes related to photosynthesis. Among the differentially expressed genes, we selected 23 DSBM genes that consistently responded to drought stress. Rehydration immediately reset the changes in expression of these DSBM genes, indicating that their expression changes reflect current drought-stress perception levels, but not stress memories. Responses of DSBM genes tended to be conserved among rice accessions, irrespective of the rice subpopulation (such as indica, aus, and japonica). We developed a machine learning model using the expression levels of DSBM genes trained by the time-series RNA-seq data for IR64. This model successfully predicted the drought-stress perception levels of various rice accessions, representing the probability of exposure to drought treatment, with an accuracy of 75%. Extreme root architecture traits, such as the largest root surface area, narrowest crown root diameter, and largest ratio of deep rooting, influenced the predicted drought-stress perception levels. Conclusion We identified DSBM genes and developed a machine learning model as a robust tool for assessing drought-stress perception levels in rice. Monitoring and predicting drought-stress perception levels should contribute to more efficient crop management and breeding schemes. Furthermore, our dataset would serve as a resource for further understanding the mechanisms of drought resistance in rice.
Why it matches plant phenotyping methodsイネの乾燥ストレス知覚レベルという植物状態を、遺伝子発現バイオマーカーと機械学習で推定する手法を開発し、複数アクセッションで検証しているため、方法論が中心である。
abstractWe therefore developed drought-stress biomarkers (DSBMs) to detect drought-stress perception levels based on gene expression profiles
Plant phenotyping relevance match · UnverifiedOpenAlex · Europe PMC · checked 6 Sept 2026
Abstract 3D models are used in plant phenotyping for non-destructive quantification and analysis of morphological characteristics. Analyzing plant structure allows breeders to select for desirable traits, associated with e.g. drought tolerance or increased productivity. In sugar beet, morphological parameters depict an essential element of the variety approval for distinguishing between genotypes. However, only a limited number of measured or scored parameters are considered at a single time point. This study aims to disclose the benefit of incorporating dynamic spatio-temporal development of 3D parameters for automated crop genotype differentiation. A greenhouse experiment was conducted covering twelve sugar beet genotypes. High-resolution 3D models were generated twice a week over the course of two months and both common and novel 3D morphological parameters were extracted. The importance of these parameters was assessed over time, and the dataset was analyzed using unsupervised pointwise clustering and time series clustering. Varying importance of parameters depending on the time point and the noticeable higher importance of plant parameters compared to leaf parameters are demonstrated by our results. Moreover, increased and more stable genotype differentiation is archived using time series clustering compared to pointwise clustering. Furthermore, taproot formation of sugar beet was found to have a crucial impact on morphological development. Substantial genotypic variations in the dynamic development of 3D morphological parameters could be demonstrated. The higher and more stable clustering performance using time series analysis underlines the importance of 4D data for plant genotype differentiation. Future work should focus on identifying important growth stages for data collection.
Why it matches plant phenotyping methods3Dモデルを用いた時系列植物形態計測、形態パラメータ抽出、クラスタリングによる遺伝型識別が研究の中心であり、4Dフェノタイピング手法の実質的な応用・評価に該当する。
abstractHigh-resolution 3D models were generated twice a week over the course of two months and both common and novel 3D morphological parameters were extracted.
Plant phenotyping relevance match · UnverifiedOpenAlex · Europe PMC · bioRxiv · Crossref · checked 7 Sept 2026
Abstract Plant phenotyping systematically quantifies plant traits such as growth, morphology, physiology, or yield, assessing genetic and environmental influences on plant performance. The integration of advanced phenotyping technologies, including imaging sensors and data analytics, facilitates the non-destructive and longitudinal acquisition of high-throughput data. Nevertheless, the sheer volume of such phenotyping data introduces significant challenges for researchers, particularly related to data processing. To overcome these challenges, researchers are turning to artificial intelligence (AI), a tool that can autonomously process and learn from large amounts of data. Despite this advantage, accurate image segmentation remains a key hurdle due to the complexity of plant morphology and environmental noise. In this study, we present the Botanical Spectrum Analyser (BSA), a user-friendly graphical user interface (GUI) that integrates a modified U-Net deep neural network for plant image segmentation. Designed for accessibility, BSA enables non-technical users to apply advanced AI segmentation to RGB and hyperspectral (VNIR and SWIR) imagery. We evaluated BSA’s performance across three case studies involving wheat, barley, and Arabidopsis, demonstrating its robustness across species and imaging modalities. Our results show that BSA achieves an average accuracy of 99.7%, with F1-scores consistently exceeding 98% and strong Jaccard and recall performance across datasets. For challenging root segmentation tasks, BSA outperformed commercial algorithms, achieving a 76% F1-score compared to 24%, representing a 50% improvement. These results highlight the adaptability of the BSA framework for diverse phenotyping scenarios, bridging the gap between advanced deep learning methods and accessible plant science applications.
Why it matches plant phenotyping methods植物画像からの表現型抽出を目的とするGUI・深層学習セグメンテーション手法を開発し、複数種・画像モダリティで性能評価しているため、方法が中心的です。
abstractwe present the Botanical Spectrum Analyser (BSA), a user-friendly graphical user interface (GUI) that integrates a modified U-Net deep neural network for plant image segmentation.
Plant phenotyping relevance match · UnverifiedOpenAlex · Europe PMC · bioRxiv · checked 6 Sept 2026
Summary The ongoing evolutionary arms race between crop plants and their parasites necessitates a constant exploration of new genetic resistance. Broomrape ( Orobanche cumana ), a devastating parasitic plant, presents a formidable challenge to sunflower production, yet the genetic mechanisms underlying host resistance are still largely unknown. To address this gap, we developed a high-throughput phenotyping platform to quantify root infestation in a highly diverse sunflower association mapping (SAM) population. Using a dual GWAS approach with both SNPs and k-mers, we were able to pinpoint the genetic basis of resistance. Our findings validate previously identified QTLs with greater resolution and reveal several novel candidate genes conferring resistance, including putative leucine-rich repeat receptor kinases. Critically, the k-mer mapping approach circumvented reference genome bias, highlighting key introgressions from wild Helianthus species that have contributed to broomrape resistance. This research provides a powerful methodology for gene discovery and demonstrates that wild relatives remain a vital source of genetic material, offering breeders a significant advantage in the ongoing battle against rapidly evolving parasites.
Why it matches plant phenotyping methodsヒマワリの根への寄生侵入を定量するハイスループット表現型計測プラットフォームの開発と適用が研究の中心であり、植物病害状態の測定法に該当する。
abstractwe developed a high-throughput phenotyping platform to quantify root infestation
Leaves are the key organs in photosynthesis and nutrient production, and leaf counting is an important indicator of banana plant health and growth rate. However, in complex orchard environments, leaves often overlap, the background is cluttered, and illumination varies, making accurate segmentation and detection challenging. To address these issues, we propose a lightweight banana leaf detection and counting method deployable on embedded devices, which integrates a space–depth-collaborative reasoning strategy with multi-scale feature enhancement to achieve efficient and precise leaf identification and counting. For complex background interference and occlusion, we design a multi-scale attention guided feature enhancement mechanism that employs a Mixed Local Channel Attention (MLCA) module and a Self-Ensembling Attention Mechanism (SEAM) to strengthen local salient feature representation, suppress background noise, and improve discriminability under occlusion. To mitigate feature drift caused by environmental changes, we introduce a task-aware dynamic scale adaptive detection head (DyHead) combined with multi-rate depthwise separable dilated convolutions (DWR_Conv) to enhance multi-scale contextual awareness and adaptive feature recognition. Furthermore, to tackle instance differentiation and counting under occlusion and overlap, we develop a detection-guided space–depth position modeling method that, based on object detection, effectively models the distribution of occluded instances through space–depth feature description, outlier removal, and adaptive clustering analysis. Experimental results demonstrate that our YOLOv8n MDSD model outperforms the baseline by 2.08% in mAP50-95, and achieves a mean absolute error (MAE) of 0.67 and a root mean square error (RMSE) of 1.01 in leaf counting, exhibiting excellent accuracy and robustness for automated banana leaf statistics.
Why it matches plant phenotyping methodsバナナ葉の検出・計数という植物形態・生育指標を対象に、複雑環境での画像解析手法を開発し、精度評価しているため、植物フェノタイピング手法が中心である。
abstractwe propose a lightweight banana leaf detection and counting method deployable on embedded devices
Floral area is a major predictor of the attractiveness of a flowering plant for pollinators, yet the measurement of floral area is time-consuming and inconsistent across studies. Here, we developed an AI-powered algorithm, FloralArea, to automate floral area measurement from an image. The FloralArea algorithm has two main components: an object segmentation module and an area estimation module. The object segmentation module extracts the pixels of flowers and the reference object in an image. The area estimation module predicts floral area based on the ratio between flower and reference object pixels. We fine-tuned two YOLOv8 segmentation models for flower and reference object segmentation. The flower segmentation model achieved moderate precision, recall, mAP0.5, and mAP0.5-0.95 of 0.794, 0.68, 0.741, and 0.455 on the test dataset, while the reference object model achieved an impressive performance of 0.907, 0.940, 0.933, and 0.832. We evaluated FloralArea using 75 images of flowering plants. We used ImageJ to calculate the actual floral area for all the images and compared them with the predicted floral area from FloralArea. The predicted floral area correlated well with the measured floral area with a coefficient of determination (R 2 ) of 0.93 and a root mean square error of 20.58 cm 2 . The FloralArea algorithm reduced the time it takes to calculate floral area from an image by 99.24% compared with traditional methods with image processing tools like ImageJ. By streamlining floral area estimation, the FloralArea algorithm provides a scalable, efficient, consistent, and accessible tool for researchers, particularly to aid in assessing plant attractiveness to different pollinator groups.
Why it matches plant phenotyping methods花画像から花の面積という植物形質を自動抽出するAI手法を開発し、実測値との比較検証と処理時間評価を行っており、植物フェノタイピング手法が研究の中心です。
abstractHere, we developed an AI-powered algorithm, FloralArea, to automate floral area measurement from an image.
Reproduction assets foundThe paper's authors publicly released the FloralArea source code on GitHub and the flower image dataset (used for fine-tuning YOLOv8 models and evaluating the algorithm) on Penn State's ScholarSphere repository, as stated in the Data Availability statement.Code · publicThe source code for the FloralArea algorithm is available on GitHub ( https://github.com/eai6/FloralArea_Web.git ).Open asset ↗GitHub · eai6/FloralArea_Weblines:137-148Dataset · publicThe image dataset used to fine-tune the YOLOv8 models and evaluate the FloralArea algorithm is on the ScholarSphere repository of the Pennsylvania State University ( https://scholarsphere.psu.edu/resources/33452dff-b807-44b0-8783-71c8c47b5242 ).Open asset ↗ScholarSphere · 33452dff-b807-44b0-8783-71c8c47b5242lines:137-148Plant phenotyping relevance match · UnverifiedOpenAlex · Crossref · Europe PMC · checked 6 Sept 2026
Accurate acquisition of tobacco phenotypic traits is crucial for growth monitoring, cultivar selection, and other scientific management practices. Traditional manual measurements are time-consuming and labor-intensive, making them unsuitable for large-scale, high-throughput field phenotyping. The integration of 3D reconstruction and stem-leaf segmentation techniques offers an effective approach for crop phenotypic data acquisition. In this study, we propose a tobacco phenotyping method that combines unmanned aerial vehicle (UAV) remote sensing with an improved PointNet++ model. First, a 3D point-cloud dataset of field-grown tobacco plants was generated using multi-view UAV imagery. Next, the PointNet++ architecture was enhanced by incorporating a Local Spatial Encoding (LSE) module and a Density-Aware Pooling (DAP) module to improve the accuracy of stem and leaf segmentation. Finally, based on the segmentation results, an automated pipeline was developed to compute key phenotypic traits, including plant height, leaf length, leaf width, leaf number, and internode length. Experimental results demonstrated that the improved PointNet++ model achieved an overall accuracy (OA) of 95.25% and a mean intersection over union (mIoU) of 93.97% for tobacco plant segmentation-improvements of 5.12% and 5.55%, respectively, over the original PointNet++ model. Moreover, using the segmentation results from the improved PointNet++ model, the predicted phenotypic values exhibited strong agreement with ground-truth measurements, with coefficients of determination (R²) ranging from 0.86 to 0.95 and root mean square errors (RMSE) between 0.31 and 2.27 cm. This study provides a technical foundation for high-throughput phenotyping of tobacco and presents a transferable framework for phenotypic analysis in other crops.
Why it matches plant phenotyping methodsUAV 3D点群、改良PointNet++による茎葉分割と形質推定パイプラインが研究の中心であり、複数のタバコ形質を自動取得・検証している。
abstractIn this study, we propose a tobacco phenotyping method that combines unmanned aerial vehicle (UAV) remote sensing with an improved PointNet++ model.
WheatRootMorphology / geometry measurementPhysiological trait estimationRoot system architecture
Root anatomical phenotyping has become a demonstrably essential part of investigating root physiology and in acquiring a holistic understanding of plant development. However, accessible high throughput methods for root anatomical analysis are still lacking. Here, we present the Rapid Anatomics Tool (RAT), a novel, low-cost system for high throughput root anatomical imaging with a shallow learning curve for obtaining high quality images suitable for comparative analysis across a number of plant species. Its efficiency comes from combining blockface-like imaging and stain-free imaging using near-ultraviolet (nUV) autofluorescence utilising a combination of low-cost commercial equipment, readily available mechanical components, and custom designed and 3D printed tools. Using this system, we investigated the anatomy of mature tissue along the axis of wheat crown roots, revealing a tendency of reduction in vascular complexity (expressed through a reduction in metaxylem number, area, and mean area per metaxylem file) from the basal to the distal region of the root. This study highlights the importance of thorough sampling strategies for investigating root anatomy in relation to organ function and introduces an accessible, relatively high-throughput method to support such research.
Why it matches plant phenotyping methods根の解剖形質を高スループットに画像取得する低コスト手法を開発し、植物種間比較に利用できるシステムとして提示しているため、植物フェノタイピング手法が中心である。
abstractHere, we present the Rapid Anatomics Tool (RAT), a novel, low-cost system for high throughput root anatomical imaging
MaizeMicroscopyRootTissueMorphology / geometry measurementRoot system architecture
Root anatomy plays a critical structural and functional role in the maize root system, and regulates edaphic stress tolerance. The function and genetic basis of several maize root anatomical traits for stress tolerance have been demonstrated. Leveraging root anatomical traits in maize thus holds great potential for developing cultivars with greater nutrient and water efficiency. Key for such approaches is the ability to characterize the root anatomy of plants of interest. Here, we outline a systematic method for preparing, imaging, and analyzing maize root cross-sections. The protocol describes root sectioning (by hand or using a vibratome), preparation of microscope slides and toluidine blue staining, imaging under a light microscope, and both manual and semiautomated methods for anatomical feature extraction from images. The protocol enables the visualization and quantification of various anatomical tissues and traits, and its simplicity, adaptability, and accessibility make it an ideal choice for both small- and large-scale phenotyping studies in maize and other plant species. This standardized protocol provides researchers with a comprehensive methodology to accurately dissect root structures, enabling in-depth analyses that are essential for understanding plant growth, development, and adaptive value for stress tolerance.
Why it matches plant phenotyping methodsトウモロコシ根の切片作製、顕微鏡画像化、画像からの解剖学的形質抽出を体系化したプロトコルであり、植物フェノタイピング手法が中心である。
abstractHere, we outline a systematic method for preparing, imaging, and analyzing maize root cross-sections.
MaizeRGB / grayscaleRootMorphology / geometry measurementRoot system architecture
The study of corn root morphology is critical for understanding root architecture, which directly influences water and nutrient uptake, plant stability, and overall yield performance. It also plays a crucial role in advancing crop breeding programs. Traditional methods of analyzing root morphology are often labor-intensive, time-consuming, and subject to variability. This research introduces a deep learning (DL)-based approach for the automated and precise extraction of morphological features from monochrome images of corn roots. While DL methods have been widely applied to various agricultural problems such as yield estimation, cultivar classification, and disease detection, its application to plant features, particularly root traits, remains limited. In this study, three DL architectures- EfficientNet_B0, DenseNet_121, and ResNet_50- were used to extract and predict 12 morphological features from both raw and background-subtracted side-view images of corn roots. The results showed that all three architectures performed similarly, with DenseNet_121 slightly outperforming the others in terms of coefficient of determination and normalized root mean square error (NRMSE) metrics for background-subtracted images (mean R² 0.9199 and mean NRMSE 0.0444), while EfficientNet_B0 showed superior performance with raw images (mean R² 0.9057 and mean NRMSE 0.0480). Importantly, no significant difference in architecture performance was observed between raw and background-subtracted images. The study shows the potential of end-to-end learning by providing a robust, automated tool for plant morphological feature extraction.
Why it matches plant phenotyping methodsトウモロコシ根画像から12種類の形態形質を自動抽出・予測する深層学習手法を開発し、複数モデルの性能を比較検証しており、植物フェノタイピング手法が研究の中心である。
abstractThis research introduces a deep learning (DL)-based approach for the automated and precise extraction of morphological features from monochrome images of corn roots.
Accurate quantification of open bolls and their distribution is crucial for understanding cotton growth, development, and yield in optimized crop management and enhanced plant breeding. Manual boll counting methods are time-consuming, labor-intensive, and subjective. Leveraging the potential of high-resolution images for high-throughput phenotyping offers a promising avenue for efficient trait quantification. The objectives of this study were to develop methods to detect and count open cotton bolls using LiDAR point cloud and RGB images and to compare the effectiveness of these two data sources. A DJI Phantom 4 RTK Unmanned Aerial System (UAS) equipped with a 4 K RGB camera was used to acquire high-resolution RGB images, and a DJI Matrice 300 RTK with a Zenmuse L1 sensor was used to acquire LiDAR point cloud data. The RGB images were converted to point cloud using photogrammetry by measuring multiple points of overlapping images. The boll detection workflow involved data filtering and clustering using the density-based spatial clustering of applications with noise (DBSCAN) method. Evaluation of the methods involved 48 plots representing small, medium, and large plant sizes using metrics including mean absolute percentage error (MAPE), root mean square error (RMSE), and coefficient of determination (r²). The methods using both data sources performed well in estimating open bolls, with LiDAR point cloud data slightly outperforming those derived from RGB images. Generally, the performance of the DBSCAN method in boll detection improved with decreasing plant sizes. Specifically, LiDAR data yielded MAPE values of 5.03 %, 8.05 %, and 13.46 %, RMSE values of 7.26, 14.33, and 23.40 bolls per m², and r² values of 0.93, 0.84, and 0.84 for small, medium, and large plant sizes, respectively. RGB image-based data exhibited MAPE values of 7.21 %, 6.49 %, and 16.41 %, RMSE values of 11.05, 13.66, and 26.49 bolls per m², and r² values of 0.82, 0.74, and 0.83 for small, medium, and large plant sizes, respectively. The method demonstrates the potential of RGB imagery and LiDAR data for estimating boll counts, offering valuable tools for enhanced plant phenotyping in plant breeding and site-specific crop management. Both data sources underestimated boll counts, with smaller plants showing less undercounting, likely due to improved light penetration and separation of bolls. These findings highlight the influence of plant structure on boll detection accuracy and the need to address challenges posed by dense canopies to enhance detection reliability.
Why it matches plant phenotyping methodsLiDARとRGB画像を用いた綿花の開花ボール数の検出・計数手法を開発し、比較評価した研究であり、植物表現型取得が中心です。
abstractThe objectives of this study were to develop methods to detect and count open cotton bolls using LiDAR point cloud and RGB images and to compare the effectiveness of these two data sources.
Understanding the genetic basis of root system architecture (RSA) in crops requires innovative approaches that enable both high-throughput and precise phenotyping in field conditions. In this study, we evaluated multiple phenotyping and analytical frameworks for quantifying RSA in mature, field-grown maize in three field experiments. We used forward and reverse genetic approaches to evaluate >1700 maize root crowns, including a diversity panel, a biparental mapping population, and maize mutant and wild-type alleles at two known RSA genes, DEEPER ROOTING 1 (DRO1) and Rootless1 (Rt1). We show the utility of increasing the dimensionality of traditional two-dimensional (2D) techniques, referred to as the "2D multi-view" method, to improve the capture of whole root system information for mapping genetic variation influencing RSA. Comparison of univariate and multivariate genome-wide association study (GWAS) approaches revealed that multivariate traits were effective at dissecting complex RSA phenotypes and identifying pleiotropic quantitative trait loci (QTLs). Overall, three-dimensional (3D) root models generated from X-ray computed tomography and digital phenotyping captured a larger proportion of RSA trait variations compared to other methods of root phenotyping, as evidenced by both genome-wide and single-gene analyses. Among the individual root traits, root pulling force emerged as a highly heritable estimate of RSA that identified the largest number of shared QTLs with 3D phenotypes. Our study shows that integrating complementary phenotyping technologies helps to provide a more comprehensive understanding of the genetic architecture of RSA in field-grown maize.
Why it matches plant phenotyping methods根系構造を定量化する複数の表現型解析法を比較・評価し、2Dマルチビュー、X線CT、デジタル表現型などの技術性能を遺伝解析で検証しており、表現型取得法が研究の中心である。
abstractwe evaluated multiple phenotyping and analytical frameworks for quantifying RSA in mature, field-grown maize
Reproduction assets foundThe paper deposits raw phenotypic metadata (root crown/RSA measurements from the field experiments) on Dryad, and uses the authors' public 3D root crown analysis pipeline (RCAP) on GitHub for the XRT feature extraction. Both are paper-specific, public, and actionable. Generic R packages and cited prior work are not.Dataset · publicRaw phenotypic metadata are available on the Dryad Digital Repository ( https://doi.org/10.5061/dryad.z34tmpgq4 , http://datadryad.org/share/HeNYoxNMdN_GrHMyZHFN3rUTN1UiG8OFhU-B107E7mM ).Open asset ↗Dryad Digital Repository · 10.5061/dryad.z34tmpgq4lines:499-731Code · publicreferred to here as the root crown analysis pipeline (RCAP). Detailed descriptions of RCAP trait implementations and related resources are available at: https://github.com/Topp‐Roots‐Lab/3d‐root‐crown‐analysis‐pipeline/ .Open asset ↗GitHub · Topp‐Roots‐Lab/3d‐root‐crown‐analysis‐pipelinelines:162-175Plant phenotyping relevance match · UnverifiedCrossref · Europe PMC · checked 14 Sept 2026
ABSTRACT Roots are crucial for enhancing crop resilience to abiotic stresses, including drought, salinity, cold, nutrient deficiency, and metal toxicity. Root system architecture and morphological traits play a significant role in enabling plants to access water and nutrients under stress conditions. However, the study of roots is challenging due to their underground nature. Here, we review advancements in high‐throughput root phenotyping methodologies that enable the non‐destructive and large‐scale analysis of root traits in controlled conditions. These include soil‐less two‐dimensional platforms, such as hydroponics and gel‐based systems, and soil‐based systems like Rhizotrons and RhizoTubes. Additionally, cutting‐edge three‐dimensional soil‐less systems and soil‐based imaging technologies, such as x‐ray‐computed tomography and magnetic resonance imaging, have significantly improved the precision of root trait analysis. Computational tools, including machine learning algorithms, are also transforming root phenotyping by automating image segmentation, trait extraction, and data analysis. Case studies and examples described here demonstrate the successful application of these methods in identifying stress‐specific root traits that improve resilience to various abiotic stresses in monocots, dicots, and legumes. Despite these advancements, challenges such as high costs, scalability, and environmental variability persist. Integrating laboratory and field‐based phenotyping systems can address these limitations and lead the way for more effective breeding programs to improve crop resilience against climate change.
Why it matches plant phenotyping methods根系形質のハイスループット画像化・計測法と計算解析を体系的にレビューしており、植物フェノタイピング手法が中心である。
abstractHere, we review advancements in high‐throughput root phenotyping methodologies that enable the non‐destructive and large‐scale analysis of root traits in controlled conditions.
Root hair counting is a specialized aspect of plant biology and agronomy research that offers valuable insights into plant health, nutrient uptake, and overall growth potential. Root hairs are tiny extensions from the root epidermis that significantly increase the surface area and constitute roughly 70% of the total root area of a plant root system, enhancing the ability of plants to absorb water and nutrients from the soil. Understanding the importance of root hair counting involves looking at various aspects of plant physiology and soil–plant interactions. Despite these benefits, counting root hairs, especially manually, can be tedious, time-consuming, and, more often, inaccurate due to differences in the perception of individuals. Therefore, we have proposed a novel method for root hair counting and further observed an improvement in root hair count measurements when utilizing image super-resolution as a preprocessing step. Our approach of counting root hairs can tackle real-world challenges and be able to count overlapping hairs as well. By visualizing the rhizosphere in binary space, we can see a considerable increase in root hair count from 37 to 68 when counting manually to our approach for Bell pepper, and from 44 to 88, when counting manually to our method for Arabidopsis root images. To the best of the authors’ knowledge, this research study is specifically designed for root hair counting and measurement improvement using super-resolution, is the first of its kind, and has yet to be acknowledged.
Why it matches plant phenotyping methods植物の根毛数という形態形質を画像から自動計測する新規手法を開発し、超解像前処理による測定改善も検証しており、フェノタイピング手法が中心である。
titleA Novel Approach for Plant Root Hair Counting and its Improvement via Image Super-Resolution
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.
Various fusion methods of optical satellite images have been proposed for monitoring heterogeneous farmlands requiring high spatial and temporal resolution. In this study, a three-meter normalized difference vegetation index (NDVI) was generated by applying the spatiotemporal fusion (STF) method to simultaneously generate a full-length normalized difference vegetation index time series (SSFIT) and enhanced spatial and temporal adaptive reflectance fusion method (ESTARFM) to the NDVI of Sentinel-2 (S2) and PlanetScope (PS), using images from 2019 to 2021 of rice paddy and heterogeneous cabbage fields in Korea. Before fusion, S2 was processed with the maximum NDVI composite (MNC) and the spatiotemporal gap-filling technique to minimize cloud effects. The fused NDVI image had a spatial resolution similar to PS, enabling more accurate monitoring of small and heterogeneous fields. In particular, the SSFIT technique showed higher accuracy than ESTARFM, with a root mean square error of less than 0.16 and correlation of more than 0.8 compared to the PS NDVI. Additionally, SSFIT takes four seconds to process data in the field area, while ESTARFM requires a relatively long processing time of five minutes. In some images where ESTARFM was applied, outliers originating from S2 were still present, and heterogeneous NDVI distributions were also observed. This spatiotemporal fusion (STF) technique can be used to produce high-resolution NDVI images for any date during the rainy season required for time-series analysis.
Why it matches plant phenotyping methods衛星画像の時空間融合により作物圃場の高解像度NDVI時系列を生成し、精度と処理時間を比較検証しており、植物状態の取得手法が研究の中心である。
abstractIn this study, a three-meter normalized difference vegetation index (NDVI) was generated by applying the spatiotemporal fusion (STF) method
Xanthomonas oryzae pv. oryzae ( Xoo ), the causal agent of bacterial blight in rice, is primarily studied in the context of foliar infections. However, infected stubble and irrigation water may serve as reservoirs and be responsible for seedling stage root infections in the field, especially during transplanting. Here, we established a coleoptile crown root-based infection protocol to investigate whether gene-for-gene interactions between Xoo TAL effectors and SWEET sucrose uniporter susceptibility genes occur in the root xylem, and whether the disease can propagate from roots to seedling shoots. Using translational SWEET11a-GUS reporter lines under control of the native SWEET11a promoter, we observed progressive infection as indicated by accumulation of SWEET11a-GUS fusion protein in infected coleoptile crown roots. However, we did not detect progression of GUS accumulation beyond the coleoptile node, nor did we detect blight symptoms on the young leaves. Notably, the xylem, at least during early stages of infection remained functional as shown by Rhodamine B tracer, consistent with transfer of xylem constituents via living cells at the coleoptile node that did not allow bacteria to pass. Furthermore, the root infection protocol is a ∼4x faster compared to standard leaf-clipping assays (roots assay: 11 days from sowing, compared to 39 days for clip infection), enabling more rapid assessment of TAL effector repertoire and plant defense responses with translational SWEET-GUS reporter lines. Our findings expand our understanding of Xoo infection routes and provide a valuable tool for resistance testing and pathogen surveillance.
Why it matches plant phenotyping methodsイネ病害の進展・抵抗性を迅速に評価する根部感染プロトコルを確立し、従来法との速度差とレポーターによる感染進展を示しているため、病害フェノタイピング手法が中心である。
abstractHere, we established a coleoptile crown root-based infection protocol to investigate whether gene-for-gene interactions between Xoo TAL effectors and SWEET sucrose uniporter susceptibility genes occur in the root xylem, and whether the disease can propagate from roots to seedling shoots.
Reproduction assets foundThe preprint explicitly states that raw data underlying the root infection, GUS, and Rhodamine B measurements are publicly deposited at a DOI (10.60534/7s2cg-r2j06), which is an allowed URL. This is a paper-specific, publicly accessible raw data repository. No author analysis code with a public URL is stated (only use-Dataset · publicData availability:
Raw data are available at https://doi.org/10.60534/7s2cg-r2j06Open asset ↗10.60534/7s2cg-r2j06pdf-page:5 lines:1-61Plant phenotyping relevance match · UnverifiedOpenAlex · Europe PMC · checked 6 Sept 2026
RootSeed / grainMorphology / geometry measurementObject detectionSegmentationRoot system architecture
spp.), ecologically and economically significant, pose unique challenges due to their curved seedling morphology. Traditional manual measurement methods are time-consuming, prone to human error, and often lack consistency. Moreover, automated approaches remain limited and often fail to accurately process seedlings with nonlinear or curved morphologies. In this study, we introduce GLEN, a deep learning-based model for detecting germinating elm seeds and accurately estimating their lengths of germinating structures. It leverages a dual-path architecture that combines pixel-level spatial features with instance-level semantic information, enabling robust measurement of curved radicles. To support training, we construct GermElmData, a curated dataset of annotated elm seedling images, and introduce a novel synthetic data generation pipeline that produces high-fidelity, morphologically diverse germination images. This reduces the dependence on extensive manual annotations and improves model generalization. Experimental results demonstrate that GLEN achieves an estimation error on the order of millimeters, outperforming existing models. Beyond quantifying germinating elm seeds, the architectural design and data augmentation strategies in GLEN offer a scalable framework for morphological quantification in both plant phenotyping and broader biomedical imaging domains.
Why it matches plant phenotyping methods発芽エルム種子の曲がった幼根長を深層学習で推定する手法を開発し、注釈付き画像データセットと合成データ生成パイプラインも構築しているため、植物表現型取得が中心である。
abstractwe introduce GLEN, a deep learning-based model for detecting germinating elm seeds and accurately estimating their lengths of germinating structures.
RootObject detection2D/3D reconstructionSkeletonization / topologyRoot system architecture
Plant roots typically exhibit a highly complex and dense architecture, incorporating numerous slender lateral roots and branches, which significantly hinders the precise capture and modeling of the entire root system. Additionally, roots often lack sufficient texture and color information, making it difficult to identify and track root traits using visual methods. Previous research on roots has been largely confined to 2D studies; however, exploring the 3D architecture of roots is crucial in botany. Since roots grow in real 3D space, 3D phenotypic information is more critical for studying genetic traits and their impact on root development. We have introduced a 3D root skeleton extraction method that efficiently derives the 3D architecture of plant roots from a few images. This method includes the detection and matching of lateral roots, triangulation to extract the skeletal structure of lateral roots, and the integration of lateral and primary roots. We developed a highly complex root dataset and tested our method on it. The extracted 3D root skeletons showed considerable similarity to the ground truth, validating the effectiveness of the model. This method can play a significant role in automated breeding robots. Through precise 3D root structure analysis, breeding robots can better identify plant phenotypic traits, especially root structure and growth patterns, helping practitioners select seeds with superior root systems. This automated approach not only improves breeding efficiency but also reduces manual intervention, making the breeding process more intelligent and efficient, thus advancing modern agriculture.
Why it matches plant phenotyping methods植物根系の3D骨格・構造という表現型を画像から抽出する手法を開発し、データセット上で検証しており、方法が研究の中心である。
abstractWe have introduced a 3D root skeleton extraction method that efficiently derives the 3D architecture of plant roots from a few images.
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.
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 · UnverifiedEurope PMC · checked 15 Sept 2026
Field / plotRootClassificationRoot system architecture
Supporting sustainable agriculture requires a deeper understanding of belowground interactions under diversified crop mixtures. Current tools do not allow differentiation of root species without destructive sampling. This makes the study of crop mixtures and their belowground interactions laborious, leading to a reduction in the scale of research. On the basis of our in-depth review, there is an urgent need for standardized, cost-effective methods for root phenotyping, particularly under field conditions where high variability and logistical difficulties are common. Physicochemical root traits related to root function offer distinctive markers that can represent a species' identity. Processing and analyzing such a unique root data type with optimized deep learning and machine learning can lead to high-throughput root mixture analysis.
Why it matches plant phenotyping methods根のフェノタイピング手法をレビューし、根形質の標準化と機械学習によるハイスループット解析を論じる方法論的レビューである。
abstractOn the basis of our in-depth review, there is an urgent need for standardized, cost-effective methods for root phenotyping
Maize, as one of the most important crops, plays a key role in phenotypic research, which promotes the development of precision agriculture and the in-depth exploration of the gene-phenotypic association mechanism. However, traditional phenotyping methods relying on manual measurements or two-dimensional images have significant limitations in terms of efficiency and accuracy, particularly in effectively analyzing the complex three-dimensional structures of plants. To address these challenges, this paper proposes a high-precision automatic segmentation method for maize stem and leaf organs based on 3D point clouds. The method consists of the following three core modules: (1) point cloud rotation correction preprocessing using a directed bounding box; (2) coarse segmentation of the stem and leaf using a dynamic root-shoot radius adjustment strategy; (3) fine segmentation incorporating dynamic misclassification detection and re-clustering mechanisms. The proposed method was systematically evaluated on maize plant point cloud data at multiple growth stages, and compared with manually annotated results. Experimental results show that the method achieved an average precision of 0.944, average recall of 0.915, Micro-F1 score of 0.920, and average overall accuracy of 0.935, demonstrating excellent segmentation performance. Furthermore, seven key phenotypic parameters, including plant height, crown diameter, stem height, stem diameter, number of leaves, leaf length, and leaf width, were automatically extracted, with the results showing highly significant correlations with manual measurements. This study provides effective technical support for high-precision 3D segmentation of maize stem and leaf organs and automated phenotypic analysis, laying a solid foundation for high-throughput plant phenotyping research and 3D reconstruction applications.
Why it matches plant phenotyping methodsトウモロコシの3D点群から茎葉を自動分割し、複数の表現型形質を抽出する手法の開発と手動測定との検証が研究の中心である。
abstractthis paper proposes a high-precision automatic segmentation method for maize stem and leaf organs based on 3D point clouds.
Plant phenotyping relevance match · UnverifiedCrossref · OpenAlex · Europe PMC · checked 15 Sept 2026
Biomass estimation of oilseed rape is crucial for optimizing crop productivity and breeding strategies. While UAV-based imaging has advanced high-throughput phenotyping, current methods often rely on orthophoto images, which struggle with overlapping leaves and incomplete structural information in complex field environments. This study integrates 3D Gaussian Splatting (3DGS) with the Segment Anything Model (SAM) for precise 3D reconstruction and biomass estimation of oilseed rape. UAV multi-view oblique images from 36 angles were used to perform 3D reconstruction, with the SAM module enhancing point cloud segmentation. The segmented point clouds were then converted into point cloud volumes, which were fitted to ground-measured biomass using linear regression. The results showed that 3DGS (7 k and 30 k iterations) provided high accuracy, with peak signal-to-noise ratios (PSNR) of 27.43 and 29.53 and training times of 7 and 49 min, respectively. This performance exceeded that of structure from motion (SfM) and mipmap Neural Radiance Fields (Mip-NeRF), demonstrating superior efficiency. The SAM module achieved high segmentation accuracy, with a mean intersection over union (mIoU) of 0.961 and an F1-score of 0.980. Additionally, a comparison of biomass extraction models found the point cloud volume model to be the most accurate, with an determination coefficient (R²) of 0.976, root mean square error (RMSE) of 2.92 g/plant, and mean absolute percentage error (MAPE) of 6.81 %, outperforming both the plot crop volume and individual crop volume models. This study highlights the potential of combining 3DGS with multi-view UAV imaging for improved biomass phenotyping.
Why it matches plant phenotyping methodsUAV多視点画像、3D再構成、SAMによる分割、体積からのバイオマス推定を統合・比較検証した、植物表現型取得法が研究の中心である。
abstractThis study integrates 3D Gaussian Splatting (3DGS) with the Segment Anything Model (SAM) for precise 3D reconstruction and biomass estimation of oilseed rape.
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
WheatField / plotRootMorphology / geometry measurementPhysiological trait estimationRoot system architectureStress response / toleranceWater status / transpiration
Abstract Background and Aims Deep roots may help plants adapt to climate change by allowing them to access deeper soil layers where water is still available, reducing water stress and increasing nitrogen (N) uptake. Water stress significantly affects yield during later developmental stages, but methods are lacking for phenotyping for deep rooting under field conditions and at maturity. Methods Over 3 years, we used minirhizotron root imaging in the RadiMax semi-field facility to compare deep rooting in winter wheat genotypes grown in field soil to 2.7 m depth. We related this to deep soil uptake of water and N using isotopic tracers injected into the soil at 1.6–1.8 m depth. Carbon isotope discrimination was used to evaluate water stress levels. Key Results Deep rooting was positively correlated with uptake of deep-placed N and water, and uptake of deep-placed N was three times higher in the genotype with deepest roots compared with the shallowest. Deep rooting was negatively correlated with water stress, measured using carbon isotope discrimination. This correlation was strongest in 2023, a dry year, highlighting the role of deep roots in mitigating water stress. Some genotypes had consistently deeper or shallower roots over the three experimental years, and there were strong correlations of isotopic measurements between genotypes across years. Conclusions Our findings show strong relationships between deep rooting and deep root functions, which indicate that deep rooting is a desirable trait that should be targeted. The significant genotypic variation observed, which can be phenotyped for even under field conditions, indicates that deep rooting is a trait that can be incorporated into breeding programmes. Furthermore, the methods used in this study are effective and should be developed for further application.
Why it matches plant phenotyping methods深根を圃場条件で評価するためのミニライゾトロン画像法を中心に、複数年・遺伝子型間で検証し、深根形質の実用性を評価しているため。
abstractmethods are lacking for phenotyping for deep rooting under field conditions and at maturity.
Salt stress is a major abiotic stress affecting wheat at various developmental stages and significantly reduces grain yield. Developing salt resilient wheat cultivars alleviate the negative impacts of salt stress and helps in maintaining sustainable grain yield under salt stress. A study was undertaken to assess the response of various seedling traits in a genetically, phenotypically, and geographically diverse panel of 228 hexaploid spring wheat accessions using greenhouse lysimeter system with two irrigation treatments: control (electrical conductivity of irrigation water as deci-Siemens per meter., (ECᵢ𝓌 = 14 dSm⁻¹) and saline (ECᵢ𝓌 = 14 dSm⁻¹). Salt stress was given on 18 days old seedlings and the targeted salinity level (ECᵢ𝓌 = 14 dSm⁻¹) was achieved gradually over two days period, to overcome any osmotic shock. Data on various seedling traits [such as shoot height (SH; inches), root length (RL; inches), tiller number (TN), shoot weight (SW; grams), and root weight (RW; grams)] were collected after three weeks of salt treatment from control and salt stress environment. Shoot and root traits were used to calculate root length by shoot height (RL-by-SH) and root weight by shoot weight (RW-by-SW) ratios. Furthermore, the salt tolerance index (STI), was calculated for each trait by dividing trait values of each accession from salt-treated tanks by those from control tanks. Raw data was subjected to mixed linear analysis to derive best linear unbiased prediction (BLUP). BLUP values were also used for Pearson's correlation coefficient analysis and principal component analysis (PCA), which gives intrinsic relationship among various seedling traits. Dataset presented here is a valuable source for identifying tolerant lines for salt stress environment. Moreover, researchers can utilize this information to identify potential genomic regions associated with salt stress tolerance and can be utilized in developing salt resilient wheat cultivars.
Why it matches plant phenotyping methods塩ストレス下のコムギ幼植物について、複数の形態・生体重形質を体系的に収集した再利用可能な表現型データセットであり、植物表現型データの提供が中心です。
titlePhenotypic data related to seedling traits of hexaploid spring wheat panel evaluated under salinity stress
We present a comprehensive mathematical model to calculate stem water potential in tomato plants cultivated under greenhouse conditions. Stem water potential is one of the variables that determines the growth of fruit as water potential gradients between the fruit and the stem are the driving forces for import of water and solutes into the fruit. Notably, the model integrates growth dynamics, environmental conditions, and plant management strategies to improve the accuracy of water potential estimation throughout the canopy. Environmental factors (i.e., temperature, relative humidity, light irradiance) were implemented at plant compartment levels, allowing for precise microclimate representation. Plant structure was used to calculate water flows and, ultimately, stem water potential by utilizing a hydraulic resistance model. The model was calibrated and validated using data collected from five growing seasons (2020 – 2024). The precision of water potential estimates across different growth stages was improved by including plant morphology dynamics. This, together with discretisation into compartments, allowed for unique realistic predictions for the whole season. Accurate predictions required accounting for growth dependency in root and xylem resistance. Temperature was the main predictor of plant growth for the investigated conditions of tomato production in Belgium. The greenhouse environment and plant management significantly influenced water fluxes and subsequent water potential estimations and should always be considered, especially for whole-season scenarios. Two hypothetical scenarios were analyzed based on 2019 environmental data, exploring the impact of greenhouse management and climate change. Simulations revealed that an increase in the greenhouse minimum temperature set points (+2 °C) had a greater positive effect on yield than a hypothetical climate change scenario with a larger temperature increase (+4 °C). The latter resulted in a higher prevalence of suboptimal growth conditions, presenting a real challenge for efficient future greenhouse management. Additionally, controlling the vapour pressure deficit instead of relative humidity was shown to significantly reduce water demand due to decreased transpiration rates. This water potential model for tomato growth can be used conjointly with fruit growth models for better crop prediction and optimisation of growing conditions. The presented model is modular and extendable, allowing integration not just with fruit growth models, but also potential inclusion of additional plant organs.
Why it matches plant phenotyping methodsトマトの茎水ポテンシャルや形態を推定する数学モデルを開発し、5作期のデータで較正・検証しており、植物状態の取得・推定手法が研究の中心である。
abstractWe present a comprehensive mathematical model to calculate stem water potential in tomato plants cultivated under greenhouse conditions.
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.
Hydropersulfides (RSSH) are an important class of reactive sulfur species (RSS) involved in a variety of physiological processes in biological systems. However, selective detection of RSSH is challenging since the persulfide group (-SSH) exhibits reactivity akin to biologically abundant biothiols. To address this issue, we designed CRBA as a ratiometric fluorescent probe for RSSH by integrating the nucleophilic ring-opening of an N -benzoylaziridine and spirolactamization of a rhodol. The ratiometric sensing of RSSH is realized by modulating Förster resonance energy transfer (FRET) in the coumarin-rhodol dyad. Nucleophilic attack of the N -benzoylaziridine moiety in CRBA by RSSH affords the corresponding secondary amide ring-opened product, which subsequently undergoes a spontaneous intramolecular spirolactamization to disrupt the π-conjugation structure of the rhodol. The tandem reaction decreases the intramolecular FRET efficiency within the probe and causes a clear dual-emission signal change. Probe CRBA possesses outstanding selectivity toward RSSH over biothiols and H 2 S, and is capable of tracking RSSH levels in Arabidopsis thaliana roots. Moreover, we observed the upregulation of RSSH levels in heavy metal (HM)-induced stress of A. thaliana with probe CRBA , and revealed the correlation between S -persulfidation and H 2 S levels in living plants.
Why it matches plant phenotyping methods植物体内のRSSH（過硫化水素）を定量的に可視化する蛍光プローブを開発・検証し、Arabidopsisの根および重金属ストレス下の生理状態を測定しているため、植物フェノタイピング手法が中心です。
abstractwe designed CRBA as a ratiometric fluorescent probe for RSSH
Legume root nodules are important for biological nitrogen fixation, a process critical for plants to gain additional nitrogen from the environment. Nodule quantification is valuable for evaluating nitrogen fixation efficiency, assessing symbiotic relationships, monitoring responses to nitrogen, and supporting genetic studies on legume adaptation and productivity. However, accurate quantification of root nodules is difficult and time-consuming due to the complexity of the root system and soil interference. Here, we explore the utility of hyperspectral imaging as a non-destructive tool to detect active fixing root nodules with minimal preparation and show that we can differentiate nodules and root tissues through unique spectral signatures while also distinguishing between fixing and non-fixing nodules. We applied deep learning techniques to develop an automated nodule counting pipeline adaptable across different legume species and under diverse growth conditions. This approach eliminates the need for labor-intensive counting and enables the detection of nodules embedded within dense root tangles with high accuracy. This automated hyperspectral approach offers a promising alternative to support assessments of nodule abundance and their activity across legume species grown under various environments.
Why it matches plant phenotyping methods根粒の検出・計数と固定活性の識別という植物形質の取得を、ハイパースペクトル画像と深層学習による自動化手法として開発しており、方法が研究の中心です。
abstractHere, we explore the utility of hyperspectral imaging as a non-destructive tool to detect active fixing root nodules