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
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This work was supported by project JPNP18016, commissioned by the New Energy and
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Industrial Technology Development Organization (NEDO), JST CREST (JPMJCR17O1),
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and JST ALCA-Next (JPMJAN23D3).
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Data availability
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The source code and sample data (optode and CT images) are available from the
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GitHub repository (https://github.com/tsubasa-kawai28/RG2DO-Root).15
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References
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Aguilar EA et al. 2003. Oxygen distribution and movement, respiration and nutrient
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loading in banana roots (Musa spp. L.) subjected to aerated and oxygen-depleted
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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 · Europe PMC · bioRxiv · checked 14 Sept 2026
Abstract Background and aims Plant volatile organic compounds (VOCs) change dynamically with plant development and in response to environmental conditions. However, their potential as non-invasive indicators of phenological progression remains poorly explored. In this study, we developed a framework integrating automated VOC sampling, time-resolved VOC profiling, and machine-learning analysis for the non-invasive assessment of plant phenology. Using soybean ( Glycine max (L.) Merr.), we investigated whether development-associated temporal variation in VOC emissions could delineate and predict developmental phases. Methods We collected VOCs daily under controlled environmental conditions from 16 to 43 days after sowing, spanning the transition from vegetative to reproductive stages, using an automated sampling system coupled with thermal desorption-gas chromatograph-mass spectrometer (TD- GC-MS). To characterise temporal changes in VOC profiles associated with phenological progression, we analysed the daily VOC data using a multi-step pipeline combining statistical filtering and similarity-based network analysis. We defined VOC-derived developmental phases from similarity patterns in the VOC profiles, then developed and evaluated machine-learning models to predict these phases. Key results Seven VOCs exhibited distinct phase-dependent dynamics, including green leaf volatiles and monoterpenes showing characteristic temporal changes during phenological progression. Network-based clustering of VOC profiles resolved five developmental phases closely aligned with conventional developmental stages. A machine-learning model predicted these phases from the VOC profiles with high predictive accuracy on independent test data, demonstrating that phenological progression could be quantitatively inferred from VOC emission patterns. Conclusions Our findings support VOC profiling as a reliable and non-invasive approach for assessing phenological progression in soybean. By extracting temporally structured VOC signals, this framework captures developmental information that may be difficult to obtain through visual observation alone, particularly after canopy closure. VOC profiling offers a practical tool for monitoring crop developmental dynamics and has broader potential for plant phenotyping and precision crop management.
Why it matches plant phenotyping methods自動VOCサンプリング、時系列VOCプロファイリング、機械学習を統合し、VOCから植物の発育段階を非破壊推定する方法を開発・評価しており、フェノタイピング手法が中心である。
abstractwe developed a framework integrating automated VOC sampling, time-resolved VOC profiling, and machine-learning analysis for the non-invasive assessment of plant phenology.
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 · publicThe peak area matrix obtained from the MS- DIAL analysis (Supplementary Dataset S1) was filtered to remove unreliable features.Open asset ↗lines:66-69Code / dataset availability confirmedOpenAlex · arXiv · checked 5 Sept 2026
While most phenotyping platforms rely primarily on image-based measurements, advanced plant characterization requires the integration of active physiological sensing modali- ties such as chlorophyll fluorescence. We present an autonomous robotic platform designed to perform targeted fluorescence measurements on plant leaves. The system combines 3D plant reconstruction, geometric analysis, and motion planning to localize suitable measurement points and generate collision-free trajectories for a robotic manipulator. A dense 3D model of the plant is reconstructed from multi-view data and used to extract candidate leaf surfaces based on orientation, accessibility, and sensing constraints. These targets are then integrated into a task-level planning framework that guides the end-effector to precise contact or near-contact configurations required for point-based fluorescence acquisition. The platform enables automated, repeatable, and spatially resolved physiological measurements that go beyond passive imaging. By tightly coupling perception, geometric reasoning, and manipulation, the proposed system provides a robotics-driven approach to high-resolution plant phenotyping and opens new directions for autonomous agricultural inspection and plant-aware manipulation.
Why it matches plant phenotyping methods植物葉の蛍光を自律ロボットで空間的・反復的に取得するプラットフォームを開発しており、植物表現型の取得手法が研究の中心です。
abstractWe present an autonomous robotic platform designed to perform targeted fluorescence measurements on plant leaves.
Reproduction assets foundThe paper states its code is publicly available in the authors' SonyCSLParis GitHub repository (Plant3DImager), which implements the phenotyping perception and motion-planning pipeline. The exact full URL is split across a line break in the supplied text, so the verifiable allowed URL prefix is used.Code · public2 The code is available at https://github.com/SonyCSLParis/ 3 See for example at https://www.youtube.com/watch?v=Open asset ↗SonyCSLParis/pdf-page:4 lines:1-61Code / dataset availability confirmedCrossref · checked 15 Sept 2026
Abstract Plants employ non-photochemical quenching (NPQ) to protect their photosynthetic apparatus from photodamage. The response latency of NPQ following changes in light intensity is thought to significantly decrease photosynthetic efficiency. The amount of NPQ is commonly quantified from chlorophyll-fluorescence techniques using the Stern–Volmer equation, which requires fully closed reaction centres (RCs) of photosystem II, yielding NPQ in the absence of photochemical quenching ( $${\rm{NPQ}}^{\rm{Closed}}$$ NPQ Closed ). However, in nature, NPQ and photochemical quenching are normally present simultaneously. Therefore, to obtain a full understanding of this process, NPQ should also be explored when the RCs are open. Here we developed two methodologies to obtain NPQ in the presence of photochemistry ( $${\rm{NPQ}}^{\rm{Open}}$$ NPQ Open ) using both fluorescence lifetime and fluorescence yield measurements. A detailed comparison in Arabidopsis thaliana plants reveals that the value of $${\mathrm{NPQ}}^{\mathrm{Open}}$$ NPQ Open is ~35% lower than that of $${\rm{NPQ}}^{\rm{Closed}}$$ NPQ Closed . This difference is consistently observed across all measurements and is seen both upon closing ( $${\rm{NPQ}}^{\rm{Open}}\to {\rm{NPQ}}^{\rm{Closed}}$$ NPQ Open → NPQ Closed ) and upon reopening ( $${\mathrm{NPQ}}^{\mathrm{Closed}}\to {\mathrm{NPQ}}^{\mathrm{Open}}$$ NPQ Closed → NPQ Open ) of the RCs. We show that this difference can be explained by the presence of RC-induced ‘instantaneous’ switching of the NPQ quenching rate. This means that, in plants, NPQ is much more economical than is widely believed, it is large when its presence is needed, and it decreases instantaneously when the need disappears.
Why it matches plant phenotyping methods植物の光合成状態（NPQ）を測定するための蛍光寿命・蛍光収率に基づく2つの方法を開発し、比較検証しているため、方法開発が中心である。
abstractHere we developed two methodologies to obtain NPQ in the presence of photochemistry ( $${\rm{NPQ}}^{\rm{Open}}$$ NPQ Open ) using both fluorescence lifetime and fluorescence yield measurements.
Reproduction assets foundThe paper's custom ultrafast fluorescence analysis code (ICA-based PSII/PSI deconvolution and NPQ calculations) is explicitly deposited by the authors on GitHub, alongside the original data contributions.Code · publicr(s) for their contribution to the peer review of this work. Peer reviewer reports are available.
Funding
This work was supported by ‘Nanoscale regulators of photosynthesis’ NWO research project (project number: OCENW.GROOT.2019.86).
Data availability
The original contributions presented in the study are available via GitHub at https://github.com/L-Ramakers/Heimdall .
Code availability
The custom analysis code used in the study is available via GitHub at https://github.com/L-Ramakers/Heimdall .
Competing interests
The authors declare no competing interests.
Footnotes
Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional afOpen asset ↗L-Ramakers/Heimdalllines:88-125Code / dataset availability confirmedEurope PMC · checked 5 Sept 2026
Idesia polycarpa Maxim. is a premier woody oil species in Guizhou Province, China, whose fruit yield and oil quality largely depend on effective pollination and fertilization. However, limited research on pollen viability and germination has hindered industrial progress. To address this gap, a comprehensive evaluation framework for elite I. polycarpa germplasm was developed, integrating micromorphological analysis, optimized staining protocols, and in vitro germination assay. Scanning electron microscopy (SEM) revealed that I. polycarpa pollen, while genetically conserved at the genus level-characterized by prolate shapes, tricolporate apertures, and reticulate exine ornamentation-exhibits notable micromorphological variation among genotypes. Of the nine staining protocols tested (2,3,5-triphenyl tetrazolium chloride [TTC], carbol fuchsin, acetocarmine, methylene blue, Alexander, peroxidase, 2,5-diphenylmonotetrazolium bromide [MTT], I2-KI, and red ink), TTC and red ink were the most effective, offering clear chromatic distinction between viable and non-viable pollen. Through orthogonal experimental designs, genotype-specific optimal media for in vitro germination were identified: 0.40 g/L H3BO3, 0.01 g/L KNO3, 0.02 g/L Ca(NO3)2·4H2O, and 0.20 g/L KH2PO4 for STZ-6; and 0.20 g/L H3BO3, 0.02 g/L KNO3, 0.02 g/L Ca(NO3)2·4H2O, and 0.10 g/L KH2PO4 for STZ-9. Regression analysis confirmed a highly significant positive correlation (P < 0.01) between in vitro germination rates and the staining results from both TTC and red ink across various concentrations. Notably, 5% TTC and 30% red ink exhibited the highest coefficients of determination. A hierarchical evaluation strategy is thus proposed: the 5% TTC method is recommended for precise laboratory quantification due to its stability, while the 30% red ink method, due to its ease of use, is suited for rapid field-based screening. This study provides valuable insights into the morphological characteristics of I. polycarpa pollen and establishes a standardized evaluation framework, supporting germplasm innovation and optimizing pollination management.
Why it matches plant phenotyping methods花粉の生存性・発芽という植物の生殖形質を対象に、染色法とin vitro発芽法を最適化・検証し、標準化した評価フレームワークを開発しているため、方法論が中心である。
abstracta comprehensive evaluation framework for elite I. polycarpa germplasm was developed, integrating micromorphological analysis, optimized staining protocols, and in vitro germination assay.
Reproduction assets foundThe article's Data Availability statement points to a public Biostudies deposit containing the study's data (pollen morphology measurements, staining viability counts, and in vitro germination results). No author analysis code or trained models are mentioned.Dataset · publicData Availability: The data that support the findings of this study are openly available in Biostudies at https://doi.org/10.6019/S-BSST3125 .Open asset ↗Biostudies · S-BSST3125lines:176-186Code / dataset availability confirmedCrossref · checked 15 Sept 2026
Accurate estimation of evapotranspiration (ET) is critical for irrigation management in water-scarce regions such as the Middle East and North Africa (MENA). This study compares sensible heat flux (H), latent heat flux (LE), and ET derived from eddy covariance (EC) and a boundary-layer scintillometer (BLS) operated with an aperture reducer, deployed simultaneously over an irrigated late-season potato field (1.8 ha) in the Beqaa Valley, Lebanon. Satellite NDVI observations indicate that the BLS–EC overlap period (13 October–27 November 2021) sampled the crop from peak canopy (NDVI ≈ 0.85–0.90) through the onset of senescence (NDVI ≈ 0.79). The BLS (Scintec BLS900) operated along a 140 m path. The EC system showed incomplete daytime energy-balance closure, with a regression slope of ≈0.69 and a seasonal Bowen-ratio-preserving correction factor of CF = 1.24 (a ~19% closure deficit) was used. Across the matched period, daily H from the BLS was strongly correlated with EC (r ≈ 0.82) but systematically lower, with a regression slope of ≈0.63 that persisted across timescales; this scale-invariant amplitude compression reflects the path-averaged, similarity-based nature of the scintillometer retrieval rather than the EC closure deficit, which instead governs the mean bias. BLS-derived daily ET showed a systematic positive bias relative to uncorrected EC (mean bias error, MBE = +0.30 mm d−1; +16% cumulative). Applying the Bowen-ratio-preserving correction (CF = 1.24) to EC reduced this to MBE = −0.14 mm d−1 (−6%), and the residual-to-LE correction yielded MBE = −0.15 mm d−1 (−6.4%); the latter comparison is only partly independent, as both methods share the same Rn and G. The Bowen-ratio-preserving method is therefore recommended for this dataset. Overall, the BLS captured the temporal variability of crop water use well, but residual-based ET estimates require careful treatment of the energy-balance-closure gap and are sensitive to the high BLS gap fraction (61.6% of 15 min records over the overlap, exceeding 90% at night). Once EC is closure-corrected to serve as the reference, the BLS offers a cost-effective alternative for field-scale ET monitoring in the MENA region, subject to the conditional agreement documented here.
Why it matches plant phenotyping methodsジャガイモ圃場の作物蒸発散量（ET）という生理・水利用状態を対象に、ECとBLSを比較検証し、補正法や測定誤差も評価している。センサー測定法の技術的妥当性が中心であり、単なる routine measurement ではない。
abstractThis study compares sensible heat flux (H), latent heat flux (LE), and ET derived from eddy covariance (EC) and a boundary-layer scintillometer (BLS) operated with an aperture reducer
Reproduction assets foundThe paper's flux/ET datasets are only available on request from the corresponding author, so they do not qualify as public assets. However, the Supplementary Information file (available at the MDPI supplementary URL) explicitly contains experiment sensor documentation and field/canopy images (Figures S1–S4: study site,Supplement · publicmeasurements along the beam. Because these results derive from a single crop, season, and phenological window, their generalization awaits multi-site, multi-season replication spanning the full-canopy cycle—the priority for subsequent campaigns.
Supplementary Materials
The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/s26144398/s1 , Figure S1: Study site and potato canopy—Beqaa Valley, Lebanon; Figure S2: Eddy covariance system—full tower view (peak canopy); Figure S3: EC sensor suite close-up and soil sensor installation; Figure S4: BLS900 scintillometer—transmitter, receiver, and meteorological station.
Author Contributions
Conceptualization, HOpen asset ↗lines:251-268Code / dataset availability confirmedCrossref · Europe PMC · checked 5 Sept 2026
Field-based phenotyping of water-related traits for precision irrigation in tropical agroecosystems poses a persistent methodological challenge, driven by high climatic variability and the complex water-use physiology of Crassulacean Acid Metabolism (CAM) crops such as pineapple (Ananas comosus var. MD2). We developed and validated a Physics-Informed Machine Learning (PIML) framework that integrates high-resolution UAV multispectral imagery, IoT-based microclimatic records, and a mechanistic soil water balance based on the FAO-56 Penman–Monteith standard to predict plot-scale soil moisture depletion as a proxy of plant water status. A six-month field campaign (March–August 2022) across 25 georeferenced commercial pineapple plots in the Colombian Orinoquia piedmont yielded a spatiotemporally balanced dataset of N=150 observations. Soil-adjusted vegetation indices (OSAVI, MSAVI) outperformed standard NDVI for capturing water-related canopy traits, effectively decoupling spectral responses from substrate noise. A Gradient Boosting regressor achieved R2=0.842 and RMSE=0.0705 on a normalized target scale, corresponding to a 7.05% error over the prediction range, while the traffic-light Decision Support System (DSS) for irrigation scheduling reached 91.1% accuracy (Cohen’s Kappa =0.91). Incorporating daily soil moisture depletion as a mechanistic feature improved predictive accuracy over a spectral-only baseline (ΔR2=+0.052) and anchored predictions within a physically consistent framework based on the FAO-56 water balance, with no false negatives observed for water deficit detection in the hold-out validation set. This framework advances high-throughput, population-scale phenotyping of water-related traits in open-canopy CAM crops, establishing a transferable methodology for operational precision irrigation under tropical savanna conditions.
Why it matches plant phenotyping methodsUAVマルチスペクトル画像と機械学習を用いて植物の水関連形質・水状態を推定する枠組みを開発・検証しており、表現型取得と予測手法が研究の中心である。
abstractWe developed and validated a Physics-Informed Machine Learning (PIML) framework that integrates high-resolution UAV multispectral imagery, IoT-based microclimatic records, and a mechanistic soil water balance based on the FAO-56 Penman–Monteith standard to predict plot-scale soil moisture depletion as a proxy of plant water status.
Reproduction assets foundThe paper's Data Availability Statement explicitly deposits the complete dataset and source code (raw UAV multispectral imagery, Python scripts, IoT sensor logs, CROPWAT 8.0 files, and XGBoost model code) in a public Mendeley Data repository, which directly reproduces this paper's phenotyping measurements and analysis.Dataset · publicThe complete dataset and source code supporting this study are publicly available at Mendeley Data: https://data.mendeley.com/datasets/9xwdvzf3bf/1 (accessed on 20 May 2026). The repository includes: (1) raw multispectral UAV imagery with calibration panel captures; (2) Python scripts for DN-to-reflectance conversion and spectral index extraction; (3) IoT sensor logs (soil moisture, temperature, relative humidity); (4) CROPWAT 8.0 project files for FAO-56 soil water balance simulation; and (5) XGBoost model source code with hyperparameter optimization routines.Open asset ↗Mendeley Data · 9xwdvzf3bf/1lines:193-228Code / dataset availability confirmedEurope PMC · checked 5 Sept 2026
Low temperature stress severely restricts the cultivation and distribution of pear ( Pyrus L.) germplasms, frequently resulting in frost injury and yield reduction. To accurately evaluate the cold resistance of pear germplasm resources, this study investigates the physiological and biochemical responses of one-year-old branches to different degrees of low-temperature stress, as well as differences in the tissue structure of these pear germplasms after low-temperature stress. In this study, 122 pear germplasms were classified into high (HR), medium (MR), and low (LR) cold-tolerance categories based on their semi-lethal temperature (LT 50 ). Further analysis of pear germplasms with different levels of cold resistance revealed that, with decreasing temperature, HR germplasms exhibited smaller increases in relative electrolyte conductivity (REC) and malondialdehyde (MDA) content and higher accumulation of proline (Pro), soluble proteins (SP), soluble sugars (SS), and peroxidase activity compared with LR germplasms. In addition, the peak values of these indicators generally occurred at lower temperatures in HR germplasms. A correlation analysis and principal component analysis indicated that physiological indices, including REC, bound water/free water ratio, SS, and MDA, as well as branch anatomical traits related to xylem and cortex proportions, were closely associated with variation in LT 50 . An integrated assessment using membership function analysis produced rankings consistent with LT 50 -based clustering, supporting the reliability of the multivariate evaluation framework. Overall, this study establishes an integrated, indicator-based approach for evaluating cold resistance in pear germplasm by integrating physiological, biochemical, and anatomical characteristics. These results provide a theoretical basis and methodological reference for screening cold resistance germplasms.
Why it matches plant phenotyping methods生理・生化学・解剖学的形質を統合し、LT50と多変量評価によってナシ遺伝資源の耐寒性を分類・スクリーニングする評価フレームワークが研究の中心である。
abstractTo accurately evaluate the cold resistance of pear germplasm resources, this study investigates the physiological and biochemical responses of one-year-old branches to different degrees of low-temperature stress, as well as differences in the tissue structure of these pear germplasms after low-temperature stress.
Reproduction assets foundThe article's Data Availability statement links a public Zenodo deposit containing the paper's raw phenotyping data (LT50, physiological/biochemical and anatomical measurements for pear germplasms). Supplemental files also contain germplasm characteristics and LT50 comparisons, but the Zenodo raw-data deposit is the明确,Dataset · publicThe data is available at Zenodo: liu186253. (2025). liu186253/Data: raw data (Version V11). Zenodo. https://doi.org/10.5281/zenodo.17524773 .Open asset ↗Zenodo · 10.5281/zenodo.17524773lines:636-710Code / dataset availability confirmedEurope PMC · checked 14 Sept 2026
Accurate assessment of leaf chlorophyll is essential for understanding plant physiological responses to environmental variation. While solvent extraction provides precise chlorophyll measurements, it is destructive and temporally limited, whereas portable optical meters such as the CCM-300 enable rapid, non-destructive measurement of the chlorophyll fluorescence ratio (CFR) but require species- and season-specific calibration. This study evaluates the performance of CCM-300 measurements and reconstructs seasonal chlorophyll dynamics in field maple (Acer campestre) across two contrasting summers in the United Kingdom. Paired CFR and acetone-extracted chlorophyll data collected in 2023 were used to develop calibration models. RF regression achieved the highest predictive performance within the calibration dataset, although substantial uncertainty remained at the leaf level; a simple linear model was therefore adopted for cross-year projection due to its stability under extrapolation. Applying this calibration to daily 2022 CFR measurements generated a continuous "virtual acetone" trajectory, enabling qualitative comparison with weekly destructive extractions in 2023. Both years exhibited mid-season chlorophyll plateaus followed by late-summer declines; however, senescence, defined as the initiation of sustained post-peak decline, occurred earlier during the warmer and drier 2022 season. Mixed-effects modelling identified positive effects of temperature and wind speed on CFR in 2022, while generalised additive modelling of the 2023 dataset revealed a non-linear seasonal decline under comparatively mild conditions. Because cross-year projections rely on a low-fit linear calibration, interannual differences are interpreted primarily in terms of relative seasonal trajectory shape and timing rather than absolute chlorophyll magnitude.
Why it matches plant phenotyping methodsCCM-300による葉クロロフィル測定を破壊的測定と比較し、校正モデルの開発・性能評価と季節軌跡の再構築を行っており、植物表現型取得法が研究の中心である。
abstractThis study evaluates the performance of CCM-300 measurements and reconstructs seasonal chlorophyll dynamics in field maple (Acer campestre) across two contrasting summers in the United Kingdom.
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 · publicData Availability: Data used in the study can be accessed via https://zenodo.org/records/17475985.Open asset ↗zenodo · 17475985pdf-page:11 lines:1-44Code / dataset availability confirmedEurope PMC · checked 14 Sept 2026
Published5 Jun 2026Proceedings of the National Academy of Sciences of the United States of AmericaCited by 0 · OpenAlex ↗
To improve crop yield and resilience, it is essential to identify the steps limiting [Formula: see text] assimilation rate in plant leaves. The combined effect of multiple traits can be resolved by mechanistic models of the underlying diffusion, biochemistry, and geometry. Yet the widely used simple serial resistance models overlook tissue geometry, and detailed anatomical models are computationally heavy and rely on parameters that are difficult to measure. Here, we develop a framework for systematic species and model comparison, and find that the necessary level of model resolution is species-specific. We apply a minimal reaction-diffusion model and reduce [Formula: see text] fixation in leaves to two key parameters. These parameters comprise a compact phase space in which three rate-limiting regimes emerge naturally: stomatal uptake, intercellular diffusion, and intracellular processes. Mapping diverse plant species into this phase space reveals: 1) dominant colimitations by stomatal and intracellular processes, 2) an equal partition between species that require spatially resolved leaf-scale models and species where intracellular models suffice. Taken together, we present a scalable path for interpreting complex trait data and bridging between models.
Why it matches plant phenotyping methods葉のCO2固定を機構モデルで2パラメータに縮約し、複数種の生理的制限状態と複雑な形質データを解釈・比較する計算フレームワークが中心であるため、植物生理形質の推定・解析手法として含める。
abstractHere, we develop a framework for systematic species and model comparison
Reproduction assets foundThe paper deposits its analysis code/scripts publicly on Zenodo (DOI 10.5281/zenodo.19087524) and GitHub (andreas-stillits/CarbonFixationModel), and uses the publicly deposited Knauer et al. leaf-trait/mesophyll-conductance dataset on Figshare (10.6084/m9.figshare.19681410) to map species into (τ, γ) space. All three, Code · publicCode and Scripts. All code is readily available at our github and at a public
repository (DOI: 10.5281/zenodo.19087524).Open asset ↗Zenodo · 10.5281/zenodo.19087524pdf-raw-page:8 lines:1-60Code / dataset availability confirmedEurope PMC · checked 6 Sept 2026
As the demand for plant-based nutrition increases, improving the protein profile of legumes like cowpea has become a breeding priority. Cowpea, a multiuse legume and staple in many low-income regions, provides important dietary protein that can help meet the demand in our growing population. Our research used genome-wide association studies (GWAS) and phenomic tools to investigate the genetic architecture of seed protein content in cowpea and integrated 4 cohorts of undergraduate researchers through a USDA-AFRI REEU program. Using wet chemistry and near-infrared spectroscopy (NIRS), we assessed crude protein (CP) within the University of California Riverside Minicore collection, developed and validated a custoMED-made NIRS calibration equation for CP (R2 = 0.86), and performed GWAS with ∼41k single-nucleotide polymorphisms (SNPs). Significant SNPs associated with protein content were identified on chromosomes 1, 3, 7, 10, and 11, and candidate genes were linked to functions including nutrient transport, stress response, and seed storage protein regulation. These results provide a foundation for future marker validation and functional studies, and demonstrate the value of pairing trait discovery with undergraduate training.
Why it matches plant phenotyping methods種子タンパク質含量という植物形質の取得に用いるNIRS校正式を開発・検証しており、表現型測定法が研究の主要な技術的要素である。
abstractdeveloped and validated a custoMED-made NIRS calibration equation for CP (R2 = 0.86)
Reproduction assets foundThe paper's Data Availability statement deposits the phenotypic data (wet chemistry CP, NIRS-derived CP phenotypes used for calibration and GWAS) in Dryad. No author analysis code or trained NIRS model files are explicitly deposited; other URLs are generic tools or citations.Dataset · publicThe phenotypic data collected and used in this research are available in the Dryad Digital Repository under DOI: https://doi.org/10.5061/dryad.8cz8w9h72 .Open asset ↗Dryad Digital Repository · 10.5061/dryad.8cz8w9h72lines:305-345Code / dataset availability confirmedEurope PMC · checked 15 Sept 2026
MaizeTomatoLeafPhysiological trait estimationCalibration / preprocessingWater status / transpiration
Within the soil-plant-atmosphere continuum, water movement is driven by the water potential gradients between these three domains. To have a comprehensive understanding of such water relations, an examination of how plants respond to variations in soil water availability is required. The methodologies employed for measuring water potential in leaf (Ψ leaf ) and soil (Ψ soil ) have undergone a significant evolution; transitioning from qualitative assessments to the use of high-precision digital sensors over the past few decades. The present protocol aims to provide a comprehensive, step-by-step guide from the germination phase of maize and tomato plants to the installation of two sensors that continuously monitor water potential in the leaf (PSY1 psychrometer) and in the soil (TEROS 21 matric potential sensor). Additionally, we present the code for processing the raw data files in RStudio.
Why it matches plant phenotyping methods葉の水ポテンシャルを連続測定するセンサー設置、データ処理コード、手順を中心とした植物生理形質の測定プロトコルであり、方法論的貢献が明確。
abstractThe present protocol aims to provide a comprehensive, step-by-step guide from the germination phase of maize and tomato plants to the installation of two sensors that continuously monitor water potential in the leaf (PSY1 psychrometer) and in the soil (TEROS 21 matric potential sensor).
Reproduction assets foundThe paper deposits its authors' R analysis notebook with an example water-potential dataset, the CR800 datalogger program, and an installation video on Zenodo, all publicly accessible.Code · publicthat were missing, zero, or otherwise aberrant. It was also programmed to identify and remove inverted day-night cycle patterns, as well as values that were statistically insignificant.
Figure 9 shows applications of data cleaning on the example dataset. For more details, please check codes that have been deposited on Zenodo (
https://doi.org/10.5281/zenodo.20080750 ,
D’Agostino, 2026 ).
Figure 9.
Example of data cleaning using the algorithm.
Green is kept data and red is discarded data.
Conclusion
In summary, the present protocol is not confined to the descriptive monitoring of Ψ
soil
and Ψ
leafOpen asset ↗Zenodo · 10.5281/zenodo.20080750lines:452-504Code · public(1) the address of each Teros 21; (2) the data transporting port (“C1” or “C3”); (3) the creation of dataset files to store the recorded soil matric potential and temperature, as well as the voltage of the battery for power supply; (4) the time interval for the data recording.
An example of the program was deposited on Zenodo (
https://doi.org/10.5281/zenodo.17158115 ), with the document name of “Program-CR800”). Before starting, install the software of “Device Configuration Utility” and “PC400” from Campbell Scientific (
https://www.campbellsci.com/devconfig ;
https://www.campbellsci.com/pc400 ). “CRBasic Editor” is integrated inside PC400. For more details about the programming, please reOpen asset ↗Zenodo · 10.5281/zenodo.17158115lines:321-378Dataset · publiculic limitation, soil-root disconnection, and recovery. Consequently, this linkage of the protocol to mechanistic analyses of water transport in the SPAC is more direct.
Ethics and consent
Ethical approval and consent were not required.
Data availability
The datasets and codes to analyze the data have been deposited on Zenodo (
https://doi.org/10.5281/zenodo.20080750 ,
D’Agostino (2026) ).
Data are available under the terms of the Creative Commons Zero v1.0 Universal.
An additional explicative video for the psychrometer installation on leaves is available on Zenodo (
https://doi.org/10.5281/zenodo.17510720 ,
Degand
et al. (2025) ).
The author(s) declare that this video is released under theOpen asset ↗Zenodo · 10.5281/zenodo.20080750lines:505-651Code / dataset availability confirmedEurope PMC · Crossref · checked 5 Sept 2026
Kinetic models of photosynthesis enable time-resolved predictions of traits related to this key process and provide the means to identify factors limiting photosynthesis. However, the use of large-scale models is currently limited by the lack of efficient approaches to estimate the hundreds of genotype-specific kinetic parameters. Here, we present C4TUNE, an artificial neural network that can efficiently predict parameters of a large-scale photosynthesis model from photosynthesis response curves. C4TUNE was trained on a biologically relevant synthetic dataset comprising matched samples of parameters and response curves obtained using a C 4 photosynthesis kinetic model. To speed up the training of C4TUNE, we devised a surrogate neural network to predict photosynthesis response curves directly from the model parameters and environmental inputs. Given response curves as input, we showed that over 99% of the parameter vectors predicted by C4TUNE could be used directly in simulation of the kinetic model and resulted in excellent fits. Finally, we applied C4TUNE to predict parameters for a population of 68 maize genotypes across two seasons. The predicted genotype-specific parameters allowed pinpointing factors that limit photosynthetic efficiency, validated using simulations. Therefore, the use of C4TUNE presents a fast and precise approach for parameter prediction based on minimal datasets.
Why it matches plant phenotyping methodsC4TUNEは光合成応答曲線から遺伝子型特異的な光合成動態パラメータを推定するニューラルネットワークであり、植物の生理形質の取得・推定手法の開発と検証が研究の中心です。
abstractHere, we present C4TUNE, an artificial neural network that can efficiently predict parameters of a large-scale photosynthesis model from photosynthesis response curves.
Reproduction assets foundThe paper's Data Availability Statement provides a public GitHub repository with the authors' custom code for artificial dataset generation, neural network definition/training, and predicted maize genotype parameters. Zenodo datasets (gas exchange measurements and synthetic training data) are mentioned via DOIs but no Code · publicCustom code for the generation of the artificial dataset as well as code for neural model definition and training is available at https://github.com/pwendering/C4TUNE . This repository also contains the predicted parameters for the maize genotypes.Open asset ↗pwendering/C4TUNElines:223-270Code / dataset availability confirmedEurope PMC · Crossref · checked 5 Sept 2026
Abstract Chlorophyll estimation is fundamental in plant physiology, crop management, and ecological studies; however, destructive and non-destructive methods are often interpreted interchangeably despite differing measurement principles. The present study compared four chlorophyll estimation approaches—two non-destructive (SPAD meter and GreenSeeker) and two destructive (80% acetone and DMSO extraction)—across eight crop species under uniform field conditions. Significant interspecific variation was observed for all methods. Correlation and regression analyses revealed generally weak relationships among methods, particularly between leaf-level (SPAD, solvent extraction) and canopy-level (GreenSeeker) measurements, reflecting scale-dependent behavior and methodological differences. Moderate associations were observed between SPAD and acetone-extracted chlorophyll for certain traits, whereas GreenSeeker showed poor agreement with solvent-based estimates. Differences between DMSO and acetone extraction further highlighted solvent-specific extraction efficiency. The results demonstrate that chlorophyll estimation methods are not directly interchangeable and should be selected based on study objectives, biological scale, and leaf anatomical characteristics. Species-specific calibration and integration of canopy structural parameters are required to improve cross-method interpretability.
Why it matches plant phenotyping methods複数の葉・キャノピーのクロロフィル推定法を作物種間で比較し、相関、回帰、スケール依存性、互換性を評価しており、植物表現型測定法の技術的検証が中心である。
abstractThe present study compared four chlorophyll estimation approaches—two non-destructive (SPAD meter and GreenSeeker) and two destructive (80% acetone and DMSO extraction)—across eight crop species under uniform field conditions.
Reproduction assets foundThe preprint declares that the datasets generated in this chlorophyll-method comparison study (SPAD, GreenSeeker, acetone and DMSO measurements across eight crop species) are publicly deposited in Figshare under DOI 10.6084/m9.figshare.31817989. This is a paper-specific, publicly actionable phenotype dataset. No authorDataset · publicThe datasets generated during the current study are available in the Figshare repository, https://doi.org/10.6084/m9.figshare.31817989Open asset ↗Figshare · 10.6084/m9.figshare.31817989lines:163-185Code / dataset availability confirmedEurope PMC · checked 5 Sept 2026
The increasing global food insecurity driven by climate-induced natural hazards and soil degradation has made the resilience of alternative agricultural systems a critical focus in risk management. This study presents a geospatially integrated monitoring framework, the Optimized Multi-Scale Adaptive Graph Neural Network (OMSA-GNN), designed to mitigate risks associated with nutrient instability in hydroponic and aeroponic environments. The proposed system leverages a Raspberry Pi-based IoT network to monitor complex interactions among microclimatic variables, plant physiological health, and nutrient concentrations, treating them as localized geospatial data points. To enhance decision-making under environmental uncertainty, an Improved Sparrow Search Algorithm (ISSA) is employed to optimize the predictive performance of the GNN. The OMSA-GNN model incorporates visual plant indices as a proximal remote sensing approach to enable early detection of physiological stress that may lead to crop failure. Evaluated using a lettuce growth dataset, the framework demonstrates superior performance in forecasting growth trajectories and managing resource-related risks compared to conventional static models. The results highlight a scalable approach for improving the reliability of urban food systems, where traditional land-based agriculture is increasingly vulnerable to natural hazards.
Why it matches plant phenotyping methods植物の生理的ストレスと成長軌跡を、視覚的植物指数およびIoTセンサーデータから推定するGNNベースの監視・解析手法が研究の中心であり、植物表現型取得と予測に該当する。
abstractThe OMSA-GNN model incorporates visual plant indices as a proximal remote sensing approach to enable early detection of physiological stress that may lead to crop failure.
Reproduction assets foundThe paper's Data Availability statement points to a public Kaggle lettuce growth dataset used for evaluation, matching an allowed URL. No author code or model checkpoints are disclosed.Dataset · publicThe datasets used and/or analyzed during the current study are available in the Kaggle repository, https://www.kaggle.com/datasets/jurijsruko/lettuce/data.Open asset ↗Kaggle · jurijsruko/lettucehtml-lines:469-500Code / dataset availability confirmedEurope PMC · checked 5 Sept 2026
Background Leaf-level biogenic volatile organic compounds (BVOCs) emissions represent a major source of organic gases in the atmosphere, influencing both climate and air quality. These emissions are strongly driven by environmental perturbations, which affect individual plant- to ecosystem-level processes. Uncovering all the BVOCs and understanding how their emissions respond to altered environmental conditions provide critical insights into vegetation-driven changes in atmospheric chemistry. We developed a tandem instrumentation setup that integrates a proton transfer reaction time-of-flight mass spectrometer (PTR-ToF-MS) with parts-per-trillion detection limits and a photosynthetic infrared gas exchange system for the untargeted survey of all the BVOCs. This novel system enables simultaneous, real-time monitoring of BVOC emissions and photosynthetic parameters at the leaf level, offering new opportunities to disentangle the physiological and environmental drivers of VOC release. Furthermore, we established the VOC Analysis and Processing Optimization Resource (VAPOR), an open-access software tool designed for rapid data post-processing and the analysis of the variability of hundreds of BVOCs. We assessed the performance of the tandem system under varying background conditions, using standard gas mixtures and a range of environmental factors. Results Blank emissions were substantially lower for major BVOCs (e.g., isoprene) compared to those observed in plant emissions. Despite this, the observation of background-level VOCs highlights the importance of routinely acquiring and accounting for blank measurements in analyses using the coupled instrumentation. Introduction of known VOC concentrations to the system demonstrated a linear response across different compounds with varying molecular compositions, indicating minimal gas loss regardless of chemical moieties within the coupled instrumentation. We applied the optimized system to investigate the physiological mechanisms driving BVOC emissions across different genotypes of poplar and pennycress. The high mass resolution capabilities of the PTR-ToF-MS, coupled with comprehensive VAPOR-driven data analysis, enabled the identification of several important BVOCs, including methanol and methanethiol; these BVOCs displayed substantial variation across pennycress genotypes and showed concentrations ~ 100-350% higher than the blank. Moreover, isoprene emissions varied significantly among poplar genotypes grown in different potting media. Conclusions Tandem instrumentation offers a powerful tool for profiling volatile molecular markers and elucidating their genetic and environmental underpinnings. This approach enhances our ability to predict BVOC emissions in response to genotype by environmental interactions and contributes to a deeper understanding of vegetation responses to environmental changes.
Why it matches plant phenotyping methods葉レベルの植物揮発性物質排出と光合成パラメータを取得するタンデム計測系を開発・検証し、解析ソフトウェアも提供しているため、植物表現型取得法が中心である。
abstractWe developed a tandem instrumentation setup that integrates a proton transfer reaction time-of-flight mass spectrometer (PTR-ToF-MS) with parts-per-trillion detection limits and a photosynthetic infrared gas exchange system for the untargeted survey of all the BVOCs.
Reproduction assets foundThe paper's authors developed VAPOR, an open-access software tool used to post-process and analyze the paper's leaf VOC emission measurements, with explicit public availability at the authors' GitHub repository.Code · publicThe open-source code for VAPOR is accessible at https://github.com/INTERSECT-BESS/ORNL-VOC . In this study, VAPOR was used to post-process the VOC results generated from the offline collection of gases from poplars with different soil media.Open asset ↗INTERSECT-BESS/ORNL-VOClines:127-146Code / dataset availability confirmedEurope PMC · checked 15 Sept 2026
TissuePhysiological trait estimationWater status / transpiration
Process-based models that mechanistically represent water-carbon balances in the atmosphere-soil-plant continuum are an attractive tool for monitoring live fuel moisture content (LFMC) dynamics, a key variable when assessing fire danger. However, their application as operational tools to assess near-term wildfire danger at regional scale faces important challenges. Here, we explored key sources of prediction uncertainty in process-based modeling of LFMC. We applied the SurEau-ECOS model of plant hydraulics embedded within the MEDFATE modeling framework to assess how the accuracy of LFMC predictions was influenced by input data sources, by the availability of species-specific plant traits and by the level of mechanistic detail used to model water content of plant tissues. A lack of accurate data describing soil physical properties compromises the application of process-based models for predicting LFMC. Nonetheless, using global meteorological and vegetation data allows for successful regional-scale applications. Fully mechanistic approaches that model LFMC from plant water status using ecophysiological knowledge yield more accurate predictions. However, when reliable plant traits are lacking, semimechanistic approaches based on empirical equations offer a robust alternative. Overall, addressing the sources of uncertainty highlighted here could pave the way for developing operational tools to forecast near-term wildfire danger through process-based modeling of LFMC dynamics.
Why it matches plant phenotyping methods植物の生体燃料水分量（LFMC）という生理状態の推定モデルを対象に、入力データ、植物形質、機構的詳細度が予測精度へ与える影響と不確実性を評価しており、植物状態の取得・推定手法が中心である。
abstractHere, we explored key sources of prediction uncertainty in process-based modeling of LFMC.
Reproduction assets foundThe paper's Data availability statement explicitly deposits the LFMC field data (Catalan and Reseau–Hydrique networks) and the analysis/figure code in a public GitHub repository, which directly reproduces this paper's phenotyping measurements (7203 LFMC values) and computational analysis. Supporting Information TablesSCode · publicof the ‘Severo Ochoa’ Centres of Excellence programme, Ref. CEX2023‐001340‐S, funded by MICIU/AEI/ https://doi.org/10.13039/501100011033 . Also it was supported by the Spanish Government project IMPROMED (grant no. PID2023‐152644NB‐I00).
Data availability
The data and code for analyses and figures are available through GitHub ( https://github.com/emf‐creaf/LFMC_FR_CAT ). Also, the data that support the findings of this study are available in the Supporting Information of this article, specifically in Tables S1–S3 .
References
Balaguer‐Romano
R
,
De Cáceres
M
,
Espelta
JM
. 2025 .
Second‐growth forests exhibit higher sensitivity to dry and wet years than long‐existing ones
. Ecosystems
28 : 6Open asset ↗emf‐creaf/LFMC_FR_CATlines:253-664Code / dataset availability confirmedOpenAlex · Europe PMC · checked 5 Sept 2026
Quantifying the kinetics of net CO2 assimilation (A) and stomatal conductance (gs) under fluctuating light typically relies on gas exchange measurements, which are slow and thus unsuited for high-throughput phenotyping. As a result, faster, non-invasive phenotyping methods are needed to further evaluate these traits at a larger scale. However, first the relationship between non-steady-state parameters must be examined in greater detail. In this study, we aimed to determine whether variations in non-steady-state values of chlorophyll fluorescence and leaf temperature reflect differences in key gas exchange traits under fluctuating light conditions. Here, the correlations between the times required for a change in non-steady-state A, gs, operating efficiency of PSII (ΦPSII), and leaf temperature (Tleaf) during stepwise changes in light intensity were evaluated across nine plant species. Both steady-state and non-steady-state photosynthetic traits varied significantly among species. Overall, we found significant positive correlations between non-steady-state A and ΦPSII for time to 50% and 90% of final steady-state values (t50; r2 = 0.70) and (t90; r2 = 0.33). The t90 of gs and that of Tleaf were also significantly correlated after both increases (r2 = 0.45) and decreases (r2 = 0.61) in light intensity. Our findings suggest that the times required for a change in ΦPSII (particularly t50) and Tleaf (particularly t90) can be used as indicators of dynamic A and gs, respectively, facilitating faster phenotyping of the complex processes of photosynthesis and stomatal conductance kinetics in the future.
Why it matches plant phenotyping methods非定常クロロフィル蛍光と葉温を用いて光合成・気孔コンダクタンス動態を推定する高速フェノタイピング手法を評価しており、相関検証が研究の中心である。
abstractfaster, non-invasive phenotyping methods are needed to further evaluate these traits at a larger scale.
Reproduction assets foundThe paper's primary gas exchange, chlorophyll fluorescence, and leaf temperature phenotyping data are explicitly deposited in the WUR data repository (DOI 10.17887/WUR01-TMWYJN), stated in the Data availability section. No author analysis code repository is stated; the agricolae R package is a generic library, not a论文-Dataset · publicThe primary data and associated metadata are publicly available through the WUR data repository at https://doi.org/10.17887/WUR01-TMWYJN .Open asset ↗WUR data repository · 10.17887/WUR01-TMWYJNlines:406-446Code / dataset availability confirmedOpenAlex · Europe PMC · Crossref · checked 15 Sept 2026
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:486-514Code / dataset availability confirmedEurope PMC · checked 5 Sept 2026
Climate change-induced drought increasingly constrains water management in mixed-species urban gardens, requiring scalable and non-destructive approaches. This study proposes an integrated framework combining chlorophyll fluorescence, RGB image indices, and machine learning to classify plant drought response patterns. Ten garden plant species were evaluated under varying soil moisture conditions. Hierarchical cluster analysis integrating fluorescence parameters and RGB indices identified three physiologically defined response clusters, and their reproducibility using RGB indices alone was assessed. A total of 1,629 samples were augmented to 1,881 using the synthetic minority over-sampling technique (SMOTE) to address class imbalance. A support vector machine (SVM) model with a radial basis function kernel, using green leaf index (GLI), normalized green-red difference index (NGRDI), blue-green pigment index (BGI), and soil moisture (%) as predictors, achieved an accuracy of 0.91 and a Kappa coefficient of 0.84. In contrast, PLS-DA showed lower performance (accuracy 0.79, Kappa 0.65), indicating limited separability under linear assumptions. These results demonstrate that RGB indices combined with nonlinear models were able to reproduce physiologically defined drought response patterns under the given conditions. As a proof of concept, this study demonstrates the potential of the proposed framework; however, its generalizability is limited by the controlled greenhouse setting, the relatively small number of species, and the lack of external validation in heterogeneous field environments. The framework may provide a cost-effective approach for classifying plant drought responses and has the potential to support the grouping of plants with similar water requirements, which could contribute to improved irrigation management in mixed-species gardens under further validation.
Why it matches plant phenotyping methodsRGB画像指標と機械学習により、植物の干ばつ応答パターンという生理状態を分類し、蛍光測定との再現性を評価しているため、表現型取得・抽出手法が中心です。
abstractThis study proposes an integrated framework combining chlorophyll fluorescence, RGB image indices, and machine learning to classify plant drought response patterns.
Reproduction assets foundThe paper explicitly states that the authors' analysis code (data processing, feature extraction, SVM/PLS-DA modeling) is publicly deposited on Zenodo with a DOI matching an allowed URL. The phenotype datasets are only said to be in the manuscript/supplementary files, so the code deposit is the qualifying paperSpecificCode · publicThe code supporting the findings of this study, including data processing, feature extraction, and machine learning modeling is available at Zenodo: https://doi.org/10.5281/zenodo.19127295 .Open asset ↗Zenodo · 10.5281/zenodo.19127295lines:98-116Code / dataset availability confirmedOpenAlex · arXiv · checked 5 Sept 2026
Purpose: Fast detection of plant stress is key to plant phenotyping, precision agriculture, and automated crop management. In particular, efficient irrigation management requires early identification of water stress to optimize resource use while maintaining crop performance. Direct physiological sensing offers the potential to detect stress responses before visible symptoms appear. Methods: In this study, we recorded electrophysiological signals from greenhouse-grown tomato plants subjected to water stress and developed a framework based on machine learning for online stress detection. The recorded time-series data were processed using a processing pipeline that includes statistical feature extraction and selection, automated machine learning or alternatively deep learning, and probability calibration. Results: Across multiple input time horizons, we found that a 30-minute look-back window strikes the best balance between rapid decision-making and classification performance. Using automated machine learning, the framework achieved classification accuracies of up to 92%, outperforming deep learning approaches. Sequential backward selection reduced the feature set while maintaining performance. Importantly, the framework detects transitions from healthy to stressed states in recordings that were not included in the training set. Conclusion: Overall, we provide a decision-support tool for farmers and establish a foundation for biofeedback-driven irrigation control to improve resource efficiency in (semi-)autonomous crop production systems.
Why it matches plant phenotyping methodsトマトの電気生理シグナルから水ストレス状態を推定するセンシング・機械学習パイプラインを開発し、未学習データ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ある。
abstractDirect physiological sensing offers the potential to detect stress responses before visible symptoms appear.
Reproduction assets foundThe paper's electrophysiological time-series and soil moisture measurements from the water-stress tomato experiment are explicitly stated to be publicly available online via a Zenodo deposit (Buss et al. 2026a), referenced in both the Methods and Data availability sections.Dataset · publicAll recorded and
processed data are available online (Buss et al. 2026a).Open asset ↗pdf-page:5 lines:1-37Code / dataset availability confirmedEurope PMC · checked 14 Sept 2026
Abstract Purpose Accurate estimation of crop transpiration is essential for optimizing irrigation management and improving water-use efficiency in precision agriculture. However, direct measurement of transpiration is often invasive, costly, and difficult to maintain at large scales. This study proposes a data-driven framework to estimate maize ( Zea mays L.) sap flow driven by transpiration using widely available climatic and soil moisture data combined with machine learning techniques. Methods Field experiments were conducted during the 2023 and 2024 growing seasons in central Italy under irrigated silage maize. Meteorological variables, soil water content, and crop growth indicators were used as inputs, while sap flow measurements served as reference outputs. Several machine learning models were evaluated, including Linear Regression, Support Vector Regression (SVR), Decision Tree Regressor, and Multi-Layer Perceptron Regressor (MLPR), using both Point Estimation and Temporal Estimation strategies. Temporal approaches incorporated short-term historical information through feature concatenation and previous-average windows. Results Results demonstrate that non-linear models, particularly MLPR and SVR, consistently outperform linear and tree-based approaches. The inclusion of short temporal windows (45 minutes to 2 hours) significantly improves predictive accuracy, enhancing reconstruction of the diurnal transpiration pattern. Feature concatenation proved more effective than averaging strategies in capturing soil–plant–atmosphere interactions. Model performance remained robust across two contrasting growing seasons, confirming good generalization capability under interannual variability and data discontinuities. Conclusion The proposed framework provides a reliable and minimally invasive solution for real-time estimation of maize transpiration, supporting precision irrigation management. These findings highlight the potential of machine learning models as practical decision-support tools for sustainable agricultural water management.
Why it matches plant phenotyping methodsトウモロコシの蒸散・樹液流という生理形質を、気象・土壌水分データと機械学習で推定する手法を開発・比較し、複数年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ある。
abstractThis study proposes a data-driven framework to estimate maize ( Zea mays L.) sap flow driven by transpiration using widely available climatic and soil moisture data combined with machine learning techniques.
Reproduction assets foundThe paper's Data Availability statement says part of the datasets generated and analyzed (maize sap flow, climate, and soil moisture measurements) are publicly available on the authors' GitHub, while the analysis source code is only promised upon acceptance.Dataset · publicon; Datacuration; Formal
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analysis; Funding acquisition; Investigation; Methodology; Project administration; Supervision;
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Validation; Visualization; Writing – original draft; Writing – review and editing.
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D t v il ility Part of the datasets generated and analyzed during the current study are
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publicly available at https://github.com/isarlab-department-690
engineering/Agritech3.1.5FIWARE. The source code used for data processing and analysis will
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be released upon acceptance of the paper in the GitHub repository https://github.com/isarlab-692
department-engineering/DD_Maize_Sap_Flow.
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Funding This work was carried out within the framework of the project Agritech National ROpen asset ↗isarlab-department-690pdf-raw-page:31 lines:1-67Code / dataset availability confirmedEurope PMC · checked 5 Sept 2026
Abstract The global issue of water scarcity and climate change requires highly efficient and intelligent irrigation systems that are capable of optimizing water consumption with high crop productivity. The paper aims to provide a holistic machine learning framework for crop water stress prediction and efficient irrigation scheduling using multi-parametric agronomic data. The paper analyzes 55,450 soybean data with 13 physiological and biochemical parameters to implement and compare six regression models for predicting the water stress index. After eliminating tautology by removing the direct water content parameter from the prediction model, LightGBM and XGBoost ensemble tree models achieved near-perfect accuracy for predicting crop water stress using regular plant parameters alone, with R² = 1.0 and RMSE = 1.57×10⁻⁸ to 5.04×10⁻⁵. The Random Forest classifier, which was implemented without any direct stress indicators, achieved perfect discrimination between low, moderate, and high stress classes with precision/recall equal to 1.0, and 5-fold cross-validation and noise tests confirmed its robustness. SHAP analysis of the results showed protein percentage (PPE) and seed yield per unit area (SYUA) to be key drivers of water stress, providing valuable insights for precision agriculture. The model for determining irrigation requirements based on crop evapotranspiration and stress level achieved R² = 1.0 with zero error, making it possible to translate trait values directly into irrigation requirements. The framework presented in this paper brings together machine learning and agronomic knowledge to provide real-time data-driven solutions for irrigation systems, which have 30–50% water savings potential while maintaining healthy crops. It lays the ground for the development of AI-assisted irrigation systems that are applicable to different crops and climatic conditions, particularly in water-scarce countries such as Iraq.
Why it matches plant phenotyping methods作物の水ストレス状態を生理・農学データから機械学習で推定し、複数モデルの比較、交差検証、ノイズ試験、解釈分析まで行う計算的フェノタイピング手法が中心である。灌漑最適化への応用を含むが、単なる日常的測定ではない。
abstractThe paper aims to provide a holistic machine learning framework for crop water stress prediction and efficient irrigation scheduling using multi-parametric agronomic data.
Reproduction assets foundThe paper's soybean phenotyping dataset (55,450 records, 13 physiological/biochemical traits) is publicly available on Kaggle; the Data Availability statement points to it, though it ambiguously labels it as the code implementation location. No separate verified code repository is provided.Dataset · publicThe dataset used in this study (Advanced Soybean Agricultural Dataset) is available from the corresponding author upon reasonable request. The code implementation for all analyses is available at: https://www.kaggle.com/datasets/wisam1985/advanced-soybean-agricultural-dataset-2025 .Open asset ↗kaggle · wisam1985/advanced-soybean-agricultural-dataset-2025lines:372-406Code / dataset availability confirmedarXiv · checked 13 Sept 2026
Natural and anthropogenic disturbances are impacting the health of forests worldwide. Monitoring forest disturbances at scale is important to inform conservation efforts. Here, we present a scalable approach for country-wide mapping of forest greenness anomalies at the 10 m resolution of Sentinel-2. Using relevant ecological and topographical context and an established representation of the vegetation cycle, we learn a predictive quantile model of the normalised difference vegetation index (NDVI) derived from Sentinel-2 data. The resulting expected seasonal cycles are used to detect NDVI anomalies across Switzerland between April 2017 and August 2025. Goodness-of-fit evaluations show that the conditional model explains 65% of the observed variations in the median seasonal cycle. The model consistently benefits from the local context information, particularly during the green-up period. The approach produces coherent spatial anomaly patterns and enables country-wide quantification of forest browning. Case studies with independent reference data from known events illustrate that the model reliably detects different types of disturbances.
Why it matches plant phenotyping methodsSentinel-2 NDVIを用いて森林キャノピーの季節変動から褐変・攪乱状態を推定する手法を開発し、適合度と独立参照データで検証しているため、単なる森林地図作成ではなく植物状態の取得・評価が中心である。
abstractwe present a scalable approach for country-wide mapping of forest greenness anomalies at the 10 m resolution of Sentinel-2.
Reproduction assets foundThe paper explicitly states that its code and interactive content are publicly available in the authors' GitHub repository. Other URLs in the article are cited third-party data sources (swisstopo, EnviDat, GDAL, TauDEM, WhiteboxTools) rather than paper-specific assets.Code · publicThe code and interactive content are available at https://github.com/SamanthaBiegel/s2-forest-browning-monitoring .Open asset ↗SamanthaBiegel/s2-forest-browning-monitoringlines:51-55Code / dataset availability confirmedOpenAlex · Europe PMC · checked 15 Sept 2026
ABSTRACT Hyperspectral remote sensing is a powerful, high-throughput phenotyping tool that quantifies physiologically and structurally relevant wavelengths across diverse genotypes and over varying temporal scales. In this study, we combined tower-based continuous hyperspectral sensing with genome-wide association studies to analyze 1,423 wavebands (400-900 nm) and derivative vegetation indices across 505 genotypes and the genetic architecture of hyperspectral phenotypes over time in Populus trichocarpa Torr. & Gray grown under field conditions. Wavelengths related to chlorophyll and carotenoid absorption spectra exhibited the strongest genetic variation resulting in 98 significant SNP associations. Notably, we found substantial overlap in genetic association between the blue and red spectral regions, indicative of carotenoids and chlorophyll, respectively, and identified more than 10 candidate genes associated with chloroplast function, underpinning photosynthetic activity. Furthermore, fluctuations in associations for vegetative indices, such as the chlorophyll:carotenoid index (CCI), across the growing season reveal a temporally dynamic genetic architecture of physiological traits associated with fall senescence of this temperate tree species. Finally, we also observed correlations (⍴=0.3, p 0.5, p<1x10 -16 ), reinforcing the value of hyperspectral measurements for predicting traits linked to tree productivity. These findings highlight the potential of high-throughput, rapid, hyperspectral genome wide association studies GWAS to uncover physiologically meaningful genetic variation and offer promising insights for future acceleration for plant breeding.
Why it matches plant phenotyping methodsタワー型連続ハイパースペクトルセンシングを用いて多数の遺伝子型の生理・構造形質を時系列で取得し、表現型解析とGWASに substantively 適用しているため、フェノタイピング手法が中心的である。
abstractHyperspectral remote sensing is a powerful, high-throughput phenotyping tool that quantifies physiologically and structurally relevant wavelengths across diverse genotypes and over varying temporal scales.
Reproduction assets foundThe paper's hyperspectral phenotype dataset (tower-based hyperspectral traits for 505 Populus trichocarpa genotypes) is explicitly stated to be publicly available through the Oak Ridge National Laboratory LabKey data portal with DOI 10.25983/CBI/3012775. This is a paper-specific, public, actionable phenotype dataset. ADataset · publicHyperspectral phenotype data are publicly available through the Oak Ridge National Laboratory LabKey data portal (DOI: 10.25983/CBI/3012775).Oak Ridge National Laboratory LabKey data portal · 10.25983/CBI/3012775lines:163-201Code / dataset availability confirmedEurope PMC · bioRxiv · Crossref · checked 5 Sept 2026
A central problem in soft and biological physics is how molecular-scale activity and remodelling coarse-grain into emergent mechanical laws at larger scales. In growing cell walls (polymeric composite materials that surround 90% of living organisms’ cells) irreversible deformation is not controlled by elastic stress alone. Instead, growth depends on the interplay between energy storage, dissipation, and the local timing of viscoelastic relaxation. Although dynamic atomic force microscopy (AFM) resolves storage and loss moduli ( E′, E″) of living walls at nanometre resolution, these observables have remained phenomenological and disconnected from constitutive field variables. Here we introduce a physics-based inversion framework that converts AFM measurements of epidermal cells of living Arabidopsis plants into spatially resolved fields of stiffness k , viscosity η , and relaxation time τ . By analysing the spatial gradients of E′ and E″, we uncover organized mechanical heterogeneities governed by cellular confinement and stress focusing. We demonstrate that the local relaxation time is encoded directly in the coupling between storage and dissipation, yielding the pointwise relation τ = (1/ ω ) ∂ E ’/∂ E ’’, where ω is the indentation frequency. This relation enables model-independent extraction of mechanical timescales and establishes a general route from nanoscale non-equilibrium rheology to continuum descriptions of growth in living and active soft materials. Significance How molecular-scale activity gives rise to tissue-scale form is a central challenge in biological physics. Although growth is fundamentally a non-equilibrium mechanical process, experimental measurements at the nanoscale have not been directly connected to the constitutive parameters that govern morphogenesis. We introduce a framework that converts dynamic atomic force microscopy maps of storage and loss moduli into spatially resolved fields of stiffness, viscosity, and relaxation time in living cell walls. By revealing that mechanical relaxation is encoded in the local coupling between elastic storage and viscous dissipation, our work provides a route from nanoscale rheology to growth-relevant mechanical timing. This establishes a quantitative bridge between molecular remodeling and continuum mechanics, enabling direct experimental constraints on multiscale theories of morphogenesis.
Why it matches plant phenotyping methods生きたArabidopsis細胞のAFM測定を物理ベースで反転し、剛性・粘性・緩和時間という植物細胞壁の機械的形質を空間的に抽出する新規フレームワークが研究の中心である。
abstractHere we introduce a physics-based inversion framework that converts AFM measurements of epidermal cells of living Arabidopsis plants into spatially resolved fields of stiffness k , viscosity η , and relaxation time τ .
Reproduction assets foundThe paper's custom AFM viscoelastic analysis code is explicitly deposited and publicly available on GitHub (ForceMetric). The underlying AFM phenotype/measurement data are only available upon request, not publicly.Code · publicAFM data were analysed in Python 3.5 using previously described routines [34] (code
available at https://github.com/jcbs/ForceMetric ).Open asset ↗jcbs/ForceMetricpdf-page:14 lines:1-56Code / dataset availability confirmedEurope PMC · OpenAlex · checked 14 Sept 2026
Sun-induced fluorescence (SIF) has emerged as a promising tool for tracking photosynthetic dynamics, yet its application in monitoring biotic stress remains underexplored in field conditions. In this study, we investigated the effects of Cercospora leaf spot (CLS), a destructive foliar disease of sugar beet (Beta vulgaris L.), for which traditional monitoring methods often fail to capture subtle disease effects or distinguish between structural and physiological stress responses. CLS infection was induced through artificial inoculation and manually scored. Canopy-level reflectance indices were acquired along with red and far-red passive SIF signals and active PSII efficiency traits using FloX and LIFT sensors mounted on an automated high-throughput phenotyping platform. The results demonstrate that SIF effectively detects CLS in sugar beet, with responses comparable with structural and disease- specific indices. Despite visible symptoms, PSII efficiency (Fq'/Fm') remained stable across treatments, indicating limited impairment of leaf photosynthetic efficiency at early stages. However, the canopy-level electron transport rate varied significantly and showed a strong relationship with red and far-red SIF, suggesting that CLS primarily affects canopy light absorption and utilization. After structural normalization, SIF yield remained largely unchanged, confirming that observed SIF reductions were mainly driven by canopy structural alterations. Overall the study demonstrates the effectiveness of SIF for large-scale disease monitoring and integration into high-throughput phenotyping, while also revealing structural and physiological factors influencing the SIF signal under disease stress.
Why it matches plant phenotyping methodsSIFおよびPSIIセンサーを搭載したハイスループット表現型解析プラットフォームで、サトウダイコンの病害状態と構造・生理応答を評価する手法の実質的な適用・検証が中心である。
abstractCanopy-level reflectance indices were acquired along with red and far-red passive SIF signals and active PSII efficiency traits using FloX and LIFT sensors mounted on an automated high-throughput phenotyping platform.
Reproduction assets foundThe paper's phenotyping dataset (SIF, reflectance indices, LIFT PSII traits, disease scores from the CLS sugar beet field trial) is deposited in the open access Jülich DATA repository under DOI 10.26165/JUELICH-DATA/FOQOFI. No separate author analysis code repository with explicit availability language is stated; R/lmeDataset · publicThe dataset has been deposited in the open access Jülich DATA reposi ease using UAV-supported image data and deep learning. Sugar Industry
tory: https://doi.org/10.26165/JUELICH-DATA/FOQOFI. 147, 79–86.
Ispizua Yamati FR, Bömer J, Noack N, Linkugel T, Paulus S, Mahlein
A-K. 2025. Configuration of a multisensor platform for advanced plant phe
References notyping and disease detection: case study on cercospora leaf spot in sugar
Ač A, Malenovský Z, Olejníč ková J, Gallé A, Rascher U, Mohammed beet. Smart AgricultOpen asset ↗Jülich DATA · 10.26165/JUELICH-DATA/FOQOFIpdf-layout-page:14 lines:52-72Code / dataset availability confirmedEurope PMC · bioRxiv · checked 15 Sept 2026
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
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methods and analysed the results. The experimental data were collected by C.R. assisted by
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G.L.M., C.T. and D.J.W. Data analysis and writing of paper by all authors.
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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
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this version posted March 18, 2026.
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https://doi.org/10.64898/2026.03.16.71158Open asset ↗Zenodo · 18184803pdf-raw-page:21 lines:1-47Code / dataset availability confirmedCrossref · checked 5 Sept 2026
Abstract Photosynthetic organisms have evolved multiple non-photochemical quenching (NPQ) processes, providing photoprotection by safely dissipating excess excitation energy. These processes involve various molecular players functioning on overlapping timescales from seconds to days, making it challenging to isolate and quantify their individual kinetics. In this study, we perform whole-leaf chlorophyll fluorescence lifetime and xanthophyll concentration measurements on wild-type and various newly characterized NPQ mutants of Nicotiana benthamiana , a vascular land plant. Based on these measurements, we construct a fluorescence lifetime-based quantitative kinetic model that disentangles individual photoprotection components and, when integrated additively, accurately predicts wild-type and mutant NPQ behaviors under various light-dark regimes. Additionally, the model quantifies the per-molecule quenching effectiveness of various xanthophylls and the contributions of six quenching components (qE V , qE A, qE Z, qE L, qZ, and qI) across different genotypes. It also suggests improved overall quenching efficiency at specific VDE:ZEP:PsbS overexpression stoichiometries, aligning with previous studies and supporting translational efforts to optimize photoprotection and enhance crop yields under dynamic light environments.
Why it matches plant phenotyping methods葉の蛍光寿命測定を基盤に、NPQ成分を分離・定量するモデルを構築しており、植物の光防護状態を取得・抽出する方法が研究の中心です。
abstractBased on these measurements, we construct a fluorescence lifetime-based quantitative kinetic model that disentangles individual photoprotection components and, when integrated additively, accurately predicts wild-type and mutant NPQ behaviors under various light-dark regimes.
Reproduction assets foundThe paper's fluorescence lifetime/pigment phenotyping data and the NPQ model code are both publicly deposited on Zenodo (DOI 10.5281/zenodo.16755870), per explicit Data availability and Code availability statements.Dataset · publicThe data supporting the findings of this study are available within the article and at https://doi.org/10.5281/zenodo.16755870 .Open asset ↗Zenodo · 10.5281/zenodo.16755870lines:171-237Code · publicThe codes for NPQ models used in this study are available at https://doi.org/10.5281/zenodo.16755870 .Open asset ↗Zenodo · 10.5281/zenodo.16755870lines:171-237Code / dataset availability confirmedOpenAlex · Crossref · Europe PMC · checked 14 Sept 2026
Pea (Pisum sativum) production is challenged by drought stress. Traditional methods for assessing drought tolerance are limited, and high-throughput phenotyping (HTP) can facilitate the rapid and automated assessment of plant traits. Herein, 180 Pisum spp. accessions were evaluated using an indoor HTP platform under two irrigation treatments, control (70% field capacity) and drought stress (30% field capacity), for 50 days. A combination of digital phenotyping via imaging and manual measurements was used to analyse biomass-related, architectural, and physiological traits. Drought conditions resulted in significant reductions in biomass-related traits including fresh weight (47%), total leaf area (43%), and dry weight (41%). In contrast, PSII photochemical efficiency, leaf weight ratio, and solidity showed negative sensitivity index values (ranging from -7% to -1%), indicating comparatively lower sensitivity to drought and suggesting relative stability of these traits under water-limited conditions. The high heritability value for water use efficiency (0.87) suggests that this parameter may be useful for distinguishing pea's responses to suboptimal soil moisture levels. Principal component analysis (PCA) highlighted patterns of trait variation and associations among biomass-related traits, such as fresh weight, dry weight, and leaf area, which were sensitive to drought conditions. This suggests that the plants may use a combination of strategies to cope with water limitations. Furthermore, studying the significant variation in drought response among the diverse Pisum species and subspecies revealed distinct adaptation strategies. These findings support the development of crops that are resilient to the negative effects of climate change.
Why it matches plant phenotyping methods屋内HTPプラットフォームと画像ベースのデジタルフェノタイピングを用いて、多数アクセッションの形態・生理形質を取得・解析しており、フェノタイピング手法の実質的な適用が研究の中心です。
abstracthigh-throughput phenotyping (HTP) can facilitate the rapid and automated assessment of plant traits
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 analysis software for the RGB side‐view imaging has been developed in Python by the NPEC data team, the source is published on Github, accessible via this link: https://github.com/NPEC‐NL/greenhouse_m5 .Open asset ↗NPEC‐NL/greenhouse_m5lines:68-83Code / dataset availability confirmedCrossref · checked 5 Sept 2026
Abstract. Climate change and extreme weather events pose challenges to food security, emphasizing the need for reliable and timely monitoring of crop and rangeland conditions. For this purpose, long-term consistent Earth Observation datasets on vegetation conditions are typically used in early warning and crop yield forecast systems. However, the near-real-time (NRT) production of high quality datasets and the need to guarantee long-term records present various challenges. To address these, we present a NRT global dataset of Fraction of Photosynthetically Active Radiation (FPAR) at 500 m resolution, optimized for agricultural applications. Our dataset combines MODIS-FPAR (Collection 6.1) and VIIRS-FPAR (Collection 2) data, ensuring continuity from 2000 to well beyond 2030. We applied a robust filtering approach based on the Whittaker smoother to produce reliable FPAR estimates in NRT, accounting for sparse and irregular spaced observations due to cloud cover. The dataset is composed of two 10 d filtered timeseries: (1) MODIS-FPAR for 2000 to 2023, being the reference dataset, and (2) intercalibrated VIIRS-FPAR for 2018 onward. While several methods can effectively smooth and gap-fill FPAR data (i.e., using observations before and after the estimation date), our method is designed for optimal filtering in NRT (i.e., using only prior observations). Our approach yields six successive estimates of the same FPAR data point with increasing quality: an inital estimate immediately after the 10 d reference period, four subsequent estimates every 10 d using new observations, and a final consolidated estimate 90 d later. The implemented filtering ingests the available FPAR observations and their original quality assessment (QA) layers. To avoid unrealistic extrapolation when observations are sparse, we impose constraints, season and location specific, to FPAR estimates. We then intercalibrated the VIIRS-FPAR with the MODIS-FPAR filtered timeseries, using a mean difference correction approach, to ensure consistency between both series. This paper describes the filtering and intercalibration method used, the quality assessment of resulting timeseries, and details the obtained products and the corresponding QA layers. The NRT FPAR dataset is publicly available through the Joint Research Centre Data Catalogue, https://doi.org/10.2905/1aac79d8-0d68-4f1c-a40f-b6e362264e50 (Seguini et al., 2025).
Why it matches plant phenotyping methodsMODIS/VIIRSから植物キャノピー状態であるFPARを推定するNRTフィルタリング・相互較正手法とデータセットの開発、品質評価が中心であり、単なる農業モニタリングへの routine measurement ではない。
abstractThis paper describes the filtering and intercalibration method used, the quality assessment of resulting timeseries, and details the obtained products and the corresponding QA layers.
Reproduction assets foundThe paper describes its own global NRT 500 m 10 d filtered FPAR dataset (MODIS and intercalibrated VIIRS timeseries with QA layers), explicitly stated to be publicly and freely available via the JRC Data Catalogue DOI and directly downloadable from the ASAP server, with visualization in the ASAP Warning Explorer. This衍Dataset · publicThe NRT FPAR dataset is publicly available through the Joint Research Centre Data Catalogue, https://doi.org/10.2905/1aac79d8-0d68-4f1c-a40f-b6e362264e50 ( Seguini et al. , 2025 ) .Open asset ↗10.2905/1aac79d8-0d68-4f1c-a40f-b6e362264e50lines:158-173Dataset · publicor can be directly downloaded from the following server https://agricultural-production-hotspots.ec.europa.eu/data/MO6_FPAR/ (last access: 30 September 2025).Open asset ↗lines:245-257Code / dataset availability confirmedEurope PMC · checked 5 Sept 2026
Published11 Jan 2026TAG. Theoretical and applied genetics. Theoretische und angewandte GenetikCited by 2 · OpenAlex ↗
Utilizing the diversity preserved in genebank collections is essential for accelerating crop improvement, yet information is often limited to selected core collections. Genome-wide prediction (GWP) offers a promising approach to large-scale phenotypic imputation, with proven utility in practical pre-breeding contexts. In this study, we leveraged GWP to expand the German Federal ex situ barley core collection (core1000) with a focus on resistance to Puccinia hordei, Blumeria graminis hordei, and Rhynchosporium commune. Using the barley core1000 collection, which was originally selected to maximize molecular diversity, we trained genomic prediction models and imputed resistance scores for 20,458 genebank accessions based on sequence data encompassing 306,049 high-quality SNPs. To empirically validate prediction accuracy, we selected 300 spring and winter barley genotypes for field evaluation across four environments, resulting in moderate-to-strong correlations between predicted and observed resistance levels. Genome-wide association mapping in this set revealed five marker-trait associations that were not detected in the original core1000 collection. These results demonstrate that prediction-informed sampling can effectively expand trait-relevant genetic diversity and increase the frequency of resistance-associated alleles, thereby improving the power to detect loci that may be overlooked in conventional panels. Accordingly, GWP supports the targeted inclusion of accessions with trait-relevant variation and enhances the value of genebank resources for trait discovery and pre-breeding applications.
Why it matches plant phenotyping methodsゲノム情報から病害抵抗性という植物形質を大規模に推定・補完する方法を開発し、圃場評価で予測精度を検証しているため、方法論が中心である。
abstractGenome-wide prediction (GWP) offers a promising approach to large-scale phenotypic imputation
Reproduction assets foundThe authors deposited the paper's raw disease-resistance phenotypic data, BLUEs for the spring/winter validation set, and the R script for curation/heritability/BLUE computation in the public e!DAL-PGP repository (Yuan 2025). The companion IPK/2024/7 dataset belongs to cited prior work (Yuan et al. 2025 training data).Dataset · publicThe raw phenotypic data described here as well as the ready-to-use phenotypic values (BLUEs) for spring and winter validation set, and the R script to import and curate the raw phenotypic data to compute heritability and BLUEs are available in the e!DAL-PGP Repository (Arend et al. 2016 ) and can be directly accessed here (Yuan 2025 ).Open asset ↗e!DAL-PGP Repositorylines:157-182Code / dataset availability confirmedOpenAlex · checked 5 Sept 2026
Field / plotMultispectral / hyperspectralLeafVisualization / data managementLeaf traitsPhotosynthesis / fluorescence
Abstract. Accurate assessment of leaf functional traits is crucial for a diverse range of applications from crop phenotyping to parameterizing global climate models. Leaf reflectance spectroscopy offers a promising avenue to advance ecological and agricultural research by complementing traditional, time-consuming gas exchange measurements. However, the development of robust hyperspectral models for predicting leaf photosynthetic capacity and associated traits from reflectance data has been hindered by limited data availability across species and environments. Here we introduce the Global Spectra-Trait Initiative (GSTI), a collaborative repository of paired leaf hyperspectral and gas exchange measurements from diverse ecosystems. The GSTI repository currently encompasses over 7500 observations from 397 species and 41 sites gathered from 36 published and unpublished studies, thereby offering a key resource for developing and validating hyperspectral models of leaf photosynthetic capacity. The GSTI database is developed on GitHub (https://github.com/plantphys/gsti, last access: 4 January 2026) and published to ESS-DIVE https://doi.org/10.15485/2530733, Lamour et al., 2025). It includes gas exchange data, derived photosynthetic parameters, and key leaf traits often associated with traditional gas exchange measurements such as leaf mass per area and leaf elemental composition. By providing a standardized repository for data sharing and analysis, we present a critical step towards creating hyperspectral models for predicting photosynthetic traits and associated leaf traits for terrestrial plants.
Why it matches plant phenotyping methods葉のハイパースペクトルとガス交換・光合成形質を標準化して収録するデータベースを構築し、植物フェノタイピングモデルの開発・検証に供することが中心である。
abstractHere we introduce the Global Spectra-Trait Initiative (GSTI), a collaborative repository of paired leaf hyperspectral and gas exchange measurements from diverse ecosystems.
Reproduction assets foundThe paper describes the GSTI database of paired leaf hyperspectral and gas-exchange measurements, with both the data and R processing/model-fitting code publicly available on GitHub and archived releases on ESS-DIVE.Code · publicThe GSTI data and code are available in the public GitHub repository at https://github.com/plantphys/gsti (last access: 4 January 2026)Open asset ↗https://github.com/plantphys/gstilines:537-549Code / dataset availability confirmedOpenAlex · Europe PMC · bioRxiv · checked 15 Sept 2026
Abstract In perennial crops, inner wood degradation by pathogens often escapes detection until irreversible damage has occurred. Grapevine trunk disease (GTD) is a well-known example in viticulture that alters plants from within, years before foliar symptoms arise, making early assessment difficult. To overcome this limitation, we present a novel non-destructive 3D + t pipeline for Magnetic Resonance Imaging (MRI) spatial quantification and monitoring of early internal tissue degradation resulting from fungal colonization. This pipeline integrates (i) anatomical alignment and rigid time-series registration of volumetric MRI scans, (ii) a generalized cylindrical coordinate transformation for cross-sectional trunk anatomy normalization, (iii) supervised classification to segment water-depleted (diseased/non-functional) regions, and (iv) population-level statistical analyses including construction of population mean images, probabilistic atlases of lesions, and 3D lesion descriptors. Applied to multiple Vitis vinifera cultivars inoculated with a fungal trunk pathogen, our approach enables time-lapse comparisons between cultivar and treatment in vivo. The results reveal consistent early degradation signals across individuals and cultivar-dependent lesion differences. By combining high-resolution MRI with advanced image processing and statistical atlas tools, this method provides a new paradigm for 3D plant phenotyping of internal disease progression. This methodological innovation allows non-invasive quantification of disease development and comparative assessment of host responses in woody plants, demonstrating its potential to advance understanding and management of GTDs.
Why it matches plant phenotyping methodsMRI画像と画像処理・統計アトラスを統合し、ブドウ樹内部の病変・組織劣化を3Dで定量化する植物フェノタイピング手法の開発が中心である。
abstractwe present a novel non-destructive 3D + t pipeline for Magnetic Resonance Imaging (MRI) spatial quantification and monitoring of early internal tissue degradation resulting from fungal colonization.
Reproduction assets foundThe paper's raw/processed MRI datasets are only available from the corresponding author upon reasonable request, but the authors' processing pipeline (scripts and parameters to reproduce processed outputs from raw data) is publicly deposited on Zenodo with an explicit DOI.Code · publiceer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made
available under a CC-BY 4.0 International license.
1 reasonable request. The processing pipeline (including scripts and parameters required to reproduce the
2 processed outputs from the raw data) is available at https://doi.org/10.5281/zenodo.17944369.
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Introduction Understanding plant responses to abiotic stress requires an insight into plant redox activity. This study proposes a novel and cost-effective method for assessing the redox state of plants. Methods The method utilizes the electrochromic properties of polyoxometalate phosphomolybdic acid hydrate (PMA). PMA is reduced proportionally by glutathione (GSH) and ascorbic acid (AsA), which results in a measurable color change. The validity of this method was confirmed through empirical experimentation in Arabidopsis thaliana under conditions of salinity and UV radiation. Results Salinity treatments revealed a non-significant, two-phase trend in redox activity with an increase at moderate levels followed by a decrease. UVC radiation led to a substantial decrease in redox activity, indicating distress. In contrast, UVA promoted resilience, also known as eustress. Notably, UVB significantly increased redox activity, suggesting the activation of an emergency antioxidant response. Discussion A demonstrable correlation has been identified between the redox activity of plants and various stress types. This correlation facilitates the classification of responses into two distinct categories: adaptive eustress and detrimental distress. This advancement contributes to the enhancement of plant metabolic and stress tolerance evaluation.
Why it matches plant phenotyping methods植物のレドックス状態を測定する新規手法を開発し、シロイヌナズナでストレス条件下の妥当性を検証しており、表現型取得が研究の中心である。
abstractThis study proposes a novel and cost-effective method for assessing the redox state of plants.
Reproduction assets foundThe paper's Data availability statement points to a public Zenodo deposit (DOI 10.5281/zenodo.17795112) containing the study's datasets, which underpin the PMA-based redox activity measurements (absorbance at 852 nm) in Arabidopsis thaliana under salinity and UV stress. No author analysis code or trained models are attDataset · publicThe datasets presented in this study can be found in online repositories. The names of the repository/repositories and accession number(s) can be found below: https://doi.org/10.5281/zenodo.17795112 .Open asset ↗zenodo · 10.5281/zenodo.17795112lines:416-483Code / dataset availability confirmedEurope PMC · checked 15 Sept 2026
Stomatal pores, formed by guard cells, govern the critical trade-off between carbon assimilation and water loss in plants. Their dynamic responses to environmental stresses, such as stomatal oscillations and drought “stress memory” (hysteresis), have lacked a unified mechanistic explanation. While abscisic acid (ABA) is believed to play key roles in water stress responses, no model has linked its core regulatory kinetics to these complex stomatal behaviors. Here, we introduce a coupled hydropassive-hydroactive (HP-HA) model that integrates leaf hydraulics with the biokinetics of guard cell-autonomous ABA regulation and plasma membrane-mediated osmoregulation. We demonstrate that this framework predicts accurate, genotype-specific stomatal regulation across wildtype, ABA-insensitive mutant ( ost1-3 ), and ABA-synthesis mutant ( aao3-2 ) in Arabidopsis thaliana ( At ) and that non-linear feedbacks in ABA autoregulation can drive both stomatal oscillations and hysteresis. This work unifies genetic, signaling, and membrane processes with leaf-scale physiological dynamics, providing a new predictive foundation for understanding and modulating plant management of water use and water stress.
Why it matches plant phenotyping methods葉の水理とABA制御を統合した予測モデルを開発し、遺伝子型別の気孔コンダクタンス制御を検証しており、植物生理表現型の取得・予測手法が中心である。
abstractHere, we introduce a coupled hydropassive-hydroactive (HP-HA) model that integrates leaf hydraulics with the biokinetics of guard cell-autonomous ABA regulation and plasma membrane-mediated osmoregulation.
Reproduction assets foundThe paper's Code Availability section explicitly archives all MATLAB code used to generate the study's stomatal conductance modeling results in a Zenodo repository (DOI 10.5281/zenodo.17888362) and on GitHub (desai-sahil/sys-bio-gs), both listed as allowed URLs. This is author analysis code directly reproducing the hydCode · publicn analysis are provided in SI sections S5. Comprehensive tables listing all model parameters,
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on steps to run the code to reproduce the results in main text.
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We thank F. E. Rockwell, V. Bacheva, S. Sen, I. Gabay, E. Wu, J. BeldiOpen asset ↗Zenodo · 10.5281/zenodo.17888362pdf-raw-page:9 lines:1-74Code · publicnd the methodology for parameter fitting are provided in SI section S7.
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on steps to run the code to reproduce the results in main text.
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We thank F. E. Rockwell, V. Bacheva, S. Sen, I. Gabay, E. Wu, J. Belding, and P. Jain for insightful
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Crop traits are the integrated outcome of genetic variation, environmental conditions, and their complex interactions, rendering accurate prediction from genetic markers alone a persistent challenge. Here, we present KineticGP, a computational framework that combines genomic prediction with genotype-specific kinetic models of C 4 photosynthesis to make predictions of leaf photosynthetic traits across genotypes from a multiple-parent advanced generation intercross maize population. Using genetic markers and gas exchange measurements from three field seasons, we show that KineticGP outperforms a baseline genomic prediction model in predicting the photosynthetic rate at saturating light by 86% for unseen genotypes across two seen seasons. In addition, KineticGP enabled us to survey genetic variability in enzyme kinetic parameters, which can be used to identify targets for the improvement of photosynthesis. This approach paves the way for interrogating and integrating the dynamic interactions between genotype and environment to improve the accuracy of photosynthetic trait predictions.
Why it matches plant phenotyping methods葉の光合成形質を予測する計算フレームワーク自体が研究の中心であり、遺伝マーカーとガス交換測定を統合した植物生理形質の推定手法を開発・評価している。
abstractHere, we present KineticGP, a computational framework that combines genomic prediction with genotype-specific kinetic models of C 4 photosynthesis to make predictions of leaf photosynthetic traits across genotypes
Reproduction assets found保存済みの本文根拠を更新済みルールで再検証し、公開資産1件を確認しました。Code · publicAll codes and data to ensure the reproducibility of the results can be accessed at https://github.com/Rudan-X/KineticGP .Open asset ↗GitHub · Rudan-X/KineticGPlines:231-264Code / dataset availability confirmedCrossref · checked 5 Sept 2026
ABSTRACT Sample preparation is an important first step to obtain high quality mass spectrometry imaging (MSI) data. Preparing plant tissues is especially challenging for MSI of thin tissues along the lateral dimensions. The unique challenges involved with plant tissues, such as fragile cell walls, hydrophobic barriers, and specific tissue structures, often lead to inefficiency and difficulties in sample preparation. Imprinting plant tissues onto porous polytetrafluoroethylene (pPTFE) sheet has been widely used to extract internal metabolites in leaves and petals while keeping spatial resolution for MSI. However, pressure applications were typically made manually using a vise or pliers leading to low reproducibility and resolution in MS images. In this study, we introduce a home‐built pneumatic press (PNP) that has been designed to precisely control the pressure application parameters during imprinting. To evaluate the performance of the new device, Lemna minor fronds, Arabidopsis thaliana , and Bacopa monnieri leaves were imprinted onto the pPTFE with PNP, vise, or pliers, and matrix‐assisted laser desorption/ionization (MALDI) MSI was obtained on the imprints. The PNP showed dramatic improvements in reproducibility and image quality compared to manual pressure application tools.
Why it matches plant phenotyping methods植物組織の空間的な代謝物情報を再現性よく取得するための空気圧式インプリンティング装置を開発し、手動法と性能比較している。植物表現型取得に関わる試料調製・イメージング手法が中心である。
abstractIn this study, we introduce a home‐built pneumatic press (PNP) that has been designed to precisely control the pressure application parameters during imprinting.
Reproduction assets foundThe paper's MALDI-MSI data (imzML files of imprinted Lemna minor, Arabidopsis, and Bacopa tissues) are openly deposited in a paper-specific METASPACE project, as stated in the Data Availability Statement. No author analysis code or trained models are disclosed.Dataset · publicData Availability Statement
The data that support the findings of this study are openly available in METASPACE (https://metaspace2020.eu/project/pnp_ptfe_imprinting_plant).Open asset ↗METASPACE · pnp_ptfe_imprinting_planthtml-lines:230-307Code / dataset availability confirmedEurope PMC · checked 14 Sept 2026
Grapevine water relations are increasingly influenced by drought under climate change, with significant implications for yield, fruit composition and wine quality. Stable isotopes of hydrogen, oxygen, carbon and nitrogen (δ 2 H, δ 18 O, δ 13 C and δ 15 N) provide sensitive tracers of plant water sources and physiological responses to stress. Here, we combined dual water isotopes (δ 2 H, δ 18 O), carbon and nitrogen isotopes (δ 13 C, δ 15 N), and high-resolution micrometeorological/soil observations to diagnose drought dynamics in Vitis vinifera cv. Sauvignon blanc (Orlești, Romania; 2023-2024). Dual-isotope relationships delineated progressive evaporative enrichment along the soil-plant-atmosphere continuum, with slopes LMWL ≈ 6.41 > stem ≈ 5.0 > leaf ≈ 2.2, consistent with kinetic fractionation during transpiration (leaf) superimposed on source-water signals (stem). Weekly leaf δ 18 O covaried strongly with relative humidity (RH; r = -0.69) and evapotranspiration (ET; r = +0.56), confirming atmospheric control of short-term enrichment, while stem isotopes showed buffered responses to soil water. We integrated Δ 18 O (leaf-stem), RH, ET, and soil matric potential at 60 cm (Soil 60 ) into an Isotopic Drought Index (IDI), which captured the onset, intensity, and persistence of the July-August 2024 drought (IDI 0-100 > 90; RH 40 mm wk -1 , Soil 60 > 100 cb). Carbon and nitrogen isotopes provided complementary, integrative diagnostics: δ 13 C increased (less negative) with drought (r = -0.52 with RH; +0.49 with IDI), reflecting higher intrinsic water-use efficiency, whereas δ 15 N rose with soil dryness and IDI (leaf: r ≈ +0.48 with Soil 60 ; +0.42 with IDI), indicating constraints on N acquisition and enhanced internal remobilization. Together, multi-isotope and environmental data yield a mechanistic, field-validated framework linking atmospheric demand and edaphic limitation to vine physiological and biogeochemical responses and demonstrate the operational value of an isotope-informed drought index for precision viticulture.
Why it matches plant phenotyping methods複数同位体と環境データからブドウの水分状態・干ばつ応答を推定するIsotopic Drought Indexを構築し、圃場で検証した研究であり、植物の生理状態取得手法が中心である。
abstractWe integrated Δ 18 O (leaf-stem), RH, ET, and soil matric potential at 60 cm (Soil 60 ) into an Isotopic Drought Index (IDI), which captured the onset, intensity, and persistence of the July-August 2024 drought
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 · publicTable S1: Isotopic data of leaf and stem of Vitis vinifera cv. Sauvignon Blanc blanc from Orlești-Vâlcea (Romania), during 2023-2024 vintage; Table S2: Meteorological and soil measurements (Romania), during the sampling campaign (Orlești – Vâlcea, Romania; 2023-2024 vintage)Open asset ↗lines:149-204Code / dataset availability confirmedEurope PMC · checked 6 Sept 2026
This dataset was generated to support research investigating the use of hyperspectral reflectance for the estimation of foliar nitrogen (N) and phosphorus (P) concentrations in apple ( Malus domestica ) trees. This article and the dataset it describes accompany an original research article submitted to Computers and Electronics in Agriculture entitled "Investigating the limits of spectroscopy for the estimation of foliar N and P in apple" [1]. Data were collected from a controlled potted experiment involving 150 'Golden Delicious' apple trees grown under varying nutrient supply regimes, including full nutrient supply, nitrogen- and phosphorus-deficient treatments, and trees infected with ' Candidatus Phytoplasma mali'. The experiment was conducted over the 2023 growing season at the Laimburg Research Centre in South Tyrol, Italy. All data and the accompanying code for its analysis is freely available in the associated GitHub repository [2]. Spectral data were collected using the Spectral Evolution SR-3500 field spectroradiometer with an attached leaf clip, producing high-resolution hyperspectral reflectance profiles (350-2500 nm) from the adaxial surface of fully expanded leaves. A total of 1189 leaf spectra were recorded and were matched to chemically analysed leaf samples. Corresponding foliar N and P concentrations (and others) were determined through laboratory analysis using the Dumas combustion method for nitrogen and ICP-OES following acid digestion for phosphorus. The dataset includes metadata detailing tree treatments, sampling dates, infection status, and shoot growth metrics. Additionally, R scripts used for data processing, spectral pre-treatment (including multiplicative scatter correction and Savitzky-Golay derivatives), feature selection (VIP and mRMR), and model development are provided. The dataset is suitable for reuse in the development and benchmarking of spectral models for nutrient estimation, especially in the context of field-based or remote sensing applications in horticulture. Its wide range of foliar nutrient values, inclusion of multiple physiological stresses, and detailed documentation make it a valuable resource for researchers working in precision agriculture, plant phenotyping, chemometrics, and hyperspectral data analysis.
Why it matches plant phenotyping methodsリンゴ葉のN・P濃度という植物生理形質を対象に、ハイパースペクトル反射データ、化学分析値、前処理・モデル開発コードを含む再利用可能なデータセットであり、植物フェノタイピング手法の開発・ベンチマークに直接資する。
abstractThis dataset was generated to support research investigating the use of hyperspectral reflectance for the estimation of foliar nitrogen (N) and phosphorus (P) concentrations in apple
Reproduction assets foundThe authors publicly release the paper's own hyperspectral leaf spectra (.sed files), matched foliar N/P concentrations, metadata, and R analysis scripts via a GitHub repository (also archived with Zenodo DOI 10.5281/zenodo.15600557), with explicit public availability and no registration required.Dataset · publicData accessibility
Repository name: Github
Data identification number: DOI 10.5281/zenodo.15600557
Direct URL to data: https://github.com/HyperspectralCameron/Investigating-the-Limits-of-Spectroscopy-for-the-Estimation-of-Foliar-N-and-P-in-Apple.gitInstructions for accessing these data: All data and code are publicly available through the GitHub repository listed above. The repository includes raw spectral files (.sed), metadata files, and R scripts for pre-processing, modelling, and visualisation. No registration or authentication is required.Open asset ↗GitHub · DOI 10.5281/zenodo.15600557html-lines:84-123Code · publicAll data and the accompanying code for its analysis is freely available in the associated GitHub repository [2].Open asset ↗GitHubhtml-lines:1-83Code / dataset availability confirmedCrossref · checked 6 Sept 2026
CoffeeMultispectral / hyperspectralLeafClassificationPhysiological trait estimationWater status / transpiration
Water potential is an important indicator used to study water relations in plants, as it reflects the level of hydration in their tissues. There are different numerical variables that describe plant properties and can be acquired from leaf reflectance. The objective of this study was to estimate water potential in coffee plants using spectral variables. For this, a range of wavelengths that provided analytical flexibility was used. After this, machine learning techniques were employed to build data-driven models. The dataset used presents spectral characteristics (wavelength) of coffee plants, collected through the CI-710 Mini-Leaf Spectrometer equipment and also the water potential of each coffee plant, measured by the Scholander Chamber equipment. The dataset was divided into two crop management groups: irrigated and rainfed. Four machine learning techniques were implemented: Multi-Layer Perceptron (MLP), Decision Tree, Random Forest and K-Nearest Neighbor (KNN). The implementation of machine learning techniques followed two distinct strategies: regression and classification. The results indicate that the decision tree-based model demonstrated superior performance under irrigated conditions for regression tasks. In contrast, the KNN technique achieved the best performance for classification. Under rainfed conditions, the MLP model outperformed the other techniques for regression, while the Random Forest method exhibited the highest accuracy in classification tasks. While no hardware prototype was developed, the machine learning-based methods presented here suggest a possible pathway toward future intelligent, user-friendly, and accessible sensing technologies for coffee plantations.
Why it matches plant phenotyping methodsコーヒー植物の葉スペクトルから水ポテンシャルという生理形質を機械学習で推定・分類する手法が研究の中心であり、植物フェノタイピング手法の開発・評価に該当する。
abstractThe objective of this study was to estimate water potential in coffee plants using spectral variables.
Reproduction assets foundThe paper's Data Availability Statement explicitly states that the study's datasets (coffee leaf spectral reflectance and water potential measurements) and the MATLAB analysis codes are publicly available at the authors' UFLA repository, which is an allowed URL. This is a paper-specific, public, actionable asset.Dataset · publicData Availability Statement: The datasets and MATLAB codes used in this study are available at
http://www.aia.ufla.br/home/filesdatasets/, accessed on 27 November 2025.Open asset ↗aia.ufla.brpdf-page:18 lines:1-54Code / dataset availability confirmedEurope PMC · checked 15 Sept 2026
MaizeTomatoLeafPhysiological trait estimationWater status / transpiration
Within the soil-plant-atmosphere continuum, water movement is driven by the water potential gradients between these three domains. To have a comprehensive understanding of such water relations, an examination of how plants respond to variations in soil water availability is required. The methodologies employed for measuring water potential in leaf (Ψ leaf ) and soil (Ψ soil ) have undergone a significant evolution; transitioning from qualitative assessments to the use of high-precision digital sensors over the past few decades. The present protocol aims to provide a comprehensive, step-by-step guide from the germination phase of maize and tomato plants to the installation of two sensors that continuously monitor water potential in the leaf (PSY1 psychrometer) and in the soil (TEROS 21 matric potential sensor). Additionally, we present the code for processing the raw data files in RStudio.
Why it matches plant phenotyping methods葉の水ポテンシャルという植物生理形質を連続測定するセンサー設置手順とデータ処理コードを中心に扱うプロトコルであり、植物フェノタイピング手法が研究の中心である。
abstractThe present protocol aims to provide a comprehensive, step-by-step guide from the germination phase of maize and tomato plants to the installation of two sensors that continuously monitor water potential in the leaf (PSY1 psychrometer) and in the soil (TEROS 21 matric potential sensor).
Reproduction assets foundThe paper deposits its example water-potential datasets (soil matric potential from Teros 21, leaf water potential from PSY1, transpiration from scales) and the authors' data extraction/cleaning/analysis code on Zenodo (10.5281/zenodo.17158115), under CC0/CC-BY. A supplementary installation video is separately on ZenodDataset · public52.
PubMed Abstract | Publisher Full Text
Cotrozzi L, Couture JJ, Cavender-Bares J, et al.: Using foliar spectral properties
References
Figure 9. Example of data cleaning using the algorithm. Green is kept data and red is discarded data.
Data availability
The datasets and codes to analyze the data have been deposited
on Zenodo (https://doi.org/10.5281/zenodo.17158115, D'Agostino
(2025)).
Data are available under the terms of the Creative Commons
Zero v1.0 Universal
An additional explicative video for the psychrometer instal-
lation on leaves is available on Zenodo (https://doi.org/10.5281/zenodo.17510720, Degand et al. (2025)).
The author(s) declare that this video is released under the
CreOpen asset ↗Zenodo · 10.5281/zenodo.17158115pdf-raw-page:11 lines:1-61Code · publicat were missing,
zero, or otherwise aberrant. It was also programmed to iden-
tify and remove inverted day-night cycle patterns, as well as
values that were statistically insignificant. Figure 9 shows appli-
cations of data cleaning on the example dataset. For more
details, please check codes that have been deposited on Zenodo
(https://doi.org/10.5281/zenodo.17158115, D'Agostino, 2025).
Ethics and consent
Ethical approval and consent were not required
Figure 8. Example of the charging effects on the data recordings.
Page 10 of 18
Open Research Europe 2025, 5:363 Last updated: 19 JUN 2026Open asset ↗Zenodo · 10.5281/zenodo.17158115pdf-raw-page:10 lines:1-58Supplement · publicavailability
The datasets and codes to analyze the data have been deposited
on Zenodo (https://doi.org/10.5281/zenodo.17158115, D'Agostino
(2025)).
Data are available under the terms of the Creative Commons
Zero v1.0 Universal
An additional explicative video for the psychrometer instal-
lation on leaves is available on Zenodo (https://doi.org/10.5281/zenodo.17510720, Degand et al. (2025)).
The author(s) declare that this video is released under the
Creative Commons CC0 1.0 Universal Public Domain Dedica-
tion. This means the video is free of all copyright restrictions
and may be copied, modified, distributed, and used without
permission, including for commercial purposes.
Data are availablOpen asset ↗Zenodo · 10.5281/zenodo.17510720pdf-raw-page:11 lines:1-61Code / dataset availability confirmedEurope PMC · checked 6 Sept 2026
Recent advancements in precision agriculture have introduced innovative approaches to addressing plant stress, a critical factor influencing crop productivity and agricultural sustainability. Accurate, real-time prediction of plant stress has become essential for optimizing water utilization and promoting healthy crop development. While existing machine learning methods have demonstrated efficacy, they often lack the adaptability required to accommodate the dynamic conditions of agricultural environments. Prior research has identified soil moisture and chlorophyll content as key indicators of plant health and stress, with conventional models relying on simplistic algorithms for stress prediction. However, these models exhibit limitations in scalability, adaptability and interpretability. To overcome these challenges, this study employed sparse additive models with learning (SAM-L) algorithms, integrated with explainable artificial intelligence (XAI), to provide a flexible and transparent solution. In this paper, we proposed a novel framework that integrates SAM-L and XAI to predict plant stress using soil moisture and chlorophyll content. The SAM-L algorithm is a machine learning method that focuses on sparsely selecting relevant features through additive models. It aims to enhance model interpretability while maintaining high prediction accuracy by learning sparse representations of input data. The SAM-L algorithm enhances interpretability while preserving high predictive accuracy by learning sparse feature representations from input data. Additionally, XAI was incorporated to ensure interpretable decision-making, enabling farmers and stakeholders to comprehend the rationale behind irrigation recommendations. The model's architecture incorporates a three-layer Long Short-Term Memory (LSTM) network to process sequential data effectively. The proposed framework achieved a high performance on publicly available dataset, yielding an overall accuracy of 89.2% on the multi-class classification task. Further analysis of the results across the three predefined stress categories (healthy, moderate stress, and high stress) revealed strong performance, with the model obtaining a macro F1-score of 0.88 and a macro recall of 0.88. The proposed framework not only can enhance prediction accuracy but also can promote sustainable farming practices by reducing water wastage and improving crop resilience.
Why it matches plant phenotyping methods植物ストレス状態を土壌水分とクロロフィルから推定するSAM-L・XAI・LSTM統合手法が研究の中心であり、植物の生理状態を対象とした計算的フェノタイピング手法に該当する。
abstractIn this paper, we proposed a novel framework that integrates SAM-L and XAI to predict plant stress using soil moisture and chlorophyll content.
Reproduction assets foundThe paper states its plant-stress phenotyping data came from a publicly available Kaggle dataset ('Real-Time Plant Health Insights: Simulated Biosensor Data for AI-Driven Monitoring'), used directly for the SAM-L/XAI stress-prediction experiments. Code is only available on request, so it does not qualify as a public,作者Dataset · publicThe dataset used in this study was sourced from Kaggle and was publicly available.Open asset ↗Kagglepdf-page:16 lines:1-70Code / dataset availability confirmedEurope PMC · checked 15 Sept 2026
Invasive plant pests and pathogens cause substantial environmental and economic damage. Visual inspection remains a central tenet of plant health surveys, but its sensitivity (probability of correctly identifying the presence of a pest) and specificity (probability of correctly identifying the absence of a pest) are not routinely quantified. As knowing sensitivity and specificity of visual inspection is critical for effective contingency planning and outbreak management, we address this deficiency using empirical data and statistical analyses. Twenty-three citizen scientist surveyors assessed up to 175 labelled oak trees for three symptoms of acute oak decline. The same trees were also assessed by an expert who has monitored these individual trees annually for over a decade. The sensitivity and specificity of surveyors was calculated using the expert data as the 'gold-standard' (i.e., assuming perfect sensitivity and specificity). The utility of an approach using Bayesian modelling to estimate the sensitivity and specificity of visual inspection in the absence of a rarely available 'gold-standard' dataset was then examined with simulated plant health survey datasets. There was large variation in sensitivity and specificity between surveyors and between different symptoms, although the sensitivity of detecting a symptom was positively related to the frequency of the symptom on a tree. By leveraging surveyor observations of two symptoms from a minimum of 80 trees on two sites, with reliable prior knowledge of sites with a higher (~0.6) and lower (~0.3) true disease prevalence we show that sensitivity and specificity can be estimated without 'gold-standard' data using Bayesian modelling. We highlight that sensitivity and specificity will depend on the symptoms of a pest or disease, the individual surveyor, and the survey protocol. This has consequences for how surveys are designed to detect and monitor outbreaks, as well as the interpretation of survey data that is used to inform outbreak management.
Why it matches plant phenotyping methods植物の病徴を対象とする目視検査の感度・特異度を定量化し、ゴールドスタンダードなしで推定するベイズモデルを検討しており、植物病害状態の取得・評価法が研究の中心である。
abstractVisual inspection remains a central tenet of plant health surveys, but its sensitivity (probability of correctly identifying the presence of a pest) and specificity (probability of correctly identifying the absence of a pest) are not routinely quantified.
Reproduction assets foundThe paper's Data Availability statement explicitly provides the code and data used for the study (plant health survey sensitivity/specificity analysis) via a public GitHub repository and an archived Zenodo DOI, both listed in allowed_urls.Code · publicne represents perfect agreement between estimated values and actual values.
(TIF)
Acknowledgments
We would like to thank all involved in the AOD survey days and the National Trust and Royal Parks for allowing workshops to take place on their properties.
Data Availability
The code and data used for this paper are available from: https://github.com/MCombess/Plant_Health_sens_spec_workflow and are archived: https://doi.org/10.5281/zenodo.15730414 .
Funding Statement
MC, NB, PC, SP undertook the work with funding from the United Kingdom’s Department for Environment, Food & Rural Affairs ( https://www.gov.uk/government/organisations/department-for-environment-food-rural-affairs ) through the FutuOpen asset ↗Plant_Health_sens_spec_workflowlines:244-268Dataset · publicIF)
Acknowledgments
We would like to thank all involved in the AOD survey days and the National Trust and Royal Parks for allowing workshops to take place on their properties.
Data Availability
The code and data used for this paper are available from: https://github.com/MCombess/Plant_Health_sens_spec_workflow and are archived: https://doi.org/10.5281/zenodo.15730414 .
Funding Statement
MC, NB, PC, SP undertook the work with funding from the United Kingdom’s Department for Environment, Food & Rural Affairs ( https://www.gov.uk/government/organisations/department-for-environment-food-rural-affairs ) through the Future Proofing Plant Health Programme (Project Reference: TH42222FR09: citizen sOpen asset ↗10.5281/zenodo.15730414lines:244-268Code / dataset availability confirmedEurope PMC · checked 14 Sept 2026
Field / plotChlorophyll fluorescenceLeafPhysiological trait estimationGrowth / development / phenologyPhotosynthesis / fluorescencePigment / colour / senescence
Abstract Accurate assessment of leaf chlorophyll is essential for understanding plant physiological responses to environmental variation. While solvent extraction provides precise chlorophyll concentrations, it is destructive and temporally limited. In contrast, portable optical meters such as the CCM-300 enable rapid, non-destructive measurements of chlorophyll fluorescence ratio (CFR), but their calibration against extracted pigments is often species- and season-specific. This study evaluated the reliability of CCM-300 measurements and reconstructed seasonal chlorophyll dynamics in Acer campestre across two contrasting summers in the United Kingdom.Paired CFR and acetone-extracted chlorophyll data collected in 2023 were used to develop calibration models. Random Forest regression achieved the best predictive accuracy (R² = 0.51, RMSE = 0.51 mg cm⁻²), although a simple linear model was adopted for cross-year projection due to its stability. Applying this calibration to daily 2022 CFR measurements generated a “virtual acetone” chlorophyll time series, allowing comparison with weekly destructive extractions in 2023. Both years exhibited mid-season chlorophyll plateaus followed by late-summer declines, but senescence occurred approximately ten days earlier in the warmer, drier 2022 season.Mixed-effects modelling of the 2022 data indicated positive effects of temperature (β = 0.0029 ± 0.0012 SE) and wind speed (β = 0.0053 ± 0.0021 SE) on CFR, whereas day of year and precipitation were not significant. A generalised additive model for 2023 explained 90% of deviance (adj. R² = 0.89) and revealed significant nonlinear effects of temperature, rainfall, and wind speed. Together, these results demonstrate that the CCM-300 can provide a robust non-destructive proxy for total chlorophyll when properly calibrated, and that Acer campestre chlorophyll dynamics are highly sensitive to interannual climatic variability.
Why it matches plant phenotyping methodsCCM-300による葉クロロフィル測定を抽出クロロフィルと比較・較正し、季節時系列へ適用して信頼性を評価しているため、植物フェノタイピング手法が中心である。
abstractThis study evaluated the reliability of CCM-300 measurements and reconstructed seasonal chlorophyll dynamics in Acer campestre across two contrasting summers in the United Kingdom.
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 · publicData Availability: Data used in the study can be accessed via https://zenodo.org/records/17475985.Open asset ↗zenodo · 17475985pdf-page:11 lines:1-44Code / dataset availability confirmedCrossref · Europe PMC · checked 6 Sept 2026
Background: Quantitatively detecting whether plants exhibit measurable bioelectric differences in the presence of nearby human movement remains challenging, in part because plant signals are low-amplitude, slow, and easily confounded by environmental factors. Methods: We recorded bioelectric activity from 2978 plant samples across three species (basil, salad, tomato) using differential electrode pairs (leaf and soil electrodes) sampling at 142 Hz. Two trained performers executed three specific eurythmic gestures near experimental plants while control plants remained isolated. Random Forest and Convolutional Neural Network classifiers were applied to distinguish the control from treatment conditions using engineered features including spectral, temporal, wavelet, and frequency domain characteristics. Results: Random Forest classification achieved 62.7% accuracy (AUC = 0.67) distinguishing differences in recordings collected near a moving human from control conditions, representing a statistically significant 12.7 percentage point improvement over chance. Individual performer signatures were detectable with 68.2% accuracy, while plant species classification achieved only 44.5% accuracy, indicating minimal species-specific artifacts. Temporal analysis revealed that the plants with repeated exposure exhibited consistently less negative bioelectric amplitudes compared to single-exposure plants. Innovation: We introduce a data-driven approach that pairs standardized, short-window bioelectric recordings with machine-learning classifiers (Random Forest, CNN) to test, in an exploratory manner, whether plant signals differ between human-moving-nearby and isolation conditions. Conclusions: Plants exhibit modest but statistically detectable bioelectric differences in the presence of nearby human movement. Rather than attributing these differences to eurythmic movement itself, the present design can only demonstrate that plant recordings collected within ~1 m of a moving human differ, modestly but statistically, from recordings taken ≥3 m away. The underlying biophysical pathways and specific contributing factors (airflow, VOCs, thermal plumes, vibration, electromagnetic fields) remain unknown. These results should therefore be interpreted as exploratory correlations, not mechanistic evidence of gesture-specific plant sensing.
Why it matches plant phenotyping methods植物の生体電気記録をセンサーで取得し、特徴抽出と機械学習によって植物の状態差を判別する方法が研究の中心であり、単なる生理測定ではない。
abstractWe introduce a data-driven approach that pairs standardized, short-window bioelectric recordings with machine-learning classifiers (Random Forest, CNN) to test, in an exploratory manner, whether plant signals differ between human-moving-nearby and isolation conditions.
Reproduction assets foundThe paper's plant bioelectric recordings (wav sensor data from basil, salad, tomato with/without nearby human movement) are publicly deposited on figshare, with an explicit Data Availability Statement and URL matching an allowed URL.Dataset · publicThe datasets generated and analyzed during the current study are available from figshare https://figshare.com/articles/dataset/Machine_Learning_Detection_of_Plant_Bioelectric_Responses_to_Human_Eurythmic_Gestures_/30227083?file=58324288 (accessed 25 October 2025).Open asset ↗figshare · 30227083lines:172-191Code / dataset availability confirmedEurope PMC · checked 6 Sept 2026
Mycorrhizal associations between fungi and plants are a fundamental aspect of terrestrial ecosystems. Mycorrhizas occur in c. 85% of extant plants, yet their geological record remains sparse. Rare fossil evidence from early terrestrial environments offers crucial insights into these ancient symbioses, but visualizing fossil fungi at the microscale within plant tissues is challenging. Here, we combine confocal laser scanning microscopy and fluorescence lifetime imaging microscopy (FLIM) to investigate a newly identified fungus and cellular structures of a 407-Myr-old plant from the Windyfield Chert, a stratigraphically distinct fossiliferous unit from Rhynie (Scotland). We also applied Raman spectroscopy to investigate the carbon framework of both fungal and plant tissues. This integrative approach revealed fungal structures in unprecedented detail. The fungus, Rugososporomyces lavoisierae gen. nov., sp. nov., exhibits features resembling extant Glomeromycotina arbuscular mycorrhizal fungi. This is the first record of mycorrhizas from the Windyfield Chert. FLIM further distinguished features at the subcellular level, while Raman spectroscopy showed that fungal arbuscules and vesicles of the plant water-conducting cells underwent geological alterations, resulting in a similar chemical composition. These findings expand our understanding of ancient and extremely rare plant-fungal symbioses and highlight the potential of confocal-FLIM for advancing palaeobotanical research.
Why it matches plant phenotyping methods植物組織内の微細構造を対象に、共焦点レーザー顕微鏡・FLIM・ラマン分光を組み合わせた観察法を中核としており、化石植物の細胞・菌根構造の状態を抽出している。
abstractHere, we combine confocal laser scanning microscopy and fluorescence lifetime imaging microscopy (FLIM) to investigate a newly identified fungus and cellular structures of a 407-Myr-old plant
Reproduction assets foundThe authors deposited all confocal imaging data used in this fossil mycorrhiza study (CLSM/FLIM datasets of Rugososporomyces lavoisierae in Aglaophyton majus) in a public Zenodo repository under a CC BY 4.0 license. This is a paper-specific, publicly accessible dataset of the phenotyping/imaging measurements.Dataset · publicAll confocal data collected and used in this study are deposited in the Zenodo repository under a Creative Commons Attribution 4.0 international license https://doi.org/10.5281/zenodo.15194427 (Strullu‐Derrien et al ., 2025 ).Open asset ↗Zenodo · 10.5281/zenodo.15194427lines:252-551Code / dataset availability confirmedCrossref · Europe PMC · checked 13 Sept 2026
In this study, we present a pilot investigation using a single Purple Heart plant (Tradescantia pallida) to explore whether bioelectrical signals for dual-purpose classification tasks: environmental state detection and human emotion recognition. Using an AD8232 ECG sensor at 400 Hz sampling rate, we recorded 3 s bioelectrical signal segments with 1 s overlap, converting them to mel-spectrograms for ResNet18 CNN (Convolutional Neural Network) classification. For lamp on/off detection, we achieved 85.4% accuracy with balanced precision (0.85–0.86) and recall (0.84–0.86) metrics across 2767 spectrogram samples. For human emotion classification, our system achieved optimal performance at 73% accuracy with 1 s lag, distinguishing between happy and sad emotional states across 1619 samples. These results should be viewed as preliminary and exploratory, demonstrating feasibility rather than definitive evidence of plant-based emotion sensing. Replication across plants, days, and experimental sites will be essential to establish robustness. The current study is limited by a single-plant setup, modest sample size, and reliance on human face-tracking labels, which together preclude strong claims about generalizability.
Why it matches plant phenotyping methods植物の生体電気信号をセンサーで取得し、スペクトログラムとCNNで環境状態を分類する手法を開発・評価しており、植物の生理状態に基づく表現型取得が中心です。ただし、人間の感情分類は植物表現型ではありません。
abstractUsing an AD8232 ECG sensor at 400 Hz sampling rate, we recorded 3 s bioelectrical signal segments with 1 s overlap, converting them to mel-spectrograms for ResNet18 CNN (Convolutional Neural Network) classification.
Reproduction assets foundThe paper's Data Availability Statement provides explicit public URLs for both the phenotype/bioelectrical signal dataset (figshare project) and the authors' analysis code (GitHub), directly reproducing this paper's plant-phenotyping measurements and computational analysis.Dataset · publicThe data is available at https://figshare.com/projects/Plant_Bioelectrical_Signals_for_Environmental_and_Emotional_State_Classification/265783 (accessed on 25 October 2025).Open asset ↗figshare · Plant_Bioelectrical_Signals_for_Environmental_and_Emotional_State_Classification/265783lines:129-206Code · publicCode is available at https://github.com/pgloor/hiddenbiosignals (accessed on 25 October 2025).Open asset ↗github · pgloor/hiddenbiosignalslines:129-206Code / dataset availability confirmedCrossref · Europe PMC · checked 6 Sept 2026
Early plant disease detection is crucial for sustainable crop production and food security. Stem rust, caused by Puccinia graminis f. sp. tritici, poses a major threat to wheat and barley. This study evaluates the feasibility of using hyperspectral imaging and machine learning for early detection of stem rust and examines the cross-crop transferability of diagnostic models. Hyperspectral datasets of wheat (Triticum aestivum L.) and barley (Hordeum vulgare L.) were collected under controlled conditions, before visible symptoms appeared. Multi-stage preprocessing, including spectral normalization and standardization, was applied to enhance data quality. Feature engineering focused on spectral curve morphology using first-order derivatives, categorical transformations, and extrema-based descriptors. Models based on Support Vector Machines, Logistic Regression, and Light Gradient Boosting Machine were optimized through Bayesian search. The best-performing feature set achieved F1-scores up to 0.962 on wheat and 0.94 on barley. Cross-crop transferability was evaluated using zero-shot cross-domain validation. High model transferability was confirmed, with F1 > 0.94 and minimal false negatives (
Why it matches plant phenotyping methods植物の病徴が現れる前の茎さび病状態を、ハイパースペクトル画像と機械学習で検出・推定する方法の開発および転移性検証が中心である。
abstractThis study evaluates the feasibility of using hyperspectral imaging and machine learning for early detection of stem rust and examines the cross-crop transferability of diagnostic models.
Reproduction assets foundThe paper's Data Availability Statement provides a public Google Drive link to the hyperspectral imaging datasets (864 hyperspectral cubes of wheat and barley, control and stem-rust-inoculated) used for the phenotyping and machine learning analysis. No code or model checkpoints are explicitly deposited.Dataset · publicData is available via online download link https://drive.google.com/drive/folders/1qAgWEAH5BruTNmT0bmSm4HTbXe_iitap (accessed on 21 October 2025).Open asset ↗1qAgWEAH5BruTNmT0bmSm4HTbXe_iitaplines:233-282Code / dataset availability confirmedCrossref · checked 15 Sept 2026
Abstract. Carbonyl sulfide (COS) has been proposed as a proxy for gross primary production (GPP), as it is taken up by plants through a pathway comparable to that of CO2. COS diffuses into the leaf, where it undergoes an essentially one-way reaction in the mesophyll cells, irreversibly catalyzed by the enzyme carbonic anhydrase (CA), and is likely not respired by the leaf. In order to use COS as a proxy for GPP, the mechanisms of COS uptake and its coupling to photosynthesis need to be well understood. Characterizing the isotopic discrimination of COS during plant uptake could provide valuable information on the physiological COS uptake process and may help to constrain the COS budget. This study presents joint measurements of isotope discrimination during plant uptake for COS (CO34S) and CO2 (13CO2 and C18O16O). A C3 plant, sunflower (Helianthus annuus), and a C4 plant, papyrus (Cyperus papyrus), were enclosed in a flow-through plant chamber and exposed to varying light levels. The incoming and outgoing gas compositions were measured online, and discrete air samples were taken for isotope analysis. Simultaneously measuring fluxes and isotope discrimination of both COS and CO2 yielded a unique dataset that includes information on the plant's behavior and allowed for the estimation of stomatal- and mesophyll conductances. The average COS uptake fluxes were 73.3 ± 1.5 pmol m−2 s−1 for sunflower and 107.3 ± 1.5 pmol m−2 s−1 for papyrus (PAR > 0) and displayed virtually no trend with increasing PAR from 200 to 600 µmol m−2 s−1. The mean observed 34Δ for COS was 3.4 ± 1.0 ‰ for sunflower and 2.6 ± 1.0 ‰ for papyrus. 34Δ was stable across all light intensities, which could be explained by a sufficient stomatal opening and low variability in the ratio of mesophyll vs. ambient COS mole fraction, CmS/CaS. For both C3 and C4 plants, for CO2, a negative relationship was observed between the uptake flux and the isotopic discriminations 13Δ and 18Δ. The CO2 uptake and 13CO2 and C16O18O discriminations of sunflower have expected values for a C3 plant, while the low CO2 flux and high 13Δ and 18Δ values observed for papyrus were not in the typical C4 range, which was perhaps due to the relatively low light conditions during our experiments.
Why it matches plant phenotyping methods植物のCOS・CO2取り込み、同位体識別、気孔・葉肉コンダクタンスをフロースルー植物チャンバーで定量する生理的表現型測定が研究の中心であり、再利用可能な測定データセットと推定手法を提示している。
abstractThis study presents joint measurements of isotope discrimination during plant uptake for COS (CO34S) and CO2 (13CO2 and C18O16O).
Reproduction assets foundThe paper's isotope discrimination and gas-exchange dataset from the flow-through chamber experiments is publicly deposited on Zenodo by the authors.Dataset · publicynthetically available radiation at the top of the chamber, 34 Δ is the discrimination against CO 34 S and LRU is the leaf relative uptake ratio.
* n =1 , error states is the single measurement precision instead of the repeatability precision.
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Data availability
The dataset is available at: https://doi.org/10.5281/zenodo.14677494 (Baartman et al., 2025).
Author contributions
Conceptualization: SLB, MCK, MEP, LW. Data curation: SLB. Formal analysis: SLB, NUL. Funding acquisition: MCK. Investigation: SLB, SMD, MW, LMJK, LM, AC, SH. Methodology: SLB, SMD, MW, LMJK, MEP. Resources: SMD, MW, LM, SH. Supervision: MEP, TR, MCK. Visualization: SLB, NUL. WritingOpen asset ↗Zenodo · 10.5281/zenodo.14677494lines:652-942Code / dataset availability confirmedEurope PMC · checked 6 Sept 2026
Quantifying crop responses to increasing temperatures is critical for predicting the productivity and sustainability of agricultural systems under environmental change. Physiological trait data associated with maximum Rubisco carboxylation ( V cmax ) and maximum electron transport ( J max ) rates are especially important predictors of crop response to elevated temperatures. However, when generating V cmax and J max data, steady-state methods of gas exchange measurements are time-consuming; thus, non-steady-state methods have been developed to obtain these measurements faster, prospectively allowing for trait data collection of considerably more varieties of crops. Globally important and geographically widespread vineyards are of particular interest due to the high economic value and the susceptibility of these managed systems to climate warming, especially in Canada, where the annual rate of warming far exceeds global averages. In this study, we examined the efficacy of the high-throughput, non-steady-state dynamic assimilation technique (DAT) for obtaining V cmax and J max data from wine grapes. Specifically, we measured V cmax and J max (alongside leaf nitrogen [N] concentrations and leaf mass per unit area [LMA]) across seven of the world's most common wine grape ( Vitis vinifera L.) varieties, namely, Cabernet franc, Cabernet sauvignon, Merlot, Pinot noir, Riesling, Sauvignon blanc, and Viognier. Our results show that V cmax and J max estimates derived from the DAT were strongly correlated to those obtained through the steady-state method ( r 2 = 0.748 and 0.908, respectively), and J max did not differ significantly between the two methods. Additionally, leaf N explained 43%-46% and 56%-58% of the variation in V cmax and J max , respectively, across both methods. Our results suggest that the DAT represents a viable tool for rapidly estimating intraspecific variation in important physiological traits and allows for increased replication and the inclusion of additional varieties when evaluating the responses of wine grape and other crops to climate warming.
Why it matches plant phenotyping methodsワインブドウの生理形質を高速取得する動的同化技術（DAT）を定常法と比較検証しており、植物表現型の測定法が中心的である。
abstractwe examined the efficacy of the high-throughput, non-steady-state dynamic assimilation technique (DAT) for obtaining V cmax and J max data from wine grapes.
Reproduction assets foundThe paper's physiological trait data (Vcmax, Jmax, leaf N, LMA for seven wine grape varieties) are openly deposited in the University of Toronto Borealis Dataverse, per the Data Availability Statement. No author analysis code or trained models are reported.Dataset · publicThe data that support the findings of this study are openly available in the Borealis Repository—University of Toronto Dataverse at https://doi.org/10.5683/SP3/URPVFF .Open asset ↗Borealis Repository—University of Toronto Dataverse · 10.5683/SP3/URPVFFlines:277-347Code / dataset availability confirmedEurope PMC · checked 6 Sept 2026
Deep understanding of slow-wilting is essential for developing drought-tolerant crops. Existing approaches to measure transpiration rates are difficult to apply to large populations due to their high cost and low throughput. To overcome these challenges, we developed a high-throughput phenotyping system that integrates a load cell sensor and an Arduino-based microcontroller device. The system tracked the transpiration rate in real time by measuring changes in the pot weight in 224 recombinant inbred lines of Taekwangkong (fast-wilting) x SS2-2 (slow-wilting) under water-restricted conditions. Among five transpiration features we determined, stress recognition time point (SRTP) and decrease in transpiration rate by stress (DTrs) are informative parameters, that are interconnected and independently affect slow-wilting as well. Quantitative trait loci (QTL) for SRTP and DTrs were identified at the same location as the major QTL for slow wilting, qSW_Gm10 , identified in the previous study. Notably, we found a novel major QTL for DTrs, qDTrs_Gm04 , with a LOD value of 42 and PVE of 47 %. As a candidate gene for qDTrs_Gm04 , GmWRKY58 was selected with differential expression between the parental lines under drought conditions as well as upstream sequence variation. Our high-throughput system is of help not only to biological research but breeding programs of drought-tolerant lines.
Why it matches plant phenotyping methods高スループットなセンサー基盤を開発し、ポット重量変化からダイズの蒸散率・乾燥ストレス応答をリアルタイム抽出することが研究の中心であるため。
abstractwe developed a high-throughput phenotyping system that integrates a load cell sensor and an Arduino-based microcontroller device.
Reproduction assets foundThe paper's data availability statement explicitly deposits the processed phenotypic data (transpiration features from the RIL drought experiment) and trained Random Forest/XGBoost model objects on Figshare, which is a paper-specific, publicly accessible asset.Dataset · publicThe processed phenotypic data, along with the trained Random Forest and XGBoost machine learning model objects (.rds files), are publicly available on Figshare at https://doi.org/10.6084/m9.figshare.c.7951601.v1.Open asset ↗Figshare · 10.6084/m9.figshare.c.7951601.v1html-lines:276-299Code / dataset availability confirmedEurope PMC · checked 6 Sept 2026
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:1-61Code / dataset availability confirmedEurope PMC · checked 6 Sept 2026
Global climate change and urbanization have posed challenges to sustainable food production and resource management in agriculture. Vertical farming, in particular, allows for high-density cultivation on limited land but requires precise control of crop height to suit vertical farming systems. Tomato, a globally significant vegetable crop, urgently requires mutant varieties that suppress indeterminate growth for effective cultivation in vertical farming systems. In this study, we utilized the CRISPR-Cas9 system to develop a new tomato cultivar optimized for vertical farming by editing the Gibberellin 20-oxidase ( SlGA20ox ) genes, which are well known for their roles in the "Green Revolution". Additionally, we proposed a volumetric model to effectively identify mutants through non-destructive analysis of chlorophyll fluorescence. The proposed model achieved over 84 % classification accuracy in distinguishing triple-determinate and slga20ox gene-edited plants, outperforming traditional machine learning methods and 1D-CNN approaches. Unlike previous studies that primarily relied on manual feature extraction from chlorophyll fluorescence data, this research introduced a deep learning framework capable of automating feature extraction in three dimensions while learning the temporal characteristics of chlorophyll fluorescence imaging data. The study demonstrated the potential to classify tomato plants customized for vertical farming, leveraging advanced phenotypic analysis methods. Our approach explores new analytical methods for chlorophyll fluorescence imaging data within AI-based phenotyping and can be extended to other crops and traits, accelerating breeding programs and enhancing the efficiency of genetic resource management.
Why it matches plant phenotyping methodsトマトのクロロフィル蛍光画像から遺伝子編集植物を識別する3次元深層学習モデルを提案し、既存手法と比較評価しており、表現型取得・抽出法が研究の中心である。
abstractAdditionally, we proposed a volumetric model to effectively identify mutants through non-destructive analysis of chlorophyll fluorescence.
Reproduction assets foundThe authors state that the dataset and source code used in this study are publicly available on GitHub, which qualifies as a paper-specific public code asset for the CF 3D-CNN phenotyping analysis.Code · publicThe dataset and source code used in this study are available at https://github.com/youzh-all/CF_3D-CNN .Open asset ↗https://github.com/youzh-all/CF_3D-CNN · CF_3D-CNNlines:358-392Code / dataset availability confirmedEurope PMC · Crossref · checked 15 Sept 2026
Background and aims Rapid, large-scale monitoring is critical to understanding spatiotemporal plant stress dynamics, but current physiological stress markers are costly, destructive and time-consuming. This study aimed to evaluate the potential of machine learning to non-destructively predict leaf betalains - yellow to reddish pigments unique to Caryophyllales species - for the first time, and to explore intra-individual variation in betalains in a clonal species and its role in responding to stressful periods. Methods We characterized the betalainic profile of an invasive clonal plant for the first time, Carpobrotus edulis (the cape fig), via high-performance liquid chromatography. We measured multiple stress markers over a year, including betalain content using our optimized method, where the species is spreading. Additionally, 3735 digital images at the leaf level were taken. Machine learning regression algorithms were trained to predict betalain accumulation from digital images, outperforming classic spectroradiometer measurements. Key results Betalain content increased sharply in non-reproductive ramets during extreme abiotic conditions in summer and during senescence in reproductive ramets. The stress markers revealed a strong intra-individual functional mosaic, underscoring the importance of spatiotemporal dimensions in stress tolerance. Conclusions We developed a scalable, non-destructive tool for betalain research that integrates digital imaging with machine learning. This approach opens new possibilities for understanding spatiotemporal stress responses, particularly in clonal plant systems, using artificial intelligence.
Why it matches plant phenotyping methods葉のデジタル画像と機械学習によりベタレイン蓄積を非破壊推定する手法を開発しており、植物ストレス状態の表現型取得が研究の中心である。
abstractThis study aimed to evaluate the potential of machine learning to non-destructively predict leaf betalains
Reproduction assets foundThe paper's Data Availability statement explicitly deposits the complete set of analysis scripts, collected leaf images, image features (predictors), and response variables (betalain/pigment measurements) in FigShare under DOI 10.6084/m9.figshare.28706588. This is a paper-specific, public phenotyping asset (images + MLDataset · publicThe complete set of scripts, collected images, image features
(predictors) and response variables are publicly available in
FigShare: 10.6084/m9.figshare.28706588.FigShare · 10.6084/m9.figshare.28706588pdf-raw-page:12 lines:1-81Code / dataset availability confirmedEurope PMC · checked 6 Sept 2026
Drought is one of the main factors affecting mung bean production in China. Screening drought-resistant germplasm resources and cultivating drought-resistant varieties are of great significance to the development of the mung bean industry in China. Combined with chlorophyll fluorescence imaging technology, this paper proposes a lightweight mung bean drought resistance identification network model based on YOLOv8, referred to as CSCA-YOLOv8. The model uses StarNet to replace the backbone network of YOLOv8 to reduce the size of the model. The C2f_Star module is introduced in the neck structure instead of the original C2f module. Then, in order to enhance the network's attention to the key regions in the feature map, the Context Anchor Attention Mechanism (CAA) module is also introduced into the fourth C2f_Star module. Then, a CGBD module is proposed in the neck structure to reconstruct the ordinary convolution to improve the feature extraction ability of the model for small targets. Finally, the SIoU loss function is used to replace CIoU to accelerate the convergence of the model. In the actual data analysis, we used the collected 4808 chlorophyll fluorescence images of the natural mung bean population under drought stress to make the Mungbean Drought Datatset(MDD) and made classification labels for each image according to different drought resistance levels, which were 0, 1, 2, 3, 4 and 5. We also verified the excellent performance and generalization performance of the model using the collected MDD dataset. The final experimental results show that compared with the YOLOv8s baseline model, the number of parameters of our proposed algorithm is reduced by 24%, the floating point number is reduced by 35%, and the accuracy is improved by 2.52%, which supports the deployment on embedded edge devices with limited computing power. Therefore, our proposed algorithm has great potential in the field of drought resistance identification and germplasm selection of mung bean.
Why it matches plant phenotyping methods乾燥抵抗性を推定するクロロフィル蛍光画像ベースのYOLOv8改良モデルを開発し、データセット上で性能・汎化性能を検証しており、表現型取得・抽出手法が中心である。
abstractCombined with chlorophyll fluorescence imaging technology, this paper proposes a lightweight mung bean drought resistance identification network model based on YOLOv8
Reproduction assets foundThe paper's data availability statement explicitly deposits the authors' MDD chlorophyll fluorescence image dataset (4808 mung bean drought-resistance images with labels) and their CSCA-YOLOv8 source code on a public GitHub repository, making both directly actionable paper-specific assets.Code · publicThe dataset and source code are
available on Github.Open asset ↗pdf-page:3 lines:1-51Code / dataset availability confirmedEurope PMC · bioRxiv · checked 15 Sept 2026
Large-scale kinetic models of photosynthesis enable time-resolved predictions of traits related to this key process, and provide the means to identify factors limiting photosynthesis. However, their use is currently limited by the lack of efficient approaches to estimate the hundreds of genotype-specific kinetic parameters. Here, we present C4TUNE, an artificial neural network, which can efficiently predict parameters of a large-scale photosynthesis model from photosynthesis response curves. C4TUNE was trained on a biologically-relevant synthetic dataset comprising matched samples of parameters and response curves obtained using a C 4 photosynthesis kinetic model. To speed up the training of C4TUNE, we devised a surrogate neural network to predict photosynthesis response curves directly from the model parameters and environmental inputs. Given response curves as input, we showed that over 99% of the parameter vectors predicted by C4TUNE could be used directly in simulation of the kinetic model and resulted in excellent fits. Finally, we applied C4TUNE to predict parameters for a population of 68 maize genotypes across two seasons. The predicted genotype-specific parameters allowed pinpointing factors that limit photosynthetic efficiency, validated using simulations. Therefore, the use of C4TUNE presents a fast and precise approach for parameter prediction based on minimal datasets.
Why it matches plant phenotyping methodsC4TUNEは光合成応答曲線から遺伝子型別の光合成パラメータを推定するニューラルネットワーク手法であり、植物生理形質の抽出が研究の中心です。
abstractHere, we present C4TUNE, an artificial neural network, which can efficiently predict parameters of a large-scale photosynthesis model from photosynthesis response curves.
Reproduction assets foundThe paper deposits its maize gas exchange phenotype measurements (Zenodo 15966533), the synthetic neural-network training dataset (Zenodo 15926601), and the C4TUNE analysis/training code with predicted genotype parameters (GitHub pwendering/C4TUNE), all with explicit availability statements and public URLs.Dataset · publicwere tuned as described above (“Surrogate
648
model”). The final model was trained for 30 epochs with a batch size of 8.
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The neural networks were implemented using Python 3.10.14 using the PyTorch library version
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2.5.1 42
.
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Data availability
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The gas exchange measurements for maize genotypes are available at
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https://doi.org/10.5281/zenodo.15966533. Part of these data has been used in another study
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linking photosynthesis-related traits and hyperspectral reflectance data 43
. The generated
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artificial data set for neural network training is available at
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https://doi.org/10.5281/zenodo.15926601.657
.
CC-BY-NC-ND 4.0 International license
made available under a
(whOpen asset ↗zenodo · 10.5281/zenodo.15966533pdf-raw-page:21 lines:1-94Code · public22
Code availability
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Custom code for the generation of the artificial dataset as well as code for neural model
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definition and training are available at https://github.com/pwendering/C4TUNE. This
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repository also contains the predicted parameters for the maize genotypes.
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References
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1. Zhu, X. G., Long, S. P. & Ort, D. R. Improving photosynthetic efficiency for greater
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yield. Annu. Rev. Plant Biol. 61, 235–261 (2010).
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2. Croce, R. et al. Perspectives on improving photosynthesis to increase crop yOpen asset ↗github · pwendering/C4TUNEpdf-raw-page:22 lines:1-69Code / dataset availability confirmedEurope PMC · checked 14 Sept 2026
Crop traits are the integrated outcome of genetic factors, environment effects, and their complex interactions, rendering accurate prediction from genetic markers alone a challenging problem. Here we present KineticGP, a computational framework that combines genomic prediction with genotype-specific kinetic models of C 4 photosynthesis to make predictions of leaf photosynthesis traits across genotypes from a multiple parent advanced generation intercross maize population. Using genetic markers and gas exchange measurements from three field seasons, we show that KineticGP outperforms a baseline genomic prediction model for photosynthesis rate at saturating light by 86% for unseen genotypes across two seen seasons. In addition, KineticGP allowed surveying the genetic variability in enzyme kinetic parameters that can be used to raise targets for improvement of photosynthesis. The approach paves the way for interrogating and integrating the dynamic interactions between genotype and environment to improve the prediction accuracy of photosynthetic traits.
Why it matches plant phenotyping methods葉の光合成形質を予測する計算フレームワークの開発が研究の中心であり、植物生理形質の推定手法として適格。
abstractHere we present KineticGP, a computational framework that combines genomic prediction with genotype-specific kinetic models of C 4 photosynthesis to make predictions of leaf photosynthesis traits across genotypes
Reproduction assets found保存済みの本文根拠を更新済みルールで再検証し、公開資産1件を確認しました。Code · publicAll codes and data to ensure reproducibility of
the results can be accessed at: https://github.com/Rudan-X/KineticGPOpen asset ↗GitHub · Rudan-X/KineticGPpdf-page:19 lines:1-43Code / dataset availability confirmedEurope PMC · checked 6 Sept 2026
Nitrogen (N) is essential for plant growth, yet excessive fertilizer use contributes to environmental degradation. Actinorhizal trees like Alnus glutinosa form symbiotic relationships with nitrogen-fixing bacteria of the genus Frankia, reducing reliance on synthetic fertilizers. However, distinguishing between soil-derived and symbiotically fixed nitrogen remains a challenge. This study investigates the potential of NIR spectroscopy as a nondestructive tool for differentiating N sources in A. glutinosa . Seedlings were grown in sterilized soil under controlled conditions with and without Frankia inoculation, and across a gradient of NH 4 NO 3 fertilization (0-20 mM). We measured leaf chlorophyll, nitrogen content, biomass, and NIR reflectance (330-1100 nm) of the third fully expanded leaf. principal component analysis (PCA) and partial least squares (PLS) regression revealed that spectral signatures significantly differed between inoculated and uninoculated plants, particularly in the visible range around 555 nm. Despite similar leaf chlorophyll levels, Frankia -inoculated plants and those fertilized with 20 mM NH 4 NO 3 exhibited spectral differences that could otherwise not be detected by SPAD measurements. PLS regression explained up to 54.8% of spectral variance based on nitrogen source, even in the absence of unique spectral peaks. These findings highlight the potential of NIR spectroscopy for rapid, in vivo and in vitro assessment of symbiotic N-fixation in trees, offering a novel and more precise approach than SPAD measurements.
Why it matches plant phenotyping methodsNIR分光とPLS回帰を用いて植物の共生的窒素固定状態・窒素源を非破壊推定する方法が研究の中心であり、SPADとの比較も行っている。
abstractThese findings highlight the potential of NIR spectroscopy for rapid, in vivo and in vitro assessment of symbiotic N-fixation in trees, offering a novel and more precise approach than SPAD measurements.
Reproduction assets foundThe paper's Data Availability Statement deposits the study's data (NIR spectra and plant phenotyping measurements) on Zenodo with an explicit public DOI, which is an allowed URL. No author analysis code repository is stated; the other allowed URLs are generic R package documentation.Dataset · publicData Availability Statement
The data is deposited in Zenodo under (DOI): https://doi.org/10.5281/zenodo.15533926 .Open asset ↗Zenodo · 10.5281/zenodo.15533926lines:126-163Code / dataset availability confirmedEurope PMC · checked 6 Sept 2026
Laboratory / benchtopX-ray / CTRootMorphology / geometry measurementRoot system architecture
Soil compaction and escalating global drought increase soil strength and stiffness. It remains unclear which plant root biomechanical mechanisms/traits enable growth in these harsh conditions. Here, we combine synchrotron X-ray computed tomography with spatially resolved X-ray diffraction to characterize the biomechanics of a replica root-soil system. We map the strain field around the root tip analog, finding strong agreement with finite element simulations, thereby demonstrating a promising new in vivo measurement protocol.
Why it matches plant phenotyping methods根の生育に関わる土壌内の力学的状態・ひずみを、X線CTと回折で可視化・定量する新しいin vivo測定プロトコルを開発・検証しており、植物表現型取得法が中心です。
abstractWe map the strain field around the root tip analog, finding strong agreement with finite element simulations, thereby demonstrating a promising new in vivo measurement protocol.
Reproduction assets foundThe paper's Data availability and Code availability statements both deposit the study's XCT/XRD imaging and diffraction data and the processing scripts in the University of Southampton Pure repository (DOI 10.5258/SOTON/D3309), which is an allowed URL. These are paper-specific, publicly declared assets directly reproduCode · publicAll scripts used to process the data can be found in the Pure repository: https://doi.org/10.5258/SOTON/D3309 .Open asset ↗Pure · 10.5258/SOTON/D3309lines:209-251Code / dataset availability confirmedCrossref · Europe PMC · checked 13 Sept 2026
GreenhouseThermalLeafPhysiological trait estimationStress response / tolerancePlant / canopy temperature
ABSTRACT Extreme heat can push plants beyond their thermal safety margin ( TSM ) if maximum leaf temperature ( T leaf_max ) exceeds leaf critical temperature ( T crit ). The TSM is potentially useful for assessing heat vulnerability across species but needs further validation, so we exposed 50 tree/shrub species in controlled glasshouses to a 6‐day heatwave (peak air temperature = 41°C). Many species increased their mean T crit during the heatwave (42%), with Δ T crit ranging from +1°C to 4°C, but other species did not acclimate or were impaired by heat stress (58%). Species T leaf_max explained ~55% of the variation in species T crit and was a key correlate of the plasticity of T crit among species. Species with high Δ T crit also had higher Δ T leaf_max , with leaves being 7°‒12°C hotter during the heatwave than under baseline conditions. Both T leaf_max and TSMs were correlated with heatwave damage across diverse species from contrasting climate zones. Species differences in TSMs were stable across measurement temperatures, correctly identified the most vulnerable species, and were strongly associated with T leaf_max . Our results suggest that (1) T leaf_max alone is more informative than T crit for ranking species heat tolerance, and (2) species vulnerability to heatwaves is most reliably assessed by using TSMs that integrate T leaf_max with T crit across species.
Why it matches plant phenotyping methods葉温・熱安全余裕度（TSM）を用いた植物の熱脆弱性評価手法を、多種の植物で検証し、損傷予測性能や種間比較の妥当性を評価しているため、方法的役割が中心である。
abstractThe TSM is potentially useful for assessing heat vulnerability across species but needs further validation
Reproduction assets foundThe article's Data Availability Statement explicitly states the supporting data (phenotype measurements: Tcrit, Tleaf_max, TSM, damage indicators for 50 species) are openly available on Figshare at the authors' public DOI, which is an allowed URL.Dataset · publicData Availability Statement
The data that support the findings of this study are openly available in Figshare at https://doi.org/10.6084/m9.figshare.29345549.v1 .Open asset ↗Figshare · 10.6084/m9.figshare.29345549.v1lines:721-817Code / dataset availability confirmedOpenAlex · Crossref · Europe PMC · checked 6 Sept 2026
Addressing the global malnutrition crisis requires precise and timely diagnostics of plant stresses to enhance the quality and yield of nutrient-rich crops, such as tomatoes. Soft wearable sensors offer a promising approach by continuously monitoring plant physiology. However, challenges remain in identifying direct physiological indicators of plant stresses, hindering the development of accurate diagnostic models for predicting symptom progression. Here, we introduce a machine-learning-powered spectral-dominant multimodal soft wearable system (MapS-Wear) for precise, long-term, and early-stage diagnosis of stresses in tomatoes. MapS-Wear continuously tracks leaf surrounding temperature, humidity, and unique in-situ transmission spectra, which are critical stress-related indicators. The machine learning framework processes these multimodal data to predict gradual stress progression and diagnose nutrient deficiencies in plants over 10 days earlier than conventional computer vision methods. Moreover, MapS-Wears enables portable and large-scale screening of grafted tomato varieties in greenhouses, accelerating the identification of compatible grafting combinations. This demonstration highlights the potential for high-throughput plant phenotyping and yield improvement.
Why it matches plant phenotyping methods植物ストレスの生理状態を連続センシングし、機械学習で早期診断・進行予測するウェアラブル計測システムが研究の中心であり、植物フェノタイピング手法として明確に該当する。
abstractHere, we introduce a machine-learning-powered spectral-dominant multimodal soft wearable system (MapS-Wear) for precise, long-term, and early-stage diagnosis of stresses in tomatoes.
Reproduction assets foundThe paper's Data and materials availability statement explicitly deposits the tomato leaf photos, transmission spectral data, and ML algorithms on Zenodo, matching an allowed URL.Dataset · publicThe photos of tomato leaves in different health statuses, the transmission spectral data of these leaves, and the ML algorithms are openly available on Zenodo ( https://zenodo.org/doi/10.5281/zenodo.15192884 ).Open asset ↗Zenodo · 10.5281/zenodo.15192884lines:129-274Code / dataset availability confirmedCrossref · Europe PMC · checked 15 Sept 2026
The integration of nanotechnology in agriculture allows for more precise nutrient delivery through nanoparticles (NPs), particularly via foliar application. To mature this technology for enhancing fertilizer efficiency, it is essential to shed new light on the transport and dissolution of NPs in plants. Available analytical methods struggle to address this challenge in a direct manner. We introduce correlative X-ray imaging as a novel analytical tool capable of tracking NP pathways, dissolution and hence nutrient release in plants. By utilizing three complementary X-ray techniques, we offer a unique insight into the plant processes associated with foliar fertilization. We demonstrate that small-angle X-ray scattering enables the characterization of NP size and concentration, while X-ray fluorescence imaging, maps the distribution of elements within the sample. Finally, micro-computed tomography integrates these findings into a complete three-dimensional digital representation of the plant’s microstructure, revealing regions of apparent densification associated with NP accumulation. Using freeze-dried barley plants infiltrated with nano-hydroxyapatite (nHAP), we observed rapid dissolution of NPs, and we are able to associate time and space attributes to the translocation process of nutrients up to three days following foliar application of NPs. With the first pilot study of applying correlative X-ray imaging to live plants, we sought to indicate the potential of this new analytical approach for future nano-enabled agricultural research.
Why it matches plant phenotyping methods植物内のナノ粒子経路・溶解・栄養輸送を可視化する相関X線イメージング手法の導入と実証が中心であり、植物の状態・生理過程を測定する方法論的研究である。
abstractWe introduce correlative X-ray imaging as a novel analytical tool capable of tracking NP pathways, dissolution and hence nutrient release in plants.
Reproduction assets foundThe article's data availability statement points to a public figshare repository hosting the study's datasets (X-ray imaging/phenotyping measurements). No author analysis code with explicit public deposit language was identified; Dragonfly is a commercial visualization tool, not a paper-specific asset.Dataset · publicng Wan , Hainan University, China
Zhansheng Li , Chinese Academy of Agricultural Sciences, China
Firozeh Solimani , Politecnico di Bari, Italy
Data availability statement
The datasets presented in this study can be found in online repositories. The names of the repository/repositories and accession number(s) can be found below: https://figshare.com/s/33ee8388a36600fe98d5 .
Author contributionsOpen asset ↗figsharelines:191-206Code / dataset availability confirmedbioRxiv · checked 15 Sept 2026
The global carbon cycle depends heavily on the carbon sequestration rates of aquatic ecosystems. Sinking of phytoplankton is a rapid mediator of carbon sequestration, because phytoplankton are globally abundant photoautotrophs that grow rapidly. Pico- and nano-phytoplankton sinking velocities vary depending on their growth state, viability, clumping, and distribution in the water column. We introduced high throughput fluorescence microscopy of well-plates, to measure sinking velocities of three diatom strains, and three cyanobacteria strains, with cell radii spanning an order of magnitude, all grown under three different light levels. Cultures were measured for sinking velocities repeatedly across their growth trajectories. Tracking multiple fluorescence wavebands allowed us to simultaneously determine sinking velocities for living vs. dead cells. Sinking velocities varied strongly across growth light levels, and across growth stages. These monoclonal cultures furthermore show distinct sub-populations of slow- and fast-sinking cells. Our results departed widely from simple Stokes Law estimates of sinking based upon radii and mass density of cells. Complex, heterogeneous phytoplankton communities likely show more complicated sinking patterns than are currently expressed in biogeochemical ocean models. Our well-plate microscopy approach using parallel imaging of many samples generates high-throughput measures of cell sinking at population- or community-scales, to in turn improve modelling of carbon export to deeper layers.
Why it matches plant phenotyping methods植物プランクトンの沈降速度を高スループット蛍光顕微鏡で測定する手法を導入し、生活状態や集団スケールの生理・機能形質を定量化しているため、測定法が研究の中心です。
abstractWe introduced high throughput fluorescence microscopy of well-plates, to measure sinking velocities of three diatom strains, and three cyanobacteria strains
Reproduction assets foundThe paper's sinking-velocity analysis code is explicitly stated to be openly available on the authors' GitHub repository. The raw phenotype data is promised for Dryad only upon acceptance, so it is not yet publicly actionable.Code · publicfunctional groups of cyanobacteria, diatoms strains with 156
diameter less than 10µm and diatoms strains with diameter larger than 10µm based on 157
growth light, viability state (living vs. dead and dying) and slow vs. fast sinking 158
velocity clustering groups. 159
The code used to analyse the data is public available at 160
https://github.com/maxberthold/PhytoplanktonSinkVelocities. 161
Sinking according to Stokes’ law 162
Sinking velocities of spherical objects falling under the case of Reynolds numbers 163
smaller than 1 can be described by Stokes’ law. Several studies have used Stokes law or 164
a modified version of Stokes’ law to estimate sinking velocities of plankton and marine 16Open asset ↗maxberthold/PhytoplanktonSinkVelocitiespdf-raw-page:8 lines:1-44Code / dataset availability confirmedCrossref · checked 6 Sept 2026
Non-destructive tree phenotyping for resistance screening and early, presymptomatic disease detection figures prominently among the most important practical limitations inherent in forest health management. The need for point-of-care tools is particularly acute for managing diseases caused by non-native pathogens, often resulting in difficult-to-control biological invasions. One such case is represented by ash dieback in Europe, caused by Hymenoscyphus fraxineus, which has led Sweden to red-list its main host, European ash ( Fraxinus excelsior ). We evaluated the use of near-infrared (NIR) spectroscopy and machine learning for detection of presymptomatic infections by H. fraxineus and identification of disease-resistance European ash accessions. Here, we show that presymptomatic infected trees can be distinguished from pathogen-free trees with a testing error rate of 0.161 in a controlled inoculation experiment. We also show that the same approach can be used to identify disease-resistant European ash accessions based on data from two independent, multiyear clonal trials, with a testing error rate of 0.155. These results confirm that NIR spectroscopy combined with machine learning is sensitive enough for early disease detection and resistance screening in this system. This is consistent with prior findings in other tree pathosystems and suggests that this approach could be developed into an operational tool to facilitate the management of biological invasions of forest environments by non-native pathogens, including habitat restoration with resistant germplasm.
Why it matches plant phenotyping methodsNIR分光と機械学習を用いて、感染樹の病徴状態と病害抵抗性を非破壊・早期推定する方法を評価しており、植物フェノタイピング手法の開発・検証が中心である。
abstractNon-destructive tree phenotyping for resistance screening and early, presymptomatic disease detection figures prominently among the most important practical limitations inherent in forest health management.
Reproduction assets foundThe paper's NIR spectral/phenotype datasets (presymptomatic infection detection and resistance phenotyping of European ash) are deposited publicly on Dryad under DOI 10.5061/dryad.s1rn8pkkn, per the data availability statement. No author analysis code repository is stated; cited R packages (caret, FDA, R) are generic,非Dataset · publicThe datasets presented in this study can be found in online repositories. The names of the repository/repositories and accession number(s) can be found at: https://datadryad.org/stash , 10.5061/dryad.s1rn8pkkn .Open asset ↗datadryad.org · 10.5061/dryad.s1rn8pkknlines:402-432Code / dataset availability confirmedEurope PMC · checked 15 Sept 2026
Fluctuations and propagation of cytosolic calcium levels at both the cellular and tissue levels show complex patterns, referred to as calcium signatures, that regulate growth, organ development, damage responses, and survival. The quantitative analysis of calcium signatures at the cellular level is essential for identifying unique patterns that coordinate biological processes. However, a versatile framework applicable to multiple tissue types, allowing researchers to compare, measure, and validate diverse responses and recognize conserved patterns across model organisms, is missing. Here, we present a post-processing tool, CalciumInsights, which leverages the R packages Shiny and Golem. This tool has a graphical user interface and does not require software programming experience to perform calcium signal analysis. The open-source software has a modular framework with standardized functionalities that can be tailored for various research approaches. CalciumInsights provides descriptive statistical analysis through various metrics extracted from dynamic calcium transients and oscillations, such as peak amplitude, area under the curve, frequency, among others. The tool was evaluated with fluorescence imaging data from three model organisms: Danio rerio , Arabidopsis thaliana , and Drosophila melanogaster , demonstrating its ability to analyze diverse biological responses and models. Finally, the open-source nature of CalciumInsights enables community-driven improvements and developments for enabling new applications. Author Summary This manuscript introduces CalciumInsights, an open-source tool for calcium signature analysis. Designed to be a versatile tool that works with various tissue types and biological systems, CalciumInsights has an easy-to-use graphical user interface. Our program simplifies metrics extraction while maintaining the quality of the analysis by integrating several algorithms. CalciumInsights stands out for its user-friendliness, ease of use, and robust data exploration features, such as tunable filters for improved accuracy. These features promote inclusivity and lower barriers to scientific research by making calcium signature analysis accessible to users of all programming skill levels.
Why it matches plant phenotyping methods植物の蛍光イメージングからカルシウム動態という生理状態を抽出・定量するオープンソース解析ツールが中心であり、植物を含む複数生物種のデータで評価されている。
abstractHere, we present a post-processing tool, CalciumInsights, which leverages the R packages Shiny and Golem.
Reproduction assets foundThe paper describes CalciumInsights, an open-source R/Shiny tool for calcium transient analysis. The authors explicitly state their code is publicly available on GitHub, which constitutes the paper's computational analysis asset. No plant-phenotyping datasets, images, or trained models are described; the tool is tissueCode · publicnt for publication
All authors have reviewed the manuscript and approved the final draft for publication.
Resource availability
Lead contact: Further information and requests for data may be directed to and will be
fulfilled by Mauricio Cabrera (mauricio.cabrera1@upr.edu)
Code: All codes used are publicly available in GitHub at https://github.com/AOG-Lab/CalciumInsights
References
1. Berridge MJ, Lipp P, Bootman MD. The versatility and universality of calcium signalling. Nat
Rev Mol Cell Biol [Internet]. 2000 Oct [cited 2024 Oct 21];1(1):11–21. Available from:
https://www.nature.com/articles/35036035
2. Sanderson MJ, Charles AC, Boitano S, Dirksen ER. Mechanisms and function of intercellularOpen asset ↗AOG-Lab/CalciumInsightspdf-raw-page:20 lines:1-37Code / dataset availability confirmedCrossref · checked 15 Sept 2026
Flow cytometry is widely applied to infer the ploidy and genome size (GS) of plant nuclei. The conventional approach of sample preparation, reliant on fresh plant material to release intact nuclei, often results in poor yields of nuclei in conditions when a plant material cannot be kept fresh due to logistical constraints. Previous attempts to use frozen plant material were mainly limited to ploidy analysis and relied on chopping methods, which restrict the material input and often result in poor nuclei yield, especially in frozen samples, due to incomplete disruption. Here, we present a modified protocol for GS estimation using frozen plant material that facilitates larger volumes of tissue to be processed while improving debris removal. Nuclei isolated from this protocol can also be used for DNA or RNA extraction. Genome size estimates from frozen material are similar to those from fresh material, with a reduction in error range, although not always significant (p > 0.05). In certain species, frozen samples can yield substantially more nuclei than fresh material. With the addition of specific debris compensation algorithms, coefficient of variation (CV%) can be maintained below 5%. This method has special value in estimating the GS of samples collected from remote locations and frozen for use in plant genome sequencing. Freezing preserves high-quality DNA and RNA, enabling the same sample to be used for both flow cytometry and genome sequencing.
Why it matches plant phenotyping methods凍結植物材料からフローサイトメトリーで植物ゲノムサイズを推定する改良プロトコルを開発・検証しており、植物形質取得法が研究の中心である。
abstractHere, we present a modified protocol for GS estimation using frozen plant material that facilitates larger volumes of tissue to be processed while improving debris removal.
Reproduction assets foundThe paper deposits its raw flow cytometry fluorescence dataset (genome size estimation of fresh vs frozen plant material) in FlowRepository and its supplementary materials (ANOVA table, histogram/peak-modeling figures, microscopy images, protocol) in a Zenodo database. Both are paper-specific, publicly accessible, andSupplement · publicg across diverse taxa and
storage durations, this method could significantly enhance field-based and conservation
genomics efforts.
Supplementary Materials: The following supplementary data can be accessed online from the
database titled “A flow cytometry protocol for measurement of plant genome size using frozen mate-
rial” at https://doi.org/10.5281/zenodo.14873353 (Accessed on 1 April 2025). Table S1: Results of
one-way ANOVA for all combinations of species, nuclei extraction method, and debris compensation
on genome size estimation. Figure S1. The process of nuclei isolation from frozen leaf material.
Figure S2. Conventional histogram analysis for the fluorescence data of fresh preparatOpen asset ↗zenodo · 10.5281/zenodo.14873353pdf-raw-page:12 lines:1-50Code / dataset availability confirmedCrossref · OpenAlex · Europe PMC · checked 6 Sept 2026
= 0.63). Our findings demonstrate that UAV-acquired multispectral data can effectively map photosynthetic traits with high spatial resolution, establishing it as a valuable tool for rapid phenotyping and spatial assessment of photosynthetic capacity in crop fields.
Why it matches plant phenotyping methodsUAVマルチスペクトルデータで作物の光合成形質を推定する高スループット表現型解析が中心であり、センサープラットフォームの実質的な適用に該当する。
titleHigh-Throughput Field Phenotyping Using Unmanned Aerial Vehicles (UAVs) for Rapid Estimation of Photosynthetic Traits.
Reproduction assets foundThe paper's authors publicly deposited the calibration and validation datasets of UAV-based spectral indices and photosynthetic trait measurements (Vcmax/Jmax) in a GitHub repository, directly reproducing this paper's phenotyping measurements and analysis inputs.Dataset · publicThe calibration and validation datasets of UAV-based spectral indices and photosynthesis supporting our results are available in the GitHub repositories at https://github.com/ljs19930709/UAV-and-Photosynthesis-dataset-.git .Open asset ↗https://github.com/ljs19930709/UAV-and-Photosynthesis-dataset-.gitlines:107-117Code / dataset availability confirmedEurope PMC · checked 14 Sept 2026
Earth's polar regions are experiencing significant climate change, impacting global oceanographic and weather patterns. Arctic "greening" is well studied, but a debate has emerged about whether similar trends are occurring in Antarctica and whether and how remote sensing can assess them. Recent studies have introduced a concept of "greening" in Antarctica, framed primarily around moss cover expansion over bare ground. This interpretation differs from Arctic greening studies, which focus mainly on changes in vascular plant productivity and successional dynamics. This paper evaluates the Antarctic greening concept, focusing on how Normalized Difference Vegetation Index (NDVI)-based methods are applied and interpreted in this context, considering regional limitations in technology, data availability, and the unique Antarctic vegetation characteristics. Unlike the Arctic, Antarctic vegetation consists mainly of nonvascular organisms (algae, cyanobacteria, lichens, and bryophytes) that interact with slow-weathering soils with minimal organic inputs. These biological and environmental differences likely influence NDVI greening metrics and their ecological relevance, but remain poorly understood due to limited long-term data and validation. Despite advances in remote sensing, Antarctic vegetation mapping remains in its early stages. The small size and patchy distribution of vegetation complicate detection of presence and extent, and even with modern satellites, capturing subcentimeter annual growth rates remains challenging. The lack of historical high-resolution imagery hampers change detection, limiting our ability to track habitat expansion, vegetation dynamics, and community composition changes over time. Based on critical assessment, we identify serious concerns regarding the accuracy and interpretation of NDVI-based greening trends in Antarctica in recent studies, particularly in relation to technological constraints and biological realism. To address these issues, we propose a refined framework for interpreting NDVI data in Antarctica, aiming to prevent misleading conclusions about vegetation changes and trends. This framework suggests an urgent need for re-evaluation of how "greening" is both quantified and interpreted in Antarctica.
Why it matches plant phenotyping methods南極植生の状態・変化をNDVIで推定するリモートセンシング手法を中心に、その精度・解釈上の問題を批判的に評価し、改良フレームワークを提案する方法論的レビューである。
abstractThis paper evaluates the Antarctic greening concept, focusing on how Normalized Difference Vegetation Index (NDVI)-based methods are applied and interpreted in this context
Reproduction assets foundThe paper's Data Availability Statement states the supporting data are openly available in Edinburgh Data Share at DOI 10.7488/ds/7945. This is a paper-specific public dataset deposit (the study's vegetation/greening analysis data). The related spectral library dataset (10.7488/ds/7720) is cited prior work, not this论文.Dataset · publicfunding provided by grants from Dartmouth's
College of Arts and Sciences and the Clare Garber Goodman Fund for
Anthropological Research.
Conflicts of Interest
The authors declare no conflicts of interest.
Data Availability Statement
The data that support the findings of this study are openly available in
Edinburgh Data Share at https://doi.org/10.7488/ds/7945.References
Aartsma, P., J. Asplund, A. Odland, S. Reinhardt, and H. Renssen.
2021. “Microclimatic Comparison of Lichen Heaths and Shrubs:
Shrubification Generates Atmospheric Heating but Subsurface Cooling
During the Growing Season.” Biogeosciences 18: 1577–1599. https://doi.org/10.5194/bg-18-1577-2021.Allison, J. S., and R. I. SmithOpen asset ↗Edinburgh Data Share · 10.7488/ds/7945pdf-raw-page:14 lines:1-79Code / dataset availability confirmedOpenAlex · Crossref · Europe PMC · checked 15 Sept 2026
Introduction: Photosynthesis is fundamental to agricultural productivity, but its relatively low light-to-biomass conversion efficiency represents an opportunity for enhancement. High-throughput phenotyping is crucial for unraveling the genetic basis of variation in photosynthetic activity. However, the heritability of chlorophyll fluorescence parameters measured during the day is often low as a result of high levels of variation introduced by environmental fluctuations. Methods: To address these limitations, we measured fluorescence phenotypes at night, leveraging natural dark adaptation to minimize environmental noise. Results: Night measurement significantly increased the heritability of fluorescence traits compared to daytime measurements, with the maximum quantum yield of photosystem II (Fv/Fm) showing an increase in heritability from 0.32 to 0.72. Genome-wide association studies (GWAS) conducted using three photosynthetic fluorescence traits measured at night across two growing seasons identified several significant single nucleotide polymorphisms (SNPs). Notably, two candidate genes near SNPs linked to multiple fluorescence traits, Zm00001eb271820 and Zm00001eb012130, have known roles in photosynthesis regulation. Four of the significant signal nucleotide polymorphisms identified in GWAS conducted using nighttime collected data also exhibited statistically significant associations with the same phenotypes during the day. In a majority of other cases, direction of effect was consistent but greater variance in day measured data relative to night measured data resulted in the differences not being statistically significant. Discussion: These results highlight the effectiveness of phenotyping photosynthetic traits at night in reducing environmental noise and enhancing the discovery of genomic intervals related to photosynthesis. While nighttime data collection may not be applicable for all photosynthetic traits, it offers a promising avenue for advancing our understanding of the genetic variation of photosynthesis in modern crop species.
Why it matches plant phenotyping methods夜間の蛍光測定による光合成形質フェノタイピングを開発・評価し、昼間測定との比較で環境変動低減と遺伝率向上を検証しているため、手法が研究の中心である。
abstractHigh-throughput phenotyping is crucial for unraveling the genetic basis of variation in photosynthetic activity.
Reproduction assets foundThe paper's fluorescence phenotype datasets and analysis outputs (trait QC cutoffs, BLUP/heritability model tables, GWAS significant SNP tables) are stated to be available via the article's online Supplementary Material hosted by Frontiers. No standalone author code repository or named data repository accession appearsSupplement · publicof their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.
Supplementary material
The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fpls.2025.1595339/full#supplementary-material
References
Ali W. Grzybowski M. Torres-Rodríguez J. V. Li F. Shrestha N. Mathivanan R. K.
. ( 2024 ).
Quantitative genetics of photosynthetic trait variation in maize
. bioRxiv , eraf198 . doi:
10.1101/2024.11.25.625283
PMC12448886
40365812
Alter P. Dreissen A. Luo F.-L. MatsubaOpen asset ↗lines:613-651Code / dataset availability confirmedCrossref · checked 14 Sept 2026
Chlorophyll fluorescenceLeafRootPhysiological trait estimationPhotosynthesis / fluorescenceWater status / transpiration
The invasive aquatic macrophyte Pontederia crassipes (water hyacinth) exhibits exceptional adaptability across a wide range of light environments, yet the mechanistic basis of its photosynthetic plasticity under both high- and low-light stress remains poorly resolved. This study integrated chlorophyll fluorescence and gas-exchange analyses to evaluate three photosynthetic models—rectangular hyperbola (RH), non-rectangular hyperbola (NRH), and the Ye mechanistic model—in capturing light-response dynamics in P. crassipes. The Ye model provided superior accuracy (R2 > 0.996) in simulating the net photosynthetic rate (Pn) and electron transport rate (J), outperforming empirical models that overestimated Pnmax by 36–46% and Jmax by 1.5–24.7% and failed to predict saturation light intensity. Mechanistic analysis revealed that P. crassipes maintains high photosynthetic efficiency in low light (LUEmax = 0.030 mol mol−1 at 200 µmol photons m−2 s−1) and robust photoprotection under strong light (NPQmax = 1.375, PSII efficiency decline), supported by a large photosynthetic pigment pool (9.46 × 1016 molecules m−2) and high eigen-absorption cross-section (1.91 × 10−21 m2). Unlike terrestrial plants, its floating leaves experience enhanced irradiance due to water-surface reflection and are decoupled from water limitation via submerged root uptake, enabling flexible stomatal and energy regulation. Distinct thresholds for carboxylation efficiency (CEmax = 0.085 mol m−2 s−1) and water-use efficiency (WUEi-max = 45.91 μmol mol−1 and WUEinst = 1.96 μmol mmol−1) highlighted its flexible energy management strategies. These results establish the Ye model as a reliable tool for characterizing aquatic photosynthesis and reveal how P. crassipes balances light harvesting and dissipation to thrive in fluctuating environments. These resulting insights have implications for both understanding invasiveness and managing eutrophic aquatic systems.
Why it matches plant phenotyping methods複数の光合成モデルを実測データで比較・検証し、植物の光合成生理形質を推定するモデルの精度と適用性を中心的に評価しているため。
abstractThis study integrated chlorophyll fluorescence and gas-exchange analyses to evaluate three photosynthetic models—rectangular hyperbola (RH), non-rectangular hyperbola (NRH), and the Ye mechanistic model—in capturing light-response dynamics in P. crassipes.
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 · publicThe following supporting information can be downloaded at https://www.mdpi.com/article/10.3390/biology14060600/s1 : Table S1. Gas-exchange measurement data.Open asset ↗lines:329-346Code / dataset availability confirmedOpenAlex · Europe PMC · checked 15 Sept 2026
Various high-throughput screening methods have been developed to explore plant phenotypes, primarily at the organ and whole plant levels. There is a need to develop phenomics methods at the cellular level to narrow down the genotype to phenotype gap. This study used double-resonator piezoelectric cytometry biosensors to capture the dynamic changes in mechanical phenotypes of living cells of two rice species, drought-resistant Lvhan No. 1 and drought-sensitive 6527, under PEG6000 drought stress. In rice cells of Lvhan No. 1 and 6527, mechanomics parameters, including cell-generated surface stress (ΔS) and viscoelastic parameters (G', G″, G″/G'), were measured and compared under 5-25% PEG6000. Lvhan No. 1 showed larger viscoelastic but smaller surface stress changes with the same concentration of PEG6000. Moreover, Lvhan No. 1 cells showed better wall-plasma membrane-cytoskeleton continuum structure maintaining ability under drought stress, as proven by transient tension stress (ΔS > 0) and linear G'~ΔS, G″~ΔS relations at higher 15-25% PEG6000, but not for 6527 cells. Additionally, two distinct defense and drought resistance mechanisms were identified through dynamic G″/G' responses: (i) transient hardening followed by softening recovery under weak drought, and (ii) transient softening followed by hardening recovery under strong drought. The abilities of Lvhan No. 1 cells to both recover from transient hardening to softening and to recover from transient softening to hardening are better than those of 6527 cells. Overall, the dynamic mechanomics phenotypic patterns (ΔS, G', G″, G″/G', G'~ΔS, G″~ΔS) verified that Lvhan No. 1 has better drought resistance than that of 6527, which is consistent with the field data.
Why it matches plant phenotyping methods植物細胞の機械的表現型を取得する高スループットなバイオセンサー手法を用い、乾燥ストレス応答を定量化しており、表現型取得法が研究の中心である。
abstractThere is a need to develop phenomics methods at the cellular level to narrow down the genotype to phenotype gap.
Reproduction assets foundThe paper's DRPC phenotyping measurements (frequency and motional resistance traces underlying the ΔS, G′, G″ analyses) are provided as downloadable supplementary figures at the MDPI supplementary URL. No standalone public dataset or author analysis code repository is stated; the Data Availability Statement only offersSupplement · publicansient softening under strong drought. The results presented in this work demonstrated the potential to develop a new cellular mechanical phenotype platform to screen for biotic and abiotic stress-resistant crop varieties, as shown in Figure 11 .
Supplementary Materials
The following supporting information can be downloaded at https://www.mdpi.com/article/10.3390/bios15060334/s1 , Figure S1: Changes in frequency and motional resistance of 9 MHz AT and BT cut chips during the adhesions of Lvhan No.1 rice cells followed by the treatments of different concentrations of PEG6000 stresses. (A, B, C, D, E): AT cut, (A1, B1, C1, D1, E1): BT cut, (A, A1): 5% PEG6000, (B, B1): 10%PEG6000, (C, C1) 15%Open asset ↗lines:136-159Code / dataset availability confirmedOpenAlex · checked 15 Sept 2026
Field / plotMultispectral / hyperspectralLeafVisualization / data managementLeaf traitsPhotosynthesis / fluorescence
Abstract. Accurate assessment of leaf functional traits is crucial for a diverse range of applications from crop phenotyping to parameterizing global climate models. Leaf reflectance spectroscopy offers a promising avenue to advance ecological and of robust hyperspectral models for predicting leaf photosynthetic capacity and associated traits from reflectance data has been hindered by limited data availability across species and environments. Here we introduce the Global Spectra-Trait Initiative (GSTI), a collaborative repository of paired leaf hyperspectral and gas exchange measurements from diverse ecosystems. The GSTI repository currently encompasses over 7500 observations from 397 species and 41 sites gathered from 36 published and unpublished studies, thereby offering a key resource for developing and validating hyperspectral models of leaf photosynthetic agricultural research by complementing traditional, time-consuming gas exchange measurements. However, the development capacity. The GSTI database is developed on GitHub (https://github.com/plantphys/gsti) and published to ESS-dive https://data.ess-dive.lbl.gov/datasets/doi:10.15485/2530733, Lamour et al., 2025). It includes gas exchange data, derived photosynthetic parameters, and key leaf traits often associated with traditional gas exchange measurements such as leaf mass per area and leaf elemental composition. By providing a standardized repository for data sharing and analysis, we present a critical step towards creating hyperspectral models for predicting photosynthetic traits and associated leaf traits for terrestrial plants.
Why it matches plant phenotyping methods葉のハイパースペクトル計測とガス交換による光合成形質を結合したデータベースで、植物形質推定モデルの開発・検証を主目的とするため、フェノタイピング手法・データセットとして中心的です。
abstractHere we introduce the Global Spectra-Trait Initiative (GSTI), a collaborative repository of paired leaf hyperspectral and gas exchange measurements from diverse ecosystems.
Reproduction assets foundThe paper's paired leaf spectroscopy–trait database and its R processing/fitting workflow are explicitly released in a public GitHub repository, with published versions archived on ESS-DIVE.Dataset · publicts of the GSTI will focus on expanding data coverage, incorporating data from under-
represented biomes and plant functional types.
6. Data and code availability
495
The GSTI data and code are available in the public GitHub repository at https://github.com/plantphys/gsti, and published
versions of GSTI are released to ESS-Dive (https://data.ess-dive.lbl.gov/datasets/doi:10.15485/2530733, Lamour et al., 2025).
7. How to contribute to future versions of the GSTI
We encourage the community to contribute new datasets to expand the scope and utility of the GSTI project. To ensure
consistency and maintain data quality, contributions should adhere to the standards and guidelines outlined in this paOpen asset ↗ESS-DIVE · doi:10.15485/2530733pdf-raw-page:22 lines:1-36Code · publicgoing refinement of spectra-trait models as new datasets are
incorporated. Future developments of the GSTI will focus on expanding data coverage, incorporating data from under-
represented biomes and plant functional types.
6. Data and code availability
495
The GSTI data and code are available in the public GitHub repository at https://github.com/plantphys/gsti, and published
versions of GSTI are released to ESS-Dive (https://data.ess-dive.lbl.gov/datasets/doi:10.15485/2530733, Lamour et al., 2025).
7. How to contribute to future versions of the GSTI
We encourage the community to contribute new datasets to expand the scope and utility of the GSTI project. To ensure
consistency and maiOpen asset ↗GitHubpdf-raw-page:22 lines:1-36Code / dataset availability confirmedCrossref · Europe PMC · checked 6 Sept 2026
Photosynthetic activity can be monitored using pulse amplitude modulated (PAM) fluorescence or gas exchange. While PAM provides insight into the light-dependent reactions, gas exchange reflects CO 2 fixation and water balance. Accurate, non-invasive prediction of photosynthetic performance under varying conditions is highly relevant for phenotyping and stress diagnostics. Despite their physiological link, data from both methods do not always correlate. To systematically investigate this relationship, photosynthetic parameters were measured in maize ( Zea mays , C4) and basil ( Ocimum basilicum , C3) under different photon densities and spectral compositions. Maize showed the highest CO 2 assimilation rate of 30.99 ± 1.54 µmol CO 2 /(m²s) under 2000 PAR green light (527 nm), while basil reached 10.56 ± 0.92 µmol CO 2 /(m²s) under red light (630 nm). PAM-derived electron transport rates (ETR) increased with light intensity in a pattern similar to CO 2 assimilation, but did not reliably reflect its absolute values under all conditions. To improve prediction accuracy, we applied a machine learning model. XGBoost, a gradient-boosted decision tree algorithm, efficiently captures nonlinear interactions between physiological and environmental parameters. It achieved superior performance (R² = 0.847; MSE = 5.24) compared to the Random Forest model. Our model enables accurate photosynthesis prediction from PAM data across light intensities and spectral conditions in both C3 and C4 plants.
Why it matches plant phenotyping methodsPAM蛍光データから光合成性能を推定する機械学習モデルを開発・比較評価しており、植物生理形質の取得・推定手法が研究の中心である。
abstractTo improve prediction accuracy, we applied a machine learning model.
Reproduction assets foundThe paper's data availability statement names a public GitHub repository (KlirS/Model-for-PAM-Fluorescence-Gas-Exchange-Correlation) hosting the study's datasets, which underpin the PAM fluorescence/gas-exchange measurements and the Random Forest/XGBoost analysis. The statement does not explicitly distinguish code vs. Dataset · publicThe datasets presented in this study can be found in online repositories. The names of the repository/repositories and accession number(s) can be found below: https://github.com/KlirS/Model-for-PAM-Fluorescence-Gas-Exchange-CorrelationOpen asset ↗KlirS/Model-for-PAM-Fluorescence-Gas-Exchange-Correlationlines:486-500Code / dataset availability confirmedCrossref · checked 6 Sept 2026
Abstract Plant cells are contained within a rigid network of cell walls. Cell walls serve as a structural material and a crucial signaling hub vital to all aspects of the plant life cycle. However, many features of the cell wall remain enigmatic, as it has been challenging to map its functional properties in live plants at subcellular resolution. Here, we introduce CarboTag, a modular toolbox for live functional imaging of plant walls. CarboTag uses a small molecular motif, a pyridine boronic acid, that directs its cargo to the cell wall. We designed a suite of cell wall imaging probes based on CarboTag in various colors for multiplexing. Additionally, we developed new functional reporters for live quantitative imaging of key cell wall characteristics: network porosity, cell wall pH and the presence of reactive oxygen species. CarboTag paves the way for dynamic and quantitative mapping of cell wall responses at subcellular resolution. Subject terms: Plant cell biology, Fluorescence imaging
Why it matches plant phenotyping methods植物細胞壁のライブ機能イメージング用ツールボックスを開発し、孔隙率、pH、活性酸素などの細胞壁特性を定量化する手法が中心である。
abstractHere, we introduce CarboTag, a modular toolbox for live functional imaging of plant walls.
Reproduction assets foundThe paper's Data availability and Code availability statements both point to a public 4TU repository DOI containing the raw imaging/phenotyping data and the analysis code for this paper's CarboTag cell wall imaging measurements.Dataset · publicThe raw data associated with the figures in this paper are publicly available at https://doi.org/10.4121/3464fadd-ccb8-4a6c-9463-e3014bcdf984 . Source data are provided with this paper.Open asset ↗10.4121/3464fadd-ccb8-4a6c-9463-e3014bcdf984lines:179-240Code · publicCode developed to process and analyze data in this paper are publicly available at https://doi.org/10.4121/3464fadd-ccb8-4a6c-9463-e3014bcdf984 .Open asset ↗10.4121/3464fadd-ccb8-4a6c-9463-e3014bcdf984lines:179-240Code / dataset availability confirmedEurope PMC · checked 15 Sept 2026
Plant growth and development strongly depend on the uptake of soil minerals and their distribution within plants. Various electrophysiological techniques have been developed to study these ion transport processes, from the single molecule-to whole plant level. An important non-invasive method is provided by Scanning Ion-Selective Electrodes (SISE), which are used to detect ion fluxes. These SISE-measurements depend on software that coordinates the perpetually electrode movement between two positions, as well as data collection and analysis. We developed two LabView-based programs; the SISE-monitor and SISE-analyser that enable ion flux recordings and their analysis, respectively. These applications are freely available, both as windows-executable files that enable routine measurements, as well as the LabView source code that allows deep insights into the routines used for measurement and further development of the programs to include new functions.
Why it matches plant phenotyping methods植物のイオンフラックスを取得・解析するソフトウェアを開発した研究であり、植物生理状態の測定ワークフローが中心です。
abstractWe developed two LabView-based programs; the SISE-monitor and SISE-analyser that enable ion flux recordings and their analysis, respectively.
Reproduction assets foundThe paper's authors publicly released their LabView-based SISE-Monitor and SISE-Analyser software (used for ion flux measurements and analysis) as *.exe and *.vi files on GitHub, with explicit availability statements and a public repository URL.Code · publiche SISE-programs would be made publicly
440 available. This could provide a range of versions of the SISE-programs, with a variety of
441 helpful features that would enable SISE-users to find an optimal solution for their
442 needs.
443
444 Availability and requirements
445
446 Project name: SISE-Software
447 Project home page: https://github.com/Rob-Roelfsema/SISE-Software-April2025
448 Operating system: Windows
449 Programming language: LabView
450 Other requirements: Supporting Virtual Instrument (VI) files (for *.vi files only)
451 License: GNU GPL
452 Any restrictions to use by non-academics: none
453
454 List of abbreviations
455 AI channel, Analog Input channel
456 ASCII, American StaOpen asset ↗Rob-Roelfsema/SISE-Software-April2025pdf-layout-page:15 lines:1-60Code / dataset availability confirmedEurope PMC · checked 14 Sept 2026
Recordings of electric potential changes on plant surfaces have been utilized to identify the components and mechanisms involved in the formation and transmission of systemic signals elicited by stimuli such as herbivory, wounding, or burning. The recorded responses, commonly referred to as slow wave or variation potentials, exhibit striking variability in their waveform. The extent to which this variability is due to differences in experimental procedures or plant biological variability remains unclear. Here, we provide a detailed and robust protocol refined from years of experience in conducting leaf surface potential recordings of Arabidopsis thaliana in response to mechanical wounding. This protocol serves as a comprehensive tutorial covering plant growth, procedures for reproducible mechanical wounding, critical aspects of electrophysiological recordings, and statistical analysis of surface potential recordings. It particularly emphasizes the construction and maintenance of electrodes, placement of the reference or ground electrode, mechanisms for wounding, and data analysis. This protocol aims to promote and facilitate the adoption, standardization, and interoperability of plant surface potential recordings among research groups, thereby increasing the reproducibility and comparability of data within the field. Key features • Recording electric potential changes on the petiole of 5-week-old Arabidopsis plants using noninvasive surface electrodes, improving the wounding procedure, reproducibility, and data processing from [1]. • Genotype-independent method for phenotyping, including parallel recordings from multiple plants. • Guidelines for plant growth conditions, unambiguous leaf assignment by order of emergence, and detailed instructions for electrode fabrication and maintenance. • Instructions for constructing devices for standardized, reproducible mechanical wounding along with a custom script for unbiased and semi-automated data analysis.
Why it matches plant phenotyping methods植物の表面電位を再現性高く記録・解析する電気生理学的フェノタイピング手法の詳細プロトコルであり、電極、標準化創傷、データ解析、再現性・相互運用性が中心的に扱われている。
abstractHere, we provide a detailed and robust protocol refined from years of experience in conducting leaf surface potential recordings of Arabidopsis thaliana in response to mechanical wounding.
Reproduction assets foundThe authors publicly deposit their paper-specific assets on GitHub: the SWPanalyzer.Rmd analysis script, raw surface potential recordings, and 3D-printed wounding grid designs, all directly used for this protocol's phenotyping measurements and analysis.Code · publicis thaliana Col-0 ecotype, other ecotypes may be used. However, differences in rosette morphology could affect petiole accessibility for electrode placement.
2. Prior to the measurement, plants should be acclimatized to the new conditions.
3. The raw data, analysis, R script, and 3D design can be found under the following link: https://github.com/jucbca/SWP-data_analysis
Troubleshooting
Recording:
Problem: You are unable to record any changes in electric potentials.
Solutions:
1. If you do not detect a signal in the wounded leaf:
a. Use a lighter to burn the leaf from the bottom. This method is the most reliable trigger of SWPs and serves as a positive control for your setup.
b. Check with aOpen asset ↗jucbca/SWP-data_analysislines:260-334Code / dataset availability confirmedEurope PMC · checked 6 Sept 2026
Remote sensing holds promise for ecosystem-level monitoring of plant drought stress but is limited by uncertain linkages between physiological stress and remotely sensed metrics of water content. Here, we investigate the stability of relationships between water potential (Ψ) and water content (measured in situ and via repeat airborne VSWIR imaging) over diel, seasonal, and spatial variation in two xeric oak tree species. We also compare these field-based relationships with ones established in laboratory settings that might be used as calibration. Due to confounding physiological processes related to growth, both in situ and remotely sensed metrics lacked consistent relationships with stress when measured across space or through time. Relationships between water content and physiological drought stress measured over the growing season were stronger and more closely related to established laboratory-based drydown methods than those measured across space (i.e., between wet trees and dry trees). These results provide insight into the utility of "space for time" approaches in remote sensing and demonstrate both important limitations and the potential power of high temporal resolution remote sensing for detecting drought stress.
Why it matches plant phenotyping methods樹木の干ばつストレスを対象に、航空機VSWIR画像による含水量推定と水ポテンシャルとの関係を時空間的に検証しており、リモートセンシング手法の妥当性・限界評価が中心です。
abstractwater content (measured in situ and via repeat airborne VSWIR imaging)
Reproduction assets foundThe paper's Data Availability Statement explicitly deposits the study's data and analysis code on Zenodo (DOI 10.5281/zenodo.15110087), and the AVIRIS-NG reflectance data used for the canopy water content analysis is publicly archived at ORNL DAAC (DOI 10.3334/ORNLDAAC/2376). Both are paper-specific, public, and match Code · publicThe data and code that support the findings of this study are openly available in Zenodo at https://doi.org/10.5281/zenodo.15110087 .Open asset ↗Zenodo · 10.5281/zenodo.15110087lines:245-270Dataset · publicThe data and code that support the findings of this study are openly available in Zenodo at https://doi.org/10.5281/zenodo.15110087 .Open asset ↗Zenodo · 10.5281/zenodo.15110087lines:467-475Dataset · publicReflectance data was obtained from ORNL DAAC at https://doi.org/10.3334/ORNLDAAC/2376 .Open asset ↗ORNL DAAC · 10.3334/ORNLDAAC/2376lines:245-270Code / dataset availability confirmedEurope PMC · checked 6 Sept 2026
Chlorophyll levels are a key indicator of plant nitrogen status, which plays a critical role in optimizing agricultural yields. This study evaluated the performance of three low-cost multi-spectral sensors, AS7262, AS7263, and AS7265x, for non-destructive chlorophyll measurement. Measurements were taken from a diverse set of five leaf types, including smooth, uniform leaves (banana and mango), textured leaves (jasmine and sugarcane), and narrow leaves (rice). Partial least squares regression models were used to fit sensor spectra to chlorophyll levels, using nested cross-validation to ensure robust model evaluation. Sensor performance was assessed using R2 and mean absolute error (MAE) scores. The AS7265x demonstrated the best performance on smooth, uniform leaves with validation R2 scores of 0.96-0.95. Its performance decreased for the other leaves, with R2 scores of 0.75-0.85. The AS7262 and AS7263 sensors, while slightly less accurate, achieved reasonable R2 scores ranging from 0.93 to 0.86 for smooth leaves, and from 0.85 to 0.73 for the other leaves. All sensors, particularly the AS7265x, show potential for non-destructive chlorophyll measurement in agricultural applications. Their low cost and reasonable accuracy make them suitable for agricultural applications such as monitoring plant nitrogen levels.
Why it matches plant phenotyping methods低コストマルチスペクトルセンサーによる葉のクロロフィル測定法を評価・比較し、交差検証で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す。
abstractThis study evaluated the performance of three low-cost multi-spectral sensors, AS7262, AS7263, and AS7265x, for non-destructive chlorophyll measurement.
Reproduction assets foundThe authors publicly release raw sensor data, analysis scripts, firmware, and GUI in the GitHub repository KyleLopin/asm_chloro_test, plus supplementary information including extracted chlorophyll reference measurements (S2) at the MDPI supplement URL.Code · publicRaw data, scripts to generate the data and figures used in the manuscript, programs to run the sensors, and GUI used to collect the data are available at https://github.com/KyleLopin/asm_chloro_test (accessed on 25 March 2025).Open asset ↗KyleLopin/asm_chloro_test · KyleLopin/asm_chloro_testlines:187-200Code · publicThe microcontroller code to operate the sensor and a GUI for data collection are available at https://github.com/KyleLopin/asm_chloro_test/tree/master/source (accessed on 25 March 2025).Open asset ↗KyleLopin/asm_chloro_test · KyleLopin/asm_chloro_testlines:155-167Supplement · publicThe following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/s25072198/s1 . Supplementary Information S1: Device Electrical Characterization. Supplementary Information S2: Extracted Chlorophyll Reference Measurements.Open asset ↗lines:176-186Code / dataset availability confirmedEurope PMC · checked 6 Sept 2026
Background Ploidy, representing the number of homologous chromosome sets, can be estimated from flow cytometry data acquired on cells stained with a fluorescent DNA dye. This estimation relies on a combination of tools that often require scripting, individual sample curation, and additional analyses. Results To automate the ploidy estimation for multiple flow cytometry files, we developed MuPETFlow-a Shiny graphical user interface tool. MuPETFlow allows users to visualize cell fluorescence histograms, detect the peaks corresponding to the different cell cycle phases, perform a linear regression using standards, make ploidy or genome size predictions, and export results as figures and table files. The tool was benchmarked with known ploidy datasets from yeast and plant species, yielding consistent ploidy results. MuPETFlow's peaks detection and performance were also compared to those of other tools. Conclusions MuPETFlow stands out as the only tool offering in-app ploidy detection, multiple peak detection, multi-sample visualization, and automation capabilities. These features significantly accelerate the analysis, making it especially valuable for projects involving large datasets.
Why it matches plant phenotyping methods植物のフローサイトメトリーデータから倍数性・ゲノムサイズを推定する解析ツールを開発し、植物データセットでベンチマークしているため、植物表現型取得・解析手法が中心である。
abstractTo automate the ploidy estimation for multiple flow cytometry files, we developed MuPETFlow-a Shiny graphical user interface tool.
Reproduction assets foundThe paper's flow cytometry analysis assets are publicly available: the authors' MuPETFlow GitHub repository hosts the tool code and the newly generated S. cerevisiae FCS datasets, and the plant (Solanum pseudocapsicum) flow cytometry data used for ploidy estimation is deposited in FlowRepository under FR-FCM-Z45W. The Dataset · publicThe S. pseudocapsicum dataset is available
http://flowrepository.org/id/FR-FCM-Z45W.Open asset ↗pdf-page:5 lines:1-74Dataset · publicThe S. cerevisiae datasets are available at https://github.com/CintiaG/MuPETOpen asset ↗GitHubpdf-page:5 lines:1-74Code / dataset availability confirmedEurope PMC · checked 14 Sept 2026
Background: The dark green coloration of bunching onion leaf blades is a key determinant of market value, nutritional quality, and visual appeal. This trait is regulated by a complex network of pigment interactions, which not only determine coloration but also serve as critical indicators of plant growth dynamics and stress responses. This study aimed to elucidate the mechanisms regulating the dark green trait and develop a predictive model for accurately assessing pigment composition. These advancements enable the efficient selection of dark green varieties and facilitate the establishment of optimal growth environments through plant growth monitoring. Methods: Seven varieties and lines of heat-tolerant bunching onions were analyzed, including two commercial F1 cultivars, along with two purebred varieties and three F1 hybrid lines bred in Yamaguchi Prefecture. The analysis was conducted on visible spectral reflectance data (400-700 nm at 20 nm intervals) and pigment compounds (chlorophyll a , chlorophyll b and pheophytin a , lutein, and β-carotene), whereas primary and secondary metabolites were assessed by using widely targeted metabolomics. In addition, a random forest regression model was constructed by using spectral reflectance data and pigment compound contents. Results: Principal component analysis based on spectral reflectance data and the comparative profiling of 186 metabolites revealed characteristic metabolite accumulation associated with each green color pattern. The "green" group showed greater accumulation of sugars, the "gray green" group was characterized by the accumulation of phenolic compounds, and the "dark green" group exhibited accumulation of cyanidins. These metabolites are suggested to accumulate in response to environmental stress, and these differences are likely to influence green coloration traits. Furthermore, among the regression models for estimating pigment compound contents, the one for chlorophyll a content achieved high accuracy, with an R2 value of 0.88 in the test dataset and 0.78 in Leave-One-Out Cross-Validation, demonstrating its potential for practical application in trait evaluation. However, since the regression model developed in this study is based on data obtained from greenhouse conditions, it is necessary to incorporate field trial results and reconstruct the model to enhance its adaptability. Conclusions: This study revealed that cyanidin is involved in the characteristics of dark green varieties. Additionally, it was demonstrated that chlorophyll a can be predicted using visible spectral reflectance. These findings suggest the potential for developing markers for the dark green trait, selecting high-pigment-accumulating varieties, and facilitating the simple real-time diagnosis of plant growth conditions and stress status, thereby enabling the establishment of optimal environmental conditions. Future studies will aim to elucidate the genetic factors regulating pigment accumulation, facilitating the breeding of dark green varieties with enhanced coloration traits for summer cultivation.
Why it matches plant phenotyping methods可視スペクトル反射データから葉のクロロフィルa含量を推定する回帰モデルを構築・検証しており、植物形質の取得・推定法が中心的です。
abstractFurthermore, among the regression models for estimating pigment compound contents, the one for chlorophyll a content achieved high accuracy, with an R2 value of 0.88 in the test dataset and 0.78 in Leave-One-Out Cross-Validation, demonstrating its potential for practical application in trait evaluation.
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 raw MS data can be downloaded from DROP Met database ( https://prime.psc.riken.jp/menta.cgi/prime/drop_index#DM0069 , accessed on 14 February 2025).Open asset ↗DROP Met · DM0069lines:156-172Code / dataset availability confirmedEurope PMC · checked 14 Sept 2026
Invasive plant pests and pathogens cause significant environmental and economic damage. Visual inspection remains a central tenet of plant health surveys, but its sensitivity (probability of correctly identifying the presence of a pest) and specificity (probability of correctly identifying the absence of a pest) is usually ignored. These parameters facilitate calculation of surveillance metrics which are critical for effective contingency planning and outbreak management. To address this, twenty-three citizen scientist surveyors assessed up to 175 oak trees for three symptoms of acute oak decline. The same trees were also assessed by an expert who has monitored these trees annually for over a decade. The sensitivity and specificity of surveyors was calculated using the expert data as the ‘gold-standard’. The utility of a workflow utilising Bayesian modelling was then examined using simulated data to estimate these parameters in the absence of a rarely available ‘gold-standard’ dataset. There was large variation in sensitivity and specificity between surveyors and symptoms, although the sensitivity was positively related to the number of symptoms on a tree. By leveraging surveyor observations of two symptoms from a minimum of 80 trees on two sites, with knowledge of whether a site has higher (∼0.6) or lower (∼0.3) true disease prevalence we show that sensitivity and specificity can be estimated without gold-standard data. We highlight that sensitivity and specificity will depend on the symptoms of a pest or disease, the individual surveyor, and the survey protocol. This has consequences for how surveys are designed to detect and monitor outbreaks, as well as the interpretation of survey data that is used to inform outbreak management. Author summary The increasing occurrence of emerging plant pests and diseases is affecting both agricultural and natural ecosystems. Effective management and control of such pests and diseases is much easier when they are detected early. Currently, visual surveys underpin plant health surveillance, but basic metrics of the reliability of visual detection such as the sensitivity (probability of correctly identifying a positive) and specificity (probability of correctly identifying a negative) are not routinely quantified. In this study, we first quantify the sensitivity and specificity of 23 trained citizen scientist surveyors at detecting three symptoms of acute oak decline, by comparing their symptom classifications against a dataset from an expert who has conducted long-term monitoring of these trees. We demonstrate how individuals vary greatly in their ability to detect symptoms, and how different symptoms are associated with different detection error. Secondly, based on this dataset we outline a workflow developed for scenarios realistic in plant health which utilises Bayesian modelling to estimate these parameters in the absence of a rarely available ‘gold-standard’ expert dataset. In summary, our results highlight variation in the reliability of visual detection, and we provide a workflow to calculate this and facilitate optimisation of risk-based surveillance strategies in plant health.
Why it matches plant phenotyping methods植物の病徴を対象とした目視検査の感度・特異度を定量化し、ゴールドスタンダードなしで推定するベイズ推論ワークフローを検証しており、病害フェノタイピング手法が中心である。
abstractwe first quantify the sensitivity and specificity of 23 trained citizen scientist surveyors at detecting three symptoms of acute oak decline
Reproduction assets foundThe paper's code and data (AOD survey data and Bayesian workflow analysis) are explicitly stated as publicly available on GitHub with a Zenodo archive DOI.Code · publicies in plant
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health. Further adaptation of these methods can be used for a broad range of visual
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environmental surveillance activities.
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Acknowledgements
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We would like to thank all involved in the AOD survey days.
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Code and data availability
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The code and data used for this paper are available from:
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https://github.com/MCombess/Plant_Health_sens_spec_workflow and are archived: DOI:
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10.5281/zenodo.14975201
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CC-BY 4.0 International license
made available under a
(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is
The copyright holder for this preprint
this version postOpen asset ↗MCombess/Plant_Health_sens_spec_workflow · 10.5281/zenodo.14975201pdf-raw-page:27 lines:1-54Code / dataset availability confirmedCrossref · checked 13 Sept 2026
Leaf rust and yellow rust are globally significant fungal diseases that severely impact wheat production, causing yield losses of up to 60% in highly susceptible cultivars. Early and accurate detection is crucial for integrating precision crop protection strategies to mitigate these losses. This study investigates the potential of 3D LiDAR technology for monitoring rust-induced physiological changes in wheat by analyzing variations in plant height, biomass, and light reflectance intensity. Results showed that grain yield decreased by 10–50% depending on cultivar susceptibility, with the durum wheat cultivar ‘Kiko Nick’ and bread wheat ‘Califa’ exhibiting the most severe reductions (~50–60%). While plant height and biomass remained relatively unaffected, LiDAR-derived intensity values strongly correlated with disease severity (R2 = 0.62–0.81, depending on the cultivar and infection stage). These findings demonstrate that LiDAR can serve as a non-destructive, high-throughput tool for early rust detection and biomass estimation, highlighting its potential for integration into precision agriculture workflows to enhance disease monitoring and improve wheat yield forecasting. To promote transparency and reproducibility, the dataset used in this study is openly available on Zenodo, and all processing code is accessible via GitHub, cited at the end of this manuscript.
Why it matches plant phenotyping methodsLiDARによる小麦の病害状態・バイオマス等の非破壊推定を中心に評価しており、植物フェノタイピング手法の実質的な適用・検証に該当する。
abstractThis study investigates the potential of 3D LiDAR technology for monitoring rust-induced physiological changes in wheat by analyzing variations in plant height, biomass, and light reflectance intensity.
Reproduction assets foundThe paper's LiDAR-derived wheat rust phenotyping dataset is openly available on Zenodo (DOI 10.5281/zenodo.14889285), and the authors' point-cloud processing and parameter-extraction code is publicly available on GitHub (eapolo/agrolidarwheatrust). Both are explicitly stated in the Data Availability Statement.Dataset · publicData Availability Statement: The dataset used in this study has been published on the Zenodo
platform under the DOI: https://doi.org/10.5281/zenodo.14889285Open asset ↗Zenodo · 10.5281/zenodo.14889285pdf-page:21 lines:1-60Code · publicalong with the code, which is
available in the GitHub repository at https://github.com/eapolo/agrolidarwheatrust, accessed on
10 March 2025.Open asset ↗github.com/eapolo/agrolidarwheatrustpdf-page:21 lines:1-60Code / dataset availability confirmedCrossref · checked 6 Sept 2026
Summary Leaf water loss after stomatal closure is key to understanding the effects of prolonged drought on vegetation. It is therefore important to accurately quantify such water losses to improve physiology‐based models of drought‐induced plant mortality. We measured water loss of detached leaves continuously during dehydration in nine woody angiosperm species. We computed minimum leaf conductance ( g min ) at different water potential thresholds along a sequence of physiological function losses, spanning from turgor loss point to hydraulic failure. A mechanistic model evaluated the impact of different g min estimations on the time to hydraulic failure (THF). Residual conductance is not steady and decreases continuously at varying rates across species during the entire dehydration process, even after correcting for leaf shrinkage and vapor pressure deficit shifts. Different estimations of g min had a significant impact on the THF predicted by the model, especially for drought‐resistant species. We demonstrate that residual conductance is variable during dehydration, and thus, it is important to use physiological or water status boundaries for its estimation in order to determine distinct g min values of water loss. We describe an accurate, repeatable and open‐source methodology to estimate g min . Such methodology could enhance models of plant mortality under drought.
Why it matches plant phenotyping methods葉の脱水過程における最小葉コンダクタンスの定量法を開発・評価し、反復可能な方法論として提示しているため、植物生理フェノタイピング手法が中心です。
abstractWe describe an accurate, repeatable and open‐source methodology to estimate g min .
Reproduction assets foundThe paper's Data Availability Statement provides two paper-specific public assets: the authors' analysis/acquisition code (gminComputation in Python, g_Residual in R, and the 'cuticular' acquisition software) hosted on a public Gitlab repository, and the manuscript's underlying dehydration/gmin measurement data on the法Code · publicCodes developed for data acquisition (software ‘cuticular’ for Windows) and computation of raw residual conductance (project ‘gminComputation’ is developed as a console version in python, and ‘g_Residual’ is a script written in R language) are available in the following public Gitlab repository: https://gitub.u‐bordeaux.fr/phenoboisOpen asset ↗https://gitub.u‐bordeaux.fr/phenoboislines:509-550Code / dataset availability confirmedCrossref · Europe PMC · checked 6 Sept 2026
We show non-invasive 3D plant disease imaging using automated monocular vision-based structure from motion. We optimize the number of key points in an image pair by using a small angular step size and detection in the extra green channel. Furthermore, we upsample the images to increase the number of key points. With the same setup, we obtain functional fluorescence information that we map onto the 3D structural plant image, in this way obtaining a combined functional and 3D structural plant image using a single setup.
Why it matches plant phenotyping methods植物の3D構造と蛍光機能情報を取得・統合する画像計測手法の開発が中心であり、植物病害の非侵襲的フェノタイピングに該当します。
abstractWe show non-invasive 3D plant disease imaging using automated monocular vision-based structure from motion.
Reproduction assets foundThe paper's Data Availability Statement explicitly deposits the code and datasets for reproducing the SfM 3D plant imaging results in the 4TU repository, with a DOI matching an allowed URL.Code · publicThe code and data sets for reproducing the results are available in 4TU repository at https://doi.org/10.4121/e6db8707-10ee-4553-9a98-753f1b4c526a .Open asset ↗4TU repository · 10.4121/e6db8707-10ee-4553-9a98-753f1b4c526alines:52-127Code / dataset availability confirmedEurope PMC · checked 14 Sept 2026
Published1 Mar 2025The Plant journal : for cell and molecular biologyCited by 6 · OpenAlex ↗
High lipid producing (HLP) tobacco (Nicotiana tabacum) is a potential biofuel crop that produces an excess of 30% dry weight as lipid bodies in the form of triacylglycerol. While using HLP tobacco as a sustainable fuel source is promising, it has not yet been tested for its tolerance to warmer environments that are expected in the near future as a result of climate change. We found that HLP tobacco had reduced stomatal conductance, which results in increased leaf temperatures up to 1.5°C higher under control and high temperature (38°C day/28°C night) conditions, reduced transpiration, and reduced CO 2 assimilation. We hypothesize this reduction in stomatal conductance is due to the presence of excessive, large lipid droplets in HLP guard cells imaged using confocal microscopy. High temperatures also significantly reduced total fatty acid levels by 55% in HLP plants; thus, additional engineering may be needed to maintain high titers of leaf oil under future climate conditions. High-throughput image analysis techniques using open-source image analysis platform PlantCV for thermal image analysis (plant temperature), stomata microscopy image analysis (stomatal conductance), and fluorescence image analysis (photosynthetic efficiency) were developed and applied in this study. A corresponding set of PlantCV tutorials are provided to enable similar studies focused on phenotyping future crops under adverse conditions.
Why it matches plant phenotyping methodsPlantCVを用いた熱画像・気孔顕微鏡画像・蛍光画像の高スループット解析手法を開発・適用し、植物温度、気孔関連指標、光合成効率を推定しているため、表現型取得手法が中心的です。
abstractHigh-throughput image analysis techniques using open-source image analysis platform PlantCV for thermal image analysis (plant temperature), stomata microscopy image analysis (stomatal conductance), and fluorescence image analysis (photosynthetic efficiency) were developed and applied in this study.
Reproduction assets foundThe paper's raw phenotyping image data (thermal, fluorescence, stomata, confocal microscopy) are deposited on Zenodo, and the authors' PlantCV analysis workflows and R scripts are on GitHub, including three PlantCV tutorials for thermal, stomata, and photosynthesis analysis.Dataset · publicaxial side of the leaf rather than a cross section. While small lipid droplets were present in the WT stomatal guard cells and epidermis, large lipid droplets were present in the HLP guard cells under both control and after 7 days of treatment (representative control images in Figure 8A–D , complete dataset available on Zenodo, https://zenodo.org/records/10711864 ). In addition, while HLP oil appeared to form spherical droplets, it did not “line” the stomatal opening as in WT (Figure 8C,D ).
Figure 8
High lipid producing (HLP) had excessive oil droplets in stomatal guard cells.
Representative confocal microscopy images, shown as focused Z‐stack, of tobacco leaf tissue fixed in paraformaOpen asset ↗Zenodolines:115-123Code · publicmated marginal means (LSMEANS) to determine which sample types were significantly different from others. Means are reported in text with standard error. Plots were made using ggplot2 package (v.3.5.0) in R. Jupyter notebooks associated with PlantCV analyses and R scripts associated with this manuscript are available on Github ( https://github.com/danforthcenter/tobacco‐heat‐paper ).
AUTHOR CONTRIBUTIONS
DKA, MAG, PDB, BSJ and KMM designed experiments. KMM and BSJ performed experiments and data analysis. KJC designed and aided KMM in confocal and brightfield microscopy experiments and advised TEM experiments. JW performed TEM experiments, and KG‐O and SK performed data analysis of TEM images.Open asset ↗GitHublines:171-182Code · publictification was used to isolate only individual plants in each mask. Then, the mask was applied to the registered thermal image to calculate the average plant temperature, as well as a histogram of pixel temperatures for each plant. A PlantCV workflow was used to analyze the images in parallel. A tutorial is available on GitHub: https://github.com/danforthcenter/plantcv‐tutorial‐thermal?tab=readme‐ov‐file (Acosta‐Gamboa et al., 2024 ). Scripts for this project are available at https://github.com/danforthcenter/tobacco‐heat‐paper . Raw image data are available on Zenodo, https://zenodo.org/records/10711864 .
Stomatal aperture measurements
To measure stomatal number and aperture, leaf impressioOpen asset ↗GitHublines:142-146Code · publicpackage was then used to calculate the number of stomata and the area of the aperture. A limitation of this method is that it does not provide the width and length of stomata, or measurements of the guard cells themselves; instead, it provides the aperture area (a result of length and width). A tutorial is available on GitHub: https://github.com/danforthcenter/plantcv‐stomata‐tutorial‐pcv4 (Murphy, 2024 ). Scripts for this project are available at https://github.com/danforthcenter/tobacco‐heat‐paper . Raw image data are available on Zenodo, https://zenodo.org/records/10711864 .
Photosynthesis and gas exchangeOpen asset ↗GitHublines:142-146Code · publicPlantCV (Gehan et al., 2017 ) using the photosynthesis package; the chlorophyll fluorescence image was used to mask the image for only plant pixels, and average F
v / F
m , F q ′ / F m ′ , NPQ, chlorophyll index, and anthocyanin index were calculated as an average per plant at each timepoint. A tutorial is available on GitHub: https://github.com/danforthcenter/plantcv‐tutorial‐photosynthesis?tab=readme‐ov‐file (Schuhl et al., 2024 ). Scripts for this project are available at https://github.com/danforthcenter/tobacco‐heat‐paper . Raw image data are available on Zenodo, https://zenodo.org/records/10711864 .
Microscopy imaging of lipids
Leaf samples analyzed for lipid content were taken from thOpen asset ↗GitHublines:156-164Code / dataset availability confirmedEurope PMC · checked 15 Sept 2026
Understanding plant hormonal responses to stress and their transport dynamics remains challenging, limiting advancements in enhancing plant resilience. Our study presents a novel approach that utilizes genetically engineered bacteria (GEB) as molecular transceivers within plants, aiming to develop revolutionary agricultural biosensors. We focus on abscisic acid (ABA), a key hormone for plant growth and stress response. We propose using Escherichia coli (E. coli) engineered with PYR1-derived receptors that exhibit high affinity for ABA, triggering a bioluminescent response. Simulations investigate the detection time for ABA, bacterial diffusion within plant roots, advection effects through shoots, and chemotaxis in response to attractant gradients in leaves. Results indicate that higher ABA concentrations correlate with shorter response times, with an average of 431.52 s based on bioluminescence. The average internalization time for bacteria through a plant root area of 2 µm 2 during the rhizophagy process is estimated at 1220.12 s. Simulations also assess bacterial movement through shoots, the impact of advection, and chemotactic responses. These findings highlight the complex interplay between plant signaling and microbial communities, validating the efficacy of our bacterial-based sensor approach and opening new avenues for agricultural biosensor technology.
Why it matches plant phenotyping methods植物内ABAを生物発光で検出する遺伝子改変細菌センサーを開発・シミュレーション検証しており、植物ストレス状態の取得手法が中心である。
abstractOur study presents a novel approach that utilizes genetically engineered bacteria (GEB) as molecular transceivers within plants, aiming to develop revolutionary agricultural biosensors.
Reproduction assets foundThe paper's MATLAB simulation code for the bacterial ABA biosensor is publicly available on the authors' GitHub repository, as stated in the data availability statement.Code · public14th Five-Year Plan period (2021YFD1700904) and by the Major Science and Technology projects of Henan Province (221100320200) and supported by the Henan Center for Outstanding Overseas Scientists (GZS2021007).
Availability of data and materials
All data and simulation files/codes are available online at Github under the link " https://github.com/Shakeel-CN/E.-coil-PYR1 ".
Declarations
Ethics approval and consent to participate
Not applicable.
Consent for publication
Not Applicable.
Competing interestsOpen asset ↗Shakeel-CN/E.-coil-PYR1lines:382-403Code / dataset availability confirmedEurope PMC · checked 6 Sept 2026
The cuticle is a polymeric membrane covering all plant aerial organs of primary origin. It regulates water loss and defends against environmental stressors and pathogens. Despite its significance, understanding of the micro-mechanical properties of the cuticle (cuticular membrane; CM) remains limited. In this study, non-invasive Brillouin light scattering (BLS) spectroscopy was applied to probe the micro-mechanics of native CM, dewaxed CM (DCM), and isolated cutin matrix (CU) of mature apple fruit. The BLS signal arises from the photon interaction with thermally induced pressure waves and allows for imaging with mechanical contrast. The derived loss tangent showed significant differences with wax extraction from the CM and further with carbohydrate extraction from the DCM, consistent with tensile test results. Spatial heterogeneity between anticlinal and periclinal regions was observed by BLS microscopy of CM and DCM, but not in CU. The key conclusions are: (1) BLS is sensitive to micro-mechanical variations, particularly the strain-stiffening effect of the cutin framework, offering insights into the CM's micro-mechanical behavior and underlying chemical structures; (2) CM and DCM exhibit spatial micro-mechanical heterogeneity between periclinal and anticlinal regions.
Why it matches plant phenotyping methodsリンゴ果実のクチクラの微力学特性を、BLS顕微鏡による非侵襲的イメージングで測定・比較しており、植物器官の物性形質取得が研究の中心である。
abstractnon-invasive Brillouin light scattering (BLS) spectroscopy was applied to probe the micro-mechanics of native CM, dewaxed CM (DCM), and isolated cutin matrix (CU) of mature apple fruit.
Reproduction assets foundThe paper deposits its underlying Brillouin light scattering measurement data (primary and supplementary figures) in a public LUIS repository (DOI 10.25835/xvsi5g6m). The Brillouin analysis python script is only available on request, so it is not a public code asset.Dataset · publicThe underlying data for all the primary and Supplementary Figs. has been deposited in a publicly accessible repository [ https://doi.org/10.25835/xvsi5g6m ] 67 . Raw data may be obtained from the authors upon reasonable request.Open asset ↗10.25835/xvsi5g6mlines:177-254Code / dataset availability confirmedOpenAlex · arXiv · checked 14 Sept 2026
Advancements in artificial intelligence (AI) have greatly benefited plant phenotyping and predictive modeling. However, unrealized opportunities exist in leveraging AI advancements in model parameter optimization for parameter fitting in complex biophysical models. This work developed novel software, PhoTorch, for fitting parameters of the Farquhar, von Caemmerer, and Berry (FvCB) biochemical photosynthesis model based the parameter optimization components of the popular AI framework PyTorch. The primary novelty of the software lies in its computational efficiency, robustness of parameter estimation, and flexibility in handling different types of response curves and sub-model functional forms. PhoTorch can fit both steady-state and non-steady-state gas exchange data with high efficiency and accuracy. Its flexibility allows for optional fitting of temperature and light response parameters, and can simultaneously fit light response curves and standard A/Ci curves. These features are not available within presently available A/Ci curve fitting packages. Results illustrated the robustness and efficiency of PhoTorch in fitting A/Ci curves with high variability and some level of artifacts and noise. PhoTorch is more than four times faster than benchmark software, which may be relevant when processing many non-steady-state A/Ci curves with hundreds of data points per curve. PhoTorch provides researchers from various fields with a reliable and efficient tool for analyzing photosynthetic data. The Python package is openly accessible from the repository: https://github.com/GEMINI-Breeding/photorch.
Why it matches plant phenotyping methods光合成ガス交換データから生理形質を推定するモデルフィッティングソフトウェアの開発・ベンチマークが中心であり、植物フェノタイピング手法に該当する。
abstractThis work developed novel software, PhoTorch, for fitting parameters of the Farquhar, von Caemmerer, and Berry (FvCB) biochemical photosynthesis model based the parameter optimization components of the popular AI framework PyTorch.
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 Python package is openly accessible from the repository: https://github.com/GEMINI-Breeding/photorch .Open asset ↗GEMINI-Breeding/photorchlines:1-57Code / dataset availability confirmedEurope PMC · checked 6 Sept 2026
Assessing the health status of vegetation is of vital importance for all stakeholders. Multi-spectral and hyper-spectral imaging systems are tools for evaluating the health of vegetation in laboratory settings, and also hold the potential of assessing vegetation of large portions of land. However, the literature lacks benchmark datasets to test algorithms for predicting plant health status, with most researchers creating tailored datasets. This work presents a dataset composed of multi-spectral images, hyper-spectral reflectance values, and measurements of weight, chlorophyll, and nitrogen content of leaves at five different drying stages, from avocado, olive, and grape trees, which are common crops in the Valparaíso region of Chile. This dataset is a valuable asset for developing tools in the field of precision agriculture and assessing the general health status of vegetation.
Why it matches plant phenotyping methods植物のマルチスペクトル・ハイパースペクトル画像と葉の水分状態・化学形質を含む評価用データセットを構築しており、フェノタイピング手法開発のためのベンチマークが中心である。
abstractThis work presents a dataset composed of multi-spectral images, hyper-spectral reflectance values, and measurements of weight, chlorophyll, and nitrogen content of leaves at five different drying stages
Reproduction assets foundThe paper's multispectral images, hyperspectral reflectance, and trait measurements (weight, chlorophyll, nitrogen, fuel moisture) are publicly deposited on Figshare with an explicit DOI. The authors' sample Matlab code is included within that dataset. The MicaSense imageprocessing repository is a generic third-party工具Dataset · publicAll the data is available at this repository DOI: https://doi.org/10.6084/m9.figshare.26950660.v2.Open asset ↗figshare · 10.6084/m9.figshare.26950660.v2pdf-page:14 lines:1-35Code / dataset availability confirmedbioRxiv · checked 15 Sept 2026
Leaf osmotic potential at full turgor ({pi}0) has been used frequently to indicate turgor loss point of plant leaves. However, even a rapid measurement of{pi} 0 using osmometry is time-consuming, if numerous leaf samples need to be measured. Because of this, researchers tend to use a small sample size to determine{pi} 0 and relate it to indices of crop performance. Yet the statistical and agronomic significance of using a small sample size of{pi} 0 to indicate crop performance is not known. We address this question using field measurements and statistical resampling. Six mature leaf samples were collected at the peak bloom stage from each of the 54 cotton plots in Texas, USA in 2024. The{pi} 0 of the collected leaves were measured using an osmometer. Seed cotton yields from the field plots were measured near the end of cotton season. To test the effect of sample size on strength of the linear relation between{pi} 0 and cotton yield, 1-6 resamples of{pi} 0 were randomly drawn with replacement from the original 6 measurements per plot for the 54 plots. The resampled data of{pi} 0 were then used as independent variable to predict cotton yield. We found that, considering the labor and cost, sampling 3 or 6 leaves per plot may not make a significant difference for the linear regression between{pi} 0 and cotton yield.
Why it matches plant phenotyping methods葉の浸透ポテンシャル測定におけるサンプル数の妥当性と、収量との関係に対する影響を再サンプリングで評価しており、測定プロトコルの技術的検証が中心である。
titleDoes sample size of leaf osmotic potential affect its relationship with cotton yield?
Reproduction assets foundThe paper's field-measured leaf osmotic potential and seed cotton yield dataset, plus the authors' resampling/regression computer code, are explicitly deposited publicly on Zenodo (record 14635663), as stated in the Data availability section.Dataset · publicect 9574-
2, is appreciated. We thank Jose Teran and Joe Gonzalez,
Farm Manager and Farm Foreman, respectively, at Uvalde
Research Center, and collaborating farmer Rick Kruger for
time/efforts invested in crop management.
Data availability
The data and computer code for reproduc-
ing the results of this paper are available from
https://zenodo.org/records/14635663.Bibliography
1. Megan K. Bartlett, Ya Zhang, Christine Scoffoni, Shanwen Sun, Rico Ardy, Kunfang Cao,
and Lawren Sack. Rapid determination of comparative drought tolerance traits: using an
osmometer to predict turgor loss point. Methods in Ecology and Evolution, 3:880–888, 2012.
2. Y. N. S. Cheung, M. T. Tyree, and J. Dainty. WOpen asset ↗Zenodo · 14635663pdf-raw-page:3 lines:1-85Code / dataset availability confirmedEurope PMC · checked 6 Sept 2026
A wide range of portable chlorophyll meters are increasingly being used to measure leaf chlorophyll content as an indicator of plant performance, providing reference data for remote sensing studies. We tested the effect of leaf anatomy on the relationship between optical assessments of chlorophyll (Chl) against biochemically determined Chl content as a reference. Optical Chl assessments included measurements taken by four chlorophyll meters: three transmittance-based (SPAD-502, Dualex-4 Scientific, and MultispeQ 2.0), one fluorescence-based (CCM-300), and vegetation indices calculated from the 400-2500 nm leaf reflectance acquired using an ASD FieldSpec and a contact plant probe. Three leaf types with different anatomy were included: dorsiventral laminar leaves, grass leaves, and needles. On laminar leaves, all instruments performed well for chlorophyll content estimation (R 2 > 0.80, nRMSE 2 > 0.90, nRMSE 2 = 0.45, nRMSE = 11%) and failed for SPAD. For Norway spruce needles, the relation of CCM-300 values to chlorophyll content was also weak (R 2 = 0.45, nRMSE = 11%). To improve the accuracy of data used for remote sensing algorithm development, we recommend calibration of chlorophyll meter measurements with biochemical assessments, especially for species with anatomy other than laminar dicot leaves. The take-home message is that portable chlorophyll meters perform well for laminar leaves and grasses with wider leaves, however, their accuracy is limited for conifer needles and narrow grass leaves. Species-specific calibrations are necessary to account for anatomical variations, and adjustments in sampling protocols may be required to improve measurement reliability.
Why it matches plant phenotyping methods携帯型クロロフィルメーターによる葉クロロフィル量推定を、葉の解剖学的差異と生化学測定を基準に比較・検証し、校正とサンプリング改善を提案しているため、植物表現型取得法が中心です。
abstractWe tested the effect of leaf anatomy on the relationship between optical assessments of chlorophyll (Chl) against biochemically determined Chl content as a reference.
Reproduction assets foundThe paper's Data availability statement explicitly deposits the study's chlorophyll measurement and trait data in a public Zenodo repository, which is an allowed URL. No separate author analysis code URL is given (analyses were in Matlab/R), so the qualifying asset is the deposited dataset.Dataset · publicData are available in Zenodo repository found by https://zenodo.org/records/14615430.Open asset ↗Zenodo · 14615430pdf-page:14 lines:1-62Code / dataset availability confirmedCrossref · checked 15 Sept 2026
Field / plotWhole plant / canopy / plot / fieldGrowth / time-series analysisPhotosynthesis / fluorescenceWater status / transpiration
Abstract. Long-term time series of transpiration, evaporation, plant net photosynthesis, and soil respiration are essential for addressing numerous research questions related to ecosystem functioning. However, quantifying these fluxes is challenging due to the lack of reliable and direct measurement techniques, which has left gaps in the understanding of their temporal cycles and spatial variability. To help address this open challenge, we generated a dataset of these four components by implementing five (conventional and novel) approaches to partition total evapotranspiration (ET) and CO2 fluxes into plant and soil fluxes across 47 National Ecological Observatory Network (NEON) sites. The final dataset (https://doi.org/10.5281/zenodo.12191876; Zahn and Bou-Zeid, 2024) spans a 5-year period and covers various ecosystems, including forests, grasslands, and agricultural terrain. This is the first comprehensive dataset covering such a wide spatial and temporal distribution. Overall, we observed good agreement across most methods for ET components, increasing confidence in these estimates. Partitioning of CO2 components, on the other hand, was found to be less robust and more dependent on prior knowledge of water use efficiency. This highlights some limitations of these present methods that we discuss, emphasizing the broader challenge posed by the lack of an accurate reference method to validate against. Despite these limitations, this dataset has several potential applications, especially in addressing critical questions regarding the response of ecosystems to extreme weather events, which are expected to become more severe and frequent with climate change.
Why it matches plant phenotyping methods複数手法で蒸散・植物純光合成などの植物生理フラックスを分離推定し、手法間比較と妥当性・限界評価を行った大規模データセットであり、植物状態の取得手法が中心である。
abstractwe generated a dataset of these four components by implementing five (conventional and novel) approaches to partition total evapotranspiration (ET) and CO2 fluxes into plant and soil fluxes across 47 National Ecological Observatory Network (NEON) sites.
Reproduction assets foundThe paper's flux-partitioning dataset (transpiration, evaporation, plant photosynthesis, soil respiration across 47 NEON sites) is publicly deposited on Zenodo, and the authors' scripts implementing all five partitioning methods are also publicly available on Zenodo with explicit availability statements.Dataset · publicl. ( 2020 ) across FLUXNET sites. By comparing different algorithms, we can further explore their uncertainties and focus on model improvement. Finally, as more data become available, other options can be used to train machine learning algorithms, focusing on gap-filling.
7 Code and data availability
The dataset is available at https://doi.org/10.5281/zenodo.12191876 ( Zahn and Bou-Zeid , 2024 ) . In addition to all the flux components, it contains the auxiliary meteorological inputs used to implement the Extreme Gradient Boosting algorithm for gap-filling and feature importance analysis. The scripts used to implement all five partitioning methods can be found at https://doi.org/10.5281/zenOpen asset ↗Zenodo · 10.5281/zenodo.12191876lines:346-361Code / dataset availability confirmedEurope PMC · checked 6 Sept 2026
Abstract Assessing the health status of vegetation is of vital importance for all stakeholders. Multi-spectral and hyper-spectral imaging systems are tools for evaluating the health of crops across large areas, particularly when deployed on robotic platforms such as unmanned aerial vehicles (UAVs). However, the literature lacks benchmark datasets to test algorithms for predicting plant health status, with most researchers creating tailored datasets. This work presents a dataset composed of multi-spectral images, hyper-spectral reflectance values, and measurements of weight, chlorophyll, and nitrogen content of leaves at five different drying stages, from avocado, olive, and vineyard trees, which are common crops in the Valparaíso region of Chile. This dataset is a valuable asset for developing tools in the field of precision agriculture and assessing the general health status of vegetation.
Why it matches plant phenotyping methods植物の健康状態を推定するためのマルチスペクトル・ハイパースペクトル画像と葉の形質測定を組み合わせた評価用データセットが主題であり、植物フェノタイピング手法のベンチマーク資源に該当する。
abstractThis work presents a dataset composed of multi-spectral images, hyper-spectral reflectance values, and measurements of weight, chlorophyll, and nitrogen content of leaves at five different drying stages
Reproduction assets foundThe paper is a dataset descriptor; its complete plant-phenotyping measurements (multispectral leaf images, hyperspectral reflectance, chlorophyll, nitrogen, weight/FMC across five drying stages for avocado, olive, and vineyard) are publicly deposited on figshare under DOI 10.6084/M9.FIGSHARE.26950660, along with aMatlåDataset · publicAll the data is available at this repository DOI: 10.6084/M9.FIGSHARE.26950660Open asset ↗figshare · 10.6084/M9.FIGSHARE.26950660pdf-page:15 lines:1-59Code / dataset availability confirmedbioRxiv · Europe PMC · checked 15 Sept 2026
Field / plotLaboratory / benchtopX-ray / CTRootWhole plant / canopy / plot / field
Soil compaction and escalating global drought increase soil strength and stiffness. It remains unclear which plant root biomechanical mechanisms/traits enable growth in these harsh conditions. Here, we combine synchrotron X-ray computed tomography with spatially resolved X-ray diffraction to characterize the biomechanics of a replica root-soil system. We map the strain field around the root tip, finding strong agreement with finite element simulations, thereby demonstrating a promising new in-vivo measurement protocol.
Why it matches plant phenotyping methods根周辺のひずみ場という根の力学的形質を、X線CT・回折と有限要素解析で測定・検証する新規プロトコルが研究の中心である。
abstractWe map the strain field around the root tip, finding strong agreement with finite element simulations, thereby demonstrating a promising new in-vivo measurement protocol.
Reproduction assets foundThe paper deposits its X-ray diffraction and X-ray imaging (XCT) measurements in the Southampton Pure repository (DOI 10.5258/SOTON/D3309.274) and its processing scripts in a companion deposit (DOI 10.5258/SOTON/D3309.276), both with explicit availability statements and public URLs.Dataset · public∇uT ), F(σ′) > 0,x ∈ Ω
σ′ = Cep
: (∇u+∇uT ), F(σ′) = 0,x ∈ Ω
u·ê1 = 0, x ∈ ΓAxis
u = 0, x ∈ ΓC,Top
u = [0,wstep]T , x ∈ Γbot ∪Γout
n̂·∇u = 0, x ∈ Γtop ∪ΓC,tip
n̂·σ = ppen, x ∈ (Γtop ∩Ω∩Ωc)∪(ΓC,tip ∩Ω∩Ωc)
. (29)
Data Records
273
All X-ray diffraction and X-ray imaging data used in this study can be found in the Pure repository: https://doi.org/10.5258/SOTON/D3309.274
Code availability
275
All scripts used to process the data can be found in the Pure repository: https://doi.org/10.5258/SOTON/D3309.276
References
277
1. Lee, H. et al. Ipcc, 2023: Climate change 2023: Synthesis report, summary for policymakers. contribution of working
278
groups i, ii and iii to the sixth assessment report ofOpen asset ↗Pure repository · 10.5258/SOTON/D3309.274pdf-raw-page:9 lines:1-96Code · publicu = 0, x ∈ Γtop ∪ΓC,tip
n̂·σ = ppen, x ∈ (Γtop ∩Ω∩Ωc)∪(ΓC,tip ∩Ω∩Ωc)
. (29)
Data Records
273
All X-ray diffraction and X-ray imaging data used in this study can be found in the Pure repository: https://doi.org/10.5258/SOTON/D3309.274
Code availability
275
All scripts used to process the data can be found in the Pure repository: https://doi.org/10.5258/SOTON/D3309.276
References
277
1. Lee, H. et al. Ipcc, 2023: Climate change 2023: Synthesis report, summary for policymakers. contribution of working
278
groups i, ii and iii to the sixth assessment report of the intergovernmental panel on climate change [core writing team, h.
279
lee and j. romero (eds.)]. ipcc, geneva, switzerland. (2023).
2Open asset ↗Pure repository · 10.5258/SOTON/D3309.276pdf-raw-page:9 lines:1-96Code / dataset availability confirmedCrossref · checked 14 Sept 2026
The analysis of fast fluorescence kinetics, specifically through the JIP test, is a valuable tool for identifying and characterizing plant stress. However, interpreting OJIP data requires a comprehensive understanding of their underlying theory. This study proposes a Machine Learning-based approach using a One-Class Support Vector Machine anomaly detection model to effectively categorize OJIP measurements into “normal”, representing healthy plants, and “anomalies”. This approach was validated using a previously published dataset. A subgroup of the identified “anomalies” was clearly linked to stress-induced reductions in photosynthesis. Furthermore, the percentage of these “anomalies” showed a meaningful correlation with both the progression and severity of stress. The results highlight the still largely unexploited potential of Machine Learning in OJIP analysis.
Why it matches plant phenotyping methods植物の高速蛍光 kinetics（OJIP）からストレス状態を抽出する機械学習手法を開発し、既存データセットで検証しており、フェノタイピング手法が中心である。
abstractThis study proposes a Machine Learning-based approach using a One-Class Support Vector Machine anomaly detection model to effectively categorize OJIP measurements into “normal”, representing healthy plants, and “anomalies”.
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 · publicSupplementary Materials: The following supporting information can be downloaded at https://
www.mdpi.com/article/10.3390/stresses4040051/s1. All OJIP data used in the study can be found in
Supplementary data (OJIP data).xlsx.Open asset ↗stresses4040051pdf-page:12 lines:1-58Code / dataset availability confirmedEurope PMC · checked 7 Sept 2026
Early stress detection of crops requires a thorough understanding of the signals showing the very first symptoms of the alterations in the photosynthetic light reactions. Detection of the activation of the regulated heat dissipation mechanism is crucial to complement passively induced fluorescence to resolve ambuiguities in energy partitioning. Using leaf spectroscopy, we evaluated the capability of pigment spectral unmixing to calculate the fluorescence quantum efficiency (FQE) and simultaneously retrieve fast absorption changes in a drought and nitrogen deficiency experiment with tomato. In addition, active fluorescence measurements and pigment analyses of xanthophylls, carotenes and chlorophylls were conducted. We observed notable responses in noninvasive proximal sensing-retrieved FQE values under stress, but as expected, these alone were not enough to identify the constraints in photosynthetic efficiency. Reflectance-based detection of the 535-nm peak absorption change was able to complement FQE and indicate the activation of regulated heat dissipation for both stress treatments under growing light conditions. However, further complexity in the light harvesting energy regulation needs to be accounted for when considering additional light stress. Our results underscore the potential of complementary in vivo quantitative spectroscopy-based products in the early and nondestructive stress diagnosis of plants, marking the path for further applications.
Why it matches plant phenotyping methods葉分光法とスペクトルアンミキシングにより、植物のFQEや熱散逸に関連する吸収変化を非破壊・定量的に取得し、ストレス診断への有効性を評価しているため、植物生理フェノタイピング手法の応用・評価が中心です。
abstractUsing leaf spectroscopy, we evaluated the capability of pigment spectral unmixing to calculate the fluorescence quantum efficiency (FQE) and simultaneously retrieve fast absorption changes in a drought and nitrogen deficiency experiment with tomato.
Reproduction assets foundThe article's Data Availability Statement explicitly deposits the paper's raw and processed phenotyping/spectroscopy measurements open access on Zenodo (doi: 10.5281/zenodo.12800064). This is a paper-specific, public, actionable dataset. However, the Zenodo URL is not among the allowed_urls, so no asset URL is providedDataset · publicData Availability Statement
Raw and processed data are available open access through the Zenodo repository (doi: 10.5281/zenodo.12800064 ).Zenodo · 10.5281/zenodo.12800064lines:539-574Code / dataset availability confirmedEurope PMC · Crossref · checked 14 Sept 2026
Net photosynthesis (AN) is a major component of the global carbon cycle, with significant feedback to decadal-scale climate change. Although plant acclimation to environmental changes can modify AN, traditional vegetation models in Earth System Models (ESMs) often rely on plant functional type (PFT)-specific parameter calibrations or simplified acclimation assumptions, both of which lacked generalizability across time, space and PFTs. In this study, we propose a differentiable photosynthesis model to learn the environmental dependencies of Vc,max25, as this genre of hybrid physics-informed machine learning can seamlessly train neural networks and process-based equations together. Compared to PFT-specific parameterization of Vc,max25, learning the environment dependencies of key photosynthetic parameters improves model spatiotemporal generalizability. Applying environmental acclimation to Vc,max25 led to substantial variation in global mean AN, calling for the attention to acclimation in ESMs. The model effectively captured multivariate observations (Vcmax25, stomatal conductance gs, and AN) simultaneously and, in fact, multivariate constraints further improved model generalization across space and PFTs. It also learned sensible acclimation relationships of Vc,max25 to different environmental conditions. The model explained more than 54%, 57% and 62% of the variance of AN, gs, and Vcmax25, respectively, presenting a first global-scale spatial test benchmark of AN and gs. These results highlight the potential of differentiable modeling to enhanced process-based modules in ESMs and effectively leverage information from large, multivariate datasets.
Why it matches plant phenotyping methods植物の光合成・気孔コンダクタンス等の生理形質を推定する微分可能な物理情報機械学習モデルを開発し、観測データで検証・ベンチマークしており、フェノタイピング手法が中心である。
abstractwe propose a differentiable photosynthesis model to learn the environmental dependencies of Vc,max25
Reproduction assets foundThe Open Research section states the differentiable photosynthesis model code is publicly available on Zenodo, and the leaf gas exchange databases (Knauer et al. 2018; Lin et al. 2015) and NGEE-Tropics leaf gas exchange datasets (Jardine et al. 2020; Rogers et al. 2022) used for the photosynthesis phenotyping analysis,Code · public611differentiable photosynthesis modelcodeisavailableathttps://zenodo.org/records/8067204whileOpen asset ↗Zenodo · 8067204pdf-page:27 lines:1-32Dataset · public608[https://ngee-tropics.lbl.gov/research/data/]. Observations of Vc,max25 wereobtainedfrom(Alietal.,Open asset ↗NGEE-Tropicspdf-page:27 lines:1-32Code / dataset availability confirmedEurope PMC · checked 7 Sept 2026
Plants respond to rapid environmental change in ways that depend on both their genetic identity and their phenotypic plasticity, impacting their survival as well as associated ecosystems. However, genetic and environmental effects on phenotype are difficult to quantify across large spatial scales and through time. Leaf hyperspectral reflectance offers a potentially robust approach to map these effects from local to landscape levels. Using a handheld field spectrometer, we analyzed leaf-level hyperspectral reflectance of the foundation tree species Populus fremontii in wild populations and in three 6-year-old experimental common gardens spanning a steep climatic gradient. First, we show that genetic variation among populations and among clonal genotypes is detectable with leaf spectra, using both multivariate and univariate approaches. Spectra predicted population identity with 100% accuracy among trees in the wild, 87%-98% accuracy within a common garden, and 86% accuracy across different environments. Multiple spectral indices of plant health had significant heritability, with genotype accounting for 10%-23% of spectral variation within populations and 14%-48% of the variation across all populations. Second, we found gene by environment interactions leading to population-specific shifts in the spectral phenotype across common garden environments. Spectral indices indicate that genetically divergent populations made unique adjustments to their chlorophyll and water content in response to the same environmental stresses, so that detecting genetic identity is critical to predicting tree response to change. Third, spectral indicators of greenness and photosynthetic efficiency decreased when populations were transferred to growing environments with higher mean annual maximum temperatures relative to home conditions. This result suggests altered physiological strategies further from the conditions to which plants are locally adapted. Transfers to cooler environments had fewer negative effects, demonstrating that plant spectra show directionality in plant performance adjustments. Thus, leaf reflectance data can detect both local adaptation and plastic shifts in plant physiology, informing strategic restoration and conservation decisions by enabling high resolution tracking of genetic and phenotypic changes in response to climate change.
Why it matches plant phenotyping methods葉のハイパースペクトル反射を用いて遺伝型、クロロフィル、水分量、光合成効率などの植物形質・生理状態を推定し、精度評価と環境間比較を行っており、フェノタイピング手法の適用が中心です。
abstractLeaf hyperspectral reflectance offers a potentially robust approach to map these effects from local to landscape levels.
Reproduction assets foundThe paper's leaf hyperspectral reflectance data (the core phenotyping measurements) are publicly deposited in EcoSIS via an explicit data availability statement with DOI. No author analysis code repository is stated; R package references (vegan, prospectr) are generic libraries, not paper-specific assets.Dataset · publicThe data that support the findings of this study are available from EcoSIS at https://doi.org/10.21232/9bbY8fVJ .Open asset ↗EcoSIS · 10.21232/9bbY8fVJlines:291-351Code / dataset availability confirmedEurope PMC · checked 15 Sept 2026
Field / plotTissuePhysiological trait estimationGrowth / time-series analysisGrowth / development / phenology
Intra-annual variations of carbon stable isotope ratios (δ13C) in different tree compartments could represent valuable indicators of plant carbon source-sink dynamics, at weekly time scale. Despite this significance, the absence of a methodological framework for tracking δ13C values in tree rings persists due to the complexity of tree ring development. To fill this knowledge gap, we developed a method to monitor weekly variability of δ13C in the cambium-xylem continuum of black spruce species [Picea mariana (Mill.) BSP.] during the growing season. We collected and isolated the weekly incremental growth of the cambial region and the developing tree ring from five mature spruce trees over three consecutive growing seasons (2019-21) in Simoncouche and two growing seasons (2020-21) in Bernatchez, both located in the boreal forest of Quebec, Canada. Our method allowed for the creation of intra-annual δ13C series for both the growing cambium (δ13Ccam) and developing xylem cellulose (δ13Cxc) in these two sites. Strong positive correlations were observed between δ13Ccam and δ13Cxc series in almost all study years. These findings suggest that a constant supply of fresh assimilates to the cambium-xylem continuum may be the dominant process feeding secondary growth in the two study sites. On the other hand, rates of carbon isotopic fractionation appeared to be poorly affected by climate variability, at an inter-weekly time scale. Hence, increasing δ13Ccam and δ13Cxc trends highlighted here possibly indicate shifts in carbon allocation strategies, likely fostering frost resistance and reducing water uptake in the late growth season. Additionally, these trends may be related to the black spruce trees' responses to the seasonal decrease in photosynthetically active radiation. Our findings provide new insights into the seasonal carbon dynamics and growth constraints of black spruce in boreal forest ecosystems, offering a novel methodological approach for studying carbon allocation at fine temporal scales.
Why it matches plant phenotyping methods樹木の形成層・木部における週次δ13C変動を追跡する測定法を開発し、複数年・地点で適用して検証しているため、植物の生理状態を取得する方法が中心である。
abstractwe developed a method to monitor weekly variability of δ13C in the cambium-xylem continuum of black spruce species
Reproduction assets foundThe paper's weekly δ13C cambium/xylem measurements are stated to be publicly available via the authors' Quebec-Labrador tree-ring dashboard. A GitHub repository for figure data is mentioned but without a URL and only 'upon publication', so it is not actionable. NOAA GML and the Arizona repository URL are external/citedDataset · publicCanada, 490 de La Couronne, Québec, QC G1K 9A9, Canada.
Conflict of interest
None declared.
Funding
This work was funded by the National Sciences and Engineering Research Council of Canada (NSERC) to É.B. (RGPIN 2021-04216).
Data availability
The weekly carbon isotope measurements published in the study will be available here: https://quebeclabradortr.shinyapps.io/TRdashboard4/ . Additional data used to produce the figures will be available from a GitHub repository, upon publication of the article.
References
Alvarez C, Bégin C, Savard MM, Dinis L, Marion J, Smirnoff A, Bégin Y. (2018). Relevance of using whole-ring stable isotopes of black spruce trees in the perspective of climate reconstrOpen asset ↗TRdashboard4lines:362-389Code / dataset availability confirmedOpenAlex · bioRxiv · checked 15 Sept 2026
Abstract Globally, vegetation biodiversity is expected to decline as the rate of plant adaptation struggles to keep pace with rising temperatures. To support conservation efforts through remote sensing, we disentangled the nested effects of genetic and environmental influences on reflectance spectra, leveraging spectroscopy to assess plant adaptations to temperature. Specifically, we quantified the relative effect of plasticity and heritability on Populus fremontii (Fremont cottonwood) leaf reflectance using clonal replicates propagated from 16 populations and grown across three common gardens spanning a mean annual temperature gradient representing the thermal range of P. fremontii . We used variance partitioning to decompose phenotypic variation expressed in the leaf spectra into genotypic and environmental components to estimate broad-sense heritability. Heritability was strongly expressed in the spectral red edge (∼680-750nm) and shortwave infrared (∼1400-3000nm), though the heritability peak in the red edge was sensitive to extreme temperatures. By comparing distances of group centroids in principal component space, we determined that P. fremontii intraspecific spectral variation was shaped by the interaction between common garden site conditions and source population. Support vector machine models indicated pronounced environmental influence on spectral variation, as P. fremontii source population and garden location were classified at 71.8% and 92.6% accuracy, respectively. These findings emphasize the utility of reflectance data in separating genetic and environmental influences on plant phenotypes, offering a pathway to scale these insights across broader landscapes and aid in the conservation and management of vulnerable ecosystems in a warming climate.
Why it matches plant phenotyping methods葉の反射スペクトルを植物表現型として取得し、遺伝性・環境効果の分離、スペクトル変異の分類、温度適応評価に体系的に利用しており、単なる補助的な測定ではなく主要な解析基盤である。
abstractwe quantified the relative effect of plasticity and heritability on Populus fremontii (Fremont cottonwood) leaf reflectance
Reproduction assets foundThe paper's analysis code is explicitly stated to be publicly available on GitHub at the authors' repository (MegsSeeley/temperature_cottonwood). The phenotype/spectral data files are promised on Figshare only 'upon acceptance', so they are not yet publicly actionable and the Figshare DOI is not in the allowed URL listCode · publicAll authors reviewed
528 several drafts and agreed with the final version.
529 Availability of data: All data files will be made available on the Figshare database upon
530 acceptance of the manuscript at DOI: 10.6084/m9.figshare.25719585.
531 Code availability: Code is available on GitHub and is maintained by Seeley (2025)
532 https://github.com/MegsSeeley/temperature_cottonwood.
533 Conflict of interest: The authors have declared that no competing interests exist.
534
535 References
536 Ahmad, P., & Prasad, M. N. V. (2011). Environmental Adaptations and Stress Tolerance of
537 Plants in the Era of Climate Change. Springer Science & Business Media.
24Open asset ↗MegsSeeley/temperature_cottonwoodpdf-layout-page:24 lines:1-55Code / dataset availability confirmedCrossref · checked 13 Sept 2026
Abstract Microscopic imaging for studying plant-pathogen interactions is limited by its reliance on invasive histological techniques, like clearing and staining, or, for in vivo imaging, on complicated generation of transgenic pathogens. We present real-time 3D in vivo visualization of pathogen dynamics with label-free optical coherence tomography. Based on intrinsic signal fluctuations as tissue contrast we image filamentous pathogens and a nematode in vivo in 3D in plant tissue. We analyze 3D images of lettuce downy mildew infection ( Bremia lactucae ) to obtain hyphal volume and length in three different lettuce genotypes with different resistance levels showing the ability for precise (micro) phenotyping and quantification of the infection level. In addition, we demonstrate in vivo longitudinal imaging of the growth of individual pathogen (sub)structures with functional contrast on the pathogen micro-activity revealing pathogen vitality thereby opening a window on the underlying molecular processes.
Why it matches plant phenotyping methods植物病原体を対象としたラベルフリーOCTによるリアルタイム3D画像化を開発し、感染植物の病原体量・感染レベル・活性を定量する手法として実証しているため、植物フェノタイピング手法が中心である。
abstractWe present real-time 3D in vivo visualization of pathogen dynamics with label-free optical coherence tomography.
Reproduction assets foundThe authors explicitly deposited supporting code for dynamic OCT processing, segmentation, and data analysis, together with a representative selection of the dynamic OCT volumes (the paper's plant-pathogen phenotyping data), in a freely-accessible Zenodo repository (10.5281/zenodo.11428245). This is a paper-specific,公开Dataset · publicA representative selection of the data, all the dynamic OCT volumes, and supporting code for data processing and plotting have been uploaded to a freely-accessible Zenodo repository 33 . [10.5281/zenodo.11428245].Zenodo · 10.5281/zenodo.11428245lines:133-155Code / dataset availability confirmedEurope PMC · checked 14 Sept 2026
RiceThermalLeafStress / disease detectionStress response / tolerancePlant / canopy temperature
Diatraea saccharalis (Fabricius) is one of the main pests of rice crops and its early detection, that is, before the plants show damage, is essential to avoid yield losses and define effective and rational control. This work aimed to model the infrared-thermal responses of rice cultivars to D. saccharalis infestation levels. Between 2019 and 2020, two experiments were conducted in a protected environment with the cultivars IR 40 and BR IRGA 409, which presented, in a previous study, different resistance reactions. Rice plants grown in pots were manually infested with first-instar larvae of D. saccharalis, from 0 to 10 caterpillars/plant, with the plants kept in cages covered with voile fabric throughout the test. With the adjustment of regression models, it was noticed that the leaf surface temperature is related to the level of infestation and could be used to detect which IR 40 is susceptible.
Why it matches plant phenotyping methods赤外線サーモグラフィーでイネ葉面温度から害虫感染レベルと感受性を推定する方法が研究の中心であり、植物状態の取得・推定に該当する。
abstractThis work aimed to model the infrared-thermal responses of rice cultivars to D. saccharalis infestation levels.
Reproduction assets foundThe preprint's Data Availability Statement points to the authors' experimental dataset (leaf temperature, infestation, and resistance trait measurements) deposited in Harvard Dataverse under DOI 10.7910/DVN/Q1DRVV. This is a paper-specific, publicly accessible phenotype dataset. No author analysis code or trained modelDataset · publicn (AIC) and the root-mean-squared-error (RMSE) were used to choose and evaluate the
goodness-of-fit of models. The analyses were performed with the R software (www.r-project.org).
Data Availability Statement: The experimental data that support the results and findings of this study are
openly available in Harvard DataverseV1 at https://doi.org/10.7910/DVN/Q1DRVV.
References
1. Bortoli, S. A. D., Dória, H. O. S., Albergaria, N. M. M. S., & Botti, M. V. (2005). Biological aspects and damage
of Diatraea saccharalis (Lepidoptera: Pyralidae) in sorghum, under different doses of nitrogen and
potassium. Ciência e Agrotecnologia, 29(2), 267-273. https://doi.org/10.1590/S1413-70542005000200001Open asset ↗Harvard Dataverse · 10.7910/DVN/Q1DRVVpdf-layout-page:7 lines:1-60Code / dataset availability confirmedEurope PMC · checked 7 Sept 2026
Kelps are vital for marine ecosystems, yet the genetic diversity underlying their capacity to adapt to climate change remains unknown. In this study, we focused on the kelp Macrocystis pyrifera a species critical to coastal habitats. We developed a protocol to evaluate heat stress response in 204 Macrocystis pyrifera genotypes subjected to heat stress treatments ranging from 21 °C to 27 °C. Here we show that haploid gametophytes exhibiting a heat-stress tolerant (HST) phenotype also produced greater biomass as genetically similar diploid sporophytes in a warm-water ocean farm. HST was measured as chlorophyll autofluorescence per genotype, presented here as fluorescent intensity values. This correlation suggests a predictive relationship between the growth performance of the early microscopic gametophyte stage HST and the later macroscopic sporophyte stage, indicating the potential for selecting resilient kelp strains under warmer ocean temperatures. However, HST kelps showed reduced genetic variation, underscoring the importance of integrating heat tolerance genes into a broader genetic pool to maintain the adaptability of kelp populations in the face of climate change.
Why it matches plant phenotyping methods熱ストレス耐性という植物状態をクロロフィル自家蛍光で定量するプロトコルを開発しており、表現型取得法が研究の中心的要素として明示されている。
abstractWe developed a protocol to evaluate heat stress response in 204 Macrocystis pyrifera genotypes subjected to heat stress treatments ranging from 21 °C to 27 °C.
Reproduction assets foundThe authors publicly deposited both the analysis scripts and the numerical source data (including raw fluorescence intensity values underlying the heat-stress phenotyping) in a Zenodo repository, with explicit availability statements and URLs in the Data availability and Code availability sections.Code · publicAll scripts used in this study are available in a Zenodo repository at https://doi.org/10.5281/zenodo.13315681 .Open asset ↗Zenodo · 10.5281/zenodo.13315681lines:168-242Dataset · publicNumerical source data for the graph presented in Figs. 1 – 3 , and Fig. 5 can be found in the Zenodo repository here: https://doi.org/10.5281/zenodo.13315681 .Open asset ↗Zenodo · 10.5281/zenodo.13315681lines:148-167Code / dataset availability confirmedEurope PMC · checked 7 Sept 2026
Huanglongbing (HLB), associated with the psyllid-vectored phloem-limited bacterium, Candidatus Liberibacter asiaticus (CLas), is a disease threat to all citrus production worldwide. Currently, there are no sustainable curative or prophylactic treatments available. In this study, we utilized mass spectrometry (MS)-based metabolomics in combination with 3D molecular mapping to visualize complex chemistries within plant tissues to explore how these chemistries change in vivo in HLB-infected trees. We demonstrate how spatial information from molecular maps of branches and single leaves yields insight into the biology not accessible otherwise. In particular, we found evidence that flavonoid biosynthesis is disrupted in HLB-infected trees, and an increase in the polyamine, feruloylputrescine, is highly correlated with an increase in disease severity. Based on mechanistic details revealed by these molecular maps, followed by metabolic modeling, we formulated and tested the hypothesis that CLas infection either directly or indirectly converts the precursor compound, ferulic acid, to feruloylputrescine to suppress the antimicrobial effects of ferulic acid and biosynthetically downstream flavonoids. Using in vitro bioassays, we demonstrated that ferulic acid and bioflavonoids are indeed highly bactericidal to CLas, with the activity on par with a reference antibiotic, oxytetracycline, recently approved for HLB management. We propose these compounds should be evaluated as therapeutics alternatives to the antibiotics for HLB treatment. Overall, the utilized 3D metabolic mapping approach provides a promising methodological framework to identify pathogen-specific inhibitory compounds in planta for potential prophylactic or therapeutic applications.
Why it matches plant phenotyping methods植物組織内の化学状態を3D分子マッピングで可視化し、HLB感染と病徴重症度に関連する状態を抽出する方法論的枠組みが研究の中心であり、単なる代謝測定ではない。
abstractwe utilized mass spectrometry (MS)-based metabolomics in combination with 3D molecular mapping to visualize complex chemistries within plant tissues
Reproduction assets foundThe paper deposits its citrus LC-MS/MS metabolomics raw data in MassIVE and provides GNPS molecular networking job links for its 2D/3D molecular mapping analyses. These are paper-specific, publicly accessible assets directly reproducing the study's measurements and computational analysis.Dataset · publicThe data were deposited in the MassIVE online repository and are available below links: https://massive.ucsd.edu/ProteoSAFe/dataset.jsp?task=bc1261c22e6c49d1b4f6490c7414845bOpen asset ↗MassIVE · bc1261c22e6c49d1b4f6490c7414845blines:146-218Code / dataset availability confirmedEurope PMC · checked 7 Sept 2026
Nitrogen (N)-fixing symbiosis is critical to terrestrial ecosystems, yet possession of this trait is known for few plant species. Broader presence of the symbiosis is often indirectly determined by phylogenetic relatedness to taxa investigated via manipulative experiments. This data gap may ultimately underestimate phylogenetic, spatial, and temporal variation in N-fixing symbiosis. Still needed are simpler field or collections-based approaches for inferring symbiotic status. N-fixing plants differ from non-N-fixing plants in elemental and isotopic composition, but previous investigations have not tested predictive accuracy using such proxies. Here we develop a regional field study and demonstrate a simple classification model for fixer status using nitrogen and carbon content measurements, and stable isotope ratios (δ 15 N and δ 13 C), from field-collected leaves. We used mixed models and classification approaches to demonstrate that N-fixing phenotypes can be used to predict symbiotic status; the best model required all predictors and was 80-94% accurate. Predictions were robust to environmental context variation, but we identified significant variation due to native vs. non-native (exotic) status and phylogenetic affinity. Surprisingly, N content-not δ 15 N-was the strongest predictor, suggesting that future efforts combine elemental and isotopic information. These results are valuable for understudied taxa and ecosystems, potentially allowing higher-throughput field-based N-fixer assessments.
Why it matches plant phenotyping methods葉の元素・安定同位体測定と分類モデルを組み合わせ、N固定という植物状態を推定する方法を開発・精度検証しており、単なる生物学的測定ではない。
abstractHere we develop a regional field study and demonstrate a simple classification model for fixer status using nitrogen and carbon content measurements, and stable isotope ratios (δ 15 N and δ 13 C), from field-collected leaves.
Reproduction assets foundThe paper's Data availability statement explicitly deposits raw isotope/elemental phenotype data and analysis scripts in a public GitHub repository with a Zenodo stable release, including the isotope CSV dataset and phylogenetic tree used in the analyses.Code · publicRaw data and analysis scripts are available via GitHub at https://github.com/ryanafolk/isotope . A stable release of this repository is available at: https://doi.org/10.5281/zenodo.8407949Open asset ↗ryanafolk/isotopelines:143-208Code / dataset availability confirmedEurope PMC · checked 7 Sept 2026
Biological nitrogen fixation (BNF) by symbiotic bacteria plays a vital role in sustainable agriculture. However, current quantification methods are often expensive and impractical. This study explores the potential of Raman spectroscopy, a non-invasive technique, for rapid assessment of BNF activity in soybeans. Raman spectra were obtained from soybean plants grown with and without rhizobia bacteria to identify spectral signatures associated with BNF. δN 15 isotope ratio mass spectrometry (IRMS) was used to determine actual BNF percentages. Partial least squares regression (PLSR) was employed to develop a model for BNF quantification based on Raman spectra. The model explained 80% of the variation in BNF activity. To enhance the model's specificity for BNF detection regardless of nitrogen availability, a subsequent elastic net (Enet) regularisation strategy was implemented. This approach provided insights into key wavenumbers and biochemicals associated with BNF in soybeans.
Why it matches plant phenotyping methodsラマン分光によるダイズの生物的窒素固定活性の非侵襲的定量法を開発し、IRMSで検証しているため、植物生理状態の取得手法が中心である。
abstractThis study explores the potential of Raman spectroscopy, a non-invasive technique, for rapid assessment of BNF activity in soybeans.
Reproduction assets foundThe paper's Data Availability Statement explicitly deposits the original Raman spectra/BNF measurement data on FigShare, a public, paper-specific asset directly reproducing the study's phenotyping measurements.Dataset · publicData Availability Statement: The original data presented in the study are openly available in FigShare
at https://figshare.com/articles/dataset/dx_doi_org_10_6084_m9_figshare_25909780/25909780 (ac-
cessed on 28 May 2024).Open asset ↗FigSharepdf-page:13 lines:1-60Code / dataset availability confirmedCrossref · checked 15 Sept 2026
Field / plotWhole plant / canopy / plot / fieldPhysiological trait estimationGrowth / time-series analysisPhotosynthesis / fluorescenceWater status / transpiration
Abstract. Long-term time series of transpiration, evaporation, plant photosynthesis, and soil respiration are essential for addressing numerous research questions related to ecosystem functioning. However, quantifying these fluxes is challenging due to the lack of reliable and direct measurement techniques, which has left gaps in the understanding of their temporal cycles and spatial variability. To help address this open challenge, we generated a dataset of these four components by implementing five (conventional and novel) approaches to partition total ET and CO2 fluxes into plant and soil fluxes across 47 NEON sites. The final dataset (https://doi.org/10.5281/zenodo.12191876) spans a five-year period and covers various ecosystems, including forests, grasslands, and agricultural terrain. This is the first comprehensive dataset covering such a wide spatial and temporal distribution. Overall, we observed good agreement across most methods for ET components, increasing the reliability of these estimates. Partitioning of CO2 components was found to be less robust and more dependent on prior knowledge of water-use efficiency. This dataset has several potential future applications, such as addressing critical questions regarding the response of ecosystems to extreme weather events, which are expected to become more severe and frequent with climate change.
Why it matches plant phenotyping methods植物・土壌フラックスを分離推定する5手法を47地点で実装し、手法間の一致度を評価した長期データセットであり、植物の生理状態（蒸散・光合成）の取得・推定法が中心的です。
abstractwe generated a dataset of these four components by implementing five (conventional and novel) approaches to partition total ET and CO2 fluxes into plant and soil fluxes across 47 NEON sites.
Reproduction assets foundThe paper's five-year NEON flux-partitioning dataset and the authors' partitioning-method scripts are explicitly deposited on Zenodo with public DOIs.Dataset · publicows the availability
of flux components as a fraction of the total number of half-
hour periods in the record. Overall, all the methods cover
a similar temporal distribution of flux partitioning and are
potential candidates for ensemble averaging.
4 Description of the final dataset
The final dataset is available for download at
https://doi.org/10.5281/zenodo.12191876 (Zahn and Bou-
Zeid, 2024). It is organized into different folders for each
site, with each site containing a .csv file for each method.
This format is selected to be user-friendly and accessible in
various programming languages and software packages. For
FVS and CECw, in addition to their ensemble averages for
https://doi.org/Open asset ↗Zenodo · 10.5281/zenodo.12191876pdf-raw-page:9 lines:136-149Code · publicnthesis, transpiration and stomatal conduc-
tance: potential and limitations, Plant Cell Environ., 35, 657–
667, https://doi.org/10.1111/j.1365-3040.2011.02451.x, 2011.
Zahn, E.: einaraz/PartitioningMethods: Processing Eddy-
Covariance Data: Five Evapotranspiration Flux Parti-
tioning Methods (v1.0.1) [Software], Zenodo [code],
https://doi.org/10.5281/zenodo.11510363, 2024.
Zahn, E. and Bou-Zeid, E.: Partitioning of water and CO2 fluxes at
NEON sites into soil and plant components: a five-year dataset
for spatial and temporal analysis [dataset], Zenodo [data set],
https://doi.org/10.5281/zenodo.12191876, 2024.
Zahn, E., Chor, T. L., and Dias, N. L.: A Simple Methodology for
Quality ControlOpen asset ↗Zenodo · 10.5281/zenodo.11510363pdf-raw-page:22 lines:1-58Code / dataset availability confirmedEurope PMC · checked 14 Sept 2026
Chlorophyll fluorescence is a well-established method to estimate chlorophyll content in leaves. A popular fluorescence-based meter, the Opti-Sciences CCM-300 Chlorophyll Content Meter (CCM-300), utilizes the fluorescence ratio F735/F700 and equations derived from experiments using broadleaf species to provide a direct, rapid estimate of chlorophyll content used for many applications. We sought to quantify the performance of the CCM-300 relative to more intensive methods, both across plant functional types and years of use. We linked CCM-300 measurements of broadleaf, conifer, and graminoid samples in 2018 and 2019 to high-performance liquid chromatography (HPLC) and/or spectrophotometric (Spec) analysis of the same leaves. We observed a significant difference between the CCM-300 and HPLC/Spec, but not between HPLC and Spec. In comparison to HPLC, the CCM-300 performed better for broadleaves (r = 0.55, RMSE = 154.76) than conifers (r = 0.52, RMSE = 171.16) and graminoids (r = 0.32, RMSE = 127.12). We observed a slight deterioration in meter performance between years, potentially due to meter calibration. Our results show that the CCM-300 is reliable to demonstrate coarse variations in chlorophyll but may be limited for cross-plant functional type studies and comparisons across years.
Why it matches plant phenotyping methodsCCM-300による葉のクロロフィル含量測定法を、HPLCおよび分光測定と比較して信頼性・校正性能を検証しており、植物表現型取得法が研究の中心である。
abstractWe sought to quantify the performance of the CCM-300 relative to more intensive methods, both across plant functional types and years of use.
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 · publicThe following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/s24154784/s1 , Figure S1: HPLC measurements from the UW-Madison dataset ( n = 26).Open asset ↗lines:262-279Code / dataset availability confirmedCrossref · Europe PMC · checked 7 Sept 2026
Hyperspectral imaging has emerged as a pivotal technology in agricultural research, offering a powerful means to non-invasively monitor stress factors, such as drought, in crops like potato plants. In this context, the integration of attention-based deep learning models presents a promising avenue for enhancing the efficiency of stress detection, by enabling the identification of meaningful spectral channels. This study assesses the performance of deep learning models on two potato plant cultivars exposed to water-deficient conditions. It explores how various sampling strategies and biases impact the classification metrics by using a dual-sensor hyperspectral imaging systems (VNIR -Visible and Near-Infrared and SWIR—Short-Wave Infrared). Moreover, it focuses on pinpointing crucial wavelengths within the concatenated images indicative of water-deficient conditions. The proposed deep learning model yields encouraging results. In the context of binary classification, it achieved an area under the receiver operating characteristic curve (AUC-ROC—Area Under the Receiver Operating Characteristic Curve) of 0.74 (95% CI: 0.70, 0.78) and 0.64 (95% CI: 0.56, 0.69) for the KIS Krka and KIS Savinja varieties, respectively. Moreover, the corresponding F1 scores were 0.67 (95% CI: 0.64, 0.71) and 0.63 (95% CI: 0.56, 0.68). An evaluation of the performance of the datasets with deliberately introduced biases consistently demonstrated superior results in comparison to their non-biased equivalents. Notably, the ROC-AUC values exhibited significant improvements, registering a maximum increase of 10.8% for KIS Krka and 18.9% for KIS Savinja. The wavelengths of greatest significance were observed in the ranges of 475–580 nm, 660–730 nm, 940–970 nm, 1420–1510 nm, 1875–2040 nm, and 2350–2480 nm. These findings suggest that discerning between the two treatments is attainable, despite the absence of prominently manifested symptoms of drought stress in either cultivar through visual observation. The research outcomes carry significant implications for both precision agriculture and potato breeding. In precision agriculture, precise water monitoring enhances resource allocation, irrigation, yield, and loss prevention. Hyperspectral imaging holds potential to expedite drought-tolerant cultivar selection, thereby streamlining breeding for resilient potatoes adaptable to shifting climates.
Why it matches plant phenotyping methodsジャガイモの水欠乏状態をハイパースペクトル画像と深層学習で識別し、性能評価および重要波長の同定を行っており、植物ストレス表現型の取得・抽出手法が中心である。
abstractHyperspectral imaging has emerged as a pivotal technology in agricultural research, offering a powerful means to non-invasively monitor stress factors, such as drought, in crops like potato plants.
Reproduction assets foundThe paper's Data Availability Statement explicitly deposits the pre-processed hyperspectral dataset on Zenodo and the authors' analysis code on GitHub, both with public URLs matching allowed_urls. The SiaPy Zenodo record is a generic open-source library, not a paper-specific asset.Code · publicand code at https://github.com/janezlapajne/manuscripts (accessed on 8 July 2024)Open asset ↗github · janezlapajne/manuscriptslines:104-424Code / dataset availability confirmedEurope PMC · checked 14 Sept 2026
The analysis of photosynthetic traits has become an integral part of plant (eco-)physiology. Many of these characteristics are not directly measured, but calculated from combinations of several, more direct, measurements. The calculations of such derived variables are based on underlying physical models and may use additional constants or assumed values. Commercially available gas-exchange instruments typically report such derived variables, but the available implementations use different definitions and assumptions. Moreover, no software is currently available to allow a fully scripted and reproducible workflow that includes importing data, pre-processing and recalculating derived quantities. The R package gasanalyzer aims to address these issues by providing methods to import data from different instruments, by translating photosynthetic variables to a standardized nomenclature, and by optionally recalculating derived quantities using standardized equations. In addition, the package facilitates performing sensitivity analyses on variables or assumptions used in the calculations to allow researchers to better assess the robustness of the results. The use of the package and how to perform sensitivity analyses are demonstrated using three different examples.
Why it matches plant phenotyping methods植物の光合成形質データを標準化・再計算・感度分析するRパッケージを開発しており、植物生理形質の取得後処理と再現可能な解析ワークフローが中心である。
abstractThe R package gasanalyzer aims to address these issues by providing methods to import data from different instruments, by translating photosynthetic variables to a standardized nomenclature, and by optionally recalculating derived quantities using standardized equations.
Reproduction assets foundThe paper's gas-exchange phenotyping data (poplar, tobacco, GFS-3000 examples) and all analysis code for the gasanalyzer package are publicly available in the authors' GitLab repository, with a stable release on CRAN.Code · publicd by an LI-6400 and 13CO2/12CO2 ratios were obtained using a Los Gatos Research CCIA-36d isotope analyser. Details of the growth conditions and experimental design are given in Tholen et al. (2012).
All data and code used to generate the figures in this article are available at the GitLab repository for the gasanalyzer package (https://gitlab.com/plantphys/gasanalyzer).
Results and Discussion
The R package gasanalyzer provides methods for importing data from different instruments and presents the data in a consistent format with a standardized nomenclature [for a complete list, seeSupporting Information—Table S1]. The package can be used not only to pre-process data for analysis but also toOpen asset ↗plantphys/gasanalyzerhtml-lines:105-113Code / dataset availability confirmedOpenAlex · Europe PMC · checked 7 Sept 2026
To maximise the throughput of novel, high-throughput phenotyping platforms, many researchers have utilised smaller pot sizes to increase the number of biological replicates that can be grown in spatially limited controlled environments. This may confound plant development through a process known as “pot binding”, particularly in larger species including potato (Solanum tuberosum), and under water-restricted conditions. We aimed to investigate the water availability hypothesis of pot binding, which predicts that small pots have insufficient water holding capacities to prevent drought stress between irrigation periods, in potato. Two cultivars of potato were grown in small (5 L) and large (20 L) pots, were kept under polytunnel conditions, and were subjected to three irrigation frequencies: every other day, daily, and twice daily. Plants were phenotyped with two Phenospex PlantEye F500s and canopy and tuber fresh mass and dry matter were measured. Increasing irrigation frequency from every other day to daily was associated with a significant increase in fresh tuber yield, but only in large pots. This suggests a similar level of drought stress occurred between these treatments in the small pots, supporting the water availability hypothesis of pot binding. Further increasing irrigation frequency to twice daily was still not sufficient to increase yields in small pots but it caused an insignificant increase in yield in the larger pots, suggesting some pot binding may be occurring in large pots under daily irrigation. Canopy temperatures were significantly higher under each irrigation frequency in the small pots compared to large pots, which strongly supports the water availability hypothesis as higher canopy temperatures are a reliable indicator of drought stress in potato. Digital phenotyping was found to be less accurate for larger plants, probably due to a higher degree of self-shading. The research demonstrates the need to define the optimum pot size and irrigation protocols required to completely prevent pot binding and ensure drought treatments are not inadvertently applied to control plants.
Why it matches plant phenotyping methodsPlantEyeを用いたデジタルフェノタイピングの適用と精度評価が研究上の主要要素であり、植物のキャノピー温度や成長状態を測定し、植物サイズによる測定精度低下も検討している。
abstractPlants were phenotyped with two Phenospex PlantEye F500s and canopy and tuber fresh mass and dry matter were measured.
Reproduction assets foundThe paper's data availability statement points to a public Zenodo deposit containing the datasets generated and analysed in this potato pot-binding phenotyping study.Dataset · publicThe datasets generated and analysed for this study can be found in the Zendo repository at https://doi.org/10.5281/zenodo.10707587 .Open asset ↗Zenodo · 10.5281/zenodo.10707587lines:845-856Code / dataset availability confirmedEurope PMC · checked 14 Sept 2026
Biological nitrogen fixation (BNF) by symbiotic bacteria plays a vital role in sustainable agriculture. However, current quantification methods are often expensive and impractical. This study explores the potential of Raman spectroscopy, a non-invasive technique, for rapid assessment of BNF activity in soybeans. Raman spectra were obtained from soybean plants grown with and without rhizobia bacteria to identify spectral signatures associated with BNF. δN15 isotope ratio mass spectrometry (IRMS) was used to determine actual BNF percentages. Partial least squares regression (PLSR) was employed to develop a model for BNF quantification based on Raman spectra. The model explained 80% of the variation in BNF activity. To enhance the model's specificity for BNF detection regardless of nitrogen availability, a subsequent elastic net (Enet) regularization strategy was implemented. This approach provided insights into key wavenumbers and biochemicals associated with BNF in soybeans.
Why it matches plant phenotyping methodsラマン分光と回帰モデルを用いてダイズの生物的窒素固定活性を定量する手法を開発・検証しており、植物の生理状態の取得が研究の中心である。
abstractThis study explores the potential of Raman spectroscopy, a non-invasive technique, for rapid assessment of BNF activity in soybeans.
Reproduction assets foundThe authors state that the original data (Raman spectra and BNF/IRMS measurements) are openly available on FigShare under DOI 10.6084/m9.figshare.25909780. This is a paper-specific, publicly deposited dataset directly reproducing the study's phenotyping measurements. No author analysis code or trained model is reportedDataset · publicData Availability Statement: The original data presented in the study are openly available in FigShare at
10.6084/m9.figshare.25909780FigShare · 10.6084/m9.figshare.25909780pdf-raw-page:13 lines:1-55Code / dataset availability confirmedEurope PMC · checked 15 Sept 2026
Canopy temperature (CT) is often interpreted as representing leaf activity traits such as photosynthetic rates, gas exchange rates, or stomatal conductance. This interpretation is based on the observation that leaf activity traits correlate with transpiration which affects leaf temperature. Accordingly, CT measurements may provide a basis for high throughput assessments of the productivity of wheat canopies during early grain filling, which would allow distinguishing functional from dysfunctional stay-green. However, whereas the usefulness of CT as a fast surrogate measure of sustained vigor under soil drying is well established, its potential to quantify leaf activity traits under high-yielding conditions is less clear. To better understand sensitivity limits of CT measurements under high yielding conditions, we generated within-genotype variability in stay-green functionality by means of differential short-term pre-anthesis canopy shading that modified the sink:source balance. We quantified the effects of these modifications on stay-green properties through a combination of gold standard physiological measurements of leaf activity and newly developed methods for organ-level senescence monitoring based on timeseries of high-resolution imagery and deep-learning-based semantic image segmentation. In parallel, we monitored CT by means of a pole-mounted thermal camera that delivered continuous, ultra-high temporal resolution CT data. Our results show that differences in stay-green functionality translate into measurable differences in CT in the absence of major confounding factors. Differences amounted to approximately 0.8°C and 1.5°C for a very high-yielding source-limited genotype, and a medium-yielding sink-limited genotype, respectively. The gradual nature of the effects of shading on CT during the stay-green phase underscore the importance of a high measurement frequency and a time-integrated analysis of CT, whilst modest effect sizes confirm the importance of restricting screenings to a limited range of morphological and phenological diversity.
Why it matches plant phenotyping methods高解像度画像・深層学習による器官レベル老化モニタリングと熱画像による連続的なキャノピー温度測定を開発・適用し、stay-green機能の表現型評価法として検証しているため、方法が中心的である。
abstractnewly developed methods for organ-level senescence monitoring based on timeseries of high-resolution imagery and deep-learning-based semantic image segmentation
Reproduction assets foundThe paper publicly deposits its manually annotated segmentation datasets (target-domain patches for the off-nadir stem/ear segmentation model) via the ETH Zurich research repository. All other raw phenotyping data (thermal images, physiological measurements) is only available on request from the authors. Generic tools/Dataset · publicd through logical operations to obtain the fractions of green, chlorotic, and necrotic tissues for each vegetation component. For details, refer to ( Anderegg et al., 2023 ). The annotated data sets representing the target domain will be made freely available via the Repository for Publications and Research data of ETH Zürich ( https://doi.org/10.3929/ethz-b-000668219 ).
Figure 2
Effects of canopy shading on agronomic traits and canopy characteristics. Effects of shading on (A) grain yield, (B) above ground vegetative dry biomass (total above ground biomass after threshing), (C) peduncle length, (D) plant height, (E) spike volume, (F) thousand kernel weight, (G) grain protein concentration.Open asset ↗Repository for Publications and Research data of ETH Zürich · 10.3929/ethz-b-000668219lines:58-67Code / dataset availability confirmedEurope PMC · checked 14 Sept 2026
Southern leaf blight (SLB) is a foliar disease caused by the fungus Cochliobolus heterostrophus infecting maize plants in humid, warm weather conditions. SLB causes production losses to corn producers in different regions of the world such as Latin America, Europe, India, and Africa. In this paper, we demonstrate a non-destructive method to quantify the signs of fungal infection in SLB-infected corn plants using a deep UV (DUV) fluorescence spectrometer, with a 248.6 nm excitation wavelength, to acquire the emission spectra of healthy and SLB-infected corn leaves. Fluorescence emission spectra of healthy and diseased leaves were used to train an Autoencoder (AE) anomaly detection algorithm-an unsupervised machine learning model-to quantify the phenotype associated with SLB-infected leaves. For all samples, the signature of corn leaves consisted of two prominent peaks around 450 nm and 325 nm. However, SLB-infected leaves showed a higher response at 325 nm compared to healthy leaves, which was correlated to the presence of C. heterostrophus based on disease severity ratings from Visual Scores (VS). Specifically, we observed a linear inverse relationship between the AE error and the VS (R2 = 0.94 and RMSE = 0.935). With improved hardware, this method may enable improved quantification of SLB infection versus visual scoring based on e.g., fungal spore concentration per unit area and spatial localization.
Why it matches plant phenotyping methodsトウモロコシ葉の病徴・感染程度という植物状態を、深紫外蛍光分光とオートエンコーダで非破壊的に定量する手法が研究の中心であり、視覚評価との相関による技術評価も行っている。
abstractwe demonstrate a non-destructive method to quantify the signs of fungal infection in SLB-infected corn plants using a deep UV (DUV) fluorescence spectrometer
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 · publicData Availability: The data can be downloaded from here: https://datadryad.org/stash/share/NW062Bv9Cpe5VslBiiA52nweUJEQCHC2yzAqKlQYx6w .Open asset ↗Dryadlines:138-151Code / dataset availability confirmedEurope PMC · checked 14 Sept 2026
Premise Poikilohydric plants respond to hydration by undergoing dry-wet-dry cycles. Carbon balance represents the net gain or loss of carbon from each cycle. Here we present the first standard protocol for measuring carbon balance, including a custom-modified chamber system for infrared gas analysis, 12-h continuous monitoring, resolution of plant-substrate relationships, and in-chamber specimen hydration. Methods and results We applied the carbon balance technique to capture responses to water stress in populations of the moss Syntrichia caninervis , comparing 19 associated physiological variables. Carbon balance was negative in desiccation-acclimated (field-collected) mosses, which exhibited large respiratory losses. Contrastingly, carbon balance was positive in hydration-acclimated (lab-cultivated) mosses, which began exhibiting net carbon uptake Conclusions Carbon balance is a functional trait indicative of physiological performance, hydration stress, and survival in poikilohydric plants, and the carbon balance method can be applied broadly across taxa to test hypotheses related to environmental stress and global change.
Why it matches plant phenotyping methodsコケ植物の炭素収支を測定する標準プロトコルとカスタムチャンバーを開発し、炭素収支を機能形質として評価しているため、植物フェノタイピング手法が中心である。
abstractHere we present the first standard protocol for measuring carbon balance, including a custom-modified chamber system for infrared gas analysis, 12-h continuous monitoring, resolution of plant-substrate relationships, and in-chamber specimen hydration.
Reproduction assets foundThe authors deposit all case-study data and analysis materials in a public GitHub repository, explicitly stated in the Data Availability Statement. The R Markdown/R analysis workflow (Appendix S3) and supporting files are also provided, making the paper's carbon-balance phenotyping data and computational analysis code,Code · publiclability Statement
A detailed carbon balance protocol, RMD file, custom chamber baseplate data files, and standard curve data are available in the Supporting Information for this manuscript. All other data used in the manuscript, including in the hydration‐acclimation case study, are available via the public GitHub repository ( https://github.com/KirstenKCoe/Coe-et-al.-2024-APPS ).Open asset ↗KirstenKCoe/Coe-et-al.-2024-APPSlines:474-476Code / dataset availability confirmedCrossref · checked 15 Sept 2026
The PlantEye multispectral scanner is an optoelectrical sensor automatically applied to a mechatronic platform that allows the non-destructive, accurate, and high-throughput detection of morphological and physiological plant parameters. In this study, we describe how the advanced phenotyping platform precisely assesses changes in plant architecture and growth parameters of wild rocket salad (Diplotaxis tenuifolia L. [DC.]) under drought stress conditions. Four different irrigation supply levels from moderate to severe, required to keep 100, 70, 50, and 30% of the water-holding capacity, were adopted. Growth rate and plant architecture were recorded through the digital measure of biomass, leaf area, Canopy Light Penetration Depth, five convex hull traits, plant height, Surface Angle Average, and Voxel Volume Total. Vegetation color assessments included hue, lightness, and saturation. Vegetation and senescence indices were calculated from canopy reflectance in the red (620–645 nm), green (530–540 nm), blue (peak wavelength 460–485 nm), near-infrared (820–850 nm), and 3D laser (940 nm) ranges. The temperature, relative humidity, and solar radiation of the environment were also recorded. Overall, morphological parameters, color, multispectral data, and vegetation indices provided over 7200 data points through daily scans over three weeks of cultivation. Although a general decrease in growth parameters with increasing stress severity was observed, plants were able to maintain the same morpho-physiological performances as the control during the early growth stages, keeping both 70% and 50% of the total water-holding capacity. Among indices, the Normalized Differential Vegetation Index (NDVI) contributed the most to the differentiation between different stress levels during the cultivation cycle. Across the 3 weeks of growth, statistically significant differences were observed for all traits except for the Saturation Average. Comparisons with respect to the control highlighted the strong impact of drought stress on morphological plant traits. This study provided meaningful insights into the health status of wild rocket salad under increasing drought stress.
Why it matches plant phenotyping methodsPlantEye multispectral3Dプラットフォームを用いた植物形態・生理形質の高スループット取得が研究の中心であり、乾燥ストレス実験への実質的なフェノタイピング適用である。
abstractThe PlantEye multispectral scanner is an optoelectrical sensor automatically applied to a mechatronic platform that allows the non-destructive, accurate, and high-throughput detection of morphological and physiological plant parameters.
Reproduction assets foundThe paper deposits its raw phenotyping and climate data on Figshare with explicit open-access availability statements: Data File 1 (climate datalogger) at DOI 10.6084/m9.figshare.25201160 and Data File 2 (PlantEye F500 drought-stress phenotyping, ~7200 data points) at DOI 10.6084/m9.figshare.25201172. No author code orDataset · publice gathered 7200 phenotypic data
points on both control and water-stressed plants from 8 June to 26 June 2023 (Table 2: Data
File 2).
Table 2. Overview of Data Files reporting raw climatic and phenotyping data.
Label Name of Data File Data Repository and DOI Identifier
Data File 1
D. tenuifolia_Trial_Climate
Datalogger
Figshare
(https://doi.org/10.6084/m9.figshare.25201160,
accessed on 6 May 2024)
Data File 2
D_tenuifolia_Water_Stress_
F500Phenotyping
Figshare
(https://doi.org/10.6084/m9.figshare.25201172,
accessed on 6 May 2024)
The applied stresses highlighted substantial changes in the morphology and canopy
of the plant (Figure 2). The Three-Dimensional Leaf Area consistently decreased witOpen asset ↗Figshare · 10.6084/m9.figshare.25201160pdf-raw-page:4 lines:1-58Dataset · publicble 2. Overview of Data Files reporting raw climatic and phenotyping data.
Label Name of Data File Data Repository and DOI Identifier
Data File 1
D. tenuifolia_Trial_Climate
Datalogger
Figshare
(https://doi.org/10.6084/m9.figshare.25201160,
accessed on 6 May 2024)
Data File 2
D_tenuifolia_Water_Stress_
F500Phenotyping
Figshare
(https://doi.org/10.6084/m9.figshare.25201172,
accessed on 6 May 2024)
The applied stresses highlighted substantial changes in the morphology and canopy
of the plant (Figure 2). The Three-Dimensional Leaf Area consistently decreased with the
incremental stress during the 3 weeks of this study. We observed how, in control conditions,
LA3D increased from the first to theOpen asset ↗Figshare · 10.6084/m9.figshare.25201172pdf-raw-page:4 lines:1-58Code / dataset availability confirmedEurope PMC · checked 7 Sept 2026
The net CO2 assimilation (A) response to intercellular CO2 concentration (Ci) is a fundamental measurement in photosynthesis and plant physiology research. The conventional A/Ci protocols rely on steady-state measurements and take 15-40 min per measurement, limiting data resolution or biological replication. Additionally, there are several CO2 protocols employed across the literature, without clear consensus as to the optimal protocol or systematic biases in their estimations. We compared the non-steady-state Dynamic Assimilation Technique (DAT) protocol and the three most used CO2 protocols in steady-state measurements, and tested whether different CO2 protocols lead to systematic differences in estimations of the biochemical limitations to photosynthesis. The DAT protocol reduced the measurement time by almost half without compromising estimation accuracy or precision. The monotonic protocol was the fastest steady-state method. Estimations of biochemical limitations to photosynthesis were very consistent across all CO2 protocols, with slight differences in Rubisco carboxylation limitation. The A/Ci curves were not affected by the direction of the change of CO2 concentration but rather the time spent under triose phosphate utilization (TPU)-limited conditions. Our results suggest that the maximum rate of Rubisco carboxylation (Vcmax), linear electron flow for NADPH supply (J), and TPU measured using different protocols within the literature are comparable, or at least not systematically different based on the measurement protocol used.
Why it matches plant phenotyping methods植物の光合成生理形質を測定するCO2応答プロトコルを比較・検証し、測定時間、精度、再現性を評価しているため、方法検証が中心です。
abstractWe compared the non-steady-state Dynamic Assimilation Technique (DAT) protocol and the three most used CO2 protocols in steady-state measurements
Reproduction assets foundThe paper's primary A/Ci gas-exchange measurement data are openly deposited in Dryad. The msuRACiFit GitHub repository is cited prior work, not this paper's analysis code, and no author analysis code URL is given for this study.Dataset · publicAll primary data to support the findings of this study are openly available in Dryad at https://doi.org/10.5061/dryad.pk0p2ngst ( Tejera-Nieves and Walker, 2024 ).Open asset ↗Dryad · 10.5061/dryad.pk0p2ngstlines:120-161Code / dataset availability confirmedEurope PMC · checked 7 Sept 2026
This paper proposes a workflow to assess the uncertainty of the Normalized Difference Vegetation Index (NDVI), a critical index used in precision agriculture to determine plant health. From a metrological perspective, it is crucial to evaluate the quality of vegetation indices, which are usually obtained by processing multispectral images for measuring vegetation, soil, and environmental parameters. For this reason, it is important to assess how the NVDI measurement is affected by the camera characteristics, light environmental conditions, as well as atmospheric and seasonal/weather conditions. The proposed study investigates the impact of atmospheric conditions on solar irradiation and vegetation reflection captured by a multispectral UAV camera in the red and near-infrared bands and the variation of the nominal wavelengths of the camera in these bands. Specifically, the study examines the influence of atmospheric conditions in three scenarios: dry-clear, humid-hazy, and a combination of both. Furthermore, this investigation takes into account solar irradiance variability and the signal-to-noise ratio (SNR) of the camera. Through Monte Carlo simulations, a sensitivity analysis is carried out against each of the above-mentioned uncertainty sources and their combination. The obtained results demonstrate that the main contributors to the NVDI uncertainty are the atmospheric conditions, the nominal wavelength tolerance of the camera, and the variability of the NDVI values within the considered leaf conditions (dry and fresh).
Why it matches plant phenotyping methodsUAVマルチスペクトル画像から植物状態を示すNDVIを取得する測定ワークフローについて、不確かさ評価・感度分析を中心に扱っており、測定法の技術的検証が主題である。
abstractThis paper proposes a workflow to assess the uncertainty of the Normalized Difference Vegetation Index (NDVI), a critical index used in precision agriculture to determine plant health.
Reproduction assets foundThe paper's Monte Carlo NDVI uncertainty analysis relies on two public datasets: the ORNL Visible and Near-Infrared Leaf Reflectance Spectra (1992–1993), which provide the dry/fresh leaf reflectance inputs underlying the NDVI variability analysis, and the NASA GES DISC TSIS-1 Level 3 Solar Spectral Irradiance 24-Hour VDataset · public43. Richard E. TSIS SIM Level 3 Solar Spectral Irradiance 24-Hour Means V09. Goddard Earth Sciences Data and Information Services Center (GES DISC); Greenbelt, MD, USA: 2022. [(accessed on 23 April 2024)]. Available online: https://disc.gsfc.nasa.gov/datasets/TSIS_SSI_L3_24HR_12/summary .Open asset ↗GES DISC · TSIS SIM Level 3 Solar Spectral Irradiance 24-Hour Means V09lines:687-687Code / dataset availability confirmedCrossref · checked 15 Sept 2026
Photosynthesis is influenced by dynamic energy allocation under various environmental conditions. Solar-induced chlorophyll fluorescence (SIF), an important pathway for dissipating absorbed energy, has been extensively used to evaluate gross primary productivity (GPP). However, the potential for photochemical reflectance index (PRI), as an indicator of non-photochemical quenching (NPQ), to improve the SIF-based GPP estimation, has not been thoroughly investigated. In this study, using continually tower-based observations, we examined how PRI affected the link between SIF and GPP for corn and soybean at half-hourly and daily timescales. The relationship of GPP to SIF and PRI is impacted by stress indicated by vapor pressure deficit (VPD) and crop water stress index (CWSI). Moreover, the ratio of GPP to SIF of corn was more sensitive to PRI compared to soybean. Whether in Pearson or Partial correlation analysis, the relationships of PRI to the ratio of GPP to SIF were almost all significant, regardless of controlling structural-physiological (stomatal conductance, vegetation indices) and environmental variables (light intensity, etc.). Therefore, PRI significantly affects the SIF–GPP relationship for corn (r > 0.31, p 0.22, p
Why it matches plant phenotyping methods作物を対象に、塔載観測によるPRI・SIFを用いたGPP推定関係の改善と検証を主題としており、植物の生理状態推定手法が中心である。
titleAssessing the Potential for Photochemical Reflectance Index to Improve the Relationship between Solar-Induced Chlorophyll Fluorescence and Gross Primary Productivity in Crop and Soybean
Reproduction assets foundThe paper's tower-based SIF, PRI, and GPP measurements for corn and soybean at US-Ne2/US-Ne3 derive from an openly downloadable ORNL DAAC dataset (ds_id=2136), explicitly cited in the Data Availability Statement. No author analysis code or models are disclosed.Dataset · publicResearch and Development Program of
China, grant number 2021YFC2600501; the National Natural Science Foundation of China, grant
number SKLNBC2023-01.
Institutional Review Board Statement: Not applicable.
Informed Consent Statement: Not applicable.
Data Availability Statement: The dataset used in this study can be downloaded at https://daac.ornl.gov/cgi-bin/dsviewer.pl?ds_id=2136 (accessed on 1 January 2024).
Acknowledgments: We appreciate the open-access dataset supported by Wu from the Agroecosystem
Sustainability Center, Institute for Sustainability, Energy, and Environment, University of Illinois at
Urbana-Champaign, Urbana, IL, USA.
Conflicts of Interest: The authors declare no conflictsOpen asset ↗daac.ornl.gov · ds_id=2136pdf-raw-page:17 lines:1-54Code / dataset availability confirmedEurope PMC · checked 7 Sept 2026
Breeding for disease resistance is a central component of strategies implemented to mitigate biotic stress impacts on crop yield. Conventionally, genotypes of a plant population are evaluated through a labor-intensive process of assigning visual scores (VS) of susceptibility (or resistance) by specifically trained staff, which limits manageable volumes and repeatability of evaluation trials. Remote sensing (RS) tools have the potential to streamline phenotyping processes and to deliver more standardized results at higher through-put. Here, we use a two-year evaluation trial of three newly developed biparental populations of maize doubled haploid lines (DH) to compare the results of genomic analyses of resistance to common rust (CR) when phenotyping is either based on conventional VS or on RS-derived (vegetation) indices. As a general observation, for each population × year combination, the broad sense heritability of VS was greater than or very close to the maximum heritability across all RS indices. Moreover, results of linkage mapping as well as of genomic prediction (GP), suggest that VS data was of a higher quality, indicated by higher -logp values in the linkage studies and higher predictive abilities for genomic prediction. Nevertheless, despite the qualitative differences between the phenotyping methods, each successfully identified the same genomic region on chromosome 10 as being associated with disease resistance. This region is likely related to the known CR resistance locus Rp1 . Our results indicate that RS technology can be used to streamline genetic evaluation processes for foliar disease resistance in maize. In particular, RS can potentially reduce costs of phenotypic evaluations and increase trialing capacities.
Why it matches plant phenotyping methodsトウモロコシのさび病抵抗性を対象に、リモートセンシング由来指標を従来の視覚評点と比較し、遺伝解析・ゲノム予測における性能を評価している。病害表現型の取得法の比較検証が中心である。
abstractRemote sensing (RS) tools have the potential to streamline phenotyping processes and to deliver more standardized results at higher through-put.
Reproduction assets foundThe paper's RS-derived vegetation indices, visual scores, and related phenotyping data for the three maize DH populations are publicly deposited in CIMMYT's dataverse repository, explicitly linked in the Data availability section. No author analysis code or trained models are stated as available.Dataset · publicThe corresponding data can be found on CIMMYT’s dataverse repository ( https://data.cimmyt.org/dataset.xhtml?persistentId=hdl:11529/10548898 ).Open asset ↗hdl:11529/10548898lines:114-253Code / dataset availability confirmedEurope PMC · checked 14 Sept 2026
Most plant thermal tolerance studies focus on single critical thresholds, which limit the capacity to generalise across studies and predict heat stress under natural conditions. In animals and microbes, thermal tolerance landscapes describe the more realistic, cumulative effects of temperature. We tested this in plants by measuring the decline in leaf photosynthetic efficiency (F V /F M ) following a combination of temperatures and exposure times and then modelled these physiological indices alongside recorded environmental temperatures. We demonstrate that a general relationship between stressful temperatures and exposure durations can be effectively employed to quantify and compare heat tolerance within and across plant species and over time. Importantly, we show how F V /F M curves translate to plants under natural conditions, suggesting that environmental temperatures often impair photosynthetic function. Our findings provide more robust descriptors of heat tolerance in plants and suggest that heat tolerance in disparate groups of organisms can be studied with a single predictive framework.
Why it matches plant phenotyping methods植物の熱耐性を、温度と曝露時間の累積効果および葉の光合成効率から定量・比較する予測フレームワークが研究の中心であり、植物生理状態のフェノタイピング手法に該当する。
abstractWe demonstrate that a general relationship between stressful temperatures and exposure durations can be effectively employed to quantify and compare heat tolerance within and across plant species and over time.
Reproduction assets foundThe article's Data Availability Statement explicitly deposits the authors' R scripts and datasets (including the FV/FM heat-tolerance measurements and analysis data) at the Dryad Digital Repository with a public DOI, making this a paper-specific, publicly actionable asset.Dataset · publicProgram Scholarship; University of
Technology.
PEER REVIEW
The peer review history for this article is available at
https://www.webofscience.com/api/gateway/wos/peer-re-view/10.1111/ele.14416.DATA AVAILABILITY STATEMENT
The R scripts and datasets used to conduct the data
analyses are available at the DRYAD Digital Repository
(https://doi.org/10.5061/dryad.wdbrv15v4).ORCID
Alicia M. Cook https://orcid.org/0000-0003-3594-3220
Enrico L. Rezende https://orcid.org/0000-0002-6245-9605
Katherina Petrou https://orcid.org/0000-0002-2703-0694
Andy Leigh https://orcid.org/0000-0003-3568-2606
REFERENCES
AGBoM. (2018a) Climate statistics for Australian Locations: Port
Augusta AERO. Available at: http:Open asset ↗DRYAD Digital Repository · 10.5061/dryad.wdbrv15v4pdf-raw-page:10 lines:1-102Code / dataset availability confirmedEurope PMC · checked 7 Sept 2026
Sun-induced chlorophyll fluorescence (SIF) provides an opportunity to study terrestrial ecosystem photosynthesis dynamics. However, the current coarse spatiotemporal satellite SIF products are challenging for mechanistic interpretations of SIF signals. Long-term ground SIF and vegetation indices (VIs) are important for satellite SIF validation and mechanistic understanding of the relationship between SIF and photosynthesis when combined with leaf- and canopy-level auxiliary measurements. In this study, we present and analyze a total of 15 site-years of ground far-red SIF (SIF at 760 nm, SIF 760 ) and VIs datasets from soybean, corn, and miscanthus grown in the U.S. Corn Belt from 2016 to 2021. We introduce a comprehensive data processing protocol, including different retrieval methods, calibration coefficient adjustment, and nadir SIF footprint upscaling to match the eddy covariance footprint. This long-term ground far-red SIF and VIs dataset provides important and first-hand data for far-red SIF interpretation and understanding the mechanistic relationship between far-red SIF and canopy photosynthesis across various crop species and environmental conditions.
Why it matches plant phenotyping methods作物キャノピーの光合成状態を推定する地上SIFについて、取得データセットと処理・校正・フットプリント拡大プロトコルが中心的に提示されており、植物フェノタイピング手法およびデータセットに該当する。
abstractWe introduce a comprehensive data processing protocol, including different retrieval methods, calibration coefficient adjustment, and nadir SIF footprint upscaling to match the eddy covariance footprint.
Reproduction assets foundThe paper's ground far-red SIF760 and vegetation indices dataset (15 site-years, US Corn Belt) is explicitly deposited in a public repository (ORNL DAAC) with a DOI matching an allowed URL.Dataset · publicThe processed half-hourly
SIF760 and VIs are available at the on Oak Ridge National Laboratory Distributed Active Archive Center (ORNL
DAAC) data repository https://doi.org/10.3334/ORNLDAAC/213653.Open asset ↗10.3334/ORNLDAAC/213653pdf-page:4 lines:1-47Code / dataset availability confirmedEurope PMC · checked 14 Sept 2026
CottonGreenhouseThermalLeafClassificationPhotosynthesis / fluorescenceStress response / toleranceWater status / transpiration
Introduction Drought detection, spanning from early stress to severe conditions, plays a crucial role in maintaining productivity, facilitating recovery, and preventing plant mortality. While handheld thermal cameras have been widely employed to track changes in leaf water content and stomatal conductance, research on thermal image classification remains limited due mainly to low resolution and blurry images produced by handheld cameras. Methods In this study, we introduce a computer vision pipeline to enhance the significance of leaf-level thermal images across 27 distinct cotton genotypes cultivated in a greenhouse under progressive drought conditions. Our approach involved employing a customized software pipeline to process raw thermal images, generating leaf masks, and extracting a range of statistically relevant thermal features (e.g., min and max temperature, median value, quartiles, etc.). These features were then utilized to develop machine learning algorithms capable of assessing leaf hydration status and distinguishing between well-watered (WW) and dry-down (DD) conditions. Results Two different classifiers were trained to predict the plant treatment-random forest and multilayer perceptron neural networks-finding 75% and 78% accuracy in the treatment prediction, respectively. Furthermore, we evaluated the predicted versus true labels based on classic physiological indicators of drought in plants, including volumetric soil water content, leaf water potential, and chlorophyll a fluorescence, to provide more insights and possible explanations about the classification outputs. Discussion Interestingly, mislabeled leaves mostly exhibited notable responses in fluorescence, water uptake from the soil, and/or leaf hydration status. Our findings emphasize the potential of AI-assisted thermal image analysis in enhancing the informative value of common heterogeneous datasets for drought detection. This application suggests widening the experimental settings to be used with deep learning models, designing future investigations into the genotypic variation in plant drought response and potential optimization of water management in agricultural settings.
Why it matches plant phenotyping methods葉の熱画像からマスクと熱特徴量を抽出し、機械学習で水分状態・乾燥処理を判定する画像解析パイプラインが中心であり、植物表現型の取得・推定手法に該当する。
abstractOur approach involved employing a customized software pipeline to process raw thermal images, generating leaf masks, and extracting a range of statistically relevant thermal features
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 · publicSupplementary Table S3
Single measurements of volumetric soil water content across all collected images.Open asset ↗lines:440-465Code / dataset availability confirmedEurope PMC · checked 14 Sept 2026
Premise The adaptive significance of amphistomy (stomata on both upper and lower leaf surfaces) is unresolved. A widespread association between amphistomy and open, sunny habitats suggests the adaptive benefit of amphistomy may be greatest in these contexts, but this hypothesis has not been tested experimentally. Understanding amphistomy informs its potential as a target for crop improvement and paleoenvironment reconstruction. Methods We developed a method to quantify "amphistomy advantage" ( AA $\text{AA}$ ) as the log-ratio of photosynthesis in an amphistomatous leaf to that of the same leaf but with gas exchange blocked through the upper surface (pseudohypostomy). Humidity modulated stomatal conductance and thus enabled comparing photosynthesis at the same total stomatal conductance. We estimated AA $\text{AA}$ and leaf traits in six coastal (open, sunny) and six montane (closed, shaded) populations of the indigenous Hawaiian species 'ilima (Sida fallax). Results Coastal 'ilima leaves benefit 4.04 times more from amphistomy than montane leaves. Evidence was equivocal with respect to two hypotheses: (1) that coastal leaves benefit more because they are thicker and have lower CO 2 conductance through the internal airspace and (2) that they benefit more because they have similar conductance on each surface, as opposed to most conductance being through the lower surface. Conclusions This is the first direct experimental evidence that amphistomy increases photosynthesis, consistent with the hypothesis that parallel pathways through upper and lower mesophyll increase CO 2 supply to chloroplasts. The prevalence of amphistomatous leaves in open, sunny habitats can partially be explained by the increased benefit of amphistomy in "sun" leaves, but the mechanistic basis remains uncertain.
Why it matches plant phenotyping methods葉の両面気孔性が光合成に与える効果を定量化する新しい実験手法を開発し、複数集団で適用しているため、植物の生理形質取得法が中心である。
abstractWe developed a method to quantify "amphistomy advantage" ( AA $\text{AA}$ ) as the log-ratio of photosynthesis in an amphistomatous leaf to that of the same leaf but with gas exchange blocked through the upper surface (pseudohypostomy).
Reproduction assets foundThe paper's raw phenotyping data (stomatal traits, leaf thickness, gas exchange) are publicly deposited on Dryad, and the authors' custom analysis scripts are on GitHub with a Zenodo archive; both are paper-specific and directly actionable.Dataset · public7341. This is publication #213 from the School of Life
Sciences, University of Hawaiʻi at Mānoa.
DATA AVAILABILITY STATEMENT
Custom scripts are available on a GitHub repository
(https://github.com/cdmuir/stomata-ilima) and archived
on Zenodo: https://doi.org/10.5281/zenodo.10369114 (Muir,
2023). Raw data are deposited on Dryad: https://doi.org/10.5061/dryad.rxwdbrvfw (Triplett et al., 2024).
ORCID
Thomas N. Buckley http://orcid.org/0000-0001-7610-7136
Christopher D. Muir http://orcid.org/0000-0003-2555-3878
REFERENCES
Anonymous. 2022. Yellow ʻilima (Sida fallax). https://www.inaturalist.org/taxa/54995-Sida-fallax. iNaturalist.
Ball, J. T., I. E. Woodrow, and J. A. Berry. 1987. A model prediOpen asset ↗Dryad · 10.5061/dryad.rxwdbrvfwpdf-raw-page:9 lines:1-93Code · publicfor advice on leaf sectioning. Startup
funds were provided by the University of Hawaiʻi, NSF
Award 1929167 to C.D.M., and T.N.B. received NSF Award
2307341. This is publication #213 from the School of Life
Sciences, University of Hawaiʻi at Mānoa.
DATA AVAILABILITY STATEMENT
Custom scripts are available on a GitHub repository
(https://github.com/cdmuir/stomata-ilima) and archived
on Zenodo: https://doi.org/10.5281/zenodo.10369114 (Muir,
2023). Raw data are deposited on Dryad: https://doi.org/10.5061/dryad.rxwdbrvfw (Triplett et al., 2024).
ORCID
Thomas N. Buckley http://orcid.org/0000-0001-7610-7136
Christopher D. Muir http://orcid.org/0000-0003-2555-3878
REFERENCES
Anonymous. 2022. YellowOpen asset ↗GitHub · cdmuir/stomata-ilimapdf-raw-page:9 lines:1-93Code / dataset availability confirmedEurope PMC · checked 7 Sept 2026
Photosynthesis drives plant physiology, biomass accumulation, and yield. Photosynthetic efficiency, specifically the operating efficiency of PSII (Fq'/Fm'), is highly responsive to actual growth conditions, especially to fluctuating photosynthetic photon fluence rate (PPFR). Under field conditions, plants constantly balance energy uptake to optimize growth. The dynamic regulation complicates the quantification of cumulative photochemical energy uptake based on the intercepted solar energy, its transduction into biomass, and the identification of efficient breeding lines. Here, we show significant effects on biomass related to genetic variation in photosynthetic efficiency of 178 climbing bean (Phaseolus vulgaris L.) lines. Under fluctuating conditions, the Fq'/Fm' was monitored throughout the growing period using hand-held and automated chlorophyll fluorescence phenotyping. The seasonal response of Fq'/Fm' to PPFR (ResponseG:PPFR) achieved significant correlations with biomass and yield, ranging from 0.33 to 0.35 and from 0.22 to 0.31 in two glasshouse and three field trials, respectively. Phenomic yield prediction outperformed genomic predictions for new environments in four trials under different growing conditions. Investigating genetic control over photosynthesis, one single nucleotide polymorphism (Chr09_37766289_13052) on chromosome 9 was significantly associated with ResponseG:PPFR in proximity to a candidate gene controlling chloroplast thylakoid formation. In conclusion, photosynthetic screening facilitates and accelerates selection for high yield potential.
Why it matches plant phenotyping methods携帯型および自動クロロフィル蛍光フェノタイピングによる光合成効率の反復測定と、収量予測への技術適用が研究の中心であるため。
abstractUnder fluctuating conditions, the Fq'/Fm' was monitored throughout the growing period using hand-held and automated chlorophyll fluorescence phenotyping.
Reproduction assets foundThe paper's field MultispeQ chlorophyll fluorescence phenotyping data (Fq'/Fm' with PPFR and environmental covariates for the Dar18B, Dar19B, and Pal19D trials) are publicly available on the PhotosynQ platform via three author-provided project URLs. Glasshouse ChlF/biomass data are only in supplementary files without aDataset · publicThe MultispeQ data are also available on the PhotosynQ data base after creating an account (Darién 2018: https://photosynq.org/projects/climbers-in-darien-2018Open asset ↗PhotosynQ · climbers-in-darien-2018lines:374-422Code / dataset availability confirmedEurope PMC · checked 14 Sept 2026
Leaf gas exchange measurements are an important tool for inferring a plant's photosynthetic biochemistry. In most cases, the responses of photosynthetic CO 2 assimilation to variable intercellular CO 2 concentrations (A/C i response curves) are used to model the maximum (potential) rate of carboxylation by ribulose-1,5-bisphosphate carboxylase/oxygenase (Rubisco, V cmax ) and the rate of photosynthetic electron transport at a given incident photosynthetically active radiation flux density (PAR; J PAR ). The standard Farquhar-von Caemmerer-Berry model is often used with default parameters of Rubisco kinetic values and mesophyll conductance to CO 2 (g m ) derived from tobacco that may be inapplicable across species. To study the significance of using such parameters for other species, here we measured the temperature responses of key in vitro Rubisco catalytic properties and g m in cotton (Gossypium hirsutum cv. Sicot 71) and derived V cmax and J 2000 (J PAR at 2000 µmol m -2 s -1 PAR) from cotton A/C i curves incrementally measured at 15°C-40°C using cotton and other species-specific sets of input parameters with our new automated fitting R package 'OptiFitACi'. Notably, parameterisation by a set of tobacco parameters produced unrealistic J 2000 :V cmax ratio of cmax above 15°C, up to 2.3-fold higher estimates of J 2000 and more variable estimates of V cmax and J 2000 , for our cotton data compared to model parameterisation with cotton-derived values. We determined that errors arise when using a g m,25 of 2.3 mol m -2 s -1 MPa -1 or less and Rubisco CO 2 -affinities in 21% O 2 (K C 21%O2 ) at 25°C outside the range of 46-63 Pa to model A/C i responses in cotton. We show how the A/C i modelling capabilities of 'OptiFitACi' serves as a robust, user-friendly, and flexible extension of 'plantecophys' by providing simplified temperature-sensitivity and species-specificity parameterisation capabilities to reduce variability when modelling V cmax and J 2000 .
Why it matches plant phenotyping methods植物のガス交換から光合成形質を推定する新規Rパッケージを開発し、種特異的パラメータによる推定性能を検証しているため、方法が研究の中心である。
abstractwith our new automated fitting R package 'OptiFitACi'
Reproduction assets foundThe paper's authors publicly released the OptiFitACi R package containing the fitacis4 function used for all A/Ci curve fitting analyses in this study, with an explicit GitHub URL. The phenotype data (A/Ci response measurements) are stated to be in the article's Supporting Information, which is part of the article and,Code · public(Walker et al., 2013). KC
21%O2
and Γ* were calculated as
described above for tobacco. Equation (2) in Walker et al. (2013) was
used to calculate the gm of antirbcS Arabidopsis at each temperature.
2.6 | Design and implementation of function
fitacis4 in R package ‘OptiFitACi’
A new R function fitacis4 (in package ‘OptiFitACi’; https://github.com/jsamthor/OptiFitACi/tree/master/R) was designed to enhance the A/
Ci analysis capabilities of functions fitaci, fitacis, fitacis2 in the packages
‘plantecophys’ (Duursma, 2015) and ‘plantecowrap’. The function
fitacis4 is used for the batch analysis of leaf photosynthetic gas
exchange data to estimate Vcmax and J2000 using the FvCB C3 model of
leaf pOpen asset ↗jsamthor/OptiFitACi · OptiFitACipdf-raw-page:5 lines:1-114Code / dataset availability confirmedEurope PMC · checked 7 Sept 2026
Diets consisting of greater quantity/diversity of phytochemicals are correlated with reduced risk of disease. This understanding guides policy development increasing awareness of the importance of consuming fruits, grains, and vegetables. Enacted policies presume uniform concentrations of phytochemicals across crop varieties regardless of production/harvesting methods. A growing body of research suggests that concentrations of phytochemicals can fluctuate within crop varieties. Improved awareness of how cropping practices influence phytochemical concentrations are required, guiding policy development improving human health. Reliable, inexpensive laboratory equipment represents one of several barriers limiting further study of the complex interactions influencing crop phytochemical accumulation. Addressing this limitation our study validated the capacity of a low-cost Reflectometer ($500) to measure phytochemical content in selected crops, against a commercial grade laboratory spectrophotometer. Our correlation results ranged from r 2 = 0.81 for protein in wheat and oats to r 2 = 0.99 for polyphenol content in lettuce in both the Reflectometer and laboratory spectrophotometer assessment, suggesting the Reflectometer provides an accurate accounting of phytochemical content within evaluated crops. Repeatability evaluation demonstrated good reproducibility of the Reflectometer to assess crop phytochemical content. Additionally, we confirmed large variation in phytochemical content within specific crop varieties, suggesting that cultivar is but one of multiple drivers of phytochemical accumulation. Our findings indicate dramatic nutrient variations could exist across the food supply, a point whose implications are not well understood. Future studies should investigate the interactions between crop phytochemical accumulation and farm management practices that influence specific soil characteristics.
Why it matches plant phenotyping methods作物の植物化学成分量を測定する低コスト反射計を、実験室用分光光度計と比較して精度・再現性検証しており、植物形質の取得手法の技術的検証が中心である。
abstractour study validated the capacity of a low-cost Reflectometer ($500) to measure phytochemical content in selected crops, against a commercial grade laboratory spectrophotometer.
Reproduction assets foundThe paper explicitly states that all Bionutrient Institute data (reflectometer/spectrometer phytochemical measurements used in this study) are publicly available in the authors' GitLab repository, and the authors' data-processing pipeline code is also publicly hosted on GitLab.Dataset · publicAll data derived from the Bionutrient Institute methods are available publicly from our repository: https://gitlab.com/our-sci/bionutrient-institute/dataset . The data used in this manuscript covers samples submitted up to 7/31/2022.Open asset ↗our-sci/bionutrient-institute/datasetlines:156-212Code · publicAn automated data pipeline was built using SurveyStacks API’s to merge data from each completed survey and mongoDB scripts ( https://gitlab.com/our-sci/real-food-campaign/lab-data-review-dashboard/-/tree/main ) calculated measurement outcomes.Open asset ↗our-sci/real-food-campaign/lab-data-review-dashboardlines:132-143Code / dataset availability confirmedCrossref · checked 15 Sept 2026
Quantitative phase imaging by digital holographic microscopy (DHM) is a nondestructive and label-free technique that has been playing an indispensable role in the fields of science, technology, and biomedical imaging. The technique is competent in imaging and analyzing label-free living cells and investigating reflective surfaces. Herein, we introduce a new configuration of a wide field-of-view single-shot common-path off-axis reflective DHM for the quantitative phase imaging of biological cells that leverages several advantages, including being less-vibration sensitive to external perturbations due to its common-path configuration, also being compact in size, simple in optical design, highly stable, and cost-effective. A detailed description of the proposed DHM system, including its optical design, working principle, and capability for phase imaging, is presented. The applications of the proposed system are demonstrated through quantitative phase imaging results obtained from the reflective surface (USAF resolution test target) as well as transparent samples (living plant cells). The proposed system could find its applications in the investigation of several biological specimens and the optical metrology of micro-surfaces.
Why it matches plant phenotyping methods植物細胞を対象に定量位相画像を取得できる新規デジタルホログラフィック顕微鏡を開発しており、植物試料での性能実証も含むため、植物フェノタイピングに利用可能な画像計測法が中心です。
abstractwe introduce a new configuration of a wide field-of-view single-shot common-path off-axis reflective DHM for the quantitative phase imaging of biological cells
Reproduction assets foundThe paper's only paper-specific public asset is the MDPI supplementary material containing the time-lapse retrieved wrapped phase imaging video of tobacco plant cells (the paper's plant-cell phenotyping measurements). No analysis code, datasets, or trained models are publicly deposited; the Data Availability Statement仅Supplement · publicdual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.
Supplementary Materials
The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/s24030720/s1 , See the supplementary material for visualization of the time-lapse retrieved wrapped phase imaging video of tobacco plant cells captured five-minute intervals.
Click here for additional data file.
Author Contributions
Conceptualization: M.K.; methodology, M.K.; software, M.K.; validation, M.K., O.M. and T.M.; formal Open asset ↗lines:51-66Code / dataset availability confirmedEurope PMC · checked 15 Sept 2026
Leaf stomata facilitate the exchange of water and CO 2 during photosynthetic gas exchange. The shape, size, and density of leaf pores have not been constant over geologic time, and each morphological trait has potentially been impacted by changing environmental and climatic conditions, especially by changes in the concentration of atmospheric carbon dioxide. As such, stomatal parameters have been used in simple regressions to reconstruct ancient carbon dioxide, as well as incorporated into more complex gas-exchange models that also leverage plant carbon isotope ecology. Most of these proxy relationships are measured on chemically cleared leaves, although newer techniques such as creating stomatal impressions are being increasingly employed. Additionally, many of the proxy relationships use angiosperms with broad leaves, which have been increasingly abundant in the last 130 million years but are absent from the fossil record before this. We focus on the methodology to define stomatal parameters for paleo-CO 2 studies using two separate methodologies (one corrosive, one non-destructive) to prepare leaves on both scale- and broad-leaves collected from herbaria with known global atmospheric CO 2 levels. We find that the corrosive and non-corrosive methodologies give similar values for stomatal density, but that measurements of stomatal sizes, particularly guard cell width (GCW), for the two methodologies are not comparable. Using those measurements to reconstruct CO 2 via the gas exchange model, we found that reconstructed CO 2 based on stomatal impressions (due to inaccurate measurements in GCW) far exceeded measured CO 2 for modern plants. This bias was observed in both coniferous (scale-shaped) and angiosperm (broad) leaves. Thus, we advise that applications of gas exchange models use cleared leaves rather than impressions.
Why it matches plant phenotyping methods葉の気孔形態（密度・サイズ）を測定する2手法を比較・検証し、CO2推定への影響を評価しており、植物表現型取得法が研究の中心です。
abstractWe focus on the methodology to define stomatal parameters for paleo-CO 2 studies using two separate methodologies (one corrosive, one non-destructive) to prepare leaves on both scale- and broad-leaves collected from herbaria with known global atmospheric CO 2 levels.
Reproduction assets foundThe paper's stomatal phenotyping measurements (stomatal density, guard cell length/width, CO2 reconstructions) are publicly deposited as supplemental data tables on Mendeley Data (DOI 10.17632/gs6rn9tjxn.1), explicitly linked in the Supplementary Materials and Data Availability sections. No author analysis code or phenDataset · publicSupplemental Data Tables: All Measurements—Data are available at Mendeley Data: 10.17632/gs6rn9tjxn.1 (accessed on 13 November 2023).Open asset ↗Mendeley Data · 10.17632/gs6rn9tjxn.1lines:58-75Code / dataset availability confirmedOpenAlex · checked 15 Sept 2026
Published1 Jan 2024IEEE Transactions on Geoscience and Remote SensingCited by 1 · OpenAlex ↗
Full-spectrum Sun-induced chlorophyll fluorescence (SIF) offers profound physiological insights into plant functional status compared to single-band SIF. We propose a Fourier series-based method (FSM) for retrieving full-spectrum SIF, aiming to address the limitations of existing methods, such as reliance on reflectance training datasets and the limited spectral range of retrieved SIF spectrum. The core principle of the FSM involves modeling reflectance as a wavelength-dependent function, which can be approximated by successive summations using high-order expansions of the Fourier series. The performance of the FSM was thoroughly evaluated through a combination of simulations and field measurements. The findings illustrate FSM’s capability to achieve high-precision full-spectrum SIF retrieval, with an average relative root-mean-square error (RRMSE) of 2.468% based on synthetic data. Moreover, the corresponding RRMSE values in the O2-A and O2-B bands, at 1.1% and 3.724%, respectively, indicate accuracy comparable to the spectral fitting method (SFM) and advanced FSR (aFSR) methods and superior to the SpecFit method. In the field full-spectrum SIF retrieval, FSM exhibited improved reflectance reconstruction and produced more reasonable results for the diurnal variation of full-spectrum SIF. The diurnal comparison of single-band SIF at both Italian and German sites further highlights the close alignment between FSM-retrieved SIF and the SFM SIF, with$R^{2}$values exceeding 0.96 and a maximum RMSE of 0.118 mW/m2/sr/nm. Conversely, the aFSR method encountered challenges stemming from an under-representation of the training dataset, resulting in the maximum RMSE at the Italian site reaching 0.506 mW/m2/sr/nm, along with a minimum$R^{2}$of 0.809. The FSM demonstrates the promising potential for full-spectrum SIF retrieval, accompanied by fewer limitations.
Why it matches plant phenotyping methods植物キャノピー計測から葉緑素蛍光を抽出する新規手法を開発し、シミュレーションと圃場計測で精度検証・既存法比較を行っており、植物生理状態のフェノタイピング手法が中心である。
abstractWe propose a Fourier series-based method (FSM) for retrieving full-spectrum SIF
Reproduction assets foundThe paper's FloX field spectral measurements (canopy upwelling radiance and apparent reflectance from Grosseto, Italy and Julich, Germany) are explicitly stated to be publicly available on Zenodo, matching the allowed URL. No author analysis code or trained model deposit is mentioned.Dataset · publicp (sparse vegetation), with observation heights of 1.5
121
m and 3 m, respectively. For this study, we used six sets of clear-sky observations
122
conducted on April 7, 16, and 25, 2018, in Italy, and on November 5, 7, and 18, 2020,
123
in Germany. These spectral datasets are already available on the shared online platform
124
(https://zenodo.org/records/7040578). For a more detailed description, please refer to
125
the work by Naethe, Julitta [17].
126
Figure 1 illustrates the diurnal measurements of upwelling radiance and apparent
127
reflectance recorded over the course of six days. The Italian measurements (upper two
128
rows) depict the vigorous growth period of the target vegetatOpen asset ↗zenodo · 7040578pdf-raw-page:6 lines:1-53Code / dataset availability confirmedCrossref · checked 14 Sept 2026
Laboratory / benchtopCell / cellular structurePhysiological trait estimationVisualization / data managementGrowth / development / phenology
Abstract Background Plant defense activators offer advantages over pesticides by avoiding the emergence of drug-resistant pathogens. However, only a limited number of compounds have been reported. Reactive oxygen species (ROS) act as not only antimicrobial agents but also signaling molecules that trigger immune responses. They also affect various cellular processes, highlighting the potential ROS modulators as plant defense activators. Establishing a high-throughput screening system for ROS modulators holds great promise for identifying lead chemical compounds with novel modes of action (MoAs). Results We established a novel in silico screening system for plant defense activators using deep learning-based predictions of ROS accumulation combined with the chemical properties of the compounds as explanatory variables. Our screening strategy comprised four phases: (1) development of a ROS inference system based on a deep neural network that combines ROS production data in plant cells and multidimensional chemical features of chemical compounds; (2) in silico extensive-scale screening of seven million commercially available compounds using the ROS inference model; (3) secondary screening by visualization of the chemical space of compounds using the generative topographic mapping; and (4) confirmation and validation of the identified compounds as potential ROS modulators within plant cells. We further characterized the effects of selected chemical compounds on plant cells using molecular biology methods, including pathogenic signal-triggered enzymatic ROS induction and programmed cell death as immune responses. Our results indicate that deep learning-based screening systems can rapidly and effectively identify potential immune signal-inducible ROS modulators with distinct chemical characteristics compared with the actual ROS measurement system in plant cells. Conclusions We developed a model system capable of inferring a diverse range of ROS activity control agents that activate immune responses through the assimilation of chemical features of candidate pesticide compounds. By employing this system in the prescreening phase of actual ROS measurement in plant cells, we anticipate enhanced efficiency and reduced pesticide discovery costs. The in-silico screening methods for identifying plant ROS modulators hold the potential to facilitate the development of diverse plant defense activators with novel MoAs.
Why it matches plant phenotyping methods植物細胞のROS蓄積という生理状態を推定する深層学習モデルと、実測による検証を組み合わせたスクリーニング手法の開発が中心である。
abstractWe established a novel in silico screening system for plant defense activators using deep learning-based predictions of ROS accumulation combined with the chemical properties of the compounds as explanatory variables.
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 learning algorithm codes used during the current study are available on the GitHub address ( https://github.com/ma1206ko/in_silico_screening ).Open asset ↗ma1206ko/in_silico_screeninglines:168-249Code / dataset availability confirmedOpenAlex · Europe PMC · checked 7 Sept 2026
Phytophthora root rot (PRR) is a major constraint to chickpea production in Australia. Management options for controlling the disease are limited to crop rotation and avoiding high risk paddocks for planting. Current Australian cultivars have partial PRR resistance, and new sources of resistance are needed to breed cultivars with improved resistance. Field- and glasshouse-based PRR resistance phenotyping methods are labour intensive, time consuming, and provide seasonally variable results; hence, these methods limit breeding programs’ abilities to screen large numbers of genotypes. In this study, we developed a new space saving (400 plants/m2), rapid (<12 days), and simplified hydroponics-based PRR phenotyping method, which eliminated seedling transplant requirements following germination and preparation of zoospore inoculum. The method also provided post-phenotyping propagation all the way through to seed production for selected high-resistance lines. A test of 11 diverse chickpea genotypes provided both qualitative (PRR symptoms) and quantitative (amount of pathogen DNA in roots) results demonstrating that the method successfully differentiated between genotypes with differing PRR resistance. Furthermore, PRR resistance hydroponic assessment results for 180 recombinant inbred lines (RILs) were correlated strongly with the field-based phenotyping, indicating the field phenotype relevance of this method. Finally, post-phenotyping high-resistance genotypes were selected. These were successfully transplanted and propagated all the way through to seed production; this demonstrated the utility of the rapid hydroponics method (RHM) for selection of individuals from segregating populations. The RHM will facilitate the rapid identification and propagation of new PRR resistance sources, especially in large breeding populations at early evaluation stages.
Why it matches plant phenotyping methods植物の根腐病抵抗性を評価する高速・高スループット水耕フェノタイピング法を開発し、遺伝子型間識別と圃場評価との相関で検証しているため、方法が研究の中心です。
abstractwe developed a new space saving (400 plants/m2), rapid (<12 days), and simplified hydroponics-based PRR phenotyping method
Reproduction assets foundThe paper's supplementary materials (MDPI S1) contain paper-specific phenotyping images (post-phenotyping propagation, genotype symptom comparisons, hydroponics setup, growth stages) and a workflow flow chart, publicly downloadable. The underlying phenotype datasets are only 'available if requested', so they do not yetSupplement · publicThe following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/plants12234069/s1 , Figure S1: Phenotypic differences between (a) plants at the time of transplanting to potting mix post-phenotyping E1 and (b) 5 weeks later showing growth and pod developmentOpen asset ↗lines:235-249Code / dataset availability confirmedOpenAlex · Crossref · Europe PMC · checked 7 Sept 2026
BACKGROUND: Thermography is a popular tool to assess plant water-use behavior, as plant temperature is influenced by transpiration rate, and is commonly used in field experiments to detect plant water deficit. Its application in indoor automated phenotyping platforms is still limited and mainly focuses on differences in plant temperature between genotypes or treatments, instead of estimating stomatal conductance or transpiration rate. In this study, the transferability of commonly used thermography analysis protocols from the field to greenhouse phenotyping platforms was evaluated. In addition, the added value of combining thermal infrared (TIR) with hyperspectral imaging to monitor drought effects on plant transpiration rate (E) was evaluated. RESULTS: The sensitivity of commonly used TIR indices to detect drought-induced and genotypic differences in water status was investigated in eight maize inbred lines in the automated phenotyping platform PHENOVISION. Indices that normalized plant temperature for vapor pressure deficit and/or air temperature at the time of imaging were most sensitive to drought and could detect genotypic differences in the plants' water-use behavior. However, these indices were not strongly correlated to stomatal conductance and E. The canopy temperature depression index, the crop water stress index and the simplified stomatal conductance index were more suitable to monitor these traits, and were consequently used to develop empirical E prediction models by combining them with hyperspectral indices and/or environmental variables. Different modeling strategies were evaluated, including single index-based, machine learning and mechanistic models. Model comparison showed that combining multiple TIR indices in a random forest model can improve E prediction accuracy, and that the contribution of the hyperspectral data is limited when multiple indices are used. However, the empirical models trained on one genotype were not transferable to all eight inbred lines. CONCLUSION: Overall, this study demonstrates that existing TIR indices can be used to monitor drought stress and develop E prediction models in an indoor setup, as long as the indices normalize plant temperature for ambient air temperature or relative humidity.
Why it matches plant phenotyping methods屋内自動植物フェノタイピング環境で、熱画像・ハイパースペクトル画像による干ばつストレス、水利用、蒸散速度の推定手法を評価・モデル比較しており、フェノタイピング手法が中心である。
abstractthe transferability of commonly used thermography analysis protocols from the field to greenhouse phenotyping platforms was evaluated
Reproduction assets foundThe article's Availability of data and materials statement deposits the datasets generated and analyzed in this study (thermal/hyperspectral phenotyping data and analyses) in three Zenodo repositories with public DOIs. These are paper-specific, publicly accessible assets. No author analysis code with an explicit publicDataset · publicThe datasets generated and analyzed during the current study are available in the zenodo repository ( https://doi.org/10.5281/zenodo.7807989 , https://doi.org/10.5281/zenodo.8164473 , https://doi.org/10.5281/zenodo.8033640 ).Open asset ↗zenodo · 10.5281/zenodo.7807989lines:198-347Dataset · publicThe datasets generated and analyzed during the current study are available in the zenodo repository ( https://doi.org/10.5281/zenodo.7807989 , https://doi.org/10.5281/zenodo.8164473 , https://doi.org/10.5281/zenodo.8033640 ).Open asset ↗zenodo · 10.5281/zenodo.8164473lines:198-347Dataset · publicThe datasets generated and analyzed during the current study are available in the zenodo repository ( https://doi.org/10.5281/zenodo.7807989 , https://doi.org/10.5281/zenodo.8164473 , https://doi.org/10.5281/zenodo.8033640 ).Open asset ↗zenodo · 10.5281/zenodo.8033640lines:198-347Code / dataset availability confirmedEurope PMC · checked 14 Sept 2026
LeafPhysiological trait estimationPhotosynthesis / fluorescencePlant / canopy temperature
We present the Fast Assimilation-Temperature Response (FAsTeR) method, a new method for measuring plant assimilation-temperature (AT) response that reduces measurement time and increases data density compared with conventional methods. The FAsTeR method subjects plant leaves to a linearly increasing temperature ramp while taking rapid, nonequilibrium measurements of gas exchange variables. Two postprocessing steps are employed to correct measured assimilation rates for nonequilibrium effects and sensor calibration drift. Results obtained with the new method are compared with those from two conventional stepwise methods. Our new method accurately reproduces results obtained from conventional methods, reduces measurement time by a factor of c. 3.3 (from c. 90 to 27 min), and increases data density by a factor of c. 55 (from c. 10 to c. 550 observations). Simulation results demonstrate that increased data density substantially improves confidence in parameter estimates and drastically reduces the influence of noise. By improving measurement speed and data density, the FAsTeR method enables users to ask fundamentally new kinds of ecological and physiological questions, expediting data collection in short-field campaigns, and improving the representativeness of data across species in the literature.
Why it matches plant phenotyping methods葉レベル光合成の温度応答を高速・高密度に測定する新手法を開発し、従来法と比較検証しているため、植物フェノタイピング手法が中心です。
abstractWe present the Fast Assimilation-Temperature Response (FAsTeR) method, a new method for measuring plant assimilation-temperature (AT) response that reduces measurement time and increases data density compared with conventional methods.
Reproduction assets foundThe paper's Data availability statement explicitly provides full data and R code (postmeasurement corrections, analyses, figures, and FAsTeR protocol) at the authors' public GitHub repository.Code · publicSTM. JCG wrote the first
draft of the manuscript, and JCG and STM revised the manu-
script.
ORCID
Josef C. Garen https://orcid.org/0000-0002-3338-6662
Sean T. Michaletz https://orcid.org/0000-0003-2158-6525
Data availability
Full data and code used for the production of figures and statis-
tics in this article are available at https://github.com/garenj/Faster-method.New Phytologist (2024) 241: 1361–1372
www.newphytologist.com
Ó 2023 The Authors
New Phytologist Ó 2023 New Phytologist Foundation
Research Methods
New
Phytologist
1370
14698137,
2024,
3,
Downloaded
from
https://nph.onlinelibrary.wiley.com/doi/10.1111/nph.19405
by
Mount
Vernon
Nazarene
University,
Wiley
Online
Library
on
[20/12/Open asset ↗garenj/Faster-methodpdf-raw-page:10 lines:89-144Code / dataset availability confirmedEurope PMC · bioRxiv · checked 7 Sept 2026
Field / plotLeafStomata / guard-cell complexPhysiological trait estimation2D/3D reconstructionPhotosynthesis / fluorescenceStomatal traitsWater status / transpiration
ABSTRACT Premise of the study The adaptive significance of stomata on both upper and lower leaf surfaces, called amphistomy, is unresolved. A widespread association between amphistomy and open, sunny habitats suggests the adaptive benefit of amphistomy may be greatest in these contexts, but this hypothesis has not been tested experimentally. Understanding why amphistomy evolves can inform its potential as a target for crop improvement and paleoenvironment reconstruction. Methods We developed a new method to quantify “amphistomy advantage”, AA, as the log-ratio of photosynthesis in an amphistomatous leaf to that of the same leaf but with gas exchange blocked through the upper (adaxial) surface, which we term “pseudohypostomy”. We used humidity to modulate stomatal conductance and thus compare photosynthetic rates at the same total stomatal conductance. We estimated AA and related physiological and anatomical traits in 12 populations, six coastal (open, sunny) and six montane (closed, shaded), of the indigenous Hawaiian species ‘ilima ( Sida fallax ). Key results Coastal ‘ilima leaves benefit 4.04 times more from amphistomy compared to their montane counterparts. Our evidence was equivocal with respect to two hypotheses – that coastal leaves benefit more because 1) they are thicker and therefore have lower CO 2 conductance through the internal airspace, and 2) that they benefit more because they have similar conductance on each surface, as opposed to most of the conductance being on the lower (abaxial) surface. Conclusions This is the first direct experimental evidence that amphistomy per se increases photosynthesis, consistent with the hypothesis that parallel pathways through upper and lower mesophyll increase the supply of CO 2 to chloroplasts. The prevalence of amphistomatous leaves in open, sunny habitats can partially be explained the increased benefit of amphistomy in ‘sun’ leaves, but the mechanistic basis of this observation is an area for future research.
Why it matches plant phenotyping methods葉の両面気孔性が光合成に与える効果を定量化する新しい生理的測定法を開発し、複数集団で比較検証しており、表現型取得が研究の中心である。
abstractWe developed a new method to quantify “amphistomy advantage”, AA, as the log-ratio of photosynthesis in an amphistomatous leaf to that of the same leaf but with gas exchange blocked through the upper (adaxial) surface, which we term “pseudohypostomy”.
Reproduction assets foundThe paper's Data Availability Statement explicitly provides a public GitHub repository containing the custom analysis scripts for this study's amphistomy advantage measurements. Raw data are only promised for future Dryad deposit (not yet available), so only the code asset qualifies.Code · publicCustom scripts are available on a GitHub repository (https://github.com/cdmuir/stomata-ilima) and will
be archived on Zenodo with a DOI and stable URL upon publication.Open asset ↗cdmuir/stomata-ilimapdf-page:16 lines:1-52Code / dataset availability confirmedEurope PMC · bioRxiv · checked 15 Sept 2026
We present a sensitive method for non-contact detection of chloroplast movements in leaves and other photosynthetic tissues, based on changes in the magnitude of cross-polarized reflectance. We examined changes in bidirectional red light reflectance during irradiation with blue light, known to trigger chloroplast relocations. Experiments on the model plant Arabidopsis thaliana , wild-type, and several mutants with disrupted chloroplast movements showed that the chloroplast avoidance response, induced by high blue light, led to a substantial increase in diffuse reflectance of unpolarized red light. The effects of the accumulation response in low blue light were the opposite. The specular reflectance of the leaf was unaffected by the chloroplast positioning. To further improve the specificity of the detection, we examined the effects of chloroplast relocations on the leaf reflectance of a linearly polarized incident beam. The greatest relative change associated with chloroplast movements was observed when the planes of polarization of the incident and detected beams were perpendicular. Further experiments revealed that the chloroplast positioning affected the magnitude of depolarization of light by the leaf. We applied the developed approach to examine chloroplast relocations in four angiosperm species collected in the field. The method allowed us to detect the chloroplast avoidance response in the green stems of bilberry, a sample not amenable to transmittance-based detection. Despite the importance of chloroplast movements for the optimization of photosynthetic efficiency and biomass production, high throughput reflectance-based methods are not routinely used for their detection. This method opens the possibility of non-invasive, non-contact detection of chloroplast relocations in a manner insensitive to the orientation of the leaf.
Why it matches plant phenotyping methods葉のクロロプラスト移動という植物状態を、偏光反射によって非接触・非侵襲的に検出する手法を開発し、複数種で適用しているため、植物フェノタイピング手法が研究の中心である。
abstractWe present a sensitive method for non-contact detection of chloroplast movements in leaves and other photosynthetic tissues, based on changes in the magnitude of cross-polarized reflectance.
Reproduction assets foundThe paper's Data availability statement openly deposits the paper's own reflectance/transmittance phenotype recordings (Arabidopsis WT/mutants and wild plants) on FigShare, and provides authors' public code: BeamJ (Java control software for the phenotyping setup) and openRayTracer (Mathematica ray-tracing package used,Dataset · publiced on the manuscript.
Conflict of interest
The authors declare no conflict of interest.
Funding
This study was supported by the National Science Centre Poland within the MINIATURA 4
project to P.H., number 2020/04/X/NZ4/01256.
Data availability
The data that support the findings of this study are openly available in FigShare at
https://doi.org/10.6084/m9.figshare.21082654 (reflectance and transmittance recordings for
Arabidopsis WT and mutants) and https://doi.org/10.6084/m9.figshare.24424843 (wild
plants). Java source code for the software is publicly available via GitHub at
https://github.com/pawelHerm/beamJ/tree/master/BeamJ. Wolfram Mathematica package for
ray tracing is available at httOpen asset ↗FigShare · 10.6084/m9.figshare.21082654pdf-raw-page:14 lines:1-47Dataset · publicthe National Science Centre Poland within the MINIATURA 4
project to P.H., number 2020/04/X/NZ4/01256.
Data availability
The data that support the findings of this study are openly available in FigShare at
https://doi.org/10.6084/m9.figshare.21082654 (reflectance and transmittance recordings for
Arabidopsis WT and mutants) and https://doi.org/10.6084/m9.figshare.24424843 (wild
plants). Java source code for the software is publicly available via GitHub at
https://github.com/pawelHerm/beamJ/tree/master/BeamJ. Wolfram Mathematica package for
ray tracing is available at https://github.com/plantPhotobiologyLab/openRayTracer.References
Banaś, A. K., Aggarwal, C., Łabuz, J., Sztatelman, O., Gabryś,Open asset ↗FigShare · 10.6084/m9.figshare.24424843pdf-raw-page:14 lines:1-47Code · publicthat support the findings of this study are openly available in FigShare at
https://doi.org/10.6084/m9.figshare.21082654 (reflectance and transmittance recordings for
Arabidopsis WT and mutants) and https://doi.org/10.6084/m9.figshare.24424843 (wild
plants). Java source code for the software is publicly available via GitHub at
https://github.com/pawelHerm/beamJ/tree/master/BeamJ. Wolfram Mathematica package for
ray tracing is available at https://github.com/plantPhotobiologyLab/openRayTracer.References
Banaś, A. K., Aggarwal, C., Łabuz, J., Sztatelman, O., Gabryś, H. (2012). Blue light
signalling in chloroplast movements. Journal of Experimental Botany, 63(4), 1559–
1574.
Baránková, B., LazáOpen asset ↗GitHub · pawelHerm/beamJpdf-raw-page:14 lines:1-47Code · publicuorescence. The filtered light was focused on a
photodetector (amplified silicon photodiode, PDA100A2, Thorlabs) with a plano-convex lens
(LA1074-A, Thorlabs). The angular size of the clear aperture of the collecting lens with
respect to the sample center was 0.019 steradian (calculated using our ray-tracing
Mathematica package https://github.com/plantPhotobiologyLab/openRayTracer). To control
the observation angle, the detector was mounted at the RBB300A/M rotation board
(Thorlabs). The experiments were performed with two angular positions of the polarizer: its
transmission axis was either parallel (transmits P) or perpendicular (transmits S component)
to the plane of incidence. The LEDs suOpen asset ↗GitHub · plantPhotobiologyLab/openRayTracerpdf-raw-page:6 lines:1-45Code / dataset availability confirmedEurope PMC · checked 7 Sept 2026
Breeding for resilience to climate change requires considering adaptive traits such as plant architecture, stomatal conductance and growth, beyond the current selection for yield. Robotized indoor phenotyping allows measuring such traits at high throughput for speed breeding, but is often considered as non-relevant for field conditions. Here, we show that maize adaptive traits can be inferred in different fields, based on genotypic values obtained indoor and on environmental conditions in each considered field. The modelling of environmental effects allows translation from indoor to fields, but also from one field to another field. Furthermore, genotypic values of considered traits match between indoor and field conditions. Genomic prediction results in adequate ranking of genotypes for the tested traits, although with lesser precision for elite varieties presenting reduced phenotypic variability. Hence, it distinguishes genotypes with high or low values for adaptive traits, conferring either spender or conservative strategies for water use under future climates.
Why it matches plant phenotyping methodsロボット化した屋内フェノタイピングによる適応形質の高スループット測定と、圃場条件への推定・整合性評価が研究の中心であり、単なる生物学的実験のルーチン測定ではない。
abstractRobotized indoor phenotyping allows measuring such traits at high throughput for speed breeding
Reproduction assets foundThe paper's Data availability statement deposits its phenotypic/genotypic datasets (diversity panel, genetic progress panel, recent hybrids panel) on Recherche Data Gouv with three public DOIs. These are paper-specific public phenotype datasets directly reproducing the study's measurements. The PhenoArch platform page,Dataset · publicThe datasets for phenotypic and genotypic values for the diversity panel are available at https://doi.org/10.15454/IASSTN .Open asset ↗10.15454/IASSTNlines:186-234Dataset · publicThe datasets for phenotypic and genotypic values for the genetic progress panel are available at https://doi.org/10.15454/KLD0GH .Open asset ↗10.15454/KLD0GHlines:186-234Dataset · publicThe dataset for the ‘recent hybrid’ panel is available at https://doi.org/10.57745/NZY1KL .Open asset ↗10.57745/NZY1KLlines:186-234Code / dataset availability confirmedCrossref · checked 8 Sept 2026
Abstract Remote sensing of vegetation by spectroscopy is increasingly used to characterize trait distributions in plant communities. How leaves interact with electromagnetic radiation is determined by their structure and contents of pigments, water, and abundant dry matter constituents like lignins, phenolics, and proteins. High-resolution (“hyperspectral”) spectroscopy can characterize trait variation at finer scales, and may help to reveal underlying genetic variation—information important for assessing the potential of populations to adapt to global change. Here, we use a set of 360 inbred genotypes of the wild coyote tobacco Nicotiana attenuata : wild accessions, recombinant inbred lines (RILs), and transgenic lines (TLs) with targeted changes to gene expression, to dissect genetic versus non-genetic influences on variation in leaf spectra across three experiments. We calculated leaf reflectance from hand-held field spectroradiometer measurements covering visible to short-wave infrared wavelengths of electromagnetic radiation (400–2500 nm) using a standard radiation source and backgrounds, resulting in a small and quantifiable measurement uncertainty. Plants were grown in more controlled (glasshouse) or more natural (field) environments, and leaves were measured both on- and off-plant with the measurement set-up thus also in more to less controlled environmental conditions. Entire spectra varied across genotypes and environments. We found that the greatest variance in leaf reflectance was explained by between-experiment and non-genetic between-sample differences, with subtler and more specific variation distinguishing groups of genotypes. The visible spectral region was most variable, distinguishing experimental settings as well as groups of genotypes within experiments, whereas parts of the short-wave infrared may vary more specifically with genotype. Overall, more genetically variable plant populations also showed more varied leaf spectra. We highlight key considerations for the application of field spectroscopy to assess genetic variation in plant populations.
Why it matches plant phenotyping methods葉の反射スペクトルを用いて植物の遺伝的変異を評価する測定法を、異なる環境・測定条件で検証・評価しており、植物フェノタイピング手法が中心です。
titleEvaluating potential of leaf reflectance spectra to monitor plant genetic variation
Reproduction assets foundThe paper's leaf reflectance spectral measurements and analysis code are publicly available: processed spectral data, metadata, and code are on the authors' GitHub repository, and the raw spectral measurement dataset is published in SPECCHIO.Code · publicAll processed spectral data, metadata and code are provided at the GitHub repository: https://github.com/licheng1221/How-leaves-reflect-genetic-variation .Open asset ↗https://github.com/licheng1221/How-leaves-reflect-genetic-variationlines:218-235Code / dataset availability confirmedOpenAlex · Europe PMC · checked 7 Sept 2026
Advancements in hyperspectral imaging (HSI) together with the establishment of dedicated plant phenotyping facilities worldwide have enabled high-throughput collection of plant spectral images with the aim of inferring target phenotypes. Here, we test the utility of HSI-derived canopy data, which were collected as part of an automated plant phenotyping system, to predict physiological traits in cultivated Asian rice ( Oryza sativa ). We evaluated 23 genetically diverse rice accessions from two subpopulations under two contrasting nitrogen conditions and measured 14 leaf- and canopy-level parameters to serve as ground-reference observations. HSI-derived data were used to (1) classify treatment groups across multiple vegetative stages using support vector machines (≥ 83% accuracy) and (2) predict leaf-level nitrogen content (N, %, n=88 ) and carbon to nitrogen ratio (C:N, n=88 ) with Partial Least Squares Regression (PLSR) following RReliefF wavelength selection (validation: R 2 = 0.797 and RMSEP = 0.264 for N; R 2 = 0.592 and RMSEP = 1.688 for C:N). Results demonstrated that models developed using training data from one rice subpopulation were able to predict N and C:N in the other subpopulation, while models trained on a single treatment group were not able to predict samples from the other treatment. Finally, optimization of PLSR-RReliefF hyperparameters showed that 300-400 wavelengths generally yielded the best model performance with a minimum calibration sample size of 62. Results support the use of canopy-level hyperspectral imaging data to estimate leaf-level N and C:N across diverse rice, and this work highlights the importance of considering calibration set design prior to data collection as well as hyperparameter optimization for model development in future studies.
Why it matches plant phenotyping methods自動ハイパースペクトル画像からイネの生理形質を推定するモデルを開発・検証しており、表現型取得・抽出手法が研究の中心である。
abstractHSI-derived data were used to (1) classify treatment groups across multiple vegetative stages using support vector machines (≥ 83% accuracy) and (2) predict leaf-level nitrogen content (N, %, n=88 ) and carbon to nitrogen ratio (C:N, n=88 ) with Partial Least Squares Regression (PLSR) following RReliefF wavelength selection
Reproduction assets foundThe paper provides two paper-specific public assets: an authors' GitHub repository containing the code for the physiological trait prediction models (RReliefF-PLSR, SVM classification of rice hyperspectral data), and a Purdue PURR repository deposit containing the study's datasets (HSI-derived and ground-reference phenCode · publicThe code for each physiological trait prediction model can be accessed through GitHub ( https://github.com/To-Chia/rice_imaging_ms ).Open asset ↗https://github.com/To-Chia/rice_imaging_mslines:384-394Dataset · publicThe datasets presented in this study can be found in online repositories. The names of the repository/repositories and accession number(s) can be found below: https://purr.purdue.edu/publications/4079/ .Open asset ↗https://purr.purdue.edu/publications/4079/lines:442-452Code / dataset availability confirmedOpenAlex · Europe PMC · checked 15 Sept 2026
The study of genomic control of drought tolerance in crops requires techniques to impose well defined and consistent levels of drought stress and efficiently measure single-plant water use for hundreds of experimental units over timescales of several months. Traditional gravimetric methods are extremely labor intensive or require expensive technology, and are subject to other errors. This study demonstrates a low-cost, passive, bottom-watered system that is easily scaled for high-throughput phenotyping. The soil water content in the pots is controlled by altering the water table height in an underlying wicking bed via a float valve. The resulting soil moisture profile is then maintained passively as water withdrawn by the plant is replaced by upward movement of water from the wicking bed, which is fed from a reservoir via the float valve. The single-plant water use can be directly measured over time intervals from one to several days by observing the water level in the reservoir. Using this method, four different drought stress levels were induced in pots containing soybean (Glycine max (L.) Merr.), producing four statistically distinct groups for shoot dry weight and seed yield, as well as clear treatment effects for other relevant parameters, including root:shoot dry weight ratio, pod number, cumulative water use, and water use efficiency. This system has a broad range of applications, and should increase feasibility of high-throughput phenotyping efforts for plant drought tolerance traits.
Why it matches plant phenotyping methods高スループット表現型解析のための低コスト灌水・水利用測定システムを開発・実証しており、植物の水利用と乾燥ストレス関連形質の取得が中心的な方法論的貢献である。
abstractThis study demonstrates a low-cost, passive, bottom-watered system that is easily scaled for high-throughput phenotyping.
Reproduction assets foundThe article's Data availability statement places the study's original contributions (phenotype measurements and supplementary experiment data) in the article/Supplementary Material, which is publicly available at the Frontiers supplementary-material URL. No author analysis code, scripts, models, or standalone phenotypeSupplement · publicof their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.
Supplementary material
The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fpls.2023.1201102/full#supplementary-material
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References
Araya Y. N.
Gowing D. J.
Dise N.
( 2010 ).
A controlled water-table depth system toOpen asset ↗lines:288-364Code / dataset availability confirmedCrossref · checked 15 Sept 2026
Abstract Remote sensing of vegetation by spectroscopy is increasingly used to characterize trait distributions in plant communities. How leaves interact with electromagnetic radiation is determined by their structure and contents of pigments, water, and abundant dry matter constituents like lignins, phenolics, and proteins. High-resolution ("hyperspectral") spectroscopy can characterize trait variation at finer scales, and may help to reveal underlying genetic variation – information important for assessing the potential of populations to adapt to global change. Here, we use a set of 360 inbred genotypes of the wild coyote tobacco Nicotiana attenuata: wild accessions, recombinant inbred lines (RILs), and transgenic lines (TLs) with targeted changes to gene expression, to dissect genetic versus non-genetic influences on variation in leaf spectra across three experiments. We calculated leaf reflectance from hand-held field spectroradiometer measurements covering visible to short-wave infrared wavelengths of electromagnetic radiation (400-2500 nm) using a standard radiation source and backgrounds, resulting in a small and quantifiable measurement uncertainty. Plants were grown in more controlled (glasshouse) or more natural (field) environments, and leaves were measured both on- and off-plant with the measurement set-up thus also in more to less controlled environmental conditions. Entire spectra varied across genotypes and environments. We found that the greatest variance in leaf reflectance was explained by between-experiment and non-genetic between-sample differences, with subtler and more specific variation distinguishing groups of genotypes. The visible spectral region was most variable, distinguishing experimental settings as well as groups of genotypes within experiments, whereas parts of the short-wave infrared may vary more specifically with genotype. Overall, more genetically variable plant populations also showed more varied leaf spectra. We highlight key considerations for the application of field spectroscopy to assess genetic variation in plant populations.
Why it matches plant phenotyping methods葉の反射スペクトルを用いて遺伝的変異を評価する分光計測法を、複数環境・遺伝子型で検証し、測定不確実性や適用上の考慮点も評価しており、植物表現型取得法が研究の中心である。
titleEvaluating potential of leaf reflectance spectra to monitor plant genetic variation
Reproduction assets foundThe paper's leaf reflectance spectral measurement data are published in SPECCHIO, and all processed spectral data, metadata, and analysis code (e.g., RawDataProcess.R, Plots PCA.R, Plots Models.R) are provided in the authors' public GitHub repository.Code · publicilability of data and code
All plant lines are available from the Max Planck Institute for Chemical Ecology. The spectral measurement data
underlying the results presented in this paper are available as a published dataset [71]. All processed spectral data,
metadata and code for this study are provided at the GitHub repository:
https://github.com/licheng1221/How-leaves-reflect-genetic-variationOpen asset ↗https://github.com/licheng1221/How-leaves-reflect-genetic-variationpdf-raw-page:32 lines:1-45Code / dataset availability confirmedOpenAlex · bioRxiv · Europe PMC · Crossref · checked 15 Sept 2026
Abstract Background Thermography is a popular tool to assess plant water use behavior, as plant temperature is influenced by transpiration rate, and is commonly used in field experiments to detect drought stress. Its application in indoor automated phenotyping platforms is still limited and mainly focuses on differences in plant temperature between genotypes or treatments, instead of estimating stomatal conductance or transpiration rate. In this study, the transferability of commonly used thermography analysis protocols from the field to greenhouse phenotyping platforms was evaluated. In addition, the added value of combining thermal infrared (TIR) with hyperspectral imaging to monitor drought effects on plant transpiration rate (E) was evaluated. Results The sensitivity of commonly used TIR indices to detect drought-induced and genotypic differences in water status was investigated in eight maize inbred lines in the automated phenotyping platform PHENOVISION. Indices that normalized plant temperature for vapor pressure deficit and/or air temperature at the time of imaging were most sensitive to drought and could detect genotypic difference in the plants’ water use behavior. However, these indices were not strongly correlated to stomatal conductance and E. The canopy temperature depression index, the crop water stress index and the simplified stomatal conductance index were more suitable to monitor these traits, and were consequently used to develop empirical E prediction models by combining them with hyperspectral indices and/or environmental variables. Different modeling strategies were evaluated including single index-based, machine learning and mechanistic models. Model comparison showed that combining multiple thermal infrared indices in a random forest model can improve E prediction accuracy, and that the contribution of the hyperspectral data is limited when multiple indices are used. However, the empirical models trained on one genotype were not transferable to all eight inbred lines. Conclusion Overall, this study demonstrates that existing TIR indices can be used to monitor drought stress and develop E prediction models in an indoor setup, as long as the indices normalize plant temperature for ambient air temperature or relative humidity.
Why it matches plant phenotyping methods屋内自動植物フェノタイピング基盤で、熱画像・ハイパースペクトル画像から乾燥ストレス、蒸散速度、気孔コンダクタンスを推定する手法の評価・モデル開発が中心である。
abstractthe transferability of commonly used thermography analysis protocols from the field to greenhouse phenotyping platforms was evaluated
Reproduction assets foundThe paper's declarations state that the datasets generated and analyzed during the study (thermal/hyperspectral imaging, environmental, and transpiration data from the maize drought phenotyping experiment) are publicly available in three Zenodo deposits with explicit DOIs. These are paper-specific, public, and directlyDataset · publicyield of photosystem II
ψ water potential
744
Declarations
745
Ethics approval and consent to participate
746
Not applicable.
747
748
Consent for publication
749
Not applicable.
750
751
Availability of data and materials
752
The datasets generated and analyzed during the current study are available in the zenodo repository
753
(https://doi.org/10.5281/zenodo.7807989, https://doi.org/10.5281/zenodo.8164473,
754
https://doi.org/10.5281/zenodo.8033640)
755
756
Competing interests
757
The authors declare that this study received funding from BASF. The funder had the following
758
involvement in the study: collaboratively conceived the original screening and research plans. J.V.,
759
and W.B. wOpen asset ↗zenodo · 10.5281/zenodo.7807989pdf-raw-page:30 lines:1-62Dataset · publicl
744
Declarations
745
Ethics approval and consent to participate
746
Not applicable.
747
748
Consent for publication
749
Not applicable.
750
751
Availability of data and materials
752
The datasets generated and analyzed during the current study are available in the zenodo repository
753
(https://doi.org/10.5281/zenodo.7807989, https://doi.org/10.5281/zenodo.8164473,
754
https://doi.org/10.5281/zenodo.8033640)
755
756
Competing interests
757
The authors declare that this study received funding from BASF. The funder had the following
758
involvement in the study: collaboratively conceived the original screening and research plans. J.V.,
759
and W.B. were employed by BASF Corporation, USA.
7Open asset ↗zenodo · 10.5281/zenodo.8164473pdf-raw-page:30 lines:1-62Dataset · publiconsent to participate
746
Not applicable.
747
748
Consent for publication
749
Not applicable.
750
751
Availability of data and materials
752
The datasets generated and analyzed during the current study are available in the zenodo repository
753
(https://doi.org/10.5281/zenodo.7807989, https://doi.org/10.5281/zenodo.8164473,
754
https://doi.org/10.5281/zenodo.8033640)
755
756
Competing interests
757
The authors declare that this study received funding from BASF. The funder had the following
758
involvement in the study: collaboratively conceived the original screening and research plans. J.V.,
759
and W.B. were employed by BASF Corporation, USA.
760
761
Funding
762
This work was supported bOpen asset ↗zenodo · 10.5281/zenodo.8033640pdf-raw-page:30 lines:1-62Code / dataset availability confirmedOpenAlex · checked 14 Sept 2026
Abstract Aims Our understanding of the rhizosphere is limited by the lack of techniques for in situ live microscopy. Current techniques are either destructive or unsuitable for observing chemical changes within the pore space. To address this limitation, we have developed artificial substrates, termed smart soils, that enable the acquisition and 3D reconstruction of chemical sensors attached to soil particles. Methods The transparency of smart soils was achieved using polymer particles with refractive index matching that of water. The surface of the particles was modified both to retain water and act as a local sensor to report on pore space pH via fluorescence emissions. Multispectral signals were acquired from the particles using a light sheet microscope, and machine learning algorithms predicted the changes and spatial distribution in pH at the surface of the smart soil particles. Results The technique was able to predict pH live and in situ within ± 0.5 units of the true pH value. pH distribution could be reconstructed across a volume of several cubic centimetres around plant roots at 10 μm resolution. Using smart soils of different composition, we revealed how root exudation and pore structure create variability in chemical properties. Conclusion Smart soils captured the pH gradients forming around a growing plant root. Future developments of the technology could include the fine tuning of soil physicochemical properties, the addition of chemical sensors and improved data processing. Hence, this technology could play a critical role in advancing our understanding of complex rhizosphere processes.
Why it matches plant phenotyping methods植物根圏のpHを生体根周辺で測定・3D再構成するセンサー基盤を開発し、精度検証まで行っており、植物状態の取得方法が研究の中心である。
abstractwe have developed artificial substrates, termed smart soils, that enable the acquisition and 3D reconstruction of chemical sensors attached to soil particles.
Reproduction assets foundThe paper's Data availability statement explicitly releases the authors' software for predicting pH from light-sheet image data (the machine-learning phenotyping analysis) on the authors' public GitHub repository SENSOIL. No separate phenotype/trait dataset or image deposit is stated; supplementary material is only a 'Code · public102 Plant Soil (2024) 500:91–104
1 3
Vol:. (1234567890)
Data availability Software developped for predicting pH
from image data is available at https://github.com/LionelDu-puy/SENSOIL/tree/main/pH_Release.Declarations
Competing interest There is no competing interest.
Open Access This article is licensed under a Creative
Commons Attribution 4.0 International License, which per-
mits use, sharing, adaptation, distribution and reproduction in
any medium or format, as long as you give appropriate credit
to the original author(s) and the source,Open asset ↗SENSOILpdf-raw-page:12 lines:1-92Code / dataset availability confirmedEurope PMC · checked 14 Sept 2026
In vivo microscopy of plants with high-frequency imaging allows observation and characterization of the dynamic responses of plants to stimuli. It provides access to responses that could not be observed by imaging at a given time point. Such methods are particularly suitable for the observation of fast cellular events such as membrane potential changes. Classical measurement of membrane potential by probe impaling gives quantitative and precise measurements. However, it is invasive, requires specialized equipment, and only allows measurement of one cell at a time. To circumvent some of these limitations, we developed a method to relatively quantify membrane potential variations in Arabidopsis thaliana roots using the fluorescence of the voltage reporter DISBAC 2 (3). In this protocol, we describe how to prepare experiments for agar media and microfluidics, and we detail the image analysis. We take an example of the rapid plasma membrane depolarization induced by the phytohormone auxin to illustrate the method. Relative membrane potential measurements using DISBAC 2 (3) fluorescence increase the spatio-temporal resolution of the measurements and are non-invasive and suitable for live imaging of growing roots. Studying membrane potential with a more flexible method allows to efficiently combine mature electrophysiology literature and new molecular knowledge to achieve a better understanding of plant behaviors. Key features Non-invasive method to relatively quantify membrane potential in plant roots. Method suitable for imaging seedlings root in agar or liquid medium. Straightforward quantification.
Why it matches plant phenotyping methods植物根の膜電位を蛍光画像から定量する非侵襲的手法の開発と画像解析プロトコルが中心であり、植物表現型計測法に該当する。
abstractwe developed a method to relatively quantify membrane potential variations in Arabidopsis thaliana roots using the fluorescence of the voltage reporter DISBAC 2 (3).
Reproduction assets foundThe protocol explicitly states that the raw imaging data re-analyzed in the paper are deposited on Zenodo and that all analysis scripts (R and Python) are available in a public SourceForge repository. Both are paper-specific, public, and actionable.Dataset · publicluorescence only in the root transition zone. Moreover, we focus on the interface between the cortex and the epidermis, as the dead lateral root cap cells were strongly fluorescent (open membranes for the dye to react to). The data presented here are re-analyzed images from Serre et al. (2021). Raw data can be found on Zenodo ( https://zenodo.org/record/4922659 ). All the scripts used in this protocol can be found on the public repository https://sourceforge.net/projects/disbac2-3-data-analysis/ .
Here, we describe a method to:
Quantify DISBAC 2 (3) fluorescence in the transition zone at a given point (agar experiment) or over time (microfluidics) using the ImageJ/Fiji software.
QuantOpen asset ↗Zenodo · 4922659lines:168-214Code · publicermis, as the dead lateral root cap cells were strongly fluorescent (open membranes for the dye to react to). The data presented here are re-analyzed images from Serre et al. (2021). Raw data can be found on Zenodo ( https://zenodo.org/record/4922659 ). All the scripts used in this protocol can be found on the public repository https://sourceforge.net/projects/disbac2-3-data-analysis/ .
Here, we describe a method to:
Quantify DISBAC 2 (3) fluorescence in the transition zone at a given point (agar experiment) or over time (microfluidics) using the ImageJ/Fiji software.
Quantify root elongation either as an average growth (agar experiment) or over time (microfluidics).
Normalize the microfluidOpen asset ↗SourceForge · disbac2-3-data-analysislines:168-214Code / dataset availability confirmedEurope PMC · checked 7 Sept 2026
Background Investigations into the growth and self-organization of plant roots is subject to fundamental and applied research in various areas such as botany, agriculture, and soil science. The growth activity of the plant tissue can be investigated by isotope labeling experiments with heavy water and subsequent detection of the deuterium in non-exchangeable positions incorporated into the plant biomass. Commonly used analytical methods to detect deuterium in plants are based on mass-spectrometry or neutron-scattering and they either suffer from elaborated sample preparation, destruction of the sample during analysis, or low spatial resolution. Confocal Raman micro-spectroscopy (CRM) can be considered a promising method to overcome the aforementioned challenges. The substitution of hydrogen with deuterium results in the measurable shift of the CH-related Raman bands. By employing correlative approaches with a high-resolution technique, such as helium ion microscopy (HIM), additional structural information can be added to CRM isotope maps and spatial resolution can be further increased. For that, it is necessary to develop a comprehensive workflow from sample preparation to data processing. Results A workflow to prepare and analyze roots of hydroponically grown and deuterium labeled Zea mays by correlative HIM-CRM micro-analysis was developed. The accuracy and linearity of deuterium detection by CRM were tested and confirmed with samples of deuterated glucose. A set of root samples taken from deuterated Zea mays in a time-series experiment was used to test the entire workflow. The deuterium content in the roots measured by CRM was close to the values obtained by isotope-ratio mass spectrometry. As expected, root tips being the most actively growing root zone had incorporated the highest amount of deuterium which increased with increasing time of labeling. Furthermore, correlative HIM-CRM analysis allowed for obtaining the spatial distribution pattern of deuterium and lignin in root cross-sections. Here, more active root zones with higher deuterium incorporation showed less lignification. Conclusions We demonstrated that CRM in combination with deuterium labeling can be an alternative and reliable tool for the analysis of plant growth. This approach together with the developed workflow has the potential to be extended to complex systems such as plant roots grown in soil.
Why it matches plant phenotyping methods植物根の成長状態を測定する相関HIM-CRMワークフローを開発し、重水素検出の精度・直線性と質量分析との一致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。
abstractA workflow to prepare and analyze roots of hydroponically grown and deuterium labeled Zea mays by correlative HIM-CRM micro-analysis was developed.
Reproduction assets foundThe paper's data availability statement deposits the datasets generated and analyzed (CRM/HIM phenotyping measurements of deuterium-labeled maize roots) in the UFZ Data Investigation Portal, a public repository with an explicit URL. No author analysis code with a public URL is stated.Dataset · publicThe datasets generated and/or analyzed during the current study are available in the UFZ Data Investigation Portal ( https://www.ufz.de/record/dmp/archive/13952 ) repository.Open asset ↗UFZ Data Investigation Portal · archive/13952lines:198-288Code / dataset availability confirmedOpenAlex · checked 15 Sept 2026
Field / plotMicroscopyCell / cellular structureMorphology / geometry measurement2D/3D reconstruction
Abstract The streptophyte green algal class Zygnematophyceae is the immediate sister lineage to land plants. Their special form of sexual reproduction via conjugation might have played a key role during terrestrialization. Thus, studying Zygnematophyceae and conjugation is crucial for understanding the conquest of land. Moreover, sexual reproduction features are important for species determination. We present a phylogenetic analysis of a field‐sampled Zygnema strain and analyze its conjugation process and zygospore morphology, both at the micro‐ and nanoscale, including 3D‐reconstructions of the zygospore architecture. Vegetative filament size (26.18 ± 1.07 μm) and reproductive features allowed morphological determination of Zygnema vaginatum, which was combined with molecular analyses based on rbcL sequencing. Transmission electron microscopy (TEM) depicted a thin cell wall in young zygospores, while mature cells exhibited a tripartite wall, including a massive and sculptured mesospore. During development, cytological reorganizations were visualized by focused ion beam scanning electron microscopy (FIB‐SEM). Pyrenoids were reorganized, and the gyroid cubic central thylakoid membranes, as well as the surrounding starch granules, degraded (starch granule volume: 3.58 ± 2.35 μm3 in young cells; 0.68 ± 0.74 μm3 at an intermediate stage of zygospore maturation). Additionally, lipid droplets (LDs) changed drastically in shape and abundance during zygospore maturation (LD/cell volume: 11.77% in young cells; 8.79% in intermediate cells, 19.45% in old cells). In summary, we provide the first TEM images and 3D‐reconstructions of Zygnema zygospores, giving insights into the physiological processes involved in their maturation. These observations help to understand mechanisms that facilitated the transition from water to land in Zygnematophyceae.
Why it matches plant phenotyping methodsFIB-SEM/TEMによる3D画像取得と再構成が研究の中心で、接合胞子の形態・細胞内構造・体積変化を定量的に評価しているため、画像ベースの植物表現型計測として含める。
abstractincluding 3D‐reconstructions of the zygospore architecture
Reproduction assets foundThe paper's FIB-SEM image stacks of Zygnema vaginatum zygospores (the raw imaging data underlying the 3D reconstructions) are publicly deposited as supplementary Movies S2–S4 on figshare. No author analysis code or trained models are explicitly deposited; other supporting data are available only upon request.Dataset · publicof a young Zygnema vaginatum zygospore. https://figshare.com/s/637190524e99459e5b4e .
MOVIE S3. FIB SEM generated stack of frames of an intermediate stage of Zygnema vaginatum zygospore maturation. https://figshare.com/s/30519eddaac8274263aa .
MOVIE S4. FIB SEM generated stack of frames of a mature Zygnema vaginatum zygospore. https://figshare.com/s/11bdf9f4e7ee785e7f9f .
ACKNOWLEDGMENTS
We thank Sabrina Obwegeser, MSc (University of Innsbruck, Austria) for expert technical help in TEM sectioning and image generation. We also thank Jiří Neustupa for his company and assistance during the fieldwork.
DATA AVAILABILITY STATEMENT
The supporting data of the present study are available upon requestOpen asset ↗figsharelines:180-264Code / dataset availability confirmedCrossref · checked 14 Sept 2026
Abstract Crop yield is determined by potential harvest organ size, source organ photosynthesis and carbohydrate partitioning. Filling the harvest organ efficiently remains a challenge. Here, we developed a kinetic model of rice grain filling, which scales from the primary biochemical and biophysical processes of photosynthesis to whole-plant carbon and nitrogen dynamics. The model reproduces the rice yield formation process under different environmental and genetic perturbations. In silico screening identified a range of post-anthesis targets—both established and novel—that can be manipulated to enhance rice yield. Remarkably, we pinpointed the stability of grain-filling rate from flowering to harvest as a critical factor for maximizing grain yield. This finding was further validated in two independent super-high-yielding rice cultivars, each yielding approximately 21 t ha−1 of rough rice at 14% moisture content. Furthermore, we revealed that stabilizing the grain-filling rate could lead to a potential yield increase of 30–40% in an elite rice cultivar. Notably, the instantaneous grain-filling rates around 15- and 38-day post-flowering significantly influence grain yield; and we introduced an innovative in situ approach using ear respiratory rates for precise quantification of these rates. We finally derived an equation to predict the maximum dried brown rice yield (Y, t ha−1) of a cultivar based on its potential gross photosynthetic accumulation from flowering to harvest (Apc, t CO2 ha−1): Y = 0.74 × Apc + 1.9. Overall, this work establishes a framework for quantitatively dissecting crop physiology and designing high-yielding ideotypes.
Why it matches plant phenotyping methods全植物の炭素・窒素動態と収量形成を推定する速度論モデルを開発し、耳の呼吸速度による粒充填速度の定量化手法も導入しているため、表現型取得・推定が中心的です。
abstractHere, we developed a kinetic model of rice grain filling, which scales from the primary biochemical and biophysical processes of photosynthesis to whole-plant carbon and nitrogen dynamics.
Reproduction assets foundThe paper's authors publicly released the WACNI model source code (the computational framework used for all simulations and analysis) on GitHub, with explicit availability language in the MODEL AND DATA AVAILABILITY section. Supplementary Data 2 contains literature-extracted experimental data but no separate public URLCode · publicip
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MODEL AND DATA AVAILABILITY
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Experimental data extracted from literature, used in model-data comparison, are tabulated in
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28Open asset ↗rootchang/WACNI-ricepdf-layout-page:28 lines:1-44Code / dataset availability confirmedEurope PMC · checked 7 Sept 2026
Multicellular organisms result from complex developmental processes largely orchestrated through the quantitative spatiotemporal regulation of gene expression. Yet, obtaining absolute counts of messenger RNAs at a three-dimensional resolution remains challenging, especially in plants, owing to high levels of tissue autofluorescence that prevent the detection of diffraction-limited fluorescent spots. In situ hybridization methods based on amplification cycles have recently emerged, but they are laborious and often lead to quantification biases. In this article, we present a simple method based on single-molecule RNA fluorescence in situ hybridization to visualize and count the number of mRNA molecules in several intact plant tissues. In addition, with the use of fluorescent protein reporters, our method also enables simultaneous detection of mRNA and protein quantity, as well as subcellular distribution, in single cells. With this method, research in plants can now fully explore the benefits of the quantitative analysis of transcription and protein levels at cellular and subcellular resolution in plant tissues.
Why it matches plant phenotyping methods植物組織内のmRNA・タンパク質量を細胞および細胞内解像度で可視化・定量するsmFISH法の開発であり、植物の状態を測定する方法が中心的です。
abstractIn this article, we present a simple method based on single-molecule RNA fluorescence in situ hybridization to visualize and count the number of mRNA molecules in several intact plant tissues.
Reproduction assets foundThe authors openly deposited all raw microscopy images (WM-smFISH mRNA/protein imaging of Arabidopsis and barley tissues) used for their quantification pipeline on Figshare. No separate author analysis code repository with explicit availability language is stated in the supplied text.Dataset · publicAll the raw microscopy images used in this manuscript are openly available in Figshare at https://doi.org/10.6084/m9.figshare.22699132 .Open asset ↗Figshare · 10.6084/m9.figshare.22699132lines:110-216Code / dataset availability confirmedCrossref · checked 8 Sept 2026
Abstract Plant hydraulic conductivity and its decline under water stress are the focal point of current plant hydraulic research. The common methods of measuring hydraulic conductivity control a pressure gradient to push water through plant samples, submitting them to conditions far away from those that are experienced in nature where flow is suction driven and determined by the leaf water demand. In this paper, we present two methods for measuring hydraulic conductivity under closer to natural conditions, an artificial plant setup and a horizontal syringe pump setup. Both approaches use suction to pull water through a plant sample while dynamically monitoring the flow rate and pressure gradients. The syringe setup presented here allows for controlling and rapidly changing flow and pressure conditions, enabling experimental assessment of rapid plant hydraulic responses to water stress. The setup also allows quantification of dynamic changes in water storage of plant samples. Our tests demonstrate that the syringe pump setup can reproduce hydraulic conductivity values measured using the current standard method based on pushing water under above-atmospheric pressure. Surprisingly, using both the traditional and our new syringe pump setup, we found a positive correlation between changes in flow rate and hydraulic conductivity. Moreover, when flow or pressure conditions were changed rapidly, we found substantial contributions to flow by dynamic and largely reversible changes in the water storage of plant samples. Although the measurements can be performed under sub-atmospheric pressures, it is not possible to subject the samples to negative pressures due to the presence of gas bubbles near the valves and pressure sensors. Regardless, this setup allows for unprecedented insights into the interplay between pressure, flow rate, hydraulic conductivity and water storage in plant segments. This work was performed using an Open Science approach with the original data and analysis to be found at https://doi.org/10.5281/zenodo.7322605.
Why it matches plant phenotyping methods植物セグメントの水理伝導度と水貯蔵変化を自然条件に近く測定する新規セットアップを開発・検証しており、植物生理状態の取得手法が研究の中心である。
abstractIn this paper, we present two methods for measuring hydraulic conductivity under closer to natural conditions, an artificial plant setup and a horizontal syringe pump setup.
Reproduction assets foundThe paper explicitly states that all original data (hydraulic conductivity, flow, and pressure measurements) and the authors' analysis code are publicly available in a Zenodo deposit, cited twice (abstract and Data Availability statement).Dataset · publicAll data and analysis code is available at https://doi.org/10.5281/zenodo.7322605 .Open asset ↗Zenodo · 10.5281/zenodo.7322605lines:124-182Code / dataset availability confirmedEurope PMC · checked 7 Sept 2026
NADP(H) is a central metabolic hub providing reducing equivalents to multiple biosynthetic, regulatory and antioxidative pathways in all living organisms. While biosensors are available to determine NADP + or NADPH levels in vivo, no probe exists to estimate the NADP(H) redox status, a determinant of the cell energy availability. We describe herein the design and characterization of a genetically-encoded ratiometric biosensor, termed NERNST, able to interact with NADP(H) and estimate E NADP(H) . NERNST consists of a redox-sensitive green fluorescent protein (roGFP2) fused to an NADPH-thioredoxin reductase C module which selectively monitors NADP(H) redox states via oxido-reduction of the roGFP2 moiety. NERNST is functional in bacterial, plant and animal cells, and organelles such as chloroplasts and mitochondria. Using NERNST, we monitor NADP(H) dynamics during bacterial growth, environmental stresses in plants, metabolic challenges to mammalian cells, and wounding in zebrafish. NERNST estimates the NADP(H) redox poise in living organisms, with various potential applications in biochemical, biotechnological and biomedical research.
Why it matches plant phenotyping methods植物を含む生体内でNADP(H)酸化還元状態を推定する遺伝子 encoded センサーを設計・特性評価しており、植物の生理状態を測定する方法開発が中心である。
abstractWe describe herein the design and characterization of a genetically-encoded ratiometric biosensor, termed NERNST, able to interact with NADP(H) and estimate E NADP(H) .
Reproduction assets foundThe paper's authors publicly deposited their image-analysis pipelines (ImageJ macros, CellProfiler/KNIME workflows, and R scripts) used to quantify NERNST biosensor fluorescence measurements, in a GitHub repository, and also cite a Zenodo archive of the same materials.Code · publicAnalysis pipelines for both software packages and R scripts are available in the GitHub repository found at https://github.com/PameeMolinari/NCOMMS-22-05375-T 94 .Open asset ↗PameeMolinari/NCOMMS-22-05375-Tlines:206-213Code / dataset availability confirmedEurope PMC · checked 7 Sept 2026
Some plant diseases can significantly reduce harvest, but their early detection in cultivation may prevent those consequential losses. Conventional methods of diagnosing plant diseases are based on visual observation of crops, but the symptoms of various diseases may be similar. It increases the difficulty of this task even for an experienced farmer and requires detailed examination based on invasive methods conducted in laboratory settings by qualified personnel. Therefore, modern agronomy requires the development of non-destructive crop diagnosis methods to accelerate the process of detecting plant infections with various pathogens. This research pathway is followed in this paper, and an approach for classifying selected Solanum lycopersicum diseases (anthracnose, bacterial speck, early blight, late blight and septoria leaf) from hyperspectral data captured on consecutive days post inoculation (DPI) is presented. The objective of that approach was to develop a technique for detecting infection in less than seven days after inoculation. The dataset used in this study included hyperspectral measurements of plants of two cultivars of S. lycopersicum: Benito and Polfast, which were infected with five different pathogens. Hyperspectral reflectance measurements were performed using a high-spectral-resolution field spectroradiometer (350-2500 nm range) and they were acquired for 63 days after inoculation, with particular emphasis put on the first 17 day-by-day measurements. Due to a significant data imbalance and low representation of measurements on some days, the collective datasets were elaborated by combining measurements from several days. The experimental results showed that machine learning techniques can offer accurate classification, and they indicated the practical utility of our approaches.
Why it matches plant phenotyping methodsトマト感染株の病害状態をハイパースペクトル測定と機械学習で非破壊・早期推定する方法を開発しており、表現型取得・判定手法が中心である。
abstractan approach for classifying selected Solanum lycopersicum diseases (anthracnose, bacterial speck, early blight, late blight and septoria leaf) from hyperspectral data captured on consecutive days post inoculation (DPI) is presented.
Reproduction assets foundThe paper's Data availability statement explicitly provides the authors' hyperspectral tomato disease measurements (the paper-specific phenotyping dataset) at a public link.Dataset · publicData availability
The hyperspectral measurements presented in this study are available at https://bit.ly/3W7VroF .Open asset ↗lines:175-230Code / dataset availability confirmedEurope PMC · checked 15 Sept 2026
Purple Chinese cabbage (PCC) has become a new breeding trend due to its attractive color and high nutritional quality since it contains abundant anthocyanidins. With the aim of rapid evaluation of PCC anthocyanidins contents and screening of breeding materials, a fast quantitative detection method for anthocyanidins in PCC was established using Near Infrared Spectroscopy (NIR). The PCC samples were scanned by NIR, and the spectral data combined with the chemometric results of anthocyanidins contents obtained by high-performance liquid chromatography were processed to establish the prediction models. The content of cyanidin varied from 93.5 mg/kg to 12,802.4 mg/kg in PCC, while the other anthocyanidins were much lower. The developed NIR prediction models on the basis of partial least square regression with the preprocessing of no-scattering mode and the first-order derivative showed the best prediction performance: for cyanidin, the external correlation coefficient (RSQ) and standard error of cross-validation (SECV) of the calibration set were 0.965 and 693.004, respectively; for total anthocyanidins, the RSQ and SECV of the calibration set were 0.966 and 685.994, respectively. The established models were effective, and this NIR method, with the advantages of timesaving and convenience, could be applied in purple vegetable breeding practice.
Why it matches plant phenotyping methods紫キャベツのアントシアニン含量という植物形質を、NIR分光とケモメトリクスで迅速推定する方法を開発・検証しており、表現型取得法が研究の中心である。
abstracta fast quantitative detection method for anthocyanidins in PCC was established using Near Infrared Spectroscopy (NIR).
Reproduction assets foundThe paper's supplementary material (Table S1) contains the paper-specific HPLC-measured anthocyanidin contents for the 106 purple Chinese cabbage samples used to build the NIR prediction models, and is publicly downloadable from MDPI. No author analysis code, spectral files, or trained model files are explicitly sharedSupplement · publicdual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.
Supplementary Materials
The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/foods12091922/s1 , Table S1: Contents of anthocyanidins in purple Chinese cabbage analyzed by high-performance liquid chromatography (mg/kg).
Click here for additional data file.
Author Contributions
Conceptualization, D.-S.Z. and H.-J.H.; software, G.-M.L.; validation, Y.-Q.W. and L.-P.H.; formal analysis, G.-M.L. and X.-Z.Z.; invOpen asset ↗lines:56-122Code / dataset availability confirmedEurope PMC · checked 15 Sept 2026
Plant electrophysiological response contains useful signature of its environment and health which can be utilized using suitable statistical analysis for developing an inverse model to classify the stimulus applied to the plant. In this paper, we have presented a statistical analysis pipeline to tackle a multiclass environmental stimuli classification problem with unbalanced plant electrophysiological data. The objective here is to classify three different environmental chemical stimuli, using fifteen statistical features, extracted from the plant electrical signals and compare the performance of eight different classification algorithms. A comparison using reduced dimensional projection of the high dimensional features via principal component analysis (PCA) has also been presented. Since the experimental data is highly unbalanced due to varying length of the experiments, we employ a random under-sampling approach for the two majority classes to create an ensemble of confusion matrices to compare the classification performances. Along with this, three other multi-classification performance metrics commonly used for unbalanced data viz. balanced accuracy, F1-score and Matthews correlation coefficient have also been analyzed. From the stacked confusion matrices and the derived performance metrics, we choose the best feature-classifier setting in terms of the classification performances carried out in the original high dimensional vs. the reduced feature space, for this highly unbalanced multiclass problem of plant signal classification due to different chemical stress. Difference in the classification performances in the high vs. reduced dimensions are also quantified using the multivariate analysis of variance (MANOVA) hypothesis testing. Our findings have potential real-world applications in precision agriculture for exploring multiclass classification problems with highly unbalanced datasets, employing a combination of existing machine learning algorithms. This work also advances existing studies on environmental pollution level monitoring using plant electrophysiological data.
Why it matches plant phenotyping methods植物電気生理信号から化学ストレス状態を抽出・分類する統計解析パイプラインが研究の中心であり、植物の生理状態を対象とする実質的なフェノタイピング手法である。
abstractwe have presented a statistical analysis pipeline to tackle a multiclass environmental stimuli classification problem with unbalanced plant electrophysiological data.
Reproduction assets foundThe paper's plant electrophysiological dataset (electrical signals from tomato/cabbage plants under NaCl, H2SO4, and O3 stimuli, used for feature extraction and classification) is publicly available via the PLEASED project on MEGA, with an explicit Data Availability statement and URL matching an allowed URL. No author-Dataset · publice of infection or pollutant it is, thereby inducing a timely cure or precaution which may save the farmers lots of money. Such an intelligent system will contribute towards further modernizing current practices in the precision agriculture methods.
Data Availability
The experimental data are available in the PLEASED website at: https://mega.nz/folder/DoJHzDYR#a8LwJy3fYb06dplqV3UcoA .
Funding Statement
The work reported in this paper used the open dataset which was supported by the project PLants Employed as SEnsor Devices (PLEASED), EC grant agreement number: 296582. SD was partially supported by the European Regional Development Fund (ERDF) Deep Digital Cornwall project number: 05R18P02820.Open asset ↗lines:424-453Code / dataset availability confirmedEurope PMC · checked 15 Sept 2026
Cultivation of wild rocket [ Diplotaxis tenuifolia (L.) D.C.] as a baby-leaf vegetable for the high-convenience food chain is constantly growing due to its nutritional and taste qualities. As is well known, these crops are particularly exposed to soil-borne fungal diseases and need to be effectively protected. At present, wild rocket disease management is performed by using permitted synthetic fungicides or through the application of agro-ecological and biological methods that must be optimized. In this regard, the implementation of innovative digital-based technologies, such as infrared thermography (IT), as supporting systems to decision-making processes is welcome. In this work, leaves belonging to wild rocket plants inoculated with the soil-borne pathogens Rhizoctonia solani Kühn and Sclerotinia sclerotiorum (Lib.) de Bary were analyzed and monitored by both active and passive thermographic methods and compared with visual detection. A comparison between the thermal analysis carried out in both medium (MWIR)- and long (LWIR)-wave infrared was made and discussed. The results achieved highlight how the monitoring based on the use of IT is promising for carrying out an early detection of the rot diseases induced by the investigated pathogens, allowing their detection in 3-6 days before the canopy is completely wilted. Active thermal imaging has the potential to detect early soil-borne rotting diseases.
Why it matches plant phenotyping methods野生ルッコラの感染植物を対象に、能動・受動熱画像法を比較し、熱画像による病害症状の早期検出性能を評価しており、植物状態の取得手法が中心である。
abstractleaves belonging to wild rocket plants inoculated with the soil-borne pathogens Rhizoctonia solani Kühn and Sclerotinia sclerotiorum (Lib.) de Bary were analyzed and monitored by both active and passive thermographic methods and compared with visual detection.
Reproduction assets foundThe paper's supplementary materials (hosted publicly by MDPI) contain the paper-specific thermographic images of inoculated vs. control wild rocket leaves (Figures S1–S5) and the Right Prevision (RP%) calculation underlying the AT analysis. No separate phenotype dataset deposit or author analysis code repository is披露; Supplement · public–6 days before they are detectable by visual analysis. Our results confirm the potential of these techniques in monitoring wild rocket for the preventive detection of soil diseases with the aim of avoiding their extension and preserving the crops.
Supplementary Materials
The following supporting information can be downloaded at https://www.mdpi.com/article/10.3390/plants12081615/s1 , Figure S1: Examples of active thermography analysis for uninoculated control leaves of rocket plants; Figures S2–S5: Examples of active thermography analysis for leaves of rocket plants inoculated with treatments T1, T2, T3, and T4. Right Prevision (RP%) calculation.
Click here for additional data file.
Author COpen asset ↗lines:153-168Code / dataset availability confirmedEurope PMC · checked 14 Sept 2026
Many studies proposed the use of stable carbon isotope ratio (δ 13 C) as a predictor of abiotic stresses in plants, considering only drought and nitrogen deficiency without further investigating the impact of other nutrient deficiencies, that is, phosphorus (P) and/or iron (Fe) deficiencies. To fill this knowledge gap, we assessed the δ 13 C of barley ( Hordeum vulgare L.), cucumber ( Cucumis sativus L.), maize ( Zea mays L.), and tomato ( Solanum lycopersicon L.) plants suffering from P, Fe, and combined P/Fe deficiencies during a two-week period using an isotope-ratio mass spectrometer. Simultaneously, plant physiological status was monitored with an infra-red gas analyzer. Results show clear contrasting time-, treatment-, species-, and tissue-specific variations. Furthermore, physiological parameters showed limited correlation with δ 13 C shifts, highlighting that the plants' δ 13 C, does not depend solely on photosynthetic carbon isotope fractionation/discrimination (Δ). Hence, the use of δ 13 C as a predictor is highly discouraged due to its inability to detect and discern different nutrient stresses, especially when combined stresses are present.
Why it matches plant phenotyping methodsδ13Cを用いた栄養ストレス予測法の有効性を複数作物で評価・検証しており、植物状態の推定手法の技術的妥当性が中心である。
titleδ 13 C as a tool for iron and phosphorus deficiency prediction in crops.
Reproduction assets foundThe paper's data availability statement points to a public GitHub repository containing the raw δ13C/physiology data and the analysis scripts used to generate figures, which is a paper-specific, publicly actionable asset.Code · publick Dr. Christian Ceccon for providing support for the isotope analysis.
DATA AVAILABILITY STATEMENT
The following information was supplied regarding data and code availability: the raw data, the version of the individual packages and scripts used to analyze the data and generate the figures of this study are available at GitHub: https://github.com/Fabio-Trevisan/13C-Experiment.git .
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Increases in phosphoenolpyruvate carboxylase activity in iron‐deficient sugar beet roots: Analysis of spatial localization and post‐translational modification
. Plant and Soil , 241 ( 1 ), 43 – 48 .
10.1023/A:1Open asset ↗Fabio-Trevisan/13C-Experiment · 13C-Experimentlines:309-505Code / dataset availability confirmedEurope PMC · checked 15 Sept 2026
Background Non-invasive reporter systems are powerful tools to query physiological and transcriptional responses in organisms. For example, fluorescent and bioluminescent reporters have revolutionized cellular and organismal assays and have been used to study plant responses to abiotic and biotic stressors. Integrated, cooled charge-coupled device (CCD) camera systems have been developed to image bioluminescent and fluorescent signals in a variety of organisms; however, these integrated long-term imaging systems are expensive. Results We have developed self-assembled systems for both growing and monitoring plant fluorescence and bioluminescence for long-term experiments under controlled environmental conditions. This system combines environmental growth chambers with high-sensitivity CCD cameras, multi-wavelength LEDs, open-source software, and several options for coordinating lights with imaging. This easy-to-assemble system can be used for short and long-term imaging of bioluminescent reporters, acute light-response, circadian rhythms, delayed fluorescence, and fluorescent-protein-based assays in vivo. Conclusions We have developed two self-assembled imaging systems that will be useful to researchers interested in continuously monitoring in vivo reporter systems in various plant species.
Why it matches plant phenotyping methods植物の蛍光・生物発光を長期的に画像取得する自作システムを開発しており、植物の生理状態・レポーター応答の非侵襲的測定が中心的な方法論的貢献である。
abstractWe have developed self-assembled systems for both growing and monitoring plant fluorescence and bioluminescence for long-term experiments under controlled environmental conditions.
Reproduction assets foundThe paper's authors explicitly state code availability for two public GitHub repositories: the LightsCameraAction µManager plug-in for coordinating lights with imaging (System 1) and the GreenhamLab/CCD_Imaging repository containing Python scripts for time-series acquisition and light scheduling plus sample data for biCode · publicd in µManager 2.0.0 to take a series of evenly-spaced images between two dates/times. Multiple time series can be acquired simultaneously by simply running multiple copies of the Python script with settings for each individual series. Documentation for installing and running the script is included in its README file on github ( https://github.com/GreenhamLab/CCD_Imaging ).
Heliospectra light scheduling for late-model lights
Starting in firmware version 3.0.0, Heliospectra removed their publicly-documented protocol for controlling their ELIXIA, DYNA, and EOS lights remotely. This renders inoperable the “LightsCameraAction” µManager plug-in used in System 1. As such, the only method available Open asset ↗GreenhamLab/CCD_Imaginglines:149-158Code / dataset availability confirmedEurope PMC · OpenAlex · Crossref · checked 14 Sept 2026
Wheat ( Triticum aestivum L.) is one of the most important crops as it provides 20% of calories and proteins to the human population. To overcome the increasing demand in wheat grain production, there is a need for a higher grain yield, and this can be achieved in particular through an increase in the grain weight. Moreover, grain shape is an important trait regarding the milling performance. Both the final grain weight and shape would benefit from a comprehensive knowledge of the morphological and anatomical determinism of wheat grain growth. Synchrotron-based phase-contrast X-ray microtomography (X-ray µCT) was used to study the 3D anatomy of the growing wheat grain during the first developmental stages. Coupled with 3D reconstruction, this method revealed changes in the grain shape and new cellular features. The study focused on a particular tissue, the pericarp, which has been hypothesized to be involved in the control of grain development. We showed considerable spatio-temporal diversity in cell shape and orientations, and in tissue porosity associated with stomata detection. These results highlight the growth-related features rarely studied in cereal grains, which may contribute significantly to the final grain weight and shape.
Why it matches plant phenotyping methodsシンクロトロンX線マイクロCTと3D再構成を中核に、発達中コムギ粒の3D形状・細胞形態・組織空隙を抽出しており、植物器官の形態表現型取得が中心である。
abstractSynchrotron-based phase-contrast X-ray microtomography (X-ray µCT) was used to study the 3D anatomy of the growing wheat grain during the first developmental stages.
Reproduction assets found保存済みの本文根拠を更新済みルールで再検証し、公開資産1件を確認しました。Code · publicThe development was integrated into the Imago software, which is
freely available at https://github.com/SciCompJ/Imago (accessed on 21 February 2023).Open asset ↗SciCompJ/Imagopdf-page:23 lines:1-59Code / dataset availability confirmedEurope PMC · checked 7 Sept 2026
PotatoMRI / PETTissueClassificationWater status / transpiration
Magnetic Resonance Imaging is a powerful non-destructive tool in the study of plant tissues. For potato tubers, it greatly assists the study of tissue defects and tissue evolution during storage. This paper describes the MRI analysis of potato tubers with internal defects in their flesh tissue at eight sampling dates from 14 to 33 weeks after harvest. Spatialized multi-exponential T2 relaxometry was used to generate bi-exponential T2 maps, coupled with a classification scheme to identify the different T2 homogeneous zones within the tubers. Six classes with statistically different relaxation parameters were identified at each sampling date, allowing the defects and the pith and cortex tissues to be detected. A further distinction could be made between three constitutive elements within the flesh, revealing the heterogeneity of this particular tissue. Relaxation parameters for each class and their evolution during storage were successfully analyzed. The work demonstrated the value of MRI for detailed non-invasive plant tissue characterization.
Why it matches plant phenotyping methodsMRIと空間化T2緩和解析を用いて、ジャガイモ塊茎の組織・内部欠陥を非破壊で分類・特性評価する方法が研究の中心であり、植物器官の状態を直接推定している。
abstractSpatialized multi-exponential T2 relaxometry was used to generate bi-exponential T2 maps, coupled with a classification scheme to identify the different T2 homogeneous zones within the tubers.
Reproduction assets foundThe paper's MRI T2 relaxometry data (potato tuber images and relaxation measurements) are openly deposited in a public repository (Recherche Data Gouv, DOI 10.57745/DR2GSS), as stated in the Data Availability Statement. The supplementary material contains only result figures, not datasets or code; no analysis code or模型Dataset · publicThe MRI data presented in this study are openly available at: https://doi.org/10.57745/DR2GSS (accessed on 29 January 2023).Open asset ↗10.57745/DR2GSSlines:388-401Code / dataset availability confirmedEurope PMC · checked 7 Sept 2026
Leaves grown at different light intensities exhibit considerable differences in physiology, morphology and anatomy. Because plant leaves develop over three dimensions, analyses of the leaf structure should account for differences in lengths, surfaces, as well as volumes. In this manuscript, we set out to disentangle the mesophyll surface area available for diffusion per leaf area ( S m,LA ) into underlying one-, two- and three-dimensional components. This allowed us to estimate the contribution of each component to S m,LA , a whole-leaf trait known to link structure and function. We introduce the novel concept of a 'stomatal vaporshed,' i.e. the intercellular airspace unit most closely connected to a single stoma, and use it to describe the stomata-to-diffusive-surface pathway. To illustrate our new theoretical framework, we grew two cultivars of Vitis vinifera L. under high and low light, imaged 3D leaf anatomy using microcomputed tomography (microCT) and measured leaf gas exchange. Leaves grown under high light were less porous and thicker. Our analysis showed that these two traits and the lower S m per mesophyll cell volume ( S m,Vcl ) in sun leaves could almost completely explain the difference in S m,LA . Further, the studied cultivars exhibited different responses in carbon assimilation per photosynthesizing cell volume ( A Vcl ). While Cabernet Sauvignon maintained A Vcl constant between sun and shade leaves, it was lower in Blaufränkisch sun leaves. This difference may be related to genotype-specific strategies in building the stomata-to-diffusive-surface pathway.
Why it matches plant phenotyping methods3D葉解剖をmicroCTで画像化し、葉の拡散面積関連形質を分解・推定する新しい理論枠組みを提示しており、表現型取得・解析法が研究の中心である。
abstractwe set out to disentangle the mesophyll surface area available for diffusion per leaf area ( S m,LA ) into underlying one-, two- and three-dimensional components.
Reproduction assets foundThe paper's Data Availability statement explicitly deposits all raw and segmented microCT imaging data plus extracted trait data on Zenodo, and the vaporshed-extraction analysis code in the public leaf-traits-microct GitHub repository. Both are paper-specific, public, and actionable.Dataset · publicAll imaging data (raw microCT scans and segmented scans) and data extracted from those images are available on Zenodo ( https://doi.org/10.5281/zenodo.5994663 ).Open asset ↗Zenodo · 10.5281/zenodo.5994663lines:219-265Code / dataset availability confirmedEurope PMC · checked 14 Sept 2026
Lodging impedes the successful cultivation of cereal crops. Complex anatomy, morphology and environmental interactions make identifying reliable and measurable traits for breeding challenging. Therefore, we present a unique collaboration among disciplines for plant science, modelling and simulations, and experimental fluid dynamics in a broader context of breeding lodging resilient wheat and oat. We ran comprehensive wind tunnel experiments to quantify the stem bending behaviour of both cereals under controlled aerodynamic conditions. Measured phenotypes from experiments concluded that the wheat stems response is stiffer than the oat. However, these observations did not in themselves establish causal relationships of this observed behaviour with the physical traits of the plants. To further investigate we created an independent finite-element simulation framework integrating our recently developed multi-scale material modelling approach to predict the mechanical response of wheat and oat stems. All the input parameters including chemical composition, tissue characteristics and plant morphology have a strong physiological meaning in the hierarchical organization of plants, and the framework is free from empirical parameter tuning. This feature of our simulation framework reveals the multi-scale origin of the observed wide differences in the stem strength of both cereals that would not have been possible with purely experimental approach.
Why it matches plant phenotyping methods風洞実験と有限要素シミュレーションを統合し、植物茎の曲げ挙動・強度という表現型を予測・説明する手法が研究の中心である。
abstractWe ran comprehensive wind tunnel experiments to quantify the stem bending behaviour of both cereals under controlled aerodynamic conditions.
Reproduction assets foundThe paper's wind tunnel plant phenotyping assets are publicly available: raw wind tunnel videos of the cereal plants (DRUM repository), the authors' video-analysis scripts (GitHub), and the multi-scale finite-element model code (Dryad). Supplementary material with sample video and analysis details is on Figshare.Code · publiche scripts used and location of the data analysed from the wind tunnel experiment. Multi-scale material model codes in Python, Abaqus model file and python script for automatized simulations at different wind speed levels pertaining to multi-scale finite-element model simulations are available from the Dryad Digital Repository: https://doi.org/10.5061/dryad.612jm644j [ 53 ].
Supplementary material is available online [ 54 ].
Authors' contributionsOpen asset ↗Dryad Digital Repository · 10.5061/dryad.612jm644jlines:229-239Code / dataset availability confirmedOpenAlex · checked 14 Sept 2026
Abstract has not been obtained from indexed metadata or an accessible article page.
Why it matches plant phenotyping methodsMultispeQ測定に基づく遺伝子型の特性評価・順位付けを行うRパッケージ基盤であり、植物の生理形質取得・解析を支援する方法論的ツールが中心と判断できる。
titleRankspeQ: An R Package Platform for Genotype Characterization and Performance-Ranking Based on MultispeQ Measurements
Reproduction assets foundThe paper's authors' analysis code (RankspeQ R package) is publicly available on GitHub, and the MultispeQ phenotype dataset used in the case study ('19-06 BASE100') is publicly hosted on PhotosynQ. Phenotype data is not on request; only other data availability is stated as on request.Code · publicll include new
algorithms (e.g. trait heritability), generalization to other species and a shiny interface to make the software user
friendly.
Required metadata
Current code version
Nr Code metadata description Please fill in this column
C1 Current code version V1.0
C2 Permanent link to code/repository used of
this code version
https://github.com/jssotob/RankspeQ
C3 Code Ocean compute capsule Not applicable
C4 Legal Code License MIT + file LICENSE
C5 Code versioning system used Git
C6 Software code languages, tools, and services
used
R
C7 Compilation requirements, operating
environments & dependencies
R ≥ 4.0
RStudio≥1.2.5
C8 If available Link to developer
documentation/manual
Not appliOpen asset ↗jssotob/RankspeQpdf-raw-page:1 lines:1-81Code / dataset availability confirmedEurope PMC · checked 8 Sept 2026
Leaf pigments, including chlorophylls and carotenoids, are important biochemical indicators of plant photosynthesis and photoprotection. In this study, we developed, optimized, and validated a sequential extraction and liquid chromatography-diode array detection method allowing for the simultaneous quantification of the main photosynthetic pigments, including chlorophyll a, chlorophyll b, β-carotene, lutein, neoxanthin, and the xanthophyll cycle (VAZ), as well as the characterization of plant pigment derivatives. Chromatographic separation was accomplished with the newest generation of core-shell columns revealing numerous pigment derivatives. The sequential extraction allowed for a better recovery of the main pigments (+25 % chlorophyll a, +30 % chlorophyll b, +42 % β-carotene, and 61% xanthophylls), and the characterization of ca. 5.3 times more pigment derivatives (i.e., up to 62 chlorophyll and carotenoid derivatives including isomers) than with a single-step extraction. A broad working range of concentrations (300-2,000 ng.mL -1 ) was achieved for most pigments and their derivatives and the limit of detection was as low as a few nanograms per milliliter. The method also showed adequate trueness (RSD Fagus sylvatica L . leaves, pigment derivatives revealed a high within-individual tree variability throughout the growing season that could not be detected using the main photosynthetic pigments alone, eventually showing that the method allowed for the monitoring of pigment dynamics at unprecedented detail.
Why it matches plant phenotyping methods葉の光合成色素を植物の生理状態・動態として定量する分析法を開発、最適化、検証しており、表現型取得法が研究の中心である。
abstractIn this study, we developed, optimized, and validated a sequential extraction and liquid chromatography-diode array detection method allowing for the simultaneous quantification of the main photosynthetic pigments
Reproduction assets found本文中に内容が明示された植物フェノタイピング関連の補足表と、その公開リンクを確認しました。Supplement · publicSupplementary Table 1 ) and calibrate optical measurements performed with a field spectroradiometer or airborne spectral sensors (Croft and Chen, 2018 ).Open asset ↗lines:285-286Code / dataset availability confirmedCrossref · checked 15 Sept 2026
In plant–insect interactions, calcium (Ca2+) variations are among the earliest events associated with the plant perception of biotic stress. Upon herbivory, Ca2+ waves travel long distances to transmit and convert the local signal to a systemic defense program. Reactive oxygen species (ROS), Ca2+ and electrical signaling are interlinked to form a network supporting rapid signal transmission, whereas the Ca2+ message is decoded and relayed by Ca2+-binding proteins (including calmodulin, Ca2+-dependent protein kinases, annexins and calcineurin B-like proteins). Monitoring the generation of Ca2+ signals at the whole plant or cell level and their long-distance propagation during biotic interactions requires innovative imaging techniques based on sensitive sensors and using genetically encoded indicators. This review summarizes the recent advances in Ca2+ signaling upon herbivory and reviews the most recent Ca2+ imaging techniques and methods.
Why it matches plant phenotyping methods植物のCa2+シグナルを測定するイメージング技術・手法をレビューしており、植物の生理状態の取得方法が中心的に扱われている。
abstractMonitoring the generation of Ca2+ signals at the whole plant or cell level and their long-distance propagation during biotic interactions requires innovative imaging techniques based on sensitive sensors and using genetically encoded indicators.
Reproduction assets foundThe paper's own supplementary material contains Video S1, the authors' calcium imaging time-course of Spodoptera littoralis feeding on R-GECO1-expressing Arabidopsis, publicly downloadable from the MDPI supplementary URL.Supplement · publiccknowledgments
The authors whish to thank A. Costa, M. Grenzi and NOLIMITS, an advanced imaging facility established by the University of Milan, for providing the video and images of the fast calcium imaging analyses upon S. littoralis herbivory.
Supplementary Materials
The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/plants11202689/s1 , Video S1: Rapid Ca 2+ signals generated when Spodoptera littoralis feeds on Arabidopsis thaliana expressing the genetically encoded R-GECO1 sensor.
Click here for additional data file.
Author Contributions
Conceptualization, M.E.M.; methodology, M.E.M.; investigation, A.S.P. and M.E.M.; resources, M.E.M.; writinOpen asset ↗lines:66-85Code / dataset availability confirmedEurope PMC · checked 14 Sept 2026
Genomic prediction has revolutionized crop breeding despite remaining issues of transferability of models to unseen environmental conditions and environments. Usage of endophenotypes rather than genomic markers leads to the possibility of building phenomic prediction models that can account, in part, for this challenge. Here, we compare and contrast genomic prediction and phenomic prediction models for 3 growth-related traits, namely, leaf count, tree height, and trunk diameter, from 2 coffee 3-way hybrid populations exposed to a series of treatment-inducing environmental conditions. The models are based on 7 different statistical methods built with genomic markers and ChlF data used as predictors. This comparative analysis demonstrates that the best-performing phenomic prediction models show higher predictability than the best genomic prediction models for the considered traits and environments in the vast majority of comparisons within 3-way hybrid populations. In addition, we show that phenomic prediction models are transferrable between conditions but to a lower extent between populations and we conclude that chlorophyll a fluorescence data can serve as alternative predictors in statistical models of coffee hybrid performance. Future directions will explore their combination with other endophenotypes to further improve the prediction of growth-related traits for crops.
Why it matches plant phenotyping methodsクロロフィル蛍光データを用いたフェノミック予測モデルを構築・比較し、成長形質の予測性能と条件間・集団間の転移性を評価しているため、植物フェノタイピング手法が中心である。
abstractThe models are based on 7 different statistical methods built with genomic markers and ChlF data used as predictors.
Reproduction assets foundThe paper's Data availability statement explicitly states that all code and datasets (including the ChlF phenomic data and growth-trait phenotypes used for GP/PP modeling) are freely available at the authors' public GitHub repository https://github.com/alainmbebi/GP-PP, which matches an allowed URL. Other URLs (BGLR CRCode · publicr and is an excellent proxy for photosynthesis in coffee, making it a tool of choice for assessing the vigor of a genotype, which the present study tends to prove.
Data availability
We implemented all statistical models using R programming language; the codes and all data sets used in the current study are freely available from https://github.com/alainmbebi/GP-PP .
Supplemental material is available at G3 online.
Supplementary Material
jkac170_Supplementary_Data_File_S1
Click here for additional data file.
jkac170_Supplementary_Data_File_S2
Click here for additional data file.
Acknowledgments
We would like to thank the 2 anonymous reviewers for their suggestions and comments.
Funding
ThOpen asset ↗alainmbebi/GP-PPlines:876-910Code / dataset availability confirmedEurope PMC · Crossref · checked 8 Sept 2026
Plants are complex organisms subject to variable environmental conditions, which influence their physiology and phenotype dynamically. We propose to interpret plants as reservoirs in physical reservoir computing. The physical reservoir computing paradigm originates from computer science; instead of relying on Boolean circuits to perform computations, any substrate that exhibits complex non-linear and temporal dynamics can serve as a computing element. Here, we present the first application of physical reservoir computing with plants. In addition to investigating classical benchmark tasks, we show that Fragaria × ananassa (strawberry) plants can solve environmental and eco-physiological tasks using only eight leaf thickness sensors. Although the results indicate that plants are not suitable for general-purpose computation but are well-suited for eco-physiological tasks such as photosynthetic rate and transpiration rate. Having the means to investigate the information processing by plants improves quantification and understanding of integrative plant responses to dynamic changes in their environment. This first demonstration of physical reservoir computing with plants is key for transitioning towards a holistic view of phenotyping and early stress detection in precision agriculture applications since physical reservoir computing enables us to analyse plant responses in a general way: environmental changes are processed by plants to optimise their phenotype.
Why it matches plant phenotyping methods植物の葉厚センサーを用いた物理リザバーコンピューティングを提案・実証し、光合成速度や蒸散速度などの生理形質推定とストレス早期検出への応用を中心に扱うため、植物フェノタイピング手法として適格。
abstractHere, we present the first application of physical reservoir computing with plants.
Reproduction assets foundThe paper's Data availability statement explicitly deposits the datasets generated/analysed (leaf thickness sensor traces, environmental variables, gas exchange data) on Zenodo and the analysis data/code on a public GitHub repository, both with exact URLs matching allowed_urls.Dataset · publicDatasets generated and/or analysed during the current study are available in the Zenodo repository, https://doi.org/10.5281/zenodo.4264624 .Open asset ↗Zenodo · 10.5281/zenodo.4264624lines:153-237Code / dataset availability confirmedEurope PMC · checked 8 Sept 2026
Canopy photosynthesis is the sum of photosynthesis of all above-ground photosynthetic tissues. Quantitative roles of nonfoliar tissues in canopy photosynthesis remain elusive due to methodology limitations. Here, we develop the first complete canopy photosynthesis model incorporating all above-ground photosynthetic tissues and validate this model on wheat with state-of-the-art gas exchange measurement facilities. The new model precisely predicts wheat canopy gas exchange rates at different growth stages, weather conditions, and canopy architectural perturbations. Using the model, we systematically study (1) the contribution of both foliar and nonfoliar tissues to wheat canopy photosynthesis and (2) the responses of wheat canopy photosynthesis to plant physiological and architectural changes. We found that (1) at tillering, heading, and milking stages, nonfoliar tissues can contribute ~4, ~32, and ~50% of daily gross canopy photosynthesis ( A cgross ; ~2, ~15, and ~-13% of daily net canopy photosynthesis, A cnet ) and absorb ~6, ~42, and ~60% of total light, respectively; (2) under favorable condition, increasing spike photosynthetic activity, rather than enlarging spike size or awn size, can enhance canopy photosynthesis; (3) covariation in tissue respiratory rate and photosynthetic rate may be a major factor responsible for less than expected increase in daily A cnet ; and (4) in general, erect leaves, lower spike position, shorter plant height, and proper plant densities can benefit daily A cnet . Overall, the model, together with the facilities for quantifying plant architecture and tissue gas exchange, provides an integrated platform to study canopy photosynthesis and support rational design of photosynthetically efficient wheat crops.
Why it matches plant phenotyping methods小麦の葉・非葉器官・群落の光合成と植物体構造を定量する統合モデルを開発し、ガス交換測定施設で検証しているため、植物フェノタイピング手法が研究の中心である。
abstractHere, we develop the first complete canopy photosynthesis model incorporating all above-ground photosynthetic tissues and validate this model on wheat with state-of-the-art gas exchange measurement facilities.
Reproduction assets foundThe paper explicitly states that the source code and user manual for the 3dCAP-wheat framework (used for plant architecture extraction, 3D reconstruction, ray tracing, and canopy photosynthesis computation) are freely available on GitHub at the authors' public URL.Code · publicSource code used for this study, together with the user manual, are freely available for noncommercial use at https://github.com/rootchang/3dCAP-wheat .Open asset ↗rootchang/3dCAP-wheatlines:162-298Code / dataset availability confirmedEurope PMC · bioRxiv · checked 15 Sept 2026
Summary Plant ecologists use functional traits to describe how plants respond to and influence their environment. Reflectance spectroscopy can provide rapid, non-destructive estimates of leaf traits, but it remains unclear whether general trait-spectra models can yield accurate estimates across functional groups and ecosystems. We measured leaf spectra and 22 structural and chemical traits for nearly 2000 samples from 104 species. These samples span a large share of known trait variation and represent several functional groups and ecosystems. We used partial least-squares regression (PLSR) to build empirical models for estimating traits from spectra. Within the dataset, our PLSR models predicted traits like leaf mass per area (LMA) and leaf dry matter content (LDMC) with high accuracy ( R 2 >0.85; %RMSE<10). Models for most chemical traits, including pigments, carbon fractions, and major nutrients, showed intermediate accuracy ( R 2 =0.55-0.85; %RMSE=12.7-19.1). Micronutrients such as Cu and Fe showed the poorest accuracy. In validation on external datasets, models for traits like LMA and LDMC performed relatively well, while carbon fractions showed steep declines in accuracy. We provide models that produce fast, reliable estimates of several widely used functional traits from leaf reflectance spectra. Our results reinforce the potential uses of spectroscopy in monitoring plant function around the world.
Why it matches plant phenotyping methods葉の反射スペクトルから構造・化学的形質を推定する分光センシングとPLSRモデルを構築し、外部データで検証しており、植物形質取得法が研究の中心です。
abstractReflectance spectroscopy can provide rapid, non-destructive estimates of leaf traits
Reproduction assets foundThe paper's fresh-leaf spectral data are publicly available via the CABO data portal, and the authors' analysis scripts are on GitHub. EcoSIS/EcoSML uploads are promised only upon publication and are not yet actionable.Dataset · publicected and curated the spectral and trait data. SK analyzed the data,
597 interpreted the results, and wrote the first draft with substantial contributions from EL. All authors
598 contributed to further revisions of the paper.
599
600 Data availability
601 All fresh-leaf spectral data are available through the CABO data portal (https://data.caboscience.org/leaf).
602 Upon publication, we will also upload all spectral data, as well as metadata and trait data, to theOpen asset ↗pdf-layout-page:38 lines:1-58Code · publicnder a CC-BY 4.0 International license.
603 Ecological Spectral Information System (EcoSIS, https://ecosis.org/), and upload models to the
604 Ecological Spectral Model Library (EcoSML, https://ecosml.org/). At that stage, we will update this
605 section accordingly. Analysis scripts are available as a repository on GitHub
606 (https://github.com/ShanKothari/CABO-trait-models).Open asset ↗ShanKothari/CABO-trait-modelspdf-layout-page:39 lines:1-14Code / dataset availability confirmedEurope PMC · checked 15 Sept 2026
The existing methods of callose quantification include epifluorescence microscopy and fluorescence spectrophotometry of aniline blue-stained callose particles, immuno-fluorescence microscopy and indirect assessment of both callose synthase and β-(1,3)-glucanase enzyme activities. Some of these methods are laborious, time consuming, not callose-specific, biased and require high technical skills. Here, we describe a method of callose quantification based on Sandwich Enzyme-Linked Immunosorbent Assay (S-ELISA). Tissue culture-derived banana plantlets were inoculated with Xanthomonas campestris pv. musacearum ( Xcm ) bacteria as a biotic stress factor inducing callose production. Banana leaf, pseudostem and corm tissue samples were collected at 14 days post-inoculation (dpi) for callose quantification. Callose levels were significantly different in banana tissues of Xcm -inoculated and control groups except in the pseudostems of both banana genotypes. The method described here could be applied for the quantification of callose in different plant species with satisfactory level of specificity to callose, and reproducibility. Additionally, the use of 96-well plate makes this method suitable for high throughput callose quantification studies with minimal sampling and analysis biases. We provide step-by-step detailed descriptions of the method.
Why it matches plant phenotyping methods植物組織中のカロース量を定量するELISA法を開発・再現性評価し、高スループット測定への適用性を示した研究であり、植物状態の取得方法が中心です。
abstractHere, we describe a method of callose quantification based on Sandwich Enzyme-Linked Immunosorbent Assay (S-ELISA).
Reproduction assets foundThe paper's supplementary material (Table S1) publicly hosts the callose quantification measurements (concentrations in leaves, pseudostems, corms of Xcm-inoculated vs. control banana plantlets) underlying this study's analysis. No author analysis code, images, or trained models are deposited; the R statistical packageDataset · publicor up to 12 months).
Dissolve para-nitrophenyl phosphate (pNPP) in substrate buffer to a working concentration of 1 mg/mL.
Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Supplementary Materials
The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/mps5040054/s1 , Table S1: Analysis of callose concentration in the leaves, pseudostems and corms of banana plants inoculated and non-inoculated (control) with Xcm (Independent sample t-test, α ≤ 0.05).
Click here for additional data file.
Author Contributions
Conceptualization, A.K.T.; methodology, A.S.M., A.K.T. and P.S.; validatiOpen asset ↗lines:167-297Code / dataset availability confirmedbioRxiv · checked 8 Sept 2026
X-ray fluorescence spectroscopy (XRF) is a powerful technique for the in vivo assessment of plant tissues. However, the potential X-ray exposure damages might affect the structure and elemental composition of living plant tissues leading to artefacts in the recorded data. Herein, we exposed soybean (Glycine max (L.) Merrill) leaves to several X-ray doses through a polychromatic benchtop microprobe X-ray fluorescence spectrometer, modulating the photon flux by adjusting either the beam size, focus, or exposure time. The structure, ultrastructure and physiological responses of the irradiated plant tissues were investigated through light and transmission electron microscopy (TEM). Depending on the dose, the X-ray exposure induced decreased K and X-ray scattering intensities, and increased Ca, P, and Mn signals on soybean leaves. Anatomical analysis indicated necrosis of the epidermal and mesophyll cells on the irradiated spots, where TEM images revealed the collapse of cytoplasm and cell-wall breaking. Furthermore, the histochemical analysis detected the production of reactive oxygen species, as well as inhibition of chlorophyll autofluorescence in these areas. Under certain X-ray exposure conditions, e.g., high photon flux and exposure time, XRF measurements may affect the soybean leaves structures, elemental composition, and cellular ultrastructure, and induce programmed cell death. These results shed light on the characterization of the radiation damage, and thus, help to assess the X-ray radiation limits and strategies for in vivo for XRF analysis. HighlightBy exposing soybean leaves to several X-ray doses, we show that the characteristic X-ray induced elemental changes stem from plants physiological signalling or responses rather than only sample dehydration.
Why it matches plant phenotyping methods植物組織のin vivo XRF測定における放射線損傷と測定アーティファクトを評価し、適用限界と測定条件を検証する研究であり、フェノタイピング手法の技術的妥当性が中心です。
abstractX-ray fluorescence spectroscopy (XRF) is a powerful technique for the in vivo assessment of plant tissues.
Reproduction assets foundThe paper's DATA AVAILABILITY section states the raw data (XRF spectra/maps and imaging measurements) are fully available on Figshare at the authors' public DOI, which matches an allowed URL.Dataset · publicThe raw data herein presented is fully available at Figshare
repository: https://doi.org/10.6084/m9.figshare.1858438Open asset ↗Figshare · 10.6084/m9.figshare.1858438pdf-page:6 lines:1-93Code / dataset availability confirmedEurope PMC · checked 15 Sept 2026
As a key variable to characterize the process of crop growth, the aboveground biomass (AGB) plays an important role in crop management and production. Process-based models and remote sensing are two important scientific methods for crop AGB estimation. In this study, we combined observations from agricultural meteorological stations and county-level yield statistics to calibrate a process-based crop growth model for winter wheat. After that, we assimilated a reprocessed temporal-spatial filtered MODIS Leaf Area Index product into the model to derive the 1 km daily AGB dataset of the main winter wheat producing areas in China from 2007 to 2015. The validation using ground measurements also suggests the derived AGB dataset agrees well with the filed observations, i.e., the R 2 is above 0.9, and the root mean square error (RMSE) reaches 1,377 kg·ha -1 . Compared to county-level statistics during 2007-2015, the ranges of R 2 , RMSE, and mean absolute percentage error (MAPE) are 0.73~0.89, 953~1,503 kg·ha -1 , and 8%~12%, respectively. We believe our dataset can be helpful for relevant studies on regional agricultural production management and yield estimation.
Why it matches plant phenotyping methods冬小麦の地上部バイオマスという明示的な植物形質を、作物モデルとMODIS LAIデータ同化で広域推定し、地上測定および統計値で検証したデータセット研究であり、形質推定手法と検証が中心です。
abstractwe assimilated a reprocessed temporal-spatial filtered MODIS Leaf Area Index product into the model to derive the 1 km daily AGB dataset of the main winter wheat producing areas in China from 2007 to 2015.
Reproduction assets foundThe paper's winter wheat AGB analysis code is publicly available on the authors' GitHub repository, and the MODIS LAI product assimilated into WOFOST is publicly accessible via the Land-Atmosphere Interaction Research Group website. The generated AGB dataset itself is on figshare (10.6084/m9.figshare.16680784.v3), but那Code · publicHuang and Jianxi Huang designed the research, performed the analysis, and wrote the paper; Xuecao Li, Wen Zhuo, Yantong Wu, Quandi Niu, Wei Su, and Wenping Yuan edited and revised the manuscript.
Code availability
Python scripts that implement model calibration, data assimilation, dataset generation, and mapping are available ( https://github.com/paperoses/CHN_Winter_Wheat_AGB ). Further questions can be directed towards Hai Huang (haihuang@cau.edu.cn).
Competing interests
The authors declare no competing interests.
Footnotes
Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Supplementary information
The onOpen asset ↗paperoses/CHN_Winter_Wheat_AGBlines:117-143Code / dataset availability confirmedEurope PMC · checked 14 Sept 2026
Photosynthesis is a key target to improve crop production in many species including soybean [Glycine max (L.) Merr.]. A challenge is that phenotyping photosynthetic traits by traditional approaches is slow and destructive. There is proof-of-concept for leaf hyperspectral reflectance as a rapid method to model photosynthetic traits. However, the crucial step of demonstrating that hyperspectral approaches can be used to advance understanding of the genetic architecture of photosynthetic traits is untested. To address this challenge, we used full-range (500-2,400 nm) leaf reflectance spectroscopy to build partial least squares regression models to estimate leaf traits, including the rate-limiting processes of photosynthesis, maximum Rubisco carboxylation rate, and maximum electron transport. In total, 11 models were produced from a diverse population of soybean sampled over multiple field seasons to estimate photosynthetic parameters, chlorophyll content, leaf carbon and leaf nitrogen percentage, and specific leaf area (with R2 from 0.56 to 0.96 and root mean square error approximately <10% of the range of calibration data). We explore the utility of these models by applying them to the soybean nested association mapping population, which showed variability in photosynthetic and leaf traits. Genetic mapping provided insights into the underlying genetic architecture of photosynthetic traits and potential improvement in soybean. Notably, the maximum Rubisco carboxylation rate mapped to a region of chromosome 19 containing genes encoding multiple small subunits of Rubisco. We also mapped the maximum electron transport rate to a region of chromosome 10 containing a fructose 1,6-bisphosphatase gene, encoding an important enzyme in the regeneration of ribulose 1,5-bisphosphate and the sucrose biosynthetic pathway. The estimated rate-limiting steps of photosynthesis were low or negatively correlated with yield suggesting that these traits are not influenced by the same genetic mechanisms and are not limiting yield in the soybean NAM population. Leaf carbon percentage, leaf nitrogen percentage, and specific leaf area showed strong correlations with yield and may be of interest in breeding programs as a proxy for yield. This work is among the first to use hyperspectral reflectance to model and map the genetic architecture of the rate-limiting steps of photosynthesis.
Why it matches plant phenotyping methods葉面ハイパースペクトル反射から光合成・葉形質を推定するモデルを構築し、精度評価と集団への適用を行っており、表現型取得・推定手法が研究の中心である。
abstractwe used full-range (500-2,400 nm) leaf reflectance spectroscopy to build partial least squares regression models to estimate leaf traits
Reproduction assets foundThe paper's leaf reflectance processing code (FieldSpec R Package, Zenodo DOI 10.5281/zenodo.6248237) and its paper-specific phenotype/reflectance data, PLSR model coefficients, and complete genetic mapping dataset are publicly available via the Genetics figshare supplemental repository (DOI 10.25386/genetics.19394693)Dataset · publicgenetic mapping and
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analyses can be found in File S18. The majority of lines and accessions used in this manuscript
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are available via GRIN (https://www.ars-grin.gov/) or by request from Soybase.org for the NAM
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lines (https://soybase.org/SoyNAM/SoyNAM_RIL_request.htm). Supplemental Material
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available at figshare: https://doi.org/10.25386/genetics.19394693
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Acknowledgements
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We thank Troy Cary, Chris Moller, and Noah Mitchell for help in setting up and maintaining the
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experimental plots, collecting data, and processing samples.
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Funding
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This work was supported by soybean checkoff funding from the United Soybean Board. ASS
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was supported by a post-doctoraOpen asset ↗figshare · 10.25386/genetics.19394693pdf-raw-page:39 lines:1-49Code / dataset availability confirmedEurope PMC · checked 8 Sept 2026
MaizeField / plotRootPhysiological trait estimationRoot system architectureStress response / tolerance
High-throughput, field-based characterization of root systems for hundreds of genotypes in thousands of plots is necessary for breeding and identifying loci underlying variation in root traits and their plasticity. We designed a large-scale sampling of root pulling force, the vertical force required to extract the root system from the soil, in a maize diversity panel under differing irrigation levels for two growing seasons. We then characterized the root system architecture of the extracted root crowns. We found consistent patterns of phenotypic plasticity for root pulling force for a subset of genotypes under differential irrigation, suggesting that root plasticity is predictable. Using genome-wide association analysis, we identified 54 SNPs as statistically significant for six independent root pulling force measurements across two irrigation levels and four developmental timepoints. For every significant GWAS SNP for any trait in any treatment and timepoint we conducted post hoc tests for genotype-by-environment interaction, using a mixed model ANOVA. We found that 8 of the 54 SNPs showed significant GxE. Candidate genes underlying variation in root pulling force included those involved in nutrient transport. Although they are often treated separately, variation in the ability of plant roots to sense and respond to variation in environmental resources including water and nutrients may be linked by the genes and pathways underlying this variation. While functional validation of the identified genes is needed, our results expand the current knowledge of root phenotypic plasticity at the whole plant and gene levels, and further elucidate the complex genetic architecture of maize root systems.
Why it matches plant phenotyping methods数百遺伝子型・数千区画を対象とする高スループットな圃場根系表現型測定を設計・適用し、根抜き力と根系構造を取得している。主目的は遺伝解析だが、表現型取得手法の大規模適用が実質的に記述されているため採録する。
abstractHigh-throughput, field-based characterization of root systems for hundreds of genotypes in thousands of plots is necessary for breeding and identifying loci underlying variation in root traits and their plasticity.
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 5 ); however, we saw no overlap in hits between our root traits and flowering, consistent with the lack of correlation in Figure 4 .Open asset ↗lines:330-340Code / dataset availability confirmedEurope PMC · checked 8 Sept 2026
Sorghum, a genetically diverse C 4 cereal, is an ideal model to study natural variation in photosynthetic capacity. Specific leaf nitrogen (SLN) and leaf mass per leaf area (LMA), as well as, maximal rates of Rubisco carboxylation ( V cmax ), phosphoenolpyruvate (PEP) carboxylation ( V pmax ), and electron transport ( J max ), quantified using a C 4 photosynthesis model, were evaluated in two field-grown training sets ( n = 169 plots including 124 genotypes) in 2019 and 2020. Partial least square regression (PLSR) was used to predict V cmax ( R 2 = 0.83), V pmax ( R 2 = 0.93), J max ( R 2 = 0.76), SLN ( R 2 = 0.82), and LMA ( R 2 = 0.68) from tractor-based hyperspectral sensing. Further assessments of the capability of the PLSR models for V cmax , V pmax , J max , SLN, and LMA were conducted by extrapolating these models to two trials of genome-wide association studies adjacent to the training sets in 2019 ( n = 875 plots including 650 genotypes) and 2020 ( n = 912 plots with 634 genotypes). The predicted traits showed medium to high heritability and genome-wide association studies using the predicted values identified four QTL for V cmax and two QTL for J max . Candidate genes within 200 kb of the V cmax QTL were involved in nitrogen storage, which is closely associated with Rubisco, while not directly associated with Rubisco activity per se . J max QTL was enriched for candidate genes involved in electron transport. These outcomes suggest the methods here are of great promise to effectively screen large germplasm collections for enhanced photosynthetic capacity.
Why it matches plant phenotyping methodsトラクター搭載ハイパースペクトルセンシングとPLSRにより光合成関連形質を推定し、独立試験でモデル性能を評価している。形質取得法の開発・検証と大規模スクリーニングへの応用が中心である。
abstractPartial least square regression (PLSR) was used to predict V cmax ( R 2 = 0.83), V pmax ( R 2 = 0.93), J max ( R 2 = 0.76), SLN ( R 2 = 0.82), and LMA ( R 2 = 0.68) from tractor-based hyperspectral sensing.
Reproduction assets foundThe authors state that all phenotypic data used to develop the PLSR models (ground truth Vcmax, Vpmax, Jmax, SLN, LMA and associated hyperspectral measurements) is publicly available via a UQ eSpace DOI. Genotypic marker data is only available upon request and is not a phenotyping asset. No author analysis code or URLsDataset · publicAll phenotypic data used to develop the models presented in this manuscript is available here: https://doi.org/10.48610/acbe0df .Open asset ↗10.48610/acbe0dflines:488-522Code / dataset availability confirmedEurope PMC · checked 8 Sept 2026
Improved accuracy of evapotranspiration (ET) estimation, including its partitioning between transpiration (T) and surface evaporation (E), is key to monitor agricultural water use in vineyards, especially to enhance water use efficiency in semi-arid regions such as California, USA. Remote-sensing methods have shown great utility in retrieving ET from surface energy balance models based on thermal infrared data. Notably, the two-source energy balance (TSEB) has been widely and robustly applied in numerous landscapes, including vineyards. However, vineyards add an additional complexity where the landscape is essentially made up of two distinct zones: the grapevine and the interrow, which is often seasonally covered by an herbaceous cover crop. Therefore, it becomes more complex to disentangle the various contributions of the different vegetation elements to total ET, especially through TSEB, which assumes a single vegetation source over a soil layer. As such, a remote-sensing-based three-source energy balance (3SEB) model, which essentially adds a vegetation source to TSEB, was applied in an experimental vineyard located in California's Central Valley to investigate whether it improves the depiction of the grapevine-interrow system. The model was applied in four different blocks in 2019 and 2020, where each block had an eddy-covariance (EC) tower collecting continuous flux, radiometric, and meteorological measurements. 3SEB's latent and sensible heat flux retrievals were accurate with an overall RMSD ~ 50 W/m 2 compared to EC measurements. 3SEB improved upon TSEB simulations, with the largest differences being concentrated in the spring season, when there is greater mixing between grapevine foliage and the cover crop. Additionally, 3SEB's modeled ET partitioning (T/ET) compared well against an EC T/ET retrieval method, being only slightly underestimated. Overall, these promising results indicate 3SEB can be of great utility to vineyard irrigation management, especially to improve T/ET estimations and to quantify the contribution of the cover crop to ET. Improved knowledge of T/ET can enhance grapevine water stress detection to support irrigation and water resource management. Supplementary information The online version contains supplementary material available at 10.1007/s00271-022-00787-x.
Why it matches plant phenotyping methodsリモートセンシングによる3SEBモデルを用いてブドウ樹・被覆作物の蒸発散分離を推定し、渦相関測定および既存モデルと比較検証している。水利用管理への応用を含むが、植物の生理状態推定手法の技術評価が中心である。
abstracta remote-sensing-based three-source energy balance (3SEB) model, which essentially adds a vegetation source to TSEB, was applied in an experimental vineyard located in California's Central Valley to investigate whether it improves the depiction of the grapevine-interrow system.
Reproduction assets foundThe paper applies the authors' 3SEB model to vineyard ET partitioning and explicitly points to the authors' public GitHub repository as the model source code. No public phenotype/trait datasets or trained models are deposited; the GRAPEX flux/radiometric data are described but no public URL is given.Code · publicRefer to Burchard-Levine et al. ( 2022 ) or the source code ( https://github.com/VicenteBurchard/3SEB ) for model details and specifications.Open asset ↗VicenteBurchard/3SEBlines:112-122Code / dataset availability confirmedEurope PMC · checked 8 Sept 2026
Ratiometric time-lapse FRET analysis requires a robust and accurate processing pipeline to eliminate bias in intensity measurements on fluorescent images before further quantitative analysis can be conducted. This level of robustness can only be achieved by supplementing automated tools with built-in flexibility for manual ad-hoc adjustments. FRET-IBRA is a modular and fully parallelized configuration file-based tool written in Python. It simplifies the FRET processing pipeline to achieve accurate, registered, and unified ratio image stacks. The flexibility of this tool to handle discontinuous image frame sequences with tailored configuration parameters further streamlines the processing of outliers and time-varying effects in the original microscopy images. FRET-IBRA offers cluster-based channel background subtraction, photobleaching correction, and ratio image construction in an all-in-one solution without the need for multiple applications, image format conversions, and/or plug-ins. The package accepts a variety of input formats and outputs TIFF image stacks along with performance measures to detect both the quality and failure of the background subtraction algorithm on a per frame basis. Furthermore, FRET-IBRA outputs images with superior signal-to-noise ratio and accuracy in comparison to existing background subtraction solutions, whilst maintaining a fast runtime. We have used the FRET-IBRA package extensively to quantify the spatial distribution of calcium ions during pollen tube growth under mechanical constraints. Benchmarks against existing tools clearly demonstrate the need for FRET-IBRA in extracting reliable insights from FRET microscopy images of dynamic physiological processes at high spatial and temporal resolution. The source code for Linux and Mac operating systems is released under the BSD license and, along with installation instructions, test images, example configuration files, and a step-by-step tutorial, is freely available at github.com/gmunglani/fret-ibra.
Why it matches plant phenotyping methods植物の動的な生理状態を画像から定量化するFRET画像処理ツールの開発・ベンチマークが中心であり、花粉管内カルシウム分布の抽出に実質的に応用されている。
abstractFRET-IBRA is a modular and fully parallelized configuration file-based tool written in Python.
Reproduction assets foundThe paper's authors publicly released the FRET-IBRA analysis toolkit (Python source code, test images, example configuration files, and tutorial) under a BSD license on GitHub. The test images include the FRET microscopy image stacks of growing Arabidopsis pollen tubes used in the paper's calcium-distribution phenotypiCode · publicThe source code for Linux and Mac operating systems is released under the BSD license and, along with installation instructions, test images, example configuration files, and a step-by-step tutorial, is freely available at github.com/gmunglani/fret-ibra.Open asset ↗github.com/gmunglani/fret-ibralines:113-128Code / dataset availability confirmedOpenAlex · Europe PMC · checked 8 Sept 2026
Eastern cottonwood (Populus deltoides W. Bartram ex Marshall) and hybrid poplars are well-known bioenergy crops. With advances in tree breeding, it is increasingly necessary to find economical ways to identify high-performing Populus genotypes that can be planted under different environmental conditions. Photosynthesis and leaf nitrogen content are critical parameters for plant growth, however, measuring them is an expensive and time-consuming process. Instead, these parameters can be quickly estimated from hyperspectral leaf reflectance if robust statistical models can be developed. To this end, we measured photosynthetic capacity parameters (Rubisco-limited carboxylation rate (Vcmax), electron transport-limited carboxylation rate (Jmax), and triose phosphate utilization-limited carboxylation rate (TPU)), nitrogen per unit leaf area (Narea), and leaf reflectance of seven taxa and 62 genotypes of Populus from two study plantations in Mississippi. For statistical modeling, we used least absolute shrinkage and selection operator (LASSO) and principal component analysis (PCA). Our results showed that the predictive ability of LASSO and PCA models was comparable, except for Narea in which LASSO was superior. In terms of model interpretability, LASSO outperformed PCA because the LASSO models needed 2 to 4 spectral reflectance wavelengths to estimate parameters. The LASSO models used reflectance values at 758 and 935 nm for estimating Vcmax (R2 = 0.51 and RMSPE = 31%) and Jmax (R2 = 0.54 and RMSPE = 32%); 687, 746, and 757 nm for estimating TPU (R2 = 0.56 and RMSPE = 31%); and 304, 712, 921, and 1021 nm for estimating Narea (R2 = 0.29 and RMSPE = 21%). The PCA model also identified 935 nm as a significant wavelength for estimating Vcmax and Jmax. Therefore, our results suggest that hyperspectral leaf reflectance modeling can be used as a cost-effective means for field phenotyping and rapid screening of Populus genotypes because of its capacity to estimate these physicochemical parameters.
Why it matches plant phenotyping methodsハイパースペクトル葉反射から光合成能力と葉窒素含量を推定する統計モデルを開発・評価しており、植物形質取得手法が中心である。
abstractTherefore, our results suggest that hyperspectral leaf reflectance modeling can be used as a cost-effective means for field phenotyping and rapid screening of Populus genotypes
Reproduction assets foundThe authors deposited the paper's phenotype measurements (photosynthetic capacity parameters, leaf nitrogen, hyperspectral leaf reflectance of Populus taxa) in Mississippi State University's institutional repository, Scholars Junction, with an explicit public DOI. No author analysis code or trained models were shared.Dataset · publicData Availability: Our data can be accessed from Scholars Junction: Mississippi State University’s Institutional Repository at the following DOI: https://doi.org/10.54718/BACR5952 .Open asset ↗Scholars Junction · 10.54718/BACR5952lines:159-169Code / dataset availability confirmedEurope PMC · checked 8 Sept 2026
Wheat ( Triticum aestivum L.) is known to be negatively affected by heat stress, and its production is threatened by global warming, particularly in arid regions. Thus, efforts to better understand the molecular responses of wheat to heat stress are required. In the present study, Fourier transform infrared (FTIR) spectroscopy, coupled with chemometrics, was applied to develop a protocol that monitors chemical changes in common wheat under heat stress. Wheat plants at the three-leaf stage were subjected to heat stress at a 42 °C daily maximum temperature for 3 days, and this led to delayed growth in comparison to that of the control. Measurement of FTIR spectra and their principal component analysis showed partially overlapping features between heat-stressed and control leaves. In contrast, supervised machine learning through linear discriminant analysis (LDA) of the spectra demonstrated clear discrimination of heat-stressed leaves from the controls. Analysis of LDA loading suggested that several wavenumbers in the fingerprinting region (400-1800 cm -1 ) contributed significantly to their discrimination. Novel spectrum-based biomarkers were developed using these discriminative wavenumbers that enabled the successful diagnosis of heat-stressed leaves. Overall, these observations demonstrate the versatility of FTIR-based chemical fingerprints for use in heat-stress profiling in wheat.
Why it matches plant phenotyping methodsFTIRとケモメトリクスを用いて熱ストレス葉の化学的状態を識別・診断するプロトコルとバイオマーカーを開発しており、植物状態の取得・抽出が中心的です。
abstractFourier transform infrared (FTIR) spectroscopy, coupled with chemometrics, was applied to develop a protocol that monitors chemical changes in common wheat under heat stress.
Reproduction assets foundThe paper's custom R script for spectral biomarker (Fm) calculation was deposited as Supplementary File S1, publicly available at the MDPI supplementary URL. The raw FTIR spectral data (358 spectra) are not stated to be publicly deposited (Data Availability Statement: 'Not applicable').Code · publicThe R scripts were deposited in Supplementary File S1 .Open asset ↗lines:186-204Code / dataset availability confirmedOpenAlex · Europe PMC · checked 8 Sept 2026
MaizeSorghumMultispectral / hyperspectralPhysiological trait estimationWater status / transpiration
Lack of high-throughput phenotyping is a bottleneck to breeding for abiotic stress tolerance in crop plants. Efficient and non-destructive hyperspectral imaging can quantify plant physiological traits under abiotic stresses; however, prediction models generally are developed for few genotypes of one species, limiting the broader applications of this technology. Therefore, the objective of this research was to explore the possibility of developing cross-species models to predict physiological traits (relative water content and nitrogen content) based on hyperspectral reflectance through partial least square regression for three genotypes of sorghum (Sorghum bicolor (L.) Moench) and six genotypes of corn (Zea mays L.) under varying water and nitrogen treatments. Multi-species models were predictive for the relative water content of sorghum and corn (R2 = 0.809), as well as for the nitrogen content of sorghum and corn (R2 = 0.637). Reflectances at 506, 535, 583, 627, 652, 694, 722, and 964 nm were responsive to changes in the relative water content, while the reflectances at 486, 521, 625, 680, 699, and 754 nm were responsive to changes in the nitrogen content. High-throughput hyperspectral imaging can be used to predict physiological status of plants across genotypes and some similar species with acceptable accuracy.
Why it matches plant phenotyping methodsハイスループット hyperspectral imaging と回帰モデルにより、植物の相対含水量・窒素含量という生理形質を非破壊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。
abstractLack of high-throughput phenotyping is a bottleneck to breeding for abiotic stress tolerance in crop plants.
Reproduction assets foundThe paper's hyperspectral reflectance measurements, RWC/NC ground-reference trait data, and metadata are explicitly deposited in the Purdue University Research Repository (PURR) with a public URL stated in the Data Availability Statement. The MDPI supplement contains only stepwise regression tables, model evaluation, VDataset · publicData and meta-data are available at The Purdue University Research Repository (PURR), https://purr.purdue.edu/publications/3958/1 (accessed on 30 January 2022).Open asset ↗The Purdue University Research Repository (PURR)lines:222-238Code / dataset availability confirmedEurope PMC · checked 15 Sept 2026
TomatoGreenhouseLeafPhysiological trait estimationGrowth / development / phenology
While the high year-round production of tomatoes has been facilitated by solar greenhouse cultivation, these yields readily fluctuate in response to changing environmental conditions. Mathematic modeling has been applied to forecast phenotypes of tomatoes using environmental measurements (e.g., temperature) as indirect parameters. In this study, metabolome data, as direct parameters reflecting plant internal status, were used to construct a predictive model of the anthesis rate of greenhouse tomatoes. Metabolome data were obtained from tomato leaves and used as variables for linear regression with the least absolute shrinkage and selection operator (LASSO) for prediction. The constructed model accurately predicted the anthesis rate, with an R 2 value of 0.85. Twenty-nine of the 161 metabolites were selected as candidate markers. The selected metabolites were further validated for their association with anthesis rates using the different metabolome datasets. To assess the importance of the selected metabolites in cultivation, the relationships between the metabolites and cultivation conditions were analyzed via correspondence analysis. Trigonelline, whose content did not exhibit a diurnal rhythm, displayed major contributions to the cultivation, and is thus a potential metabolic marker for predicting the anthesis rate. This study demonstrates that machine learning can be applied to metabolome data to identify metabolites indicative of agricultural traits.
Why it matches plant phenotyping methodsトマトの開花率という植物形質をメタボロームデータから予測するLASSOモデルを構築し、別データセットで検証しており、形質推定手法が中心である。
abstractmetabolome data, as direct parameters reflecting plant internal status, were used to construct a predictive model of the anthesis rate of greenhouse tomatoes.
Reproduction assets foundThe paper's tomato leaf metabolome dataset (DM0041) used for the LASSO anthesis-rate modeling is publicly deposited in DROP Met at PRIMe, with an explicit Data Availability Statement and URL. No author analysis code or trained model checkpoint is stated as publicly available; the supplementary material link is generic.Dataset · publicss spectrometer (LC-QqQ-MS) (UPLC coupled with Xevo TQ-S, Waters, Milford, MA, United States) ( Sawada et al., 2009 ; Sawada et al., 2019 ). The analytical conditions are described in detail in Supplementary Tables S1–S3 . The metabolome data were deposited in the DROP Met in PRIMe (the Platform for RIKEN Metabolomics) (DM0041, http://prime.psc.riken.jp/archives/data/DropMet/059/ ).
2.3.3 Measurement of Relative Metabolite Contents
For the Tsukuba data (TK01), the peak areas of 501 target metabolites (including two internal standards) were processed as follows. Values below the detection limit were set to zero. The peak area of each metabolite in a leaf sample was divided by the mean peak arOpen asset ↗DROP Met · DM0041lines:87-98Code / dataset availability confirmedEurope PMC · checked 14 Sept 2026
ArabidopsisLaboratory / benchtopRootMorphology / geometry measurementTrackingGrowth / development / phenologyRoot system architecture
Directional root growth control is crucial for plant fitness. The degree of root growth deviation depends on several factors, whereby exogenous growth conditions have a profound impact. The perception of mechanical impedance by wild-type roots results in the modulation of root growth traits, and it is known that gravitropic stimulus influences distinct root movement patterns in concert with mechanoadaptation. Mutants with reduced shootward auxin transport are described as being numb towards mechanostimulus and gravistimulus, whereby different growth conditions on agar-supplemented medium have a profound effect on how much directional root growth and root movement patterns differ between wild types and mutants. To reduce the impact of unilateral mechanostimulus on roots grown along agar-supplemented medium, we compared the root movement of Col-0 and auxin resistant 1-7 in a root penetration assay to test how both lines adjust the growth patterns of evenly mechanostimulated roots. We combined the assay with the D-root system to reduce light-induced growth deviation. Moreover, the impact of sucrose supplementation in the growth medium was investigated because exogenous sugar enhances root growth deviation in the vertical direction. Overall, we observed a more regular growth pattern for Col-0 but evaluated a higher level of skewing of aux1-7 compared to the wild type than known from published data. Finally, the tracking of the growth rate of the gravistimulated roots revealed that Col-0 has a throttling elongation rate during the bending process, but aux1-7 does not.
Why it matches plant phenotyping methodsD-rootシステムと根貫通アッセイを組み合わせ、根の成長パターン・伸長速度を追跡して評価する測定ワークフローが研究の中心であるため。
abstractWe combined the assay with the D-root system to reduce light-induced growth deviation.
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 · publicThe following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/plants11050650/s1 , Table S1: raw data.Open asset ↗lines:48-103Code / dataset availability confirmedEurope PMC · checked 8 Sept 2026
Soybean is most often grown under rainfed conditions and negatively impacted by drought stress in the upper mid-south of the United States. Therefore, identification of drought-tolerance traits and their corresponding genetic components are required to minimize drought impacts on productivity. Limited transpiration (TR lim ) under high vapor pressure deficit (VPD) is one trait that can help conserve soybean water-use during late-season drought. The main research objective was to evaluate a recombinant inbred line (RIL) population, from crossing two mid-south soybean lines ("Jackson" × "KS4895"), using a high-throughput technique with an aquaporin inhibitor, AgNO 3 , for the TR lim trait. A secondary objective was to undertake a genetic marker/quantitative trait locus (QTL) genetic analysis using the AgNO 3 phenotyping results. A set of 122 soybean genotypes (120-RILs and parents) were grown in controlled environments (32/25-d/n °C). The transpiration rate (TR) responses of derooted soybean shoots before and after application of AgNO 3 were measured under 37°C and >3.0 kPa VPD. Then, the decrease in transpiration rate (DTR) for each genotype was determined. Based on DTR rate, a diverse group (slow, moderate, and high wilting) of 26 RILs were selected and tested for the whole plant TRs under varying levels of VPD (0.0-4.0 kPa) at 32 and 37°C. The phenotyping results showed that 88% of slow, 50% of moderate, and 11% of high wilting genotypes expressed the TR lim trait at 32°C and 43, 10, and 0% at 37°C, respectively. Genetic mapping with the phenotypic data we collected revealed three QTL across two chromosomes, two associated with TR lim traits and one associated with leaf temperature. Analysis of Gene Ontologies of genes within QTL regions identified several intriguing candidate genes, including one gene that when overexpressed had previously been shown to confer enhanced tolerance to abiotic stress. Collectively these results will inform and guide ongoing efforts to understand how to deploy genetic tolerance for drought stress.
Why it matches plant phenotyping methodsAgNO3を用いた高スループット測定法でダイズの蒸散制限形質を取得し、表現型データをQTL解析に用いており、フェノタイピング手法の適用が研究の中心です。
abstractThe main research objective was to evaluate a recombinant inbred line (RIL) population, from crossing two mid-south soybean lines ("Jackson" × "KS4895"), using a high-throughput technique with an aquaporin inhibitor, AgNO 3 , for the TR lim trait.
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 File 3
R/QTL package file containing genotypic and phenotypic data used for genetic mapping.Open asset ↗lines:1432-1530Code / dataset availability confirmedEurope PMC · checked 14 Sept 2026
Background Most plants encounter water stress at one or more different stages of their life cycle. The maintenance of genetic stability is the integral component of desiccation tolerance that defines the storage ability and long-term survival of seeds. Embryonic axes of desiccation-sensitive recalcitrant seeds of Acer pseudoplatnus L. were used to investigate the genotoxic effect of desiccation. Alkaline single-cell gel electrophoresis (comet assay) methodology was optimized and used to provide unique insights into the onset and repair of DNA strand breaks and 8-oxo-7,8-dihydroguanine (8-oxoG) formation during progressive steps of desiccation and rehydration. Results The loss of DNA integrity and impairment of damage repair were significant predictors of the viability of embryonic axes. In contrast to the comet assay, automated electrophoresis failed to detect changes in DNA integrity resulting from desiccation. Notably, no significant correlation was observed between hydroxyl radical ( ٠ OH) production and 8-oxoG formation, although the former is regarded to play a major role in guanine oxidation. Conclusions The high-throughput comet assay represents a sensitive tool for monitoring discrete changes in DNA integrity and assessing the viability status in plant germplasm processed for long-term storage.
Why it matches plant phenotyping methods植物胚軸のDNA完全性と生存性を評価する高スループットコメットアッセイを最適化・検証しており、表現型状態の取得法が研究の中心である。
abstractAlkaline single-cell gel electrophoresis (comet assay) methodology was optimized and used to provide unique insights into the onset and repair of DNA strand breaks
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 · publicAdditional file 3: Fig. S3. (download PDF )
The representative comet measurements and images captured by Comet Assay IV analysis software.Open asset ↗lines:516-614Code / dataset availability confirmedOpenAlex · Europe PMC · checked 8 Sept 2026
The root system of a plant provides vital functions including resource uptake, storage, and anchorage in soil. The uptake of macro-nutrients like nitrogen (N), phosphorus (P), potassium (K), and sulphur (S) from the soil is critical for plant growth and development. Small signaling peptide (SSP) hormones are best known as potent regulators of plant growth and development with a few also known to have specialized roles in macronutrient utilization. Here we describe a high throughput phenotyping platform for testing SSP effects on root uptake of multiple nutrients. The SSP, CEP1 (C-TERMINALLY ENCODED PEPTIDE) enhanced nitrate uptake rate per unit root length in Medicago truncatula plants deprived of N in the high-affinity transport range. Single structural variants of M. truncatula and Arabidopsis thaliana specific CEP1 peptides, MtCEP1D1:hyp4,11 and AtCEP1:hyp4,11, enhanced uptake not only of nitrate, but also phosphate and sulfate in both model plant species. Transcriptome analysis of Medicago roots treated with different MtCEP1 encoded peptide domains revealed that hundreds of genes respond to these peptides, including several nitrate transporters and a sulfate transporter that may mediate the uptake of these macronutrients downstream of CEP1 signaling. Likewise, several putative signaling pathway genes including LEUCINE-RICH REPEAT RECPTOR-LIKE KINASES and Myb domain containing transcription factors, were induced in roots by CEP1 treatment. Thus, a scalable method has been developed for screening synthetic peptides of potential use in agriculture, with CEP1 shown to be one such peptide.
Why it matches plant phenotyping methods植物の栄養吸収率を測定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、合成ペプチドのスクリーニングに適用しているため、測定手法が研究の中心である。
abstractHere we describe a high throughput phenotyping platform for testing SSP effects on root uptake of multiple nutrients.
Reproduction assets foundThe paper's nutrient uptake rate calculations were performed with R code publicly available on Zenodo (Griffiths et al., 2021), which the authors state they used with minor modifications for this paper's phenotyping analysis. The NCBI BioProject (PRJNA764762) is an RNA-seq omics deposit and is excluded per criteria; TrCode · publicdata processing to determine specific nutrient uptake rates was conducted using R version 3.6.0 (Team, 2020)( R Core Team, 2020 ) with minor modification to the R code available at https://doi.org/10.5281/zenodo.3893945 ( Griffiths et al., 2021 )Open asset ↗zenodo · 10.5281/zenodo.3893945lines:318-326Code / dataset availability confirmedEurope PMC · checked 15 Sept 2026
In plants, secretion of cell wall components and membrane proteins plays a fundamental role in growth and development as well as survival in diverse environments. Exocytosis, as the last step of the secretory trafficking pathway, is a highly ordered and precisely controlled process involving tethering, docking, and fusion of vesicles at the plasma membrane (PM) for cargo delivery. Although the exocytic process and machinery are well characterized in yeast and animal models, the molecular players and specific molecular events that underpin late stages of exocytosis in plant cells remain largely unknown. Here, by using the delivery of functional, fluorescent-tagged cellulose synthase (CESA) complexes (CSCs) to the PM as a model system for secretion, as well as single-particle tracking in living cells, we describe a quantitative approach for measuring the frequency of vesicle tethering events. Genetic and pharmacological inhibition of cytoskeletal function, reveal that the initial vesicle tethering step of exocytosis is dependent on actin and myosin XI. In contrast, treatments with the microtubule inhibitor, oryzalin, did not significantly affect vesicle tethering or fusion during CSC exocytosis but caused a minor increase in transient or aborted tethering events. With data from this new quantitative approach and improved spatiotemporal resolution of single particle events during secretion, we generate a revised model for the role of the cortical cytoskeleton in CSC trafficking.
Why it matches plant phenotyping methods植物細胞内の小胞テザリング頻度を単一粒子追跡で定量する新しい測定手法を開発・適用しており、植物状態の取得・定量が研究の中心である。
abstractsingle-particle tracking in living cells, we describe a quantitative approach for measuring the frequency of vesicle tethering events.
Reproduction assets foundThe paper's supplementary materials include Video S1, a live-cell imaging movie of a CSC particle insertion event next to a cortical microtubule, which directly reproduces the paper's plant phenotyping (single-particle CSC trafficking) measurements. No author analysis code or datasets with explicit deposit language areSupplement · publicnt care and maintenance of plant materials and to all members of the Staiger laboratory for helpful discussions and input.
Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Supplementary Materials
The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/ijms23010317/s1 , Video S1: A CSC particle is inserted next to a cortical microtubule and translocates on the microtubule during the steady movement phase.
Click here for additional data file.
Author Contributions
W.Z. and C.J.S. designed the research. W.Z. performed the experiments and analyzed the data. W.Z. and C.J.S. wrote the Open asset ↗lines:64-115Code / dataset availability confirmedEurope PMC · checked 9 Sept 2026
Identification of high Nitrogen Use Efficiency (NUE) phenotypes has been a long-standing challenge in breeding rice and sustainable agriculture to reduce the costs of nitrogen (N) fertilizers. There are two main challenges: (1) high NUE genetic sources are biologically scarce and (2) on the technical side, few easy, non-destructive, and reliable methodologies are available to evaluate plant N variations through the entire growth duration (GD). To overcome the challenges, we captured a unique higher NUE phenotype in rice as a dynamic time-series N variation curve through the entire GD analysis by canopy reflectance data collected by Unmanned Aerial Vehicle Remote Sensing Platform (UAV-RSP) for the first time. LY9348 was a high NUE rice variety with high Nitrogen Uptake Efficiency (NUpE) and high Nitrogen Utilization Efficiency (NUtE) shown in nitrogen dosage field analysis. Its canopy nitrogen content (CNC) was analyzed by the high-throughput UAV-RSP to screen two mixed categories (51 versus 42 varieties) selected from representative higher NUE indica rice collections. Five Vegetation Indices (VIs) were compared, and the Normalized Difference Red Edge Index (NDRE) showed the highest correlation with CNC ( r = 0.80). Six key developmental stages of rice varieties were compared from transplantation to maturation, and the high NUE phenotype of LY9348 was shown as a dynamic N accumulation curve, where it was moderately high during the vegetative developmental stages but considerably higher in the reproductive developmental stages with a slower reduction rate. CNC curves of different rice varieties were analyzed to construct two non-linear regression models between N% or N% × leaf area index (LAI) with NDRE separately. Both models could determine the specific phenotype with the coefficient of determination ( R 2 ) above 0.61 (Model I) and 0.86 (Model II). Parameters influencing the correlation accuracy between NDRE and N% were found to be better by removing the tillering stage data, separating the short and long GD varieties for the analysis and adding canopy structures, such as LAI, into consideration. The high NUE phenotype of LY9348 could be traced and reidentified across different years, locations, and genetic germplasm groups. Therefore, an effective and reliable high-throughput method was proposed for assisting the selection of the high NUE breeding phenotype.
Why it matches plant phenotyping methodsUAVマルチスペクトル画像によるイネの窒素状態・高NUE表現型の非破壊かつ高スループットな推定手法を開発・検証しており、表現型取得法が研究の中心である。
abstractfew easy, non-destructive, and reliable methodologies are available to evaluate plant N variations through the entire growth duration (GD)
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 ), two main aspects were considered: (1) Many varieties from the 3,000 rice genome project were germplasm collections and not used in field practices because of their lower-yielding, varied GD, and/or weak agricultural traits.Open asset ↗lines:380-387Code / dataset availability confirmedEurope PMC · checked 14 Sept 2026
Recent advances in leaf fluorescence measurements and canopy proximal remote sensing currently enable the non-destructive collection of rich diurnal and seasonal time series, which are required for monitoring vegetation function at the temporal and spatial scales relevant to the natural dynamics of photosynthesis. Remote sensing assessments of vegetation function have traditionally used actively excited foliar chlorophyll fluorescence measurements, canopy optical reflectance data and vegetation indices (VIs), and only recently passive solar induced chlorophyll fluorescence (SIF) measurements. In general, reflectance data are more sensitive to the seasonal variations in canopy chlorophyll content and foliar biomass, while fluorescence observations more closely relate to the dynamic changes in plant photosynthetic function. With this dataset we link leaf level actively excited chlorophyll fluorescence, canopy proximal reflectance and SIF, with eddy covariance measurements of gross ecosystem productivity (GEP). The dataset was collected during the 2017 growing season on maize, using three automated systems (i.e., Monitoring Pulse-Amplitude-Modulation fluorimeter, Moni-PAM; Fluorescence Box, FloX; and from eddy covariance tower). The data were quality checked, filtered and collated to a common 30 minutes timestep. We derived vegetation indices related to canopy functioning (e.g., Photochemical Reflectance Index, PRI; Normalized Difference Vegetation Index, NDVI; Chlorophyll Red-edge, Clre) to investigate how SIF and VIs can be coupled for monitoring vegetation photosynthesis. The raw datasets and the filtered and collated data are provided to enable new processing and analyses.
Why it matches plant phenotyping methods葉・キャノピーの蛍光、反射、植生指数を用いて植物の光合成機能を測定・統合した再利用可能なデータセットであり、センサー計測系とデータ処理が研究の中心である。
abstractWith this dataset we link leaf level actively excited chlorophyll fluorescence, canopy proximal reflectance and SIF, with eddy covariance measurements of gross ecosystem productivity (GEP).
Reproduction assets foundThis Data in Brief article explicitly deposits its maize leaf/canopy fluorescence, reflectance, VI and GEP time-series dataset (raw and collated files) in Mendeley Data under DOI 10.17632/b84jk376c3.1, and the FloX reflectance/SIF processing was performed with two author-maintained open-source R packages on GitHub (tomDataset · publicral Research Center (BARC)
City/Town/Region: Beltsville, Maryland
Country: United States of America
Latitude and longitude (and GPS coordinates, if possible) for collected samples/data: 39.030686, 76.84546
Timeframe: 2017 growing season (June-October)
Data accessibility
Repository name: Mendeley Data
Data identification number: http://dx.doi.org/10.17632/b84jk376c3.1
https://data.mendeley.com/datasets/b84jk376c3/draft?a=09b70ff8-599e-4405-a0f1-7a0c39e118fd
Related research articles
Campbell, P., K. Huemmrich, E. Middleton, et al. 2019. ``Diurnal and Seasonal Variations in Chlorophyll Fluorescence Associated with Photosynthesis at Leaf and Canopy Scales.'' Remote Sensing , 11 (5): 488 [ 10.3Open asset ↗Mendeley Data · 10.17632/b84jk376c3.1lines:36-69Dataset · publicion: Beltsville, Maryland
Country: United States of America
Latitude and longitude (and GPS coordinates, if possible) for collected samples/data: 39.030686, 76.84546
Timeframe: 2017 growing season (June-October)
Data accessibility
Repository name: Mendeley Data
Data identification number: http://dx.doi.org/10.17632/b84jk376c3.1
https://data.mendeley.com/datasets/b84jk376c3/draft?a=09b70ff8-599e-4405-a0f1-7a0c39e118fd
Related research articles
Campbell, P., K. Huemmrich, E. Middleton, et al. 2019. ``Diurnal and Seasonal Variations in Chlorophyll Fluorescence Associated with Photosynthesis at Leaf and Canopy Scales.'' Remote Sensing , 11 (5): 488 [ 10.3390/rs11050488 ]
Yang, P., C. van der TolOpen asset ↗Mendeley Data · b84jk376c3lines:36-69Code / dataset availability confirmedEurope PMC · checked 9 Sept 2026
Background The need for rapid in-field measurement of key traits contributing to yield over many thousands of genotypes is a major roadblock in crop breeding. Recently, leaf hyperspectral reflectance data has been used to train machine learning models using partial least squares regression (PLSR) to rapidly predict genetic variation in photosynthetic and leaf traits across wheat populations, among other species. However, the application of published PLSR spectral models is limited by a fixed spectral wavelength range as input and the requirement of separate custom-built models for each trait and wavelength range. In addition, the use of reflectance spectra from the short-wave infrared region requires expensive multiple detector spectrometers. The ability to train a model that can accommodate input from different spectral ranges would potentially make such models extensible to more affordable sensors. Here we compare the accuracy of prediction of PLSR with various deep learning approaches and an ensemble model, each trained and tested using previously published data sets. Results We demonstrate that the accuracy of PLSR to predict photosynthetic and related leaf traits in wheat can be improved with deep learning-based and ensemble models without overfitting. Additionally, these models can be flexibly applied across spectral ranges without significantly compromising accuracy. Conclusion The method reported provides an improved prediction of wheat leaf and photosynthetic traits from leaf hyperspectral reflectance and do not require a full range, high cost leaf spectrometer. We provide a web service for deploying these algorithms to predict physiological traits in wheat from a variety of spectral data sets, with important implications for wheat yield prediction and crop breeding.
Why it matches plant phenotyping methods小麦のハイパースペクトル反射から生理・光合成形質を推定する深層学習モデルを開発・比較し、精度を検証した研究であり、表現型取得・推定法が中心である。
abstractHere we compare the accuracy of prediction of PLSR with various deep learning approaches and an ensemble model, each trained and tested using previously published data sets.
Reproduction assets foundThe paper publicly releases its authors' model code (GitHub) and hosts the training data and pre-trained models via the Wheat Physiology Predictor web server. The SAMS repository is a generic third-party tool and is excluded.Code · publicThe full code of these models is located at https://github.com/ashwhall/hyperspec-trait-prediction .Open asset ↗ashwhall/hyperspec-trait-predictionlines:132-148Code / dataset availability confirmedEurope PMC · checked 9 Sept 2026
Plant pathogens pose increasing threats to global food security, causing yield losses that exceed 30% in food-deficit regions. Xylella fastidiosa (Xf) represents the major transboundary plant pest and one of the world's most damaging pathogens in terms of socioeconomic impact. Spectral screening methods are critical to detect non-visual symptoms of early infection and prevent spread. However, the subtle pathogen-induced physiological alterations that are spectrally detectable are entangled with the dynamics of abiotic stresses. Here, using airborne spectroscopy and thermal scanning of areas covering more than one million trees of different species, infections and water stress levels, we reveal the existence of divergent pathogen- and host-specific spectral pathways that can disentangle biotic-induced symptoms. We demonstrate that uncoupling this biotic-abiotic spectral dynamics diminishes the uncertainty in the Xf detection to below 6% across different hosts. Assessing these deviating pathways against another harmful vascular pathogen that produces analogous symptoms, Verticillium dahliae, the divergent routes remained pathogen- and host-specific, revealing detection accuracies exceeding 92% across pathosystems. These urgently needed hyperspectral methods advance early detection of devastating pathogens to reduce the billions in crop losses worldwide.
Why it matches plant phenotyping methods航空分光法と熱スキャンを用いて植物病原体感染を非視覚的な生理・スペクトル形質として検出し、複数病原体・宿主で精度を検証しており、表現型取得手法が研究の中心である。
abstractSpectral screening methods are critical to detect non-visual symptoms of early infection and prevent spread.
Reproduction assets foundThe paper's data availability and code availability statements point to a public GitHub repository (HyperSens/HyperSens-Divergent-spectral-responses-Nature-Communications) with a Zenodo DOI (10.5281/zenodo.5535095) containing the study's spectral trait datasets and analysis code. The large airborne hyperspectral imagecDataset · publicThe data used in this study 74 are available at the repository https://github.com/HyperSens/HyperSens-Divergent-spectral-responses-Nature-Communications and can be cited as https://doi.org/10.5281/zenodo.5535095Open asset ↗HyperSens/HyperSens-Divergent-spectral-responses-Nature-Communications · 10.5281/zenodo.5535095lines:133-192Code · publicThe codes used for this study 74 are available at the repository https://github.com/HyperSens/HyperSens-Divergent-spectral-responses-Nature-Communications and can be cited as https://doi.org/10.5281/zenodo.5535095Open asset ↗HyperSens/HyperSens-Divergent-spectral-responses-Nature-Communications · 10.5281/zenodo.5535095lines:133-192Code / dataset availability confirmedEurope PMC · checked 14 Sept 2026
Bacterial leaf blight (BLB) is caused by Xanthomonas oryzae pv. oryzae and is a major cause of rice yield reductions around the world. When diseased, plants produce a variety of metabolites to resist pathogens. In this study, the various defense metabolites were quantified using high-performance liquid chromatography (HPLC) after Xoo inoculation in a 120 Cheongcheong/Nagdong double haploid (CNDH) population. Quantitative trait locus (QTL) mapping was conducted using the concentration of the plant defense metabolites. HPLC analyzes the concentration of substances according to the severity of disease symptoms. Searching for BLB resistance candidate genes by applying this analysis method is very effective when mapping related genes. These resistance genes can be mapped directly to the causative pathogens. A total of 17 metabolites were detected by means of HPLC analysis after Xoo inoculation in the 120 CNDH population. QTL mapping of the metabolite concentrations resulted in the detection of the BLB resistance candidate gene, OsWRKYq6, in RM3343 of chromosome 6. OsWRKYq6 has a very high homology sequence with WRKY transcription factor 39, and when inoculated with Xoo, the relative expression level of the resistant population was higher than that of the susceptible population. Resistance genes have previously been detected using only phenotypic change data. In this study, resistance candidate genes were detected using the concentration of metabolites produced in plants after inoculation with pathogens. This newly developed analysis method can be used to effectively detect and identify genes directly involved in disease resistance for future studies.
Why it matches plant phenotyping methodsHPLCによる防御代謝物濃度の定量を、イネの病害抵抗性状態の表現型取得・QTL検出法として明示的に開発・適用しており、単なるルーチン測定ではない。
abstractIn this study, the various defense metabolites were quantified using high-performance liquid chromatography (HPLC) after Xoo inoculation in a 120 Cheongcheong/Nagdong double haploid (CNDH) population.
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 · publicThe following are available online at https://www.mdpi.com/article/10.3390/plants10102145/s1 , Figure S1: PCA (Principal Component Analysis) statistical analysis between the peak area in the HPLC analysis results and the infection length data of the corresponding leaf samples. Table S1: The plant traits and peak no. of 120 CNDH (Cheongcheong/Nagdong double haploid) populations by HPLC (high-performance liquid chromatography). Table S2: Details of QTL mapping using HPLC analysis results of 120 CNDH population after Xanthomonas oryzae pv. oryzae inoculation.Open asset ↗MDPIlines:71-91Code / dataset availability confirmedCrossref · Europe PMC · checked 9 Sept 2026
Significance Wide adoption of algae cultivation to produce environmentally sustainable biofuels and fine chemicals is currently hampered by large losses (10 to 30%) incurred by grazer infections. We show the usage of real-time chemical ionization mass spectrometry to rapidly identify gaseous indicators of grazer infections in cyanobacteria cultures. Grazing was detected significantly faster (up to 3 d) using real-time mass spectrometry than the current methods of microscopy and qPCR. By employing this technology, cultivators may be empowered to treat grazer infestations sooner, thereby protecting the crop and enhancing profitability.
Why it matches plant phenotyping methodsシアノバクテリア培養の摂食感染状態を揮発性ガスから検出する化学センシング手法を開発・既存法と比較検証しており、植物状態の取得が中心である。
abstractWe show the usage of real-time chemical ionization mass spectrometry to rapidly identify gaseous indicators of grazer infections in cyanobacteria cultures.
Reproduction assets foundThe authors deposited the paper's CIMS volatile-gas time-series measurements (CSV and raw HDF files) in the UC San Diego Library repository, publicly accessible via DOI. The SI supplement link is generic supplementary material without explicit data content, and OpenChrom is a third-party analysis tool, not an authors' Dataset · publicComma Separated Value (CSV) and raw Hierarchical Data Format (HDF) data have been deposited in the University of California San Diego Library, https://doi.org/10.6075/J0GH9GHW ( 52 ).Open asset ↗University of California San Diego Library · 10.6075/J0GH9GHWlines:113-147Code / dataset availability confirmedOpenAlex · Crossref · Europe PMC · checked 14 Sept 2026
Spectroscopic methods can contribute to addressing the field phenotyping bottleneck problem in crop breeding programs. In disease resistance phenotyping, spectral signatures can be analysed to derive infection severity scores and to screen breeding lines. Hyperspectra of winter wheat spikes were acquired in a Fusarium head blight phenotyping trial at the milk- and wax-ripening phenological phases. Disease severity ratings were simultaneously performed by an expert on a 9-point visual scale. Ordinal support vector machine models were then trained to assign hill plots to the individual severity levels. The predictive models' performance was evaluated for data collection timing, spectral pre-processing and permitted rating-error tolerance. The models trained to spectra acquired at the milk-ripening phase were sufficiently accurate to reliably distinguish between low, medium and high symptom severity; with accuracy approaching 100% for two-point error tolerance. However, deterioration in prediction quality was noted for the wax-ripening campaign, presumably due to spike-drying. After aggregation of the spectra using the median function no gain could be associated with further pre-processing. Modest performance improvements obtained with two schemes do not justify the additional data acquisition costs involved, but standard normal variate could be advantageous for some scenarios with mean-aggregated spectra. In addition to phenotyping, the results are discussed in relation to large-scale farming applications. Elevated infection risk detection prior to anthesis is recommended for fungicide treatment, considering the pathogen biology. The study is accompanied by a publicly-available dataset and the computational scripts employed to obtain the results.
Why it matches plant phenotyping methodsスペクトル測定と機械学習によりコムギ赤かび病の感染重症度を推定し、収集時期・前処理・誤差許容度を評価しているため、植物表現型取得法の検証・応用が中心です。
abstractSpectroscopic methods can contribute to addressing the field phenotyping bottleneck problem in crop breeding programs.
Reproduction assets foundThe authors deposited the paper's spectral phenotyping dataset (hyperspectra of winter wheat spikes, visual symptom scores) together with the computational analysis scripts and a GNU Guix environment specification in a public Zenodo repository, explicitly excluding only the unused hyperspectral image cubes.Dataset · publicl., 2018), with the scheme, plant health deterioration is associated with less
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pre-registration form (Zelazny et al., 2020) hosted by the pronounced features, except for the longest wavelengths,
Center of Open Science. The dataset is available from a Zen- where the relationship is reversed. This pre-processing
odo repository (https://doi.org/10.5281/zenodo.4536881), accentuated the effect of the infection on the left shoulder
excluding the hyperspectral data cubes because of their of the NIR plateau. All of these patterns occurred also after
excessive size and the fact that they were not analysed. transforming mean-aggregated spectra (Supplement S2).
The analysis was coded in the R languOpen asset ↗Zenodo · 10.5281/zenodo.4536881pdf-layout-page:6 lines:1-49Code / dataset availability confirmedEurope PMC · checked 14 Sept 2026
In this study, the feasibility of classifying soybean frogeye leaf spot (FLS) is investigated. Leaf images and hyperspectral reflectance data of healthy and FLS diseased soybean leaves were acquired. First, image processing was used to classify FLS to create a reference for subsequent analysis of hyperspectral data. Then, dimensionality reduction methods of hyperspectral data were used to obtain the relevant information pertaining to FLS. Three single methods, namely spectral index (SI), principal component analysis (PCA), and competitive adaptive reweighted sampling (CARS), along with a PCA and SI combined method, were included. PCA was used to select the effective principal components (PCs), and evaluate SIs. Characteristic wavelengths (CWs) were selected using CARS. Finally, the full wavelengths, CWs, effective PCs, SIs, and significant SIs were divided into 14 datasets (DS1-DS14) and used as inputs to build the classification models. Models' performances were evaluated based on the classification accuracy for both the overall and individual classes. Our results suggest that the FLS comprised of five classes based on the proportion of total leaf surface covered with FLS. In the PCA and SI combination model, 5 PCs and 20 SIs with higher weight coefficient of each PC were extracted. For hyperspectral data, 20 CWs and 26 effective PCs were also selected. Out of the 14 datasets, the model input variables provided by five datasets (DS2, DS3, DS4, DS10, and DS11) were more superior than those of full wavelengths (DS1) both in support vector machine (SVM) and least squares support vector machine (LS-SVM) classifiers. The models developed using these five datasets achieved overall accuracies ranging from 91.8% to 94.5% in SVM, and 94.5% to 97.3% in LS-SVM. In addition, they improved the classification accuracies by 0.9% to 3.6% (SVM) and 0.9% to 3.7% (LS-SVM).
Why it matches plant phenotyping methods葉の画像およびハイパースペクトル反射を用いて、病斑被覆率に基づくダイズ葉の病害状態を分類する手法を開発・評価しており、植物表現型取得と解析が中心である。
abstractimage processing was used to classify FLS to create a reference for subsequent analysis of hyperspectral 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。Dataset · publicS4 Data. Hyperspectral data used in this study.Open asset ↗lines:335-377Code / dataset availability confirmedEurope PMC · checked 14 Sept 2026
Drought is the most important limitation on crop yield. Understanding and detecting drought stress in crops is vital for improving water use efficiency through effective breeding and management. Leaf reflectance spectroscopy offers a rapid, non-destructive alternative to traditional techniques for measuring plant traits involved in a drought response. We measured drought stress in six glasshouse-grown agronomic species using physiological, biochemical, and spectral data. In contrast to physiological traits, leaf metabolite concentrations revealed drought stress before it was visible to the naked eye. We used full-spectrum leaf reflectance data to predict metabolite concentrations using partial least-squares regression, with validation R2 values of 0.49-0.87. We show for the first time that spectroscopy may be used for the quantitative estimation of proline and abscisic acid, demonstrating the first use of hyperspectral data to detect a phytohormone. We used linear discriminant analysis and partial least squares discriminant analysis to differentiate between watered plants and those subjected to drought based on measured traits (accuracy: 71%) and raw spectral data (66%). Finally, we validated our glasshouse-developed models in an independent field trial. We demonstrate that spectroscopy can detect drought stress via underlying biochemical changes, before visual differences occur, representing a powerful advance for measuring limitations on yield.
Why it matches plant phenotyping methods葉のハイパースペクトル反射から植物の干ばつストレスおよび関連形質を推定する手法を開発・検証しており、独立圃場試験での検証も含むため、表現型取得法が中心である。
abstractLeaf reflectance spectroscopy offers a rapid, non-destructive alternative to traditional techniques for measuring plant traits involved in a drought response.
Reproduction assets foundThe authors deposited the full raw hyperspectral/phenotype dataset on EcoSIS (DOI 10.21232/UTK8zaW4.669) and the supplementary dataset containing raw gas exchange and leaf metabolic trait data (DOI 10.21232/UTK8zaW4.665). Both are public, paper-specific phenotype/spectral datasets directly reproducing the paper's PLSR/Dataset · public.
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Supplementary Figure 2. ROC analysis for LDA models.
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Supplementary Figure 3. ROC analysis for PLS-DA models.
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Supplementary Dataset. Available online at https://doi.org/10.21232/UTK8zaW4.665
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The full raw dataset accompanying this manuscript is available online at EcoSIS (ecosis.org) at
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Acknowledgements
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This work was supported by the United States Department of Energy contract No. DE-
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SC0012704 to Brookhaven National Laboratory. We thank A. Brinton, M. J. B. Burnett, E.
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O’Connor, G. Hilles, K. Scanlon and D. Yang for assisting with data collection in the glasshouse;
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D. Anderson, S. Drew, C.Open asset ↗EcoSIS · 10.21232/UTK8zaW4.669pdf-raw-page:36 lines:1-44Dataset · public34
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Supplementary Figure 1. Example workflow for visual identification of drought.
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Supplementary Figure 2. ROC analysis for LDA models.
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Supplementary Figure 3. ROC analysis for PLS-DA models.
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The full raw dataset accompanying this manuscript is available online at EcoSIS (ecosis.org) at
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Acknowledgements
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This work was supported by the United States Department of Energy contract No. DE-
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SC0012704 to Brookhaven National Laboratory. We thankOpen asset ↗EcoSIS · 10.21232/UTK8zaW4.665pdf-raw-page:36 lines:1-44Code / dataset availability confirmedCrossref · checked 13 Sept 2026
Abstract A key impediment to studying water-related mechanisms in plants is the inability to non-invasively image water fluxes in cells at high temporal and spatial resolution. Here, we report that Raman microspectroscopy, complemented by hydrodynamic modelling, can achieve this goal - monitoring hydrodynamics within living root tissues at cell- and sub-second-scale resolutions. Raman imaging of water-transporting xylem vessels in Arabidopsis thaliana mutant roots reveals faster xylem water transport in endodermal diffusion barrier mutants. Furthermore, transverse line scans across the root suggest water transported via the root xylem does not re-enter outer root tissues nor the surrounding soil when en-route to shoot tissues if endodermal diffusion barriers are intact, thereby separating ‘two water worlds’.
Why it matches plant phenotyping methodsRaman顕微分光と流体力学モデリングを組み合わせ、根組織内の水輸送を非侵襲・細胞解像度で測定する手法が研究の中心であり、植物の生理状態を定量化している。
abstractRaman microspectroscopy, complemented by hydrodynamic modelling, can achieve this goal - monitoring hydrodynamics within living root tissues at cell- and sub-second-scale resolutions.
Reproduction assets foundThe paper's MECHA 3D solute advection-diffusion model and MATLAB inverse modeling code are openly available on GitHub under a GPL.2 license. Other deposits (Raman data, hydraulic conductivity data, custom RMS analysis code on FigShare) exist but their URLs are not in the allowed list, so only the GitHub code asset is aCode · publicThe latest code of MECHA working in 3D with solute advection-diffusion and associated Matlab codes for inverse modeling schemes are openly available online under GPL.2 open-source licence at FigShare [10.6084/m9.figshare.14892408.v2] or GitHub [ https://github.com/MECHARoot/MECHA/blob/master/MECHA_4Dsolute.zip ].Open asset ↗MECHARoot/MECHA · MECHA_4Dsolute.ziplines:111-143Code / dataset availability confirmedCrossref · checked 9 Sept 2026
Abstract Background Drought is a major consequence of global heating that has negative impacts on agriculture. Potato is a drought-sensitive crop; tuber growth and dry matter content may both be impacted. Moreover, water deficit can induce physiological disorders such as glassy tubers and internal rust spots. The response of potato plants to drought is complex and can be affected by cultivar type, climatic and soil conditions, and the point at which water stress occurs during growth. The characterization of adaptive responses in plants presents a major phenotyping challenge. There is therefore a demand for the development of non-invasive analytical techniques to improve phenotyping. Results This project aimed to take advantage of innovative approaches in MRI, phenotyping and molecular biology to evaluate the effects of water stress on potato plants during growth. Plants were cultivated in pots under different water conditions. A control group of plants were cultivated under optimal water uptake conditions. Other groups were cultivated under mild and severe water deficiency conditions (40 and 20% of field capacity, respectively) applied at different tuber growth phases (initiation, filling). Water stress was evaluated by monitoring soil water potential. Two fully-equipped imaging cabinets were set up to characterize plant morphology using high definition color cameras (top and side views) and to measure plant stress using RGB cameras. The response of potato plants to water stress depended on the intensity and duration of the stress. Three-dimensional morphological images of the underground organs of potato plants in pots were recorded using a 1.5 T MRI scanner. A significant difference in growth kinetics was observed at the early growth stages between the control and stressed plants. Quantitative PCR analysis was carried out at molecular level on the expression patterns of selected drought-responsive genes. Variations in stress levels were seen to modulate ABA and drought-responsive ABA-dependent and ABA-independent genes. Conclusions This methodology, when applied to the phenotyping of potato under water deficit conditions, provides a quantitative analysis of leaves and tubers properties at microstructural and molecular levels. The approaches thus developed could therefore be effective in the multi-scale characterization of plant response to water stress, from organ development to gene expression.
Why it matches plant phenotyping methodsジャガイモの水ストレス表現型を取得するための非侵襲的イメージング・生理計測手法と装置構成が研究の中心であり、単なる生物学的測定ではない。
abstractThere is therefore a demand for the development of non-invasive analytical techniques to improve phenotyping.
Reproduction assets foundThe paper's MRI phenotyping data (3D images of potato tubers in pots under water deficit) are openly deposited in Data INRAE with an explicit DOI, as stated in the Availability of data and materials section. No author analysis code or trained models are reported.Dataset · publicThe MRI data presented in this study are openly available in Data INRAE ( https://data.inrae.fr/ ) repository at: https://data.inrae.fr/dataset.xhtml?persistentId=doi:10.15454/SFAXAA ).Open asset ↗Data INRAE · doi:10.15454/SFAXAAlines:160-172Code / dataset availability confirmedOpenAlex · Europe PMC · bioRxiv · checked 15 Sept 2026
Abstract Temperature is a main driver of plant growth and development. New phenotyping tools enable quantifying the temperature response of hundreds of genotypes. Yet, for field-derived data, temperature response modeling bears flaws and pitfalls concerning the interpretation of derived parameters. In this study, climate data from five growing seasons with differing temperature distributions served as starting point for a growth simulation of wheat stem elongation, based on a four-parametric temperature response function (Wang-Engel) including all cardinal temperatures. In a novel approach, we re-extracted dose-responses from the simulation by combining high-resolution (hours) temperature courses with low-resolution (days) height data. The collection of such data is common in field phenotyping platforms. To take advantage of the lack of supra-optimal temperatures during the stem elongation, simpler (linear and asymptotic) models to predict temperature-response parameters were investigated. The asymptotic model extracted the base temperature of growth and the maximum absolute growth rate with high precision, whereas simpler, linear models failed to do so. Additionally, the asymptotic model provided a proxy estimate for the optimum temperature. However, when including seasonally changing cardinal temperatures, the prediction accuracy of the asymptotic model was strongly reduced. In a field study with three winter wheat varieties, significant differences were found for all three asymptotic dose-response curve parameters. We conclude that the asymptotic model based on high-resolution temperature courses is suitable to extract meaningful parameters from field-based data.
Why it matches plant phenotyping methods高解像度の温度データと低解像度の草丈データから、作物の温度応答パラメータを抽出するモデル手法が研究の中心であり、植物フェノタイピング手法に該当する。
abstractIn a novel approach, we re-extracted dose-responses from the simulation by combining high-resolution (hours) temperature courses with low-resolution (days) height data.
Reproduction assets foundThe paper's data and source code (phenotyping analysis for temperature dose-response extraction) are openly available in the ETH GitLab repository and archived in the ETH research collection.Code · publiconceptualization, Methodology, Writing - Review & Editing. Andreas Hund: Con-
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ceptualization, Supervision, Project administration, Funding acquisition, Writing - Review & Editing.
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Data availability
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Data and source code that support the findings of this study are openly available in the ETH gitlab reposi-
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tory at https://gitlab.ethz.ch/crop_phenotyping/htfp_data_processing and archived in the ETH
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research collection (http://doi.org/10.5905/ethz-1007-385).358
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(which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission.
The copyright holder for this preprint
this version posted July 23, 2021.
;
https://doi.org/10Open asset ↗crop_phenotyping/htfp_data_processingpdf-raw-page:16 lines:1-23Code · publicProject administration, Funding acquisition, Writing - Review & Editing.
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Data availability
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Data and source code that support the findings of this study are openly available in the ETH gitlab reposi-
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tory at https://gitlab.ethz.ch/crop_phenotyping/htfp_data_processing and archived in the ETH
357
research collection (http://doi.org/10.5905/ethz-1007-385).358
16
(which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission.
The copyright holder for this preprint
this version posted July 23, 2021.
;
https://doi.org/10.1101/2021.07.23.453040
doi:
bioRxiv preprintOpen asset ↗10.5905/ethz-1007-385pdf-raw-page:16 lines:1-23Code / dataset availability confirmedEurope PMC · checked 9 Sept 2026
MaizeRootMorphology / geometry measurementPhysiological trait estimationWater status / transpiration
Root hydraulic properties play a central role in the global water cycle, in agricultural systems productivity, and in ecosystem survival as they impact the canopy water supply. However, the existing experimental methods to quantify root hydraulic conductivities, such as the root pressure probing, are particularly challenging, and their applicability to thin roots and small root segments is limited. Therefore, there is a gap in methods enabling easy estimations of root hydraulic conductivities in diverse root types. Here, we present a new pipeline to quickly estimate root hydraulic conductivities across different root types, at high resolution along root axes. Shortly, free-hand root cross-sections were used to extract a selected number of key anatomical traits. We used these traits to parametrize the Generator of Root Anatomy in R (GRANAR) model to simulate root anatomical networks. Finally, we used these generated anatomical networks within the Model of Explicit Cross-section Hydraulic Anatomy (MECHA) to compute an estimation of the root axial and radial hydraulic conductivities ( k x and k r , respectively). Using this combination of anatomical data and computational models, we were able to create a root hydraulic conductivity atlas at the root system level, for 14-day-old pot-grown Zea mays (maize) plants of the var. B73 . The altas highlights the significant functional variations along and between different root types. For instance, predicted variations of radial conductivity along the root axis were strongly dependent on the maturation stage of hydrophobic barriers. The same was also true for the maturation rates of the metaxylem vessels. Differences in anatomical traits along and across root types generated substantial variations in radial and axial conductivities estimated with our novel approach. Our methodological pipeline combines anatomical data and computational models to turn root cross-section images into a detailed hydraulic atlas. It is an inexpensive, fast, and easily applicable investigation tool for root hydraulics that complements existing complex experimental methods. It opens the way to high-throughput studies on the functional importance of root types in plant hydraulics, especially if combined with novel phenotyping techniques such as laser ablation tomography.
Why it matches plant phenotyping methods根の断面画像と計算モデルを組み合わせ、根の解剖形質から油圧伝導性を推定する新規パイプラインを開発しており、植物表現型の取得・推定手法が研究の中心である。
abstractHere, we present a new pipeline to quickly estimate root hydraulic conductivities across different root types, at high resolution along root axes.
Reproduction assets foundThe paper provides two paper-specific public assets: the GRANAR–MECHA coupling workflow (Jupyter notebook via Binder, GitHub repo HeymansAdrien/GranarMecha, Zenodo DOI 10.5281/zenodo.4316762) and the Rmarkdown script plus all input/output data used to compute the B73 root hydraulic atlas (GitHub repo granar/B73_HydraulCode · publicThe whole script that was used to compute the root hydraulic atlas from the root anatomical measurement is presented as a Rmarkdown script stored in a GitHub repository ( https://github.com/granar/B73_HydraulicMap doi: https://doi.org/10.5281/zenodo.4320861 ). All input and output data of this study are stored in the same repository.Open asset ↗granar/B73_HydraulicMap · 10.5281/zenodo.4320861lines:267-339Code · publicThe whole script that was used to compute the root hydraulic atlas from the root anatomical measurement is presented as a Rmarkdown script stored in a GitHub repository ( https://github.com/granar/B73_HydraulicMap doi: https://doi.org/10.5281/zenodo.4320861 ). All input and output data of this study are stored in the same repository.Open asset ↗granar/B73_HydraulicMap · 10.5281/zenodo.4320861lines:267-339Code / dataset availability confirmedOpenAlex · Europe PMC · checked 14 Sept 2026
The recent years have witnessed the emergence of high-throughput phenotyping techniques. In particular, these techniques can characterize a comprehensive landscape of physiological traits of plants responding to dynamic changes in the environment. These innovations, along with the next-generation genomic technologies, have brought plant science into the big-data era. However, a general framework that links multifaceted physiological traits to DNA variants is still lacking. Here, we developed a general framework that integrates functional physiological phenotyping (FPP) with functional mapping (FM). This integration, implemented with high-dimensional statistical reasoning, can aid in our understanding of how genotype is translated toward phenotype. As a demonstration of method, we implemented the transpiration and soil-plant-atmosphere measurements of a tomato introgression line population into the FPP-FM framework, facilitating the identification of quantitative trait loci (QTLs) that mediate the spatiotemporal change of transpiration rate and the test of how these QTLs control, through their interaction networks, phenotypic plasticity under drought stress.
Why it matches plant phenotyping methods植物の生理形質を取得・解析するFPP-FM統合フレームワークを開発し、トマト集団の蒸散測定で実証しており、表現型取得と解析手法が中心である。
abstractHere, we developed a general framework that integrates functional physiological phenotyping (FPP) with functional mapping (FM).
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 · publicWe have coded all statistical algorithms that build our framework into a user-friendly R package for public use ( https://github.com/FFP-FM/Version1 ).Open asset ↗FFP-FM/Version1lines:232-250Code / dataset availability confirmedOpenAlex · Crossref · Europe PMC · checked 15 Sept 2026
Abstract Chlorophyll fluorescence (ChlF) is a powerful non‐invasive technique for probing photosynthesis. Although proposed as a method for drought tolerance screening, ChlF has not yet been fully adopted in physiological breeding, mainly due to limitations in high‐throughput field phenotyping capabilities. The light‐induced fluorescence transient (LIFT) sensor has recently been shown to reliably provide active ChlF data for rapid and remote characterisation of plant photosynthetic performance. We used the LIFT sensor to quantify photosynthesis traits across time in a large panel of durum wheat genotypes subjected to a progressive drought in replicated field trials over two growing seasons. The photosynthetic performance was measured at the canopy level by means of the operating efficiency of Photosystem II ( ) and the kinetics of electron transport measured by reoxidation rates ( and ). Short‐ and long‐term changes in ChlF traits were found in response to soil water availability and due to interactions with weather fluctuations. In mild drought, and were little affected, while was consistently accelerated in water‐limited compared to well‐watered plants, increasingly so with rising vapour pressure deficit. This high‐throughput approach allowed assessment of the native genetic diversity in ChlF traits while considering the diurnal dynamics of photosynthesis.
Why it matches plant phenotyping methodsLIFTセンサーを用いた高スループットな圃場キャノピー蛍光計測が研究の中心であり、光合成形質を定量するフェノタイピング手法を実質的に適用している。
abstractThe light‐induced fluorescence transient (LIFT) sensor has recently been shown to reliably provide active ChlF data for rapid and remote characterisation of plant photosynthetic performance.
Reproduction assets foundThe paper's raw and processed/cleaned LIFT chlorophyll fluorescence and spectral phenotyping datasets for both growing seasons are openly deposited on Zenodo (DOI 10.5281/zenodo.4305673), as stated in the methods and data availability statement. TERRA-REF is only cited as the meteorological data provider (infraction: aDataset · public) and 77,946 (97%) ChlF
transients in Y1 and Y2, respectively, were averaged, resulting in one
value per trait per plot per time of measurement (N = 5,544 data
points per trait in Y1; and N = 4,032 data points per trait in Y2). The
raw data and the processed and cleaned datasets for both growing
seasons are publicly accessible (https://doi.org/10.5281/zenodo.4305673).2.9 | Statistical analysis
A linear mixed model (LMM) approach was used to analyse the resolv-
able row-column designs with repeated measures for both Y1 and Y2.
Single-stage analysis models were applied to partition variance com-
ponents and to estimate genotypic effects for all traits based on “Best
Linear Unbiased PredictioOpen asset ↗Zenodo · 10.5281/zenodo.4305673pdf-raw-page:6 lines:1-96Code / dataset availability confirmedEurope PMC · checked 9 Sept 2026
Urochloa (including Brachiaria , Megathyrus and some Panicum ) tropical grasses are native to Africa and are now, after selection and breeding, planted worldwide, particularly in South America, as important forages with huge potential for further sustainable improvement and conservation of grasslands. We aimed to develop an optimized approach to determine ploidy of germplasm collection of this tropical forage grass group using dried leaf material, including approaches to collect, dry and preserve plant samples for flow cytometry analysis. Our methods enable robust identification of ploidy levels (coefficient of variation of G0/G1 peaks, CV, typically x to 9 x ), from international genetic resource collections, showing variation in basic chromosome numbers and reproduction modes (apomixis and sexual), were determined using our defined standard protocol. Two major Urochloa agamic complexes are used in the current breeding programs at CIAT and EMBRAPA: the ' brizantha ' and ' humidicola ' agamic complexes are variable, with multiple ploidy levels. Some U. brizantha accessions have odd level of ploidy (5 x ), and the relative differences in fluorescence values of the peak positions between adjacent cytotypes is reduced, thus more precise examination of this species is required. Ploidy measurement of U. humidicola revealed aneuploidy.
Why it matches plant phenotyping methods乾燥葉を用いたフローサイトメトリーによる植物の倍数性測定法を最適化し、標準プロトコルとして確立することが研究の中心であるため、植物表現型測定法として採用する。
abstractWe aimed to develop an optimized approach to determine ploidy of germplasm collection of this tropical forage grass group using dried leaf material, including approaches to collect, dry and preserve plant samples for flow cytometry analysis.
Reproduction assets foundThe paper's Supplementary Data Table S1 (hosted at the MDPI supplementary URL) contains the paper-specific phenotype measurements: the list of 348 Urochloa accessions with their flow cytometry fluorescence peak positions and G0/G1 CVs. This is a public, directly actionable dataset reproducing the paper's ploidy/phenotyDataset · publicThe following are available online at https://www.mdpi.com/article/10.3390/genes12070957/s1 , Supplementary Data Table S1. List of accessions used in the study, their fluorescence values of the peak positions, and coefficient of variations (CVs) of the G0/G1 peaks.Open asset ↗lines:351-363Code / dataset availability confirmedCrossref · checked 9 Sept 2026
Abstract. Plant shoots can act as sources or sinks of trace gases including methane and nitrous oxide. Accurate measurements of these trace gas fluxes require enclosing of shoots in closed non-steady-state chambers. Due to plant physiological activity, this type of enclosure, however, leads to CO2 depletion in the enclosed air volume, condensation of transpired water, and warming of the enclosures exposed to sunlight, all of which may bias the flux measurements. Here, we present ShoTGa-FluMS (SHOot Trace Gas FLUx Measurement System), a novel measurement system designed for continuous and automated measurements of trace gas and volatile organic compound (VOC) fluxes from plant shoots. The system uses transparent shoot enclosures equipped with Peltier cooling elements and automatically replaces fixated CO2 and removes transpired water from the enclosure. The system is designed for measuring trace gas fluxes over extended periods, capturing diurnal and seasonal variations, and linking trace gas exchange to plant physiological functioning and environmental drivers. Initial measurements show daytime CH4 emissions of two pine shoots of 0.056 and 0.089 nmol per gram of foliage dry weight (d.w.) per hour or 7.80 and 13.1 nmolm-2h-1. Simultaneously measured CO2 uptake rates were 9.2 and 7.6 mmolm-2h-1, and transpiration rates were 1.24 and 0.90 molm-2h-1. Concurrent measurement of VOC emissions demonstrated that potential effects of spectral interferences on CH4 flux measurements were at least 10-fold smaller than the measured CH4 fluxes. Overall, this new system solves multiple technical problems that have so far prevented automated plant shoot trace gas flux measurements and holds the potential for providing important new insights into the role of plant foliage in the global CH4 and N2O cycles.
Why it matches plant phenotyping methods植物シュートからの微量ガス・VOCフラックスを自動・連続測定する装置を開発し、技術的課題を解決しているため、植物生理状態の取得方法が研究の中心である。
abstractHere, we present ShoTGa-FluMS (SHOot Trace Gas FLUx Measurement System), a novel measurement system designed for continuous and automated measurements of trace gas and volatile organic compound (VOC) fluxes from plant shoots.
Reproduction assets foundThe paper's Code and data availability section states that raw measurement data and the analysis script are deposited on Zenodo (doi 10.5281/zenodo.4609836) and that the custom control software (Koppi/koppismear) is publicly available on Bitbucket. Both are paper-specific, public, and actionable.Dataset · publicRaw measurement data and the analysis script are available at Zenodo ( https://doi.org/10.5281/zenodo.4609836 ; Kohl et al. , 2021 ).Open asset ↗Zenodo · 10.5281/zenodo.4609836lines:435-476Code · publicRaw measurement data and the analysis script are available at Zenodo ( https://doi.org/10.5281/zenodo.4609836 ; Kohl et al. , 2021 ). The software used to operate both systems is available online at https://bitbucket.org/makoskinen/koppismear/ ( Koskinen , 2021 ) .Open asset ↗Bitbucket · koppismearlines:435-476Code / dataset availability confirmedEurope PMC · checked 9 Sept 2026
Background The fraction of intercepted photosynthetically active radiation (fPARi) is typically described with a non-linear function of leaf area index (LAI) and k, the light extinction coefficient. The parameter k is used to make statistical inference, as an input into crop models, and for phenotyping. It may be estimated using a variety of statistical techniques that differ in assumptions, which ultimately influences the numerical value k and associated uncertainty estimates. A systematic search of peer-reviewed publications for maize (Zea Mays L.) revealed: (i) incompleteness in reported estimation techniques; and (ii) that most studies relied on dated techniques with unrealistic assumptions, such as log-transformed linear models (LogTLM) or normally distributed data. These findings suggest that knowledge of the variety and trade-offs among statistical estimation techniques is lacking, which hinders the use of modern approaches such as Bayesian estimation (BE) and techniques with appropriate assumptions, e.g. assuming beta-distributed data. Results The parameter k was estimated for seven maize genotypes with five different methods: least squares estimation (LSE), LogTLM, maximum likelihood estimation (MLE) assuming normal distribution, MLE assuming beta distribution, and BE assuming beta distribution. Methods were compared according to the appropriateness for statistical inference, point estimates' properties, and predictive performance. LogTLM produced the worst predictions for fPARi, whereas both LSE and MLE with normal distribution yielded unrealistic predictions (i.e. fPARi 1) and the greatest coefficients for k. Models with beta-distributed fPARi (either MLE or Bayesian) were recommended to obtain point estimates. Conclusion Each estimation technique has underlying assumptions which may yield different estimates of k and change inference, like the magnitude and rankings among genotypes. Thus, for reproducibility, researchers must fully report the statistical model, assumptions, and estimation technique. LogTLMs are most frequently implemented, but should be avoided to estimate k. Modeling fPARi with a beta distribution was an absent practice in the literature but is recommended, applying either MLE or BE. This workflow and technique comparison can be applied to other plant canopy models, such as the vertical distribution of nitrogen, carbohydrates, photosynthesis, etc. Users should select the method balancing benefits and tradeoffs matching the purpose of the study.
Why it matches plant phenotyping methodsトウモロコシの光遮断係数kとfPARiを推定する統計手法を比較・評価し、植物キャノピー形質の再現可能な推定ワークフローとして推奨手法を提示しているため、方法論が中心である。
abstractThe parameter k was estimated for seven maize genotypes with five different methods: least squares estimation (LSE), LogTLM, maximum likelihood estimation (MLE) assuming normal distribution, MLE assuming beta distribution, and BE assuming beta distribution.
Reproduction assets foundThe paper's authors explicitly state that the R code implementing the k-estimation analysis (LSE, LogTLM, MLE, Bayesian estimation) is freely available in a public GitHub repository. The underlying phenotype datasets (fPARi/LAI measurements for seven maize genotypes) are only available from the corresponding author on,Code · publicR code is freely available at https://github.com/jlacasa/k-estimation/blob/main/k_estimation_02182021.Rmd .Open asset ↗https://github.com/jlacasa/k-estimation · k_estimation_02182021.Rmdlines:172-210Code / dataset availability confirmedOpenAlex · Europe PMC · checked 14 Sept 2026
Many biochemical and physiological properties of plants that are of interest to breeders and geneticists have extremely low throughput and/or can only be measured destructively. This has limited the use of information on natural variation in nutrient and metabolite abundance, as well as photosynthetic capacity in quantitative genetic contexts where it is necessary to collect data from hundreds or thousands of plants. A number of recent studies have demonstrated the potential to estimate many of these traits from hyperspectral reflectance data, primarily in ecophysiological contexts. Here, we summarize recent advances in the use of hyperspectral reflectance data for plant phenotyping, and discuss both the potential benefits and remaining challenges to its application in plant genetics contexts. The performances of previously published models in estimating six traits from hyperspectral reflectance data in maize were evaluated on new sample datasets, and the resulting predicted trait values shown to be heritable (e.g., explained by genetic factors) were estimated. The adoption of hyperspectral reflectance-based phenotyping beyond its current uses may accelerate the study of genes controlling natural variation in biochemical and physiological traits.
Why it matches plant phenotyping methods植物形質をハイパースペクトル反射データから推定する手法をレビューし、トウモロコシの新規サンプルで既存モデルを評価しており、表現型取得・推定法が中心である。
abstractHere, we summarize recent advances in the use of hyperspectral reflectance data for plant phenotyping, and discuss both the potential benefits and remaining challenges to its application in plant genetics contexts.
Reproduction assets foundThe paper's Data availability statement explicitly deposits the authors' spectral reflectance data and ground truth phenotyping measurements in a public repository (Zenodo-style DOI 10.21232/y5TTxY3N), which is an allowed URL. This is a paper-specific, publicly actionable hyperspectral phenotyping dataset.Dataset · publicd the potential for reusable genotypic datasets, that makes the potential of hyperspectral reflectance phenotyping to both expand our current genetic knowledge and address the challenges of breeding for the 21st century so exciting.
Data availability
Spectral reflectance data and ground truth measurements have been deposited in https://doi.org/10.21232/y5TTxY3N .
Funding
This research was supported by the Office of Science (BER), 10.13039/100000015 U.S. Department of Energy , grant no. DE-SC0020355 to J.C.S. and Y.G., the 10.13039/100000001 National Science Foundation under grant OIA-1557417 to Y.G. and J.C.S. and OIA-1826781 to J.C.S. This project was completed utilizing the HollandOpen asset ↗10.21232/y5TTxY3Nlines:311-337Code / dataset availability confirmedEurope PMC · checked 9 Sept 2026
Roots are at the core of plant water dynamics. Nonetheless, root morphology and functioning are not easily assessable without destructive approaches. Nuclear Magnetic Resonance (NMR), and particularly low-field NMR (LF-NMR), is an interesting noninvasive method to study water in plants, as measurements can be performed outdoors and independent of sample size. However, as far as we know, there are no reported studies dealing with the water dynamics in plant roots using LF-NMR. Thus, the aim of this study is to assess the feasibility of using LF-NMR to characterize root water status and water dynamics non-invasively. To achieve this goal, a proof-of-concept study was designed using well-controlled environmental conditions. NMR and ecophysiological measurements were performed continuously over one week on three herbaceous species grown in rhizotrons. The NMR parameters measured were either the total signal or the transverse relaxation time T 2 . We observed circadian variations of the total NMR signal in roots and in soil and of the root slow relaxing T 2 value. These results were consistent with ecophysiological measurements, especially with the variation of fluxes between daytime and nighttime. This study assessed the feasibility of using LF-NMR to evaluate root water status in herbaceous species.
Why it matches plant phenotyping methodsLF-NMRによる根の水分状態・水動態の非破壊的測定可能性を中心に検証した proof-of-concept 研究であり、植物生理状態の取得手法が主要な貢献である。
abstractthe aim of this study is to assess the feasibility of using LF-NMR to characterize root water status and water dynamics non-invasively.
Reproduction assets foundThe authors deposited the paper's NMR and ecophysiological measurement data openly in Data INRAE (DOI 10.15454/NWRHDA), and supplementary materials at MDPI contain the CPMG decay curves and NNLS processing methods used for the T2 analysis.Dataset · publicThe data presented in this study are openly available in Data INRAE ( https://data.inrae.fr/ , accessed on 12 March 2021) repository at https://doi.org/10.15454/NWRHDA (accessed on 12 March 2021).Open asset ↗Data INRAE · 10.15454/NWRHDAlines:115-137Code / dataset availability confirmedCrossref · checked 14 Sept 2026
Abstract. Plant shoots can act as sources or sinks of trace gases including methane and nitrous oxide. Accurate measurementsof these trace gas fluxes require enclosing of shoots in closed non-steady state chambers. Due to plant physiological activity, this type of enclosures, however, lead to CO2 depletion in the enclosed air volume, condensation of transpired water, and warmingof the enclosures exposed to sunlight, all of which may bias the flux measurements. Here, we present PlasTraGAS, ab novel measurement system designed for continuous and automated measurements of trace gas and volatile organic compound (VOC) fluxes from plant shoots. The system uses transparent shoot enclosures equipped with Peltier cooling elements and automatically replaces fixated CO2 and removes transpired water from the enclosure. The system is designed for measuring trace gasfluxes over extended periods, capturing diurnal and seasonal variations and linking trace gas exchange to plant physiologicalfunctioning and environmental drivers. Initial measurements show daytime CH4 emissions two pine shoots of 0.056 and 0.089 nmol g−1 foliage d.w.h−1or 7.80 and 13.1 nmol m−2 h−1. Simultaneously measured CO2 uptake rates were 9.2 and 7.6 mmol m−2 sec−1 and transpiration rates of 1.24 and 0.90 mol m−2 h−1. Concurrent measurement of VOC emissionsdemonstrated that potential effects of spectral interferences on CH4 flux measurements were at least ten-fold smaller than themeasured CH4 fluxes. Overall, this new system solves multiple technical problems that so far prevented automated plant shoottrace gas flux measurements, and holds the potential for providing important new insights into the role of plant foliage in the global CH4 and N2O cycles.
Why it matches plant phenotyping methods植物シュートからのガスフラックスを連続・自動測定する新規システムの開発が中心であり、植物の生理状態に関わる測定法を提供している。
abstractHere, we present PlasTraGAS, ab novel measurement system designed for continuous and automated measurements of trace gas and volatile organic compound (VOC) fluxes from plant shoots.
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 used to operate both systems is available online at
https://bitbucket.org/makoskinen/koppismear/ (Koskinen, 2021).Open asset ↗Bitbucket · makoskinen/koppismearpdf-page:14 lines:1-64Code / dataset availability confirmedCrossref · checked 15 Sept 2026
Abstract. Root water uptake by plants is a vital process that influences terrestrial energy, water, and carbon exchanges. At the soil, vegetation, and atmosphere interfaces, root water uptake and solar radiation predominantly regulate the dynamics and health of vegetation growth, which can be remotely monitored by satellites, using the soil–plant relationship proxy – solar-induced chlorophyll fluorescence. However, most current canopy photosynthesis and fluorescence models do not account for root water uptake, which compromises their applications under water-stressed conditions. To address this limitation, this study integrated photosynthesis, fluorescence emission, and transfer of energy, mass, and momentum in the soil–plant–atmosphere continuum system, via a simplified 1D root growth model and a resistance scheme linking soil, roots, leaves, and the atmosphere. The coupled model was evaluated with field measurements of maize and grass canopies. The results indicated that the simulation of land surface fluxes was significantly improved by the coupled model, especially when the canopy experienced moderate water stress. This finding highlights the importance of enhanced soil heat and moisture transfer, as well as dynamic root growth, on simulating ecosystem functioning.
Why it matches plant phenotyping methods植物の光合成・蛍光などの生理状態を推定する結合モデルを開発し、トウモロコシおよび草本キャノピーで評価しており、モデル開発と検証が研究の中心です。
abstractTo address this limitation, this study integrated photosynthesis, fluorescence emission, and transfer of energy, mass, and momentum in the soil–plant–atmosphere continuum system, via a simplified 1D root growth model and a resistance scheme linking soil, roots, leaves, and the atmosphere.
Reproduction assets foundThe paper's Code and data availability section explicitly archives the exact STEMMUS–SCOPE model version on Zenodo and publishes the Yangling eddy-covariance validation dataset on 4TU, both with public DOIs.Dataset · publicdoi.org/10.5281/zenodo.3839092 , Wang et al., 2020). The original source of the SCOPE model and STEMMUS model was obtained from Van der Tol et al. (2009) and Zeng et al. (2011a, b), respectively. The tower-based eddy-covariance measurements used for model validation were provided by the authors for the Yangling station, China ( https://doi.org/10.4121/uuid:aa0ed483-701e-4ba0-b7b0-674695f5f7a7 , Wang et al., 2019), and were obtained from the FLUXNET2015 Dataset and PLUMBER2 program for the Vaira Ranch (US-Var) FLUXNET site.
Author contributions
YW, YZ, HC, and ZS designed the study. YW developed the code, conducted the analysis, and wrote the paper. YW and HC collected and shared their eddy-cOpen asset ↗10.4121/uuid:aa0ed483-701e-4ba0-b7b0-674695f5f7a7lines:657-684Code / dataset availability confirmedOpenAlex · Crossref · Europe PMC · checked 8 Sept 2026
One of the objectives of many studies conducted by breeding programs is to characterize and select rootstocks well-adapted to drought conditions. In recent years, field high-throughput phenotyping methods have been developed to characterize plant traits and to identify the most water use efficient varieties and rootstocks. However, none of these studies have been able to quantify the behavior of crop evapotranspiration in almond rootstocks under different water regimes. In this study, remote sensing phenotyping methods were used to assess the evapotranspiration of almond cv. “Marinada” grafted onto a rootstock collection. In particular, the two-source energy balance and Shuttleworth and Wallace models were used to, respectively, estimate the actual and potential evapotranspiration of almonds grafted onto 10 rootstock under three different irrigation treatments. For this purpose, three flights were conducted during the 2018 and 2019 growing seasons with an aircraft equipped with a thermal and multispectral camera. Stem water potential (Ψstem) was also measured concomitant to image acquisition. Biophysical traits of the vegetation were firstly assessed through photogrammetry techniques, spectral vegetation indices and the radiative transfer model PROSAIL. The estimates of canopy height, leaf area index and daily fraction of intercepted radiation had root mean square errors of 0.57 m, 0.24 m m–1 and 0.07%, respectively. Findings of this study showed significant differences between rootstocks in all of the evaluated parameters. Cadaman® and Garnem® had the highest canopy vigor traits, evapotranspiration, Ψstem and kernel yield. In contrast, Rootpac® 20 and Rootpac® R had the lowest values of the same parameters, suggesting that this was due to an incompatibility between plum-almond species or to a lower water absorption capability of the rooting system. Among the rootstocks with medium canopy vigor, Adesoto and IRTA 1 had a lower evapotranspiration than Rootpac® 40 and Ishtara®. Water productivity (WP) (kg kernel/mm water evapotranspired) tended to decrease with Ψstem, mainly in 2018. Cadaman® and Garnem® had the highest WP, followed by INRA GF-677, IRTA 1, IRTA 2, and Rootpac® 40. Despite the low Ψstem of Rootpac® R, the WP of this rootstock was also high.
Why it matches plant phenotyping methodsリモートセンシングによる植物形質・蒸発散の推定が研究の中心で、熱・マルチスペクトル画像、フォトグラメトリ、モデルを用いた推定精度も評価している。
abstractIn recent years, field high-throughput phenotyping methods have been developed to characterize plant traits and to identify the most water use efficient varieties and rootstocks.
Reproduction assets foundThe paper's data availability statement points to the author's public GitHub profile (Héctor Nieto, pyTSEB developer) as the location of the datasets analyzed, which include the remote sensing phenotyping measurements (thermal/multispectral imagery-derived ETa, LAI, fiPAR, Ψstem relationships) and the TSEB-based model.Dataset · publicPublicly available datasets were analyzed in this study. This data can be found here: https://github.com/hectornieto .Open asset ↗hectornietolines:1046-1107Code / dataset availability confirmedEurope PMC · bioRxiv · checked 15 Sept 2026
Field / plotRaman / spectroscopyLeafWhole plant / canopy / plot / fieldObject detectionCalibration / preprocessingGrowth / development / phenologyLeaf traitsWater status / transpiration
The measurement of leaf optical properties (LOP) using reflectance and scattering properties of light allows a continuous, time-resolved, and rapid characterization of many species traits including water status, chemical composition, and leaf structure. Variation in trait values expressed by individuals result from a combination of biological and environmental variations. Such species trait variations are increasingly recognized as drivers and responses of biodiversity and ecosystem properties. However, little has been done to comprehensively characterize or monitor such variation using leaf reflectance, where emphasis is more often on species average values. Furthermore, although a variety of platforms and protocols exist for the estimation of leaf reflectance, there is neither a standard method, nor a best practise of treating measurement uncertainty which has yet been collectively adopted. In this study, we investigate what level of uncertainty can be accepted when measuring leaf reflectance while ensuring the detection of species trait variation at several levels: within individuals, over time, between individuals, and between populations. As a study species, we use an economically and ecologically important dominant European tree species, namely Fagus sylvatica . We first use fabrics as standard material to quantify the measurement uncertainties associated with leaf clip (0.0001 to 0.4 reflectance units) and integrating sphere measurements (0.0001 to 0.01 reflectance units) via error propagation. We then quantify spectrally resolved variation in reflectance from F. sylvatica leaves. We show that the measurement uncertainty associated with leaf reflectance, estimated using a field spectroradiometer with attached leaf clip, represents on average a small portion of the spectral variation within a single individual sampled over time (2.7 ± 1.7%), or between individuals (1.5 ± 1.3% or 3.4 ± 1.7%, respectively) in a set of monitored F. sylvatica trees located in Swiss and French forests. In all forests, the spectral variation between individuals exceeded the spectral variation of a single individual measured within one week. However, measurements of variation within an individual at different canopy positions over time indicate that sampling design (e.g., standardized sampling, and sample size) strongly impacts our ability to measure between-individual variation. We suggest best practice approaches towards a standardized protocol to allow for rigorous quantification of species trait variation using leaf reflectance. Highlights We partition biological variation from measurement uncertainty for leaf spectra. Measurement uncertainty represents ca. 3% of spectral variation among beech trees. Biological variation within an individual increases by 80% as leaves mature. Maxima of uncertainty correspond to maxima of biological variation (water content). We recommend procedures to quantify biological variation in spectral measurements.
Why it matches plant phenotyping methods葉の反射分光測定における不確実性を定量化し、植物形質変異の検出能力と標準化プロトコルを評価する研究であり、フェノタイピング手法が中心です。
abstractWe first use fabrics as standard material to quantify the measurement uncertainties associated with leaf clip (0.0001 to 0.4 reflectance units) and integrating sphere measurements (0.0001 to 0.01 reflectance units) via error propagation.
Reproduction assets foundThe paper deposits its analysis scripts and source data in Dryad and its FieldSpec leaf/fabric reflectance spectra in the SPECCHIO spectral database, both publicly accessible with explicit identifiers.Dataset · publicumption. The number of
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replicates is indicated for each dataset. Data processing and statistical analyses were all
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performed in Matlab R2020a. Normality tests, t-tests and ANOVAs were performed on
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individual wavelengths; see Results and figure captions for details. Scripts and source data
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are available in Dryad (https://doi.org/10.5061/dryad.gtht76hkx). FieldSpec
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spectroradiometer data are also deposited in SPECCHIO
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(http://sc22.geo.uzh.ch:8080/SPECCHIO_Web_Interface/search, Hueni et al., 2020) and can
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be found with the identifiers ‘Field spectroscopy Fabrics’ (dataset A), ‘Field spectroscopy F.
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Sylvatica individual’ (dataset B), ‘Field spectroscopy F. sylvatOpen asset ↗Dryad · 10.5061/dryad.gtht76hkxpdf-raw-page:17 lines:1-49Dataset · publicall
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performed in Matlab R2020a. Normality tests, t-tests and ANOVAs were performed on
392
individual wavelengths; see Results and figure captions for details. Scripts and source data
393
are available in Dryad (https://doi.org/10.5061/dryad.gtht76hkx). FieldSpec
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spectroradiometer data are also deposited in SPECCHIO
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(http://sc22.geo.uzh.ch:8080/SPECCHIO_Web_Interface/search, Hueni et al., 2020) and can
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be found with the identifiers ‘Field spectroscopy Fabrics’ (dataset A), ‘Field spectroscopy F.
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Sylvatica individual’ (dataset B), ‘Field spectroscopy F. sylvatica La Massane’ (dataset C),
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‘Field spectroscopy F. sylvatica SwissForest’ (dataset D). Visualization and descriOpen asset ↗SPECCHIOpdf-raw-page:17 lines:1-49Code / dataset availability confirmedEurope PMC · checked 9 Sept 2026
The seeds of Arabidopsis thaliana become encapsulated by a layer of mucilage when imbibed. This polysaccharide-rich hydrogel is constituted of two layers, an outer layer that can be easily extracted with water and an inner layer that must be examined in situ in order to study its properties and structure in a non-destructive manner or disintegrated through hydrolysis or physical means in order to analyze its constituents. Mucilage production is an adaptive trait and we have exploited 19 natural accessions previously found to have atypical and varied outer mucilage characteristics. A detailed study using biochemical, histological and Time-Domain NMR analyses has been used to generate three related datasets covering 33 traits measured in four biological replicates. This data will be a rich resource for genetic, biochemical, structural and functional analyses investigating mucilage constituent polysaccharides or their role as adaptive traits.
Why it matches plant phenotyping methodsアラビドプシス種子の粘液形質を対象に、33形質・4反復の再利用可能なデータセットを生成した研究であり、植物形質データセットの構築が中心です。
abstractA detailed study using biochemical, histological and Time-Domain NMR analyses has been used to generate three related datasets covering 33 traits measured in four biological replicates.
Reproduction assets foundThe paper deposits its plant-phenotyping measurements in three Data INRAE datasets. Two of them (dataset 1: 33 mucilage/seed traits; dataset 3: individual microscopy measurements of mucilage and seed width) have DOIs matching allowed_urls entries and are directly citable public assets. Dataset 2's DOI (10.15454/EYABB2)Dataset · publicCambert, M. et al. Seed mucilage traits for Arabidopsis thaliana natural accessions with atypical outer mucilage - dataset 1. Portail
Data INRAE https://doi.org/10.15454/1MZ1ZC (2021).Open asset ↗10.15454/1MZ1ZCpdf-page:9 lines:1-70Dataset · publicBerger, A., Sallé, C. & North, H. M. Measurements of inner mucilage and seed width for Arabidopsis natural accessions - dataset 3.
Portail Data INRAE https://doi.org/10.15454/LBUN4X (2021).Open asset ↗Portail Data INRAE · 10.15454/LBUN4Xpdf-page:9 lines:1-70Code / dataset availability confirmedOpenAlex · Crossref · Europe PMC · checked 8 Sept 2026
Summary The root economics space is a useful framework for plant ecology but is rarely considered for crop ecophysiology. In order to understand root trait integration in winter wheat, we combined functional phenomics with trait economic theory, utilizing genetic variation, high‐throughput phenotyping, and multivariate analyses. We phenotyped a diversity panel of 276 genotypes for root respiration and architectural traits using a novel high‐throughput method for CO 2 flux and the open‐source software RhizoVision Explorer to analyze scanned images. We uncovered substantial variation in specific root respiration (SRR) and specific root length (SRL), which were primary indicators of root metabolic and structural costs. Multiple linear regression analysis indicated that lateral root tips had the greatest SRR, and the residuals from this model were used as a new trait. Specific root respiration was negatively correlated with plant mass. Network analysis, using a Gaussian graphical model, identified root weight, SRL, diameter, and SRR as hub traits. Univariate and multivariate genetic analyses identified genetic regions associated with SRR, SRL, and root branching frequency, and proposed gene candidates. Combining functional phenomics and root economics is a promising approach to improving our understanding of crop ecophysiology. We identified root traits and genomic regions that could be harnessed to breed more efficient crops for sustainable agroecosystems.
Why it matches plant phenotyping methods根の呼吸と構造を対象に、CO2フラックスの新規ハイスループット法と画像解析ソフトウェアを用いた機能的フェノミクスを中心的に実施しており、植物形質取得法が研究の主要部分である。
abstractWe phenotyped a diversity panel of 276 genotypes for root respiration and architectural traits using a novel high‐throughput method for CO 2 flux and the open‐source software RhizoVision Explorer to analyze scanned images.
Reproduction assets foundThe paper explicitly deposits its trait data, GEMMA GWAS output, and R analysis scripts at Zenodo (10.5281/zenodo.4247894), and separately deposits the root respiration measurement protocol and flux-calculation R scripts at Zenodo (10.5281/zenodo.4247873). Both are paper-specific, public, and actionable. The Triticeae-Dataset · publicAll trait data, gemma output, and R analysis scripts necessary for the statistical analysis and plotting are publicly available at https://doi.org/10.5281/zenodo.4247894 (Guo et al.,
2020b ).Open asset ↗Zenodo · 10.5281/zenodo.4247894lines:608-654Code · publicThe protocol for the root respiration measurements and the R script for calculating total flux from a directory of text files are available at https://doi.org/10.5281/zenodo.4247873 (Guo et al.,
2020a ).Open asset ↗Zenodo · 10.5281/zenodo.4247873lines:85-97Code / dataset availability confirmedOpenAlex · Europe PMC · checked 8 Sept 2026
Hyperspectral imaging is a promising tool for non-destructive phenotyping of plant physiological traits, which has been transferred from remote to proximal sensing applications, and from manual laboratory setups to automated plant phenotyping platforms. Due to the higher resolution in proximal sensing, illumination variation and plant geometry result in increased non-biological variation in plant spectra that may mask subtle biological differences. Here, a better understanding of spectral measurements for proximal sensing and their application to study drought, developmental and diurnal responses was acquired in a drought case study of maize grown in a greenhouse phenotyping platform with a hyperspectral imaging setup. The use of brightness classification to reduce the illumination-induced non-biological variation is demonstrated, and allowed the detection of diurnal, developmental and early drought-induced changes in maize reflectance and physiology. Diurnal changes in transpiration rate and vapor pressure deficit were significantly correlated with red and red-edge reflectance. Drought-induced changes in effective quantum yield and water potential were accurately predicted using partial least squares regression and the newly developed Water Potential Index 2, respectively. The prediction accuracy of hyperspectral indices and partial least squares regression were similar, as long as a strong relationship between the physiological trait and reflectance was present. This demonstrates that current hyperspectral processing approaches can be used in automated plant phenotyping platforms to monitor physiological traits with a high temporal resolution.
Why it matches plant phenotyping methods近接ハイパースペクトル画像を用いた植物生理形質の非破壊フェノタイピング手法を扱い、照明変動補正、形質予測、プラットフォーム適用を技術的に検証しているため、方法が中心である。
abstractHyperspectral imaging is a promising tool for non-destructive phenotyping of plant physiological traits
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
The relationship between relative reflectance and physiological traits.Open asset ↗lines:646-777Code / dataset availability confirmedOpenAlex · Europe PMC · checked 14 Sept 2026
Deep understanding of genetic architecture of water-stress tolerance is critical for efficient and optimal development of water-stress tolerant cultivars, which is the most economical and environmentally sound approach to maintain lettuce production with limited irrigation. Lettuce ( Lactuca sativa L.) production in areas with limited precipitation relies heavily on the use of ground water for irrigation. Lettuce plants are highly susceptible to water-stress, which also affects their nutrient uptake efficiency. Water stressed plants show reduced growth, lower biomass, and early bolting and flowering resulting in bitter flavors. Traditional phenotyping methods to evaluate water-stress are labor intensive, time-consuming and prone to errors. High throughput phenotyping platforms using kinetic chlorophyll fluorescence and hyperspectral imaging can effectively attain physiological traits related to photosynthesis and secondary metabolites that can enhance breeding efficiency for water-stress tolerance. Kinetic chlorophyll fluorescence and hyperspectral imaging along with traditional horticultural traits identified genomic loci affected by water-stress. Supervised machine learning models were evaluated for their accuracy to distinguish water-stressed plants and to identify the most important water-stress related parameters in lettuce. Random Forest (RF) had classification accuracy of 89.7% using kinetic chlorophyll fluorescence parameters and Neural Network (NN) had classification accuracy of 89.8% using hyperspectral imaging derived vegetation indices. The top ten chlorophyll fluorescence parameters and vegetation indices selected by sequential forward selection by RF and NN were genetically mapped using a L. sativa × L. serriola interspecific recombinant inbred line (RIL) population. A total of 25 quantitative trait loci (QTL) segregating for water-stress related horticultural traits, 26 QTL for the chlorophyll fluorescence traits and 34 QTL for spectral vegetation indices (VI) were identified. The percent phenotypic variation (PV) explained by the horticultural QTL ranged from 6.41 to 19.5%, PV explained by chlorophyll fluorescence QTL ranged from 6.93 to 13.26% while the PV explained by the VI QTL ranged from 7.2 to 17.19%. Eight QTL clusters harboring co-localized QTL for horticultural traits, chlorophyll fluorescence parameters and VI were identified on six lettuce chromosomes. Molecular markers linked to the mapped QTL clusters can be targeted for marker-assisted selection to develop water-stress tolerant lettuce.
Why it matches plant phenotyping methods水ストレス関連形質の取得を目的に、キネティッククロロフィル蛍光、ハイパースペクトル画像、機械学習を用いる高スループット表現型解析基盤を評価・適用しており、方法が研究の中心である。
abstractTraditional phenotyping methods to evaluate water-stress are labor intensive, time-consuming and prone to errors. High throughput phenotyping platforms using kinetic chlorophyll fluorescence and hyperspectral imaging can effectively attain physiological traits related to photosynthesis and secondary metabolites that can enhance breeding efficiency for water-stress tolerance.
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 Table 1
Phenotypic values of selected chlorophyll fluorescence parameters and vegetation indices during drought stress progression.Open asset ↗lines:1465-1521Code / dataset availability confirmedCrossref · Europe PMC · checked 9 Sept 2026
Basil is one of the most widespread aromatic and medicinal plants, which is often grown in drought- and salinity-prone regions. Often co-occurrence of drought and salinity stresses in agroecosystems and similarities of symptoms which they cause on plants complicates the differentiation among them. Development of automated phenotyping techniques with integrative and simultaneous quantification of multiple morphological and physiological traits enables early detection and quantification of different stresses on a whole plant basis. In this study, we have used different phenotyping techniques including chlorophyll fluorescence imaging, multispectral imaging, and 3D multispectral scanning, aiming to quantify changes in basil phenotypic traits under early and prolonged drought and salinity stress and to determine traits which could differentiate among drought and salinity stressed basil plants. Ocimum basilicum “Genovese” was grown in a growth chamber under well-watered control [45–50% volumetric water content (VWC)], moderate salinity stress (100 mM NaCl), severe salinity stress (200 mM NaCl), moderate drought stress (25–30% VWC), and severe drought stress (15–20% VWC). Phenotypic traits were measured for 3 weeks in 7-day intervals. Automated phenotyping techniques were able to detect basil responses to early and prolonged salinity and drought stress. In addition, several phenotypic traits were able to differentiate among salinity and drought. At early stages, low anthocyanin index (ARI), chlorophyll index (CHI), and hue (HUE 2 D ), and higher reflectance in red (R Red ), reflectance in green (R Green ), and leaf inclination (LINC) indicated drought stress. At later stress stages, maximum fluorescence (F m ), HUE 2 D , normalized difference vegetation index (NDVI), and LINC contribute the most to the differentiation among drought and non-stressed as well as among drought and salinity stressed plants. ARI and electron transport rate (ETR) were best for differentiation of salinity stressed plants from non-stressed plants both at early and prolonged stress.
Why it matches plant phenotyping methods複数の自動フェノタイピング技術を用いて、形態・生理形質を統合的に定量し、乾燥・塩ストレスの早期検出と識別を評価しており、フェノタイピング手法の応用が研究の中心である。
abstractDevelopment of automated phenotyping techniques with integrative and simultaneous quantification of multiple morphological and physiological traits enables early detection and quantification of different stresses on a whole plant basis.
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
Analysis of variance (ANOVA) for measured phenotypic traits of basil grown in different treatments: control (C), moderate salinity stress (S1), severe salinity stress (S2), moderate drought (D1), and severe drought (D2).Open asset ↗lines:494-517Code / dataset availability confirmedOpenAlex · Europe PMC · checked 8 Sept 2026
Plants have evolved several adaptive mechanisms to cope with water-limited conditions. While most of them are through constitutive traits, certain "acquired tolerance" traits also provide significant improvement in drought adaptation. Most abiotic stresses, especially drought, show a gradual progression of stress and hence provide an opportunity to upregulate specific protective mechanisms collectively referred to as "acquired tolerance" traits. Here, we demonstrate a significant genetic variability in acquired tolerance traits among rice germplasm accessions after standardizing a novel gradual stress progress protocol. Two contrasting genotypes, BPT 5204 (drought susceptible) and AC 39000 (tolerant), were used to standardize methodology for capturing acquired tolerance traits at seedling phase. Seedlings exposed to gradual progression of stress showed higher recovery with low free radical accumulation in both the genotypes compared to rapid stress. Further, the gradual stress progression protocol was used to examine the role of acquired tolerance at flowering phase using a set of 17 diverse rice genotypes. Significant diversity in free radical production and scavenging was observed among these genotypes. Association of these parameters with yield attributes showed that genotypes that managed free radical levels in cells were able to maintain high spikelet fertility and hence yield under stress. This study, besides emphasizing the importance of acquired tolerance, explains a high throughput phenotyping approach that significantly overcomes methodological constraints in assessing genetic variability in this important drought adaptive mechanism.
Why it matches plant phenotyping methodsイネの乾燥適応形質を評価するための段階的ストレス付与プロトコルを標準化し、高スループット表現型解析として方法論的制約を克服する手法を提示しているため、表現型取得法が中心的です。
abstractafter standardizing a novel gradual stress progress protocol
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 ) during the Kharif season of 2019 to confirm the trait diversity, particularly for acquired tolerance traits.Open asset ↗lines:351-362Code / dataset availability confirmedCrossref · checked 9 Sept 2026
Abstract Mass spectrometry–based imaging (MSI) has emerged as a promising method for spatial metabolomics in plant science. Several ionisation techniques have shown great potential for the spatially resolved analysis of metabolites in plant tissue. However, limitations in technology and methodology limited the molecular information for irregular 3D surfaces with resolutions on the micrometre scale. Here, we used atmospheric-pressure 3D-surface matrix-assisted laser desorption/ionisation mass spectrometry imaging (3D-surface MALDI MSI) to investigate plant chemical defence at the topographic molecular level for the model system Asclepias curassavica . Upon mechanical damage (simulating herbivore attacks) of native A. curassavica leaves, the surface of the leaves varies up to 700 μm, and cardiac glycosides (cardenolides) and other defence metabolites were exclusively detected in damaged leaf tissue but not in different regions of the same leaf. Our results indicated an increased latex flow rate towards the point of damage leading to an accumulation of defence substances in the affected area. While the concentration of cardiac glycosides showed no differences between 10 and 300 min after wounding, cardiac glycosides decreased after 24 h. The employed autofocusing AP-SMALDI MSI system provides a significant technological advancement for the visualisation of individual molecule species on irregular 3D surfaces such as native plant leaves. Our study demonstrates the enormous potential of this method in the field of plant science including primary metabolism and molecular mechanisms of plant responses to abiotic and biotic stress and symbiotic relationships. Graphical abstract
Why it matches plant phenotyping methods植物葉の不規則な3D表面で防御化合物を空間可視化するMSI技術の技術的進展と適用が中心であり、植物状態・応答の表現型取得法に該当する。
abstractHere, we used atmospheric-pressure 3D-surface matrix-assisted laser desorption/ionisation mass spectrometry imaging (3D-surface MALDI MSI) to investigate plant chemical defence at the topographic molecular level
Reproduction assets foundThe paper's MALDI mass spectrometry imaging data (MS image files of Asclepias curassavica leaf measurements) are publicly deposited in the METASPACE database, as stated in the Data availability section. This is a paper-specific, publicly accessible dataset directly reproducing the study's imaging measurements. No code,Dataset · publicAll MS image files are available from the METASPACE database ( https://metaspace2020.eu/project/DD_Asclepias_3DMSI ).Open asset ↗METASPACE · DD_Asclepias_3DMSIlines:109-164Code / dataset availability confirmedEurope PMC · checked 14 Sept 2026
Reflected light carries ample information about the biochemical composition, tissue architecture, and physiological condition of plants. Recent technical progress has paved the way for affordable imaging hyperspectrometers (IH) providing spatially resolved spectral information on plants on different levels, from individual plant organs to communities. The extraction of sensible information from hyperspectral images is difficult due to inherent complexity of plant tissue and canopy optics, especially when recorded under ambient sunlight. We report on the changes in hyperspectral reflectance accompanying the accumulation of anthocyanins in healthy apple (cultivars Ligol, Gala, Golden Delicious) fruits as well as in fruits affected by pigment breakdown during sunscald development and phytopathogen attacks. The measurements made outdoors with a snapshot IH were compared with traditional "point-type" reflectance measured with a spectrophotometer under controlled illumination conditions. The spectra captured by the IH were suitable for processing using the approaches previously developed for "point-type" apple fruit and leaf reflectance spectra. The validity of this approach was tested by constructing a novel index mBRI (modified browning reflectance index) for detection of tissue damages on the background of the anthocyanin absorption. The index was suggested in the form of mBRI = ( R 640 -1 + R 800 -1 ) - R 678 -1 . Difficulties of the interpretation of fruit hyperspectral reflectance images recorded in situ are discussed with possible implications for plant physiology and precision horticulture practices.
Why it matches plant phenotyping methodsリンゴ果実のハイパースペクトル画像から組織損傷を検出する指標を開発・検証しており、植物状態の取得手法が研究の中心である。
abstractThe validity of this approach was tested by constructing a novel index mBRI (modified browning reflectance index) for detection of tissue damages on the background of the anthocyanin absorption.
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 sampling of the spectral data and rendering of the index images have been carried out using Gelion, the original software for processing hyperspectral images ( https://github.com/AlexanderMipt/Gelion ).Open asset ↗github.com/AlexanderMipt/Gelionlines:105-152Code / dataset availability confirmedEurope PMC · checked 14 Sept 2026
Background With up to 200 published contributions, the GreenLab mathematical model of plant growth, developed since 2000 under Sino-French co-operation for agronomic applications, is descended from the structural models developed in the AMAP unit that characterize the development of plants and encompass them in a conceptual mathematical framework. The model also incorporates widely recognized crop model concepts (thermal time, light use efficiency and light interception), adapting them to the level of the individual plant. Scope Such long-term research work calls for an overview at some point. That is the objective of this review paper, which retraces the main history of the model's development and its current status, highlighting three aspects. (1) What are the key features of the GreenLab model? (2) How can the model be a guide for defining relevant measurement strategies and experimental protocols? (3) What kind of applications can such a model address? This last question is answered using case studies as illustrations, and through the Discussion. Conclusions The results obtained over several decades illustrate a key feature of the GreenLab model: owing to its concise mathematical formulation based on the factorization of plant structure, it comes along with dedicated methods and experimental protocols for its parameter estimation, in the deterministic or stochastic cases, at single-plant or population levels. Besides providing a reliable statistical framework, this intense and long-term research effort has provided new insights into the internal trophic regulations of many plant species and new guidelines for genetic improvement or optimization of crop systems.
Why it matches plant phenotyping methodsGreenLabモデルの開発史と、植物構造・成長の測定戦略およびパラメータ推定用実験プロトコルを中心にレビューしており、植物形質の取得・モデル化手法が主要テーマである。
abstractWhat are the key features of the GreenLab model? (2) How can the model be a guide for defining relevant measurement strategies and experimental protocols?
Reproduction assets foundThe review explicitly states that the data and source codes for its maize and coffee GreenLab calibration case studies are publicly available as Supplementary Data S1 and via the authors' URL http://greenlab.cirad.fr/StemGL/AoB_19945R_Codes.zip. The GLUVED eLearning site is a general course resource, not a paper-phenotCode · public06 ). Other heuristic methods have also been used, such as particle swarm optimization ( Qi et al. , 2010 ) and neural networks ( Fan et al. , 2015 ).
We illustrate here the parameter estimations on maize and coffee. Data and sources codes are available as Supplementary Data S1 . These are also available from the following link http://greenlab.cirad.fr/StemGL/AoB_19945R_Codes.zip .
These case studies are somewhat iconic. The study of maize is interesting as it has a simple non-branched deterministic structure but complex organ expansions, due to their duration and sink strength variation. The fruits are not numerous, but their biomass is significant due to their high sink strength. Thus, thiOpen asset ↗lines:306-320Code / dataset availability confirmedCrossref · Europe PMC · checked 9 Sept 2026
Abstract Arbuscular mycorrhiza fungi (AMF) are beneficial soil fungi that can promote the growth of their host plants. Accurate quantification of AMF in plant roots is important because the level of colonization is often indicative of the activity of these fungi. Root colonization is traditionally measured with microscopy methods which visualize fungal structures inside roots. Microscopy methods are labor-intensive, and results depend on the observer. In this study, we present a relative qPCR method to quantify AMF in which we normalized the AMF qPCR signal relative to a plant gene. First, we validated the primer pair AMG1F and AM1 in silico, and we show that these primers cover most AMF species present in plant roots without amplifying host DNA. Next, we compared the relative qPCR method with traditional microscopy based on a greenhouse experiment with Petunia plants that ranged from very high to very low levels of AMF root colonization. Finally, by sequencing the qPCR amplicons with MiSeq, we experimentally confirmed that the primer pair excludes plant DNA while amplifying mostly AMF. Most importantly, our relative qPCR approach was capable of discriminating quantitative differences in AMF root colonization and it strongly correlated (Spearman Rho = 0.875) with quantifications by traditional microscopy. Finally, we provide a balanced discussion about the strengths and weaknesses of microscopy and qPCR methods. In conclusion, the tested approach of relative qPCR presents a reliable alternative method to quantify AMF root colonization that is less operator-dependent than traditional microscopy and offers scalability to high-throughput analyses.
Why it matches plant phenotyping methods植物根のAMF菌根 colonization を定量する相対qPCR法を開発・検証し、顕微鏡法との比較で性能を評価しているため、植物状態の取得手法が中心である。
abstractIn this study, we present a relative qPCR method to quantify AMF in which we normalized the AMF qPCR signal relative to a plant gene.
Reproduction assets foundThe paper provides two paper-specific public assets: the authors' analysis code repository on GitHub (R/DADA2/qPCR analysis workflow) and raw amplicon sequencing data deposited in the European Nucleotide Archive under study accession PRJEB20127 (sample SAMEA103939171), which contains the qPCR amplicon sequences used toCode · publicAll code is available under https://github.com/PMI-Basel/Bodenhausen_et_al_AMF_qPCR .Open asset ↗PMI-Basel/Bodenhausen_et_al_AMF_qPCRlines:90-98Code / dataset availability confirmedEurope PMC · checked 9 Sept 2026
Image-based symptom scoring of plant diseases is a powerful tool for associating disease resistance with plant genotypes. Advancements in technology have enabled new imaging and image processing strategies for statistical analysis of time-course experiments. There are several tools available for analyzing symptoms on leaves and fruits of crop plants, but only a few are available for the model plant Arabidopsis thaliana (Arabidopsis). Arabidopsis and the model fungus Botrytis cinerea (Botrytis) comprise a potent model pathosystem for the identification of signaling pathways conferring immunity against this broad host-range necrotrophic fungus. Here, we present two strategies to assess severity and symptom progression of Botrytis infection over time in Arabidopsis leaves. Thus, a pixel classification strategy using color hue values from red-green-blue (RGB) images and a random forest algorithm was used to establish necrotic, chlorotic, and healthy leaf areas. Secondly, using chlorophyll fluorescence (ChlFl) imaging, the maximum quantum yield of photosystem II (F v /F m ) was determined to define diseased areas and their proportion per total leaf area. Both RGB and ChlFl imaging strategies were employed to track disease progression over time. This has provided a robust and sensitive method for detecting sensitive or resistant genetic backgrounds. A full methodological workflow, from plant culture to data analysis, is described.
Why it matches plant phenotyping methods植物病害の症状・重症度・進展を画像から定量化する方法の開発とワークフロー提示が中心であり、植物状態の表現型取得に該当する。
abstractHere, we present two strategies to assess severity and symptom progression of Botrytis infection over time in Arabidopsis leaves.
Reproduction assets foundThe authors explicitly state that all R and ImageJ scripts and the study data are openly available in their public GitHub repository, which directly reproduces this paper's Botrytis symptom phenotyping analysis.Code · publicAll R and ImageJ script generated to process are available at https://github.com/mipavici/MDPI_leaf_infection .Open asset ↗mipavici/MDPI_leaf_infectionlines:69-123Dataset · publicThe data presented in this study are openly available at https://github.com/mipavici/MDPI_leaf_infection .Open asset ↗mipavici/MDPI_leaf_infectionlines:69-123Code / dataset availability confirmedOpenAlex · Crossref · checked 15 Sept 2026
The persistence and productivity of forage grasses, important sources for feed production, are threatened by climate change-induced drought. Breeding programs are in search of new drought tolerant forage grass varieties, but those programs still rely on time-consuming and less consistent visual scoring by breeders. In this study, we evaluate whether Unmanned Aerial Vehicle (UAV) based remote sensing can complement or replace this visual breeder score. A field experiment was set up to test the drought tolerance of genotypes from three common forage types of two different species: Festuca arundinacea, diploid Lolium perenne and tetraploid Lolium perenne. Drought stress was imposed by using mobile rainout shelters. UAV flights with RGB and thermal sensors were conducted at five time points during the experiment. Visual-based indices from different colour spaces were selected that were closely correlated to the breeder score. Furthermore, several indices, in particular H and NDLab, from the HSV (Hue Saturation Value) and CIELab (Commission Internationale de l’éclairage) colour space, respectively, displayed a broad-sense heritability that was as high or higher than the visual breeder score, making these indices highly suited for high-throughput field phenotyping applications that can complement or even replace the breeder score. The thermal-based Crop Water Stress Index CWSI provided complementary information to visual-based indices, enabling the analysis of differences in ecophysiological mechanisms for coping with reduced water availability between species and ploidy levels. All species/types displayed variation in drought stress tolerance, which confirms that there is sufficient variation for selection within these groups of grasses. Our results confirmed the better drought tolerance potential of Festuca arundinacea, but also showed which Lolium perenne genotypes are more tolerant.
Why it matches plant phenotyping methodsUAVのRGB・熱画像から植生指数と水ストレス指標を抽出し、目視スコアとの相関や遺伝率を評価する高スループット表現型解析が研究の中心です。
abstractwe evaluate whether Unmanned Aerial Vehicle (UAV) based remote sensing can complement or replace this visual breeder score
Reproduction assets foundThe authors state their phenotyping data (UAV RGB/thermal-derived vegetation indices, breeder scores, and related measurements) are publicly available on Zenodo under DOI 10.5281/zenodo.4415643. This is a paper-specific, public, directly actionable dataset. No author analysis code repository is disclosed; the Matlab/CHDataset · publicData Availability Statement: Data is publicly available at 10.5281/zenodo.4415643.Open asset ↗Zenodo · 10.5281/zenodo.4415643pdf-raw-page:19 lines:1-46Code / dataset availability confirmedOpenAlex · Europe PMC · Crossref · checked 14 Sept 2026
Nutrient uptake is critical for crop growth and is determined by root foraging in soil. Growth and branching of roots lead to effective root placement to acquire nutrients, but relatively little is known about absorption of nutrients at the root surface from the soil solution. This knowledge gap could be alleviated by understanding sources of genetic variation for short-term nutrient uptake on a root length basis. A modular platform called RhizoFlux was developed for high-throughput phenotyping of multiple ion-uptake rates in maize (Zea mays L.). Using this system, uptake rates were characterized for the crop macronutrients nitrate, ammonium, potassium, phosphate, and sulfate among the Nested Association Mapping (NAM) population founder lines. The data revealed substantial genetic variation for multiple ion-uptake rates in maize. Interestingly, specific nutrient uptake rates (nutrient uptake rate per length of root) were found to be both heritable and distinct from total uptake and plant size. The specific uptake rates of each nutrient were positively correlated with one another and with specific root respiration (root respiration rate per length of root), indicating that uptake is governed by shared mechanisms. We selected maize lines with high and low specific uptake rates and performed an RNA-seq analysis, which identified key regulatory components involved in nutrient uptake. The high-throughput multiple ion-uptake kinetics pipeline will help further our understanding of nutrient uptake, parameterize holistic plant models, and identify breeding targets for crops with more efficient nutrient acquisition.
Why it matches plant phenotyping methods根の複数イオン吸収速度を高スループットで測定するRhizoFluxプラットフォームを開発し、性能・遺伝的変異を評価しており、植物表現型取得法が研究の中心です。
abstractA modular platform called RhizoFlux was developed for high-throughput phenotyping of multiple ion-uptake rates in maize (Zea mays L.).
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 statistical analysis R codes including the packages needed are available at https://doi.org/10.5281/zenodo.3893944 ( Griffiths and York, 2020b )Open asset ↗Zenodo · 10.5281/zenodo.3893944lines:83-89Code / dataset availability confirmedbioRxiv · checked 15 Sept 2026
Root hydraulic properties play a central role in the global water cycle, agricultural systems productivity, and ecosystem survival as they impact the global canopy water supply. However, the available experimental methods to quantify root hydraulic conductivities, such as the root pressure probing, are particularly challenging and their applicability on thin roots and small root segments is limited. There is a gap in methods enabling easy estimations of root hydraulic conductivities across a diversity of root types and at high resolution along root axes. In this case study, we analysed Zea mays (maize) plants of the var. B73 that were grown in pots for 14 days. Root cross-section data were used to extract anatomical measurements. We used the Generator of Root Anatomy in R (GRANAR) model to generate root anatomical networks from anatomical features. Then we used the Model of Explicit Cross-section Hydraulic Anatomy (MECHA) to compute an estimation of the root axial and radial hydraulic conductivities (kx and kr, respectively), based on the generated anatomical networks and cell hydraulic properties from the literature. The root hydraulic conductivity maps obtained from the root cross-sections suggest significant functional variations along and between different root types. Predicted variations of kr along the root axis were strongly dependent on the maturation stage of hydrophobic barriers. The same was also true for the maturation rates of the metaxylem. The different anatomical features, as well as their evolution along the root type add significant variation to the kr estimation in between root type and along the root axe. Under the prism of root types, anatomy, and hydrophobic barriers, our results highlight the diversity of root radial and axial hydraulic conductivities, which may be veiled under low-resolution measurements of the root system hydraulic conductivity. While predictions of our root hydraulic maps match the range and trend of measurements reported in the literature, future studies could focus on the quantitative validation of hydraulic maps. From now on, a novel method, which turns root cross-section images into hydraulic maps will offer an inexpensive and easily applicable investigation tool for root hydraulics, in parallel to root pressure probing experiments. One-Sentence summaryThe use of cross-section images and modelling tools to generate a map the axial and radial hydraulic conductivity along different root types for the maize cultivar B73.
Why it matches plant phenotyping methods根の断面画像から解剖学的形質を抽出し、モデルで軸方向・半径方向の根 hydraulic conductivity を推定する手法が研究の中心であるため、植物表現型計測手法として収録する。
abstractWe used the Generator of Root Anatomy in R (GRANAR) model to generate root anatomical networks from anatomical features. Then we used the Model of Explicit Cross-section Hydraulic Anatomy (MECHA) to compute an estimation of the root axial and radial hydraulic conductivities (kx and kr, respectively)
Reproduction assets foundThe paper provides two public, paper-specific assets: the GRANAR-MECHA coupling workflow (Jupyter/R repository with Zenodo DOI) and the B73_HydraulicMap repository containing the Rmarkdown script used to compute the root hydraulic maps plus all input and output data of the study.Code · publicection can be visualized through different figures that show the
186 proportion of the water fluxes in each compartiment (apoplastic and symplastic fluxes).
The whole script that was used to compute the root hydraulic maps from the root anatomical
188 measurement is presented as a Rmarkdown script stored in a GitHub repository
(https://github.com/granar/B73_HydraulicMap doi: 10.5281/zenodo.4320861). In the same
190 repository are stored all input and output data of this study.
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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 Open asset ↗granar/B73_HydraulicMap · 10.5281/zenodo.4320861pdf-raw-page:10 lines:1-20Code / dataset availability confirmedEurope PMC · checked 14 Sept 2026
Early prediction of pathogen infestation is a key factor to reduce the disease spread in plants. Macrophomina phaseolina (Tassi) Goid, as one of the main causes of charcoal rot disease, suppresses the plant productivity significantly. Charcoal rot disease is one of the most severe threats to soybean productivity. Prediction of this disease in soybeans is very tedious and non-practical using traditional approaches. Machine learning (ML) techniques have recently gained substantial traction across numerous domains. ML methods can be applied to detect plant diseases, prior to the full appearance of symptoms. In this paper, several ML techniques were developed and examined for prediction of charcoal rot disease in soybean for a cohort of 2,000 healthy and infected plants. A hybrid set of physiological and morphological features were suggested as inputs to the ML models. All developed ML models were performed better than 90% in terms of accuracy. Gradient Tree Boosting (GBT) was the best performing classifier which obtained 96.25% and 97.33% in terms of sensitivity and specificity. Our findings supported the applicability of ML especially GBT for charcoal rot disease prediction in a real environment. Moreover, our analysis demonstrated the importance of including physiological featured in the learning. The collected dataset and source code can be found in https://github.com/Elham-khalili/Soybean-Charcoal-Rot-Disease-Prediction-Dataset-code.
Why it matches plant phenotyping methods大豆の炭腐病という植物の疾病状態を、生理・形態特徴と機械学習で予測する手法を開発・比較しており、表現型取得・推定が研究の中心です。
abstractIn this paper, several ML techniques were developed and examined for prediction of charcoal rot disease in soybean for a cohort of 2,000 healthy and infected plants.
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 collected dataset and source code can be found in https://github.com/Elham-khalili/Soybean-Charcoal-Rot-Disease-Prediction-Dataset-code .Open asset ↗GitHub · Elham-khalili/Soybean-Charcoal-Rot-Disease-Prediction-Dataset-codelines:227-299Code / dataset availability confirmedOpenAlex · Europe PMC · bioRxiv · checked 8 Sept 2026
WheatRootMorphology / geometry measurementPhysiological trait estimationRoot system architecture
Summary The root economics space is a useful framework for plant ecology, but rarely considered for crop ecophysiology. In order to understand root trait integration in winter wheat, we combined functional phenomics with trait economic theory utilizing genetic variation, high-throughput phenotyping, and multivariate analyses. We phenotyped a diversity panel of 276 genotypes for root respiration and architectural traits using a novel high-throughput method for CO 2 flux and the open-source software RhizoVision Explorer for analyzing scanned images. We uncovered substantial variation for specific root respiration (SRR) and specific root length (SRL), which were primary indicators of root metabolic and construction costs. Multiple linear regression estimated that lateral root tips had the greatest SRR, and the residuals of this model were used as a new trait. SRR was negatively correlated with plant mass. Network analysis using a Gaussian graphical model identified root weight, SRL, diameter, and SRR as hub traits. Univariate and multivariate genetic analyses identified genetic regions associated with aspects of the root economics space, with underlying gene candidates. Combining functional phenomics and root economics is a promising approach to understand crop ecophysiology. We identified root traits and genomic regions that could be harnessed to breed more efficient crops for sustainable agroecosystems.
Why it matches plant phenotyping methods根の呼吸と形態を対象に、CO2フラックスの新規ハイスループット測定法と画像解析ソフトウェアを用いたフェノタイピングが研究の中心である。
abstractWe phenotyped a diversity panel of 276 genotypes for root respiration and architectural traits using a novel high-throughput method for CO 2 flux and the open-source software RhizoVision Explorer for analyzing scanned images.
Reproduction assets foundThe paper explicitly deposits two paper-specific public assets: (1) the root respiration measurement protocol and R script for computing CO2 flux from LI-850 text files (Zenodo 4247873), and (2) all trait data, GEMMA output, and R analysis scripts for the statistical analysis and plotting (Zenodo 4247894). Both are theCode · publicThe protocol for the root
respiration measurements and the R script for calculating total flux from a directory of text files
are available at https://doi.org/10.5281/zenodo.4247873 (Guo et al., 2020a).Open asset ↗Zenodo · 10.5281/zenodo.4247873pdf-page:8 lines:1-41Dataset · publicAll trait data, GEMMA output, and R analysis scripts necessary for doing the statistical
analysis and plotting are available at https://doi.org/10.5281/zenodo.4247894 (Guo et al.,
2020b).Open asset ↗Zenodo · 10.5281/zenodo.4247894pdf-page:12 lines:1-35Code / dataset availability confirmedCrossref · checked 9 Sept 2026
Abstract Plant interactions are as important belowground as aboveground. Belowground plant interactions are however inherently difficult to quantify, as roots of different species are difficult to disentangle. Although for a couple of decades molecular techniques have been successfully applied to quantify root abundance, root identification and quantification in multispecies plant communities remains particularly challenging. Here we present a novel methodology, multispecies genotyping by sequencing (msGBS), as a next step to tackle this challenge. First, a multispecies meta‐reference database containing thousands of gDNA clusters per species is created from GBS derived High Throughput Sequencing (HTS) reads. Second, GBS derived HTS reads from multispecies root samples are mapped to this meta‐reference which, after a filter procedure to increase the taxonomic resolution, allows the parallel quantification of multiple species. The msGBS signal of 111 mock‐mixture root samples, with up to 8 plant species per sample, was used to calculate the within‐species abundance. Optional subsequent calibration yielded the across‐species abundance. The within‐ and across‐species abundances highly correlated ( R 2 range 0.72–0.94 and 0.85–0.98, respectively) to the biomass‐based species abundance. Compared to a qPCR based method which was previously used to analyse the same set of samples, msGBS provided similar results. Additional data on 11 congener species groups within 105 natural field root samples showed high taxonomic resolution of the method. msGBS is highly scalable in terms of sensitivity and species numbers within samples, which is a major advantage compared to the qPCR method and advances our tools to reveal hidden belowground interactions.
Why it matches plant phenotyping methods根サンプル中の各植物種の相対 abundance（バイオマスに対応する植物状態）を定量する分子ベースの測定法を開発し、模擬試料および自然試料で検証しているため、方法中心の植物フェノタイピング研究と判断します。
abstractHere we present a novel methodology, multispecies genotyping by sequencing (msGBS), as a next step to tackle this challenge.
Reproduction assets foundThe paper's msGBS analysis scripts are publicly available on GitHub, and metadata underlying the study are deposited on Dryad; both are explicitly stated in the Data Availability Statement with authors' public URLs.Code · publicAll scripts used were made available on GitHub ( https://github.com/NielsWagemaker/scripts_msGBS/tree/msGBS-1.0 ).Open asset ↗GitHub · NielsWagemaker/scripts_msGBSlines:804-868Code / dataset availability confirmedEurope PMC · checked 9 Sept 2026
Common beanMaizeField / plotRootClassificationMorphology / geometry measurementRoot system architecture
A soil coring protocol was developed to cooptimize the estimation of root length distribution (RLD) by depth and detection of functionally important variation in root system architecture (RSA) of maize and bean. The functional-structural model OpenSimRoot was used to perform in silico soil coring at six locations on three different maize and bean RSA phenotypes. Results were compared to two seasons of field soil coring and one trench. Two one-sided T -test (TOST) analysis of in silico data suggests a between-row location 5 cm from plant base (location 3), best estimates whole-plot RLD/D of deep, intermediate, and shallow RSA phenotypes, for both maize and bean. Quadratic discriminant analysis indicates location 3 has ~70% categorization accuracy for bean, while an in-row location next to the plant base (location 6) has ~85% categorization accuracy in maize. Analysis of field data suggests the more representative sampling locations vary by year and species. In silico and field studies suggest location 3 is most robust, although variation is significant among seasons, among replications within a field season, and among field soil coring, trench, and simulations. We propose that the characterization of the RLD profile as a dynamic rhizo canopy effectively describes how the RLD profile arises from interactions among an individual plant, its neighbors, and the pedosphere.
Why it matches plant phenotyping methods根系長分布と根系構造を推定する土壌コア採取プロトコルを開発し、シミュレーション・圃場データ・トレンチで比較検証しており、植物表現型取得法が研究の中心である。
abstractA soil coring protocol was developed to cooptimize the estimation of root length distribution (RLD) by depth and detection of functionally important variation in root system architecture (RSA) of maize and bean.
Reproduction assets foundThe authors publicly deposited the field and simulation phenotype data, OpenSimRoot parameterizations/outputs, Voronoi R code, and analysis scripts on Zenodo (DOI 10.5281/zenodo.3952179), explicitly stated in the Data Availability section and Methods.Dataset · publicThe field and simulation data, model parameterization, R package to calculate Voronoi-adjusted root length distribution, and R scripts used to analyze data are available at Zenodo ( https://doi.org/10.5281/zenodo.3952179 ).Open asset ↗Zenodo · 10.5281/zenodo.3952179lines:95-122Code / dataset availability confirmedEurope PMC · checked 9 Sept 2026
MaizeField / plotRootStem / branchMorphology / geometry measurementGrowth / development / phenologyRoot system architectureStress response / tolerance
Mechanical failure, known as lodging, negatively impacts yield and grain quality in crops. Limiting crop loss from lodging requires an understanding of the plant traits that contribute to lodging-resistance. In maize, specialized aerial brace roots are reported to reduce root lodging. However, their direct contribution to plant biomechanics has not been measured. In this manuscript, we use a non-destructive field-based mechanical test on plants before and after the removal of brace roots. This precisely determines the contribution of brace roots to establish a rigid base (i.e. stalk anchorage) that limits plant deflection in maize. These measurements demonstrate that the more brace root whorls that contact the soil, the greater their overall contribution to anchorage, but that the contributions of each whorl to anchorage were not equal. Previous studies demonstrated that the number of nodes that produce brace roots is correlated with flowering time in maize. To determine if flowering time selection alters the brace root contribution to anchorage, a subset of the Hallauer's Tusón tropical population was analyzed. Despite significant variation in flowering time and anchorage, selection neither altered the number of brace root whorls in the soil nor the overall contribution of brace roots to anchorage. These results demonstrate that brace roots provide a rigid base in maize and that the contribution of brace roots to anchorage was not linearly related to flowering time.
Why it matches plant phenotyping methodsトウモロコシの茎基部アンカレッジという植物力学形質を、非破壊の野外機械試験で定量する測定法が研究の中心であり、単なるルーチン測定ではない。
abstractwe use a non-destructive field-based mechanical test on plants before and after the removal of brace roots. This precisely determines the contribution of brace roots to establish a rigid base (i.e. stalk anchorage) that limits plant deflection in maize.
Reproduction assets foundThe paper's data availability statement explicitly deposits all raw data, processing code, and analyzed data (DARLING force-deflection phenotyping measurements) in a public authors' GitHub repository.Code · publicAll raw data, the code used to process data, and the analyzed data are available at: https://github.com/EESparksL/ab/Reneau_et_al_2020 .Open asset ↗EESparksL/ab/Reneau_et_al_2020lines:132-295Code / dataset availability confirmedOpenAlex · Crossref · Europe PMC · checked 14 Sept 2026
BACKGROUND: Restricting transpiration under high vapor pressure deficit (VPD) is a promising water-saving trait for drought adaptation. However, it is often measured under controlled conditions and at very low throughput, unsuitable for breeding. A few high-throughput phenotyping (HTP) studies exist, and have considered only maximum transpiration rate in analyzing genotypic differences in this trait. Further, no study has precisely identified the VPD breakpoints where genotypes restrict transpiration under natural conditions. Therefore, outdoors HTP data (15 min frequency) of a chickpea population were used to automate the generation of smooth transpiration profiles, extract informative features of the transpiration response to VPD for optimal genotypic discretization, identify VPD breakpoints, and compare genotypes. RESULTS: Fifteen biologically relevant features were extracted from the transpiration rate profiles derived from load cells data. Genotypes were clustered (C1, C2, C3) and 6 most important features (with heritability > 0.5) were selected using unsupervised Random Forest. All the wild relatives were found in C1, while C2 and C3 mostly comprised high TE and low TE lines, respectively. Assessment of the distinct p-value groups within each selected feature revealed highest genotypic variation for the feature representing transpiration response to high VPD condition. Sensitivity analysis on a multi-output neural network model (with R of 0.931, 0.944, 0.953 for C1, C2, C3, respectively) found C1 with the highest water saving ability, that restricted transpiration at relatively low VPD levels, 56% (i.e. 3.52 kPa) or 62% (i.e. 3.90 kPa), depending whether the influence of other environmental variables was minimum or maximum. Also, VPD appeared to have the most striking influence on the transpiration response independently of other environment variable, whereas light, temperature, and relative humidity alone had little/no effect. CONCLUSION: Through this study, we present a novel approach to identifying genotypes with drought-tolerance potential, which overcomes the challenges in HTP of the water-saving trait. The six selected features served as proxy phenotypes for reliable genotypic discretization. The wild chickpeas were found to limit water-loss faster than the water-profligate cultivated ones. Such an analytic approach can be directly used for prescriptive breeding applications, applied to other traits, and help expedite maximized information extraction from HTP data.
Why it matches plant phenotyping methods屋外HTPのロードセルデータから蒸散応答の特徴量とVPDブレークポイントを自動抽出し、遺伝型を識別する解析手法が研究の中心であるため。
abstractoutdoors HTP data (15 min frequency) of a chickpea population were used to automate the generation of smooth transpiration profiles, extract informative features of the transpiration response to VPD for optimal genotypic discretization, identify VPD breakpoints, and compare genotypes.
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 · publicInterested readers can find the R scripts on the open-source GitHub platform, https://github.com/KSoumya/EZTr .Open asset ↗KSoumya/EZTrlines:185-203Code / dataset availability confirmedEurope PMC · checked 9 Sept 2026
Plant simulation models are abstractions of plant physiological processes that are useful for investigating the responses of plants to changes in the environment. Because photosynthesis and transpiration are fundamental processes that drive plant growth and water relations, a leaf gas-exchange model that couples their interdependent relationship through stomatal control is a prerequisite for explanatory plant simulation models. Here, we present a coupled gas-exchange model for C4 leaves incorporating two widely used stomatal conductance submodels: Ball-Berry and Medlyn models. The output variables of the model includes steady-state values of CO2 assimilation rate, transpiration rate, stomatal conductance, leaf temperature, internal CO2 concentrations, and other leaf gas-exchange attributes in response to light, temperature, CO2, humidity, leaf nitrogen, and leaf water status. We test the model behavior and sensitivity, and discuss its applications and limitations. The model was implemented in Julia programming language using a novel modeling framework. Our testing and analyses indicate that the model behavior is reasonably sensitive and reliable in a wide range of environmental conditions. The behavior of the two model variants differing in stomatal conductance submodels deviated substantially from each other in low humidity conditions. The model was capable of replicating the behavior of transgenic C4 leaves under moderate temperatures as found in the literature. The coupled model, however, underestimated stomatal conductance in very high temperatures. This is likely an inherent limitation of the coupling approaches using Ball-Berry type models in which photosynthesis and stomatal conductance are recursively linked as an input of the other.
Why it matches plant phenotyping methodsC4葉のガス交換特性を推定する結合モデルを開発し、感度・信頼性・文献データ再現性・限界を検証しており、植物表現型の取得・推定手法が中心である。
abstractHere, we present a coupled gas-exchange model for C4 leaves incorporating two widely used stomatal conductance submodels: Ball-Berry and Medlyn models.
Reproduction assets foundThe authors explicitly state that a Jupyter notebook containing the model source code, calibration datasets (maize gas-exchange/SPAD measurements), and figure-generation scripts is publicly available on GitHub. The authors' Julia modeling framework (Cropbox.jl) used for the analysis is also publicly available.Code · publicA Jupyter notebook containing source code of the model with calibration datasets and scripts for producing figures presented in this paper is available at https://github.com/cropbox/plants2020 .Open asset ↗cropbox/plants2020lines:500-598Code / dataset availability confirmedEurope PMC · checked 9 Sept 2026
Arbuscular mycorrhizal fungi function as conduits for underground nutrient transport. While the fungal partner is dependent on the plant host for its carbon (C) needs, the amount of nutrients that the fungus allocates to hosts can vary with context. Because fungal allocation patterns to hosts can change over time, they have historically been difficult to quantify accurately. We developed a technique to tag rock phosphorus (P) apatite with fluorescent quantum-dot (QD) nanoparticles of three different colors, allowing us to study nutrient transfer in an in vitro fungal network formed between two host roots of different ages and different P demands over a 3-week period. Using confocal microscopy and raster image correlation spectroscopy, we could distinguish between P transfer from the hyphae to the roots and P retention in the hyphae. By tracking QD-apatite from its point of origin, we found that the P demands of the younger root influenced both: (1) how the fungus distributed nutrients among different root hosts and (2) the storage patterns in the fungus itself. Our work highlights that fungal trade strategies are highly dynamic over time to local conditions, and stresses the need for precise measurements of symbiotic nutrient transfer across both space and time.
Why it matches plant phenotyping methods量子ドット標識と共焦点画像解析を開発し、植物根へのリン移行および菌根内保持を時空間的に定量する手法が研究の中心であるため、植物の栄養生理状態を測定するフェノタイピング手法として含める。
abstractWe developed a technique to tag rock phosphorus (P) apatite with fluorescent quantum-dot (QD) nanoparticles of three different colors
Reproduction assets foundThe paper's authors publicly deposited all data, scripts, and analysis for this study in a GitHub repository, explicitly stated in the Methods. This is a paper-specific, publicly actionable code/data asset reproducing the paper's QD-apatite phenotyping measurements and statistical analysis.Code · publicWe performed all statistical analysis in R version 3.6.1 [ 48 ]. All data, scripts, and analysis are available at: https://github.com/anoukvantpadje/Two_roots .Open asset ↗anoukvantpadje/Two_rootslines:57-192Code / dataset availability confirmedbioRxiv · Europe PMC · checked 15 Sept 2026
Plant thermal tolerance is a crucial research area as the climate warms and extreme weather events become more frequent. Leaves exposed to temperature extremes have inhibited photosynthesis and will accumulate damage to photosystem II (PSII) if tolerance thresholds are exceeded. Temperature-dependent changes in basal chlorophyll fluorescence (T-F0) can be used to identify the critical temperature at which PSII is inhibited. We developed and tested a high-throughput method for measuring the critical temperatures for PSII at low (CTMIN) and high (CTMAX) temperatures using a Maxi-Imaging fluorimeter and a thermoelectric Peltier plate heating/cooling system. We examined how experimental conditions: wet vs dry surfaces for leaves and heating/cooling rate, affect CTMIN and CTMAX across four species. CTMAX estimates were not different whether measured on wet or dry surfaces, but leaves were apparently less cold tolerant when on wet surfaces. Heating/cooling rate had a strong effect on both CTMAX and CTMIN that was species-specific. We discuss potential mechanisms for these results and recommend settings for researchers to use when measuring T-F0. The approach that we demonstrated here allows the high-throughput measurement of a valuable ecophysiological parameter that estimates the critical temperature thresholds of leaf photosynthetic performance in response to thermal extremes.
Why it matches plant phenotyping methods葉の熱耐性・PSII機能の臨界温度を高スループットに測定する蛍光イメージング手法を開発・検証しており、表現型取得法が研究の中心である。
abstractWe developed and tested a high-throughput method for measuring the critical temperatures for PSII at low (CTMIN) and high (CTMAX) temperatures using a Maxi-Imaging fluorimeter and a thermoelectric Peltier plate heating/cooling system.
Reproduction assets foundThe paper provides authors' public R code and example files for extracting Tcrit values from T-F0 chlorophyll fluorescence curves, hosted on the authors' GitHub repository. The paper also states phenotype data are openly available in figshare (10.6084/m9.figshare.12545093), but no figshare URL is present in the allowedCode · publican leaf temperature estimated from
227 two thermocouples attached to leaves on the plate and relative F0 values using the segmented R
228 package (Muggeo 2017) using the R Environment for Statistical Computing (R Core Team
229 2020). We provide example files and example R code for extracting Tcrit values from T-F0
230 curves at https://github.com/pieterarnold/Tcrit-extraction.
231
232 Surface wetness experiment: effect of wet vs dry surfaces for leaves on CTMIN and CTMAX
233 Most experiments that measure T-F0 have measured leaf samples with all excess surface
234 moisture removed, on a dry surface. However, maintaining water content of detached leaves by
235 providing a wet surface where leaOpen asset ↗pieterarnold/Tcrit-extractionpdf-layout-page:8 lines:1-46Code / dataset availability confirmedEurope PMC · checked 14 Sept 2026
Future increases in yield potential will rely largely on improved photosynthesis. Whereas emphasis has traditionally been given to measuring leaf photosynthesis, wheat spikes have an important role in filling grains since they can intercept up to a third of incident light. In the present study, 196 genetically diverse spring wheat lines were evaluated for spike photosynthesis (SP) under temperate (yield potential) and heat stressed, irrigated conditions. Two different methods to estimate SP were used: (i) gas exchange measurements of SP rate and (ii) integrative measurements using a SP inhibition treatment (consisting of a permeable textile covering the spikes). Rate of SP was measured directly in 45 selected genotypes under yield potential conditions using a custom-made illuminating chamber. In these lines, a variation of 2.8-fold for spike photosynthetic rate is reported for the first time with good heritability estimates. Correlations between SP rate and yield, thousand grain weight, number of grains per spike and radiation use efficiency are reported across different panels. Genotypic variation in SP was independent from flag leaf photosynthesis suggesting that any strategy aiming to increase canopy photosynthesis should also consider SP. The SP inhibition treatments were applied on the 196 lines in both environments to estimate SP contribution to grain weight per spike, which was 30–40 % under both heat stressed and yield potential conditions averaged across lines. Positive correlations with grain yield were observed for spike photosynthesis contribution across all of the panels under heat stress and when combining heat and yield potential environments (P < 0.001, r = 0.401). These results indicate a highly significant genotypic variation of spike photosynthetic rate and spike photosynthesis contribution to grain yield among wheat lines and highlight its importance under irrigated and heat stressed conditions.
Why it matches plant phenotyping methods小麦穂の光合成速度・寄与を高スループットに取得する2種類の測定法を用い、カスタム照明チャンバーによる直接測定も実施しており、植物生理形質のフェノタイピング手法の実質的適用が研究の中心である。
titleSpike photosynthesis measured at high throughput indicates genetic variation independent of flag leaf photosynthesis
Reproduction assets foundThe article reports spike photosynthesis phenotyping of 196 wheat lines (gas-exchange rates and SP inhibition treatments) but contains no explicit public dataset or code deposit. The only paper-specific, publicly accessible asset indicated is the article's supplementary material (Supplementary Tables 4-5 and Fig. 1), 'Supplement · publicnical assistance with measurements, data
and trial management. A special thanks to J.M. Esquer who was re-
sponsible to design the spike illumination chamber used in these ex-
periments for the measurements.
Appendix A. Supplementary data
Supplementary material related to this article can be found, in the
online version, at doi:https://doi.org/10.1016/j.fcr.2020.107866.References
Abbad, H., El Jaafari, S., Bort, J., Araus, J.L., Jaafari, S.E., Bort, J., Araus, J.L., 2004.
Comparison of flag leaf and ear photosynthesis with biomass and grain yield of
durum wheat under various water conditions and genotypes. Agronomie 24, 19–28.
https://doi.org/10.1051/agro:2003056.Acreche, M.M., Slafer, G.A., Open asset ↗pdf-raw-page:11 lines:1-57Code / dataset availability confirmedCrossref · checked 9 Sept 2026
Field / plotRootPhysiological trait estimationWater status / transpiration
Abstract Background and aims Monitoring root water uptake dynamics under water deficit (WD) conditions in fields are crucial to assess plant drought tolerance. In this study, we investigate the ability of Electrical Resistivity Tomography (ERT) to capture specific soil water depletion induced by root water uptake. Methods A combination of surface and depth electrodes with a high spatial resolution (10 cm) was used to map 2-D changes of bulk soil electrical conductivity (EC) in an agronomic trial with different herbaceous species. A synthetic experiment was performed with a mechanistic model to assess the ability of the electrode configuration to discriminate abstraction patterns due to roots. The impact of root segments was incorporated in the forward electrical model using the power-law mixing model. Results The time-lapse analysis of the synthetic ERT experiment shows that different root water uptake patterns can be delineated for measurements collected under WD conditions but not under wet conditions. Three indices were found (depletion amount, maximum depth, and spread), which allow capturing plant-specific water signatures based moisture profile changes derived from EC profiles. When root electrical properties were incorporated in the synthetic experiments, it led to the wrong estimation of the amount of water depletion, but a correct ranking of plants depletion depth. When applied to the filed data, our indices showed that Cocksfoot and Ryegrass had shallower soil water depletion zones than white clover and white clover combined with Ryegrass. However, in terms of water depletion amount, Cocksfoot consumed the largest amount of water, followed by White Clover, Ryegrass+White Clover mixture, and Ryegrass. Conclusion ERT is a well-suited method for phenotyping root water uptake ability in field trials under WD conditions.
Why it matches plant phenotyping methodsERTによる根の吸水動態・耐乾性の表現型取得法を、合成実験と圃場データで検証・適用しており、フェノタイピング手法が中心である。
abstractA synthetic experiment was performed with a mechanistic model to assess the ability of the electrode configuration to discriminate abstraction patterns due to roots.
Reproduction assets foundThe paper's Data availability statement explicitly deposits the field datasets acquired and analyzed (ERT/TDR soil water depletion measurements used for plant phenotyping) on Zenodo, with a public URL matching an allowed entry.Dataset · publicThe field datasets acquired and analyzed in the current study are available at https://zenodo.org/record/3750199#.XpS-bVwzY2x , https://doi.org/10.5281/zenodo.3750199Open asset ↗zenodo · 10.5281/zenodo.3750199lines:218-250Code / dataset availability confirmedEurope PMC · checked 15 Sept 2026
Combining information of plant physiological processes with climate control systems can improve control accuracy in controlled environments as greenhouses and plant factories. Through that, resource optimization can be achieved. To predict the plant physiological processes and implement them in control actions of interest, a reliable monitoring system and a capable control system are needed. In this paper, we focused on the option to use real-time crop monitoring for precision climate control in greenhouses. For that, we studied the processes and external factors influencing leaf net CO 2 assimilation rate ( A L , µmol CO 2 m -2 s -1 ) as possible variables of a plant performance indicator. While measured greenhouse environmental variables such as light, temperature, or humidity showed a direct relation between A L and light-quantum yield of photosystem II (Φ 2 ), we defined three objectives: (1) to explore the relationship between climate variables and A L , as well as Φ 2 ; (2) create a simple and reliable method for real-time prediction of A L with continuously Φ 2 measurements; and (3) calibrate parameters to predict chloroplast electron transport rate as input in A L modelling. Due to practical obstacles in measuring CO 2 gas-exchange in commercial production, we explored a method to predict A L by measuring Φ 2 of leaves in a commercial hydroponic greenhouse tomato crop ("Pureza"). We calculated A L with two different approaches based on either the negative exponential response model with simplified biochemical equations (marked as Model I) or the non-rectangular hyperbola full biochemical photosynthetic models (marked as Model II). Using Model I can only be used to predict A L with large uncertainty (R 2 0.64; RMSE 2.21), while using Φ 2 as input to Model II could be used to improve the prediction accuracy of A L (R 2 0.71; RMSE 1.98). Our results suggests that (1) Φ 2 light signals can be used to predict net photosynthesis rate with high accuracy; (2) a parameterized photosynthetic electron transport rate model is suitable predicting measured electron transport rate ( J ) and A L . The system can be used as decision support system (DSS) for plant and crop performance monitoring when leaf-dynamics are up-scaled to the plant or crop level.
Why it matches plant phenotyping methods葉のΦ2測定とモデル化により光合成速度・電子伝達速度をリアルタイム推定する監視システムを開発・評価しており、植物生理形質の取得手法が研究の中心である。
abstractcreate a simple and reliable method for real-time prediction of A L with continuously Φ 2 measurements
Reproduction assets foundThe paper's measured phenotyping data (leaf CO2 assimilation, chlorophyll fluorescence Φ2, and greenhouse environmental variables from the tomato crop) are stated to be included in the article's Supplementary Material (Supplementary Material A–B), publicly accessible at the Frontiers supplementary-material URL. No codeDataset · publicconstrued as a potential conflict of interest.
Acknowledgments
The authors thank the financial support from the program of China Scholarships Council for the first author and Wolfgang Pfeiffer for the technique support with the BERMONIS.
Supplementary Material
The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fpls.2020.01038/full#supplementary-material
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References
Aggarwal C., Yu S. (2005). An effective and efficient algorithm for high-dimensional outlier detection. VLDB J.
14, 211–221.
Allen J. F. (2003). Cyclic, pseudocOpen asset ↗lines:575-613Code / dataset availability confirmedCrossref · checked 9 Sept 2026
One of the essential factors in the root zone environment that affects plant growth is temperature. Determining the optimal root zone temperature condition in a hydroponic system during cultivation could lead to an improvement in plant growth. An optimal control strategy can be determined by identifying the eco-physiological process using a dynamic model. However, it is difficult to develop a dynamic model of the responses of plant growth to root zone temperature because the eco-physiological processes of plants are quite complicated. We propose an intelligent approach that can deal with this complex system. Non-linear autoregressive with exogenous input (NARX) neural networks were used to develop a dynamic model of the responses of plant growth to root zone temperature. The responses of chili pepper plant growth as affected by root zone temperature were measured during 60 days of cultivation inside a growth chamber using a non-destructive and continuous system based on a load cell. Five datasets of dynamic responses of plant growth were obtained for system identification. The results suggest that the application of a neural network is useful for modeling the dynamic response of plant growth to root zone temperature in hydroponic cultivation, with promising performance.
Why it matches plant phenotyping methods植物成長を連続・非破壊に測定するロードセル系と、成長応答を推定するNARXニューラルネットワーク動的モデルが研究の中心であり、植物成長という形質の取得・モデル化手法に該当する。
abstractWe propose an intelligent approach that can deal with this complex system. Non-linear autoregressive with exogenous input (NARX) neural networks were used to develop a dynamic model of the responses of plant growth to root zone temperature.
Reproduction assets foundThe paper cites the authors' own public Matlab program script for the NARX modeling of plant growth response to root zone temperature, hosted on GitHub (reference 47), which qualifies as a paper-specific public analysis code asset. No public phenotype dataset deposit is stated; the five measurement datasets are not明确lyCode · publicAji, G. K.; Hatou, K.; Morimoto, T. Matlab Program Script for Modeling the Dynamic Response of Plant
Growth to Root Zone Temperature in Hydroponic Chili Pepper Plant using Neural Network Available
online: https://github.com/mradjie/narx‐plant‐growthOpen asset ↗mradjie/narx‐plant‐growthpdf-page:14 lines:1-47Code / dataset availability confirmedOpenAlex · Europe PMC · bioRxiv · checked 15 Sept 2026
MaizeRootPhysiological trait estimationGrowth / development / phenologyRoot system architecture
Nutrient uptake is critical for crop growth and determined by root foraging in soil. Growth and branching of roots lead to effective root placement to acquire nutrients, but relatively less is known about absorption of nutrients at the root surface from the soil solution. This knowledge gap could be alleviated by understanding sources of genetic variation for short-term nutrient uptake on a root length basis. A new modular platform for high-throughput phenotyping of multiple ion uptake kinetics was designed to determine nutrient uptake rates in Zea mays . Using this system, uptake rates were characterized for the crop macronutrients nitrate, ammonium, potassium, phosphate and sulfate among the Nested Association Mapping (NAM) population founder lines. The data revealed that substantial genetic variation exists for multiple ion uptake rates in maize. Interestingly, specific nutrient uptake rates (nutrient uptake rate per length of root) were found to be both heritable and distinct from total uptake and plant size. The specific uptake rates of each nutrient were positively correlated with one another and with specific root respiration (root respiration rate per length of root), indicating that uptake is governed by shared mechanisms. We selected maize lines with high and low specific uptake rates and performed an RNA-seq analysis, which identified key regulatory components involved in nutrient uptake. The high-throughput multiple ion uptake kinetics pipeline will help further our understanding of nutrient uptake, parameterize holistic plant models, and identify breeding targets for crops with more efficient nutrient acquisition. Significance Statement Nutrient uptake is among the most limiting factors for plant growth and yet has not been used as a selection criterion in breeding. This is partly due to the lack of high-throughput phenotyping methods for measuring nutrient uptake. Here we describe a novel high-throughput phenotyping pipeline for quantification of multiple ion uptake rates. Using this new phenotyping system, our results demonstrate that specific ion uptake performance by maize plants is positively correlated among the macronutrients nitrogen, phosphorus, potassium and sulfur, and that substantial variation exists within a genetically diverse population. The findings reveal components of regulatory pathways possibly related with enhanced uptake, and confirm that nutrient uptake itself is a potential target for breeding of nutrient-efficient crops.
Why it matches plant phenotyping methods複数イオンの吸収速度を定量する高スループット植物表現型解析プラットフォーム自体の設計・記述が中心であり、植物の生理形質を測定する方法論研究に該当する。
abstractA new modular platform for high-throughput phenotyping of multiple ion uptake kinetics was designed to determine nutrient uptake rates in Zea mays .
Reproduction assets foundThe paper's RhizoFlux phenotyping analysis R scripts and statistical analysis code are explicitly deposited on Zenodo with an authors' public URL (https://doi.org/10.5281/zenodo.3893945), directly reproducing the paper's ion-uptake and trait analysis. No public phenotype dataset or image deposit is stated in the blocksCode · publics quantified based on the ∆ ∆Ct method using normal-
681
ized geo-metric means of the two reference genes (Zm00001d002944,
682
Zm00001d020826; (59)).
683
Statistical analysis. Statistical analyses were conducted using R ver-
684
sion 3.6.0 (60); the statistical analysis R codes including the pack-
685
ages needed are available (https://doi.org/10.5281/zenodo.3893945).686
The depletion rate of a nutrient from a solution is commonly
687
accepted as equal to the net uptake rate by roots (assuming both
688
influx and efflux). Therefore, the following equation was used
689
to determine the total net influx rates for nitrate, ammonium,
690
potassium, phosphate and sulfate:
691
In =
(Ct − C0)
(t0Open asset ↗zenodo · 10.5281/zenodo.3893945pdf-raw-page:9 lines:1-156Code / dataset availability confirmedOpenAlex · checked 13 Sept 2026
Abstract Remote sensing of solar‐induced chlorophyll fluorescence (SIF) offers potential to infer photosynthesis across scales and biomes. Many retrieval methods have been developed to estimate top‐of‐canopy SIF using ground‐based spectroscopy. However, inconsistencies among methods may confound interpretation of SIF dynamics, eco‐physiological/environmental drivers, and its relationship with photosynthesis. Using high temporal‐ and spectral resolution ground‐based spectroscopy, we aimed to (1) evaluate performance of SIF retrieval methods under diverse sky conditions using continuous field measurements; (2) assess method sensitivity to fluctuating light, reflectance, and fluorescence emission spectra; and (3) inform users for optimal ground‐based SIF retrieval. Analysis included field measurements from bi‐hemispherical and hemispherical‐conical systems and synthetic upwelling radiance constructed from measured downwelling radiance, simulated reflectance, and simulated fluorescence for benchmarking. Fraunhofer‐based differential optical absorption spectroscopy (DOAS) and singular vector decomposition (SVD) retrievals exhibit convergent SIF‐PAR relationships and diurnal consistency across different sky conditions, while O2A‐based spectral fitting method (SFM), SVD, and modified Fraunhofer line discrimination (3FLD) exhibit divergent SIF‐PAR relationships across sky conditions. Such behavior holds across system configurations, though hemispherical‐conical systems diverge less across sky conditions. O2A retrieval accuracy, influenced by atmospheric distortion, improves with a narrower fitting window and when training SVD with temporally local spectra. This may impact SIF‐photosynthesis relationships interpreted by previous studies using O2A‐based retrievals with standard (759–767.76 nm) fitting windows. Fraunhofer‐based retrievals resist atmospheric impacts but are noisier and more sensitive to assumed SIF spectral shape than O2A‐based retrievals. We recommend SVD or SFM using reduced fitting window (759.5–761.5 nm) for robust far‐red SIF retrievals across sky conditions.
Why it matches plant phenotyping methods高頻度フィールド分光による植物キャノピーの蛍光・光合成関連状態の取得法を、複数のSIF検索手法について系統的に評価・ベンチマークしており、フェノタイピング手法の技術的検証が中心である。
abstractevaluate performance of SIF retrieval methods under diverse sky conditions using continuous field measurements
Reproduction assets foundThe paper's field spectroscopy data (PhotoSpec and bi-hemispherical system) and SIF retrieval code are explicitly stated to be publicly available at Caltech, Cornell, and GitHub repositories with DOIs.Dataset · publicthe PhotoSpec data used in this study is publicly available
at a data repository hosted at the California Institute of Technology
(https://data.caltech.edu/records/1226) and associated with DOI 10.22002/D1.1226.Open asset ↗data.caltech.edu · 10.22002/D1.1226lines:4420-4536Dataset · publicData from
the bi-hemispherical system is publicly available at a data repository hosted by Cornell
University (https://ecommons.cornell.edu/handle/1813/69711) and associated with DOI
10.7298/wqx5-ba07.Open asset ↗ecommons.cornell.edu · 10.7298/wqx5-ba07lines:4420-4536Code · publicCode used for SIF retrievals can be found on Github
(https://github.com/SunCornell/SIF retrieval methods) and is associated with DOI
10.5281/zenodo.3759965.Open asset ↗github.com/SunCornell/SIF · 10.5281/zenodo.3759965lines:4420-4536Code / dataset availability confirmedCrossref · checked 14 Sept 2026
CottonMaizeLaboratory / benchtopRootStem / branch2D/3D reconstructionRoot system architecture
Abstract Aims The flow of electric current in the root-soil system relates to the pathways of water and solutes, its characterization provides information on the root architecture and functioning. We developed a current source density approach with the goal of non-invasively image the current pathways in the root-soil system. Methods A current flow is applied from the plant stem to the soil, the proposed geoelectrical approach images the resulting distribution and intensity of the electric current in the root-soil system. The numerical inversion procedure underlying the approach was tested in numerical simulations and laboratory experiments with artificial metallic roots. We validated the method using rhizotron laboratory experiments on maize and cotton plants. Results Results from numerical and laboratory tests showed that our inversion approach was capable of imaging root-like distributions of the current source. In maize and cotton, roots acted as “leaky conductors”, resulting in successful imaging of the root crowns and negligible contribution of distal roots to the current flow. In contrast, the electrical insulating behavior of the cotton stems in dry soil supports the hypothesis that suberin layers can affect the mobility of ions and water. Conclusions The proposed approach with rhizotrons studies provides the first direct and concurrent characterization of the root-soil current pathways and their relationship with root functioning and architecture. This approach fills a major gap toward non-destructive imaging of roots in their natural soil environment.
Why it matches plant phenotyping methods根圏の電流経路を非侵襲的に画像化し、根の構造・機能を推定する新規手法を開発・検証しており、植物フェノタイピング手法が研究の中心である。
abstractWe developed a current source density approach with the goal of non-invasively image the current pathways in the root-soil system.
Reproduction assets found保存済みの本文根拠を更新済みルールで再検証し、公開資産2件を確認しました。Code · publicERT and iCSD codes, with data from this study, are maintained at https://github.com/Peruz/ERTpm and https://github.com/Peruz/icsd .Open asset ↗Peruz/ERTpmlines:342-431Code · publicERT and iCSD codes, with data from this study, are maintained at https://github.com/Peruz/ERTpm and https://github.com/Peruz/icsd .Open asset ↗Peruz/icsdlines:342-431Code / dataset availability confirmedEurope PMC · checked 9 Sept 2026
Crop improvement is crucial to ensuring global food security under climate change, and hence there is a pressing need for phenotypic observations that are both high throughput and improve mechanistic understanding of plant responses to environmental cues and limitations. In this study, chlorophyll a fluorescence light response curves and gas-exchange observations are combined to test the photosynthetic response to moderate drought in four genotypes of Brassica rapa The quantum yield of PSII ( ϕ PSII ) is here analyzed as an exponential decline under changing light intensity and soil moisture. Both the maximum ϕ PSII and the rate of ϕ PSII decline across a large range of light intensities (0-1,000 μmol photons m -2 s -1 ; β PSII ) are negatively affected by drought. We introduce an alternative photosynthesis model ( β PSII model) incorporating parameters from rapid fluorescence response curves. Specifically, the model uses β PSII as an input for estimating the photosynthetic electron transport rate, which agrees well with two existing photosynthesis models (Farquhar-von Caemmerer-Berry and Yin). The β PSII model represents a major improvement in photosynthesis modeling through the integration of high-throughput fluorescence phenotyping data, resulting in gained parameters of high mechanistic value.
Why it matches plant phenotyping methods高速クロロフィル蛍光フェノタイピングデータを用いた光合成モデルを新規に構築し、既存モデルと比較検証しており、表現型取得・解析手法が研究の中心である。
abstractWe introduce an alternative photosynthesis model ( β PSII model) incorporating parameters from rapid fluorescence response curves.
Reproduction assets foundThe paper's phenotyping measurements are publicly available via two PhotosynQ projects (chlorophyll fluorescence and ECS protocols/data for the B. rapa drought experiment), and the authors' analysis code for the βPSII decline model and three photosynthesis models is publicly hosted on the first author's GitHub.Code · publicThe code for all three photosynthesis models as well as the simple β PSII decline model are available at https://github.com/jrpleban/ .Open asset ↗jrplebanlines:556-568Code / dataset availability confirmedEurope PMC · OpenAlex · checked 14 Sept 2026
Genetic selection for whole-plant water use efficiency (yield per transpiration; WUE plant ) in any crop-breeding programme requires high-throughput phenotyping of component traits of WUE plant such as intrinsic water use efficiency (WUE i ; CO 2 assimilation rate per stomatal conductance). Measuring WUE i by gas exchange measurements is laborious and time consuming and may not reflect an integrated WUE i over the life of the leaf. Alternatively, leaf carbon stable isotope composition (δ 13 C leaf ) has been suggested as a potential time-integrated proxy for WUE i that may provide a tool to screen for WUE plant . However, a genetic link between δ 13 C leaf and WUE plant in a C 4 species has not been well established. Therefore, to determine if there is a genetic relationship in a C 4 plant between δ 13 C leaf and WUE plant under well watered and water-limited growth conditions, a high-throughput phenotyping facility was used to measure WUE plant in a recombinant inbred line (RIL) population created between the C 4 grasses Setaria viridis and S. italica. Three quantitative trait loci (QTL) for δ 13 C leaf were found and co-localized with transpiration, biomass accumulation, and WUE plant . Additionally, WUE plant for each of the δ 13 C leaf QTL allele classes was negatively correlated with δ 13 C leaf , as would be predicted when WUE i influences WUE plant . These results demonstrate that δ 13 C leaf is genetically linked to WUE plant , likely to be through their relationship with WUE i , and can be used as a high-throughput proxy to screen for WUE plant in these C 4 species.
Why it matches plant phenotyping methods葉の炭素安定同位体比を全植物体の水利用効率の高スループット代理指標として検証し、WUEとの遺伝的関連を評価しており、表現型取得・スクリーニング法が中心的です。
abstractleaf carbon stable isotope composition (δ 13 C leaf ) has been suggested as a potential time-integrated proxy for WUE i that may provide a tool to screen for WUE plant
Reproduction assets foundThe paper's QTL analysis was performed with the authors' custom Python and R scripts, publicly deposited on GitHub (foxy_qtl_pipeline). No public phenotype dataset URL is stated in the supplied blocks.Code · publicin Feldman et al. (2017); Feldman et al. (2018) were
308
used in this study, and here the methods used are repeated. QTL mapping was
309
performed on day 27 within each treatment group using functions in the R/qtl and
310
funqtl packages (Kwak et al., 2016). The functions were called by a set of custom
311
Python and R scripts (https://github.com/maxjfeldman/foxy_qtl_pipeline). Two
312
complimentary analysis methods were utilized. First, a single QTL model genome
313
scan using Haley-Knott regression was performed to identify QTL exhibiting LOD
314
score peaks greater than a permutation-based significance threshold (α = 0.05, n =
315
1000). Second, a stepwise forward/backward selection proceOpen asset ↗maxjfeldman/foxy_qtl_pipelinepdf-raw-page:11 lines:1-77Code / dataset availability confirmedOpenAlex · Europe PMC · checked 14 Sept 2026
Root system architecture has received increased attention in recent years; however, significant knowledge gaps remain for physiological phenes, or units of phenotype, that have been relatively less studied. Ion uptake kinetics studies have been invaluable in uncovering distinct nutrient uptake systems in plants with the use of Michaelis-Menten kinetic modeling. This review outlines the theoretical framework behind ion uptake kinetics, provides a meta-analysis for macronutrient uptake parameters, and proposes new strategies for using uptake kinetics parameters as selection criteria for breeding crops with improved resource acquisition capability. Presumably, variation in uptake kinetics is caused by variation in type and number of transporters, assimilation machinery, and anatomical features that can vary greatly within and among species. Critically, little is known about what determines transporter properties at the molecular level or how transporter properties scale to the entire root system. A meta-analysis of literature containing measures of crop nutrient uptake kinetics provides insights about the need for standardization of reporting, the differences among crop species, and the relationships among various uptake parameters and experimental conditions. Therefore, uptake kinetics parameters are proposed as promising target phenes that integrate several processes for functional phenomics and genetic analysis, which will lead to a greater understanding of this fundamental plant process. Exploiting this genetic and phenotypic variation has the potential to greatly advance breeding efforts for improved nutrient use efficiency in crops.
Why it matches plant phenotyping methods植物の栄養吸収速度を表現型（phene）として扱い、理論枠組み、メタ解析、測定報告の標準化、育種利用を検討する方法論的レビューであり、表現型測定が中心です。
abstractThis review outlines the theoretical framework behind ion uptake kinetics, provides a meta-analysis for macronutrient uptake parameters, and proposes new strategies for using uptake kinetics parameters as selection criteria for breeding crops with improved resource acquisition capability.
Reproduction assets foundThe authors deposited the meta-analysis data and statistical analysis code for this paper's ion uptake kinetics meta-analysis on Zenodo, with an explicit availability statement and public DOI link. The same Zenodo DOI also hosts the supplemental materials.Code · publicthat the K m values were relatively low, so nitrate can be reduced to very low concentrations by plants. Here, a new meta-analysis is presented for uptake kinetics across multiple crop species and for multiple nutrient types: nitrate, phosphate, and potassium. The meta-analysis data and statistical analysis code are available ( https://doi.org/10.5281/zenodo.3605654 ).
To summarize the current state of crop ion uptake kinetic research, maize is the most widely characterized crop for ion uptake kinetics, with approximately half of all studies focusing on maize; however, there are also a substantial number of studies for barley and rice ( Fig. 3A ). By comparison, wheat ( Triticum aestivum )Open asset ↗Zenodo · 10.5281/zenodo.3605654lines:121-127Code / dataset availability confirmedCrossref · checked 15 Sept 2026
Miscanthus x giganteus (Mxg) is a promising second-generation biofuel crop with high production of energetic biomass. Our aim was to determine the level of plant stress of Mxg grown in poor quality soils using non-invasive physiological parameters and to test whether the stress could be reduced by application of plant growth regulators (PGRs). Plant fitness was quantified by measuring of leaf fluorescence using 24 indexes to select the most suitable fluorescence indicators for quantification of this type of abiotic stress. Simultaneously, visible stress signs were observed on stems and leaves and differences in variants were revealed also by microscopy of leaf sections. Leaf fluorescence analysis, visual observation and changes of leaf anatomy revealed significant stress in all studied subjects compared to those cultivated in good quality soil. Besides commonly used Fv/Fm (potential photosynthetic efficiency) and P.I. (performance index), which showed very low sensitivity, we suggest other fluorescence parameters (like dissipation, DIo/RC) for revealing finer differences. We can conclude that measurement of leaf fluorescence is a suitable method for revealing stress affecting Mxg in poor soils. However, none of investigated parameters proved significant positive effect of PGRs on stress reduction. Therefore, direct improvement of soil quality by fertilization should be considered for stress reduction and improving the biomass quality in this type of soils.
Why it matches plant phenotyping methods葉の蛍光指標を用いた非侵襲的ストレス定量と指標選定が研究目的の中心であり、植物の生理状態を測定するフェノタイピング手法の適用・検証に該当する。
abstractOur aim was to determine the level of plant stress of Mxg grown in poor quality soils using non-invasive physiological parameters
Reproduction assets foundThe paper's supplementary materials hosted on MDPI contain the paper-specific fluorescence index measurements (Table S1 means/SDs for all PGR concentrations, boxplots, experiment photos, climate data), which directly reproduce this study's plant-phenotyping measurements. No author analysis code or trained models are deSupplement · publics established that application of PGRs Stimpo and Regoplant did not reduce the stress level of Mxg, the direct improvement of soil shall be considered for stress reduction.
Acknowledgments
We would like to thank Agrobiotech for providing us with Stimpo and Regoplant.
Supplementary Materials
The following are available online at https://www.mdpi.com/2223-7747/9/2/194/s1 , Table S1: Means and standard deviations of fluorescence indexes for all PGRs concentrations in experiment; Figure S2: Boxplots of fluorescence indexes; Figure S3: Photograph of the experiment; Figure S4: Average month temperatures, precipitation and light period in Ústí nad Labem in 2017.
Click here for additional data file.Open asset ↗lines:96-146Code / dataset availability confirmedEurope PMC · checked 9 Sept 2026
The ability of a genotype to stay green affects the primary target traits grain yield (GY) and grain protein concentration (GPC) in wheat. High throughput methods to assess senescence dynamics in large field trials will allow for (i) indirect selection in early breeding generations, when yield cannot yet be accurately determined and (ii) mapping of the genomic regions controlling the trait. The aim of this study was to develop a robust method to assess senescence based on hyperspectral canopy reflectance. Measurements were taken in three years throughout the grain filling phase on >300 winter wheat varieties in the spectral range from 350 to 2500 nm using a spectroradiometer. We compared the potential of spectral indices (SI) and full-spectrum models to infer visually observed senescence dynamics from repeated reflectance measurements. Parameters describing the dynamics of senescence were used to predict GY and GPC and a feature selection algorithm was used to identify the most predictive features. The three-band plant senescence reflectance index (PSRI) approximated the visually observed senescence dynamics best, whereas full-spectrum models suffered from a strong year-specificity. Feature selection identified visual scorings as most predictive for GY, but also PSRI ranked among the most predictive features while adding additional spectral features had little effect. Visually scored delayed senescence was positively correlated with GY ranging from r = 0.173 in 2018 to r = 0.365 in 2016. It appears that visual scoring remains the gold standard to quantify leaf senescence in moderately large trials. However, using appropriate phenotyping platforms, the proposed index-based parameterization of the canopy reflectance dynamics offers the critical advantage of upscaling to very large breeding trials.
Why it matches plant phenotyping methodsコムギの老化動態をハイパースペクトル反射から推定する頑健な表現型計測法の開発が研究の中心であり、スペクトル指標と全スペクトルモデルを比較・評価している。
abstractThe aim of this study was to develop a robust method to assess senescence based on hyperspectral canopy reflectance.
Reproduction assets foundThe paper's data availability statement points to an ETH research-collection deposit for the experimental phenotyping data (hyperspectral reflectance, visual senescence scorings, GY/GPC) and states that all analysis scripts are publicly available, with a GitHub development repository and an archived ETH version.Dataset · publicExperimental data supporting the conclusions of this article can be downloaded here: https://doi.org/10.3929/ethz-b-000365618 .Open asset ↗10.3929/ethz-b-000365618lines:853-864Code · publicAll analysis scripts required to reproduce the results published in this article are publicly available.Open asset ↗lines:853-864Code / dataset availability confirmedEurope PMC · checked 15 Sept 2026
Providing global food security requires a better understanding of how plants function and how their products, including important crops are influenced by environmental factors. Prominent biological factors influencing food security are pests and pathogens of plants and crops. Traditional pest control, however, has involved chemicals that are harmful to the environment and human health, leading to a focus on sustainability and prevention with regards to modern crop protection. A variety of physical and chemical analytical tools is available to study the structure and function of plants at the whole-plant, organ, tissue, cellular, and biochemical levels, while acting as sensors for decision making in the applied crop sciences. Vibrational spectroscopy, among them mid-infrared and Raman spectroscopy in biology, known as biospectroscopy are well-established label-free, nondestructive, and environmentally friendly analytical methods that generate a spectral “signature” of samples using mid-infrared radiation. The generated wavenumber spectrum containing hundreds of variables as unique as a biochemical “fingerprint”, and represents biomolecules (proteins, lipids, carbohydrates, nucleic acids) within biological ... (continues)
Why it matches plant phenotyping methods植物の構造・機能を対象に、振動分光法を用いて害虫・病原体の感染状態を発症前に検出するセンサー手法が中心であり、植物状態の取得方法に該当する。
titleSensor based pre-symptomatic detection of pests and pathogens for precision scheduling of crop protection products
Reproduction assets foundThe thesis states that all PCA-LDA computational analysis of the ATR-FTIR plant spectra was performed using the open-source IRootlab toolbox, with an explicit public GitHub URL provided in the text. No paper-specific phenotype datasets, raw spectra deposits, or trained models are reported in the supplied blocks.Code · publicPCA-LDA was
performed using the open source IRootlab toolbox (https://github.com/trevisanj/ irootlab)
specialized for analysis of IR spectra (Trevisan et al. 2013), in conjunction with Matlab 2016aOpen asset ↗pdf-raw-page:149 lines:1-34Code / dataset availability confirmedEurope PMC · checked 14 Sept 2026
Enzyme biosensors are useful tools that can monitor rapid changes in metabolite levels in real-time. However, current approaches are largely constrained to metabolites within a limited chemical space. With the rising development of artificial metalloenzymes (ArM), a unique opportunity exists to design biosensors from the ground-up for metabolites that are difficult to detect using current technologies. Here we present the design and development of the ArM ethylene probe (AEP), where an albumin scaffold is used to solubilize and protect a quenched ruthenium catalyst. In the presence of the phytohormone ethylene, cross metathesis can occur to produce fluorescence. The probe can be used to detect both exogenous- and endogenous-induced changes to ethylene biosynthesis in fruits and leaves. Overall, this work represents an example of an ArM biosensor, designed specifically for the spatial and temporal detection of a biological metabolite previously not accessible using enzyme biosensors.
Why it matches plant phenotyping methods植物組織中のエチレンを空間・時間的に検出する新規バイオセンサーの設計・開発が中心であり、植物の生理状態を測定する方法論的貢献に該当する。
abstractHere we present the design and development of the ArM ethylene probe (AEP)
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 · publicSource data are provided as a Source Data file.Open asset ↗lines:95-101Code / dataset availability confirmedEurope PMC · Crossref · OpenAlex · checked 15 Sept 2026
The rapid selection of salinity-tolerant crops to increase food production in salinized lands is important for sustainable agriculture. Recently, high-throughput plant phenotyping technologies have been adopted that use plant morphological and physiological measurements in a non-destructive manner to accelerate plant breeding processes. Here, a hyperspectral imaging (HSI) technique was implemented to monitor the plant phenotypes of 13 okra (Abelmoschus esculentus L.) genotypes after 2 and 7 days of salt treatment. Physiological and biochemical traits, such as fresh weight, SPAD, elemental contents and photosynthesis-related parameters, which require laborious, time-consuming measurements, were also investigated. Traditional laboratory-based methods indicated the diverse performance levels of different okra genotypes in response to salinity stress. We introduced improved plant and leaf segmentation approaches to RGB images extracted from HSI imaging based on deep learning. The state-of-the-art performance of the deep-learning approach for segmentation resulted in an intersection over union score of 0.94 for plant segmentation and a symmetric best dice score of 85.4 for leaf segmentation. Moreover, deleterious effects of salinity affected the physiological and biochemical processes of okra, which resulted in substantial changes in the spectral information. Four sample predictions were constructed based on the spectral data, with correlation coefficients of 0.835, 0.704, 0.609 and 0.588 for SPAD, sodium concentration, photosynthetic rate and transpiration rate, respectively. The results confirmed the usefulness of high-throughput phenotyping for studying plant salinity stress using a combination of HSI and deep-learning approaches.
Why it matches plant phenotyping methodsHSIと深層学習による植物・葉のセグメンテーションおよび生理形質推定が研究の中心であり、高スループット表現型取得手法を実装・評価している。
titleHyperspectral imaging combined with machine learning as a tool to obtain high‐throughput plant salt‐stress phenotyping
Reproduction assets foundThe authors publicly deposited the plant/leaf segmentation models in CodeOcean and the MMD clustering source code on GitHub, both directly supporting this paper's phenotyping analysis. The CVPPP 2015 dataset and Hitachi annotation tool are third-party/generic resources, not paper-specific assets.Code · publicels were constructed using Python3.6 (Guido van Ros-
sum, Python Dev Team).
DATA AVAILABILITY STATEMENT
Data further supporting this work, such as details of plant
and leaf segmentation models used in this study, are open
and available in codeocean (https://doi.org/10.24433/CO.3430273.v1). The source code of MMD is available on
https://github.com/jinnuozhang/Coderoom/blob/master/CLUS
TER.ipynb.
ACKNOWLEDGEMENT
The authors would like to thank Hui Fang for helping in illustrat-
ing.
CONFLICT OF INTEREST
The authors declare no conflicts of interest.
AUTHOR CONTRIBUTIONS
XF designed the research. YH and DJ supervised the pro-
ject. XF, YZ, XY, CY, HW and ZT performed the experi-
ments. QW analyzOpen asset ↗github.com/jinnuozhang/Coderoompdf-raw-page:13 lines:1-89Code / dataset availability confirmedEurope PMC · checked 14 Sept 2026
Conventional methods for screening for stress-tolerant cereal varieties rely on expensive, labour-intensive field testing and molecular biology techniques. Here, we use the root hair assay (RHA) as a rapid screening tool to identify stress-tolerant varieties at the early seedling stage. Wheat and barley seedlings had stress applied, and the response quantified in terms of programmed cell death (PCD), viability and necrosis. Heat shock experiments of seven barley varieties showed that winter and spring barley varieties could be partitioned into their two distinct seasonal groups based on their PCD susceptibility, allowing quick data-driven evaluation of their thermotolerance at an early seedling stage. In addition, evaluating the response of eight wheat varieties to heat and salt stress allowed identification of their PCD inflection points (35°C and 150 mM NaCl), where the largest differences in PCD levels arise. Using the PCD inflection points as a reference, we compared different stress effects and found that heat-susceptible wheat varieties displayed similar vulnerabilities to salt stress. Stress-induced PCD levels also facilitated the assessment of the basal, induced and cross-stress tolerance of wheat varieties using single, combined and multiple individual stress exposures by applying concurrent heat and salt stress in a time-course experiment. Two stress-susceptible varieties were found to have low constitutive resistance as illustrated by their high PCD levels in response to single and combined stress exposure. However, both varieties had a fast, adaptive response as PCD levels declined at the other time-points, showing that even with low constitutive resistance, the initial stress cue primes cross-stress tolerance adaptations for enhanced resistance even to a second, different stress type. Here, we demonstrate the RHA's suitability for high-throughput analysis (∼4 days from germination to data collection) of multiple cereal varieties and stress treatments. We also showed the versatility of using stress-induced PCD levels to investigate the role of constitutive and adaptive resistance by exploring the temporal progression of cross-stress tolerance. Our results show that by identifying suboptimal PCD levels in vivo in a laboratory setting, we can preliminarily identify stress-susceptible cereal varieties and this information can guide further, more efficiently targeted, field-scale experimental testing.
Why it matches plant phenotyping methods根毛アッセイ（RHA）を用いてストレス誘導性PCD・生存性・壊死を定量し、作物品種の耐性を迅速かつハイスループットにスクリーニングする手法を実証しており、表現型取得法が研究の中心である。
abstractHere, we use the root hair assay (RHA) as a rapid screening tool to identify stress-tolerant varieties at the early seedling stage.
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 · publicAll datasets generated for this study are included in the article/ Supplementary Material .Open asset ↗lines:703-766Code / dataset availability confirmedEurope PMC · OpenAlex · bioRxiv · checked 15 Sept 2026
The worldwide rise in heatwave frequency poses a threat to plant survival and productivity. Determining the new marker phenotypes that show reproducible response to heat stress and contribute to heat stress tolerance is becoming a priority. In this study, we describe a protocol focusing on the daily changes in plant morphology and photosynthetic performance after exposure to heat stress using an automated non-invasive phenotyping system. Heat stress exposure resulted in an acute reduction of quantum yield of photosystem II and increased leaf angle. In the longer term, exposure to heat also affected plant growth and morphology. By tracking the recovery period of WT and mutants impaired in thermotolerance (hsp101), we observed that the difference in maximum quantum yield, quenching, rosette size, and morphology. By examining the correlation across the traits throughout time, we observed that early changes in photochemical quenching corresponded with the rosette size at later stages, which suggests the contribution of quenching to overall heat tolerance. We also determined that 6h of heat stress provides the most informative insight in plant responses to heat, as it shows a clear separation between treated and non-treated plants as well as WT and hsp101. Our work streamlines future discoveries by providing an experimental protocol, data analysis pipeline and new phenotypes that could be used as targets in thermotolerance screenings.
Why it matches plant phenotyping methods自動化・非破壊フェノタイピングシステムを用いた形態・光合成表現型の取得プロトコル、データ解析パイプライン、新規表現型を中心的に提示しており、耐暑性スクリーニングへの再利用可能な方法論である。
abstractwe describe a protocol focusing on the daily changes in plant morphology and photosynthetic performance after exposure to heat stress using an automated non-invasive phenotyping system.
Reproduction assets foundThe paper publicly deposits its authors' analysis code: an R-notebook for data analysis and a Jupyter notebook for machine learning, both on Zenodo. No phenotype dataset or image deposit is stated in the supplied blocks.Code · public5
statistical analysis using ggpubr. Machine learning classification was implemented using
1
Sci-kit learn in Python (Pedregosa et al., 2011). The script used for data analysis in R is
2
publicly available as an R-notebook (http://doi.org/10.5281/zenodo.3534239), as well as
3
the Jupyter notebook containing the command lines used for machine learning
4
(http://doi.org/10.5281/zenodo.3534148).5
6
3. Results
7
8
3.1 Extended exposure to heat stress results in a proportional decrease of the rosette
9
size and photosynthetic efficiency
10
11
To assess whether high-throughput phenotyping cOpen asset ↗zenodo · 10.5281/zenodo.3534239pdf-raw-page:5 lines:1-56Code · publicng ggpubr. Machine learning classification was implemented using
1
Sci-kit learn in Python (Pedregosa et al., 2011). The script used for data analysis in R is
2
publicly available as an R-notebook (http://doi.org/10.5281/zenodo.3534239), as well as
3
the Jupyter notebook containing the command lines used for machine learning
4
(http://doi.org/10.5281/zenodo.3534148).5
6
3. Results
7
8
3.1 Extended exposure to heat stress results in a proportional decrease of the rosette
9
size and photosynthetic efficiency
10
11
To assess whether high-throughput phenotyping can capture significant alterations in plant
12
physiology caused by exposure to heat stress, we exposed three weeks old ArabidopsisOpen asset ↗zenodo · 10.5281/zenodo.3534148pdf-raw-page:5 lines:1-56Code / dataset availability confirmedEurope PMC · checked 15 Sept 2026
Premise When plants are exposed to stress conditions, irreversible damage can occur, negatively impacting yields. It is therefore important to detect stress symptoms in plants, such as the accumulation of anthocyanin, as early as possible. Methods and results Twenty-two regression models in five color spaces were trained to develop a prediction model for plant anthocyanin levels from digital color imaging data. Of these, a quantile random forest regression model trained with standard red, green, blue (sRGB) color space data most accurately predicted the actual anthocyanin levels. This model was then used to noninvasively monitor the spatial and temporal accumulation of anthocyanin in Arabidopsis thaliana leaves. Conclusions The digital imaging-based nature of this protocol makes it a low-cost and noninvasive method for the detection of plant stress. Applying a similar protocol to more economically viable crops could lead to the development of large-scale, cost-effective systems for monitoring plant health.
Why it matches plant phenotyping methods植物アントシアニン量という生理状態をデジタル画像から推定する回帰モデルと非侵襲的モニタリング手法の開発が中心であり、植物フェノタイピング手法に該当する。
abstractTwenty-two regression models in five color spaces were trained to develop a prediction model for plant anthocyanin levels from digital color imaging data.
Reproduction assets foundThe paper's authors publicly deposited all R code used for leaf image processing, regression training, and accuracy evaluation in a GitHub repository, explicitly stated in the DATA AVAILABILITY section. This is a paper-specific, publicly actionable analysis code asset for the anthocyanin phenotyping method.Code · publicments; B.C.A. and W.S.L. developed
the methods used for data analysis; and B.C.A. and J.K. wrote the
manuscript.All authors read and approved the final manuscript.
DATA AVAILABILITY
All R code used to process and sort the leaf images, train the regres-
sions, and evaluate the accuracy of the regressions can be down-
loaded from https://github.com/bryceaskey/anthocyanin_accum
ulation.
SUPPORTING INFORMATION
Additional Supporting Information may be found online in the
supporting information tab for this article.
APPENDIX S1. Drought stress induces anthocyanin accumulation.
Side view (A) and overhead (B) photos of wild-type Arabidopsis
thaliana (Col-0) under either well-watered (control) or watOpen asset ↗bryceaskey/anthocyanin_accumpdf-raw-page:6 lines:1-82Code / dataset availability confirmedEurope PMC · checked 9 Sept 2026
Hyperspectral remote sensing holds the potential to detect and quantify crop diseases in a rapid and non-invasive manner. Such tools could greatly benefit resistance breeding, but their adoption is hampered by i) a lack of specificity to disease-related effects and ii) insufficient robustness to variation in reflectance caused by genotypic diversity and varying environmental conditions, which are fundamental elements of resistance breeding. We hypothesized that relying exclusively on temporal changes in canopy reflectance during pathogenesis may allow to specifically detect and quantify crop diseases while minimizing the confounding effects of genotype and environment. To test this hypothesis, we collected time-resolved canopy hyperspectral reflectance data for 18 diverse genotypes on infected and disease-free plots and engineered spectral-temporal features representing this hypothesis. Our results confirm the lack of specificity and robustness of disease assessments based on reflectance spectra at individual time points. We show that changes in spectral reflectance over time are indicative of the presence and severity of Septoria tritici blotch (STB) infections. Furthermore, the proposed time-integrated approach facilitated the delineation of disease from physiological senescence, which is pivotal for efficient selection of STB-resistant material under field conditions. A validation of models based on spectral-temporal features on a diverse panel of 330 wheat genotypes offered evidence for the robustness of the proposed method. This study demonstrates the potential of time-resolved canopy reflectance measurements for robust assessments of foliar diseases in the context of resistance breeding.
Why it matches plant phenotyping methods圃場ハイパースペクトル反射の時系列特徴量を開発・検証し、コムギの病害存在と重症度を定量化する手法が研究の中心であるため。
abstractwe collected time-resolved canopy hyperspectral reflectance data for 18 diverse genotypes on infected and disease-free plots and engineered spectral-temporal features representing this hypothesis.
Reproduction assets foundThe paper's data availability statement explicitly deposits the datasets generated and analyzed (canopy hyperspectral reflectance, STB scorings, PLACL leaf-scan measurements) in the ETH Zürich research repository with a DOI, and makes all analysis scripts (R/Python, including the stb_placl leaf-image analysis pipeline)Dataset · publicThe datasets generated and analyzed for this study can be found in the ETH Zürich publications and research data repository ( https://www.research-collection.ethz.ch/ ) and can be downloaded from the following link: https://doi.org/10.3929/ethz-b-000370027 .Open asset ↗ETH Zürich publications and research data repository · 10.3929/ethz-b-000370027lines:684-694Code · publicAll analysis scripts are publicly available. Development repositories: https://github.com/and-jonas/Andereggetal2019b and https://github.com/and-jonas/stb_placl. Programming language: R, Python. License: GNU General Public License, version 3 (GPL-3.0).Open asset ↗github.com/and-jonas/Andereggetal2019blines:684-694Code · publicAll analysis scripts are publicly available. Development repositories: https://github.com/and-jonas/Andereggetal2019b and https://github.com/and-jonas/stb_placl. Programming language: R, Python. License: GNU General Public License, version 3 (GPL-3.0).Open asset ↗github.com/and-jonas/stb_placllines:684-694Code / dataset availability confirmedEurope PMC · checked 9 Sept 2026
Climate change is steering tree breeding programs towards the development of families and genotypes that will be adapted and more resilient to changing environments. Making genotype-phenotype-environment connections is central to these predictions and it requires the evaluation of functional traits such as photosynthetic rates that can be linked to environmental variables. However, the ability to rapidly measure photosynthetic parameters has always been limiting. The estimation of V c,max and J max using CO 2 response curves has traditionally been time consuming, taking anywhere from 30 min to more than an hour, thereby drastically limiting the number of trees that can be assessed per day. Technological advancements have led to the development of a new generation of portable photosynthesis measurement systems offering greater chamber environmental control and automated sampling and, as a result, the proposal of a new, faster, method (RACiR) for measuring V c,max and J max . This method was developed using poplar trees and involves measuring photosynthetic responses to CO 2 over a range of CO 2 concentrations changing at a constant rate. The goal of the present study was to adapt the RACiR method for use on conifers whose measurement usually requires much larger leaf chambers. We demonstrate that the RACiR method can be used to estimate V c,max and J max in conifers and provide recommendations to enhance the method. The use our method in conifers will substantially reduce measurement time, thus greatly improving genotype evaluation and selection capabilities based on photosynthetic traits. This study led to the developpement of an R package (RapidACi, https://github.com/ManuelLamothe/RapidACi) that facilitates the correction of multiple RACiR files and the post-measurement correction of leaf areas.
Why it matches plant phenotyping methods針葉樹向けに光合成形質（V_c,max、J_max）の高速測定法を適応・検証し、解析用Rパッケージも開発しており、植物表現型取得法が研究の中心です。
abstractthe proposal of a new, faster, method (RACiR) for measuring V c,max and J max
Reproduction assets foundThe paper's RACiR correction/analysis R package (RapidACi) is publicly available on GitHub, and example raw data (one uncorrected RACiR curve with its corresponding ECRC and A-Ci-TRAD) are provided in a second public GitHub repository for use with the supplementary example analysis. The full measurement datasets are onCode · publicThe script used to make the corrections is available on Github ( https://github.com/ManuelLamothe/RapidACi ) it can be used to automatically correct multiple files at a time and to carry out post-measurement corrections to leaf areaOpen asset ↗ManuelLamothe/RapidACilines:301-307Dataset · publicdata from one uncorrected RACiR curve and its corresponding ECRC and ACi-TRAD are provided at https://github.com/GuillaumeOtisPrudhomme/TestFiles for use with the example analyses in Data Sheet 1Open asset ↗GuillaumeOtisPrudhomme/TestFileslines:480-531Code / dataset availability confirmedOpenAlex · Europe PMC · Crossref · checked 9 Sept 2026
BACKGROUND: Assessing the impact of the environment on plant performance requires growing plants under controlled environmental conditions. Plant phenotypes are a product of genotype × environment (G × E), and the Enviratron at Iowa State University is a facility for testing under controlled conditions the effects of the environment on plant growth and development. Crop plants (including maize) can be grown to maturity in the Enviratron, and the performance of plants under different environmental conditions can be monitored 24 h per day, 7 days per week throughout the growth cycle. RESULTS: The Enviratron is an array of custom-designed plant growth chambers that simulate different environmental conditions coupled with precise sensor-based phenotypic measurements carried out by a robotic rover. The rover has workflow instructions to periodically visit plants growing in the different chambers where it measures various growth and physiological parameters. The rover consists of an unmanned ground vehicle, an industrial robotic arm and an array of sensors including RGB, visible and near infrared (VNIR) hyperspectral, thermal, and time-of-flight (ToF) cameras, laser profilometer and pulse-amplitude modulated (PAM) fluorometer. The sensors are autonomously positioned for detecting leaves in the plant canopy, collecting various physiological measurements based on computer vision algorithms and planning motion via "eye-in-hand" movement control of the robotic arm. In particular, the automated leaf probing function that allows the precise placement of sensor probes on leaf surfaces presents a unique advantage of the Enviratron system over other types of plant phenotyping systems. CONCLUSIONS: The Enviratron offers a new level of control over plant growth parameters and optimizes positioning and timing of sensor-based phenotypic measurements. Plant phenotypes in the Enviratron are measured in situ-in that the rover takes sensors to the plants rather than moving plants to the sensors.
Why it matches plant phenotyping methodsロボット rover、複数センサー、コンピュータビジョン、葉面プロービングを統合した植物フェノタイピング基盤の開発・実証が中心である。
abstractThe Enviratron is an array of custom-designed plant growth chambers that simulate different environmental conditions coupled with precise sensor-based phenotypic measurements carried out by a robotic rover.
Reproduction assets foundThe paper describes the Enviratron phenotyping facility and explicitly states that a Git repository containing the code developed to operate and support the Enviratron is publicly available on GitLab. This is an authors' public code asset directly tied to this paper's phenotyping system. No phenotype datasets or image/Code · publicA git repository containing code developed to operate and support the Enviratron is available online at https://gitlab.com/dill_picl/enviratron .Open asset ↗dill_picl/enviratronlines:165-207Code / dataset availability confirmedEurope PMC · checked 15 Sept 2026
-Solar-induced chlorophyll a Fluorescence (SIF), which is distributed over a relatively broad (~200 nm) spectral range, is a signal intricately connected to the efficiency of photosynthesis and is now observable from space. Variants of the Fraunhofer Line Depth/Discriminator (FLD) method are used as the basis of retrieval algorithms for estimating SIF from space. Although typically unobserved directly, recent advances in FLD-based algorithms now facilitate the prediction (by model inversion) of the canopy emitted fluorescence spectrum from the discrete-feature FLD retrievals.-Here we present first canopy scale measurements of chlorophyll a fluorescence spectra emitted from Scots pine at two times of year, and also from a lingonberry dominated understory. We used a high power multispectral Light Emitting Diode (LED) array to illuminate the respective canopies at night and measured under standardised conditions using a field spectrometer mounted in the nadir position above the canopy. We refer to the technique, which facilitates the in situ upscaling of a commonly measured leaf scale quantity to the canopy, as nocturnal LED-Induced chlorophyll a Fluorescence (LEDIF).-The shape of the LEDIF spectra was dependant on the colour of the excitation light and also on the dominant species. Because we measured pine at two different times of year we were also able to show an increase in the canopy scale apparent quantum yield of fluorescence which was consistent with leaf-level increase in fluorescence yield recorded with a monitoring PAM fluorometer.-The automation of the LEDIF technique could be used to estimate seasonal changes in canopy fluorescence spectra and yield from fixed or mobile platforms and provide a window into functional traits across species and architectures. LEDIF could also be used to evaluate FLD and inversion-based retrievals of canopy spectra, as well as different irradiance normalisation schemes typically applied to SIF data to account for the dependence of SIF on ambient light conditions.
Why it matches plant phenotyping methods植物キャノピーのクロロフィル蛍光スペクトルと見かけの量子収率を測定する新規LEDIF手法を開発・実証しており、植物の生理形質取得が研究の中心である。
abstractHere we present first canopy scale measurements of chlorophyll a fluorescence spectra emitted from Scots pine at two times of year, and also from a lingonberry dominated understory.
Reproduction assets foundThe paper's LEDIF fluorescence spectra and analysis code are not stated to be publicly available. The only qualifying public asset is the SMARTSMEAR database, from which the authors downloaded the site PAR and temperature measurements used in the study; it is a public, paper-relevant data source with an explicit URL,但它Dataset · publicAcross Space and Time (FAST2017) campaign, at
the Station for Measuring Atmosphere-Ecosystem Relations II
(SMEARII), Hyytiälä, Finland (61°51N, 24°17E). Site measurements of
above canopy photosynthetically active radiation (PAR) and in canopy
(16.8 m) temperature were downloaded from the publicly accessible
SMARTSMEAR database (https://avaa.tdata.fi/web/smart).2.2. Canopy spectral measurements
Canopy scale steady state chlorophyll a fluorescence spectra were
excited at night using a multispectral LED light source (BPP210 Beamz
Professional, distributed by Tronios BV, Twente, Netherlands) from a
scaffold tower at a height of approximately 0.5 m above a mature 15 m
tall Scots pine treeOpen asset ↗SMARTSMEARpdf-raw-page:2 lines:72-128Code / dataset availability confirmedEurope PMC · checked 14 Sept 2026
Precise sensor-based non-destructive estimation of crop nitrogen (N) status is essential for low-cost, objective optimization of N fertilization, as well as for early estimation of yield potential and N use efficiency. Several studies assessed the performance of spectral vegetation indices (SVI) for winter wheat ( Triticum aestivum L.), often either for conditions of low N status or across a wide range of the target traits N uptake (Nup), N concentration (NC), dry matter biomass (DM), and N nutrition index (NNI). This study aimed at a critical assessment of the estimation ability depending on the level of the target traits. It included seven years' data with nine measurement dates from early stem elongation until flowering in eight N regimes (0-420 kg N ha -1 ) for selected SVIs. Tested across years, a pronounced date-specific clustering was found particularly for DM and NC. While for DM, only the R900_970 gave moderate but saturated relationships (R 2 = 0.47, p 2 = 0.59, p NC ≈ DM. Depending on the number (n = 1-3) and characteristic of cultivars included, the relationships improved when testing within instead of across cultivars, with the relatively lowest cultivar effect on the estimation of DM and the strongest on NC. For assessing the trait estimation under conditions of high-excessive N fertilization, the range of the target traits was divided into two intervals with NNI values 0.8 (interval 2: high N status). Although better estimations were found in interval 1, useful relationships were also obtained in interval 2 from the best indices (DM: R780_740: average R 2 = 0.35, RMSE = 567 kg ha -1 ; NC: REIP: average R 2 = 0.40, RMSE = 0.25%; NNI: REIP: average R 2 = 0.46, RMSE = 0.10; Nup: REIP: average R 2 = 0.48, RMSE = 21 kg N ha -1 ). While in interval 1, all indices performed rather similarly, the three red edge-based indices were clearly better suited for the three N-related traits. The results are promising for applying SVIs also under conditions of high N status, aiming at detecting and avoiding excessive N use. While in canopies of lower N status, the use of simple NIR/VIS indices may be sufficient without losing much precision, the red edge information appears crucial for conditions of higher N status. These findings can be transferred to the configuration and use of simpler multispectral sensors under conditions of contrasting N status in precision farming.
Why it matches plant phenotyping methods冬コムギのスペクトル植生指数によるN状態・バイオマス等の非破壊推定性能を複数年・品種・施肥条件で批判的に評価しており、センサー型表現型計測と検証が中心である。
abstractPrecise sensor-based non-destructive estimation of crop nitrogen (N) status is essential
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 · publicThe following are available online at https://www.mdpi.com/1424-8220/19/17/3712/s1 , Supplementary Table S1: Treatment effects of main plot treatment factors; Supplementary Table S2: Treatment effects of N fertilization; Supplementary Table S3: RMSE and mean-normalized RMSE for regressions across main plots; Supplementary Table S4: Significant ( p < 0.05) coefficients of determination (R²) for the whole data and for data subsets; Supplementary Table S5: Index ranking by data and statistical approachOpen asset ↗lines:241-279Code / dataset availability confirmedOpenAlex · checked 13 Sept 2026
Retrieval of Sun-Induced Chlorophyll Fluorescence (F) spectrum is one of the challenging perspectives for further advancing F studies towards a better characterization of vegetation structure and functioning. In this study, a simplified Spectral Fitting retrieval algorithm suitable for retrieving the F spectrum with a limited number of parameters is proposed (two parameters for F). The novel algorithm is developed and tested on a set of radiative transfer simulations obtained by coupling SCOPE and MODTRAN5 codes, considering different chlorophyll content, leaf area index and noise levels to produce a large variability in fluorescence and reflectance spectra. The retrieval accuracy is quantified based on several metrics derived from the F spectrum (i.e., red and far-red peaks, O2 bands and spectrally-integrated values). Further, the algorithm is employed to process experimental field spectroscopy measurements collected over different crops during a long-lasting field campaign. The reliability of the retrieval algorithm on experimental measurements is evaluated by cross-comparison with F values computed by an independent retrieval method (i.e., SFM at O2 bands). For the first time, the evolution of the F spectrum along the entire growing season for a forage crop is analyzed and three diverse F spectra are identified at different growing stages. The results show that red F is larger for young canopy; while red and far-red F have similar intensity in an intermediate stage; finally, far-red F is significantly larger for the rest of the season.
Why it matches plant phenotyping methods作物群落の蛍光スペクトルから植物の生理状態を推定する検索アルゴリズムを開発し、シミュレーションと圃場分光測定で精度・信頼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。
abstracta simplified Spectral Fitting retrieval algorithm suitable for retrieving the F spectrum with a limited number of parameters is proposed
Reproduction assets foundThe paper's spectral fitting (SpecFit) retrieval algorithm is the core computational analysis of this study, and the authors explicitly state its Matlab source code is publicly available via a GitLab repository. No phenotype datasets or field measurement data are stated as publicly deposited.Code · publicThe Matlab source code of the present algorithm is available for
download through the git repository https://gitlab.com/ltda/flox-specfit.Open asset ↗gitlab.com/ltda/flox-specfitpdf-page:6 lines:1-108Code / dataset availability confirmedOpenAlex · Europe PMC · checked 15 Sept 2026
Verticillium dahliae is a highly detrimental pathogen of soil cultivated strawberry (Fragaria × ananassa). Breeding of Verticillium wilt resistance into commercially viable strawberry cultivars can help mitigate the impact of the disease. In this study we describe novel sources of resistance identified in multiple strawberry populations, creating a wealth of data for breeders to exploit. Pathogen-informed experiments have allowed the differentiation of subclade-specific resistance responses, through studying V. dahliae subclade II-1 specific resistance in the cultivar ‘Redgauntlet’ and subclade II-2 specific resistance in ‘Fenella’ and ‘Chandler’. A large-scale low-cost phenotyping platform was developed utilising automated unmanned vehicles and near infrared imaging cameras to assess field-based disease trials. The images were used to calculate disease susceptibility for infected plants through the normalized difference vegetation index score. The automated disease scores showed a strong correlation with the manual scores. A co-dominant resistant QTL; FaRVd3D, present in both ‘Redgauntlet’ and ‘Hapil’ cultivars exhibited a major effect of 18.3 % when the two resistance alleles were combined. Another allele, FaRVd5D, identified in the ‘Emily’ cultivar was associated with an increase in Verticillium wilt susceptibility of 17.2%, though whether this allele truly represents a susceptibility factor requires further research, due to the nature of the F1 mapping population. Markers identified in populations were validated across a set of 92 accessions to determine whether they remained closely linked to resistance genes in the wider germplasm. The resistant markers FaRVd2B from ‘Redgauntlet’ and FaRVd6D from ‘Chandler’ were associated with resistance across the wider germplasm. Furthermore, comparison of imaging versus manual phenotyping revealed the automated platform could identify three out of four disease resistance markers. As such, this automated wilt disease phenotyping platform is considered to be a good, time saving, substitute for manual assessment.
Why it matches plant phenotyping methods自動無人車両と近赤外画像を用いた圃場病害表現型計測プラットフォームを開発し、手動評価との相関で検証しているため、表現型取得法が研究の中心です。
abstractA large-scale low-cost phenotyping platform was developed utilising automated unmanned vehicles and near infrared imaging cameras to assess field-based disease trials.
Reproduction assets foundThe paper used the authors' public Crosslink tool for linkage map generation (GitHub URL given in a footnote), and the Frontiers supplementary material contains paper-specific phenotype data (per-genotype AUDPC disease scores, validation set individuals, linkage maps). The bioRxiv preprint is the article itself and is;Supplement · publicFIGURE S7
Relative Area Under the Disease Progression Curve (AUDPC) for each of the seven phenotyping events illustrating the phenotypic range of disease symptoms.Open asset ↗lines:945-962Code / dataset availability confirmedCrossref · checked 9 Sept 2026
Abstract Afforestation projects for mitigating CO 2 emissions require to monitor the carbon fixation and plant growth as key indicators. We proposed a monitoring method for predicting carbon fixation in afforestation projects, combining a process‐based ecosystem model and field data and addressed the uncertainty of predicted carbon fixation and ecophysiological characteristics with plant growth. Carbon pools were simulated using the Biome‐BGC model tuned by parameter optimization using measured carbon density of biomass pools on an 11‐year‐old Eucommia ulmoides plantation on Loess Plateau, China. The allocation parameters fine root carbon to leaf carbon (FRC:LC) and stem carbon to leaf carbon (SC:LC), along with specific leaf area (SLA) and maximum stomatal conductance ( g smax ) strongly affected aboveground woody (AC) and leaf carbon (LC) density in sensitivity analysis and were selected as adjusting parameters. We assessed the uncertainty of carbon fixation and plant growth predictions by modeling three growth phases with corresponding parameters: (i) before afforestation using default parameters, (ii) early monitoring using parameters optimized with data from years 1 to 5, and (iii) updated monitoring at year 11 using parameters optimized with 11‐year data. The predicted carbon fixation and optimized parameters differed in the three phases. Overall, 30‐year average carbon fixation rate in plantation (AC, LC, belowground woody parts and soil pools) was ranged 0.14–0.35 kg‐C m −2 y −1 in simulations using parameters of phases (i)–(iii). Updating parameters by periodic field surveys reduced the uncertainty and revealed changes in ecophysiological characteristics with plant growth. This monitoring method should support management of afforestation projects by carbon fixation estimation adapting to observation gap, noncommon species and variable growing conditions such as climate change, land use change.
Why it matches plant phenotyping methods植物器官・生態系の炭素固定と成長を推定する監視手法を、プロセスモデルと圃場データ、パラメータ最適化で構築・不確実性評価しており、植物の生理状態推定が中心です。
abstractWe proposed a monitoring method for predicting carbon fixation in afforestation projects, combining a process‐based ecosystem model and field data
Reproduction assets foundThe paper's Data Availability Statement explicitly deposits the authors' biometric data for E. ulmoides allometric relationships and the files related to parameter optimization and simulation results on Zenodo, a public repository with a DOI. This is a paper-specific, publicly actionable asset. The NCDC GSOD meteoricalDataset · publicThe biometric data for allometric relationships of E. ulmoides and the files related to optimization and simulation results are available on Zenodo ( https://doi.org/10.5281/zenodo.2815612 ).Open asset ↗Zenodo · 10.5281/zenodo.2815612lines:393-549Code / dataset availability confirmedOpenAlex · Europe PMC · checked 15 Sept 2026
Plant phenotyping to date typically comprises morphological and physiological profiling in a high-throughput manner. A powerful method that allows for subcellular characterization of organelle stoichiometric/functional characteristics is still missing. Organelle abundance and crosstalk in cell dynamics and signaling plays an important role for understanding crop growth and stress adaptations. However, microscopy can not be considered a high-throughput technology. The aim of the present study was to develop an approach that enables the estimation of organelle functional stoichiometry and to determine differential subcellular dynamics within and across cultivars in a high-throughput manner. A combination of subcellular non-aqueous fractionation and liquid chromatography mass spectrometry was applied to assign membrane-marker proteins to cell compartmental abundances and functions of Pisum sativum leaves. Based on specific subcellular affiliation, proteotypic marker peptides of the chloroplast, mitochondria and vacuole membranes were selected and synthesized as heavy isotope labelled standards. The rapid and unbiased Mass Western approach for accurate stoichiometry and targeted absolute protein quantification allowed for a proportional organelle abundances measure linked to their functional properties. A 3D Confocal Laser Scanning Microscopy approach was developed to evaluate the Mass Western. Two P. sativum cultivars of varying morphology and physiology were compared. The Mass Western assay enabled a cultivar specific discrimination of the chloroplast to mitochondria to vacuole relations.
Why it matches plant phenotyping methods植物の細胞内オルガネラ量と機能的特性を高スループットに推定するフェノタイピング手法を開発し、3D共焦点顕微鏡で評価・検証しているため、方法が中心的である。
abstractThe aim of the present study was to develop an approach that enables the estimation of organelle functional stoichiometry and to determine differential subcellular dynamics within and across cultivars in a high-throughput manner.
Reproduction assets foundThe paper's plant-phenotyping measurements and analysis outputs are available as public supplementary material hosted on the Frontiers article page: Tables S1–S4 (Mass Western target peptide lists, confocal organelle volume/area abundance values, NAF LFQ peak intensities, and proteotypic peptide subcellular localizatonSupplement · publicand Thomas Joch for plant cultivation at the department-associated greenhouse facility.
Footnotes
Funding. This study was funded by the Austrian Science Fund (FWF) [ P24870 -B22] and [W 1257-820], and supported by the COST action FA1306.
Supplementary Material
The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fpls.2019.00638/full#supplementary-material
Figure S1
Morphological phenotypes of the Pisum sativum cultivars Protecta (left) and Messire (right). Length of internodes and leaf weight n = 5 biol. replicates, error bars = standard error, p < 0.05 (Kruskal Wallis). ** p < 0.01, *** p < 0.005.
Click here for additional data filOpen asset ↗lines:102-128Code / dataset availability confirmedOpenAlex · Europe PMC · checked 10 Sept 2026
High throughput phenotyping technologies are lagging behind modern marker technology impairing the use of secondary traits to increase genetic gains in plant breeding. We aimed to assess whether the combined use of hyperspectral data with modern marker technology could be used to improve across location pre-harvest yield predictions using different statistical models. A maize bi-parental doubled haploid (DH) population derived from F1, which consisted of 97 lines was evaluated in testcross combination under heat stress as well as combined heat and drought stress during the 2014 and 2016 summer season in Ciudad Obregon, Sonora, Mexico (27°20" N, 109°54" W, 38 m asl). Full hyperspectral data, indicative of crop physiological processes at the canopy level, was repeatedly measured throughout the grain filling period and related to grain yield. Partial least squares regression (PLSR), random forest (RF), ridge regression (RR) and Bayesian ridge regression (BayesB) were used to assess prediction accuracies on grain yield within (two-fold cross-validation) and across environments (leave-one-environment-out-cross-validation) using molecular markers (M), hyperspectral data (H) and the combination of both (HM). Highest prediction accuracy for grain yield averaged across within and across location predictions (rGP) were obtained for BayesB followed by RR, RF and PLSR. The combined use of hyperspectral and molecular marker data as input factor on average had higher predictions for grain yield than hyperspectral data or molecular marker data alone. The highest prediction accuracy for grain yield across environments was measured for BayesB when molecular marker data and hyperspectral data were used as input factors, while the highest within environment prediction was obtained when BayesB was used in combination with hyperspectral data. It is discussed how the combined use of hyperspectral data with molecular marker technology could be used to introduce physiological genomic estimated breeding values (PGEBV) as a pre-harvest decision support tool to select genetically superior lines.
Why it matches plant phenotyping methods作物キャノピーの反復ハイパースペクトル計測を用いた表現型情報の取得と、収量予測における統計モデルの比較評価が研究の中心であり、実質的な植物表現型計測の応用研究である。
abstractHigh throughput phenotyping technologies are lagging behind modern marker technology impairing the use of secondary traits to increase genetic gains in plant breeding.
Reproduction assets foundThe paper's underlying hyperspectral, marker, and grain yield phenotyping data are publicly deposited in the CIMMYT Research Data & Software Repository Network, with an explicit availability statement and handle URL. No author analysis code repository is stated; R packages cited are generic libraries.Dataset · publicData Availability: The data underlying this study have been uploaded to the CIMMYT data repository and are accessible using the following link: http://hdl.handle.net/11529/10548168 .Open asset ↗CIMMYT data repository · hdl.handle.net/11529/10548168lines:130-141Code / dataset availability confirmedEurope PMC · checked 10 Sept 2026
Published12 Mar 2019Metabolomics : Official journal of the Metabolomic SocietyCited by 21 · OpenAlex ↗
Introduction Oats (Avena sativa L.) are a whole grain cereal recognised for their health benefits and which are cultivated largely in temperate regions providing both a source of food for humans and animals, as well as being used in cosmetics and as a potential treatment for a number of diseases. Oats are known as being a cereal source high in dietary fibre (e.g. β-glucans), as well as being high in antioxidants, minerals and vitamins. Recently, oats have been gaining increased global attention due to their large number of beneficial health effects. Consumption of oats has been proven to lower blood LDL cholesterol levels and blood pressure, thus reducing the risk of heart disease, as well as reducing blood-sugar and insulin levels. Objectives Oats are seen as a low input cereal. Current agricultural guidelines on nitrogen application are believed to be suboptimal and only consider the effect of nitrogen on grain yield. It is important to understand the role of both variety and of crop management in determining nutritional quality of oats. In this study the response of yield, grain quality and grain metabolites to increasing nitrogen application to levels greater than current guidelines were investigated. Methods Four winter oat varieties (Mascani, Tardis, Balado and Gerald) were grown in a replicated nitrogen response trial consisting of a no added nitrogen control and four added nitrogen treatments between 50 and 200 kg N ha -1 in a randomised split-plot design. Grain yield, milling quality traits, β-glucan, total protein and oil content were assessed. The de-hulled oats (groats) were also subjected to a rapid Ultra High Performance Liquid Chromatography-Mass Spectrometry (UHPLC-MS) metabolomic screening approach. Results Application of nitrogen had a significant effect on grain yield but there was no significant difference between the response of the four varieties. Grain quality traits however displayed significant differences both between varieties and nitrogen application level. β-glucan content significantly increased with nitrogen application. The UHPLC-MS approach has provided a rapid, sub 15 min per sample, metabolite profiling method that is repeatable and appropriate for the screening of large numbers of cereal samples. The method captured a wide range of compounds, inclusive of primary metabolites such as the amino acids, organic acids, vitamins and lipids, as well as a number of key secondary metabolites, including the avenanthramides, caffeic acid, and sinapic acid and its derivatives and was able to identify distinct metabolic phenotypes for the varieties studied. Amino acid metabolism was massively upregulated by nitrogen supplementation as were total protein levels, whilst the levels of organic acids were decreased, likely due to them acting as a carbon skeleton source. Several TCA cycle intermediates were also impacted, potentially indicating increased TCA cycle turn over, thus providing the plant with a source of energy and reductant power to aid elevated nitrogen assimilation. Elevated nitrogen availability was also directed towards the increased production of nitrogen containing phospholipids. A number of both positive and negative impacts on the metabolism of phenolic compounds that have influence upon the health beneficial value of oats and their products were also observed. Conclusions Although the developed method has broad applicability as a rapid screening method or a rapid metabolite profiling method and in this study has provided valuable metabolic insights, it still must be considered that much greater confidence in metabolite identification, as well as quantitative precision, will be gained by the application of higher resolution chromatography methods, although at a large expense to sample throughput. Follow up studies will apply higher resolution GC (gas chromatography) and LC (reversed phase and HILIC) approaches, oats will be also analysed from across multiple growth locations and growth seasons, effectively providing a cross validation for the results obtained within this preliminary study. It will also be fascinating to perform more controlled experiments with sampling of green tissues, as well as oat grains, throughout the plants and grains development, to reveal greater insight of carbon and nitrogen metabolism balance, as well as resource partitioning into lipid and secondary metabolism.
Why it matches plant phenotyping methodsUHPLC-MSによる植物代謝表現型の迅速取得法を開発・反復性評価し、大規模穀類サンプルへの適用可能性と限界も示しているため、代謝測定が単なる生物学的実験の補助ではなく方法論の中心である。
abstractThe UHPLC-MS approach has provided a rapid, sub 15 min per sample, metabolite profiling method that is repeatable and appropriate for the screening of large numbers of cereal samples.
Reproduction assets foundThe paper's UHPLC-MS metabolite profiling data (oat nitrogen supplementation study) is publicly deposited in MetaboLights (MTBLS804), and the authors' ASCA/PLS-S analysis scripts are publicly available on GitHub.Code · publicASCA and PLS-S with RFE were performed within MATLAB 2016a using in-house scripts which are made available freely online at https://github.com/Biospec/cluster-toolbox-v2.0 .Open asset ↗GitHub · Biospec/cluster-toolbox-v2.0lines:106-112Code / dataset availability confirmedCrossref · checked 10 Sept 2026
Background and aims Non- or minimally invasive methods are urgently needed to characterize and monitor crop root systems to foster progress in phenotyping and general system understanding. Electrical methods have come into focus due to their unique sensitivity to various structural and functional root characteristics. The aim of this study is to highlight imaging capabilities of these methods with regard to crop root systems and to investigate changes in electrical signals caused by physiological reactions. Methods Spectral electrical impedance tomography (sEIT) and electrical impedance spectroscopy (EIS) were used in three laboratory experiments to characterize oilseed root systems embedded in nutrient solution. Two experiments imaged the root extension with sEIT, including one experiment monitoring a nutrient stress situation. In the third experiment electrical signatures were observed over the diurnal cycle using EIS. Results Root system extension was imaged using sEIT under static conditions. During continuous nutrient deprivation, electrical polarization signals decreased steadily. Systematic changes were observed over the diurnal cycle, indicating further sensitivity to associated physiological processes. Spectral parameters suggest polarization processes at the μm scale. Conclusions Electrical imaging methods are able to non-invasively characterize crop root systems in controlled laboratory conditions, thereby offering links to root structure and function. The methods have the potential to be upscaled to the field scale.
Why it matches plant phenotyping methods電気インピーダンス画像化・分光法を用いて作物根系の構造と生理状態を非侵襲的に測定する方法が研究の中心であり、根系伸長や栄養ストレス・日周生理変化の表現型取得を実証している。
abstractNon- or minimally invasive methods are urgently needed to characterize and monitor crop root systems to foster progress in phenotyping and general system understanding.
Reproduction assets foundThe paper's Data Availability statement explicitly deposits the sEIT/EIS measurement data and analysis scripts in a public Zenodo repository, which directly reproduces this paper's root-phenotyping measurements and computational analysis.Dataset · publicData Availability
Measurement data and analysis scripts are available under the https://doi.org/10.5281/zenodo.1320755Open asset ↗zenodo · 10.5281/zenodo.1320755lines:233-271Code / dataset availability confirmedEurope PMC · checked 15 Sept 2026
Experimental high-throughput analysis of molecular networks is a central approach to characterize the adaptation of plant metabolism to the environment. However, recent studies have demonstrated that it is hardly possible to predict in situ metabolic phenotypes from experiments under controlled conditions, such as growth chambers or greenhouses. This is particularly due to the high molecular variance of in situ samples induced by environmental fluctuations. An approach of functional metabolome interpretation of field samples would be desirable in order to be able to identify and trace back the impact of environmental changes on plant metabolism. To test the applicability of metabolomics studies for a characterization of plant populations in the field, we have identified and analyzed in situ samples of nearby grown natural populations of Arabidopsis thaliana in Austria. A. thaliana is the primary molecular biological model system in plant biology with one of the best functionally annotated genomes representing a reference system for all other plant genome projects. The genomes of these novel natural populations were sequenced and phylogenetically compared to a comprehensive genome database of A. thaliana ecotypes. Experimental results on primary and secondary metabolite profiling and genotypic variation were functionally integrated by a data mining strategy, which combines statistical output of metabolomics data with genome-derived biochemical pathway reconstruction and metabolic modeling. Correlations of biochemical model predictions and population-specific genetic variation indicated varying strategies of metabolic regulation on a population level which enabled the direct comparison, differentiation, and prediction of metabolic adaptation of the same species to different habitats. These differences were most pronounced at organic and amino acid metabolism as well as at the interface of primary and secondary metabolism and allowed for the direct classification of population-specific metabolic phenotypes within geographically contiguous sampling sites.
Why it matches plant phenotyping methods植物集団の代謝表現型を対象に、メタボロミクス、データマイニング、代謝経路再構築を統合して分類・予測する手法の適用可能性を検証しており、単なる生物学的測定ではない。
abstractTo test the applicability of metabolomics studies for a characterization of plant populations in the field
Reproduction assets foundThe paper's supplementary material, publicly hosted at the Frontiers article page, contains paper-specific phenotyping assets: example plant images of the sampled Arabidopsis populations (Data Sheet S1), metabolomics analysis outputs (PCA loadings of GC-MS/LC-MS metabolites, Jacobian entry tables), and SNP-enriched-genSupplement · publicof the department-associated greenhouse facility for their support and advice.
1
http://www.1001genomes.org
2
http://www.freizeitkarte-osm.de/de/oesterreich.html
3
https://www.rdocumentation.org/packages/stats/versions/3.5.1/topics/hclust
Supplementary Material
The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fpls.2018.01556/full#supplementary-material
Figure S1
PCA analysis of primary metabolites.
Click here for additional data file.
Table S1
PCA loadings of GC-MS and LC-MS metabolites.
Click here for additional data file.
Table S2
Table of Jacobian entries and their associated metabolite, pathway and enzyme reaction (EC numberOpen asset ↗lines:91-238Code / dataset availability confirmedCrossref · checked 14 Sept 2026
Abstract. The investigation of plant roots is inherently difficult and often neglected. Being out of sight, roots are often out of mind. Nevertheless, roots play a key role in the exchange of mass and energy between soil and the atmosphere, in addition to the many practical applications in agriculture. In this paper, we propose a method for roots imaging based on the joint use of two electrical noninvasive methods: electrical resistivity tomography (ERT) and mise-à-la-masse (MALM). The approach is based on the key assumption that the plant root system acts as an electrically conductive body, so that injecting electrical current into the plant stem will ultimately result in the injection of current into the subsoil through the root system, and particularly through the root terminations via hair roots. Evidence from field data, showing that voltage distribution is very different whether current is injected into the tree stem or in the ground, strongly supports this hypothesis. The proposed procedure involves a stepwise inversion of both ERT and MALM data that ultimately leads to the identification of electrical resistivity (ER) distribution and of the current injection root distribution in the three-dimensional soil space. This, in turn, is a proxy to the active (hair) root density in the ground. We tested the proposed procedure on synthetic data and, more importantly, on field data collected in a vineyard, where the estimated depth of the root zone proved to be in agreement with literature on similar crops. The proposed noninvasive approach is a step forward towards a better quantification of root structure and functioning.
Why it matches plant phenotyping methods植物根系の三次元画像化と活動根密度の推定を目的とした非侵襲的センシング手法を提案し、合成データおよび圃場データ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。
abstractIn this paper, we propose a method for roots imaging based on the joint use of two electrical noninvasive methods: electrical resistivity tomography (ERT) and mise-à-la-masse (MALM).
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 · publicMeasured and simulated raw data, electrical imaging, and MALM data used to generate the
figures can be accessed at https://doi.org/10.5281/zenodo.1464825 (Mary et al., 2018).Open asset ↗Zenodo · 10.5281/zenodo.1464825lines:872-946Code / dataset availability confirmedbioRxiv · Crossref · checked 15 Sept 2026
Food production in conventional agriculture faces numerous challenges such as reducing waste, meeting demand, maintaining flavor, and providing nutrition. Contained environments under artificial climate control, or cyber-agriculture, could in principle be used to meet many of these challenges. Through such environments, phenotypic expression of the plant---mass, edible yield, flavor, and nutrients---can be actuated through a "climate recipe," where light, water, nutrients, temperature, and other climate and ecological variables are optimized to achieve a desired result. This paper describes a method for doing this optimization for the desired result of flavor by combining cyber-agriculture, metabolomic phenotype (chemotype) measurements, and machine learning. In a pilot experiment, (1) environmental conditions, i.e. photoperiod and ultraviolet (UV) light (known to affect production of flavor-active molecules in edible plants) were applied under different regimes to basil plants (Ocimum basilicum) growing inside a hydroponic farm with an open-source design; (2) flavor-active volatile molecules were measured in each plant using gas chromatography-mass spectrometry (GC-MS); and (3) symbolic regression was used to construct a surrogate model of this chemistry from the input environmental variables, and this model was used to discover new combinations of photoperiod and UV light to increase this chemistry. These new combinations, or climate recipes, were then implemented in the hydroponic farm, and several of them resulted in a marked increase in volatiles over control. The process also led to two important insights: it demonstrated a "dilution effect", i.e. a negative correlation between weight and desirable chemical species, and it discovered the surprising effect that a 24-hour photoperiod of photosynthetic-active radiation, the equivalent of all-day light, induces the most flavor molecule production in basil. In this manner, surrogate optimization through machine learning can be used to discover effective recipes for cyber-agriculture that would be difficult and time-consuming to find using hand-designed experiments.
Why it matches plant phenotyping methods植物の代謝表現型（風味関連揮発性物質）をGC-MSで測定し、機械学習による代理モデルで環境条件から表現型を予測・最適化するワークフローが研究の中心である。
abstractThis paper describes a method for doing this optimization for the desired result of flavor by combining cyber-agriculture, metabolomic phenotype (chemotype) measurements, and machine learning.
Reproduction assets foundThe paper's Data availability statement points to a public GitHub repository containing the underlying GC-MS chemotype/phenotype data and experimental results used for surrogate modeling.Dataset · publict on analysis instrumentation and Babak Hodjat and Hormoz Shahrzad for
514 modeling and optimization insights and comments on the manuscript.
515
516 Data availability
517 The data underlying the results presented in this study are freely available on the Open
518 Agriculture Initiative's public Github repository located at
519 https://github.com/OpenAgInitiative/flavor-data
520
521 Author contributions
522 AJJ and EM contributed to the conceptualization, analysis, methodology development,
523 investigation, visualization, and preparing the original draft of the manuscript. AJ ran the
524 biological and GC-MS experiments and EM ran the computational experiments. JdlP supervised
525 and contrOpen asset ↗OpenAgInitiative/flavor-datapdf-layout-page:24 lines:1-52Code / dataset availability confirmedEurope PMC · checked 10 Sept 2026
In order to exploit plants as environmental biosensors, previous researches have been focused on the electrical signal response of the plants to different environmental stimuli. One of the important outcomes of those researches has been the extraction of meaningful features from the electrical signals and the use of such features for the classification of the stimuli which affected the plants. The classification results are dependent on the classifier algorithm used, features extracted and the quality of data. This paper presents an innovative way of extracting features from raw plant electrical signal response to classify the external stimuli which caused the plant to produce such a signal. A curve fitting approach in extracting features from the raw signal for classification of the applied stimuli has been adopted in this work, thereby evaluating whether the shape of the raw signal is dependent on the stimuli applied. Four types of curve fitting models-Polynomial, Gaussian, Fourier and Exponential, have been explored. The fitting accuracy (i.e., fitting of curve to the actual raw signal) depicted through R-squared values has allowed exploration of which curve fitting model performs best. The coefficients of the curve fit models were then used as features. Thereafter, using simple classification algorithms such as Linear Discriminant Analysis (LDA), Quadratic Discriminant Analysis (QDA) etc. within the curve fit coefficient space, we have verified that within the available data, above 90% classification accuracy can be achieved. The successful hypothesis taken in this work will allow further research in implementing plants as environmental biosensors.
Why it matches plant phenotyping methods植物の電気生理応答から特徴量を抽出・分類する方法自体が中心であり、植物の生理状態（刺激応答）を測定するセンサ型フェノタイピング手法に該当する。
abstractThis paper presents an innovative way of extracting features from raw plant electrical signal response to classify the external stimuli which caused the plant to produce such a signal.
Reproduction assets foundThe paper's plant electrical signal response datasets (Tomato, Cucumber, Cabbage under NaCl, H2SO4, and O3 stimuli) are explicitly stated to be publicly available via a MEGA repository cited as Reference [60]. This is the paper-specific phenotype/sensor time-series data used for the curve-fitting feature extraction andDataset · public60. Plant Electrical Signal Response Dataset. Available online: https://mega.nz/#F!DoJHzDYR!Open asset ↗mega.nzpdf-page:21 lines:1-24Code / dataset availability confirmedEurope PMC · checked 15 Sept 2026
Accurate estimation of terrestrial photosynthesis has broad scientific and societal impacts. Measurements of photosynthesis can be used to assess plant health, quantify crop yield, and determine the largest CO₂ flux in the carbon cycle. Long-term and continuous monitoring of vegetation optical properties can provide valuable information about plant physiology. Recent developments of the remote sensing of solar-induced chlorophyll fluorescence (SIF) and vegetation spectroscopy have shown promising results in using this information to quantify plant photosynthetic activities and stresses at the ecosystem scale. However, there are few automated systems that allow for unattended observations over months to years. Here we present FluoSpec 2, an automated system for collecting irradiance and canopy radiance that has been deployed in various ecosystems in the past years. The instrument design, calibration, and tests are recorded in detail. We discuss the future directions of this field spectroscopy system. A network of SIF sensors, FluoNet, is established to measure the diurnal and seasonal variations of SIF in several ecosystems. Automated systems such as FluoSpec 2 can provide unique information on ecosystem functioning and provide important support to the satellite remote sensing of canopy photosynthesis.
Why it matches plant phenotyping methodsFluoSpec 2は植物キャノピーのSIF・光学特性を継続取得する自動センサーシステムであり、装置設計、校正、試験が論文の中心です。植物の光合成・生理状態を測定するフェノタイピング基盤に該当します。
abstractHere we present FluoSpec 2, an automated system for collecting irradiance and canopy radiance
Reproduction assets foundThe paper's acknowledgments explicitly state that source codes used to control the FluoSpec 2 system and for postprocessing are publicly available at two GitHub repositories (persl/SeaBreeze and zhangyaonju/seabreeze_control), both of which are in the allowed URL list. These are authors' public code assets directly支撑该仪Code · publicDong Yan, Xian Wang, and Matt Dannenberg for the help with the installation of FluoSpec 2. We also thank Christian Frankenberg, Ari Kornfeld, Joe Berry, Troy Magney, Lianhong Gu, and Jochen Stutz for providing important and useful feedbacks. Source codes that are used to control the system and for postprocesing can be found in https://github.com/persl/SeaBreeze and https://github.com/zhangyaonju/seabreeze_control .
Author Contributions
X.Y. designed the FluoSpec 2 system. X.Y., H.S., A.S. designed all the tests of FluoSpec 2 and installed three FluoSpec 2. All authors contributed to the improvement of FluoSpec 2 and the writing and editing of the manuscript.
FundingOpen asset ↗persl/SeaBreezelines:139-146Code · publicrg for the help with the installation of FluoSpec 2. We also thank Christian Frankenberg, Ari Kornfeld, Joe Berry, Troy Magney, Lianhong Gu, and Jochen Stutz for providing important and useful feedbacks. Source codes that are used to control the system and for postprocesing can be found in https://github.com/persl/SeaBreeze and https://github.com/zhangyaonju/seabreeze_control .
Author Contributions
X.Y. designed the FluoSpec 2 system. X.Y., H.S., A.S. designed all the tests of FluoSpec 2 and installed three FluoSpec 2. All authors contributed to the improvement of FluoSpec 2 and the writing and editing of the manuscript.
FundingOpen asset ↗zhangyaonju/seabreeze_controllines:139-146Code / dataset availability confirmedEurope PMC · checked 10 Sept 2026
Studies of long-distance transport of tracer isotopes in plants offer a high potential for functional phenotyping, but so far measurement time is a bottleneck because continuous time series of at least 1 h are required to obtain reliable estimates of transport properties. Hence, usual throughput values are between 0.5 and 1 samples h -1 . Here, we propose to increase sample throughput by introducing temporal gaps in the data acquisition of each plant sample and measuring multiple plants one after each other in a rotating scheme. In contrast to common time series analysis methods, mechanistic tracer transport models allow the analysis of interrupted time series. The uncertainties of the model parameter estimates are used as a measure of how much information was lost compared to complete time series. A case study was set up to systematically investigate different experimental schedules for different throughput scenarios ranging from 1 to 12 samples h -1 . Selected designs with only a small amount of data points were found to be sufficient for an adequate parameter estimation, implying that the presented approach enables a substantial increase of sample throughput. The presented general framework for automated generation and evaluation of experimental schedules allows the determination of a maximal sample throughput and the respective optimal measurement schedule depending on the required statistical reliability of data acquired by future experiments.
Why it matches plant phenotyping methods植物の長距離トレーサー輸送を機能的フェノタイピングとして測定するため、測定スケジュールとモデル解析を設計・評価し、スループット向上と推定精度を検討した方法開発研究。
abstractStudies of long-distance transport of tracer isotopes in plants offer a high potential for functional phenotyping
Reproduction assets foundThe paper's analysis code is explicitly stated to be publicly available on GitHub (mdate repository), and the Supplementary Material includes Supplementary Table 2 with all design definitions and fit results, which directly reproduce the paper's computational analysis of tracer transport experimental designs.Code · publicAll numerical routines have been implemented in MATLAB (R2016a, MathWorks, Inc.). The code is publicly available at https://github.com/ForschungszentrumJuelich/mdate .Open asset ↗ForschungszentrumJuelich/mdatelines:36-53Code / dataset availability confirmedOpenAlex · Europe PMC · checked 10 Sept 2026
Conventional field phenotyping for drought tolerance, the most important factor limiting yield at a global scale, is labor-intensive and time-consuming. Automated greenhouse platforms can increase the precision and throughput of plant phenotyping and contribute to a faster release of drought tolerant varieties. The aim of this work was to establish a framework of analysis to identify early traits which could be efficiently measured in a greenhouse automated phenotyping platform, for predicting the drought tolerance of field grown soybean genotypes. A group of genotypes was evaluated, which showed variation in their drought susceptibility index (DSI) for final biomass and leaf area. A large number of traits were measured before and after the onset of a water deficit treatment, which were analyzed under several criteria: the significance of the regression with the DSI, phenotyping cost, earliness, and repeatability. The most efficient trait was found to be transpiration efficiency measured at 13 days after emergence. This trait was further tested in a second experiment with different water deficit intensities, and validated using a different set of genotypes against field data from a trial network in a third experiment. The framework applied in this work for assessing traits under different criteria could be helpful for selecting those most efficient for automated phenotyping.
Why it matches plant phenotyping methods温室自動フェノタイピング platformで測定する早期形質を選定・評価し、異なる実験と圃場データで検証しており、形質取得と評価フレームワークが研究の中心である。
abstractThe aim of this work was to establish a framework of analysis to identify early traits which could be efficiently measured in a greenhouse automated phenotyping platform
Reproduction assets found本文中に内容が明示された植物フェノタイピング関連の補足表と、その公開リンクを確認しました。Supplement · publicSupplementary Table 1
Phenotypic traits evaluated during the Experiment 1 with GlyPh and four criteria considered for the evaluation of phenotyping efficiency.Open asset ↗lines:584-646Code / dataset availability confirmedEurope PMC · checked 15 Sept 2026
Agronomists have used statistical crop models to predict yield on a genotype-by-genotype basis. Mechanistic models, based on fundamental physiological processes common across plant taxa, will ultimately enable yield prediction applicable to diverse genotypes and crops. Here, genotypic information is combined with multiple mechanistically based models to characterize photosynthetic trait differentiation among genotypes of Brassica rapa . Infrared leaf gas exchange and chlorophyll fluorescence observations are analyzed using Bayesian methods. Three advantages of Bayesian approaches are employed: a hierarchical model structure, the testing of parameter estimates with posterior predictive checks and a multimodel complexity analysis. In all, eight models of photosynthesis are compared for fit to data and penalized for complexity using deviance information criteria (DIC) at the genotype scale. The multimodel evaluation improves the credibility of trait estimates using posterior distributions. Traits with important implications for yield in crops, including maximum rate of carboxylation ( V cmax ) and maximum rate of electron transport ( J max ) show genotypic differentiation. B. rapa shows phenotypic diversity in causal traits with the potential for genetic enhancement of photosynthesis. This multimodel screening represents a statistically rigorous method for characterizing genotypic differences in traits with clear biophysical consequences to growth and productivity within large crop breeding populations with application across plant processes.
Why it matches plant phenotyping methods光合成形質を推定するマルチモデル・ベイズ統計手法が研究の中心であり、遺伝子型間の形質差を定量化・検証しているため。
titlePhenotypic Trait Identification Using a Multimodel Bayesian Method: A Case Study Using Photosynthesis in Brassica rapa Genotypes.
Reproduction assets foundThe paper's authors provide the model and implementation code used for the Bayesian photosynthesis phenotyping analysis in a public GitHub repository, explicitly stated in the text. No public phenotype dataset deposit is mentioned; the raw A/Ci curve data availability is not stated.Code · publicg a suite of eight models following this approach. The data, parameters, and predictions made by these eight models are described in Table 1 , with model equations identified in Table 2 . Each model has a coded name based on the assumptions therein (Table 3 ). The model and implementation codes used for analysis are provided at https://github.com/jrpleban/Bayes_Farquhar_Models_2_level_Hierarchy . Priors on parameters are shown in Table 4 . We have chosen to estimate some parameters often set as constants ( K c , K o ) to evaluate a given model's ability to discern traits expected to be conserved in this population. A literature survey for each parameter was used to provide statistical distriOpen asset ↗jrpleban/Bayes_Farquhar_Models_2_level_Hierarchylines:45-184Code / dataset availability confirmedEurope PMC · checked 15 Sept 2026
We explore a recent, innovative variation of closed-system respirometry for terrestrial organisms, whereby oxygen partial pressure ( P O 2 ) is repeatedly measured fluorometrically in a constant-volume chamber over multiple time points. We outline a protocol that aligns this technology with the broader literature on aerial respirometry, including the calculations required to accurately convert O 2 depletion to metabolic rate (MR). We identify a series of assumptions, and sources of error associated with this technique, including thresholds where O 2 depletion becomes limiting, that impart errors to the calculation and interpretation of MR. Using these adjusted calculations, we found that the resting MR of five species of angiosperm seeds ranged from 0.011 to 0.640 ml g -1 h -1 , consistent with published seed MR values. This innovative methodology greatly expands the lower size limit of terrestrial organisms that can be measured, and offers the potential for measuring MR changes over time as a result of physiological processes of the organism.
Why it matches plant phenotyping methods蛍光式閉鎖系呼吸測定法のプロトコル、計算補正、誤差要因を開発・検証し、植物種子の代謝率という生理形質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 explore a recent, innovative variation of closed-system respirometry for terrestrial organisms, whereby oxygen partial pressure ( P O 2 ) is repeatedly measured fluorometrically in a constant-volume chamber over multiple time points.
Reproduction assets foundThe paper's authors developed an annotated R script (Script 1) that automates their metabolic-rate calculations from Q2 fluorometric respirometry data, and it is explicitly stated to be available in the journal's supplementary material, which is publicly accessible at the supplemental URL. The seed respirometry raw/repCode · publicThe R script is available in the
supplementary material, and is heavily annotated to provide
guidance to its use (Script 1).Open asset ↗pdf-raw-page:2 lines:1-110Code / dataset availability confirmedOpenAlex · Europe PMC · checked 10 Sept 2026
Genomics and phenomics have promised to revolutionize the field of plant breeding. The integration of these two fields has just begun and is being driven through big data by advances in next-generation sequencing and developments of field-based high-throughput phenotyping (HTP) platforms. Each year the International Maize and Wheat Improvement Center (CIMMYT) evaluates tens-of-thousands of advanced lines for grain yield across multiple environments. To evaluate how CIMMYT may utilize dynamic HTP data for genomic selection (GS), we evaluated 1170 of these advanced lines in two environments, drought (2014, 2015) and heat (2015). A portable phenotyping system called 'Phenocart' was used to measure normalized difference vegetation index and canopy temperature simultaneously while tagging each data point with precise GPS coordinates. For genomic profiling, genotyping-by-sequencing (GBS) was used for marker discovery and genotyping. Several GS models were evaluated utilizing the 2254 GBS markers along with over 1.1 million phenotypic observations. The physiological measurements collected by HTP, whether used as a response in multivariate models or as a covariate in univariate models, resulted in a range of 33% below to 7% above the standard univariate model. Continued advances in yield prediction models as well as increasing data generating capabilities for both genomic and phenomic data will make these selection strategies tractable for plant breeders to implement increasing the rate of genetic gain.
Why it matches plant phenotyping methods圃場型HTPシステムを用いたNDVI・群落温度の取得と、ゲノム選抜モデルへの統合を技術的に評価しており、表現型取得基盤の適用が中心的です。
abstractA portable phenotyping system called 'Phenocart' was used to measure normalized difference vegetation index and canopy temperature simultaneously while tagging each data point with precise GPS coordinates.
Reproduction assets foundThe authors explicitly state that all data sets and analysis scripts for this wheat HTP/genomic-selection study are publicly deposited in the Dryad Digital Repository under DOI 10.5061/dryad.7f138. This covers the paper-specific phenotype data (NDVI, canopy temperature, grain yield BLUPs) and scripts. Note: no Dryad/DODataset · publicAll data sets and scripts are available from the Dryad Digital Repository: 10.5061/dryad.7f138 .Dryad Digital Repository · 10.5061/dryad.7f138lines:223-263Code / dataset availability confirmedOpenAlex · Europe PMC · checked 14 Sept 2026
Biofuels derived from lignocellulosic plant material are an important component of current renewable energy strategies. Improvement efforts in biofuel feedstock crops have been primarily focused on increasing biomass yield with less consideration for tissue quality or composition. Four primary components found in the plant cell wall contribute to the overall quality of plant tissue and conversion characteristics, cellulose and hemicellulose polysaccharides are the primary targets for fuel conversion, while lignin and ash provide structure and defense. We explore the genetic architecture of tissue characteristics using a quantitative trait loci (QTL) mapping approach in Panicum hallii , a model lignocellulosic grass system. Diversity in the mapping population was generated by crossing xeric and mesic varietals, comparative to northern upland and southern lowland ecotypes in switchgrass. We use near-infrared spectroscopy with a primary analytical method to create a P. hallii specific calibration model to quickly quantify cell wall components. Ash, lignin, glucan, and xylan comprise 68% of total dry biomass in P. hallii : comparable to other feedstocks. We identified 14 QTL and one epistatic interaction across these four cell wall traits and found almost half of the QTL to localize to a single linkage group. Panicum hallii serves as the genomic model for its close relative and emerging biofuel crop, switchgrass ( P. virgatum ). We used high throughput phenotyping to map genomic regions that impact natural variation in leaf tissue composition. Understanding the genetic architecture of tissue traits in a tractable model grass system will lead to a better understanding of cell wall structure as well as provide genomic resources for bioenergy crop breeding programs.
Why it matches plant phenotyping methods近赤外分光法による植物組織成分の定量と、P. hallii固有の校正モデル構築が主要な測定技術として明示され、高スループットな表現型解析に用いられているため。
abstractWe use near-infrared spectroscopy with a primary analytical method to create a P. hallii specific calibration model to quickly quantify cell wall components.
Measuring changes in protein or organelle abundance in the cell is an essential, but challenging aspect of cell biology. Frequently-used methods for determining organelle abundance typically rely on detection of a very few marker proteins, so are unsatisfactory. In silico estimates of protein abundances from publicly available protein spectra can provide useful standard abundance values but contain only data from tissue proteomes, and are not coupled to organelle localization data. A new protein abundance score, the normalized protein abundance scale (NPAS), expands on the number of scored proteins and the scoring accuracy of lower-abundance proteins in Arabidopsis. NPAS was combined with subcellular protein localization data, facilitating quantitative estimations of organelle abundance during routine experimental procedures. A suite of targeted proteomics markers for subcellular compartment markers was developed, enabling independent verification of in silico estimates for relative organelle abundance. Estimation of relative organelle abundance was found to be reproducible and consistent over a range of tissues and growth conditions. In silico abundance estimations and localization data have been combined into an online tool, multiple marker abundance profiling, available in the SUBA4 toolbox (http://suba.live).
Why it matches plant phenotyping methodsArabidopsisの細胞内オルガネラ量という植物状態を、標的プロテオミクスと計算推定で定量する手法を開発・検証し、オンラインツールとして提供しているため、測定法が中心である。
abstractA new protein abundance score, the normalized protein abundance scale (NPAS), expands on the number of scored proteins and the scoring accuracy of lower-abundance proteins in Arabidopsis.
Reproduction assets foundThe paper deposits its own shotgun proteomics raw data (whole plant and CSC samples) in PRIDE (PXD005408), its SRM organelle-marker transitions in PeptideAtlas (PASS00906), and integrates its MMAP analysis tool (NPAS + HC-marker based organelle abundance estimation) into the public SUBA4 web interface at suba.live. AllDataset · publicd ProtScore (Conf) > 2.0] and a Thorough ID was applied for the Search Effort. The data processing and matching by ProteinPilot results in recalibration of data, which were subsequently exported as MGF Peaklist(s) for HC‐data matching. These raw data for the whole plant ( n = 3) and CSCs ( n = 3) are available at PRIDE (Project https://doi.org/10.6019/pxd005408 ).
For Arabidopsis low/high‐light samples, analysis was undertaken with about 1 μg protein and performed with a Q‐Exactive+ (Thermo Fisher Scientific) with a nanoACQUITY UltraPerformance LC system (Waters), incorporating a C 18 reverse phase column (Waters; 100 μm × 100 mm, 1.7 μm particle, BEH130C18, column temperature 40°C).Open asset ↗PRIDElines:305-307Code / dataset availability confirmedEurope PMC · checked 10 Sept 2026
The two factors defining male reproductive success in plants are pollen quantity and quality, but our knowledge about the importance of pollen quality is limited due to methodological constraints. Pollen quality in terms of chemical composition may be either genetically fixed for high performance independent of environmental conditions, or it may be plastic to maximize reproductive output under different environmental conditions. In this study, we validated a new approach for studying the role of chemical composition of pollen in adaptation to local climate. The approach is based on high-throughput Fourier infrared (FTIR) characterization and biochemical interpretation of pollen chemical composition in response to environmental conditions. The study covered three grass species, Poa alpina , Anthoxanthum odoratum , and Festuca ovina . For each species, plants were grown from seeds of three populations with wide geographic and climate variation. Each individual plant was divided into four genetically identical clones which were grown in different controlled environments (high and low levels of temperature and nutrients). In total, 389 samples were measured using a high-throughput FTIR spectrometer. The biochemical fingerprints of pollen were species and population specific, and plastic in response to different environmental conditions. The response was most pronounced for temperature, influencing the levels of proteins, lipids, and carbohydrates in pollen of all species. Furthermore, there is considerable variation in plasticity of the chemical composition of pollen among species and populations. The use of high-throughput FTIR spectroscopy provides fast, cheap, and simple assessment of the chemical composition of pollen. In combination with controlled-condition growth experiments and multivariate analyses, FTIR spectroscopy opens up for studies of the adaptive role of pollen that until now has been difficult with available methodology. The approach can easily be extended to other species and environmental conditions and has the potential to significantly increase our understanding of plant male function.
Why it matches plant phenotyping methods花粉の化学組成という植物形質を、高スループットFTIRで取得・解釈する手法を検証し、適応研究への適用可能性を示したため、方法が中心的です。
abstractIn this study, we validated a new approach for studying the role of chemical composition of pollen in adaptation to local climate.
Reproduction assets foundThe article states that the study's data (FTIR pollen spectra and associated measurements) are deposited in the Dryad Digital Repository with an explicit public DOI, making this a paper-specific, publicly actionable dataset.Dataset · publicDATA ACCESSIBILITY
Data are available from the Dryad Digital Repository: https://doi.org/10.5061/dryad.2mm71 .Open asset ↗Dryad Digital Repository · 10.5061/dryad.2mm71lines:319-388Code / dataset availability confirmedEurope PMC · checked 15 Sept 2026
Legumes are protein sources for billions of humans and livestock. These traits are enabled by symbiotic nitrogen fixation (SNF), whereby root nodule-inhabiting rhizobia bacteria convert atmospheric nitrogen (N) into usable N. Unfortunately, SNF rates in legume crops suffer from undiagnosed incompatible/suboptimal interactions between crop varieties and rhizobia strains. There are opportunities to test much large numbers of rhizobia strains if cost/labor-effective diagnostic tests become available which may especially benefit researchers in developing countries. Inside root nodules, fixed N from rhizobia is assimilated into amino acids including glutamine (Gln) for export to shoots as the major fraction (amide-exporting legumes) or as the minor fraction (ureide-exporting legumes). Here, we have developed a new leaf punch based technique to screen rhizobia inoculants for SNF activity following inoculation of both amide exporting and ureide exporting legumes. The assay is based on measuring Gln output using the GlnLux biosensor, which consists of Escherichia coli cells auxotrophic for Gln and expressing a constitutive lux operon. Subsistence farmer varieties of an amide exporter (lentil) and two ureide exporters (cowpea and soybean) were inoculated with different strains of rhizobia under controlled conditions, then extracts of single leaf punches were incubated with GlnLux cells, and light-output was measured using a 96-well luminometer. In the absence of external N and under controlled conditions, the results from the leaf punch assay correlated with 15 N-based measurements, shoot N percentage, and shoot total fixed N in all three crops. The technology is rapid, inexpensive, high-throughput, requires minimum technical expertise and very little tissue, and hence is relatively non-destructive. We compared and contrasted the benefits and limitations of this novel diagnostic assay to methods.
Why it matches plant phenotyping methods葉片穿孔とGlnLuxバイオセンサーを用いて窒素固定関連状態を迅速・高スループットに測定する新規アッセイを開発し、15N測定等と比較検証しているため、植物フェノタイピング手法が中心である。
abstractHere, we have developed a new leaf punch based technique to screen rhizobia inoculants for SNF activity
Reproduction assets foundThe article's Supplementary Material (Figures S1–S4) contains the paper's own phenotyping measurements (GlnLux luminometer outputs, Gln standard curves, nodulation counts, root/shoot morphometric data, and root images) and is publicly available at the Frontiers supplementary-material URL stated in the text. No author分析Supplement · publicewan) and Alice Chang (University of British Columbia) for 15 N analysis.
Footnotes
Funding. This research was supported by CIFSRF grant 107791 to MR from the International Food Development Centre (IDRC, Ottawa) and Global Affairs Canada.
Supplementary Material
The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fpls.2017.01714/full#supplementary-material
FIGURE S1
Luminescence measurement of Gln standards using the GlnLux 96-well luminometer bioassay to demonstrate the linearity of the assay. Luminescence was measured using a concentration gradient of pure Gln standards (0, 125 × 10 -8 , 25 × 10 -7 , 5 × 10 -6 , and 1 × 10 -5 M) uOpen asset ↗lines:155-178Code / dataset availability confirmedEurope PMC · checked 10 Sept 2026
Vibrational spectroscopy provides non-destructively the molecular fingerprint of plant cells in the native state. In combination with microscopy, the chemical composition can be followed in context with the microstructure, and due to the non-destructive application, in-situ studies of changes during, e.g., degradation or mechanical load are possible. The two complementary vibrational microspectroscopic approaches, Fourier-Transform Infrared (FT-IR) Microspectroscopy and Confocal Raman spectroscopy, are based on different physical principles and the resulting different drawbacks and advantages in plant applications are reviewed. Examples for FT-IR and Raman microscopy applications on plant cell walls, including imaging as well as in-situ studies, are shown to have high potential to get a deeper understanding of structure-function relationships as well as biological processes and technical treatments. Both probe numerous different molecular vibrations of all components at once and thus result in spectra with many overlapping bands, a challenge for assignment and interpretation. With the help of multivariate unmixing methods (e.g., vertex components analysis), the most pure components can be revealed and their distribution mapped, even tiny layers and structures (250 nm). Instrumental as well as data analysis progresses make both microspectroscopic methods more and more promising tools in plant cell wall research.
Why it matches plant phenotyping methods植物細胞壁の構造・化学組成を対象とする振動顕微分光法（FT-IRおよび共焦点ラマン）の植物への応用、画像化、データ解析を方法論としてレビューしており、植物状態の取得・抽出法が中心である。
abstractThe two complementary vibrational microspectroscopic approaches, Fourier-Transform Infrared (FT-IR) Microspectroscopy and Confocal Raman spectroscopy, are based on different physical principles and the resulting different drawbacks and advantages in plant applications are reviewed.
Reproduction assets foundThe review mentions an author-established public spectral database of plant cell wall reference components and spectra, hosted at bionami.at/spectra.html, which directly supports the paper's vibrational microspectroscopy measurements and band-assignment analysis. No code, models, or image datasets with explicit public-Dataset · publica spectral database, including reference components as well as different plant cell walls is currently established and made available to the scientific community ( http://bionami.at/spectra.html ).Open asset ↗bionami.atlines:98-107Code / dataset availability confirmedbioRxiv · Europe PMC · OpenAlex · Crossref · checked 14 Sept 2026
MaizeGrowth / time-series analysisGrowth / development / phenology
Recent advances in automated plant phenotyping have enabled the collection time series measurements from the same plants of a wide range of traits over different developmental time scales. The availability of time series phenotypic datasets has increased interest in statistical approaches for comparing patterns of change between different plant genotypes and different treatment conditions. Two widely used methods of modeling growth over time are point-wise analysis of variance (ANOVA) and parametric sigmoidal curve fitting. Point-wise ANOVA yields discontinuous growth curves, which do not reflect the true dynamics of growth patterns in plants. In contrast, fitting a parametric model to a time series of observations does capture the trend of growth, however these models require assumptions regarding the true pattern of plant growth. Depending on the species, treatment regime, and subset of the plant lifecycle sampled this assumptions will not always hold true. Here we introduce a different approach - functional ANOVA - which yields continuous growth curves without requiring assumptions regarding patterns of plant growth. We compare and validate this approach using data from an experiment measuring growth of two maize (Zea mays ssp. mays) genotypes under two water availability treatments over a 21-day period. Functional ANOVA enables a nonparametric estimation of the dynamics of changes in plant traits over time without assumptions regarding curve shape. In addition to estimating smooth curves of trait values over time, functional ANOVA also estimates the the derivatives of these curves - e.g. growth rates - simultaneously. Using two different subsampling strategies, we demonstrate that this functional ANOVA method enables the comparison of growth curves between plants phenotyped on non-overlapping days with little reduction in estimation accuracy. This means functional ANOVA based approaches can allow larger numbers of samples and biological replicates to be scored in a single experiment given fixed amounts of phenotyping infrastructure and personnel.
Why it matches plant phenotyping methods植物の時系列表現型データから成長曲線と成長率を推定・比較する機能的ANOVAを導入し、実データで検証しているため、表現型解析手法が中心である。
abstractHere we introduce a different approach - functional ANOVA - which yields continuous growth curves without requiring assumptions regarding patterns of plant growth.
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 raw image data employed in this study is hosted at CyVerse under doi 10.7946/P22K7V.Open asset ↗CyVerse · 10.7946/P22K7Vpdf-page:9 lines:1-40Code / dataset availability confirmedbioRxiv · Europe PMC · checked 14 Sept 2026
How complex developmental-genetic networks are translated into organs with specific 3D shapes remains an open question. This question is particularly challenging because the elaboration of specific shapes is in essence a question of mechanics. In plants, this means how the genetic circuitry affects the cell wall. The mechanical properties of the wall and their spatial variation are the key factors controlling morphogenesis in plants. However, these properties are difficult to measure and investigating their relation to genetic regulation is particularly challenging. To measure spatial variation of mechanical properties, one must determine the deformation of a tissue in response to a known force with cellular resolution. Here we present an automated confocal micro-extensometer (ACME), which greatly expands the scope of existing methods for measuring mechanical properties. Unlike classical extensometers, ACME is mounted on a confocal microscope and utilizes confocal images to compute the deformation of the tissue directly from biological markers, thus providing cellular scale information and improved accuracy. ACME is suitable for measuring the mechanical responses in live tissue. As a proof of concept we demonstrate that the plant hormone gibberellic acid induces a spatial gradient in mechanical properties along the length of the Arabidopsis hypocotyl.\n\nTerms
Why it matches plant phenotyping methods植物組織の力学的性質を細胞解像度で定量する自動共焦点マイクロ伸展計を開発し、画像から変形を抽出する方法を中心に実証しているため。
abstractHere we present an automated confocal micro-extensometer (ACME), which greatly expands the scope of existing methods for measuring mechanical properties.
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 positioners are controlled by a SmarAct MCS3D (SmarAct GmbH) controller (Figure 1C,
label 15) accompanied by its software library, which in turn is controlled by custom-made software
(available here: https://github.com/ACME-Robinson/InstallPackage)Open asset ↗ACME-Robinson/InstallPackagepdf-page:14 lines:1-54Code / dataset availability confirmedEurope PMC · checked 14 Sept 2026
Flooding is a devastating abiotic stress that endangers crop production in the twenty-first century. Because of the severe susceptibility of common bean ( Phaseolus vulgaris L.) to flooding, an understanding of the genetic architecture and physiological responses of this crop will set the stage for further improvement. However, challenging phenotyping methods hinder a large-scale genetic study of flooding tolerance in common bean and other economically important crops. A greenhouse phenotyping protocol was developed to evaluate the flooding conditions at early stages. The Middle-American diversity panel ( n = 272) of common bean was developed to capture most of the diversity exits in North American germplasm. This panel was evaluated for seven traits under both flooded and non-flooded conditions at two early developmental stages. A subset of contrasting genotypes was further evaluated in the field to assess the relationship between greenhouse and field data under flooding condition. A genome-wide association study using ~150 K SNPs was performed to discover genomic regions associated with multiple physiological responses. The results indicate a significant strong correlation ( r > 0.77) between greenhouse and field data, highlighting the reliability of greenhouse phenotyping method. Black and small red beans were the least affected by excess water at germination stage. At the seedling stage, pinto and great northern genotypes were the most tolerant. Root weight reduction due to flooding was greatest in pink and small red cultivars. Flooding reduced the chlorophyll content to the greatest extent in the navy bean cultivars compared with other market classes. Races of Durango/Jalisco and Mesoamerica were separated by both genotypic and phenotypic data indicating the potential effect of eco-geographical variations. Furthermore, several loci were identified that potentially represent the antagonistic pleiotropy. The GWAS analysis revealed peaks at Pv08/1.6 Mb and Pv02/41 Mb that are associated with root weight and germination rate, respectively. These regions are syntenic with two QTL reported in soybean ( Glycine max L.) that contribute to flooding tolerance, suggesting a conserved evolutionary pathway involved in flooding tolerance for these related legumes.
Why it matches plant phenotyping methods洪水耐性を評価する温室フェノタイピングプロトコルを開発し、圃場データとの相関で信頼性を検証しており、表現型取得法が研究の中心である。
abstractA greenhouse phenotyping protocol was developed to evaluate the flooding conditions at early stages.
Reproduction assets found保存済みの本文根拠を更新済みルールで再検証し、公開資産1件を確認しました。Supplement · publicThe phenotypic responses of seven traits were measured in both non-flooded and flooded conditions (Supplementary Material, Data Sheet 1).Open asset ↗lines:55-103Code / dataset availability confirmedEurope PMC · Crossref · checked 14 Sept 2026
The arrangement of leaf material is critical in determining the light environment, and subsequently the photosynthetic productivity of complex crop canopies. However, links between specific canopy architectural traits and photosynthetic productivity across a wide genetic background are poorly understood for field grown crops. The architecture of five genetically diverse rice varieties-four parental founders of a multi-parent advanced generation intercross (MAGIC) population plus a high yielding Philippine variety (IR64)-was captured at two different growth stages using a method for digital plant reconstruction based on stereocameras. Ray tracing was employed to explore the effects of canopy architecture on the resulting light environment in high-resolution, whilst gas exchange measurements were combined with an empirical model of photosynthesis to calculate an estimated carbon gain and total light interception. To further test the impact of different dynamic light patterns on photosynthetic properties, an empirical model of photosynthetic acclimation was employed to predict the optimal light-saturated photosynthesis rate ( P max ) throughout canopy depth, hypothesizing that light is the sole determinant of productivity in these conditions. First, we show that a plant type with steeper leaf angles allows more efficient penetration of light into lower canopy layers and this, in turn, leads to a greater photosynthetic potential. Second the predicted optimal P max responds in a manner that is consistent with fractional interception and leaf area index across this germplasm. However, measured P max , especially in lower layers, was consistently higher than the optimal P max indicating factors other than light determine photosynthesis profiles. Lastly, varieties with more upright architecture exhibit higher maximum quantum yield of photosynthesis indicating a canopy-level impact on photosynthetic efficiency.
Why it matches plant phenotyping methodsステレオカメラによる3D植物再構成を用いてイネの群落構造形質を取得し、光環境・光合成との関係を解析しており、表現型取得ワークフローが研究の中心である。
abstractRay tracing was employed to explore the effects of canopy architecture on the resulting light environment in high-resolution
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 Table S2
Physiological characteristics of the 15 parental MAGIC lines + IR64 used in the initial screening . All measurements, apart from harvest dry weight and seed dry weight, were taken 55–60 days after transplanting (DAT), corresponding to the vegetative growth stage.Open asset ↗lines:538-570Code / dataset availability confirmedEurope PMC · checked 15 Sept 2026
As integral parts of plant signaling networks, phytohormones are involved in the regulation of plant metabolism and growth under adverse environmental conditions, including salinity. Globally, salinity is one of the most severe abiotic stressors with an estimated 800 million hectares of arable land affected. Roots are the first plant organ to sense salinity in the soil, and are the initial site of sodium (Na + ) exposure. However, the quantification of phytohormones in roots is challenging, as they are often present at extremely low levels compared to other plant tissues. To overcome this challenge, we developed a high-throughput LC-MS method to quantify ten endogenous phytohormones and their metabolites of diverse chemical classes in roots of barley. This method was validated in a salinity stress experiment with six barley varieties grown hydroponically with and without salinity. In addition to phytohormones, we quantified 52 polar primary metabolites, including some phytohormone precursors, using established GC-MS and LC-MS methods. Phytohormone and metabolite data were correlated with physiological measurements including biomass, plant size and chlorophyll content. Root and leaf elemental analysis was performed to determine Na + exclusion and K + retention ability in the studied barley varieties. We identified distinct phytohormone and metabolite signatures as a response to salinity stress in different barley varieties. Abscisic acid increased in the roots of all varieties under salinity stress, and elevated root salicylic acid levels were associated with an increase in leaf chlorophyll content. Furthermore, the landrace Sahara maintained better growth, had lower Na + levels and maintained high levels of the salinity stress linked metabolite putrescine as well as the phytohormone metabolite cinnamic acid, which has been shown to increase putrescine concentrations in previous studies. This study highlights the importance of root phytohormones under salinity stress and the multi-variety analysis provides an important update to analytical methodology, and adds to the current knowledge of salinity stress responses in plants at the molecular level.
Why it matches plant phenotyping methods根の植物ホルモンを定量するLC-MS法の開発と検証が中心で、塩ストレス状態に関連する植物表現型・生理状態の抽出法として扱われているため。
abstractwe developed a high-throughput LC-MS method to quantify ten endogenous phytohormones and their metabolites of diverse chemical classes in roots of barley.
Reproduction assets foundThe article reports LC-MS/GC-MS phytohormone and metabolite quantification plus physiological measurements (biomass, lengths, chlorophyll, Na+/K+) for six barley varieties under salinity stress. No author analysis code, models, or image/sensor datasets are described. The only paper-specific public asset is the article'Supplement · publicr providing barley seeds and advice. We also want to thank Mrs. Nirupama Jayasinghe, Mrs. Natalie Pereira, and Mrs. Himasha Mendis (Metabolomics Australia) for primary metabolite quantification and analysis.
1
http://www.metaboanalyst.ca/
Supplementary Material
The Supplementary Material for this article can be found online at: http://journal.frontiersin.org/article/10.3389/fpls.2016.02070/full#supplementary-material
Click here for additional data file.
Click here for additional data file.
Click here for additional data file.
References
Achard P. Cheng H. De Grauwe L. Decat J. Schoutteten H. Moritz T.
( 2006 ).
Integration of plant responses to environmentally activated phytohormonal signalsOpen asset ↗lines:623-682Code / dataset availability confirmedOpenAlex · Crossref · Europe PMC · checked 11 Sept 2026
High-throughput phenotyping produces multiple measurements over time, which require new methods of analyses that are flexible in their quantification of plant growth and transpiration, yet are computationally economic. Here we develop such analyses and apply this to a rice population genotyped with a 700k SNP high-density array. Two rice diversity panels, indica and aus, containing a total of 553 genotypes, are phenotyped in waterlogged conditions. Using cubic smoothing splines to estimate plant growth and transpiration, we identify four time intervals that characterize the early responses of rice to salinity. Relative growth rate, transpiration rate and transpiration use efficiency (TUE) are analysed using a new association model that takes into account the interaction between treatment (control and salt) and genetic marker. This model allows the identification of previously undetected loci affecting TUE on chromosome 11, providing insights into the early responses of rice to salinity, in particular into the effects of salinity on plant growth and transpiration.
Why it matches plant phenotyping methods植物の成長・蒸散をハイスループットに定量する解析手法を開発し、イネ集団への適用と遺伝子座同定まで行っており、表現型取得・抽出が研究の中心である。
abstractHere we develop such analyses and apply this to a rice population genotyped with a 700k SNP high-density array.
Reproduction assets foundThe paper's phenotyping data (raw data underlying trait calculation, trait values) and the authors' analysis code (trait production code and GWAS interaction-model code) are publicly deposited in Dryad under doi:10.5061/dryad.3118j, with explicit availability language in the Data availability section.Code · publicthe codes used in producing the trait values and the code for the interaction model used for GWAS analyses are all available in Dryad ( http://datadryad.org/, doi:10.5061/dryad.3118j ).Open asset ↗Dryad · doi:10.5061/dryad.3118jlines:88-149Code / dataset availability confirmedEurope PMC · checked 14 Sept 2026
Plants are permanently exposed to rapidly changing environments, therefore it is evident that they had to evolve mechanisms enabling them to dynamically adapt to such fluctuations. Here we study how plants can be trained to enhance their photoprotection and elaborate on the concept of the short-term illumination memory in Arabidopsis thaliana. By monitoring fluorescence emission dynamics we systematically observe the extent of non-photochemical quenching (NPQ) after previous light exposure to recognise and quantify the memory effect. We propose a simplified mathematical model of photosynthesis that includes the key components required for NPQ activation, which allows us to quantify the contribution to photoprotection by those components. Due to its reduced complexity, our model can be easily applied to study similar behavioural changes in other species, which we demonstrate by adapting it to the shadow-tolerant plant Epipremnum aureum. Our results indicate that a basic mechanism of short-term light memory is preserved. The slow component, accumulation of zeaxanthin, accounts for the amount of memory remaining after relaxation in darkness, while the fast one, antenna protonation, increases quenching efficiency. With our combined theoretical and experimental approach we provide a unifying framework describing common principles of key photoprotective mechanisms across species in general, mathematical terms.
Why it matches plant phenotyping methodsNPQ蛍光動態を用いた植物の光防御状態の定量と、他種にも適用可能な数学モデルの開発が研究の中心であり、単なる生物学的 routine 測定ではない。
abstractBy monitoring fluorescence emission dynamics we systematically observe the extent of non-photochemical quenching (NPQ) after previous light exposure to recognise and quantify the memory effect.
Reproduction assets foundThe authors explicitly provide open-source code (the npqmodel repository) that reproduces all figures in the paper, including the simulations of the PAM fluorescence measurements. No separate public phenotype dataset deposit is stated (data extraction is referenced only vaguely as 'the database').Code · publiccode (available from https://github.com/QTB-HHU/npqmodel), with chlorophyll fluorescence quenching in spinach thylakoids from light treated orOpen asset ↗QTB-HHU/npqmodelpdf-page:9 lines:1-51Code / dataset availability confirmedEurope PMC · checked 15 Sept 2026
I show that Arabidopsis leaf growth can be described with good precision by a conformal map, where expansion is locally isotropic (the same in all directions) but the amount of expansion can vary with position. Data obtained by tracking leaf growth over time can be reproduced with almost 90% accuracy by such a map. The growth follows a Möbius transformation, which is a type of conformal map that would arise if there were an underlying linear gradient of growth rate. From the data one can derive the parameters that describe this linear gradient and show how it changes over time. Such a rule has the property of maintaining the flatness of a leaf.
Why it matches plant phenotyping methods葉の経時追跡データから成長を定量化し、成長勾配パラメータを推定する数理モデルが研究の中心であり、植物成長表現型の抽出手法に該当する。
abstractData obtained by tracking leaf growth over time can be reproduced with almost 90% accuracy by such a map.
Reproduction assets foundThe paper's analysis is built on publicly available Arabidopsis leaf bead-tracking data (from Remmler & Rolland-Lagan 2012 and Rolland-Lagan et al. 2014), which the authors state are deposited with a package of Matlab leaf-shape analysis programs at a public repository handle. This is a paper-specific, publicly and行动可及Dataset · publicthe Discussion that re-
conciles the anisotropy of clones with the conformal model.
A.1. Data and data-analysis programs
The two papers from Prof. Rolland-Lagan's laboratory, (Re-
mmler and Rolland-Lagan, 2012; Rolland-Lagan et al., 2014), give a
thorough description of growth patterns and point the reader to-
wards a website (http://hdl.handle.net/10393/30401) where the
bead data for individual leaves are available, and also a package of
Matlab programs for leaf shape analysis. Leaves are referred to by
pot and plant numbers in their data sets, and Table A1 shows how
this relates to the numbering used here.
A.2. Complex functions and their derivatives
Let f be a complex analytic funOpen asset ↗pdf-raw-page:9 lines:1-233Code / dataset availability confirmedEurope PMC · checked 11 Sept 2026
CoffeeGrapevineLeafMorphology / geometry measurementPhysiological trait estimationLeaf traitsWater status / transpiration
Fresh water is a key natural resource for food production, sanitation and industrial uses and has a high environmental value. The largest water use worldwide (~70%) corresponds to irrigation in agriculture, where use of water is becoming essential to maintain productivity. Efficient irrigation control largely depends on having access to reliable information about the actual plant water needs. Therefore, fast, portable and non-invasive sensing techniques able to measure water requirements directly on the plant are essential to face the huge challenge posed by the extensive water use in agriculture, the increasing water shortage and the impact of climate change. Non-contact resonant ultrasonic spectroscopy (NC-RUS) in the frequency range 0.1-1.2 MHz has revealed as an efficient and powerful non-destructive, non-invasive and in vivo sensing technique for leaves of different plant species. In particular, NC-RUS allows determining surface mass, thickness and elastic modulus of the leaves. Hence, valuable information can be obtained about water content and turgor pressure. This work analyzes and reviews the main requirements for sensors, electronics, signal processing and data analysis in order to develop a fast, portable, robust and non-invasive NC-RUS system to monitor variations in leaves water content or turgor pressure. A sensing prototype is proposed, described and, as application example, used to study two different species: Vitis vinifera and Coffea arabica, whose leaves present thickness resonances in two different frequency bands (400-900 kHz and 200-400 kHz, respectively), These species are representative of two different climates and are related to two high-added value agricultural products where efficient irrigation management can be critical. Moreover, the technique can also be applied to other species and similar results can be obtained.
Why it matches plant phenotyping methods植物葉の水分量・膨圧を非接触超音波で測定するセンサー方式の要件分析、試作、応用を中心に扱っており、植物表現型取得法が明確に中心的である。
abstractThis work analyzes and reviews the main requirements for sensors, electronics, signal processing and data analysis in order to develop a fast, portable, robust and non-invasive NC-RUS system to monitor variations in leaves water content or turgor pressure.
Reproduction assets foundThe paper's inverse-problem analysis code for extracting leaf parameters (thickness, density, ultrasound velocity, attenuation) from measured resonance spectra is explicitly stated to be publicly available via the authors' GitHub repository and the US-BIOMAT resource page. No phenotype dataset deposit is mentioned.Code · publicUS-BIOMAT
Available online: https://us-biomat.com/resources/code-2/ or https://github.com/usbiomat/ultrasonic-thickness-resonance (accessed on 12 July 2016)Open asset ↗usbiomat/ultrasonic-thickness-resonancelines:327-413Code / dataset availability confirmedEurope PMC · checked 14 Sept 2026
Within the HarvestPlus program there are many collaborators currently using X-Ray Fluorescence (XRF) spectroscopy to measure Fe and Zn in their target crops. In India, five HarvestPlus wheat collaborators have laboratories that conduct this analysis and their throughput has increased significantly. The benefits of using XRF are its ease of use, minimal sample preparation and high throughput analysis. The lack of commercially available calibration standards has led to a need for alternative calibration arrangements for many of the instruments. Consequently, the majority of instruments have either been installed with an electronic transfer of an original grain calibration set developed by a preferred lab, or a locally supplied calibration. Unfortunately, neither of these methods has been entirely successful. The electronic transfer is unable to account for small variations between the instruments, whereas the use of a locally provided calibration set is heavily reliant on the accuracy of the reference analysis method, which is particularly difficult to achieve when analyzing low levels of micronutrient. Consequently, we have developed a calibration method that uses non-matrix matched glass disks. Here we present the validation of this method and show this calibration approach can improve the reproducibility and accuracy of whole grain wheat analysis on 5 different XRF instruments across the HarvestPlus breeding program.
Why it matches plant phenotyping methods小麦粒のFe・Zn濃度という植物形質を測定するXRF校正法を開発し、5台の装置で再現性と精度を検証しており、測定法が中心的です。
abstractConsequently, we have developed a calibration method that uses non-matrix matched glass disks.
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 material
The Supplementary Material for this article can be found online at: http://journal.frontiersin.org/article/10.3389/fpls.2016.00784
Click here for additional data file.Open asset ↗lines:369-504Code / dataset availability confirmedOpenAlex · Europe PMC · checked 15 Sept 2026
Robustness in lettuce, defined as the ability to produce stable yields across a wide range of environments, may be associated with below-ground traits such as water and nitrate capture. In lettuce, research on the role of root traits in resource acquisition has been rather limited. Exploring genetic variation for such traits and shoot performance in lettuce across environments can contribute to breeding for robustness. A population of 142 lettuce cultivars was evaluated during two seasons (spring and summer) in two different locations under organic cropping conditions, and water and nitrate capture below-ground and accumulation in the shoots were assessed at two sampling dates. Resource capture in each soil layer was measured using a volumetric method based on fresh and dry weight difference in the soil for soil moisture, and using an ion-specific electrode for nitrate. We used these results to carry out an association mapping study based on 1170 single nucleotide polymorphism markers. We demonstrated that our indirect, high-throughput phenotyping methodology was reliable and capable of quantifying genetic variation in resource capture. QTLs for below-ground traits were not detected at early sampling. Significant marker-trait associations were detected across trials for below-ground and shoot traits, in number and position varying with trial, highlighting the importance of the growing environment on the expression of the traits measured. The difficulty of identifying general patterns in the expression of the QTLs for below-ground traits across different environments calls for a more in-depth analysis of the physiological mechanisms at root level allowing sustained shoot growth.
Why it matches plant phenotyping methods水・硝酸の資源捕捉を定量する間接的な高スループット表現型計測法を明示的に評価し、信頼性と遺伝変異の定量能力を検証しているため、方法が研究の中心的要素です。
abstractWe demonstrated that our indirect, high-throughput phenotyping methodology was reliable and capable of quantifying genetic variation in resource capture.
Reproduction assets foundThe paper's phenotypic measurements (soil water/nitrate capture, shoot traits) and genotype scores/linkage map are stated to be available as Supplementary Material (Tables S1 and S2) hosted at the article's public Frontiers URL. No author analysis code or trained models are mentioned.Supplement · publicey also thank Jan Velema, Marcel van Diemen and Pieter Schwegman, Vitalis Organic Seeds, for providing seeds, advice, and insight. The project was financially supported through the Top Institute Green Genetics (project number: 2CFD024RP).
Supplementary material
The Supplementary Material for this article can be found online at: http://journal.frontiersin.org/article/10.3389/fpls.2016.00343
Click here for additional data file.
Click here for additional data file.
Click here for additional data file.
References
Biddington N. L., Dearman A. S. (1985). The effects of mechanically-induced stress on water loss and drought resistance in lettuce, cauliflower and celery seedlings. Ann. Bot.
56, 795–8Open asset ↗lines:840-871Code / dataset availability confirmedOpenAlex · Crossref · Europe PMC · checked 5 Sept 2026
Modern phenotyping and plant disease detection methods, based on optical sensors and information technology, provide promising approaches to plant research and precision farming. In particular, hyperspectral imaging have been found to reveal physiological and structural characteristics in plants and to allow for tracking physiological dynamics due to environmental effects. In this work, we present an approach to plant phenotyping that integrates non-invasive sensors, computer vision, as well as data mining techniques and allows for monitoring how plants respond to stress. To uncover latent hyperspectral characteristics of diseased plants reliably and in an easy-to-understand way, we "wordify" the hyperspectral images, i.e., we turn the images into a corpus of text documents. Then, we apply probabilistic topic models, a well-established natural language processing technique that identifies content and topics of documents. Based on recent regularized topic models, we demonstrate that one can track automatically the development of three foliar diseases of barley. We also present a visualization of the topics that provides plant scientists an intuitive tool for hyperspectral imaging. In short, our analysis and visualization of characteristic topics found during symptom development and disease progress reveal the hyperspectral language of plant diseases.
Why it matches plant phenotyping methods非侵襲的ハイパースペクトル画像から植物の病害進展・生理状態を抽出し、トピックモデルで自動追跡する新しい表現・解析手法が研究の中心である。
abstractwe present an approach to plant phenotyping that integrates non-invasive sensors, computer vision, as well as data mining techniques
Reproduction assets foundThe paper's authors publicly released the Python implementation of online regularized LDA used for their hyperspectral plant phenotyping analysis on GitHub. No public phenotype dataset or image deposit is stated; the hyperspectral data itself is only described, not deposited.Code · publicThe Python implementation of online regularized LDA is freely available at https://github.com/mirwaes/sclda .Open asset ↗mirwaes/scldalines:78-87