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 confirmedCrossref · Europe PMC · checked 5 Sept 2026
Shoot apical meristem (SAM) homeostasis integrates environmental and genetic cues to regulate growth dynamics that drive biomass accumulation and crop yield; however, no robust, non-destructive, quantitative proxy has been established for modeling or monitoring SAM-homeostasis-associated dynamics. Here, we developed a novel robot-based 3D imaging system and a custom pot-chamber gas exchange system to non-destructively measure plant occupation volume (POV) and whole-plant photosynthetic rate in wild-type Arabidopsis plants and nine mutants with disrupted SAM homeostasis. We demonstrate that POV robustly captures 3D plant architecture, whereas whole-plant photosynthetic rate serves as a superior proxy for optimal growth dynamics and final biomass associated with SAM homeostasis, outperforming conventional traits such as leaf number, leaf size, total leaf area, and rosette diameter. The strong positive correlations among POV, whole plant photosynthesis, and biomass accumulation establish a powerful new framework for quantitative studies of SAM homeostasis and data-driven evaluation of plant architecture.
Why it matches plant phenotyping methodsロボット3D画像とカスタムガス交換による非破壊的な植物形態・光合成表現型測定系を開発し、従来形質との比較検証も行っており、方法が研究の中心である。
abstractwe developed a novel robot-based 3D imaging system and a custom pot-chamber gas exchange system to non-destructively measure plant occupation volume (POV) and whole-plant photosynthetic rate
Reproduction assets foundThe paper's authors explicitly state that the Python source code for whole-plant leaf-area segmentation, 3D point cloud processing, POV calculation, and Mask3D-based segmentation is publicly available on GitHub at https://github.com/songqingfeng/AtPOVcalculator. This is a paper-specific, public, actionable analysis/PhDCode · publicThe Python source code for whole-plant leaf-area segmentation and calculation is publicly available on GitHub ( https://github.com/songqingfeng/AtPOVcalculator ).Open asset ↗songqingfeng/AtPOVcalculatorlines:224-233Code / dataset availability confirmedOpenAlex · Europe PMC · bioRxiv · checked 5 Sept 2026
Abstract Waterlogging is a major constraint on barley productivity, yet its dynamic, multi-phase nature makes it challenging to dissect using traditional phenotyping approaches. High-throughput phenotyping (HTP) platforms address this by enabling temporal, multi-sensor imaging of large populations, but generate complex datasets that demand new analytical frameworks. Here, we imaged 230 barley accessions over 14 days of waterlogging stress and seven days of recovery using visible, chlorophyll fluorescence, and hyperspectral sensors. Explainable AI was applied to classify stress responses into early stress, late stress, and recovery phases, achieving 86% classification accuracy, and to identify the hyperspectral indices most informative for each phase. Water index (WATER1) and structure insensitive pigment index (SIPI) emerged as primary predictors of stress response. Longitudinal genome-wide association studies (GWAS), using a treatment-by-marker interaction model, identified 236 significant loci across 12 linkage disequilibrium blocks, implicating candidate genes involved in oxidative stress regulation, transcriptional control, and auxin transport. MYB transcription factors were consistently identified across all stress phases, underscoring their central role in waterlogging adaptation. To support interpretation of longitudinal GWAS results, we developed 3D-QTLVis, an interactive visualisation tool that extends Manhattan plots across time, enabling clearer identification of dynamic genomic regions underlying stress tolerance.
Why it matches plant phenotyping methods長期マルチセンサー画像による水ストレス応答の表現型取得と、AIによるフェーズ分類・指標抽出が研究の中心であり、3D-QTLVisも開発している。
abstractHigh-throughput phenotyping (HTP) platforms address this by enabling temporal, multi-sensor imaging of large populations
Reproduction assets foundThe paper's authors publicly release their GWAS Interaction model R scripts and the 3D-QTLVis Shiny visualization tool on GitHub; no public phenotype dataset or trained model deposit is stated (phenotypic data only as summary statistics in supplements).Code · publicCode used for running the GWAS interaction model in R and the 3D-QTLVis tool are available at https://github.com/Walshj73/3D-QTLVis .Open asset ↗Walshj73/3D-QTLVislines:216-267Code / 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 · 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 · 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 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 confirmedEurope PMC · checked 15 Sept 2026
Background High-throughput automated image analysis holds great promise for plant breeding by enabling faster, more accurate assessment of traits relevant to crop improvement. Imaging-based systems, such as the CropReporter, allow automated quantification of photosynthetic parameters like PSII efficiency under ambient light from a top-down 2D perspective. However, standard analysis tools average values across the 2D top view, overrepresenting upper leaves and underrepresenting those in the lower canopy. Upper leaves may occlude lower ones, and due to the pinhole projection of the camera, lower leaves of the same size appear smaller in the image. Consequently, vertical heterogeneity in PSII efficiency within the canopy cannot be resolved using a single 2D image. Results To address these issues, we integrated top-view PSII efficiency data (by CropReporter) with 3D structural data from RGB point clouds (by MaxiMarvin). Alignment accuracy between MaxiMarvin and CropReporter was high, with R² ≥ 0.98 for the x-axis and R² ≥ 0.99 for the y-axis. The method was tested using Chenopodium quinoa, Glycine max, and Solanum tuberosum, exposed to salinity, waterlogging and drought stress respectively. In Chenopodium quinoa, it allowed precise determination of when senescence began in the lower leaves. In Solanum tuberosum, the reduction in PSII efficiency by drought was the same for all leaf layers, while in Glycine max, waterlogging stress most strongly affected the middle layer of the canopy. Conclusions This framework enables the 3D mapping of PSII efficiency across the vertical plant profile by combining top-view chlorophyll fluorescence imaging (CropReporter) with 3D structural data (MaxiMarvin). It reveals vertical variation in photosynthetic activity across canopy layers. With standard 2D chlorophyll fluorescence imaging it is difficult to distinguish between non-photosynthetic tissues like flower heads and lower layers of leaves, that might have the same PSII values. Using height-based filtering, taking data from the 3D mapping, such distinction can be made with the method presented in this paper. This allows estimating the PSII efficiencies of leaves only. By capturing layer-specific responses to abiotic stress and developmental changes, the method provides physiologically relevant input for crop growth modelling and highlights the importance of accounting for canopy structure in photosynthetic analyses.
Why it matches plant phenotyping methods2Dクロロフィル蛍光によるPSII効率を3D植物構造へ投影し、群落層別の葉の生理形質を推定する手法の開発・検証が中心である。
abstractTo address these issues, we integrated top-view PSII efficiency data (by CropReporter) with 3D structural data from RGB point clouds (by MaxiMarvin).
Reproduction assets foundThe authors state that the analysis scripts (2D–3D alignment pipeline) and the phenotyping data used in the study are included with the publication as supplementary material, accessible via the article DOI. This is a paper-specific, publicly available asset containing the authors' analysis code and data.Dataset · publicThe scripts and the data that were used in the current study are available and added to this publication.Open asset ↗lines:143-180Code / 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 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 confirmedEurope PMC · checked 5 Sept 2026
Time-series point clouds have emerged as an effective approach for precise, continuous crop monitoring and quantitative growth analysis. This study constructed a spatiotime-series point cloud dataset containing four species and eleven plant varieties, exploring crop organ instance segmentation, phenotypic parameter extraction, growth quantification, and canopy photosynthesis assessment. A skeleton-based framework for organ-level instance segmentation and time-series analysis is proposed, demonstrating robust performance across all four crops. To fully utilize the time-series data, a novel time-series leaf matching method was introduced, achieving a matching accuracy, defined as the proportion of correctly matched leaves, of over 0.823 for all species. By integrating the matching results with phenotypic parameter extraction, time-series phenotypic data were generated, and a phenotypic variation rate was defined as a suitable metric for quantifying crop growth. Furthermore, these results were integrated into a canopy photosynthesis model to derive key time-series photosynthetic metrics, including photosynthetic rate, absorbed light quantity, light energy utilization efficiency, and each crop organ's contribution to photosynthesis. These metrics provide insights into the crop's growth patterns and photosynthetic strategy. This study offers refined quantitative analysis of crop morphology and photosynthetic parameters through time-series point cloud segmentation, contributing valuable data for advancing plant biology research and enhancing the understanding of crop growth dynamics.
Why it matches plant phenotyping methods時系列点群から作物器官をセグメンテーションし、葉追跡、形態形質、成長量、光合成関連指標を抽出する手法が研究の中心であるため。
abstractA skeleton-based framework for organ-level instance segmentation and time-series analysis is proposed
Reproduction assets foundThe paper's Data availability statement explicitly provides authors' public URLs for a subset of the analysis code (GitHub) and the complete time-series 3D crop point cloud dataset (Baidu pan), both directly supporting this paper's phenotyping measurements and analysis.Code · publicA subset of the code and dataset used in this study is publicly available on our GitHub repository: https://github.com/JiarenZhou/LTPCDCCM .Open asset ↗JiarenZhou/LTPCDCCMlines:578-686Dataset · publicThe complete time-series 3D crop point cloud dataset can be downloaded from https://pan.baidu.com/s/1mNSDz4F0ZjOwmqzMuXozSQ?pwd=1234 .Open asset ↗lines:578-686Code / dataset availability confirmedCrossref · Europe PMC · OpenAlex · checked 5 Sept 2026
Quantitative characterization of complete canopy architecture is essential for accurate evaluation of crop photosynthesis and yield potential, thereby supporting crop ideotype design. Although various sensing technologies enable three-dimensional (3D) reconstruction of individual plants and canopies, they often fail to describe canopy architecture accurately because of severe occlusion in dense populations. To address this limitation, we developed an effective framework for the 3D reconstruction of complex and dynamic population-scale canopy architecture in rapeseed using unmanned aerial vehicle multi-view imagery combined with a novel point cloud completion model. A complete point cloud generation pipeline was first established to enable automated training data annotation, allowing discrimination between surface points and occluded points within the canopy. The proposed crop population point cloud completion network (CP-PCN) integrates a multi-resolution dynamic graph convolutional encoder, a point pyramid decoder, a dynamic graph convolutional feature extractor, and a generative adversarial network-based loss function to predict occluded canopy points. CP-PCN achieved chamfer distance values of 3.35 to 4.51 cm across four growth stages, outperforming the state-of-the-art transformer-based method PoinTr. Ablation analyses confirmed that each of the four modules contributes to overall model accuracy. In addition, validation experiments showed that the improved architectural completeness achieved by CP-PCN resulted in more accurate yield estimation compared with incomplete and PoinTr-completed point clouds. CP-PCN also demonstrated strong cross-crop generalizability by successfully reconstructing mature rice canopies. Overall, this framework provides a scalable approach for quantitative analysis of complex canopy architectures in field-grown crops.
Why it matches plant phenotyping methodsUAVマルチビュー画像から遮蔽点を補完し、作物群落の3Dキャノピー構造を再構成する手法を開発・検証しており、植物表現型取得が研究の中心です。
abstractwe developed an effective framework for the 3D reconstruction of complex and dynamic population-scale canopy architecture in rapeseed using unmanned aerial vehicle multi-view imagery combined with a novel point cloud completion model
Reproduction assets foundThe paper's Data and code availability statement explicitly deposits all source code and test data for the CP-PCN phenotyping pipeline on a public GitHub repository, matching an allowed URL.Code · publicAll source code and test data used in this study are publicly available on GitHub ( https://github.com/Ziyue-Guo/CP-PCN.git ).Open asset ↗https://github.com/Ziyue-Guo/CP-PCN.git · CP-PCNlines:133-158Code / dataset availability confirmedCrossref · checked 14 Sept 2026
This data descriptor presents a dataset comprising crop and soil parameters measured in winter wheat fields near the town of Knezha, Bulgaria. The data were collected as part of a project evaluating the potential of vegetation indices derived from Sentinel-2 satellite imagery to predict biophysical and biochemical crop parameters. The core dataset consists of measurements obtained from 20 m × 20 m field plots and includes a broad range of parameters: leaf area index, fraction of absorbed photosynthetically active radiation, vegetation cover fraction, chlorophyll content, above-ground biomass, plant nitrogen content, biological yield, surface soil moisture, spectral reflectance, plant density, crop height, visual assessments of disease or pest damage, and data on weed occurrence. The dataset is complemented by unmanned aerial vehicle imagery, crop calendars, and field management information. The main soil types in the study area were characterized through soil profiles, while meteorological data were obtained from an automated weather station. The data were collected during the 2016–2017 and 2017–2018 agricultural seasons. The dataset is freely available for download and serves as a valuable resource for researchers in remote sensing—particularly for validating satellite-derived products—as well as for specialists involved in winter wheat monitoring, modeling, and agronomic studies.
Why it matches plant phenotyping methods冬小麦の複数の植物形質を含む再利用可能なデータセットを提示し、UAV画像や衛星由来指標の検証を主目的としているため、植物フェノタイピング用データセットとして採用。
abstractThis data descriptor presents a dataset comprising crop and soil parameters measured in winter wheat fields near the town of Knezha, Bulgaria.
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 · publicDataset: In situ and UAV dataset with crop and soil parameters obtained from winter
wheat fields. https://doi.org/10.5281/zenodo.17475742.Open asset ↗zenodo · 10.5281/zenodo.17475742pdf-page:1 lines:1-56Code / dataset availability confirmedEurope PMC · checked 5 Sept 2026
Hyperspectral reflectance provides rapid, non-destructive phenotyping of plant leaves. These data have been used to develop machine learning models for predicting diverse plant traits, yet key challenges remain. We collected hyperspectral reflectance data together with 25 anatomical, gas exchange, and chlorophyll fluorescence traits from 320 recombinant inbred lines grown over three seasons. Using these data, we systematically (1) compare the performance of PLSR and SVR across a wide range of traits, including also slow fluorescence kinetics, (2) assess model generalizability and transferability, and (3) investigate how different aggregation strategies affect predictive accuracy. Based on a nested cross-validation framework, single cross-validation with MSE as metric performed comparably to repeated cross-validation or PRESS-based calibration. Optimal performance of trait-specific predictions was found to be dependent on the combination of model and data aggregation levels. Structural and biochemical traits showed the best generalizability and transferability, whereas physiological traits, particularly those derived from gas exchange and fluorescence kinetics, exhibited markedly reduced transferability. Together, these results provide a rigorous benchmark for evaluating machine learning models for trait prediction from hyperspectral reflectance data, and highlight both the opportunities and limitations for achieving robust generalization across diverse environments and genotypes.
Why it matches plant phenotyping methodsハイパースペクトル反射データから植物形質を予測する機械学習手法を、複数形質・環境・遺伝子型で系統的に比較し、一般化性と転移性を厳密にベンチマークしているため、方法論が中心である。
abstractHyperspectral reflectance provides rapid, non-destructive phenotyping of plant leaves.
Reproduction assets foundThe paper's Data availability statement explicitly deposits all code and raw hyperspectral/trait data in a public GitHub repository, matching an allowed URL.Code · publicAll code and raw data to ensure reproducibility of the results can be accessed at: [https://github.com/Rudan-X/HyperspectralML](https:/github.com/Rudan-X/HyperspectralML).Open asset ↗Rudan-X/HyperspectralMLlines:158-246Code / 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 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 confirmedEurope PMC · checked 14 Sept 2026
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 confirmedEurope PMC · checked 5 Sept 2026
Plants move chloroplasts in response to light, changing the optical properties of leaves. Low irradiance induces chloroplast accumulation, while high irradiance triggers chloroplast avoidance. Chloroplast movements may be monitored through changes in leaf transmittance and reflectance, typically in red light. We present a step-by-step procedure for the detection of chloroplast positioning using reflectance hyperspectral imaging in white light. We show how to employ machine learning methods to classify leaves according to the chloroplast positioning. The convolutional network is a method of choice for the analysis of the reflectance spectra, as it allows low levels of misclassification. As a complementary approach, we propose a vegetation index, called the Chloroplast Movement Index (CMI), which is sensitive to chloroplast positioning. Our method offers a high-throughput, contactless way of chloroplast movement detection. Key features • Protocol for detached leaves handled in laboratory conditions. • Based on differential (dark-adapted versus irradiated) hyperspectral images of plant leaves. • Data analysis includes machine learning methods and the calculation of a vegetation index. • Requires irradiation equipment apart from the hyperspectral camera set.
Why it matches plant phenotyping methods葉の反射ハイパースペクトル画像から葉緑体位置を検出・分類する手法と指標を開発し、高スループット測定として提示しており、植物表現型取得が中心である。
abstractWe present a step-by-step procedure for the detection of chloroplast positioning using reflectance hyperspectral imaging in white light.
Reproduction assets foundThe protocol explicitly deposits its authors' analysis code (HyperspectralImageProcessing.m, including the pretrained CNN classifier for chloroplast positioning) on GitHub and makes the original hyperspectral images of Arabidopsis and Nicotiana leaves used in the paper's figures available on figshare. Both are paper-‐Code · publicAll code has been deposited to GitHub: https://github.com/plantPhotobiologyLab/machine-learning-for-chloroplast-movement-detection (access date, 08/18/2025)Open asset ↗plantPhotobiologyLab/machine-learning-for-chloroplast-movement-detectionhtml-lines:104-130Dataset · publicOriginal files with hyperspectral images of Nicotiana benthamiana and Arabidopsis thaliana (WT and phot2) leaves, including recordings shown in Figure 3 and Figure 4 of this protocol, can be downloaded from https://figshare.com/articles/dataset/Hyperspectral_images_of_Arabidopsis_thaliana_and_Nicotiana_benthamiana_leaves/30402409?file=58898569Open asset ↗html-lines:104-130Code / 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 14 Sept 2026
Overcoming the strong chlorophyll background poses a significant challenge for measuring and optimizing plant growth. This research investigates the novel application of specialized quantum light emitters introduced into intact leaves of tobacco ( Nicotiana tabacum ), a well-characterized model plant system for studies of plant health and productivity. Leaves were harvested from plants cultivated under two distinct conditions: low light (LL), representing unhealthy leaves with reduced photosynthesis and high light (HL), representing healthy leaves with highly active photosynthesis. Higher-order correlation data were collected and analyzed using machine learning (ML) techniques, specifically a Convolutional Neural Network (CNN), to classify the photon emitter states. This CNN efficiently identified unique patterns and created distinct fingerprints for Nicotiana leaves grown under LL and HL, demonstrating significantly different quantum profiles between the two conditions. These quantum fingerprints serve as a foundation for a novel unified analysis of plant growth parameters associated with different photosynthetic states. By employing CNN, the emitter profiles were able to reproducibly classify the leaves as healthy or unhealthy. This model achieved high probability values for each classification, confirming its accuracy and reliability. The findings of this study pave the way for broader applications, including the application of advanced quantum and machine learning technologies in plant health monitoring systems.
Why it matches plant phenotyping methods量子発光体による葉の光子プロファイル取得とCNN解析を組み合わせ、光合成状態および植物の健康状態を分類する手法が研究の中心である。
abstractThis CNN efficiently identified unique patterns and created distinct fingerprints for Nicotiana leaves grown under LL and HL
Reproduction assets foundThe article's Data availability statement explicitly deposits the paper's time-tagged photon correlation data (the raw measurements underlying the quantum fingerprinting and CNN analysis) in the Dryad Digital Repository, a public, paper-specific dataset.Dataset · publicay: conceptualization, funding acqui-
sition, supervision, project administration, visualization,
writing – original draft, writing – review & editing.
Conflicts of interest
There are no conflicts to declare.
Data availability
Data for this article, including time tangled photon data, are
available at Dryad Digital Repository at https://doi.org/10.5061/dryad.1rn8pk15f.Supplementary information is available. See DOI: https://
doi.org/10.1039/d5an00326a.
Acknowledgements
This research was funded in part by the Faculty Industry
Applied Research (FIAR) program, by the University at
Buffalo’s Center of Excellence in Materials Informatics.
References
1 E. Murchie and T. Lawson, J. Exp. Bot., 2013, 6Open asset ↗Dryad Digital Repository · 10.5061/dryad.1rn8pk15fpdf-raw-page:7 lines:1-96Code / 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 confirmedEurope PMC · bioRxiv · Crossref · checked 14 Sept 2026
ABSTRACT The development of remote sensing methods to estimate plant functional diversity is limited by mismatches between ecology and remote sensing sampling schemes, and the limited representativeness of local field campaigns. The Biodiversity Observing System Simulation Experiment (BOSSE) provides a modeling framework for benchmarking new methodologies. We used BOSSE to simulate 180 different synthetic “Scenes” encompassing a two-year-long time series of plant trait maps and imagery of hyperspectral reflectance factors, spectral indices, sun-induced chlorophyll fluorescence, land surface temperature, and estimates of plant traits (optical traits). We used these simulations to answer five fundamental, yet unsolved, questions: Q1. How should remote sensing characterize functional diversity in large surfaces (sites)? Diversity metric values saturate with the number of pixels involved, hampering comparisons between plant traits and remote sensing estimates in large areas. The average value of metrics computed over small samples should be used instead. Q2. Which sources of spectral information (or combinations thereof) can best capture plant functional diversity at the site scale? Accounting for background effects is the key. Optical traits (remote sensing estimates of plant traits) are the best estimators for plant functional diversity. Other variables succeed when filtered out of the soil pixels; their combination did not yield additional advantages. Q3. How should remote sensing estimates be validated/compared with plant functional diversity measurements? Leaf area index (LAI) is a better proxy of abundance than the pixel for Q Rao, but not for variance-based partitioning. It is more sensitive to sample size, but also more resistant to suboptimal spatial resolution. Q4. When (in the phenological year) can remote sensing best capture site-scale plant functional diversity? The estimation error decreased with LAI and stabilized at values above 1 m²/m². Q5. Which approaches and remote sensing variables are more resistant to the effects of suboptimal spatial resolution? Optical traits, fluorescence, and reflectance factors were the most robust variables. Still, field data resolution needs to be degraded to match the sensor’s resolution. We found a relative spatial resolution threshold of ∼30 % (where the pixel is around three times larger than the plants). Simulation frameworks like BOSSE enable testing methodologies beyond local contexts and address the current shortage of suitable global datasets, supporting the application and development of methods for assessing plant functional diversity with remote sensing. In the future, BOSSE could contribute to understanding observational results, refining and pre-testing new methodologies, and supporting the development of comparable experimental datasets.
Why it matches plant phenotyping methodsBOSSEを用いてリモートセンシングによる植物形質・機能多様性推定手法をシミュレーションベンチマークし、検証・比較する研究であり、植物フェノタイピング手法が中心である。
abstractThe Biodiversity Observing System Simulation Experiment (BOSSE) provides a modeling framework for benchmarking new methodologies.
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 · publicvariables, we used the “pyGNDiv” package (https://github.com/JavierPachecoLabrador/pyGNDiv-Open asset ↗JavierPachecoLabrador/pyGNDiv- · pyGNDivpdf-page:11 lines:1-60Code / 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.
Download Print Version | Download XLSX
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 confirmedOpenAlex · Europe PMC · checked 6 Sept 2026
Complex omics approaches and high-throughput phenotyping generate large, heterogeneous datasets that make linking molecular signatures to plant traits challenging. To address this challenge, here we introduce panomiX, a user-friendly toolbox for multi-omics integration, designed to enable non-experts to apply advanced computational methods with ease. PanomiX automates data preprocessing, variance analysis, multi-omics prediction, and interaction modeling through machine learning, revealing meaningful molecular interactions and synergies. We applied panomiX to a tomato heat-stress experiment combining image-based phenotyping, transcriptomics, and Fourier-transform infrared spectroscopy data, with the aim of identification of condition-specific, cross-domain relationships between gene expression, metabolite levels, and phenotypic traits. Our approach identified a network of such connections, with those linking photosynthesis traits with stress-responsive kinases in elevated temperatures among most significant ones. By simplifying complex analyses and improving interpretability, panomiX offers a platform to accelerate the discovery of trait emergence in plants and select specific candidate genes based on multi-omics analyses.
Why it matches plant phenotyping methods植物の画像ベース表現型を含むマルチオミクス統合と機械学習解析を自動化するツールを開発・適用しており、表現型解析ワークフローが中心的です。
abstracthere we introduce panomiX, a user-friendly toolbox for multi-omics integration, designed to enable non-experts to apply advanced computational methods with ease.
Reproduction assets foundThe paper's tomato heat-stress phenotyping/FTIR data and pre-processed analysis inputs are publicly deposited at IPK e!DAL, and the panomiX analysis code is on GitHub with a Zenodo archive; the rnaseq-mapper pipeline is also public. ENA RNA-seq deposit is molecular omics and excluded.Dataset · publicPhenotyping and FTIR data as well as pre-processed inputs for reproducing the results of this article with panomiX are available at https://doi.org/10.5447/ipk/2025/3 .Open asset ↗10.5447/ipk/2025/3lines:156-172Code · publicThe code for panomiX is freely available at https://github.com/NAMlab/panomiX-tool under the terms of the MIT license (also archived at Zenodo at time of publication: https://doi.org/10.5281/zenodo.15193421 ).Open asset ↗GitHub · NAMlab/panomiX-toollines:156-172Code · publicThe code for panomiX is freely available at https://github.com/NAMlab/panomiX-tool under the terms of the MIT license (also archived at Zenodo at time of publication: https://doi.org/10.5281/zenodo.15193421 ).Open asset ↗Zenodo · 10.5281/zenodo.15193421lines:156-172Code / dataset availability confirmedCrossref · Europe PMC · checked 6 Sept 2026
This dataset was generated to characterize the physiological and morphological mechanisms underlying tolerance and resilience to combined drought and heat stress using a panel of 106 Mediterranean maize inbred lines. To achieve this, high-throughput non-invasive phenotyping combined with genome-wide association analysis was applied to accurately capture the dynamic responses of the maize lines to stress and to dissect the genetic basis of maize tolerance and resilience. Two experiments were conducted under control (25/20 °C, 70 % field capacity (FC)) and stress conditions (35/25 °C, 30 % FC). Stress was applied from 18 to 32 DAS (days after sowing), followed by a recovery period under control conditions. Plants were grown under controlled air temperature and soil water content, and were harvested at 45 DAS. Throughout the cultivation period, multiple camera sensors captured images daily, allowing agronomic traits to be extracted for analysis. The dataset includes raw and processed images, phenotypic data obtained from these images, results of two photosynthesis related parameters, Genome-Wide Association Study (GWAS) results from one parameter as an example, and scripts used for data analysis. Additionally, metadata and a detailed description of the experimental setup are provided. This resource is suitable for researchers interested in stress phenotyping and quantitative genetics. It allows further exploration of genotype-by-environment interactions and integration with other omics datasets. The dataset provides a valuable foundation for studies aiming to understand and improve crop resilience to climate-related abiotic stresses.
Why it matches plant phenotyping methods植物の高スループット表現型取得を中心とするデータセットで、画像から農業形質を抽出するセンサー基盤、処理画像、表現型データ、解析スクリプトを提供しているため。
abstracthigh-throughput non-invasive phenotyping combined with genome-wide association analysis was applied to accurately capture the dynamic responses of the maize lines to stress
Reproduction assets foundThe authors deposited the paper's raw/processed phenotyping images, phenotypic and photosynthesis data, GWAS inputs/results, and R analysis scripts in the public e!DAL repository (DOI 10.5447/ipk/2025/8) in ISA-Tab/MIAPPE format.Dataset · publicThe produced raw datasets and source code were uploaded to the e!DAL repository in ISA-Tab format (http://dx.doi.org/10.5447/ipk/2025/8) according to the MIAPPE standard.Open asset ↗e!DAL · 10.5447/ipk/2025/8html-lines:126-157Code / 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 · 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
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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
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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 · bioRxiv · checked 15 Sept 2026
Hyperspectral reflectance provides rapid and precise phenotyping of plants in a non-destructive manner both in field and well-controlled settings. The resulting data have been used to devise machine learning (ML) models for paired measurements of different traits in diverse plants and crops. Yet, despite advances in using of hyperspectral data to reliably predict crop traits of interest, there are pressing issues concerning the training of ML models, the aggregation of data from crop field trials, and the generalizability of the models in different prediction settings. We collected hyperspectral reflectance data along with 25 anatomical, gas exchange, and chlorophyll fluorescence traits from 320 recombinant inbred lines of a maize Multi-Parent Advanced Generation Inter-Cross population grown across three consecutive seasons. We use these data to systematically: (1) compare the performance of representative ML models for different traits, including slow fluorescence kinetics whose predictability by hyperspectral data has not yet been investigated, (2) evaluate the ML model performance in prediction scenarios concerning unseen genotypes, unseen seasons, and the combination thereof, (3) investigate the effects of data aggregation of ML model performance. These problems are addressed in a rigorous nested cross-validation setting that provides a template for adequate assessment of performance of ML models for diverse crop traits considering the particularities of the experimental design.
Why it matches plant phenotyping methodsトウモロコシのハイパースペクトル反射データによる形質推定について、複数の機械学習モデル、未知遺伝子型・季節への汎化性能、データ統合の影響を系統的かつネスト化交差検証で評価しており、フェノタイピング手法の検証が中心である。
abstractWe use these data to systematically: (1) compare the performance of representative ML models for different traits, including slow fluorescence kinetics whose predictability by hyperspectral data has not yet been investigated, (2) evaluate the ML model performance in prediction scenarios concerning unseen genotypes, unseen seasons, and the combination thereof, (3) investigate the effects of data aggregation of ML model performance.
Reproduction assets foundThe paper's data availability statement explicitly provides all code and raw data (hyperspectral reflectance and trait measurements) for reproducibility via the authors' public GitHub repository.Code · publicidge, Cambridge, UK
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†
These authors contributed equally.
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*
Corresponding authors.
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Email address:
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rudan.xu@uni-potsdam.de
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jfergu@essex.ac.uk
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jk417@cam.ac.uk
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nikoloski@mpimp-golm.mpg.de
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Data availability statement
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All code and raw data to ensure reproducibility of the results can be accessed at:
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https://github.com/Rudan-X/HyperspectralML
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Funding statement:
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J.F. was supported by the European Union’s Horizon 2020 research and innovation program
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grant 862201 (to J.K. and Z.N.). R.X. was supported by the International Max Planck Research
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School "Molecular Plant Science" between the Max Planck Institute of Molecular Plant
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Physiology and the UniveOpen asset ↗Rudan-X/HyperspectralMLpdf-raw-page:1 lines:1-71Code / dataset availability confirmedOpenAlex · arXiv · checked 15 Sept 2026
Quantitative descriptions of the complete canopy architecture are essential for accurately evaluating crop photosynthesis and yield performance to guide ideotype design. Although various sensing technologies have been developed for three-dimensional (3D) reconstruction of individual plants and canopies, they failed to obtain an accurate description of canopy architectures due to severe occlusion among complex canopy architectures. We proposed an effective method for 3D reconstruction of complex, dynamic population canopy architecture for rapeseed crops with a novel point cloud completion model. A complete point cloud generation framework was developed for automated annotation of the training dataset by distinguishing surface points from occluded points within canopies. The crop population point cloud completion network (CP-PCN) was then designed with a multi-resolution dynamic graph convolutional encoder (MRDG) and a point pyramid decoder (PPD) to predict occluded points. To further enhance feature extraction, a dynamic graph convolutional feature extractor (DGCFE) module was proposed to capture structural variations over the whole rapeseed growth period. The results demonstrated that CP-PCN achieved chamfer distance (CD) values of 3.35 cm -4.51 cm over four growth stages, outperforming the state-of-the-art transformer-based method (PoinTr). Ablation studies confirmed the effectiveness of the MRDG and DGCFE modules. Moreover, the validation experiment demonstrated that the silique efficiency index developed from CP-PCN improved the overall accuracy of rapeseed yield prediction by 11.2% compared to that of using incomplete point clouds. The CP-PCN pipeline has the potential to be extended to other crops, significantly advancing the quantitatively analysis of in-field population canopy architectures.
Why it matches plant phenotyping methods作物群落キャノピーの3D形態を復元する点群補完法を開発し、既存法との比較、アブレーション、収量予測への有効性検証まで行っており、植物フェノタイピング手法が研究の中心である。
abstractWe proposed an effective method for 3D reconstruction of complex, dynamic population canopy architecture for rapeseed crops with a novel point cloud completion model.
Reproduction assets foundThe paper's availability statement explicitly deposits all source code and test data (rapeseed canopy point cloud completion, CP-PCN) on GitHub at the allowed URL.Code · publicn Wang, Yi Feng,
Mengjie Gong and Guangyu Wu, for their participation in the experiments, and
to the Jiaxing Academy of Agricultural Sciences for their assistance with the
experimental data acquisition.
Availability of supporting data and source code
All source codes and test data involved in this study are available on
GitHub (https://github.com/Ziyue-Guo/RP-PCN.git).
Declaration of Competing Interest
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.
Contributions
Z. G. designed the study, conducted the experiments, and wrote the
manuscript. Y. S. contributed to the expeOpen asset ↗Ziyue-Guo/RP-PCNpdf-layout-page:42 lines:1-42Code / dataset availability confirmedOpenAlex · Europe PMC · checked 13 Sept 2026
Forest phenotypic responses are significantly influenced by extreme climate conditions, particularly canopy structure and photosynthetic traits. However, the underlying mechanisms driving these responses, especially in conifer species, remain poorly understood. This study employs advanced phenotyping technologies, combining three-dimensional (3D) canopy reconstruction with high-resolution physiological trait analysis, quantifying changes in key physiological traits that light interception, gas exchange parameters stomatal conductance, and chlorophyll content. Developing 3D reconstruction algorithms tailored to conifer canopies is essential for simulating forest ecosystem responses under varying canopy densities. We investigate the following questions: (1) How does thinning affect canopy light penetration and photosynthetic efficiency? Thinning significantly increased light penetration from 15 % (CK) to 22 %, enhancing photosynthetic efficiency, resulting in an 18 % increase in carbon absorption under drought conditions. (2) How does reduced-rainfall affect photosynthetically active radiation (PAR) and stomatal conductance? Reduced-rainfall caused a 12 % decrease in PAR, a 20 % reduction in stomatal conductance, and an 8 % decrease in chlorophyll content. (3) What are the synergistic effects of thinning and reduced-rainfall in carbon absorption? Thinning under reduced-rainfall increased carbon absorption by 25 %. This study reveals a significant correlation between chlorophyll content, leaf nitrogen content, and canopy structural dynamics under drought and elevated temperature conditions, offering new insights into the adaptive mechanisms plants employ to adjust their photosynthetic processes. In conclusion, the development of 3D reconstruction algorithms tailored for conifer canopies, in regulating photosynthetic traits, is crucial for improving forest adaptation, contributing to functional trait-based forest management and ecosystem modeling.
Why it matches plant phenotyping methods針葉樹林冠の3D再構成アルゴリズム開発と生理形質推定が明示されており、植物表現型取得法が研究の中心的要素である。
abstractThis study employs advanced phenotyping technologies, combining three-dimensional (3D) canopy reconstruction with high-resolution physiological trait analysis
Reproduction assets foundThe paper's Data Availability statement explicitly deposits the datasets generated during the study (phenotype/physiological measurements and 3D canopy reconstruction outputs) in a public GitHub repository with an authors' URL, making it a paper-specific, publicly actionable asset.Dataset · publicThe datasets generated during this study are available in the GitHub repository: https://github.com/wuchunyanhehe/Plant-Phenomics-Wu-2025 .Open asset ↗https://github.com/wuchunyanhehe/Plant-Phenomics-Wu-2025lines:270-306Code / dataset availability confirmedCrossref · checked 15 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-day 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: a inital estimate immediately after the 10-day reference period, four subsequent estimates every 10 days using new observations, and a final consolidated estimate 90 days 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://data.jrc.ec.europa.eu/dataset/1aac79d8-0d68-4f1c-a40f-b6e362264e50 (Seguini et al., 2025).
Why it matches plant phenotyping methodsFPARという作物・植生キャノピーの明示的な状態量を対象に、MODIS/VIIRSデータのNRTフィルタリング、相互校正、品質評価を開発・記述しており、単なる農業利用ではなく再利用可能な測定データセットと抽出手法が中心である。
abstractwe present a NRT global dataset of Fraction of Photosynthetically Active Radiation (FPAR) at 500 m resolution, optimized for agricultural applications.
Reproduction assets foundThe paper's own filtered and intercalibrated MODIS/VIIRS FPAR dataset (the paper's core output) is explicitly described as open and freely available in near real time via the JRC Data Catalogue and the ASAP website, both of which appear in allowed_urls. No author analysis code is mentioned.Dataset · publicec.europa.eu/, last
access: 30 September 2025) early warning system. The
FPAR dataset is accompanied by associated quality layers
and has a temporal resolution of 10 d, a time step often
used in operational agricultural monitoring. The dataset
is open and freely available in NRT through the Joint
Research Centre Data Catalogue (https://data.jrc.ec.europa.eu/dataset/1aac79d8-0d68-4f1c-a40f-b6e362264e50, last
access: 30 September 2025) and on the ASAP website
(https://agricultural-production-hotspots.ec.europa.eu/data/MO6_FPAR, last access: 30 September 2025). This paper
has the following specific objectives: (i) to introduce the
method used to produce a long-term archive of NRT filtered
FPAR Open asset ↗1aac79d8-0d68-4f1c-a40f-b6e362264e50pdf-raw-page:3 lines:1-86Dataset · publichas a temporal resolution of 10 d, a time step often
used in operational agricultural monitoring. The dataset
is open and freely available in NRT through the Joint
Research Centre Data Catalogue (https://data.jrc.ec.europa.eu/dataset/1aac79d8-0d68-4f1c-a40f-b6e362264e50, last
access: 30 September 2025) and on the ASAP website
(https://agricultural-production-hotspots.ec.europa.eu/data/MO6_FPAR, last access: 30 September 2025). This paper
has the following specific objectives: (i) to introduce the
method used to produce a long-term archive of NRT filtered
FPAR data; (ii) to present the intercalibration performed
between the filtered MODIS-FPAR and the filtered VIIRS-
FPAR; (iii) to evaluate tOpen asset ↗ASAP websitepdf-raw-page:3 lines:1-86Code / 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 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 · 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 confirmedOpenAlex · Europe PMC · bioRxiv · checked 7 Sept 2026
Abstract Complex omics approaches and high-throughput phenotyping generate large, heterogeneous datasets that make linking molecular signatures to plant traits challenging. To address this challenge, here we introduce panomiX, a user-friendly toolbox for multi-omics integration, designed to enable non-experts to apply advanced computational methods with ease. panomiX automates data preprocessing, variance analysis, multi-omics prediction, and interaction modeling through machine learning, revealing meaningful molecular interactions and synergies. We applied panomiX to a tomato heat-stress experiment combining image-based phenotyping, transcriptomics, and Fourier-transform infrared spectroscopy data, with the aim of identification of condition-specific, cross-domain relationships between gene expression, metabolite levels, and phenotypic traits. Our approach identified a network of such connections, with those linking photosynthesis traits with stress-responsive kinases in elevated temperatures among most significant ones. By simplifying complex analyses and improving interpretability, panomiX offers a platform to accelerate the discovery of trait emergence in plants and select specific candidate genes based on multi-omics analyses.
Why it matches plant phenotyping methods植物形質データを含むマルチオミクス統合用ツール panomiX を開発・提示し、画像ベース表現型データを統合解析する再利用可能な計算ワークフローを示しているため、表現型取得そのものより解析ツールが中心的な方法論的貢献である。
abstracthere we introduce panomiX, a user-friendly toolbox for multi-omics integration
Reproduction assets foundThe paper's computational analysis assets are publicly available: the panomiX toolbox source code (GitHub) and its deployed Shiny app, plus the authors' rnaseq-mapper pipeline used to process this study's RNA-seq data. No public deposit of the paper-specific phenotype/FTIR/RNA-seq datasets is stated in the supplied.Code · publicThe source
code for the platform is available on GitHub: https://github.com/NAMlab/panomiX-tool. The
repository contains all the necessary R scripts for data processing, visualization, and machine
learning prediction.Open asset ↗NAMlab/panomiX-toolpdf-page:4 lines:1-42Code · publicThe source code is managed with a GitHub repository connected to
the Shinyapps.io via ‘rsconnect’ [53]: https://szymanskilab.shinyapps.io/panomiX/.Open asset ↗pdf-page:4 lines:1-42Code / 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 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 confirmedOpenAlex · Europe PMC · checked 14 Sept 2026
In agriculture, the plant leaf angle influences light use efficiency and photosynthesis and, consequently, the overall crop performance. Leaf angle measurements are used in plant phenotyping, plant breeding, and remote sensing to study plant function and structure. Traditional manual leaf angle measurements have limited precision as they are labor- and time-intensive due to challenging environmental conditions and highly dynamic plant processes. To enable more detailed studies on leaf angles, we modified a well-established automated farming robot to obtain high-resolution 3D point clouds at customizable intervals of individual plants using stereo vision. We demonstrate the system's accuracy and reliability, with minimal deviation from reference values. The method can be utilized by other researchers to gather data on leaf angles and other structural plant traits at regular intervals to access the dynamics of leaves, plants, and canopies. The system's low cost and adaptability can enhance the efficiency of crop monitoring in plant breeding and phenotyping experiments. Detailed documentation and code are available on GitHub.•An open-source farming robot is retrofitted to function as an automatic data collection platform•Hard to access leaf angles can be retrieved with high accuracy•Leaf angle dynamics can be observed with high temporal resolution.
Why it matches plant phenotyping methodsステレオビジョンを用いて葉角度を高精度・高頻度に測定するロボット基盤を開発・改良し、精度と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ある。
abstractwe modified a well-established automated farming robot to obtain high-resolution 3D point clouds at customizable intervals of individual plants using stereo vision.
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 used codes and recorded data are available at: https://github.com/FrederikHennecke/PointCloudHarvest .Open asset ↗FrederikHennecke/PointCloudHarvestlines:218-236Code / dataset availability confirmedOpenAlex · checked 14 Sept 2026
In this paper, we present a new methodology that directly extracts the geometry of woody features (wood and bark) and foliage from 3D data originating from terrestrial laser scans. Our goal was to enhance the precision of radiative transfer models for modelling tree shading by using highly resolved 3D tree models. The approach was tested on a single apple tree (Malus domestica (Suckow) Borkh.) in a peri-urban setting and was validated by utilising an open-source radiative transfer model and comparing the simulation output with in-situ measurements of photosynthetically active radiation (PAR) as well as simulations utilizing turbid voxels of 0.2 m and 1 m edge length. The in-situ measurements of 60 PAR sensors showed a correlation coefficient (r) of 0.92 with the simulated light intensities for the reconstructed polygons which was higher than for the voxel-based approaches (0.2 m: r = 0.85, 1 m: r = 0.73). We were able to demonstrate that our approach effectively simulates light extinction through the canopy. This innovative method has the potential to easily provide detailed insights into high resolution radiation patterns within forests, which are connected to multiple ecosystem functions like species and habitat diversity.
Why it matches plant phenotyping methodsTLSデータから樹木の木部・樹皮・葉の3D形状を抽出する手法を開発し、PARシミュレーションとの比較で検証しており、植物形態の取得が中心的です。
abstractwe present a new methodology that directly extracts the geometry of woody features (wood and bark) and foliage from 3D data originating from terrestrial laser scans.
Reproduction assets foundThe authors state that all study data (TLS-derived point clouds, PAR measurements) and the full R code for the leaf/wood polygon reconstruction are openly available in their GitHub repository, archived as Frey & Kröner 2024 (JulFrey/dotshadow, Zenodo DOI 10.5281/zenodo.14204435, cited in the references). The Zenodo URLCode · publicAll data relevant to the study and the full R code for the reconstruction of the leaves and woody
compartments can be found at our GitHub repository under open source license (Frey and Kröner
2024).Open asset ↗pdf-page:10 lines:1-56Code / dataset availability confirmedCrossref · checked 15 Sept 2026
Investigating greenhouse gases (GHGs) and water flux dynamics within the soil–plant–atmosphere interphase is key for understanding ecosystem functioning, as they reflect the ecosystem's responses to environmental changes. Understanding these responses is essential for developing sustainable agricultural systems that can help to adapt to global challenges such as increased drought. Typically, an initial understanding of GHGs and water flux dynamics is gained through laboratory or greenhouse pot experiments, where gas exchange is often measured using commercially available manual closed-chamber (leaf) systems. However, these systems are rather expensive and often labor-intensive, thus limiting the number of different treatments and their repetitions that can be studied. Here, we present a fully automatic, low-cost (EUR 2 and evapotranspiration (ET) fluxes. It can operate in two modes: an independent and a dependent measurement mode. The independent measurement mode utilizes low-cost NDIR (non-dispersive infrared) CO 2 (K30 FR) and relative humidity (SHT31) sensors, thus making each greenhouse coffin a fully independent measurement device. The dependent measurement mode connects multiple greenhouse coffins via a low-cost multiplexer (EUR 2 O, CH 4 and stable isotopes). In both modes, CO 2 and ET fluxes are determined through the respective concentration increase during closure time. We tested both modes and demonstrated that the presented system is able to deliver precise and accurate CO 2 and ET flux measurements using low-cost sensors, with an emphasis on calibrating the sensors to improve measurement precision. By connecting multiple greenhouse coffins via our low-cost multiplexer to a single infrared gas analyzer in the dependent mode, we could additionally show that the system can efficiently measure CO 2 and ET fluxes in a high temporal resolution across various treatments with both labor and cost efficiency. Therefore, the developed system is expected to be a valuable tool for conducting greenhouse experiments, enabling comprehensive testing of plant–soil dynamic responses to various treatments and conditions.
Why it matches plant phenotyping methods低コストセンサーと自動閉鎖チャンバーによる植物・土壌系のCO2および蒸発散フラックス測定システムを開発・検証しており、測定手法自体が中心である。
abstractHere, we present a fully automatic, low-cost
Reproduction assets foundThe paper's CO2/ET flux measurements and Arduino analysis/control code are publicly deposited on Bonares (ZALF), explicitly stated in the Code and data availability section and the reference list.Dataset · publicy those with a high level of complexity (e.g., mesocosm experiment), allowing for holistic assessment of the dynamic responses of plants to various treatments and conditions while significantly reducing the required cost and labor.
Code and data availability
The data and code referred to in this study are publicly accessible at https://doi.org/10.4228/ZALF-JG04-HV79 (Al Hamwi et al., 2024).
Author contributions
MH, WA, and MD conceptualized and developed the system and codes. WA carried out the sealing and validation experiments. WA, MH, MD, and JS wrote and prepared the paper with contributions from all co-authors. All authors reviewed and agreed to the final version of the paper.
CompetiOpen asset ↗10.4228/ZALF-JG04-HV79lines:279-306Code / 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 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 7 Sept 2026
Recent research has shown that optimizing photosynthetic and stomatal traits holds promise for improved crop performance. However, standard phenotyping tools such as gas exchange systems have limited throughput. In this work, a novel approach based on a bespoke gas exchange chamber allowing combined measurement of the quantum yield of PSII (Fq'/Fm'), with an estimation of stomatal conductance via thermal imaging was used to phenotype a range of bread wheat (Triticum aestivum L.) genotypes. Using the dual-imaging methods and traditional approaches, we found broad and significant variation in key traits, including photosynthetic CO2 uptake at saturating light and ambient CO2 concentration (Asat), photosynthetic CO2 uptake at saturating light and elevated CO2 concentration (Amax), the maximum velocity of Rubisco for carboxylation (Vcmax), time for stomatal opening (Ki), and leaf evaporative cooling. Anatomical analysis revealed significant variation in flag leaf adaxial stomatal density. Associations between traits highlighted significant relationships between leaf evaporative cooling, leaf stomatal conductance, and Fq'/Fm', highlighting the importance of stomatal conductance and stomatal rapidity in maintaining optimal leaf temperature for photosynthesis in wheat. Additionally, gsmin and gsmax were positively associated, indicating that potential combinations of preferable traits (i.e. inherently high gsmax, low Ki, and maintained leaf evaporative cooling) are present in wheat. This work highlights the effectiveness of thermal imaging in screening dynamic gs in a panel of wheat genotypes. The wide phenotypic variation observed suggested the presence of exploitable genetic variability in bread wheat for dynamic stomatal conductance traits and photosynthetic capacity for targeted optimization within future breeding programmes.
Why it matches plant phenotyping methods特注ガス交換チャンバーと熱画像を組み合わせた動的な気孔コンダクタンス・光合成形質の取得手法を開発し、複数のコムギ遺伝子型で実証しているため、植物フェノタイピング手法が中心です。
abstracta novel approach based on a bespoke gas exchange chamber allowing combined measurement of the quantum yield of PSII (Fq'/Fm'), with an estimation of stomatal conductance via thermal imaging was used to phenotype a range of bread wheat (Triticum aestivum L.) genotypes.
Reproduction assets foundThe paper's Data Availability statement points to a public Dryad repository containing the raw phenotyping data (photosynthesis and stomatal kinetics measurements) for this study, matching an allowed URL. Supplementary datasets S1–S2 are calculation spreadsheets but no standalone public URL is given for them beyond theDataset · publicRaw data can be accessed from the Dryad Digital Repository ( Faralli et al. , 2024 ) ( https://doi.org/10.5061/dryad.79cnp5j4d ).Open asset ↗Dryad Digital Repository · 10.5061/dryad.79cnp5j4dlines:117-171Code / 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 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 · bioRxiv · checked 15 Sept 2026
SUMMARY There is an increasing demand to boost photosynthesis in rice to increase yield potential. Chloroplasts are the site of photosynthesis, and increasing the number and size of these organelles in the in leaf is a potential route to elevate leaf-level photosynthetic activity. Notably, bundle sheath cells do not make a significant contribution to overall carbon fixation in rice and thus various attempts are being made to increase chloroplast content in this cell type. In this study we developed and applied a deep learning tool named Chloro-Count to demonstrate that loss of OsHAP3H function in rice increases chloroplast occupancy in bundle sheath cells by 50%. Although limited to a single season, when grown in the field Oshap3H mutants exhibited increased numbers of tillers and panicles as compared to controls or gain of function mutants. The implementation of Chloro-Count enabled precise quantification of chloroplasts in loss- and gain-of-function OsHAP3H mutants and facilitated a comparison between 2D and 3D quantification methods. In wild-type rice, as the dimensions of bundle sheath cells increase, the volume of individual chloroplasts also increases. However, the larger the chloroplasts the fewer there are per bundle sheath cell. This observation revealed that a mechanism operates in bundle sheath cells to restrict chloroplast occupancy as cell dimensions increase. That mechanism is unperturbed in Oshap3H mutants. The use of Chloro-Count also revealed that 2D quantification, upon which most previous studies have relied, is compromised by the positioning of chloroplasts within the cell. Chloro-Count is therefore a valuable tool for accurate and high-throughput quantification of chloroplasts that has enabled the robust characterization of OsHAP3H effects on chloroplast biogenesis in rice. Whereas previous studies have increased chloroplast occupancy in bundle sheath cells by increasing the size of individual chloroplasts, loss of OsHAP3H function leads to an increase in chloroplast numbers.
Why it matches plant phenotyping methodsChloro-Countという深層学習ツールを開発し、葉肉細胞内の葉緑体数・占有率を高精度かつハイスループットに定量する手法が研究の中心であるため。
abstractwe developed and applied a deep learning tool named Chloro-Count
Reproduction assets foundThe paper's Chloro-Count deep learning tool (Mask R-CNN segmentation of chloroplasts and bundle sheath cells) is the authors' own analysis code, explicitly stated to be publicly available on GitHub. No public image/phenotype dataset deposit is stated; training images and Table S1 raw data are not linked to a public URLCode · publicn validated, they are mapped to
566 individual organelles/cells for volumetric analysis. An overview of the system for detecting and
567 measuring volumes of chloroplasts is presented in Figure 3A. The process for detecting and
568 measuring bundle sheaths follows an analogous workflow. The Chloro-Count code is available on
569 https://github.com/pedropgusmao/chloro-count.
570
571 Data collection and pre-processing
572 A total of 327 slices from 39 different cells were used during the training of both image segmentation
573 networks. Images from 29 cells were used for training, five for validation and five for testing. A total of
574 3,790 segments of chloroplasts were used for training, 287Open asset ↗pedropgusmao/chloro-countpdf-layout-page:16 lines:1-47Code / 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 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
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 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 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 14 Sept 2026
Predicting the plant cell response in complex environmental conditions is a challenge in plant biology. Here we developed a resource allocation model of cellular and molecular scale for the leaf photosynthetic cell of Arabidopsis thaliana, based on the Resource Balance Analysis (RBA) constraint-based modeling framework. The RBA model contains the metabolic network and the major macromolecular processes involved in the plant cell growth and survival and localized in cellular compartments. We simulated the model for varying environmental conditions of temperature, irradiance, partial pressure of CO 2 and O 2 , and compared RBA predictions to known resource distributions and quantitative phenotypic traits such as the relative growth rate, the C:N ratio, and finally to the empirical characteristics of CO 2 fixation given by the well-established Farquhar model. In comparison to other standard constraint-based modeling methods like Flux Balance Analysis, the RBA model makes accurate quantitative predictions without the need for empirical constraints. Altogether, we show that RBA significantly improves the autonomous prediction of plant cell phenotypes in complex environmental conditions, and provides mechanistic links between the genotype and the phenotype of the plant cell.
Why it matches plant phenotyping methodsRBAモデルを用いて植物細胞の生長率やC:N比などの表現型を定量予測する計算手法の開発が中心であり、単なる生物学的実験ではない。
abstractHere we developed a resource allocation model of cellular and molecular scale for the leaf photosynthetic cell of Arabidopsis thaliana
Reproduction assets foundThe authors publicly release the paper-specific RBA leaf model (XML) and the PlantCellRBA simulation/analysis software on Forgemia, with explicit availability statements in the Data availability and Supplementary material sections. No plant image/sensor/phenotype measurement datasets from this paper are deposited; the Code · publicinterest, such as the seed, in order to define and forecast quality
determinants under diverse environmental conditions. These insights will also be valuable in
fine-tuning plant breeding programs.
Data availability
The RBA leaf model (encoded in XML files) and the PlantCellRBA software for running simulations
are available at https://forgemia.inra.fr/anne.goelzer/rba-plant-cell-model.
Acknowledgements
We thank Wolfram Liebermeister, Ana Bulovic, Sophie Colombié and Jean-Denis Faure for critical
comments on the manuscript and the Métaprogramme Digitbio of INRAE for funding.
Author Contributions
AG and VF conceived the study. AG developed, implemented and simulated the different models
(RBA, Open asset ↗forgemia.inra.fr/anne.goelzer/rba-plant-cell-modelpdf-layout-page:28 lines:1-50Supplement · publicSupplementary Table 1) led to changes in growth rate
greater than 1% (Fig.Open asset ↗pdf-raw-page:20 lines:1-49Code / 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 confirmedOpenAlex · Europe PMC · Crossref · checked 15 Sept 2026
BACKGROUND: The study of plant photosynthesis is essential for productivity and yield. Thanks to the development of high-throughput phenotyping (HTP) facilities, based on chlorophyll fluorescence imaging, photosynthetic traits can be measured in a reliable, reproducible and efficient manner. In most state-of-the-art HTP platforms, these traits are automatedly analyzed at individual plant level, but information at leaf level is often restricted by the use of manual annotation. Automated leaf tracking over time is therefore highly desired. Methods for tracking individual leaves are still uncommon, convoluted, or require large datasets. Hence, applications and libraries with different techniques are required. New phenotyping platforms are initiated now more frequently than ever; however, the application of advanced computer vision techniques, such as convolutional neural networks, is still growing at a slow pace. Here, we provide a method for leaf segmentation and tracking through the fine-tuning of Mask R-CNN and intersection over union as a solution for leaf tracking on top-down images of plants. We also provide datasets and code for training and testing on both detection and tracking of individual leaves, aiming to stimulate the community to expand the current methodologies on this topic. RESULTS: We tested the results for detection and segmentation on 523 Arabidopsis thaliana leaves at three different stages of development from which we obtained a mean F-score of 0.956 on detection and 0.844 on segmentation overlap through the intersection over union (IoU). On the tracking side, we tested nine different plants with 191 leaves. A total of 161 leaves were tracked without issues, accounting to a total of 84.29% correct tracking, and a Higher Order Tracking Accuracy (HOTA) of 0.846. In our case study, leaf age and leaf order influenced photosynthetic capacity and photosynthetic response to light treatments. Leaf-dependent photosynthesis varies according to the genetic background. CONCLUSION: The method provided is robust for leaf tracking on top-down images. Although one of the strong components of the method is the low requirement in training data to achieve a good base result (based on fine-tuning), most of the tracking issues found could be solved by expanding the training dataset for the Mask R-CNN model.
Why it matches plant phenotyping methodsCNNによる葉のセグメンテーション・追跡手法を開発し、検出・追跡精度を検証した植物フェノタイピング研究である。
abstractHere, we provide a method for leaf segmentation and tracking through the fine-tuning of Mask R-CNN and intersection over union as a solution for leaf tracking on top-down images of plants.
Reproduction assets foundThe paper's authors explicitly state that the full project library (leaf detection/tracking code and dataset) is available as a public GitHub repository, which directly reproduces this paper's phenotyping analysis.Code · publicof the PyTorch-Vision GitHub repository was used for the model training, specifically the reference scripts found in the folder detection. These scripts are included in the project GitHub under the modelTraining folder without relevant modifications. The full library of this project is available as a public repository at GitHub https://github.com/Fedjurrui/Leaf-Tracking .
Declarations
Ethics approval and consent to participate
Not applicable.
Consent for publication
Not applicable.
Competing interests
No competing interests declared.
References
1.Open asset ↗Fedjurrui/Leaf-Trackinglines:174-269Code / 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 confirmedEurope PMC · Crossref · bioRxiv · checked 15 Sept 2026
Chloroplasts are essential organelles in plants that are involved in plant development and photosynthesis. Accurate quantification of chloroplast numbers is important for understanding the status and type of plant cells, as well as assessing photosynthetic potential and efficiency. Traditional methods of counting chloroplasts using microscopy are time-consuming and face challenges such as the possibility of missing out-of-focus samples or double counting when adjusting the focal position. Here, we developed an innovative approach called Detecting- and-Counting-chloroplasts (D&Cchl) for automated detection and counting of chloroplasts. This approach utilizes a deep-learning-based object detection algorithm called You-Only-Look-Once (YOLO), along with the Intersection Over Union (IOU) strategy. The application of D&Cchl has shown excellent performance in accurately identifying and quantifying chloroplasts. This holds true when applied to both a single image and a three-dimensional (3D) structure composed of a series of images. Furthermore, by integrating Cellpose, a cell-segmentation tool, we were able to successfully perform single-cell 3D chloroplast counting. Compared to manual counting methods, this approach improved the accuracy of detection and counting to over 95%. Together, our work not only provides an efficient and reliable tool for accurately analyzing the status of chloroplasts, enhancing our understanding of plant photosynthetic cells and growth characteristics, but also makes a significant contribution to the convergence of botany and deep learning. One-sentence summary This deep learning-based approach enables the accurate complete detection and counting of chloroplasts in 3D single cells using microscopic image stacks, and showcases a successful example of utilizing deep learning methods to analyze subcellular spatial information in plant cells. The authors responsible for distribution of materials integral to the findings presented in this article in accordance with the policy described in the Instructions for Authors ( https://academic.oup.com/plcell/ ) is: Zhao Dong ( dongzhao@hebeu.edu.cn ), Shaokai Yang, ( shaokai1@ualberta.ca ), Ningjing Liu ( liuningjing1@yeah.net ), and Qiong Zhao ( qzhao@bio.ecnu.edu.cn ).
Why it matches plant phenotyping methods植物細胞の顕微鏡画像から葉緑体数を自動検出・定量する深層学習手法を開発し、手動計数と比較して精度検証しているため、植物フェノタイピング手法が中心である。
abstractHere, we developed an innovative approach called Detecting- and-Counting-chloroplasts (D&Cchl) for automated detection and counting of chloroplasts.
Reproduction assets foundThe authors explicitly state that all code and the training dataset (annotated chloroplast microscopy images) are shared on their public GitHub repository, which is a paper-specific asset for this chloroplast counting study. Other URLs (labelImg, yolov7, ImageJ Falk plugins) are generic third-party tools, not paper-ownCode · publicltiple times during the stacking process. Through this approach, we
460
successfully constructed a comprehensive 3D cell model from the series of 2D
461
images, enabling more accurate chloroplast detection and counting in a 3D space.
462
463
Code and software
464
All the code and training dataset have been shared on GitHub
465
(https://github.com/xiaoli111111111/-AI4CELLBIO-ECNU), with detailed
466
explanations in the supplementary manual.
467
468
References
469
preprint (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission.
The copyright holder for this
this version posted December 18, 2023.
;
https://doi.org/10.1101/2023.12.Open asset ↗xiaoli111111111/-AI4CELLBIO-ECNUpdf-raw-page:16 lines:1-65Code / 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
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https://nph.onlinelibrary.wiley.com/doi/10.1111/nph.19405
by
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[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 · OpenAlex · checked 7 Sept 2026
Crop yield potential is intrinsically related to canopy photosynthesis; therefore, improving canopy photosynthetic efficiency is a major focus of current efforts to enhance crop yield. Canopy photosynthesis rate ( A c ) is influenced by several factors, including plant architecture, leaf chlorophyll content, and leaf photosynthetic properties, which interact with each other. Identifying factors that restrict canopy photosynthesis and target adjustments to improve canopy photosynthesis in a specific crop cultivar pose an important challenge for the breeding community. To address this challenge, we developed a novel pipeline that utilizes factorial analysis, canopy photosynthesis modeling, and phenomics data collected using a 64-camera multi-view stereo system, enabling the dissection of the contributions of different factors to differences in canopy photosynthesis between maize cultivars. We applied this method to 2 maize varieties, W64A and A619, and found that leaf photosynthetic efficiency is the primary determinant (17.5% to 29.2%) of the difference in A c between 2 maize varieties at all stages, and plant architecture at early stages also contribute to the difference in A c (5.3% to 6.7%). Additionally, the contributions of each leaf photosynthetic parameter and plant architectural trait were dissected. We also found that the leaf photosynthetic parameters were linearly correlated with A c and plant architecture traits were non-linearly related to A c . This study developed a novel pipeline that provides a method for dissecting the relationship among individual phenotypes controlling the complex trait of canopy photosynthesis.
Why it matches plant phenotyping methods64台カメラのマルチビュー・ステレオ計測によるフェノミクスデータと、キャノピー光合成モデル・因子分析を統合した新規パイプラインの開発が中心であり、植物形態形質とキャノピー光合成の関係を定量化している。
titleDevelopment of a Novel 3D Canopy Modeling Pipeline
Reproduction assets foundThe paper's Data Availability section explicitly deposits the authors' 3D canopy modeling pipeline source code and the FastTracer ray tracing software used for the canopy photosynthesis simulations, both on public GitHub repositories. No phenotype/image datasets are explicitly deposited.Code · publicThe source code used in this study is available for non-commercial use and the code can be downloaded from https://github.com/PlantSystemsBiology/3DCanopyModelOpen asset ↗PlantSystemsBiology/3DCanopyModellines:224-402Code · publicThe FastTracer software is available from https://github.com/PlantSystemsBiology/fastTracerPublicOpen asset ↗PlantSystemsBiology/fastTracerPubliclines:224-402Code / 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
help improve model parameterization.
cr
MODEL AND DATA AVAILABILITY
us
an
Experimental data extracted from literature, used in model-data comparison, are tabulated in
Supplementary Data 2.
M
The source code used for this study, along with the operational commands and user guide, is
freely available for non-commercial use at https://github.com/rootchang/WACNI-rice.git.
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Ac
28Open asset ↗rootchang/WACNI-ricepdf-layout-page:28 lines:1-44Code / 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 .
REFERENCES
Andaluz , S.
,
López‐Millán , A. F.
,
Peleato , M. L.
,
Abadía , J.
, &
Abadía , A.
( 2002 ).
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 confirmedOpenAlex · bioRxiv · Europe PMC · checked 7 Sept 2026
Abstract C 4 photosynthesis has evolved multiple times in the angiosperms and typically involves alterations to the biochemistry, cell biology and development of leaves. One common modification found in C 4 plants compared with the ancestral C 3 state is an increase in vein density such that the leaf contains a larger proportion of bundle sheath cells. Recent findings indicate that there may be significant intra-specific variation in traits such as vein density in C 4 plants but to use such natural variation for trait-mapping, rapid phenotyping would be required. Here we report a high-throughput method to quantify vein density that leverages the bundle sheath specific accumulation of starch found in C 4 species. Starch staining allowed high-contrast images to be acquired that permitted image analysis using a MATLAB-based program. The method works for the dicotyledon Gynandropsis gynandra where significant variation in vein density was detected between natural accessions, and the monocotyledon Zea mays where no variation was apparent in the genotypically diverse lines assessed. We anticipate this approach will be useful to map genes controlling vein density in C 4 species demonstrating natural variation for this trait. One sentence summary Preferential accumulation of starch in bundle sheath cells of C 4 plants allows high-throughput phenotyping of vein density.
Why it matches plant phenotyping methodsC4植物の葉脈密度を定量する高スループット染色・画像解析法の開発と適用が研究の中心であり、植物形質の取得手法を扱っている。
abstractHere we report a high-throughput method to quantify vein density that leverages the bundle sheath specific accumulation of starch found in C 4 species.
Reproduction assets foundThe paper's Starch4Kranz MATLAB pipeline for quantifying vein density in C4 plants is explicitly stated to be publicly available on GitHub at the authors' repository.Code · publicScript is available at https://github.com/plycs5/Starch4Kranz.Open asset ↗plycs5/Starch4Kranzpdf-page:11 lines:1-28Code / 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 · Crossref · checked 8 Sept 2026
Introduction Evaluations of interspecific hybrids are limited, as classical genebank accession descriptors are semi-subjective, have qualitative traits and show complications when evaluating intermediate accessions. However, descriptors can be quantified using recognized phenomic traits. This digitalization can identify phenomic traits which correspond to the percentage of parental descriptors remaining expressed/visible/measurable in the particular interspecific hybrid. In this study, a line of P. vulgaris , P. acutifolius and P. parvifolius accessions and their crosses were sown in the mesh house according to CIAT seed regeneration procedures. Methodology Three accessions and one derived breeding line originating from their interspecific crosses were characterized and classified by selected phenomic descriptors using multivariate and machine learning techniques. The phenomic proportions of the interspecific hybrid (line INB 47) with respect to its three parent accessions were determined using a random forest and a respective confusion matrix. Results The seed and pod morphometric traits, physiological behavior and yield performance were evaluated. In the classification of the accession, the phenomic descriptors with highest prediction force were Fm', Fo', Fs', LTD, Chl, seed area, seed height, seed Major, seed MinFeret, seed Minor, pod AR, pod Feret, pod round, pod solidity, pod area, pod major, pod seed weight and pod weight. Physiological traits measured in the interspecific hybrid present 2.2% similarity with the P. acutifolius and 1% with the P. parvifolius accessions. In addition, in seed morphometric characteristics, the hybrid showed 4.5% similarity with the P. acutifolius accession. Conclusions Here we were able to determine the phenomic proportions of individual parents in their interspecific hybrid accession. After some careful generalization the methodology can be used to: i) verify trait-of-interest transfer from P. acutifolius and P. parvifolius accessions into their hybrids; ii) confirm selected traits as "phenomic markers" which would allow conserving desired physiological traits of exotic parental accessions, without losing key seed characteristics from elite common bean accessions; and iii) propose a quantitative tool that helps genebank curators and breeders to make better-informed decisions based on quantitative analysis.
Why it matches plant phenotyping methodsインタースペシフィック雑種の形質を定量化・分類し、ランダムフォレストと混同行列で親由来のフェノミック形質割合を検証する方法論が研究の中心である。
abstractThis digitalization can identify phenomic traits which correspond to the percentage of parental descriptors remaining expressed/visible/measurable in the particular interspecific hybrid.
Reproduction assets foundThe paper's MultispeQ physiological phenotyping measurements (1,022 observations) are publicly available on the PhotosynQ platform as the authors' own project 'domestication-syndrome' (ID 5685). No author analysis code or trained model deposit is stated; the data availability statement only promises raw data on requestDataset · publicThe classical protocol was used: Leaf Photosynthesis MultispeQ V1.0 (the raw data are available at: https://photosynq.org/projects/domestication-syndrome ; ID 5685).Open asset ↗PhotosynQ · ID 5685lines:319-327Code / dataset availability confirmedEurope PMC · checked 8 Sept 2026
Dormancy is a physiological state that confers winter hardiness to and orchestrates phenological phase progression in temperate perennial plants. Weather fluctuations caused by climate change increasingly disturb dormancy onset and release in plants including tree crops, causing aberrant growth, flowering and fruiting. Research in this field suffers from the lack of affordable non-invasive methods for online dormancy monitoring. We propose an automatic framework for low-cost, long-term, scalable dormancy studies in deciduous plants. It is based on continuous sensing of the photosynthetic activity of shoots via pulse-amplitude-modulated chlorophyll fluorescence sensors connected remotely to a data processing system. The resulting high-resolution time series of JIP-test parameters indicative of the responsiveness of the photosynthetic apparatus to environmental stimuli were subjected to frequency-domain analysis. The proposed approach overcomes the variance coming from diurnal changes of insolation and provides hints on the depth of dormancy. Our approach was validated over three seasons in an apple ( Malus × domestica Borkh.) orchard by collating the non-invasive estimations with the results of traditional methods (growing of the cuttings obtained from the trees at different phases of dormancy) and the output of chilling requirement models. We discuss the advantages of the proposed monitoring framework such as prompt detection of frost damage along with its potential limitations.
Why it matches plant phenotyping methods植物の休眠状態をクロロフィル蛍光センサーと周波数解析で非侵襲的に推定する監視手法を開発し、複数季節・従来法との比較で検証しており、表現型取得が研究の中心です。
abstractWe propose an automatic framework for low-cost, long-term, scalable dormancy studies in deciduous plants.
Reproduction assets foundThe authors explicitly state that the analysis code, accompanied by a subset of the data, is publicly available on GitHub (Lodinn/PAM-timeseries). The MDPI supplementary materials (S1) also contain paper-specific CF transient plots, correlation matrices, regression fits, and JIP-test parameter tables. Raw data and fullCode · publicCode used in the analysis, accompanied with a subset of the data, is available on GitHub ( https://github.com/Lodinn/PAM-timeseries , accessed on 20 September 2022).Open asset ↗Lodinn/PAM-timeserieslines:128-144Supplement · publicThe following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/plants11212811/s1 . A description of the PAM fluorimeter used in the work, including: Figure S1. The scheme of the experimental orchard plot.Open asset ↗lines:128-144Code / dataset availability confirmedEurope PMC · checked 14 Sept 2026
Dormancy is a physiological state that confers winter hardiness to and orchestrates phenological phase progression in temperate perennial plants. Weather fluctuations caused by climate change increasingly disturb dormancy onset and release in many plant species including tree crops leading to aberrant growth, flowering, and fruiting. Currently, research in this field is impeded by the lack of affordable non-invasive methods for on-line monitoring of dormancy. We report on an automatic framework for low-cost, long-term, and scalable dormancy studies in deciduous plants. The proposed method is based on continuous near-field sensing of the photosynthetic activity of shoots via pulse-amplitude modulated chlorophyll fluorescence sensors connected remotely to a data processing system. The resulting high-resolution time series of JIP-test parameters indicative of the responsiveness of the photosynthetic apparatus to environmental stimuli are subjected to frequency-domain analysis. The proposed approach allows to overcome the variance coming from diurnal changes of insolation and to derive estimations on the depth of dormancy. Our approach was validated over three seasons in an experimental apple (Malus × domestica Borkh.) orchard by collating the non-invasive estimations with the results of traditional methods (growing of the cuttings obtained from the tress at different phases of dormancy) and the output of commonly used chilling requirement models. We discuss the advantages of the proposed monitoring framework such as prompt detection of freeze damages along with its potential limitations.
Why it matches plant phenotyping methods植物の休眠深度を非侵襲的に推定する蛍光センサーと時系列解析のフレームワークを開発し、従来法およびモデルと照合検証しており、表現型取得法が研究の中心である。
abstractWe report on an automatic framework for low-cost, long-term, and scalable dormancy studies in deciduous plants.
Reproduction assets foundThe authors state that the analysis code, accompanied by a subset of the data, is publicly available on GitHub. This is a paper-specific computational analysis asset for the chlorophyll fluorescence/JIP-test time-frequency analysis. The full raw data and derived parameters are only available on request, so they are notCode · publicic projects in priority areas of scientific and technological
development (grant number 075-15-2020-774).
Data Availability Statement: The raw data and derived parameters are available from the corre-
sponding author on reasonable request.
Code used in the analysis, accompanied with a subset of the data, is available on GitHub
(https://github.com/Lodinn/PAM-timeseries).
Acknowledgments: The indoors chlorophyll fluorescence measurements were carried out at the
Phototrophic Organisms Phenotyping user facilities of Lomonosov Moscow State University. TheOpen asset ↗Lodinn/PAM-timeseriespdf-layout-page:15 lines:1-58Code / 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 confirmedbioRxiv · checked 15 Sept 2026
O_LIAnnual rings from 30 year old vines in a California rootstock trial were measured to determine the effects of 15 different rootstocks on Chardonnay and Cabernet Sauvignon scions. Viticultural traits measuring vegetative growth, yield, berry quality, and nutrient uptake were collected at the beginning and end of the lifetime of the vineyard. C_LIO_LIX-ray Computed Tomography (CT) was used to measure ring widths in 103 vines. Ring width was modeled as a function of ring number using a negative exponential model. Early and late wood ring widths, cambium width, and scion trunk radius were correlated with 27 traits. C_LIO_LIModeling of annual ring width shows that scions alter the width of the first rings but that rootstocks alter the decay thereafter, consistently shortening ring width throughout the lifetime of the vine. The ratio of yield to vegetative growth, juice pH, photosynthetic assimilation and transpiration rates, and stomatal conductance are correlated with scion trunk radius. C_LIO_LIRootstocks modulate secondary growth over years, altering hydraulic conductance, physiology, and agronomic traits. Rootstocks act in similar but distinct ways from climate to modulate ring width, which borrowing techniques from dendrochronology, can be used to monitor both genetic and environmental effects in woody perennial crop species. C_LI
Why it matches plant phenotyping methodsX線CTによる年輪幅・形成層幅・幹半径の測定が研究の主要な表現型取得手段であり、樹体の二次成長を遺伝的・環境的影響のモニタリングに用いる方法として扱われている。
abstractX-ray Computed Tomography (CT) was used to measure ring widths in 103 vines.
Reproduction assets foundThe paper deposits its X-ray CT cross-section images with landmarks (the phenotyping inputs for ring-width measurement) on Dryad, and all data plus analysis code in a public GitHub repository/Jupyter notebook. Both are paper-specific, publicly available, and actionable.Dataset · publicBMG, IK, MRM, ELM, AWS, ALD, SS, and DHC analyzed data. ZM and DHC
510 coordinated research, data analysis, and manuscript writing. DHC wrote a first draft of the
511 manuscript which all authors read, commented on, and edited.
512
513 Data Availability
514
515 X-ray CT cross-sections with landmarks are deposited on Dryad:
516 http://dx.doi.org/10.5061/dryad.gqnk98sqf. All data and code to reproduce results are posted on
517 the Github repository https://github.com/DanChitwood/grapevine_rings.
518
519 Supporting Information Table S1: Numbers of measured samples for each trait, for each
520 scion, for each year.
521
522 Table 1: Rootstock parentage
523
Rootstock Parentage
775 Paulsen V. berlaOpen asset ↗Dryad · 10.5061/dryad.gqnk98sqfpdf-layout-page:13 lines:1-51Code · publict writing. DHC wrote a first draft of the
511 manuscript which all authors read, commented on, and edited.
512
513 Data Availability
514
515 X-ray CT cross-sections with landmarks are deposited on Dryad:
516 http://dx.doi.org/10.5061/dryad.gqnk98sqf. All data and code to reproduce results are posted on
517 the Github repository https://github.com/DanChitwood/grapevine_rings.
518
519 Supporting Information Table S1: Numbers of measured samples for each trait, for each
520 scion, for each year.
521
522 Table 1: Rootstock parentage
523
Rootstock Parentage
775 Paulsen V. berlandieri Rességuier 2 × V. rupestris du Lot
1103 Paulsen V. berlandieri Rességuier 2 × V. rupestris du Lot
3309 Couderc V. Open asset ↗GitHub · DanChitwood/grapevine_ringspdf-layout-page:13 lines:1-51Code / 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
804
analyses can be found in File S18. The majority of lines and accessions used in this manuscript
805
are available via GRIN (https://www.ars-grin.gov/) or by request from Soybase.org for the NAM
806
lines (https://soybase.org/SoyNAM/SoyNAM_RIL_request.htm). Supplemental Material
807
available at figshare: https://doi.org/10.25386/genetics.19394693
808
809
Acknowledgements
810
We thank Troy Cary, Chris Moller, and Noah Mitchell for help in setting up and maintaining the
811
experimental plots, collecting data, and processing samples.
812
Funding
813
This work was supported by soybean checkoff funding from the United Soybean Board. ASS
814
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
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 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 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 15 Sept 2026
Environmental factors might influence the carbon balance and sugar content in grapevine. In this two-year research, the STELLA software was employed to predict dry matter accumulation in Sangiovese vines, comparing the traditional vertical shoot positioning (VSP) and the single high wire (SHW) trellis systems. Every week, vegetative, eco-physiological and grape quality parameters were collected for 15 tagged vines per trellis system to set up the software. Significant differences in photosynthesis were recorded in 2014, with higher values in VSP (23-25% more). Shoot growth was significantly higher in VSP (20-25% more), whereas higher dry matter (30%) and yield (9-11% more) were detected for SHW. At harvest, berry composition suggested a slower ripening in SHW compared to VSP, which was linked to the shading of clusters in SHW. Finally, for the first time, linear regressions were found between measured berry sugar content and STELLA-estimated dry matter (R 2 = 0.96 in VSP; R 2 = 0.95 in SHW). This latter evidence allowed the estimation of berry sugar content, showing this software to be a practical tool to support winegrowers in decision making. Other studies are already underway to calibrate and validate the model for other varieties, training systems and environments.
Why it matches plant phenotyping methodsSTELLAモデルによるブドウの乾物蓄積・果実糖含量の推定と、実測値との回帰による検証が研究の中心であり、植物形質の計算推定手法として扱える。
abstractthe STELLA software was employed to predict dry matter accumulation in Sangiovese vines
Reproduction assets foundThe paper's phenotyping measurements (gas exchange, dry matter, berry composition) are reported only within the article itself ('Data is contained within the article'), with no public dataset deposit. However, the authors provide a public supplement containing paper-specific assets: Figure S1 (experimental site images)Supplement · publicbut, above all, herself for the tenacity in being able to finally publish the results of her master’s thesis. Another chapter is closed or not?
Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Supplementary Materials
The following are available online at https://www.mdpi.com/article/10.3390/plants10081675/s1 , Figure S1: Experimental site pictures, Figure S2: simplified model structure of STELLA software.
Click here for additional data file.
Author Contributions
Conceptualization, G.B.M. and L.S.; methodology and software validation, L.S. and E.C.; formal analysis, investigation and data curation, L.S., E.C., S.S., F.Open asset ↗lines:74-114Code / dataset availability confirmedCrossref · checked 15 Sept 2026
Abstract Sorghum (Sorghum bicolor) is a model C4 crop made experimentally tractable by extensive genomic and genetic resources. Biomass sorghum is studied as a feedstock for biofuel and forage. Mechanistic modeling suggests that reducing stomatal conductance (gs) could improve sorghum intrinsic water use efficiency (iWUE) and biomass production. Phenotyping to discover genotype-to-phenotype associations remains a bottleneck in understanding the mechanistic basis for natural variation in gs and iWUE. This study addressed multiple methodological limitations. Optical tomography and a machine learning tool were combined to measure stomatal density (SD). This was combined with rapid measurements of leaf photosynthetic gas exchange and specific leaf area (SLA). These traits were the subject of genome-wide association study and transcriptome-wide association study across 869 field-grown biomass sorghum accessions. The ratio of intracellular to ambient CO2 was genetically correlated with SD, SLA, gs, and biomass production. Plasticity in SD and SLA was interrelated with each other and with productivity across wet and dry growing seasons. Moderate-to-high heritability of traits studied across the large mapping population validated associations between DNA sequence variation or RNA transcript abundance and trait variation. A total of 394 unique genes underpinning variation in WUE-related traits are described with higher confidence because they were identified in multiple independent tests. This list was enriched in genes whose Arabidopsis (Arabidopsis thaliana) putative orthologs have functions related to stomatal or leaf development and leaf gas exchange, as well as genes with nonsynonymous/missense variants. These advances in methodology and knowledge will facilitate improving C4 crop WUE.
Why it matches plant phenotyping methods光学トモグラフィーと機械学習ツールによる気孔密度測定を中心的な方法として開発・適用し、ガス交換等の表現型を大規模集団で評価しているため。
abstractThis study addressed multiple methodological limitations. Optical tomography and a machine learning tool were combined to measure stomatal density (SD).
Reproduction assets foundThe paper's optical tomography leaf images (the sensor inputs used for machine-learning stomatal density phenotyping) are publicly deposited in the Illinois Data Bank. Phenotypic trait data (Supplemental Table S12) are public but only via the article's supplemental material without a listed URL; RNA-seq (PRJNA522466) GDataset · publichttps://www.ncbi.nlm.nih.gov/bioproject/PRJNA522466/ .
Genotyping-by-sequencing data are available at: https://doi.org/10.5281/zenodo.5019227 . Phenotypic data are available as
part of the supplemental
material ( Supplemental Table
S12 ). Optical tomography images from this article can be found in the Illinois
Data Bank under: https://doi.org/10.13012/B2IDB-1411926_V1 .
Supplemental data
The following materials are available in the online version of this article.Open asset ↗10.13012/B2IDB-1411926_V1lines:985-1051Code / 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 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 confirmedOpenAlex · Europe PMC · bioRxiv · checked 8 Sept 2026
ABSTRACT Advancements in the use of genome-wide markers have provided new opportunities for dissecting the genetic components that control phenotypic trait variation. However, cost-effectively characterizing agronomically important phenotypic traits on a large scale remains a bottleneck. Unmanned aerial vehicle (UAV)-based high-throughput phenotyping has recently become a prominent method, as it allows large numbers of plants to be analyzed in a time-series manner. In this experiment, 233 inbred lines from the maize diversity panel were grown in a replicated incomplete block under both nitrogen-limited conditions and following conventional agronomic practices. UAV images were collected during different plant developmental stages throughout the growing season. A pipeline for extracting plot-level images, filtering images to remove non-foliage elements, and calculating canopy coverage and greenness ratings based on vegetation indices (VIs) was developed. After applying the pipeline, about half a million plot-level image clips were obtained for 12 different time points. High correlations were detected between VIs and ground truth physiological and yield-related traits collected from the same plots, i.e., Vegetative Index (VEG) vs. leaf nitrogen levels (Pearson correlation coefficient, R = 0.73), Woebbecke index vs. leaf area ( R = -0.52), and Visible Atmospherically Resistant Index (VARI) vs. 20 kernel weight – a yield component trait ( R = 0.40). The genome-wide association study was performed using canopy coverage and each of the VIs at each date, resulting in N = 29 unique genomic regions associated with image extracted traits from three or more of the 12 total time points. A candidate gene Zm00001d031997 , a maize homolog of the Arabidopsis HCF244 ( high chlorophyll fluorescence 244 ), located underneath the leading SNPs of the canopy coverage associated signals that were repeatedly detected under both nitrogen conditions. The plot-level time-series phenotypic data and the trait-associated genes provide great opportunities to advance plant science and to facilitate plant breeding.
Why it matches plant phenotyping methodsUAV画像から作物プロットの被覆率・緑色度を抽出するパイプラインを開発し、地上測定との相関で検証した研究であり、フェノタイピング手法が中心です。
abstractA pipeline for extracting plot-level images, filtering images to remove non-foliage elements, and calculating canopy coverage and greenness ratings based on vegetation indices (VIs) was developed.
Reproduction assets foundThe paper's raw UAV RGB imagery used for the maize phenotyping pipeline is publicly deposited on CyVerse (DOI: 10.25739/4t1v-ab64), as stated in the supplied text. No author analysis code or trained models are described with public availability.Dataset · publicThe original UAV images
taken for this study are available at CyVerse (DOI: 10.25739/4t1v-ab64).Open asset ↗CyVerse · 10.25739/4t1v-ab64pdf-page:5 lines:1-38Code / 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 confirmedCrossref · checked 9 Sept 2026
Brassica oleracea is an important crop species that at early growth stages may exhibit failure of the apical growing point, an abnormality called “blindness”. The occurrence of blindness is promoted by exposure to low temperatures during imbibition and germination, but the causes of sensitivity to such conditions are unknown. We combined three analytical seed technology instruments to explore seed physical properties that are highly correlated with quality parameters and might be used directly for grading or sorting seed lots into subpopulations varying in potential susceptibility to blindness. For image analysis, we used the VideometerLab instrument, which can scan 19 wavelengths from ultraviolet to infrared and utilize that information in any combination to potentially identify unique criteria related to seed quality. The iXeed CF Analyzer was utilized to obtain chlorophyll fluorescence values for individual seeds. Chlorophyll contents of many seeds can be used as an indicator of seed maturity, a major contributor to seed quality. Finally, oxygen consumption measurements of individual seeds as obtained with the Q2 instrument are highly correlated with their performance under a wide variety of conditions. Six Brassica seed lots differed in their susceptibility to induction of blindness or loss of viability due to 48 h hydrated incubation at 1.5 ∘C. Analysis of physical and respiratory parameters identified some measurements that were highly correlated with the occurrence of blindness. Higher chlorophyll content, as detected by the CF-Mobile and certain wavelengths in the Videometer, was associated with greater occurrence of blindness or death following the induction treatment, suggesting that more immature seeds may be susceptible to blindness. Further research is required, but methods to detect and sort such seeds based on physical characteristics appear to be feasible.
Why it matches plant phenotyping methods種子の画像・蛍光・呼吸測定を組み合わせ、物理特性から発芽品質やblindness感受性を評価・選別する方法が研究の中心であり、単なる生物学的結果測定ではない。
abstractWe combined three analytical seed technology instruments to explore seed physical properties that are highly correlated with quality parameters and might be used directly for grading or sorting seed lots into subpopulations varying in potential susceptibility to blindness.
Reproduction assets foundThe paper's individual-seed phenotyping measurements (chlorophyll fluorescence, multispectral imaging, Q2 respiration, plant blindness scores) are consolidated in Supplemental Table S1 (Seed parameters database) and related supplements, publicly hosted on the MDPI article site. No author analysis code was deposited; CRDataset · publicSupplementary Materials: The following are available at https://www.mdpi.com/2077-0472/11/3
/220/s1, Table S1: Seed parameters database, Table S2: Q2 parameters, Table S3: MFA EigenvaluesOpen asset ↗pdf-page:20 lines:1-58Code / 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 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 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 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
Click here for additional data file.
Click here for additional data file.
Click here for additional data file.
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 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 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 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 · Crossref · OpenAlex · checked 14 Sept 2026
Background Over the last years, several plant science labs have started to employ fluctuating growth light conditions to simulate natural light regimes more closely. Many plant mutants reveal quantifiable effects under fluctuating light despite being indistinguishable from wild-type plants under standard constant light. Moreover, many subtle plant phenotypes become intensified and thus can be studied in more detail. This observation has caused a paradigm shift within the photosynthesis research community and an increasing number of scientists are interested in using fluctuating light growth conditions. However, high installation costs for commercial controllable LED setups as well as costly phenotyping equipment can make it hard for small academic groups to compete in this emerging field. Results We show a simple do-it-yourself approach to enable fluctuating light growth experiments. Our results using previously published fluctuating light sensitive mutants, stn7 and pgr5, confirm that our low-cost setup yields similar results as top-prized commercial growth regimes. Moreover, we show how we increased the throughput of our Walz IMAGING-PAM, also found in many other departments around the world. We have designed a Python and R-based open source toolkit that allows for semi-automated sample segmentation and data analysis thereby reducing the processing bottleneck of large experimental datasets. We provide detailed instructions on how to build and functionally test each setup. Conclusions With material costs well below USD$1000, it is possible to setup a fluctuating light rack including a constant light control shelf for comparison. This allows more scientists to perform experiments closer to natural light conditions and contribute to an emerging research field. A small addition to the IMAGING-PAM hardware not only increases sample throughput but also enables larger-scale plant phenotyping with automated data analysis.
Why it matches plant phenotyping methods低コストの生育光環境、IMAGING-PAMのスループット向上、植物画像の半自動セグメンテーションとデータ解析を開発・検証しており、植物表現型取得手法が中心である。
abstractWe have designed a Python and R-based open source toolkit that allows for semi-automated sample segmentation and data analysis thereby reducing the processing bottleneck of large experimental datasets.
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 scripts described in the text can be downloaded from https://github.com/CougPhenomics/ImagingPAMProcessing and the accompanying 11 day dataset can be downloaded from https://doi.org/10.17605/OSF.IO/P32AY .Open asset ↗OSF · 10.17605/OSF.IO/P32AYlines:160-190Code / 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 · 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 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 confirmedEurope PMC · OpenAlex · Crossref · bioRxiv · checked 14 Sept 2026
Background Over the last years, several plant science labs have started to employ fluctuating growth light conditions to simulate natural light regimes more closely. Many plant mutants reveal quantifiable effects under fluctuating light despite being indistinguishable from wild-type plants under standard constant light. Moreover, many subtle plant phenotypes become intensified and thus can be studied in more detail. This observation has caused a paradigm shift within the photosynthesis research community and an increasing number of scientists are interested in using fluctuating light growth conditions. However, high installation costs for commercial controllable LED setups as well as costly phenotyping equipment can make it hard for small academic groups to compete in this emerging field. Results We show a simple do-it-yourself approach to enable fluctuating light growth experiments. Our results using previously published fluctuating light sensitive mutants, stn7 and pgr5 , confirm that our low-cost setup yields similar results as top-prized commercial growth regimes. Moreover, we show how we increased the throughput of our Walz IMAGING-PAM, also found in many other departments around the world. We have designed a Python and R-based open source toolkit that allows for semi-automated sample segmentation and data analysis thereby reducing the processing bottleneck of large experimental datasets. We provide detailed instructions on how to build and functionally test each setup. Conclusions With material costs well below USD$1000, it is possible to setup a fluctuating light rack including a constant light control shelf for comparison. This allows more scientists to perform experiments closer to natural light conditions and contribute to an emerging research field. A small addition to the IMAGING-PAM hardware not only increases sample throughput but also enables larger-scale plant phenotyping with automated data analysis.
Why it matches plant phenotyping methods低コストの生育環境、IMAGING-PAMのスループット向上、植物画像の半自動セグメンテーションとデータ解析を開発・検証しており、植物表現型取得手法が中心です。
abstractWe have designed a Python and R-based open source toolkit that allows for semi-automated sample segmentation and data analysis thereby reducing the processing bottleneck of large experimental datasets.
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 scripts described in the text can be downloaded from
https://github.com/CougPhenomics/ImagingPAMProcessing and the accompanying 11 day
dataset can be downloaded from https://doi.org/10.17605/OSF.IO/P32AYOpen asset ↗OSF · 10.17605/OSF.IO/P32AYpdf-page:13 lines:1-51Code / dataset availability confirmedEurope PMC · checked 9 Sept 2026
Background As yields of major crops such as wheat ( T. aestivum ) have begun to plateau in recent years, there is growing pressure to efficiently phenotype large populations for traits associated with genetic advancement in yield. Photosynthesis encompasses a range of steady state and dynamic traits that are key targets for raising Radiation Use Efficiency (RUE), biomass production and grain yield in crops. Traditional methodologies to assess the full range of responses of photosynthesis, such a leaf gas exchange, are slow and limited to one leaf (or part of a leaf) per instrument. Due to constraints imposed by time, equipment and plant size, photosynthetic data is often collected at one or two phenological stages and in response to limited environmental conditions. Results Here we describe a high throughput procedure utilising chlorophyll fluorescence imaging to phenotype dynamic photosynthesis and photoprotection in excised leaves under controlled gaseous conditions. When measured throughout the day, no significant differences ( P > 0.081) were observed between the responses of excised and intact leaves. Using excised leaves, the response of three cultivars of T. aestivum to a user-defined dynamic lighting regime was examined. Cultivar specific differences were observed for maximum PSII efficiency ( F v '/ F m '- P F q '/ F m '- P = 0.04) under both low and high light. In addition, the rate of induction and relaxation of non-photochemical quenching (NPQ) was also cultivar specific. A specialised imaging chamber was designed and built in-house to maintain gaseous conditions around excised leaf sections. The purpose of this is to manipulate electron sinks such as photorespiration. The stability of carbon dioxide (CO 2 ) and oxygen (O 2 ) was monitored inside the chambers and found to be within ± 4.5% and ± 1% of the mean respectively. To test the chamber, T. aestivum 'Pavon76' leaf sections were measured under at 20 and 200 mmol mol -1 O 2 and ambient [CO 2 ] during a light response curve. The F v '/ F m 'was significantly higher ( P 2 ] for the majority of light intensities while values of NPQ and the proportion of open PSII reaction centers (qP) were significantly lower under > 130 μmol m -2 s -1 photosynthetic photon flux density (PPFD). Conclusions Here we demonstrate the development of a high-throughput (> 500 samples day -1 ) method for phenotyping photosynthetic and photo-protective parameters in a dynamic light environment. The technique exploits chlorophyll fluorescence imaging in a specifically designed chamber, enabling controlled gaseous environment around leaf sections. In addition, we have demonstrated that leaf sections do not different from intact plant material even > 3 h after sampling, thus enabling transportation of material of interest from the field to this laboratory based platform. The methodologies described here allow rapid, custom screening of field material for variation in photosynthetic processes.
Why it matches plant phenotyping methods葉緑素蛍光イメージングと専用チャンバーを用いた高スループットな光合成・光防御形質の取得法を開発し、葉およびチャンバー条件を検証しているため、植物フェノタイピング手法が中心である。
abstractHere we describe a high throughput procedure utilising chlorophyll fluorescence imaging to phenotype dynamic photosynthesis and photoprotection in excised leaves under controlled gaseous conditions.
Reproduction assets foundThe paper's phenotyping pipeline (chlorophyll fluorescence imaging of excised wheat leaves in custom gas-controlled chambers) is supported by a paper-specific public asset: Additional file 2, a ZIP supplement containing the CAD files, printer settings, and construction notes for the custom imaging chambers, explicitly'Supplement · publicThe CAD files for the final chamber design are fully available with this manuscript (Additional file 2 ) including printer settings and additional notes, so that users can either print their own, outsource the printing or modify the designs.Open asset ↗lines:94-102Code / 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 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 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 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 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 present a high throughput crop physiology condition monitoring system and corresponding monitoring method. The monitoring system can perform large-area chlorophyll fluorescence imaging and multispectral imaging. The monitoring method can determine the crop current condition continuously and non-destructively. We choose chlorophyll fluorescence parameters and relative reflectance of multispectral as the indicators of crop physiological status. Using tomato as experiment subject, the typical crop physiological stress, such as drought, nutrition deficiency and plant disease can be distinguished by the monitoring method. Furthermore, we have studied the correlation between the physiological indicators and the degree of stress. Besides realizing the continuous monitoring of crop physiology, the monitoring system and method provide the possibility of machine automatic diagnosis of the plant physiology. Highlights: A newly designed high throughput crop physiology monitoring system and the corresponding monitoring method are described in this study. Different types of stress can induce distinct fluorescence and spectral characteristics, which can be used to evaluate the physiological status of plants.
Why it matches plant phenotyping methods植物の生理状態・ストレスをクロロフィル蛍光とマルチスペクトル画像から非破壊・連続的に推定する高スループット監視システムと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。
abstractWe present a high throughput crop physiology condition monitoring system and corresponding monitoring method.
Reproduction assets foundThe paper's supplementary material publicly hosts the paper-specific phenotyping images: pseudo-color ΦPSII, Fv/Fm, and 550/510 parameter images and photos of tomato plants under drought, nitrogen deficiency, and Botrytis cinerea stress, plus a photo of the monitoring system. These are the plant images/phenotyping dataDataset · publicinterest.
Funding. This work was supported by the National High Technology Research, Development Program of China (863 Program) (Grant No. 2012AA10A503).
1
http://www.walz.com/
2
http://www.psi.cz/
3
http://www.hansatech-instruments.com/
Supplementary Material
The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fpls.2018.00407/full#supplementary-material
FIGURE S1
The physiology monitoring system with chlorophyll fluorescence module and multispectral module (A,B) and the scene when the system is working (C) .
Click here for additional data file.
FIGURE S2
Φ PSII , F v / F m , and 550/510 pseudo color images and photos of tomatoes uOpen asset ↗lines:96-124Code / 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
Field / plotLeafPhysiological trait estimationPhotosynthesis / fluorescence
Across the global flora, interspecific variation in photosynthetic and metabolic rates depends more strongly on leaf area than leaf mass. In contrast, intraspecific variation in these rates is strongly mass-dependent. These contrasting patterns suggest that the causes of variation in leaf mass per area (LMA) may be fundamentally different within vs. among species. We developed a statistical modeling framework to decompose LMA into two conceptual components – metabolic LMAm (which determines photosynthetic capacity and dark respiration) and structural LMAs (which determines leaf toughness and potential leaf lifespan) - using leaf trait data from tropical forests in Panama and a global leaf-trait database. Decomposing LMA into LMAm and LMAs improves predictions of leaf trait variation (photosynthesis, respiration, and lifespan). We show that strong area-dependence of metabolic traits across species can result from multiple factors, including high LMAs variance and/or a slow increase in photosynthetic capacity with increasing LMAm. In contrast, strong mass-dependence of metabolic traits within species results from LMAm increasing from sunny to shady conditions. LMAm and LMAs were nearly independent of each other in both global and Panama datasets. Synthesis : Our results suggest that leaf functional variation is multi-dimensional and that biogeochemical models should treat metabolic and structural leaf components separately.
Why it matches plant phenotyping methodsLMAを代謝成分と構造成分に分解する統計モデリング枠組みを開発し、葉形質の推定・予測に用いており、植物形質の計算的抽出が中心である。
abstractWe developed a statistical modeling framework to decompose LMA into two conceptual components
Reproduction assets foundThe paper's Stan analysis code for fitting the LMA decomposition models is explicitly stated to be publicly available on the authors' GitHub repository. The GLOPNET trait data are cited prior work, and the Panama dataset's availability is not stated, so neither qualifies as a paper-specific public asset.Code · publicfit using the Hamiltonian Monte Carlo algorithm (HMC) implemented
279
in Stan (Carpenter et al., 2016). Posterior estimates were obtained from three independent
280
chains of 20,000 iterations after a burn-in of 10,000 iterations, thinning at intervals of 20. The
281
Stan code use to fit models are available from Github at:
282
https://github.com/mattocci27/LMApLMAs. Convergence of the posterior distribution was
283
assessed with the Gelman-Rubin statistic with a convergence threshold of 1.1 for all
284
diagnostics (Gelman et al., 2014a).
285
286
Model selection
287
Alternative LL models (Eqs. 5 and 13; see also Notes S3) fit to Panama data were compared
288
using the WAIC (Watanabe-AkaikeOpen asset ↗mattocci27/LMApLMAspdf-raw-page:9 lines:1-76Code / 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:1-51