Growth chamberMultimodalStereoRootStem / branchTrackingGrowth / development / phenology
Understanding plant behaviour requires the integration of multiple phenotypic and physiological signals measured over time under controlled conditions. However, different plant signals are typically studied using separate experimental setups, limiting temporal alignment and integrative analyses.We present Mind(the)Plant, a modular experimental facility designed for the synchronized, long-term acquisition of multimodal plant data, including three-dimensional shoot kinematics, above- and below-ground volatile organic compounds (VOCs) and root imaging. Its modular architecture is designed to accommodate additional acquisition modules, such as electrophysiological signalling, as future extensions. The platform integrates a controlled growth environment with stereovision imaging, high-resolution time-of-flight mass spectrometry and custom rhizocameras. These components are connected through a unified network infrastructure that ensures synchronized acquisition and centralized data handling.We validate the performance of each acquisition module through multi-week recordings, demonstrating high-temporal stability, reliable stereovision synchronization, effective isolation of VOCs signals and robust operation of below-ground imaging. We further illustrate the analytical potential of the platform using a one-day continuous multimodal acquisition combining shoot kinematics, above-ground VOC emissions, rhizocameras observations and environmental data.Mind(the)Plant provides a novel methodological framework for studying plant behaviour, signalling and phenotypic plasticity in ecological and evolutionary research. By enabling coordinated measurements of multiple plant response modalities, the platform supports investigations of dynamic plant-environment and plant-plant interactions from a behavioural perspective.
Why it matches plant phenotyping methods植物の複数の表現型・生理シグナルを同期取得する施設を開発し、各取得モジュールの性能を検証しているため、表現型計測プラットフォームが研究の中心です。
abstractWe present Mind(the)Plant, a modular experimental facility designed for the synchronized, long-term acquisition of multimodal plant data, including three-dimensional shoot kinematics, above- and below-ground volatile organic compounds (VOCs) and root imaging.
Reproduction assets foundThe paper's data availability statement explicitly deposits data, code and processing pipelines (supporting the multimodal plant phenotyping measurements and analysis) in a public Zenodo archive with an authors' URL matching an allowed URL.Code · publicf Interest Statement
The authors have no conflicts of interest to declare.
Peer Review
The peer review history for this article is available at https://www.webofscience.com/api/gateway/wos/peer-review/10.1111/2041-210x.70411 .
Data availability Statement
Data, code and processing pipelines supporting this study are available at https://doi.org/10.5281/zenodo.22095454 ( Simonetti & Castiello, 2026 ).
References
Avesani S, Bonato B, Simonetti V, Guerra S, Ravazzolo L, Gjinaj G, Dadda M, Castiello U. Comparing proton transfer reaction (PTR) and adduct ionization mechanism (AIM) for the study of volatile organic compounds. Molecules. 2026;31(3):402. doi: 10.3390/molecules31030402.
Baluška F, LeOpen asset ↗zenodo · 10.5281/zenodo.22095454lines:482-508Code / dataset availability confirmedEurope PMC · bioRxiv · checked 5 Sept 2026
ABSTRACT Leaf shape is a fundamental trait of plant ecological strategies, influencing biotic interactions and ecosystem functioning. However, established quantitative metrics fail to capture subtle variations and irregularities, require user-based reference points or are challenging to compare among taxa with broadly different leaf shapes. In addition, established metrics typically conflate (aggregate) leaf edge complexity and macro-shape complexity, despite their independent functional significance and genetic foundations. Here, we introduce an entropy-based framework to quantify two new complexity metrics: edge complexity and macro-shape complexity. Based on three case studies, we show that these metrics outperform aggregate metrics in predicting Quercus robur chemical traits, provide more intuitive interspecific classifications, and strongly align with human perception. In addition, edge and macro-shape complexity show high complementarity, while aggregate metrics are highly redundant and typically strongly related to leaf area. Emerging as the strongest predictor of leaf chemistry and key visual cue for complexity as perceived by humans, the effects of edge complexity highlight the under-appreciated functional significance of leaf margins. Our framework and the proposed entropy-based complexity metrics thus promise to help unlock the potential of growing digital image archives of leaves, including images from herbaria and fossils, and are technically readily applicable to shapes of algae, bacteria, pollen, and beyond. The accompanying package ShapeComplexity enables the broad application of entropy-based metrics, providing a powerful tool to explore how the shape of organisms and biological structures influences ecological strategies, biotic interactions, and ecosystem functioning while tracking spatial and temporal variation.
Why it matches plant phenotyping methods葉の画像からエッジ複雑性とマクロ形状複雑性を定量化する新規指標とソフトウェアを開発しており、植物形質抽出法が研究の中心である。
abstractHere, we introduce an entropy-based framework to quantify two new complexity metrics: edge complexity and macro-shape complexity.
Reproduction assets foundThe paper's authors publicly release their ShapeComplexity analysis code (Rust) on GitHub, used to compute the paper's leaf edge- and macro-shape complexity metrics. Supplementary data/analysis code are on Dryad, but that URL is not in the allowed list. RMBG is a generic third-party background-removal model, not a phenCode · publicThe complete, open-source Rust-code (The Rust Team, 2025 ) is publicly available on GitHub ( https://github.com/Thornbach/ShapeComplexity ), ensuring transparency and reproducibilityOpen asset ↗Thornbach/ShapeComplexitylines:86-94Code / dataset availability confirmedOpenAlex · Europe PMC · checked 5 Sept 2026
leaf development was tracked dynamically from high-throughput phenotyping image series using the SAM-2 deep learning model, enabling automated segmentation and tracking of individual leaves across hundreds of plants from different accessions grown under different water and nitrogen conditions. From these time series, leaf-level growth dynamics were reconstructed and used to calculate key developmental traits, including phyllochron and relative expansion rates. These measurements were further integrated into the GreenLab model to infer plant-scale parameters such as radiation use efficiency (RUE) and leaf demand parameters. Statistical analyses revealed significant genotype, treatment, and interaction effects on several traits, with stable genotype rankings but contrasted sensitivities to environmental conditions. While whole-rosette growth captured strong and consistent responses, individual leaf contributions to global growth remained limited, highlighting the integrative nature of rosette-scale dynamics. Model-derived parameters provided additional insight into plant strategies, showing that genotypes differ in both the duration and timing of leaf demand, with generally stable patterns across environments. In parallel, QTL mapping was performed using leaf-level measurements and phyllochron traits. This analysis identified shared and trait-specific genetic loci, including loci associated with leaf emergence dynamics that were not detected using traditional rosette-scale integrative traits. Overall, this study demonstrates that combining deep learning-based image analysis with mechanistic modeling enables large-scale and quantitative characterization of plant development, providing biologically interpretable traits and new insights into their genetic and environmental determinism. It highlights the potential of combining high-throughput image-based phenotyping, deep learning, and mechanistic modeling to generate biologically interpretable traits and to assess genetic and environmental influences on these traits.
Why it matches plant phenotyping methods深層学習による葉の自動セグメンテーション・追跡を開発的に適用し、時系列画像から葉レベルおよび植物体レベルの発達形質を定量化しているため、表現型取得・抽出が研究の中心である。
abstractleaf development was tracked dynamically from high-throughput phenotyping image series using the SAM-2 deep learning model, enabling automated segmentation and tracking of individual leaves across hundreds of plants
Reproduction assets foundThe paper explicitly states that the authors' leaf segmentation software (AraLeaf_segmentation), the GreenLab Arabidopsis model (AraGreenlab), and the statistical analysis code (AraParameters_statistical_analyses) are open-source and publicly hosted on forge.inrae.fr, and that the data and results used in the paper areCode · publicThe leaf segmentation software (AraLeaf_segmentation) and the GreenLab model of Arabidopsis thaliana (AraGreenlab) are open-source software, and distributed under the GNU GPL v3 licence. The two software packages, and the code to run statistical analyses (AraParameters_statistical_analyses) are available at:https://forge.inrae.fr/phenoscope-public/plant_phenomics_suppmat.Open asset ↗phenoscope-public/plant_phenomics_suppmathtml-lines:419-444Code / dataset availability confirmedEurope PMC · Crossref · checked 5 Sept 2026
Accurate tracking and measurement of pollen dispersal in the atmosphere are essential for assessing cross-pollination risks, particularly in the case of genetically engineered (GE) crops. We conducted a series of unique release-recapture field studies with GE switchgrass in Oliver Springs, TN, USA. Two hundred transgenic switchgrass plants (Panicum virgatum L. "Performer") were planted at the center of a clear-cut field, with one block of 100 plants expressing orange fluorescent protein (OFP) under a switchgrass ubiquitin promoter (PvUBI1) and another block of 100 plants expressing OFP driven by a maize pollen-specific promoter (Zm13). Pollen was sampled from the atmosphere using fixed (ground-based) and mobile (drone-based) sampling devices at different distances from the source field, with Lagrangian stochastic dispersal simulations run for sampling periods using high-resolution wind measurements. The pollen emission rate was estimated by combining simulated and measured pollen concentrations, and strong diurnal trends were observed. Diurnal emission rate trends were positively correlated with wind speed, temperature, and vapor pressure deficit, while negatively correlated with relative humidity. In low-wind meandering conditions, incorporating changing wind direction into the dispersal modeling improved pollen emission rate estimation and model-measurement comparisons. This study assesses the effectiveness of high- and low-volume pollen samplers in relation to source strength up to 1 km from the source, enhancing understanding of pollen measurement techniques. Additionally, it is a proof-of-concept for drone-based pollen sampling and GMO pollen tracking using fluorescence measurements. Results from our experiments have significant implications for cross-pollination risk assessment, prediction, and management of airborne allergens.
Why it matches plant phenotyping methods固定・ドローン搭載サンプラー、蛍光測定、風況モデルを組み合わせて植物由来の花粉放出率を推定し、花粉測定技術を評価することが中心であるため、植物の生殖状態・放出特性に関するフェノタイピング手法として採用。
abstractPollen was sampled from the atmosphere using fixed (ground-based) and mobile (drone-based) sampling devices
Reproduction assets foundThe paper's Data Availability statement deposits all sampling data, modeling code, and simulation results on the Virginia Tech Data Repository (DOI 10.7294/25733604), which is an allowed URL. This directly covers the paper's pollen concentration measurements and Lagrangian stochastic dispersal modeling. Other URLs (e.gDataset · publicAll sampling data, modeling code, and simulation results underlying this manuscript are made available on the Virginia Tech Data Repository at https://doi.org/10.7294/25733604 .Open asset ↗Virginia Tech Data Repository · 10.7294/25733604lines:201-219Code / dataset availability confirmedEurope PMC · bioRxiv · checked 11 Sept 2026
Solitary bee species that use artificial trap nests are important for agricultural crop production and as indicators of habitat quality. Quantifying cavity-nesting solitary bee foraging and nesting behavior is essential for real-time analysis of population numbers and pollination activity, as well as understanding how environmental conditions shape reproductive success and population dynamics. However, manual observation is labor-intensive, prone to observer bias, and unable to deliver continuous data. Existing automated systems either require individual bee marking or detect presence without resolving nest-tube-level entry and exit events. We developed BeeMonitor, an integrated hardware and computer-vision pipeline that detects nest entry and exit events in cavity-nesting solitary bees from continuous video, using Osmia cornifrons (the horn-faced mason bee) as a model system. A low-cost Raspberry Pi handles solar-powered field recording, while the software combines object detection (YOLOv26), a custom multiple-object tracker (BeeTrack), and a Random Forest classifier trained on trajectory-derived features to distinguish genuine events from incidental detections. Over a 29-day deployment, hardware reliability averaged 97.5% recording coverage. The pipeline achieved 91.3% precision and 87.3% recall (F1 = 0.893), generalizing robustly under leave-one-video-out cross-validation (mean F1 = 0.904). Detected foraging trips correlated strongly with brood cell counts (R2 = 0.849, p < 0.001, n = 19), and a Random Forest model (AUC = 0.820) identified solar radiation as the dominant driver of foraging activity, followed by temperature. BeeMonitor demonstrates that automated computer vision can reliably extract ecologically relevant behavioral data from continuous video, enabling real-time analysis of pollinator behavior and abundance at a temporal and spatial resolution unattainable through manual observation. Its modular design supports adaptation to other species and monitoring contexts.
Why it matches plant phenotyping methods植物ではなく昆虫を対象とするが、映像から採餌・営巣行動を抽出する技術開発として中心的であり、指定スコープの植物表現型ではないため除外。
abstractWe developed BeeMonitor, an integrated hardware and computer-vision pipeline that detects nest entry and exit events in cavity-nesting solitary bees from continuous video
Reproduction assets foundThe paper explicitly states that source code, 3D STL files, and validation datasets/code are publicly available on the authors' GitHub repository and ScholarSphere. These directly support reproducing the paper's behavioral-event detection pipeline and its evaluation (annotated videos, classifier training/LOVO cross-vaCode · publicSource code for
software and 3D stl files can be found on the official GitHub repository here
https://github.com/Team-Insect-Net/BeeMonitor.Open asset ↗Team-Insect-Net/BeeMonitorpdf-page:2 lines:1-57Dataset · publicValidation datasets and code are available on
Scholars Sphere here
https://scholarsphere.psu.edu/resources/55f1f34b-959f-4c60-8dd3-9b33fb09357f.Open asset ↗Scholars Sphere · 55f1f34b-959f-4c60-8dd3-9b33fb09357fpdf-page:2 lines:1-57Code / dataset availability confirmedCrossref · checked 14 Sept 2026
Aims Climate change is altering northern peatland plant communities, shifting from Sphagnum mosses to vascular plants. This transition impacts ecological functions like carbon sequestration, making long-term vegetation monitoring at the site scale more critical than ever. However, current monitoring methods tend to focus on specific species or functional groups with limited spatial coverage. This study uses remote sensing to infer the spatial structure and temporal variations of peatland plant communities. Location Temperate peatland in Pyrenees Mountains, France (Bernadouze, Vicdessos). Methods Nine plots were selected across diverse microhabitats and sampled three times over the growing season of 2023 (May, June, and July). Plant species abundances were recorded, and 45 vegetation indices were derived from drone and Sentinel-2 multispectral imagery. Five vegetation indices were selected to fit a joint Species Distribution Model (JSDM) and a Random Forests (RF) model, and map species spatial distribution. Principal Coordinates Analysis (PCoA) identified plant community composition, and spatiotemporal variations were quantified in relation to environmental variables. Results Plant species occurrences could be predicted from multispectral imagery using the JSDM, with drone-based inferences (mean R 2 = 0.36) outperforming Sentinel-2 (mean R 2 = 0.29). Model performance was high for abundant species ( R 2 > 0.5), whereas predictions for rare species were less accurate ( R 2 R 2 > 0.65, P R 2 = 0.40; P R 2 = 0.04; P Conclusion This study demonstrates that drone multispectral imagery can be used to predict peatland vegetation richness and community composition and capture fine-scale heterogeneity in a small and fragmented peatland site, outperforming satellite data in spatial precision. Although our model was less accurate using satellite imagery, the use of Sentinel-2 imagery enabled long-term community tracking. By combining both, our predictive modelling framework provides a promising preliminary tool to monitor climate-induced shifts in species distributions, supporting targeted conservation.
Why it matches plant phenotyping methodsドローンおよび衛星マルチスペクトル画像から植物種の空間分布、植生多様性、群集組成を推定する画像・モデリング手法が研究の中心であり、植物状態の測定に直接結びつく。
abstractThis study uses remote sensing to infer the spatial structure and temporal variations of peatland plant communities.
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 · publicCodes to replicate main analyses are available at https://github.com/vjassey/peatland_vegetation_mapping .Open asset ↗vjassey/peatland_vegetation_mappinglines:369-375Code / dataset availability confirmedCrossref · checked 14 Sept 2026
Abstract. As key components of agricultural management, planting and harvesting schedules have strongly influenced crop production by defining the length of the crop growing season and shaping the environmental conditions crops experience. Accurate knowledge of these management data is crucial for enhancing crop yield estimates by capturing the timing of crop development relative to weather and soil conditions, assessing climate adaptation by tracking shifts in farming practices over time, and supporting agricultural carbon accounting. Yet, existing planting and harvesting date datasets are largely based on state-level statistics or rule-based calendars that overlook intra-regional variability and the influence of human decision-making. The absence of long-term, high-resolution planting and harvesting date information hinders our ability to reconstruct historical agricultural practices and assess their agronomic and environmental consequences. In this study, we introduce CropPlantHarvest, the first dataset of annual corn and soybean planting and harvesting dates across the U.S. Midwest at 500 m resolution from 2001 to 2024. Planting dates are estimated using CropSow, an integrative remotely sensed crop modeling system that aligns simulated crop growth trajectories with satellite observations to retrieve field-level planting dates. Harvesting dates are retrieved using the Normalized Harvest Phenology Index (NHPI), a novel index that integrates Normalized Difference Vegetation Index (NDVI) and near-infrared (NIR) reflectance to detect harvesting events by capturing the distinct spectral transition from senescent crops to exposed crop residues. Validation against USDA crop progress reports and field-level dataset demonstrates high accuracy of CropPlantHarvest, with a mean absolute error of approximately 5 d for both crop species. This large spatial and temporal dataset captures management-driven variability in crop season timing and duration, supporting improved modeling of crop yields, greenhouse gas emissions, and resource use. It could also serve as a benchmark for refining remote-sensing phenology products and evaluating the agro-environmental impacts of evolving crop management decisions. CropPlantHarvest is available at https://doi.org/10.5281/zenodo.16967482 (Liu and Diao, 2025).
Why it matches plant phenotyping methods衛星観測と作物モデルによる圃場レベルの作付・収穫時期推定手法を開発し、NHPIを提案して独立データで検証した大規模データセット研究であり、植物の生育・収穫状態の取得が中心的です。
abstractPlanting dates are estimated using CropSow, an integrative remotely sensed crop modeling system that aligns simulated crop growth trajectories with satellite observations to retrieve field-level planting dates.
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 · publicOur CropPlantHarvest dataset, which provides planting and harvesting dates for corn and soybean fields at 500 m spatial resolution across the U.S. Midwest from 2001 to 2024, can be accessed via Zenodo: https://doi.org/10.5281/zenodo.16967482 (Liu and Diao, 2025).Open asset ↗Zenodo · 10.5281/zenodo.16967482lines:322-333Code / dataset availability confirmedCrossref · checked 13 Sept 2026
AppleStrawberryGreenhouseLiDAR / point cloudRGB / grayscaleFruitSegmentationTracking
Accurate and consistent fruit monitoring over time is a key step towards automated agricultural production systems. However, this task is inherently difficult due to variations in fruit size, shape, occlusion, orientation, and the dynamic nature of orchards where fruits may appear or disappear between observations. In this article, we propose a novel method for fruit instance segmentation and re-identification on 3D terrestrial point clouds collected over time. Our approach directly operates on dense colored point clouds, capturing fine-grained 3D spatial detail. We segment individual fruits using a learning-based instance segmentation method applied directly to the point cloud. For each segmented fruit, we extract a compact and discriminative descriptor using a 3D sparse convolutional neural network. To track fruits across different times, we introduce an attention-based matching network that associates fruits with their counterparts from previous sessions. Matching is performed using a probabilistic assignment scheme, selecting the most likely associations across time. We evaluate our approach on real-world datasets of strawberries and apples, demonstrating that it outperforms existing methods in both instance segmentation and temporal re-identification, enabling robust and precise fruit monitoring across complex and dynamic orchard environments. • We propose a new performant approach to autonomous fruit tracking in real greenhouses. • It segments fruits using learning-based instance segmentation and RGB 3D point clouds. • Segmented fruits are encoded by a 3D CNN and matched via attentive data association. • Experiments on real strawberry and apple datasets show our method outperforms others. • Our approach enables precise temporal fruit monitoring in real and complex scenarios.
Why it matches plant phenotyping methods果実を個体単位で3D点群からセグメンテーションし、時系列追跡する画像解析手法の開発・評価が研究の中心であり、植物器官の状態を抽出するため適格。
abstractwe propose a novel method for fruit instance segmentation and re-identification on 3D terrestrial point clouds collected over time
Reproduction assets foundThe paper explicitly states that the authors' implementation of the fruit matching method (IRIS3D) is publicly available on GitHub, which is the computational analysis code for this paper's fruit segmentation and re-identification phenotyping pipeline.Code · publicThe implementation of our fruit matching method is publicly available at https://github.com/PRBonn/IRIS3D .Open asset ↗PRBonn/IRIS3Dlines:72-99Code / dataset availability confirmedCrossref · checked 13 Sept 2026
Accurate timing of rice panicle anthesis is critical for quantifying sterility risk under heat and humidity, yet minute-scale field measurement remains challenging because anthesis is transient and spikelets are tiny and difficult to detect. To address this, we present OPAL-Flow, a pipeline that provides single-panicle anthesis start and peak times under field conditions, consisting of a detector for panicle detection and tracking, a super-resolution reconstruction model, and an event-time pinpointing model. For slender panicle detection and panicle pose normalization, YOLO-SnakePanNet was introduced by using Dynamic Snake Convolution with a lightweight box-rotated head. Ablation experiments show that YOLO-SnakePanNet achieved mAP@50 of 94.4%, improving by 3.6% over the YOLOv11 while reducing computation by 0.7 GFLOPs. For panicle-level anthesis pinpointing, PanicleTimeMAE was proposed by incorporating a pyramid-dilated temporal convolutional network and a confidence-aware smoothing gate into the transformer, reaching Acc@±1 of 0.85 on 5-min sampled sequences (±1 frame = ±5 min), yielding a 40% decrease in MAE over VideoMAEv2. Finally, correlation analysis between variety-level anthesis start time (T start ) and peak time (T peak ) and same-day meteorology showed that higher photosynthetically active radiation (r = -0.543/-0.573 for T start /T peak ) and temperature (r = -0.288/-0.272) advanced anthesis, whereas higher relative humidity (r = 0.397/0.438) and rainfall (r = 0.428/0.502) delayed anthesis. The variance decomposition within fixed-effects model for Tstart ( R2 = 0.651) and Tpeak ( R2 = 0.648) prediction shows that variance mainly attributed to meteorological effects (64%) and variety effects (33.5%). Overall, OPAL-Flow enables variety selection for heat- and humidity-resilient anthesis in rice breeding and supports ecophysiological dissection of anthesis regulation.
Why it matches plant phenotyping methodsイネ穂の開花時刻という植物形質を圃場画像・動画から推定する検出、追跡、超解像、時刻推定パイプラインを開発し、性能評価も行っているため、植物フェノタイピング手法が研究の中心である。
abstractwe present OPAL-Flow, a pipeline that provides single-panicle anthesis start and peak times under field conditions, consisting of a detector for panicle detection and tracking, a super-resolution reconstruction model, and an event-time pinpointing model.
Reproduction assets foundThe paper's Data availability statement explicitly states that the source code and test samples for OPAL-Flow are publicly available on GitHub at the authors' repository. This is a paper-specific, publicly actionable code asset. The phenotype datasets (panicle detection dataset, start/peak annotation sequences) are notCode · publicThe source code and test samples used in this study are publicly available at: https://github.com/gfjiyue/OPAL-FLOW . Additional data can be made available upon reasonable request.Open asset ↗gfjiyue/OPAL-FLOWlines:578-590Code / dataset availability confirmedEurope PMC · checked 5 Sept 2026
The actin cytoskeleton forms a dynamic network composed of filaments that remain flexible when bundled up, leading to complex filamentous structures in plant cells. Understanding the properties of these filamentous structures under different conditions and in different cell types can provide insight into their function. Yet, despite developments in the study of the plant actin cytoskeleton, it remains challenging to segment and identify actin filamentous structures, preventing quantification of their spatiotemporal properties. To address this problem, we devised a network-based approach termed Graph of Filaments over Time (GraFT) to trace and track filamentous structures in cytoskeleton networks extracted from two-dimensional time series imaging data. Our comparative analyses using both synthetic test cases and real-world actin cytoskeleton networks of Arabidopsis thaliana hypocotyls exposed to different treatments demonstrated that GraFT accurately traces and tracks actin filamentous structures. Moreover, GraFT facilitates automated quantification of properties for filamentous structures, providing fine-grained insights of effects of different treatments on the level of individual structures. Therefore, GraFT offers a substantial step toward an automated framework facilitating robust spatiotemporal studies of the plant actin cytoskeleton.
Why it matches plant phenotyping methods植物細胞の画像時系列からアクチン繊維構造を追跡・セグメント化し、その時空間特性を自動定量する手法の開発と検証が中心であるため。
abstractwe devised a network-based approach termed Graph of Filaments over Time (GraFT) to trace and track filamentous structures in cytoskeleton networks extracted from two-dimensional time series imaging data.
Reproduction assets foundThe paper's authors publicly release the GraFT tool and data-processing code on GitHub (MIT licensed) with an archived Zenodo version. The paper-specific data files are stated to be on Zenodo (DOI 10.5281/zenodo.10476058), but that URL is not among the allowed URLs, so only the code assets are reported. The SciencePlotCode · publicThe tool GraFT and code created for data processing can be found on the GitHub repository https://github.com/Oesterlund/GraFT and is MIT licensed together with an archived version for reproducibilityOpen asset ↗https://github.com/Oesterlund/GraFTlines:159-261Code / dataset availability confirmedEurope PMC · checked 15 Sept 2026
Service crops are grown to provide ecosystem services in viticulture, but their adoption remains limited due to their competition with grapevine for soil resources. To identify trade-offs between services, the effect of service crops management strategies on grapevine performances still need further research. This dataset presents data from two experiments conducted to study the effect of service crops management on soil resources and grapevine performances. The inter-row vegetation was sampled in two Mediterranean vineyards using quadrats for biomass estimation. In addition, an unmanned aerial vehicle (UAV) was regularly flown over the vineyards for a period spanning more than four years in total over the two vineyards. The dataset presented here includes both raw data acquired during fieldwork and processed data derived from this raw inputs. The raw data consists of image series captured by two UAVs during each flight campaign, including RGB and multispectral imagery. Images were acquired between 2021-06-10 and 2022-07-29 for the first vineyard, and between 2023-06-08 and 2025-03-12 for the second vineyard. Based on these raw data, the processed data comprises spatial vectors, raster layers, and dense point clouds generated from UAV images using a Structure from Motion (SfM) photogrammetry workflow, at a 5 cm spatial resolution. The raster layers and dense point clouds provide specific information on vineyard characteristics for each UAV flight date, including elevation, vegetation indices, visible and near-infrared reflectance, and canopy height. In addition, the processed data include measurements of vegetation dry biomass, as well as separate measurements of dry biomass and leaf area measured for selected service crops species. This dataset can be reused for the calibration and/or evaluation of classification algorithms aimed at discriminating vines from the inter-row vegetation, or as part of a larger dataset to explore relationships between remotely-sensed vegetation indices and field-measured vegetation biomass or surface.
Why it matches plant phenotyping methodsUAV画像とSfM処理により、植生指数・樹冠高・バイオマス等の植物形質を取得した再利用可能なデータセットで、分類アルゴリズムの校正・評価用途も明示されており、植物フェノタイピング手法・データ基盤が中心です。
abstractThe dataset presented here includes both raw data acquired during fieldwork and processed data derived from this raw inputs.
Reproduction assets foundThe paper is a Data in Brief article describing a public dataset on Research Data Gouv (doi: 10.57745/MXM55R) containing UAV RGB/multispectral imagery, SfM-derived rasters and point clouds, and field-measured vegetation biomass/leaf-area data from two Mediterranean vineyards — directly the paper's phenotyping inputs. ADataset · publicollected in vineyards located in southern France near Montpellier (43°32.5243′N, 3°50.8240′E). Data are stored on Research Data Gouv, a remote storage solution curated by the French Department of Research.
Data accessibility
Repository name: Research Data Gouv
Data identification number: doi: 10.57745/MXM55R
Direct URL to data: https://doi.org/10.57745/MXM55R
Related research article
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Value of the Data
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The fine scale imaging of vineyards (i.e., 5 cm resolution) allows for classification of the vegetation in the vineyard inter-rows, and subsequent exploration of its respective dynamics.
•Open asset ↗Research Data Gouv · 10.57745/MXM55Rlines:1-47Code / dataset availability confirmedEurope PMC · checked 15 Sept 2026
Abstract Accurate tracking and measurement of pollen dispersal in the atmosphere are essential for assessing cross-pollination risks, particularly in the case of genetically engineered (GE) crops. We conducted a series of unique release-recapture field studies with GE switchgrass in Oliver Springs, Tennessee, USA. Two hundred transgenic switchgrass plants ({\it Panicum virgatum L.} `Performer') were planted at the center of a clear-cut field, with one block of 100 plants expressing orange fluorescent protein (OFP) under a maize ubiquitin promoter (PvUBI1) and another block of 100 plants expressing OFP driven by a maize pollen-specific promoter (Zm13). Pollen was sampled from the atmosphere using fixed (ground-based) and mobile (drone-based) sampling devices at different distances from the source field, with Lagrangian Stochastic dispersal simulations run for sampling periods using high-resolution wind measurements. The pollen emission rate was estimated by combining simulated and measured pollen concentrations, and strong diurnal trends were observed. Diurnal emission rate trends were positively correlated with wind speed, temperature, and vapor pressure deficit, while negatively correlated with relative humidity. In low-wind meandering conditions, incorporating changing wind direction into the dispersal modeling improved pollen emission rate estimation and model-measurement comparisons. This study assesses the effectiveness of high and low volume pollen samplers in relation to source strength up to 1 km from the source, enhancing understanding of pollen measurement techniques. Additionally, it is a proof-of-concept for drone-based pollen sampling and GMO pollen tracking using fluorescence measurements. Results from our experiments have significant implications for cross-pollination risk assessment, prediction, and management of airborne allergens.
Why it matches plant phenotyping methods固定・ドローン型サンプラーと蛍光測定、分散モデルを用いて植物由来花粉の放出量を推定し、サンプリング技術を評価することが中心であるため。
abstractPollen was sampled from the atmosphere using fixed (ground-based) and mobile (drone-based) sampling devices
Reproduction assets foundThe authors state that all sampling data, modeling code, and simulation results from this switchgrass pollen dispersal study are publicly available in a Virginia Tech figshare repository. The GitHub 3D-printing files are cited prior work (Powers et al. 2018), not a paper-specific asset.Dataset · public737 Statements and Declarations
738 Data and code availability
739 All sampling data, modeling code, and simulation results are made available in the
740 Virginia Tech Data repository:
741 https://figshare.com/s/54a308163b60865d55bf.
742 Competing interests
743 The authors have no competing interests to declare.
744 Funding
745 This work is supported in part by the Biotechnology Risk Assessment Program, project
746 award no. 2019-33522-29989, from the U.S. Department of Agriculture’s National
747 Institute of Food and Agriculture.
748 References
749 AdamovOpen asset ↗figsharepdf-layout-page:28 lines:1-46Code / 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 · checked 15 Sept 2026
Pepper / chilliField / plotLiDAR / point cloudWhole plant / canopy / plot / fieldClassificationObject detectionSegmentationTrackingGrowth / development / phenology
Pepper (Capsicum annuum) is a globally significant horticultural crop cultivated for its culinary, medicinal, and economic value. Traditional approaches for boosting the agricultural production of pepper, notably, expanding farmland, have become increasingly unsustainable. Recent advancements in artificial intelligence and 3D computer vision have started to transform crop cultivation and phenotyping, which has shed new light on increasing production by advanced breeding. However, currently, the field still lacks 3D pepper data that contains enough detail for organ-level analysis. Therefore, we propose Pepper-4D, a new, high-precision 4D point cloud dataset that records both the spatial structure and temporal development of pepper plants across various continuous growth stages. Our dataset is divided into three subsets, including a total of 916 individual point clouds from 29 indoor-cultivated pepper plant samples. Our dataset provides manual annotations at both the plant-level and organ-level, supporting phenotyping tasks such as pepper growth status classification, organ semantic segmentation, organ instance segmentation, organ growth tracking, new organ detection, and even the generation of synthetic 3D pepper plants.
Why it matches plant phenotyping methods植物の器官レベル表現型解析を支援する4D点群データセットを構築し、成長状態分類・器官分割・追跡などを可能にする研究であり、フェノタイピング用データ基盤が中心です。
abstractPepper-4D, a new, high-precision 4D point cloud dataset that records both the spatial structure and temporal development of pepper plants across various continuous growth stages.
Reproduction assets foundThe authors publicly release the Pepper-4D spatiotemporal 3D pepper point cloud dataset (with plant- and organ-level annotations) and associated code via a GitHub repository stated in the Data Availability Statement. CloudCompare is a generic third-party tool, not a paper-specific asset.Dataset · public.J.; writing—original draft preparation, F.A.; writing—review and editing, D.L.; visualization, F.A. and D.L.; supervision, D.L.; project administration, H.Y.; funding acquisition, D.L. and H.Y. All authors have read and agreed to the published version of the manuscript.
Data Availability Statement
Data and code can be found at https://github.com/foysalahmed10/Pepper-4D (accessed on 9 February 2026).
Conflicts of Interest
The authors declare no conflicts of interest.
Funding Statement
This work was supported in part by the Shanghai Sailing Program under Grant 24YF2701200, in part by the Fundamental Research Funds for the Central Universities under Grant 2232025D-50, and in part by Donghua UnOpen asset ↗foysalahmed10/Pepper-4Dlines:238-264Code · publicang Q., Zeng Y., Hou J., Zhe X. WarpingGAN: Warping multiple uniform priors for adversarial 3D point cloud generation; Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition; New Orleans, LA, USA. 18–24 June 2022; pp. 6397–6405.
Associated Data
Data Availability Statement
Data and code can be found at https://github.com/foysalahmed10/Pepper-4D (accessed on 9 February 2026).Open asset ↗foysalahmed10/Pepper-4Dlines:308-314Code / dataset availability confirmedOpenAlex · Europe PMC · checked 5 Sept 2026
LiDAR / point cloudWhole plant / canopy / plot / fieldSegmentationGrowth / time-series analysisTrackingGrowth / development / phenology
To monitor the growth and structural changes of crop organs, dynamic plant phenotyping based on time-series point clouds has become a cutting-edge research topic. However, existing organ tracking methods based on crop time-series point clouds either rely on complete organ instance segmentation results or lack real-time performance in capturing spatiotemporal correlations among organs. To address these limitations, we propose 3D-OGT: a framework capable of performing continuous organ tracking throughout the entire growth sequence with only the minimal segmentation information. The 3D-OGT framework can automatically propagate organ labels from the previous moment's crop point cloud to the subsequent point cloud, while completing organ segmentation and tracking on multiple crop growth sequences. The framework can recognize and track new organs, mature organs, and even suddenly disappeared organs. Experimental results on a spatiotemporal point cloud dataset demonstrate that 3D-OGT achieves satisfactory organ tracking performance, with an average organ tracking accuracy (TrackAcc) reaching 88.10%, which is superior to three other mainstream methods participating in the comparison.
Why it matches plant phenotyping methods作物器官の3D点群から成長を追跡・分割する手法を開発し、データセット上で他手法と比較検証しており、植物表現型取得が中心です。
abstractwe propose 3D-OGT: a framework capable of performing continuous organ tracking throughout the entire growth sequence with only the minimal segmentation information.
Reproduction assets foundThe paper's data and analysis code are publicly released in the authors' GitHub repository, explicitly stated in the Data availability section. The dataset itself is the public Pheno4D spatiotemporal point cloud dataset, but the paper-specific asset is the authors' code/data repository.Code · publicOur data and code are available at: https://github.com/zingersu/3D-organ-growth-tracking-with-minimum-segmentation.Open asset ↗zingersu/3D-organ-growth-tracking-with-minimum-segmentationhtml-lines:292-314Code / dataset availability confirmedCrossref · Europe PMC · checked 5 Sept 2026
Understanding how plants perceive and respond to environmental and developmental cues requires tools capable of monitoring molecular signals in vivo, across whole tissues, and in real time. Genetically encoded fluorescent indicators, coupled with fluorescence microscopy, have transformed plant biology, but their application remains largely confined to small model organisms and specialized microscopy instrumentation. Here, we present MAcro Plant Projection Imaging (MAPPI), an open-source, low-cost, and modular fluorescence imaging platform for soil-grown plants beyond the model organism or seedling stage. MAPPI enables wide field-of-view, dual-projection imaging of fluorescent reporters, supporting real-time visualization of systemic signals under near-physiological conditions. We validate MAPPI by tracking calcium and l -glutamate dynamics in adult Nicotiana benthamiana plants, revealing developmentally regulated long-distance calcium waves triggered by wounding, burning, or submergence, including bidirectional shoot-to-root and root-to-shoot signaling. By democratizing access to whole-plant functional imaging, MAPPI provides a scalable tool for dissecting signal propagation, stress adaptation, and systemic communication in both model and nonmodel species.
Why it matches plant phenotyping methods植物全体の蛍光シグナルをリアルタイム取得する低コスト・オープンな画像プラットフォームを開発し、成体植物で検証しているため、植物表現型取得法が研究の中心です。
abstractHere, we present MAcro Plant Projection Imaging (MAPPI), an open-source, low-cost, and modular fluorescence imaging platform for soil-grown plants beyond the model organism or seedling stage.
Reproduction assets foundThe authors publicly deposit raw imaging data and MAPPI analysis code on Zenodo, host the MAPPI acquisition/analysis code on GitHub, and release the napari-roi-registration image registration plugin on GitHub. All are paper-specific, public, and actionable.Dataset · publicThe raw data for the images presented in the manuscript and the code to run the MAPPI system are available on Zenodo ( https://doi.org/10.5281/zenodo.15845576 ).Open asset ↗Zenodo · 10.5281/zenodo.15845576lines:170-466Code · publicThe code to run the MAPPI system is also available on the dedicated GitHub repository ( https://github.com/micropolimi/MAPPI ) along with the code used to analyze the data.Open asset ↗GitHub · micropolimi/MAPPIlines:170-466Code · publicThe software is open-source and available on GitHub ( https://github.com/GiorgiaTortora/napari-roi-registration ) and the napari-hub ( www.napari-hub.org/plugins/napari-roi-registration ).Open asset ↗GitHub · GiorgiaTortora/napari-roi-registrationlines:156-169Code / dataset availability confirmedOpenAlex · Crossref · Europe PMC · checked 15 Sept 2026
BACKGROUND: Plant developmental plasticity, particularly in root system architecture, is fundamental to understanding adaptability and agricultural sustainability. Existing automated phenotyping solutions face limitations, including binary segmentation approaches, restricted structural analysis capabilities, and text-based interfaces that limit accessibility, with most focusing solely on root structures while overlooking valuable information from simultaneous analysis of multiple plant organs. FINDINGS: ChronoRoot 2.0 builds upon established low-cost hardware while significantly enhancing software capabilities and usability. The system employs nnUNet architecture for multi-class segmentation, demonstrating significant accuracy improvements while simultaneously tracking 6 distinct plant structures encompassing root, shoot, and seed components: main root, lateral roots, seed, hypocotyl, leaves, and petiole. This architecture enables easy retraining and incorporation of additional training data without requiring machine learning expertise. The platform introduces dual specialized graphical interfaces: a Standard Interface for detailed architectural analysis with novel gravitropic response parameters and a Screening Interface enabling high-throughput analysis of multiple plants through automated tracking. Functional principal component analysis integration enables discovery of novel phenotypic parameters through temporal pattern comparison. We demonstrate multi-species analysis, with Arabidopsis thaliana and Solanum lycopersicum, both morphologically distinct plant species. Three use cases in Arabidopsis thaliana and validation with tomato seedlings demonstrate enhanced capabilities: circadian growth pattern characterization, gravitropic response analysis in transgenic plants, and high-throughput etiolation screening across multiple genotypes. CONCLUSIONS: ChronoRoot 2.0 maintains the low-cost, modular hardware advantages of its predecessor while dramatically improving accessibility through intuitive graphical interfaces and expanded analytical capabilities. The open-source platform makes sophisticated temporal plant phenotyping more accessible to researchers without computational expertise. SOFTWARE AVAILABILITY: https://chronoroot.github.io.
Why it matches plant phenotyping methods根・シュート・種子を時系列追跡し、植物形態・成長・重力応答などの表現型を抽出するオープンプラットフォームの開発と検証が中心である。
titleChronoRoot 2.0: An Open AI-Powered Platform for 2D Temporal Plant Phenotyping
Reproduction assets foundThe paper publicly releases its authors' analysis code (GitHub), the annotated plant image dataset used for segmentation training/validation (HuggingFace), a pre-configured Docker image, and a project home page, all with explicit availability statements and URLs matching allowed entries.Code · publicapproach to open science will not only ensure transparency and reproducibility but also allow the system to evolve alongside the changing needs of the plant biology community.
Availability of source code and requirements
Project name: ChronoRoot 2.0.
Project home page: https://chronoroot.github.io .
Main Source Code repository: https://github.com/ChronoRoot/ChronoRoot2 .
Operating system(s): Platform independent.
Programming language: Python.
Other requirements: Conda, Apptainer, or Docker.
License: GNU GPL 3.0.
Additional files
Supplementary Text S1 : Functional PCA. Provides an intuitive explanation of functional principal component analysis (FPCA) for readers without a quantitative backgrOpen asset ↗https://github.com/ChronoRoot/ChronoRoot2lines:439-479Dataset · publicgulates LAZY genes. Plant J. 2025;121:e70016. 10.1111/tpj.70016.
19.
Gaggion N, Boccardo N A, Bonazzola R, et al. ChronoRoot 2.0 Main Source Code Repository. 2026. https://github.com/ChronoRoot/ChronoRoot2 . Accessed 25 February 2026.
20.
Gaggion N, Boccardo N A, Bonazzola R, et al. ChronoRoot 2.0 Annotated Image Dataset. 2026. https://huggingface.co/datasets/ngaggion/ChronoRoot2 . Accessed 25 February 2026.
21.
Gaggion N, Boccardo N A, Bonazzola R, et al. ChronoRoot 2.0 Docker Image. 2026. https://hub.docker.com/r/ngaggion/chronoroot . Accessed 25 February 2026.
22.
Gaggion N, Boccardo N A, Bonazzola R, et al. ChronoRoot 2.0 Project Home Page. 2026. https://chronoroot.github.io . Accessed 2Open asset ↗https://huggingface.co/datasets/ngaggion/ChronoRoot2lines:568-618Code · publicical modules, and experimental protocols. We hope that this approach to open science will not only ensure transparency and reproducibility but also allow the system to evolve alongside the changing needs of the plant biology community.
Availability of source code and requirements
Project name: ChronoRoot 2.0.
Project home page: https://chronoroot.github.io .
Main Source Code repository: https://github.com/ChronoRoot/ChronoRoot2 .
Operating system(s): Platform independent.
Programming language: Python.
Other requirements: Conda, Apptainer, or Docker.
License: GNU GPL 3.0.
Additional files
Supplementary Text S1 : Functional PCA. Provides an intuitive explanation of functional princOpen asset ↗lines:439-479Code / dataset availability confirmedOpenAlex · arXiv · checked 6 Sept 2026
High resolution phenotyping at the level of individual leaves offers fine-grained insights into plant development and stress responses. However, the full potential of accurate leaf tracking over time remains largely unexplored due to the absence of robust tracking methods-particularly for structurally complex crops such as canola. Existing plant-specific tracking methods are typically limited to small-scale species or rely on constrained imaging conditions. In contrast, generic multi-object tracking (MOT) methods are not designed for dynamic biological scenes. Progress in the development of accurate leaf tracking models has also been hindered by a lack of large-scale datasets captured under realistic conditions. In this work, we introduce CanolaTrack, a new benchmark dataset comprising 5,704 RGB images with 31,840 annotated leaf instances spanning the early growth stages of 184 canola plants. To enable accurate leaf tracking over time, we introduce LeafTrackNet, an efficient framework that combines a YOLOv10-based leaf detector with a MobileNetV3-based embedding network. During inference, leaf identities are maintained over time through an embedding-based memory association strategy. LeafTrackNet outperforms both plant-specific trackers and state-of-the-art MOT baselines, achieving a 9% HOTA improvement on CanolaTrack. With our work we provide a new standard for leaf-level tracking under realistic conditions and we provide CanolaTrack - the largest dataset for leaf tracking in agriculture crops, which will contribute to future research in plant phenotyping. Our code and dataset are publicly available at https://github.com/shl-shawn/LeafTrackNet.
Why it matches plant phenotyping methods葉レベルの時系列追跡という植物表現型取得手法を開発し、専用ベンチマークデータセットで評価しているため、方法が中心である。
abstractTo enable accurate leaf tracking over time, we introduce LeafTrackNet, an efficient framework that combines a YOLOv10-based leaf detector with a MobileNetV3-based embedding network.
Reproduction assets foundThe authors explicitly state that the CanolaTrack dataset (5,704 annotated RGB images of 184 canola plants), the LeafTrackNet code, and trained model weights are publicly available at their GitHub repository.Code · publicOur code and dataset are publicly
available at https://github.com/shl-shawn/LeafTrackNet.Open asset ↗shl-shawn/LeafTrackNet · LeafTrackNetpdf-page:1 lines:1-53Code / dataset availability confirmedEurope PMC · checked 15 Sept 2026
Introduction The real-time, accurate detection and classification of rice seeds are crucial for improving agricultural productivity, ensuring grain quality, and promoting smart agriculture. Although significant progress has been made using deep learning, particularly convolutional neural networks (CNNs) and attention-based models, earlier methods such as threshold segmentation and single-grain classification faced challenges related to computational efficiency and latency, especially in high-density seed agglutination scenarios. This study addresses these limitations by proposing an integrated intelligent analysis model that combines object detection, real-time tracking, precise classification, and high-accuracy phenotypic measurement. Methods The proposed model utilizes the lightweight YOLOv11-LA for real-time grain segmentation, which builds upon the YOLOv11 architecture. YOLOv11-LA incorporates several enhancements over YOLOv11, including separable convolutions, CBAM (Convolutional Block Attention Module) attention mechanisms, and module pruning strategies. These modifications not only improve detection accuracy but also significantly reduce the number of parameters by 63.2% and decrease computational complexity by 51.6%. For classification, the model employs a custom-designed, lightweight RiceLCNN classifier. Additionally, the DeepSORT algorithm is employed for real-time multi-object tracking, and sub-pixel edge detection along with dynamic scale calibration mechanisms are applied for precise phenotypic feature measurement. Results Compared to YOLOv11, the YOLOv11-LA model increases the mAP@0.5:0.95 score by 1.9%, showcasing its superior detection performance while maintaining lower computational overhead. The RiceLCNN classifier achieved classification accuracies of 89.78% on private datasets and 96.32% on public benchmark datasets. The system demonstrated high accuracy in measuring phenotypic features such as seed size and roundness, with measurement errors kept within 0.1 millimeters. The DeepSORT algorithm effectively managed multi-object tracking, reducing duplicate identifications and frame loss in real-time. Discussion Experimental validation confirmed that the YOLOv11-LA model outperforms the original YOLOv11 in terms of both detection speed and accuracy, while also maintaining low computational complexity. The integration of the YOLOv11-LA, RiceLCNN, and DeepSORT algorithms, combined with advanced measurement techniques, underscores the model's potential for industrial applications, particularly in enhancing smart agricultural practices.
Why it matches plant phenotyping methodsイネ種子画像からサイズや真円度を抽出するリアルタイム画像解析手法を開発し、精度・速度・測定誤差を検証しており、表現型取得が研究の中心です。
abstractThe proposed model utilizes the lightweight YOLOv11-LA for real-time grain segmentation
Reproduction assets foundThe paper's data availability statement points to a public GitHub repository (RiceLCNN) containing the study's rice seed datasets and analysis code. The supplementary material link is generic and not confirmed to contain paper-specific assets.Dataset · publicang , Southwest Forestry University, China
Guodong Sun , Beijing Forestry University, China
Xiaofei Fan , Hebei Agricultural University, China
Data availability statement
The datasets presented in this study can be found in online repositories. The names of the repository/repositories and accession number(s) can be found below: https://github.com/5120191452/RiceLCNN .
Author contributions
DZ: Methodology, Software, Writing – original draft. SS: Funding acquisition, Resources, Writing – review & editing. JL: Validation, Writing – review & editing. WX: Data curation, Resources, Writing – review & editing. NX: Formal Analysis, Visualization, Writing – review & editing.
Conflict of interest
ThOpen asset ↗https://github.com/5120191452/RiceLCNN · RiceLCNNlines:619-662Code / dataset availability confirmedCrossref · OpenAlex · Europe PMC · checked 14 Sept 2026
MultimodalLiDAR / point cloudAnnotation / quality controlClassificationObject detectionCalibration / preprocessingSegmentationTracking
Plant phenomics, the comprehensive study of plant phenotypes, has gained prominence as a vital tool for understanding the intricate relationships between genotypes and the environment. Image-based plant phenomics has progressed rapidly, and three-dimensional (3D) phenotyping is a valuable extension of traditional 2D phenomics. However, the increased data dimensionality poses challenges to feature extraction and phenotyping. In recent decades, deep learning has led to remarkable progress in revolutionizing 3D phenotyping. Therefore, this review highlights the importance of using deep learning in 3D plant phenomics. It systematically overviews the capabilities of deep learning for 3D computer vision, covering 3D representation, classification, detection and tracking, semantic segmentation, instance segmentation, and generation. Additionally, deep learning techniques for 3D point preprocessing (e.g., annotation, downsampling, and dataset organization) and various plant phenotyping tasks are discussed. Finally, the challenges and perspectives associated with deep learning in 3D plant phenomics are summarized, including (1) benchmark dataset construction by using synthetic datasets and methods such as generative artificial intelligence and unsupervised or weakly supervised learning; (2) accurate and efficient 3D point cloud analysis by leveraging multitask learning, lightweight models, and self-supervised learning; and (3) deep learning for 3D plant phenomics by exploring interpretability, extensibility, and multimodal data utilization. The exploration of deep learning in 3D plant phenomics is poised to spur breakthroughs in a new dimension of plant science.
Why it matches plant phenotyping methods3D植物フェノミクスにおける深層学習手法を体系的にレビューしており、植物形質の抽出・推定手法が中心である。
abstractTherefore, this review highlights the importance of using deep learning in 3D plant phenomics.
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 dataset can be downloaded from https://github.com/Jinlab-AiPhenomics/Mazie3D.Open asset ↗Jinlab-AiPhenomics/Mazie3Dhtml-lines:332-336Code / dataset availability confirmedOpenAlex · checked 15 Sept 2026
Published19 Nov 2025ISPRS Journal of Photogrammetry and Remote SensingCited by 3 · OpenAlex ↗
Accurate fruit shape reconstruction under real-world field conditions is essential for high-throughput phenotyping, sensor-based yield estimation, and orchard management. Existing approaches based on 2D imaging or explicit 3D reconstruction often suffer from occlusions, sparse views, and complex scene dynamics as a result of the plant geometries. This paper presents a novel UAV-based monocular 3D panoptic mapping framework for robust and scalable fruit shape completion in orchards. The proposed method integrates (1) Grounded-SAM2 for multi-object tracking and segmentation (MOTS), (2) photogrammetric structure-from-motion for 3D scene reconstruction, and (3) DeepSDF, an implicit neural representation, for completing occluded fruit geometries with a neural network. We furthermore propose a new MOTS evaluation protocol to assess tracking performance without requiring ground truth annotations. Experiments conducted in both controlled laboratory conditions and an operational apple orchard demonstrate the accuracy of our 3D fruit reconstruction at the centimeter level. The Chamfer distance error of the proposed shape completion method using the DeepSDF shape prior reduces this to the millimeter level, and outperforms the traditional method, while Grounded-SAM2 enables robust fruit tracking across challenging viewpoints. The approach is highly scalable and applicable to real-world agricultural scenarios, offering a promising solution to reconstruct complete fruits with visibility higher than 10% for precise 3D fruit phenotyping at a large scale under occluded conditions.
Why it matches plant phenotyping methods果実形状を対象とするUAV画像・3D再構成・形状補完法を開発し、実験で精度評価しており、植物表現型取得が研究の中心である。
abstractThis paper presents a novel UAV-based monocular 3D panoptic mapping framework for robust and scalable fruit shape completion in orchards.
Reproduction assets foundThe paper's authors publicly release their UAV orchard video data, lab 3D apple scans, and analysis code via a GitHub repository explicitly stated in the text. A Zenodo deposit (10.5281/zenodo.15635994) is also mentioned for the data, but its URL is not among the allowed URLs, so only the GitHub asset is reported.Code · publicing in orchard environments,(2) to propose a novel method to evaluate
MOTS without any annotations, and (3) to provide a highly accurate
3D apple dataset collected in a laboratory environment, along with
UAV-captured high-resolution videos in the field. The dataset and codes
for this research are publicly available at: https://github.com/Kaiwen-Robotics/Mono3DOrchard.2. Study area and materials
This study contains two data collection areas: field data collection
and laboratory data collection.
2.1. Field data collection
2.1.1. Study area
The field data collection was conducted within an apple orchard
located in Randwijk, Overbetuwe, the Netherlands (51.9376, 5.703057
in WGS84 UTM 31U), as shOpen asset ↗Kaiwen-Robotics/Mono3DOrchard.2pdf-raw-page:2 lines:75-128Code / dataset availability confirmedEurope PMC · bioRxiv · checked 15 Sept 2026
Understanding how plants capture light and maintain their energy balance is crucial for predicting how ecosystems respond to environmental changes. By monitoring leaf inclination angle distributions (LIADs), we can gain insights into plant behaviour that directly influences ecosystem functioning. LIADs affect radiative transfer processes and reflectance signals, which are essential components of satellite-based vegetation monitoring. Despite their importance, scalable methods for continuously observing these dynamics across different plant species throughout day-night cycles are limited. We present AngleCam V2, a deep learning model that estimates LIADs from both RGB and near-infrared (NIR) night-vision imagery. We compiled a dataset of over 4,500 images across 200 globally distributed species to facilitate generalization across taxa. Moreover, we developed a method to simulate pseudo-NIR imagery from RGB imagery to enable an efficient training of a deep learning model for tracking LIADs across day and night. The model is based on a vision transformer architecture with mixed-modality training using the RGB and the synthetic NIR images. AngleCam V2 achieved substantial improvements in generalization compared to AngleCam V1 (R 2 = 0.62 vs 0.12 on the same holdout dataset). Phylogenetic analysis across 100 genera revealed no systematic taxonomic bias in prediction errors. Testing against leaf angle dynamics obtained from multitemporal terrestrial laser scanning demonstrated the reliable tracking of diurnal leaf movements (R 2 = 0.61-0.75) and the successful detection of water limitation-induced changes over a 14-day monitoring period. This method enables continuous monitoring of leaf angle dynamics using conventional cameras, enabling applications in ecosystem monitoring networks, plant stress detection, interpreting satellite vegetation signals, and citizen science platforms for global-scale understanding of plant structural responses.
Why it matches plant phenotyping methods葉の傾斜角分布という植物形質を画像から推定する深層学習手法を開発し、大規模データセット、既存モデル比較、レーザースキャンによる検証、水ストレス下での追跡評価まで実施しており、フェノタイピング手法が研究の中心です。
abstractWe present AngleCam V2, a deep learning model that estimates LIADs from both RGB and near-infrared (NIR) night-vision imagery.
Reproduction assets foundThe paper's Data Availability Statement explicitly provides public access to the authors' analysis code (Anonymous GitHub), the phenotyping image/trait dataset (Zenodo), and the pretrained AngleCam V2 model weights (Zenodo). All three are paper-specific, public, and actionable.Code · publicLK and TK conceived the ideas, designed the methodology, and led the analysis. TK, JP, RR, JF, LK,
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and DL collected the data. LK and TK led the writing of the manuscript. All authors contributed
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critically to the drafts and gave final approval for publication.
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Data Availability Statement
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The code is available here (https://anonymous.4open.science/r/AngleCamV2-2B38). The data
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is available at (https://doi.org/10.5281/zenodo.17086253). The pretrained model is available
31
at (https://doi.org/10.5281/zenodo.17101166).32
Conflicts of Interest
33
All authors declare that they have no conflicts of interest.
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CC-BY 4.0 International license
perpetuity. It is made available underOpen asset ↗anonymous.4open.science/r/AngleCamV2-2B38pdf-raw-page:2 lines:1-30Dataset · publicK, JP, RR, JF, LK,
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and DL collected the data. LK and TK led the writing of the manuscript. All authors contributed
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critically to the drafts and gave final approval for publication.
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Data Availability Statement
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The code is available here (https://anonymous.4open.science/r/AngleCamV2-2B38). The data
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is available at (https://doi.org/10.5281/zenodo.17086253). The pretrained model is available
31
at (https://doi.org/10.5281/zenodo.17101166).32
Conflicts of Interest
33
All authors declare that they have no conflicts of interest.
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2
.
CC-BY 4.0 International license
perpetuity. It is made available under a
preprint (which was not certified by peer review) is the author/funder, whoOpen asset ↗zenodo · 10.5281/zenodo.17086253pdf-raw-page:2 lines:1-30Model / weights · publicanuscript. All authors contributed
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critically to the drafts and gave final approval for publication.
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Data Availability Statement
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The code is available here (https://anonymous.4open.science/r/AngleCamV2-2B38). The data
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is available at (https://doi.org/10.5281/zenodo.17086253). The pretrained model is available
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at (https://doi.org/10.5281/zenodo.17101166).32
Conflicts of Interest
33
All authors declare that they have no conflicts of interest.
34
2
.
CC-BY 4.0 International license
perpetuity. It is made available under a
preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in
The copyright holder for tOpen asset ↗zenodo · 10.5281/zenodo.17101166pdf-raw-page:2 lines:1-30Code / dataset availability confirmedEurope PMC · bioRxiv · checked 6 Sept 2026
Plant development and adaptation are highly dependent on cell morphology and growth. High turgor pressure in plants causes stress on the cell wall, followed by cell extension. In tip-growing cells, the localization of vesicles and cytoskeleton components has been well studied. However, there has been a lack of attention to the spatial profile of mechanical properties, specifically the cell wall elasticity. In this study, we introduce a new surface morphology-based method to measure the elasticity of the cell wall in tip-growing cells. Previous work is based on measurements from the wall meridional outline, a technique that cannot track the elastic deformation of the cell wall experimentally. Instead, we developed a way to infer the bulk modulus distribution from the cell surface by triangulating experimental marker points coming from fluorescent labeling. To justify the use of our protocol in tip-growing cells from the moss Physcomitrium patens , we replicated the experimental noise and moss morphology in simulated cells. In practice, we found that a larger triangulation improved robustness against noise, which agreed with our theoretical study. With multiple cell sampling, we determined that 10 cells were sufficient to recover the elasticity distribution with noise, but only when the elastic stretches were high enough. We then created a dimensionless map of inference error to verify a spatial change of P. patens bulk modulus within two folds. This technique will open the field to more comprehensive measurements of cell wall elasticity, providing a key step in understanding tip cell growth and morphogenesis. Author summary Tip-growing cells can be characterized by their fast growth concentrated at the cell’s apex. Their growth and morphogenesis are tightly regulated processes involving cell wall addition and rearrangement while the cell wall is under stress originating from the cell’s internal turgor pressure. We start by studying the cell wall’s elastic properties, one aspect of the cell growth process. We use a method of marker point tracking across the surface of the tip-growing cell to measure the wall’s elasticity profile. In this work, we present a parameter sensitivity study of this method on synthetic cells and report our results on experimental moss tip-growing cells. Our results suggest that this inference method can reliably measure a cell wall elasticity gradient under combined geometric and mechanical conditions that create elastic strains within 5% at the tip.
Why it matches plant phenotyping methodsコケの先端成長細胞における細胞壁弾性分布を、蛍光マーカーと表面形態から推定する新規測定法を開発し、シミュレーションおよび実細胞で検証しているため、植物フェノタイピング手法が中心である。
abstractIn this study, we introduce a new surface morphology-based method to measure the elasticity of the cell wall in tip-growing cells.
Reproduction assets foundThe authors' Data Availability statement explicitly deposits all relevant data and code, including code demonstrations, in a public GitHub repository (rholee-xu/surface-model), which contains the analysis code for the cell wall elasticity inference method.Code · publicAll relevant data and code, including code demonstrations, are available on the GitHub repository found here: https://github.com/rholee-xu/surface-modelOpen asset ↗rholee-xu/surface-modellines:45-63Code / dataset availability confirmedCrossref · Europe PMC · checked 15 Sept 2026
The integration of nanotechnology in agriculture allows for more precise nutrient delivery through nanoparticles (NPs), particularly via foliar application. To mature this technology for enhancing fertilizer efficiency, it is essential to shed new light on the transport and dissolution of NPs in plants. Available analytical methods struggle to address this challenge in a direct manner. We introduce correlative X-ray imaging as a novel analytical tool capable of tracking NP pathways, dissolution and hence nutrient release in plants. By utilizing three complementary X-ray techniques, we offer a unique insight into the plant processes associated with foliar fertilization. We demonstrate that small-angle X-ray scattering enables the characterization of NP size and concentration, while X-ray fluorescence imaging, maps the distribution of elements within the sample. Finally, micro-computed tomography integrates these findings into a complete three-dimensional digital representation of the plant’s microstructure, revealing regions of apparent densification associated with NP accumulation. Using freeze-dried barley plants infiltrated with nano-hydroxyapatite (nHAP), we observed rapid dissolution of NPs, and we are able to associate time and space attributes to the translocation process of nutrients up to three days following foliar application of NPs. With the first pilot study of applying correlative X-ray imaging to live plants, we sought to indicate the potential of this new analytical approach for future nano-enabled agricultural research.
Why it matches plant phenotyping methods植物内のナノ粒子経路・溶解・栄養輸送を可視化する相関X線イメージング手法の導入と実証が中心であり、植物の状態・生理過程を測定する方法論的研究である。
abstractWe introduce correlative X-ray imaging as a novel analytical tool capable of tracking NP pathways, dissolution and hence nutrient release in plants.
Reproduction assets foundThe article's data availability statement points to a public figshare repository hosting the study's datasets (X-ray imaging/phenotyping measurements). No author analysis code with explicit public deposit language was identified; Dragonfly is a commercial visualization tool, not a paper-specific asset.Dataset · publicng Wan , Hainan University, China
Zhansheng Li , Chinese Academy of Agricultural Sciences, China
Firozeh Solimani , Politecnico di Bari, Italy
Data availability statement
The datasets presented in this study can be found in online repositories. The names of the repository/repositories and accession number(s) can be found below: https://figshare.com/s/33ee8388a36600fe98d5 .
Author contributionsOpen asset ↗figsharelines:191-206Code / dataset availability confirmedbioRxiv · checked 15 Sept 2026
The global carbon cycle depends heavily on the carbon sequestration rates of aquatic ecosystems. Sinking of phytoplankton is a rapid mediator of carbon sequestration, because phytoplankton are globally abundant photoautotrophs that grow rapidly. Pico- and nano-phytoplankton sinking velocities vary depending on their growth state, viability, clumping, and distribution in the water column. We introduced high throughput fluorescence microscopy of well-plates, to measure sinking velocities of three diatom strains, and three cyanobacteria strains, with cell radii spanning an order of magnitude, all grown under three different light levels. Cultures were measured for sinking velocities repeatedly across their growth trajectories. Tracking multiple fluorescence wavebands allowed us to simultaneously determine sinking velocities for living vs. dead cells. Sinking velocities varied strongly across growth light levels, and across growth stages. These monoclonal cultures furthermore show distinct sub-populations of slow- and fast-sinking cells. Our results departed widely from simple Stokes Law estimates of sinking based upon radii and mass density of cells. Complex, heterogeneous phytoplankton communities likely show more complicated sinking patterns than are currently expressed in biogeochemical ocean models. Our well-plate microscopy approach using parallel imaging of many samples generates high-throughput measures of cell sinking at population- or community-scales, to in turn improve modelling of carbon export to deeper layers.
Why it matches plant phenotyping methods植物プランクトンの沈降速度を高スループット蛍光顕微鏡で測定する手法を導入し、生活状態や集団スケールの生理・機能形質を定量化しているため、測定法が研究の中心です。
abstractWe introduced high throughput fluorescence microscopy of well-plates, to measure sinking velocities of three diatom strains, and three cyanobacteria strains
Reproduction assets foundThe paper's sinking-velocity analysis code is explicitly stated to be openly available on the authors' GitHub repository. The raw phenotype data is promised for Dryad only upon acceptance, so it is not yet publicly actionable.Code · publicfunctional groups of cyanobacteria, diatoms strains with 156
diameter less than 10µm and diatoms strains with diameter larger than 10µm based on 157
growth light, viability state (living vs. dead and dying) and slow vs. fast sinking 158
velocity clustering groups. 159
The code used to analyse the data is public available at 160
https://github.com/maxberthold/PhytoplanktonSinkVelocities. 161
Sinking according to Stokes’ law 162
Sinking velocities of spherical objects falling under the case of Reynolds numbers 163
smaller than 1 can be described by Stokes’ law. Several studies have used Stokes law or 164
a modified version of Stokes’ law to estimate sinking velocities of plankton and marine 16Open asset ↗maxberthold/PhytoplanktonSinkVelocitiespdf-raw-page:8 lines:1-44Code / dataset availability confirmedCrossref · Europe PMC · checked 14 Sept 2026
Plants adapt seed traits in response to different environmental triggers, supporting the survival of the next generation. To elucidate the mechanistic understanding of such adaptations it is important to characterize the distributions of seed traits by phenotyping seeds on an individual scale and to correlate these traits with corresponding plant properties. Here we introduce a seed-to-plant-tracking pipeline which enables automated handling and high precision phenotyping of Arabidopsis seeds as well as germination detection and early growth quantification of emerging plants. It includes previously published measurement platforms ( pheno Seeder, Growscreen), which were improved for very small seeds. We demonstrate the performance of the pipeline by comparing seeds from two consecutive generations of elevated temperature during flowering with control seeds. Relative standard deviation of repeated seed mass measurements was reduced to 0.2%. We identified an increase in seed mass, volume, length, width, height, and germination time as well as a darkening of the seeds under the treatment. A correlation analysis revealed relationships between seed and plant traits, e.g., a highly significant negative correlation between seed brightness and germination time, and a positive correlation between seed mass and early growth rate, but no correlation between time of emergence and morphometric seed traits (e.g., mass, volume). Thus, the seed-to-plant tracking provides the basis for investigating the mechanism of seed and plant trait variation and transgenerational inheritance.
Why it matches plant phenotyping methods種子から植物までを追跡し、種子形質の高精度自動計測、発芽検出、初期成長定量を行うパイプラインを開発・改良しており、表現型取得法が研究の中心である。
abstractHere we introduce a seed-to-plant-tracking pipeline which enables automated handling and high precision phenotyping of Arabidopsis seeds as well as germination detection and early growth quantification of emerging plants.
Reproduction assets foundThe paper deposits its seed and plant phenotyping datasets in Jülich DATA (DOI 10.26165/JUELICH-DATA/KZDQYD), explicitly stated in the data availability statement. Supplementary tables also contain the paper's measurement data. No author analysis code repository is stated.Dataset · publicThe author(s) declare that no financial support was received for the research and/or publication of this article.
Data availability statement
The datasets presented in this study can be found in online repositories. The names of the repository/repositories and accession number(s) can be found below: https://doi.org/10.26165/JUELICH-DATA/KZDQYD .
Author contributions
DK: Formal Analysis, Investigation, Validation, Visualization, Writing – original draft, Writing – review & editing. AF: Investigation, Methodology, Resources, Software, Writing – review & editing. VS: Formal Analysis, Investigation, Methodology, Resources, Software, Writing – review & editing. JK: MethodOpen asset ↗JUELICH-DATA · 10.26165/JUELICH-DATA/KZDQYDlines:333-387Supplement · 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.2025.1539424/full#supplementary-material
Supplementary Table 1
Data of seed mass vs volume and projected seed area, respectively, shown in
Figure 6
.
Supplementary Table 2
Data of repeatability measurements analysed in
Table 1
and
2
.
Supplementary Table 3
Data of leaf area time series used for estimation of plant growOpen asset ↗lines:333-387Code / dataset availability confirmedarXiv · checked 6 Sept 2026
ArabidopsisTomatoLeafRootSeed / grainSegmentationGrowth / time-series analysisTrackingGrowth / development / phenologyRoot system architecture
Plant developmental plasticity, particularly in root system architecture, is fundamental to understanding adaptability and agricultural sustainability. ChronoRoot 2.0 builds upon established low-cost hardware while significantly enhancing software capabilities and usability. The system employs nnUNet architecture for multi-class segmentation, demonstrating significant accuracy improvements while simultaneously tracking six distinct plant structures encompassing root, shoot, and seed components: main root, lateral roots, seed, hypocotyl, leaves, and petiole. This architecture enables easy retraining and incorporation of additional training data without requiring machine learning expertise. The platform introduces dual specialized graphical interfaces: a Standard Interface for detailed architectural analysis with novel gravitropic response parameters, and a Screening Interface enabling high-throughput analysis of multiple plants through automated tracking. Functional Principal Component Analysis integration enables discovery of novel phenotypic parameters through temporal pattern comparison. We demonstrate multi-species analysis, with Arabidopsis thaliana and Solanum lycopersicum, both morphologically distinct plant species. Three use cases in Arabidopsis thaliana and validation with tomato seedlings demonstrate enhanced capabilities: circadian growth pattern characterization, gravitropic response analysis in transgenic plants, and high-throughput etiolation screening across multiple genotypes.ChronoRoot 2.0 maintains the low-cost, modular hardware advantages of its predecessor while dramatically improving accessibility through intuitive graphical interfaces and expanded analytical capabilities. The open-source platform makes sophisticated temporal plant phenotyping more accessible to researchers without computational expertise.
Why it matches plant phenotyping methods植物の時系列画像から根・地上部・種子などの形態形質を抽出・追跡するオープンなAI基盤を開発し、精度向上、再学習、GUI、高スループット解析、検証まで扱っており、フェノタイピング手法が研究の中心です。
titleChronoRoot 2.0: An Open AI-Powered Platform for 2D Temporal Plant Phenotyping
Reproduction assets foundThe paper explicitly releases its full analysis source code (GitHub), the annotated infrared image dataset with multiclass segmentation masks (HuggingFace), demo phenotype video datasets, and a Docker image — all paper-specific, public, and actionable.Code · publicThe complete source code of ChronoRoot 2.0, including the implementation of all analysis methods described in this paper, is freely available under the GNU General Public License v3.0 at https://github.com/ChronoRoot/ChronoRoot2Open asset ↗ChronoRoot/ChronoRoot2lines:491-523Dataset · publicThe annotated image dataset used for training and validation contains 911 infrared images of Arabidopsis thaliana seedlings and 480 images of tomato with expert annotations for multiclass segmentation. This dataset is publicly available without restrictions at https://huggingface.co/datasets/ngaggion/ChronoRoot2Open asset ↗ngaggion/ChronoRoot2lines:491-523Code / dataset availability confirmedEurope PMC · Crossref · checked 14 Sept 2026
Posture is a critical phenotypic trait that reflects crop growth and serves as an essential indicator for both agricultural production and scientific research. Accurate pose estimation enables real-time tracking of crop growth processes, but in field environments, challenges such as variable backgrounds, dense planting, occlusions, and morphological changes hinder precise posture analysis. To address these challenges, we propose PFLO (Pose Estimation Model of Field Maize Based on YOLO Architecture), an end-to-end model for maize pose estimation, coupled with a novel data processing method to generate bounding boxes and pose skeleton data from a"keypoint-line"annotated phenotypic database which could mitigate the effects of uneven manual annotations and biases. PFLO also incorporates advanced architectural enhancements to optimize feature extraction and selection, enabling robust performance in complex conditions such as dense arrangements and severe occlusions. On a fivefold validation set of 1,862 images, PFLO achieved 72.2% pose estimation mean average precision (mAP50) and 91.6% object detection mean average precision (mAP50), outperforming current state-of-the-art models. The model demonstrates improved detection of occluded, edge, and small targets, accurately reconstructing skeletal poses of maize crops. PFLO provides a powerful tool for real-time phenotypic analysis, advancing automated crop monitoring in precision agriculture.
Why it matches plant phenotyping methodsトウモロコシの姿勢という植物表現型を、圃場画像から推定するモデルとデータ処理法を開発・検証しており、フェノタイピング手法が研究の中心である。
abstractwe propose PFLO (Pose Estimation Model of Field Maize Based on YOLO Architecture), an end-to-end model for maize pose estimation, coupled with a novel data processing method to generate bounding boxes and pose skeleton data
Woody plant encroachment (WPE)—a phenomenon similar to species invasion—is shifting many grasslands and savannas into shrub and evergreen-dominated ecosystems. Tracking WPE is difficult because shrubs and small trees are much smaller than the coarse resolution of common remote sensing platforms (> 10 m 2 ) and the impassibility of encroaching woody thickets slows ground-based approaches. Many agencies have been investing in fine resolution ( 90%), with the NEON-based models a few percent more accurate than NAIP. A model using both inputs had the highest accuracy. However, the accuracies of NAIP and NEON models differed for woody vegetation: compared to NEON, NAIP accuracy was, 82-93% compared to 94-98% for shrubs, 72-92% compared to 93-98% for deciduous trees, and 52-78% compared to 83-86% for evergreen trees (specifically Juniperus virginiana ). NEON-based models relied on canopy height (LiDAR) to make classifications, whereas the several bands of light make similar contributions to accuracy in the NAIP models. Finally, we found that both machine learning approaches had similar accuracy, but random forests ran substantially faster. We conclude that with large training datasets, publicly available aerial imagery and similar products (e.g., UAVs, micro-satellites) can produce fine-scale, high-accuracy remote sensing of WPE in this region with low up-front costs.
Why it matches plant phenotyping methods航空画像・LiDARと機械学習を用いて低木・樹木の植生状態を高解像度で推定し、NAIPとNEONおよび手法間の精度を比較しており、植物状態の取得・推定法が研究の中心である。
abstractTracking WPE is difficult because shrubs and small trees are much smaller than the coarse resolution of common remote sensing platforms
Reproduction assets foundThe authors deposited their paper-specific training/classification dataset (ground-truthed and computer-drawn vegetation polygons for Konza Prairie) publicly on EDI. The analysis code is only 'private-for-peer review' on Figshare, so it does not qualify as a public asset. NEON and NAIP imagery are generic third-party平台Dataset · public, U.S.A.
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Open research statement:
14
Data sets utilized for this research are as follows:
15
Noble, B. and Z. Ratajczak. 2022. WPE01 Assessing the value added of NEON for using
16
machine learning to quantify vegetation mosaics and woody plant encroachment at
17
Konza Prairie ver 1. Environmental Data Initiative.
18
https://doi.org/10.6073/pasta/a7b40e41080460bb1123dcc7b6d4d942 (Accessed 2022-12-
19
08). https://portal.edirepository.org/nis/mapbrowse?scope=knb-lter-20
knz&identifier=167&revision=1
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(which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission.
The copyright holder for this preprint
this version posted FeOpen asset ↗Environmental Data Initiative · 10.6073/pasta/a7b40e41080460bb1123dcc7b6d4d942pdf-raw-page:1 lines:1-47Code / dataset availability confirmedCrossref · Europe PMC · checked 14 Sept 2026
Tracking biodiversity across biomes over space and time has emerged as an imperative in unified global efforts to manage our living planet for a sustainable future for humanity. We harness the National Ecological Observatory Network to develop routines using airborne spectroscopic imagery to predict multiple dimensions of plant biodiversity at continental scale across biomes in the US. Our findings show strong and positive associations between diversity metrics based on spectral species and ground-based plant species richness and other dimensions of plant diversity, whereas metrics based on distance matrices did not. We found that spectral diversity consistently predicts analogous metrics of plant taxonomic, functional, and phylogenetic dimensions of biodiversity across biomes. The approach demonstrates promise for monitoring dimensions of biodiversity globally by integrating ground-based measures of biodiversity with imaging spectroscopy and advances capacity toward a Global Biodiversity Observing System.
Why it matches plant phenotyping methods航空分光画像を用いて植物多様性を予測するルーチンを開発し、地上データとの関連を評価しており、植物状態の推定手法が研究の中心である。
abstractWe harness the National Ecological Observatory Network to develop routines using airborne spectroscopic imagery to predict multiple dimensions of plant biodiversity at continental scale across biomes in the US.
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 · publicR codes, including functions and examples, are available at Zenodo: https://doi.org/10.5281/zenodo.13983114Open asset ↗Zenodo · 10.5281/zenodo.13983114lines:153-226Code / dataset availability confirmedbioRxiv · Europe PMC · checked 7 Sept 2026
Pollen function is critical for successful plant reproduction and crop productivity and it is important to develop accessible methods to quantitatively analyze pollen performance to enhance reproductive resilience. Here we introduce TubeTracker as a method to quantify key parameters of pollen performance such as, time to pollen grain germination, pollen tube tip velocity and pollen tube survival. TubeTracker integrates manual and automatic image processing routines and the graphical user interface allows the user to interact with the software to make manual corrections of automated steps. TubeTracker does not depend on training data sets required to implement machine learning approaches and thus can be immediately implemented using readily available imaging systems. Furthermore, TubeTracker is an excellent tool to produce the pollen performance data sets necessary to take advantage of emerging AI-based methods to fully automate analysis. We tested TubeTracker and found it to be accurate in measuring pollen tube germination and pollen tube tip elongation across multiple cultivars of tomato. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=114 SRC="FIGDIR/small/624782v2_ufig1.gif" ALT="Figure 1"> View larger version (33K): org.highwire.dtl.DTLVardef@1fc2a63org.highwire.dtl.DTLVardef@42f3a2org.highwire.dtl.DTLVardef@18911d6org.highwire.dtl.DTLVardef@1f236f0_HPS_FORMAT_FIGEXP M_FIG Graphical Abstract Graphical user interface of TubeTracker showing all supported functionalities. C_FIG
Why it matches plant phenotyping methods植物の花粉管画像から発芽時間、伸長速度、生存性などの表現型を抽出するソフトウェア手法を開発し、複数トマト品種で精度検証しているため。
abstractHere we introduce TubeTracker as a method to quantify key parameters of pollen performance such as, time to pollen grain germination, pollen tube tip velocity and pollen tube survival.
Reproduction assets foundThe paper's authors publicly released TubeTracker, the Python software used to perform all automated pollen germination, elongation, and survival phenotyping measurements in this study, on GitHub with explicit availability language and a video sample for training.Code · publicWe further encourage users to independently
improve upon our tool and have provided the complete python code at
https://github.com/souonkap/TubeTracker, along with installation instructions and a video
sample for training purposes.Open asset ↗souonkap/TubeTrackerpdf-page:22 lines:1-44Code / dataset availability confirmedEurope PMC · checked 7 Sept 2026
Background Quantitative disease resistance (QR) is a complex, dynamic trait that is most reliably quantified in field-grown crops. Traditional disease assessments offer limited potential to disentangle the contributions of different components to overall QR at critical crop developmental stages. Yet, a better functional understanding of QR could greatly support a more targeted, knowledge-based selection for QR and improve predictions of seasonal epidemics. Image-based approaches together with advanced image processing methodologies recently emerged as valuable tools to standardize relevant disease assessments, increase measurement throughput, and describe diseases along multiple dimensions. Results We present a simple, affordable, and easy-to-operate imaging set-up and imaging procedure for in-field acquisition of wheat leaf image sequences. The development of Septoria tritici blotch and leaf rusts was monitored over time via robust methods for symptom detection and segmentation, spatial alignment of images, symptom tracking, and leaf- and symptom characterization. The average accuracy of the spatial alignment of images in a time series was approximately 5 pixels (~ 0.15 mm). Leaf-level symptom counts as well as individual symptom property measurements revealed stable patterns over time that were generally in excellent agreement with visual impressions. This provided strong evidence for the robustness of the methodology to variability typically inherent in field data. Contrasting patterns in the number of lesions resulting from separate infection events and lesion expansion dynamics were observed across wheat genotypes. The number of separate infection events and average lesion size contributed to different degrees to overall disease intensity, possibly indicating distinct and complementary mechanisms of QR. Conclusions The proposed methodology enables rapid, non-destructive, and reproducible measurement of several key epidemiological parameters under field conditions. Such data can support decomposition and functional understanding of QR as well as the parameterization, fine-tuning, and validation of epidemiological models. Details of pathogenesis can translate into specific symptom phenotypes resolvable using time series of high-resolution RGB images, which may improve biological understanding of plant-pathogen interactions as well as interactions in disease complexes.
Why it matches plant phenotyping methods圃場での植物病徴を画像から取得・追跡・定量する撮像および画像解析手法を開発・検証しており、植物表現型測定が中心である。
abstractWe present a simple, affordable, and easy-to-operate imaging set-up and imaging procedure for in-field acquisition of wheat leaf image sequences.
Reproduction assets foundThe paper explicitly states that all image-processing/analysis code is publicly available on the authors' GitHub repository, and that a sample dataset plus the trained reference mark detection model are downloadable from the ETH Research Collection. Both are paper-specific, public, and actionable.Code · publicAll code related to the processing of image time series and leaf- and lesion-level trait extraction is available from https://github.com/and-jonas/sympathique-wheat for documentation.Open asset ↗and-jonas/sympathique-wheatlines:98-107Dataset · publicA sample data set and the trained reference mark detection model can be downloaded from ETH research collection at https://doi.org/10.3929/ethz-b-000659812 .Open asset ↗10.3929/ethz-b-000659812lines:98-107Code / dataset availability confirmedEurope PMC · checked 14 Sept 2026
Understanding lignocellulosic biomass resistance to enzymatic deconstruction is crucial for its sustainable conversion into bioproducts. Despite scientific advances, quantitative morphological analysis of plant deconstruction at cell and tissue scales remains under-explored. In this study, an original pipeline is devised, involving four-dimensional (space + time) fluorescence confocal imaging, and a novel computational tool, to track and quantify deconstruction at cell and tissue scales. By applying this pipeline to poplar wood, dynamics of cellular parameters was computed and cellulose conversion during enzymatic deconstruction was measured. Results showed that enzymatic deconstruction predominantly impacts cell wall volume rather than surface area. Additionally, a negative correlation was observed between pre-hydrolysis compactness measures and volumetric cell wall deconstruction rate, whose strength was modulated by enzymatic activity. Results also revealed a strong positive correlation between average volumetric cell wall deconstruction rate and cellulose conversion rate. These findings link key deconstruction parameters across nano and micro scales.
Why it matches plant phenotyping methods植物細胞・組織の分解状態を定量する4次元蛍光共焦点イメージングと計算ツールが研究の中心であり、植物状態の形態的変化を抽出する方法を開発している。
abstractIn this study, an original pipeline is devised, involving four-dimensional (space + time) fluorescence confocal imaging, and a novel computational tool, to track and quantify deconstruction at cell and tissue scales.
Reproduction assets foundThe paper's WallTrack computational pipeline (used to track and quantify 4D confocal imaging of poplar cell wall deconstruction) is publicly available on the authors' FARE laboratory GitLab repository. The underlying imaging/phenotype data are not publicly deposited; the authors state data will be made available on.Code · publicnano and micro
scales.
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.
Data availability
The WallTrack code is accessible through the FARE laboratory
GitLab repository at: https://gitlab.com/farelab/teamyr/publications/refahi_et_al_4d. Data will be made available on request.
Acknowledgments
The authors thank Anouck Habrant for her help in confocal imaging
and Grégoire Malandain, Solmaz Hossein Khani, Khadidja Ould Amer,
and Ali Faraj for their comments on the manuscript. This work was
supported by Agence Nationale de la Recherche (ANR) Open asset ↗https://gitlab.com/farelab/teamyr/publications/refahi_et_al_4d · refahi_et_al_4dpdf-raw-page:11 lines:1-66Code / dataset availability confirmedEurope PMC · checked 15 Sept 2026
Arabidopsis thaliana sepals are excellent models for analyzing growth of entire organs due to their relatively small size, which can be captured at a cellular resolution under a confocal microscope. To investigate how differential growth of connected cell layers generate unique organ morphologies, it is necessary to live-image deep into the tissue. However, imaging deep cell layers of the sepal (or plant tissues in general) is practically challenging. Image processing is also difficult due to the low signal-to-noise ratio of the deeper tissue layers, an issue mainly associated with live imaging datasets. Addressing some of these challenges, we provide an optimized methodology for live imaging sepals, and subsequent image processing. For live imaging early-stage sepals, we found that the use of a bright fluorescent membrane marker, coupled with increased laser intensity and an enhanced Z- resolution produces high-quality images suitable for downstream image processing. Our optimized parameters allowed us to image the bottommost cell layer of the sepal (inner epidermal layer) without compromising viability. We used a 'voxel removal' technique to visualize the inner epidermal layer in MorphoGraphX image processing software. We also describe the MorphoGraphX parameters for creating a 2.5D mesh surface for the inner epidermis. Our parameters allow for the segmentation and parent tracking of individual cells through multiple time points, despite the weak signal of the inner epidermal cells. While we have used sepals to illustrate our approach, the methodology will be useful for researchers intending to live-image and track growth of deeper cell layers in 2.5D for any plant tissue.
Why it matches plant phenotyping methods植物組織の深部をライブイメージングし、画像処理・細胞セグメンテーション・追跡によって成長を解析する方法自体が中心的に開発・最適化されているため。
abstractwe provide an optimized methodology for live imaging sepals, and subsequent image processing.
Reproduction assets foundThe paper's Data availability statement deposits the study's datasets (live-imaging/phenotyping data underlying the sepal growth analysis) in two public OSF repositories with explicit DOIs, making them paper-specific, public, and actionable.Dataset · publicg and Michelle Heeney for their comments on the manuscript. We thank Richard Smith (John Innes Centre, UK) for his advice on image processing.
Data availability statement
The datasets presented in this study can be found in online repositories. The names of the repository/repositories and accession number(s) can be found below: https://doi.org/10.17605/OSF.IO/UMW9B , https://doi.org/10.17605/OSF.IO/P5Q39 .
Author contributions
AS: Conceptualization, Data curation, Formal analysis, Investigation, Methodology, Validation, Visualization, Writing – original draft, Writing – review & editing. AR: Conceptualization, Data curation, Funding acquisition, Project administration, Supervision, WritinOpen asset ↗OSF · 10.17605/OSF.IO/UMW9Blines:234-260Dataset · publicon the manuscript. We thank Richard Smith (John Innes Centre, UK) for his advice on image processing.
Data availability statement
The datasets presented in this study can be found in online repositories. The names of the repository/repositories and accession number(s) can be found below: https://doi.org/10.17605/OSF.IO/UMW9B , https://doi.org/10.17605/OSF.IO/P5Q39 .
Author contributions
AS: Conceptualization, Data curation, Formal analysis, Investigation, Methodology, Validation, Visualization, Writing – original draft, Writing – review & editing. AR: Conceptualization, Data curation, Funding acquisition, Project administration, Supervision, Writing – review & editing.
Conflict of intereOpen asset ↗OSF · 10.17605/OSF.IO/P5Q39lines:234-260Code / dataset availability confirmedarXiv · OpenAlex · checked 13 Sept 2026
AppleCitrusMangoPeachPearPlumField / plotLiDAR / point cloudRGB / grayscaleFruit
We introduce FruitNeRF, a unified novel fruit counting framework that leverages state-of-the-art view synthesis methods to count any fruit type directly in 3D. Our framework takes an unordered set of posed images captured by a monocular camera and segments fruit in each image. To make our system independent of the fruit type, we employ a foundation model that generates binary segmentation masks for any fruit. Utilizing both modalities, RGB and semantic, we train a semantic neural radiance field. Through uniform volume sampling of the implicit Fruit Field, we obtain fruit-only point clouds. By applying cascaded clustering on the extracted point cloud, our approach achieves precise fruit count.The use of neural radiance fields provides significant advantages over conventional methods such as object tracking or optical flow, as the counting itself is lifted into 3D. Our method prevents double counting fruit and avoids counting irrelevant fruit.We evaluate our methodology using both real-world and synthetic datasets. The real-world dataset consists of three apple trees with manually counted ground truths, a benchmark apple dataset with one row and ground truth fruit location, while the synthetic dataset comprises various fruit types including apple, plum, lemon, pear, peach, and mango.Additionally, we assess the performance of fruit counting using the foundation model compared to a U-Net.
Why it matches plant phenotyping methods果実を対象に、画像・NeRF・点群クラスタリングを組み合わせて3D果実数を推定する手法を開発し、実データおよび合成データで評価しているため、植物表現型取得法が中心である。
abstractWe introduce FruitNeRF, a unified novel fruit counting framework that leverages state-of-the-art view synthesis methods to count any fruit type directly in 3D.
Reproduction assets foundThe paper's real-world apple tree image dataset with manual ground-truth counts and synthetic Blender fruit tree data are publicly released via the project website, and the FruitNeRF analysis code is open-source on GitHub. The Zenodo DOI refers to the third-party BlenderNeRF plugin (cited tool), not a paper-specific.Dataset · publicThe data has been made publicly available, and visualizations can be accessed on the project website.Open asset ↗lines:183-221Code · publicFruitNeRF code: https://github.com/meyerls/FruitNeRF has been made open-source.Open asset ↗meyerls/FruitNeRFlines:74-108Code / dataset availability confirmedEurope PMC · checked 7 Sept 2026
ArabidopsisLeafTrackingArchitecture / morphology / geometryGrowth / development / phenology
Plant organs move throughout the diurnal cycle, changing leaf and petiole positions to balance light capture, leaf temperature, and water loss under dynamic environmental conditions. Upward movement of the petiole, called hyponasty, is one of several traits of the shade avoidance syndrome (SAS). SAS traits are elicited upon perception of vegetation shade signals such as far-red light (FR) and improve light capture in dense vegetation. Monitoring plant movement at a high temporal resolution allows studying functionality and molecular regulation of hyponasty. However, high temporal resolution imaging solutions are often very expensive, making this unavailable to many researchers. Here, we present a modular and low-cost imaging setup, based on small Raspberry Pi computers that can track leaf movements and elongation growth with high temporal resolution. We also developed an open-source, semiautomated image analysis pipeline. Using this setup, we followed responses to FR enrichment, light intensity, and their interactions. Tracking both elongation and the angle of the petiole, lamina, and entire leaf in Arabidopsis (Arabidopsis thaliana) revealed insight into R:FR sensitivities of leaf growth and movement dynamics and the interactions of R:FR with background light intensity. The detailed imaging options of this system allowed us to identify spatially separate bending points for petiole and lamina positioning of the leaf.
Why it matches plant phenotyping methods低コスト画像計測プラットフォームとオープンソース解析パイプラインを開発し、葉の伸長・運動・角度を高時間分解能で抽出する方法が研究の中心である。
abstractHere, we present a modular and low-cost imaging setup, based on small Raspberry Pi computers that can track leaf movements and elongation growth with high temporal resolution.
Reproduction assets foundThe paper's authors publicly deposited their Python image-analysis scripts and R analysis/statistical scripts at the Pierik-Lab GitHub organization, with explicit open-source availability language. The underlying phenotype data, however, is only available on request.Code · publicThe full, open-source scripts with descriptions per step are accessible at https://github.com/Pierik-Lab .Open asset ↗Pierik-Lablines:90-103Code / dataset availability confirmedCrossref · Europe PMC · checked 7 Sept 2026
Abstract Background The sweet potato whitefly ( Bemisia tabaci ) is a globally important insect pest that damages crops through direct feeding and by transmitting viruses. Current B. tabaci management revolves around the use of insecticides, which are economically and environmentally costly. Host plant resistance is a sustainable option to reduce the impact of whiteflies, but progress in deploying resistance in crops has been slow. A major obstacle is the high cost and low throughput of screening plants for B. tabaci resistance. Oviposition rate is a popular metric for host plant resistance to B. tabaci because it does not require tracking insect development through the entire life cycle, but accurate quantification is still limited by difficulties in observing B. tabaci eggs, which are microscopic and translucent. The goal of our study was to improve quantification of B. tabaci eggs on several important crop species: cassava, cowpea, melon, sweet potato and tomato. Results We tested a selective staining process originally developed for leafhopper eggs: submerging the leaves in McBryde’s stain (acetic acid, ethanol, 0.2% aqueous acid Fuchsin, water; 20:19:2:1) for three days, followed by clearing under heat and pressure for 15 min in clearing solution (LGW; lactic acid, glycerol, water; 17:20:23). With a less experienced individual counting the eggs, B. tabaci egg counts increased after staining across all five crops. With a more experienced counter, egg counts increased after staining on melons, tomatoes, and cowpeas. For all five crops, there was significantly greater agreement on egg counts across the two counting individuals after the staining process. The staining method worked particularly well on melon, where egg counts universally increased after staining for both counting individuals. Conclusions Selective staining aids visualization of B. tabaci eggs across multiple crop plants, particularly species where leaf morphological features obscure eggs, such as melons and tomatoes. This method is broadly applicable to research questions requiring accurate quantification of B. tabaci eggs, including phenotyping for B. tabaci resistance.
Why it matches plant phenotyping methods植物葉上のコナジラミ卵を染色して定量し、計数値と計数者間一致を改善する方法を評価しており、抵抗性フェノタイピングへの応用が明示された中心的な手法研究。
abstractThe goal of our study was to improve quantification of B. tabaci eggs on several important crop species: cassava, cowpea, melon, sweet potato and tomato.
Reproduction assets foundThe paper's Data availability statement explicitly deposits the study's egg-count datasets and the R Markdown analysis code in a Dryad repository, which is a public, paper-specific asset directly reproducing the phenotyping measurements and analysis.Dataset · publicThe datasets generated and analyzed during this study, and an R Markdown document containing the code used to perform these analyses are available in a Dryad repository (DOI: doi: https://doi.org/10.5061/dryad.vmcvdnd1m ).Open asset ↗Dryad · 10.5061/dryad.vmcvdnd1mlines:138-163Code / dataset availability confirmedEurope PMC · checked 15 Sept 2026
The technique of detecting and tracking an area's physical properties from a distance by measuring its reflected and emitted radiation is known as remote sensing. It gathered data accurately in near real-time. For this purpose, multispectral cameras mounted on UAVs that capture images with different bands can be used to generate vegetation indexes (NDVI, NDRE), which are useful in precision agriculture. In this study UAV image dataset contains 336 multispectral images from a 0.06 ha paddy field with three different phonological cycles of the crop (vegetative, reproductive, and ripening) in the north-western province of Sri Lanka. The selected sample rice variety is BG300. The images were taken over five days, starting from August 14 to October 5, 2023. The UAV flight took place at 30 m from the canopy level with the multispectral camera titled at an angle of 900. The SPAD Chlorophyll Meter was used to collect ground truth data, which is proportional to the nitrogen level of the leaf. There were 50 randomly selected readings throughout the paddy field. Relevant climate data for five days was provided by the Rice Research and Development Institute, Bathalagoda, which belongs to the paddy field. The purpose of this data creation was to aid researchers who are generally interested in disease diagnosis. Moreover, this dataset allows for studying the effect of using different tilt angles on the 3D reconstruction of the paddy fields and the generation of orthomosaics.
Why it matches plant phenotyping methodsUAVマルチスペクトル画像とSPADによる地上真値を含む、植物キャノピーの状態推定に再利用可能なデータセットであり、画像取得・オルソモザイク生成・3D再構成が中心的な方法的貢献です。
abstractIn this study UAV image dataset contains 336 multispectral images from a 0.06 ha paddy field with three different phonological cycles of the crop (vegetative, reproductive, and ripening)
Reproduction assets foundThe paper is a Data in Brief article describing a public Mendeley Data repository containing the authors' own UAV multispectral images, SPAD ground-truth readings, GPS shapefile, and climate data for paddy nitrogen phenotyping. This is a paper-specific, publicly available dataset with an explicit direct URL and DOI.Dataset · publicructions, the flight path was configured to fly on its own (DJI). The dataset includes a shapefile containing the GPS positions of the BG300 rice clusters. The same dates were used to gather SPAD meter values.
Data accessibility
Repository name: Mendeley Data
Data identification number: 10.17632/h8s5mn52j6.1
Direct URL to data: https://data.mendeley.com/datasets/h8s5mn52j6/1
Data source location
Institution: Rice Research and Development Institute
City/Town/Region: Batalagoda, Ibbagamuwa, Kurunegala
Country: Sri Lanaka
Latitude and longitude (and GPS coordinates) for collected samples/data: 7.53240 N, 80.43400E
1.
Value of the Data
•
Data is useful for researchers interested in UAV (unmannedOpen asset ↗Mendeley Data · 10.17632/h8s5mn52j6.1lines:1-80Code / dataset availability confirmedEurope PMC · checked 14 Sept 2026
The deployment of intelligent surveillance systems to monitor tomato plant growth poses substantial challenges due to the dynamic nature of disease patterns and the complexity of environmental conditions such as background and lighting. In this study, an integrated cascade framework that synergizes detectors and trackers was introduced for the simultaneous identification of tomato leaf diseases and fruit counting. We applied an autonomous robot with smartphone camera to collect images for leaf disease and fruits in greenhouses. Further, we improved the deep learning network YOLO-TGI by incorporating Ghost and CBAM modules, which was trained and tested in conjunction with premier lightweight detection models like YOLOX and NanoDet in evaluating leaf health conditions. For the cascading with various base detectors, we integrated state-of-the-art trackers such as Byte-Track, Motpy, and FairMot to enable fruit counting in video streams. Experimental results indicated that the combination of YOLO-TGI and Byte-Track achieved the most robust performance. Particularly, YOLO-TGI-N emerged as the model with the least computational demands, registering the lowest FLOPs at 2.05 G and checkpoint weights at 3.7 M, while still maintaining a mAP of 0.72 for leaf disease detection. Regarding the fruit counting, the combination of YOLO-TGI-S and Byte-Track achieved the best R 2 of 0.93 and the lowest RMSE of 9.17, boasting an inference speed that doubles that of the YOLOX series, and is 2.5 times faster than the NanoDet series. The developed network framework is a potential solution for researchers facilitating the deployment of similar surveillance models for a broad spectrum of fruit and vegetable crops.
Why it matches plant phenotyping methodsトマト葉の病害状態と果実数という植物形質を、ロボット撮影画像から検出・計数する深層学習および追跡フレームワークを開発・評価しており、表現型取得手法が中心である。
abstractan integrated cascade framework that synergizes detectors and trackers was introduced for the simultaneous identification of tomato leaf diseases and fruit counting.
Reproduction assets foundThe paper's greenhouse tomato leaf/fruit image dataset is publicly hosted on Roboflow, and the authors' analysis code (YOLO-TGI detection/tracking framework) is publicly available on GitHub. NanoDet is a cited third-party library, not a paper-specific asset.Code · publicssisted in the creation and programming of the deep learning networks. R.K. was responsible for drafting the manuscript and conducting all programming tasks, under the supervision of N.R. and S.S.
Competing interests: The authors declare that they have no competing interests.
Data Availability
Dataset and code can be reached at https://github.com/RuiKangnj/TGI/tree/main .
References
1. Dorais M, Ehret DL, Papadopoulos AP.Open asset ↗github.com/RuiKangnj/TGIlines:272-285Code / dataset availability confirmedEurope PMC · Crossref · checked 7 Sept 2026
Abstract Background. Quantitative disease resistance (QR) is a complex, dynamic trait that is most reliably quantified in field-grown crops. Traditional disease assessments offer limited potential to disentangle the contributions of different components to overall QR at critical crop developmental stages. Yet, a better functional understanding of QR could greatly support a more targeted, knowledge-based selection for QR and improve predictions of seasonal epidemics. Image-based approaches together with advanced image processing methodologies recently emerged as valuable tools to standardize relevant disease assessments, increase measurement throughput, and describe diseases along multiple dimensions. Results. We present a simple, affordable, and easy-to-operate imaging set-up and imaging procedure for in-field acquisition of wheat leaf image sequences. The development of Septoria tritici blotch and leaf rusts was monitored over time via robust methods for symptom detection and segmentation, image registration, symptom tracking, and leaf- and symptom characterization. The average accuracy of the co-registration of images in a time series was approximately 5 pixels (~ 0.15 mm). Leaf-level symptom counts as well as individual symptom property measurements revealed stable patterns over time that were generally in excellent agreement with visual impressions. This provided strong evidence for the robustness of the methodology to variability typically inherent in field data. Contrasting patterns in lesion numbers and lesion expansion dynamics were observed across wheat genotypes. The number of separate infection events and average lesion size contributed to different degrees to overall disease intensity, possibly indicating distinct and complementary mechanisms of QR. Conclusions. The proposed methodology enables rapid, non-destructive, and reproducible measurement of several key epidemiological parameters under natural field conditions. Such data can support decomposition and functional understanding of QR as well as the parameterization, fine-tuning, and validation of epidemiological models. Details of pathogenesis can translate into specific symptom phenotypes resolvable using time series of high-resolution RGB images, which may improve biological understanding of plant-pathogen interactions as well as interactions in disease complexes.
Why it matches plant phenotyping methods圃場での画像取得、症状検出・追跡・セグメンテーション、葉および病斑形質の定量化手法を開発・検証しており、植物病害表現型の取得が研究の中心です。
abstractWe present a simple, affordable, and easy-to-operate imaging set-up and imaging procedure for in-field acquisition of wheat leaf image sequences.
Reproduction assets foundThe paper explicitly states that all image-processing/analysis code is available at the authors' GitHub repository (and-jonas/sympathique-wheat), and that a sample data set plus the trained reference mark detection model can be downloaded from the ETH research collection (doi 10.3929/ethz-b-000659812). Both are paper-Code · publicAll code related to the processing of image time series and leaf- and lesion-level trait extraction is available from https://github.com/and-jonas/sympathique-wheat for documentation.Open asset ↗and-jonas/sympathique-wheatlines:80-87Dataset · publicA sample data set and the trained reference mark detection model can be downloaded from ETH research collection at https://doi.org/10.3929/ethz-b-000659812 .Open asset ↗10.3929/ethz-b-000659812lines:80-87Code / dataset availability confirmedEurope PMC · checked 14 Sept 2026
Published4 Feb 2024The Plant journal : for cell and molecular biologyCited by 0 · OpenAlex ↗
Confocal microscopy has greatly aided our understanding of the major cellular processes and trafficking pathways responsible for plant growth and development. However, a drawback of these studies is that they often rely on the manual analysis of a vast number of images, which is time-consuming, error-prone, and subject to bias. To overcome these limitations, we developed Dot Scanner, a Python program for analyzing the densities, lifetimes, and displacements of fluorescently tagged particles in an unbiased, automated, and efficient manner. Dot Scanner was validated by performing side-by-side analysis in Fiji-ImageJ of particles involved in cellulose biosynthesis. We found that the particle densities and lifetimes were comparable in both Dot Scanner and Fiji-ImageJ, verifying the accuracy of Dot Scanner. Dot Scanner largely outperforms Fiji-ImageJ, since it suffers far less selection bias when calculating particle lifetimes and is much more efficient at distinguishing between weak signals and background signal caused by bleaching. Not only does Dot Scanner obtain much more robust results, but it is a highly efficient program, since it automates much of the analyses, shortening workflow durations from weeks to minutes. This free and accessible program will be a highly advantageous tool for analyzing live-cell imaging in plants.
Why it matches plant phenotyping methods植物のライブセル画像から粒子密度・寿命・変位を自動抽出するソフトウェアを開発し、Fiji-ImageJと比較検証しており、表現型取得・解析手法が中心である。
abstractwe developed Dot Scanner, a Python program for analyzing the densities, lifetimes, and displacements of fluorescently tagged particles
Reproduction assets foundThe paper's own computational analysis tool, Dot Scanner (Python software for quantifying densities, lifetimes, and displacements of fluorescently labeled particles in plant tissues), is explicitly stated to be publicly available on GitHub with a full URL. No public phenotype/trait datasets or raw imaging data depositsCode · public= 2, blob size = 5, dot lower = 0.9, dot
upper = 4.5, and blob lower = 2, skips = 1, and remove edge
frames = false. All lifetimes that were 60 sec long were removed
from the analysis.
Dot scanner
Dot Scanner was developed using the Python programming lan-
guage. The software is available on GitHub, and the project home-
page (https://github.com/bdavis222/dotscanner) contains all the
documentation needed for its installation and use, including
the README file (https://github.com/bdavis222/dot-scanner/blob/main/README.md). As mentioned in the README, Python 3 must
be installed prior to Dot Scanner installation (https://www.python.org/downloads/).ACKNOWLEDGMENTS
We thank S. Bednarek for provOpen asset ↗bdavis222/dotscannerpdf-raw-page:9 lines:1-87Code / dataset availability confirmedEurope PMC · checked 15 Sept 2026
Abstract The actin cytoskeleton forms a dynamic network composed of filaments that remain flexible when bundled up, leading to complex filamentous structures in plant cells. Understanding the properties of these filamentous structures under different conditions and in different cell types can provide insight into their function. Yet, despite developments in the study of the plant actin cytoskeleton, it remains challenging to segment and identify actin filamentous structures, preventing quantification of their spatiotemporal properties. To address this problem, we devised a network-based approach termed Gra ph of F ilaments over T ime (GraFT) to trace and track filamentous structures in cytoskeleton networks extracted from imaging data. Our comparative analyses using both synthetic and real-world actin cytoskeleton networks of Arabidopsis thaliana hypocotyls exposed to different treatments demonstrated that GraFT accurately traces and tracks actin filamentous structures. Moreover, GraFT facilitates automated quantification of properties for filamentous structures, providing fine-grained insights of effects of different treatments on the level of individual structures. Therefore, GraFT offers a substantial step towards an automated framework facilitating robust spatiotemporal studies of the plant actin cytoskeleton.
Why it matches plant phenotyping methods植物細胞の画像からアクチン繊維構造を追跡・定量するGraFT手法を開発し、合成データと実画像で精度検証しており、表現型取得・抽出が研究の中心です。
abstractYet, despite developments in the study of the plant actin cytoskeleton, it remains challenging to segment and identify actin filamentous structures, preventing quantification of their spatiotemporal properties.
Reproduction assets foundThe preprint provides a public GitHub repository with the GraFT tool and data-processing code (MIT licensed), and states that all data files (the Arabidopsis actin cytoskeleton imaging datasets used for the phenotyping analyses) are deposited on Zenodo. The GitHub URL is an allowed URL; the Zenodo DOI is not among the,Code · publicuthors contributed to the discussion and manuscript preparation.
Competing interests
The authors declare no competing interests.
Availability of data
All data files can be found on Zenodo with DOI: 10.5281/zenodo.10476058
Code Availability
The tool GraFT and code created for data processing can be found on the GitHub repository https://github.com/Oesterlund/GraFT and is MIT licensed.
References
Østerlund, I., Persson, S. & Nikoloski, Z. Tracing and tracking filamentous structures across scales: A systematic review. Comput Struct Biotechnol J 21 , 452–462 (2023).
Takatani, S. et al. Microtubule Response to Tensile Stress Is Curbed by NEK6 to Buffer Growth Variation in the ArOpen asset ↗Oesterlund/GraFTlines:123-155Code / dataset availability confirmedEurope PMC · bioRxiv · checked 14 Sept 2026
ArabidopsisMesh / voxelMicroscopyFlowerSegmentationGrowth / time-series analysisTrackingGrowth / development / phenology
Background Arabidopsis thaliana sepals are excellent models for analyzing growth of entire organs due to their relatively small size, which can be captured at a cellular resolution under a confocal microscope [1]. To investigate how growth of different tissue layers generates unique organ morphologies, it is necessary to live-image deep into the tissue. However, imaging deep cell layers of the sepal is practically challenging, as it is hindered by the presence of extracellular air spaces between mesophyll cells, among other factors which causes optical aberrations. Image processing is also difficult due to the low signal-to-noise ratio of the deeper tissue layers, an issue mainly associated with live imaging datasets. Addressing some of these challenges, we provide an optimized methodology for live imaging sepals and subsequent image processing. This helps us track the growth of individual cells on the outer and inner epidermal layers, which are the key drivers of sepal morphogenesis. Results For live imaging sepals across all tissue layers at early stages of development, we found that the use of a bright fluorescent membrane marker, coupled with increased laser intensity and an enhanced Z-resolution produces high-quality images suitable for downstream image processing. Our optimized parameters allowed us to image the bottommost cell layer of the sepal (inner epidermal layer) without compromising viability. We used a ‘voxel removal’ technique to visualize the inner epidermal layer in MorphoGraphX [2, 3] image processing software. Finally, we describe the process of optimizing the parameters for creating a 2.5D mesh surface for the inner epidermis. This allowed segmentation and parent tracking of individual cells through multiple time points, despite the weak signal of the inner epidermal cells. Conclusion We provide a robust pipeline for imaging and analyzing growth across inner and outer epidermal layers during early sepal development. Our approach can potentially be employed for analyzing growth of other internal cell layers of the sepals as well. For each of the steps, approaches, and parameters we used, we have provided in-depth explanations to help researchers understand the rationale and replicate our pipeline.
Why it matches plant phenotyping methodsライブイメージング、画像処理、細胞セグメンテーションと追跡を統合した、萼片の成長・形態解析パイプラインの最適化が中心であり、植物表現型取得法に該当する。
abstractwe provide an optimized methodology for live imaging sepals and subsequent image processing
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 images of the WT flowers, as well as the final edited images can be accessed at https://doi.org/10.17605/OSF.IO/UMW9B . The images shown in this manuscript correspond to WT replicate 2.Open asset ↗OSF.IO/UMW9Blines:137-166Code / 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 confirmedEurope PMC · checked 14 Sept 2026
One of the fundamental questions in plant developmental biology is how cell proliferation and cell expansion coordinately determine organ growth and morphology. An amenable system to address this question is the Arabidopsis root tip, where cell proliferation and elongation occur in spatially separated domains, and cell morphologies can easily be observed using a confocal microscope. While past studies revealed numerous elements of root growth regulation including gene regulatory networks, hormone transport and signaling, cell mechanics and environmental perception, how cells divide and elongate under possible constraints from cell lineages and neighboring cell files has not been analyzed quantitatively. This is mainly due to the technical difficulties in capturing cell division and elongation dynamics at the tip of growing roots, as well as an extremely labor-intensive task of tracing the lineages of frequently dividing cells. Here, we developed a motion-tracking confocal microscope and an Artificial Intelligence (AI)-assisted image-processing pipeline that enables semi-automated quantification of cell division and elongation dynamics at the tip of vertically growing Arabidopsis roots. We also implemented a data sonification tool that facilitates human recognition of cell division synchrony. Using these tools, we revealed previously unnoted lineage-constrained dynamics of cell division and elongation, and their contribution to the root zonation boundaries.
Why it matches plant phenotyping methods生長中のシロイヌナズナ根端における細胞分裂・伸長という植物形態動態を、4D顕微鏡、AI画像処理、追跡、データソニフィケーションで半自動定量する手法を開発しており、フェノタイピング手法が中心である。
abstractHere, we developed a motion-tracking confocal microscope and an Artificial Intelligence (AI)-assisted image-processing pipeline that enables semi-automated quantification of cell division and elongation dynamics at the tip of vertically growing Arabidopsis roots.
Reproduction assets found保存済みの本文根拠を更新済みルールで再検証し、公開資産1件を確認しました。Code · publicThe codes for the nuclei detection and cell tracking are available on the GitHub ( https://github.com/JerrySongCST/Arabidopsis_root_cortex_cell_tracking ).Open asset ↗JerrySongCST/Arabidopsis_root_cortex_cell_trackinglines:152-227Code / dataset availability confirmedEurope PMC · checked 7 Sept 2026
ArabidopsisLaboratory / benchtopRGB / grayscaleRootTrackingRoot system architecture
Plant roots elongate when cells produced in the apical meristem enter a transient period of rapid expansion. To measure the dynamic process of root cell expansion in the elongation zone, we captured digital images of growing Arabidopsis roots with horizontal microscopes and analyzed them with a custom image analysis program (PatchTrack) designed to track the growth-driven displacement of many closely spaced image patches. Fitting a flexible logistics equation to patch velocities plotted versus position along the root axis produced the length of the elongation zone (mm), peak relative elemental growth rate (% h -1 ), the axial position of the peak (mm from the tip), and average root elongation rate (mm h -1 ). For a wild-type root, the average values of these kinematic traits were 0.52 mm, 23.7% h -1 , 0.35 mm, and 0.1 mm h -1 , respectively. We used the platform to determine the kinematic phenotypes of auxin transport mutants. The results support a model in which the PIN2 auxin transporter creates an area of expansion-suppressing, supraoptimal auxin concentration that ends 0.1 mm from the quiescent center (QC), and that ABCB4 and ABCB19 auxin transporters maintain expansion-limiting suboptimal auxin levels beginning approximately 0.5 mm from the QC. This study shows that PatchTrack can quantify dynamic root phenotypes in kinematic terms.
Why it matches plant phenotyping methodsPatchTrackによる画像解析で根の動的な伸長・細胞伸長形質を抽出する手法とプラットフォームを開発・実証しており、表現型取得が研究の中心です。
abstractanalyzed them with a custom image analysis program (PatchTrack) designed to track the growth-driven displacement of many closely spaced image patches.
Reproduction assets foundThe paper's Data Availability Statement explicitly deposits the authors' PatchTrack image-analysis code (used to produce the kinematic phenotyping measurements) on a public GitHub repository. No phenotype dataset or image deposit is stated.Code · publicThe computer code for PatchTrack is available at https://github.com/phytoMorph/phytoMorph_kinematics .Open asset ↗phytoMorph/phytoMorph_kinematicslines:96-111Code / dataset availability confirmedOpenAlex · Europe PMC · Crossref · checked 15 Sept 2026
Abstract Background Significant effort has been made in manually tracking plant maturity and to measure early-stage plant density, and crop height in experimental breeding plots. Agronomic traits such as relative maturity (RM), stand count (SC) and plant height (PH) are essential to cultivar development, production recommendations and management practices. The use of RGB images collected via drones may replace traditional measurements in field trials with improved throughput, accuracy, and reduced cost. Recent advances in deep learning (DL) approaches have enabled the development of automated high-throughput phenotyping (HTP) systems that can quickly and accurately measure target traits using low-cost RGB drones. In this study, a time series of drone images was employed to estimate dry bean relative maturity (RM) using a hybrid model combining Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM) for features extraction and capturing the sequential behavior of time series data. The performance of the Faster-RCNN object detection algorithm was also examined for stand count (SC) assessment during the early growth stages of dry beans. Various factors, such as flight frequencies, image resolution, and data augmentation, along with pseudo-labeling techniques, were investigated to enhance the performance and accuracy of DL models. Traditional methods involving pre-processing of images were also compared to the DL models employed in this study. Moreover, plant architecture was analyzed to extract plant height (PH) using digital surface model (DSM) and point cloud (PC) data sources. Results The CNN-LSTM model demonstrated high performance in predicting the RM of plots across diverse environments and flight datasets, regardless of image size or flight frequency. The DL model consistently outperformed the pre-processing images approach using traditional analysis (LOESS and SEG models), particularly when comparing errors using mean absolute error (MAE), providing less than two days of error in prediction across all environments. When growing degree days (GDD) data was incorporated into the CNN-LSTM model, the performance improved in certain environments, especially under unfavorable environmental conditions or weather stress. However, in other environments, the CNN-LSTM model performed similarly to or slightly better than the CNN-LSTM + GDD model. Consequently, incorporating GDD may not be necessary unless weather conditions are extreme. The Faster R-CNN model employed in this study was successful in accurately identifying bean plants at early growth stages, with correlations between the predicted SC and ground truth (GT) measurements of 0.8. The model performed consistently across various flight altitudes, and its accuracy was better compared to traditional segmentation methods using pre-processing images in OpenCV and the watershed algorithm. An appropriate growth stage should be carefully targeted for optimal results, as well as precise boundary box annotations. On average, the PC data source marginally outperformed the CSM/DSM data to estimating PH, with average correlation results of 0.55 for PC and 0.52 for CSM/DSM. The choice between them may depend on the specific environment and flight conditions, as the PH performance estimation is similar in the analyzed scenarios. However, the ground and vegetation elevation estimates can be optimized by deploying different thresholds and metrics to classify the data and perform the height extraction, respectively. Conclusions The results demonstrate that the CNN-LSTM and Faster R-CNN deep learning models outperforms other state-of-the-art techniques to quantify, respectively, RM and SC. The subtraction method proposed for estimating PH in the absence of accurate ground elevation data yielded results comparable to the difference-based method. In addition, open-source software developed to conduct the PH and RM analyses can contribute greatly to the phenotyping community.
Why it matches plant phenotyping methodsRGBドローン画像と深層学習を用いて、乾燥豆の成熟期、株数、草高を推定する手法を開発・比較・検証しており、表現型取得が研究の中心である。
abstractThe use of RGB images collected via drones may replace traditional measurements in field trials with improved throughput, accuracy, and reduced cost.
Reproduction assets foundThe preprint explicitly states that the authors' open-source phenotyping software (RM, SC, PH pipelines) is available on GitHub, with specific tools (matuRity, Vegetation index calculator, PlantHeightR, draw-plots-qgis) hosted at public URLs, and that the datasets (orthomosaics, shapefiles, ground notes, clipped plots,Code · publicle 2: Data S1). The GCPs were input and identified into the Pix4D project using the basic manual editor before initial processing.
R [ 57 ] software integrated with QGIS [ 58 ] was used to generate the polygon shapefiles according to plot boundary delimitation using the function ‘Draw plots from clicks’ available at https://github.com/diegojgris/draw-plots-qgis (Fig. 1 -b). Shapefiles were defined using images collected from the first flight available from each location. GDAL (Geospatial Data Abstraction Library) tool plugin in QGIS was used to spatial polygon vectors (or shapefiles) adjustments with a buffer zone for each plot to prevent any influence of neighboring plots. AdditOpen asset ↗diegojgris/draw-plots-qgislines:82-143Code · public3
4. DISCUSSION
The available open source HTP tools, matuRity [ 69 ], PlantHeightR [ 93 ], and Vegetation index calculator provided in this study, have the potential to facilitate and increase the data analysis performance in plant breeding and related areas. The user can either access them on-line or download the repository at https://github.com/msudrybeanbreeding?tab=repositories . Additionally, the step-by-step pipelines deployed in this study using DL methods are available at the GitHub repositories, as well as the complete data set used to perform the analysis including orthomosaics, shapefiles, ground notes, clipped plots, and programming codes. Thus, researchers may be able to replicaOpen asset ↗msudrybeanbreedinglines:572-648Code / dataset availability confirmedOpenAlex · arXiv · checked 15 Sept 2026
GreenhouseWhole plant / canopy / plot / fieldTrackingGrowth / development / phenologyWater status / transpiration
The AlphaGarden is an automated testbed for indoor polyculture farming which combines a first-order plant simulator, a gantry robot, a seed planting algorithm, plant phenotyping and tracking algorithms, irrigation sensors and algorithms, and custom pruning tools and algorithms. In this paper, we systematically compare the performance of the AlphaGarden to professional horticulturalists on the staff of the UC Berkeley Oxford Tract Greenhouse. The humans and the machine tend side-by-side polyculture gardens with the same seed arrangement. We compare performance in terms of canopy coverage, plant diversity, and water consumption. Results from two 60-day cycles suggest that the automated AlphaGarden performs comparably to professional horticulturalists in terms of coverage and diversity, and reduces water consumption by as much as 44%. Code, videos, and datasets are available at https://sites.google.com/berkeley.edu/systematiccomparison.
Why it matches plant phenotyping methods自動栽培プラットフォームに植物フェノタイピング・追跡アルゴリズムが組み込まれ、キャノピー被覆率や植物多様性を用いて性能比較しているため、フェノタイピング手法の実質的な応用・評価が中心的です。
abstractThe AlphaGarden is an automated testbed for indoor polyculture farming which combines a first-order plant simulator, a gantry robot, a seed planting algorithm, plant phenotyping and tracking algorithms, irrigation sensors and algorithms, and custom pruning tools and algorithms.
Reproduction assets foundThe paper explicitly states that code, videos, and datasets from the AlphaGarden vs. horticulturalist comparison (including time-lapse data from 120 days of physical experiments) are publicly available at the authors' project site.Code · publicormance in terms of canopy coverage, plant diversity, and water consumption. Results from two 60-day cycles suggest that the automated AlphaGarden performs comparably to professional horticulturalists in terms of coverage and diversity, and reduces water consumption by as much as 44%.
Code, videos, and datasets are available at https://sites.google.com/berkeley.edu/systematiccomparison .
I Introduction
In 1950, Alan Turing considered the question “Can Machines Think?” and proposed a test based on comparing human vs. machine ability to answer questions.
In this paper, we consider the question “Can Machines Garden?” based on comparing human vs. machine ability to tend a real polyculture gardenOpen asset ↗lines:1-83Code / dataset availability confirmedEurope PMC · checked 7 Sept 2026
With the increasing popularity of online fruit sales, accurately predicting fruit yields has become crucial for optimizing logistics and storage strategies. However, existing manual vision-based systems and sensor methods have proven inadequate for solving the complex problem of fruit yield counting, as they struggle with issues such as crop overlap and variable lighting conditions. Recently CNN-based object detection models have emerged as a promising solution in the field of computer vision, but their effectiveness is limited in agricultural scenarios due to challenges such as occlusion and dissimilarity among the same fruits. To address this issue, we propose a novel variant model that combines the self-attentive mechanism of Vision Transform, a non-CNN network architecture, with Yolov7, a state-of-the-art object detection model. Our model utilizes two attention mechanisms, CBAM and CA, and is trained and tested on a dataset of apple images. In order to enable fruit counting across video frames in complex environments, we incorporate two multi-objective tracking methods based on Kalman filtering and motion trajectory prediction, namely SORT, and Cascade-SORT. Our results show that the Yolov7-CA model achieved a 91.3% mAP and 0.85 F1 score, representing a 4% improvement in mAP and 0.02 improvement in F1 score compared to using Yolov7 alone. Furthermore, three multi-object tracking methods demonstrated a significant improvement in MAE for inter-frame counting across all three test videos, with an 0.642 improvement over using yolov7 alone achieved using our multi-object tracking method. These findings suggest that our proposed model has the potential to improve fruit yield assessment methods and could have implications for decision-making in the fruit industry.
Why it matches plant phenotyping methodsリンゴ果実を画像から検出・追跡して収量（果実数）を推定する手法を開発・評価しており、植物表現型取得が研究の中心です。
titleFruit Detection and Counting in Apple Orchards Based on Improved Yolov7 and Multi-Object Tracking Methods.
Reproduction assets foundThe paper's apple detection/counting dataset was assembled from publicly available sources, and the Data Availability Statement explicitly links the public tropical fruit dataset of Pawara et al. used as image input. No author analysis code, trained model checkpoints, or paper-specific supplements are disclosed.Dataset · publicPublicly available datasets were analyzed in this study. This data can be found here: https://www.ai.rug.nl/~p.pawara/ (accessed on 23 May 2023).Open asset ↗lines:234-247Code / dataset availability confirmedOpenAlex · Europe PMC · Crossref · checked 7 Sept 2026
Plant traits are informative for ecosystem functions and processes and help to derive general rules and predictions about responses to environmental gradients, global change and perturbations. Ecological field studies often use 'low-throughput' methods to assess plant phenotypes and integrate species-specific traits to community-wide indices. In contrast, agricultural greenhouse or lab-based studies often employ 'high-throughput phenotyping' to assess plant individuals tracking their growth or fertilizer and water demand. In ecological field studies, remote sensing makes use of freely movable devices like satellites or unmanned aerial vehicles (UAVs) which provide large-scale spatial and temporal data. Adopting such methods for community ecology on a smaller scale may provide novel insights on the phenotypic properties of plant communities and fill the gap between traditional field measurements and airborne remote sensing. However, the trade-off between spatial resolution, temporal resolution and scope of the respective study requires highly specific setups so that the measurements fit the scientific question. We introduce small-scale, high-resolution digital automated phenotyping as a novel source of quantitative trait data in ecological field studies that provides complementary multi-faceted data of plant communities. We customized an automated plant phenotyping system for its mobile application in the field for 'digital whole-community phenotyping' (DWCP), capturing the 3-dimensional structure and multispectral information of plant communities. We demonstrated the potential of DWCP by recording plant community responses to experimental land-use treatments over two years. DWCP captured changes in morphological and physiological community properties in response to mowing and fertilizer treatments and thus reliably informed about changes in land-use. In contrast, manually measured community-weighted mean traits and species composition remained largely unaffected and were not informative about these treatments. DWCP proved to be an efficient method for characterizing plant communities, complements other methods in trait-based ecology, provides indicators of ecosystem states, and may help to forecast tipping points in plant communities often associated with irreversible changes in ecosystems.
Why it matches plant phenotyping methods植物群集の3次元構造とマルチスペクトル情報を取得する自動フェノタイピングシステムをフィールド用に改変・実証しており、植物表現型取得法が研究の中心である。
abstractWe introduce small-scale, high-resolution digital automated phenotyping as a novel source of quantitative trait data in ecological field studies
Reproduction assets foundThe paper's Data availability statement points to a public repository DOI (10.17616/R32P9Q, a re3data registry DOI) for the datasets presented in this study, which include the DWCP scan-derived morphological/physiological parameters, manual trait measurements, and vegetation data. No author analysis code or trained模型的公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: http://doi.org/10.17616/R32P9Q.Open asset ↗10.17616/R32P9Qpdf-page:11 lines:1-61Code / dataset availability confirmedEurope PMC · checked 7 Sept 2026
As human population continue to increase, our food production system is challenged. With tomatoes as the main indoor produced fruit, the selection of adapter varieties to each specific condition and higher yields is an imperative task if we wish to supply the growing demand of coming years. To help farmers and researchers in the task of phenotyping, we here present a study case of the A gro s cope ph en otyping tool (ASPEN) in tomato under indoor conditions. We prove that using the ASPEN pipeline it is possible to obtain real time in situ yield estimation not only in a commercial-like greenhouse level but also within growing line. To discuss our results, we analyse the two main steps of the pipeline in a desktop computer: object detection and tracking, and yield prediction. Thanks to the use of YOLOv5, we reach a mean average precision for all categories of 0.85 at interception over union 0.5 with an inference time of 8 ms, who together with the best multiple object tracking (MOT) tested allows to reach a 0.97 correlation value compared with the real harvest number of tomatoes and a 0.91 correlation when considering yield thanks to the usage of a SLAM algorithm. Moreover, the ASPEN pipeline demonstrated to predict also the sub following harvests. Confidently, our results demonstrate in situ size and quality estimation per fruit, which could be beneficial for multiple users. To increase accessibility and usage of new technologies, we make publicly available the required hardware material and software to reproduce this pipeline, which include a dataset of more than 850 relabelled images for the task of tomato object detection and the trained YOLOv5 model[1] [1]https://github.com/camilochiang/aspen
Why it matches plant phenotyping methodsASPENはトマト果実の検出・追跡から収量、果実サイズ、品質を推定する画像ベースの表現型解析パイプラインであり、手法の評価と再現可能なソフトウェア・データセット提供が中心です。
abstractwe here present a study case of the A gro s cope ph en otyping tool (ASPEN) in tomato under indoor conditions.
Reproduction assets foundThe authors explicitly make publicly available the ASPEN pipeline software/hardware materials, a dataset of 850+ relabelled tomato images, and the trained YOLOv5 model via their GitHub repository.Dataset · publicThe dataset supporting the conclusions of this article is available in the github repository (https://github.com/camilochiang/aspen).Open asset ↗github.com/camilochiang/aspenlines:119-149Code / dataset availability confirmedEurope PMC · checked 14 Sept 2026
MaizeTissueSegmentationGrowth / time-series analysisTrackingGrowth / development / phenology
The emergence timing of a plant, i.e., the time at which the plant is first visible from the surface of the soil, is an important phenotypic event and is an indicator of the successful establishment and growth of a plant. The paper introduces a novel deep-learning based model called EmergeNet with a customized loss function that adapts to plant growth for coleoptile (a rigid plant tissue that encloses the first leaves of a seedling) emergence timing detection. It can also track its growth from a time-lapse sequence of images with cluttered backgrounds and extreme variations in illumination. EmergeNet is a novel ensemble segmentation model that integrates three different but promising networks, namely, SEResNet, InceptionV3, and VGG19, in the encoder part of its base model, which is the UNet model. EmergeNet can correctly detect the coleoptile at its first emergence when it is tiny and therefore barely visible on the soil surface. The performance of EmergeNet is evaluated using a benchmark dataset called the University of Nebraska-Lincoln Maize Emergence Dataset (UNL-MED). It contains top-view time-lapse images of maize coleoptiles starting before the occurrence of their emergence and continuing until they are about one inch tall. EmergeNet detects the emergence timing with 100% accuracy compared with human-annotated ground-truth. Furthermore, it significantly outperforms UNet by generating very high-quality segmented masks of the coleoptiles in both natural light and dark environmental conditions.
Why it matches plant phenotyping methodsコレオプタイルの出芽時期と成長を画像から抽出する深層学習セグメンテーション手法を開発し、ベンチマークデータセットで性能検証しているため、植物フェノタイピング手法が中心である。
abstractThe performance of EmergeNet is evaluated using a benchmark dataset called the University of Nebraska-Lincoln Maize Emergence Dataset (UNL-MED).
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 dataset can be freely downloaded from https://plantvision.unl.edu/dataset .Open asset ↗lines:324-339Code / dataset availability confirmedEurope PMC · checked 7 Sept 2026
Background Live imaging is the gold standard for determining how cells give rise to organs. However, tracking many cells across whole organs over large developmental time windows is extremely challenging. In this work, we provide a comparably simple method for confocal live imaging entire Arabidopsis thaliana first leaves across early development. Our imaging method works for both wild-type leaves and the complex curved leaves of the jaw-1D mutant. Results We find that dissecting the cotyledons, affixing a coverslip above the samples and mounting samples with perfluorodecalin yields optimal imaging series for robust cellular and organ level analysis. We provide details of our complementary image processing steps in MorphoGraphX software for segmenting, tracking lineages, and measuring a suite of cellular properties. We also provide MorphoGraphX image processing scripts we developed to automate analysis of segmented images and data presentation. Conclusions Our imaging techniques and processing steps combine into a robust imaging pipeline. With this pipeline we are able to examine important nuances in the cellular growth and differentiation of jaw-D versus WT leaves that have not been demonstrated before. Our pipeline is approachable and easy to use for leaf development live imaging.
Why it matches plant phenotyping methodsArabidopsis葉の生細胞イメージング、画像処理、細胞追跡・形質測定を統合した再利用可能な表現型解析パイプラインの開発が中心である。
abstractIn this work, we provide a comparably simple method for confocal live imaging entire Arabidopsis thaliana first leaves across early development.
Reproduction assets foundThe paper publicly deposits its live-imaging datasets (confocal imaging data for the figures) on OSF under CC-BY 4.0, and its MorphoGraphX/R analysis scripts on the authors' GitHub repositories, all explicitly linked in the Availability of data and materials section.Dataset · publicData for Figs. 1 , 2 , 3 , 4 , 5 A, B is available at https://doi.org/10.17605/OSF.IO/V2TKWOpen asset ↗OSF · 10.17605/OSF.IO/V2TKWlines:139-172Dataset · publicData for Figs. 5 C, 6 and 7 is available at https://doi.org/10.17605/OSF.IO/D7X3YOpen asset ↗OSF · 10.17605/OSF.IO/D7X3Ylines:139-172Code · publicavailable at https://github.com/kateharline/live_img_paper , https://github.com/kateharline/roeder_lab_projects/tree/master/imagej_scripts and https://github.com/kateharline/jawd-paperOpen asset ↗github.com/kateharline/roeder_lab_projectslines:139-172Code · publicavailable at https://github.com/kateharline/live_img_paper , https://github.com/kateharline/roeder_lab_projects/tree/master/imagej_scripts and https://github.com/kateharline/jawd-paperOpen asset ↗github.com/kateharline/jawd-paperlines:139-172Code / dataset availability confirmedEurope PMC · checked 14 Sept 2026
Counting the number of grape bunches at an early stage of development offers relevant information to the winegrower about the potential yield to be harvested. However, manual counting on the fields is laborious and time-consuming. Remote sensing, and more precisely unmanned aerial vehicles mounted with RGB or multispectral cameras, facilitate this task rapidly and accurately. This dataset contains 40 RGB videos from a 1.06-ha vineyard located in northern Spain. Moreover, the dataset includes mask labels of visible grape bunches. The videos were acquired throughout four UAV flights with an RGB camera tilted at 60 degrees. Each flight recorded one side of a row of the vineyard. The grape berries were between pea-size (BBCH75) and bunch closure (BBCH79) stage, which is two months before harvesting. No operations other than those usual in a commercial vineyard, such as pruning, cane tying, fertilization, and pest treatment, have been carried out, hence, the dataset presents leaf occlusion. The dataset was gathered and labelled to train object detection and tracking algorithms for grape bunch counting. Furthermore, it eases the work of winegrowers to check the sanitary status of the vineyard.
Why it matches plant phenotyping methodsブドウ房数という植物の収量関連形質をUAV画像から推定するための、ラベル付き動画データセットであり、物体検出・追跡手法の開発を支援する中心的な成果である。
abstractThis dataset contains 40 RGB videos from a 1.06-ha vineyard located in northern Spain. Moreover, the dataset includes mask labels of visible grape bunches.
Reproduction assets foundThis Data in Brief article describes its own public dataset: 40 UAV RGB videos over a vineyard with grape bunch mask annotations (MOTS-style PNG labels) for object detection/tracking and phenotyping, deposited on Zenodo with an explicit direct URL and DOI. This is a paper-specific, publicly available, directly reproducDataset · publice location
Institution: Wageningen University & Research
City/Town/Region: Tomiño, Pontevedra, Galicia
Country: Spain
Latitude and longitude (and GPS coordinates) for collected samples/data:
41°57′18.3″N 8°47′41.9″W
Data accessibility
Repository name: Zenodo
Data identification number: 10.5281/zenodo.7330951
Direct URL to data: https://zenodo.org/record/7330951#.Y3tU3nbMKUk
Related research article
Ariza-Sentís, M., Vélez, S., Baja, H., & Valente, J. (2022). IPPS 2022 Conference Book . 231.
Value of the Data
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Dataset is useful for researchers interested in instance segmentation, as it allows the detection and tracking of the clusters [2] .
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Dataset can be employed to count the number of viOpen asset ↗Zenodo · 10.5281/zenodo.7330951lines:1-68Code / dataset availability confirmedEurope PMC · checked 8 Sept 2026
RiceLaboratory / benchtopX-ray / CTRootImage / point-cloud registrationGrowth / time-series analysisTrackingGrowth / development / phenologyRoot system architecture
Background Root system architecture (RSA) is an essential characteristic for efficient water and nutrient absorption in terrestrial plants; its plasticity enables plants to respond to different soil environments. Better understanding of root plasticity is important in developing stress-tolerant crops. Non-invasive techniques that can measure roots in soils nondestructively, such as X-ray computed tomography (CT), are useful to evaluate RSA plasticity. However, although RSA plasticity can be measured by tracking individual root growth, only a few methods are available for tracking individual roots from time-series three-dimensional (3D) images. Results We developed a semi-automatic workflow that tracks individual root growth by vectorizing RSA from time-series 3D images via two major steps. The first step involves 3D alignment of the time-series RSA images by iterative closest point registration with point clouds generated by high-intensity particles in potted soils. This alignment ensures that the time-series RSA images overlap. The second step consists of backward prediction of vectorization, which is based on the phenomenon that the root length of the RSA vector at the earlier time point is shorter than that at the last time point. In other words, when CT scanning is performed at time point A and again at time point B for the same pot, the CT data and RSA vectors at time points A and B will almost overlap, but not where the roots have grown. We assumed that given a manually created RSA vector at the last time point of the time series, all RSA vectors except those at the last time point could be automatically predicted by referring to the corresponding RSA images. Using 21 time-series CT volumes of a potted plant of upland rice (Oryza sativa), this workflow revealed that the root elongation speed increased with age. Compared with a workflow that does not use backward prediction, the workflow with backward prediction reduced the manual labor time by 95%. Conclusions We developed a workflow to efficiently generate time-series RSA vectors from time-series X-ray CT volumes. We named this workflow 'RSAtrace4D' and are confident that it can be applied to the time-series analysis of RSA development and plasticity.
Why it matches plant phenotyping methods根系形態を時系列X線CT画像から抽出・追跡する半自動ワークフローを開発しており、植物フェノタイピング手法が研究の中心である。
abstractWe developed a semi-automatic workflow that tracks individual root growth by vectorizing RSA from time-series 3D images via two major steps.
Reproduction assets foundThe paper's authors publicly released RSAtrace4D, the software implementing the backward-prediction workflow for time-series X-ray CT root system architecture analysis, on GitHub, and state that the datasets used are available via their GitHub account and project homepage.Code · publicThe implementation of this workflow, which is specified for rice, was named RSAtrace4D and is available at the GitHub repository ( https://github.com/st707311g/RSAtrace4D ).Open asset ↗st707311g/RSAtrace4Dlines:116-124Dataset · publicThe datasets used in this study are available at the GitHub repository ( https://github.com/st707311g/ ) and the project homepage ( https://rootomics.dna.affrc.go.jp/en/ ).Open asset ↗st707311glines:136-191Code / dataset availability confirmedOpenAlex · Europe PMC · Crossref · checked 8 Sept 2026
BACKGROUND: High-throughput phenotyping platforms allow the study of the form and function of a large number of genotypes subjected to different growing conditions (GxE). A number of image acquisition and processing pipelines have been developed to automate this process, for micro-plots in the field and for individual plants in controlled conditions. Capturing shoot development requires extracting from images both the evolution of the 3D plant architecture as a whole, and a temporal tracking of the growth of its organs. RESULTS: We propose PhenoTrack3D, a new pipeline to extract a 3D + t reconstruction of maize. It allows the study of plant architecture and individual organ development over time during the entire growth cycle. The method tracks the development of each organ from a time-series of plants whose organs have already been segmented in 3D using existing methods, such as Phenomenal [Artzet et al. in BioRxiv 1:805739, 2019] which was chosen in this study. First, a novel stem detection method based on deep-learning is used to locate precisely the point of separation between ligulated and growing leaves. Second, a new and original multiple sequence alignment algorithm has been developed to perform the temporal tracking of ligulated leaves, which have a consistent geometry over time and an unambiguous topological position. Finally, growing leaves are back-tracked with a distance-based approach. This pipeline is validated on a challenging dataset of 60 maize hybrids imaged daily from emergence to maturity in the PhenoArch platform (ca. 250,000 images). Stem tip was precisely detected over time (RMSE < 2.1 cm). 97.7% and 85.3% of ligulated and growing leaves respectively were assigned to the correct rank after tracking, on 30 plants × 43 dates. The pipeline allowed to extract various development and architecture traits at organ level, with good correlation to manual observations overall, on random subsets of 10-355 plants. CONCLUSIONS: We developed a novel phenotyping method based on sequence alignment and deep-learning. It allows to characterise the development of maize architecture at organ level, automatically and at a high-throughput. It has been validated on hundreds of plants during the entire development cycle, showing its applicability on GxE analyses of large maize datasets.
Why it matches plant phenotyping methodsトウモロコシ器官の3D時系列形態を抽出・追跡する新規パイプラインを開発し、大規模データセットで精度検証しているため、植物フェノタイピング手法が中心である。
abstractWe propose PhenoTrack3D, a new pipeline to extract a 3D + t reconstruction of maize.
Reproduction assets foundThe paper's own PhenoTrack3D pipeline (source code and examples) is publicly available on GitHub under an open-source licence. Phenomenal (GitHub/Zenodo) is cited prior work used as an input pipeline, not a paper-specific asset; no public phenotype dataset or trained model deposit is stated.Code · publicThe source code and examples are available on Github ( https://github.com/openalea/phenotrack3d ) under an Open Source licence (Cecill-C).Open asset ↗openalea/phenotrack3dlines:189-246Code / dataset availability confirmedEurope PMC · bioRxiv · checked 15 Sept 2026
Live imaging is the gold standard for determining how cellular development gives rise to organs. However, tracking all individual cells across whole organs over large developmental time windows is extremely challenging. In this work, we provide a comparably simple method for confocal live imaging of Arabidopsis thaliana first leaves across early development. Our imaging method works for both wild-type leaves and the complex curved leaves of the jaw-1D mutant. We find that dissecting the cotyledons, affixing a coverslip above the samples and mounting samples with perfluorodecalin yields optimal imaging series for robust cellular and organ level analysis. We provide details of our complementary image processing steps in MorphGraphX software for segmenting cells, tracking the cell lineages, and measuring a suite of cellular growth properties. We also provide MorphoGraphX image processing scripts that we developed to automate analysis of segmented images and data presentation. Our imaging techniques and processing steps combine into a robust imaging pipeline. With this pipeline we are able to examine important nuances in the cellular growth and differentiation of jaw-D versus WT leaves that have not been demonstrated before. Our pipeline is a practical starting place for researchers new to live imaging plant leaves, but also to anyone interested in improving the throughput and reliability of their live imaging process.
Why it matches plant phenotyping methods葉全体の共焦点ライブイメージング、細胞セグメンテーション・系譜追跡・成長特性測定を統合した実用的な表現型解析パイプラインの開発であり、方法が研究の中心です。
abstractIn this work, we provide a comparably simple method for confocal live imaging of Arabidopsis thaliana first leaves across early development.
Reproduction assets foundThe paper's phenotyping analysis code is publicly available in authors' GitHub repositories: MorphoGraphX processing/quantification scripts (iterative_growth_and_measures.py, multi_resize.py, batch_tiff.py) in roeder_lab_projects/mgx_scripts, ImageJ scripts, and R analysis/figure scripts in live_img_paper and jawdPaperCode · publice heat map representations of the
data with standardized parameters across time point comparisons and replicates (Video 4).
Data analysis
All data processing, analysis and plotting was performed in RStudio (2020; 2021). Scripts used
to process the data and create figures are enclosed as Supplemental Information and available at
https://github.com/kateharline/live_img_paper, https://github.com/kateharline/roeder_lab_proj-ects/tree/master/imagej_scripts and https://github.com/kateharline/jawd-paper.Data availability
Imaging data will be deposited XXXX.
Funding
Kate Harline was supported by NSF Graduate Research Fellowship (DGE-1650441). This work
was funded by NSF MCB-2203275 (AHKR), The SchwaOpen asset ↗kateharline/live_img_paperpdf-raw-page:20 lines:1-63Code · publicndardized parameters across time point comparisons and replicates (Video 4).
Data analysis
All data processing, analysis and plotting was performed in RStudio (2020; 2021). Scripts used
to process the data and create figures are enclosed as Supplemental Information and available at
https://github.com/kateharline/live_img_paper, https://github.com/kateharline/roeder_lab_proj-ects/tree/master/imagej_scripts and https://github.com/kateharline/jawd-paper.Data availability
Imaging data will be deposited XXXX.
Funding
Kate Harline was supported by NSF Graduate Research Fellowship (DGE-1650441). This work
was funded by NSF MCB-2203275 (AHKR), The Schwartz Research Fund Award (AHKR), and
the NationaOpen asset ↗kateharline/roeder_lab_proj-ectspdf-raw-page:20 lines:1-63Code · publicnalysis
All data processing, analysis and plotting was performed in RStudio (2020; 2021). Scripts used
to process the data and create figures are enclosed as Supplemental Information and available at
https://github.com/kateharline/live_img_paper, https://github.com/kateharline/roeder_lab_proj-ects/tree/master/imagej_scripts and https://github.com/kateharline/jawd-paper.Data availability
Imaging data will be deposited XXXX.
Funding
Kate Harline was supported by NSF Graduate Research Fellowship (DGE-1650441). This work
was funded by NSF MCB-2203275 (AHKR), The Schwartz Research Fund Award (AHKR), and
the National Institute Of General Medical Sciences of the National Institutes of Health under
Open asset ↗kateharline/jawd-paperpdf-raw-page:20 lines:1-63Code / dataset availability confirmedbioRxiv · checked 8 Sept 2026
Background information to give context to the studyCanopy gaps are the most evident manifestation of how disturbances disrupt forest landscapes. The size distribution and return frequency of gaps, and subsequent recovery processes, determine whether the old-growth state can be reached. The aim or research questionWe used remote sensing metrics to compare the disturbance regime of four Amazon regions based on the size distribution of gaps, their dynamics and geometric characteristics. A brief summary of the methodology usedWe assessed gap dynamics at four sites in the central, central eastern, southeastern, and northeastern regions of the Brazilian Amazon using repeated airborne laser scanning surveys. We developed a novel analysis to quantify four possible stages of gap dynamics: formation, expansion, persisting and recovering. For that, we overlapped layers of gap locations from two consecutive airborne laser scanning surveys. Key results with some significance measuresThe gap fraction in our study sites varied between 1.26% to 7.84%. All the sites have similar proportion of gaps among size classes. What notably changed between sites was not the gap size-distribution, but the relative importance of stages of gap dynamics. Growing and persisting rates were greatest in the site with the stronger seasonal variation in climate, lower annual precipitation, higher mean wind speed and higher solar radiation. The conclusions, which address the main aimsThe concept of stability reflects the tendency of a system to quickly return to a position of equilibrium when disturbed. We showed that gap dynamics varied among sites, with one example of low recovery rate contrasted to three other sites with faster recovery. Our results support that such as assessing the size distribution of gaps, investigating their return frequency and severity is crucial for understanding forest dynamics at the landscape and regional scales.
Why it matches plant phenotyping methods航空レーザースキャンの反復データを用いて森林キャノピーギャップの動態を定量化する新規解析法を開発しており、植物キャノピー状態の抽出が研究の中心である。
abstractWe developed a novel analysis to quantify four possible stages of gap dynamics: formation, expansion, persisting and recovering.
Reproduction assets foundThe paper's core phenotyping input — repeated airborne laser scanning data for the four Amazon study sites (Ducke, Tapajos, Tanguro, Jari) from the Sustainable Landscapes Brazil project — is explicitly stated to be freely available at the EMBRAPA Paisagens Lidar webgis portal. No author analysis code, scripts, or gap-dDataset · publicThe Sustainable Landscape Brazil Project has repeatedly surveyed Amazonian sites
with airborne laser scanning. This data set gave us a unique opportunity to assess gap
dynamics across Amazonia (data freely available at:
https://www.paisagenslidar.cnptia.embrapa.br/webgis/).Open asset ↗pdf-page:4 lines:1-46Code / dataset availability confirmedEurope PMC · checked 8 Sept 2026
Mitochondria form highly dynamic populations in the cells of plants (and almost all eukaryotes). The characteristics and benefits of this collective behaviour, and how it is influenced by nuclear features, remain to be fully elucidated. Here, we use a recently developed quantitative approach to reveal and analyse the physical and collective 'social' dynamics of mitochondria in an Arabidopsis msh1 mutant where the organelle DNA maintenance machinery is compromised. We use a newly created line combining the msh1 mutant with mitochondrially targeted green fluorescent protein (GFP), and characterize mitochondrial dynamics with a combination of single-cell time-lapse microscopy, computational tracking, and network analysis. The collective physical behaviour of msh1 mitochondria is altered from that of the wild type in several ways: mitochondria become less evenly spread, and networks of inter-mitochondrial encounters become more connected, with greater potential efficiency for inter-organelle exchange-reflecting a potential compensatory mechanism for the genetic challenge to the mitochondrial DNA population, supporting more inter-organelle exchange. We find that these changes are similar to those observed in friendly, where mitochondrial dynamics are altered by a physical perturbation, suggesting that this shift to higher connectivity may reflect a general response to mitochondrial challenges, where physical dynamics of mitochondria may be altered to control the genetic structure of the mtDNA population.
Why it matches plant phenotyping methods植物ミトコンドリアの動態を、タイムラプス顕微鏡・計算追跡・ネットワーク解析で定量化する手法の実質的な適用であり、単なる生物学的ルーチン測定ではない。
abstractcharacterize mitochondrial dynamics with a combination of single-cell time-lapse microscopy, computational tracking, and network analysis.
Reproduction assets foundThe paper states that all data and analysis code for the mitochondrial dynamics phenotyping are publicly available on the authors' GitHub repository, which matches an allowed URL.Code · publicAll data and analysis codes are available from Github at https://github.com/StochasticBiology/plant-mito-dynamicsOpen asset ↗StochasticBiology/plant-mito-dynamicslines:109-163Code / dataset availability confirmedOpenAlex · Europe PMC · bioRxiv · checked 8 Sept 2026
Abstract Background High-throughput phenotyping platforms allow the study of the form and function of a large number of genotypes subjected to different growing conditions (GxE). A number of image acquisition and processing pipelines have been developed to automate this process, for micro-plots in the field and for individual plants in controlled conditions. Capturing shoot development requires extracting from images both the evolution of the 3D plant architecture as a whole, and a temporal tracking of the growth of its organs. Results We propose PhenoTrack3D, a new pipeline to extract a 3D+t reconstruction of maize at organ level from plant images. It allows the study of plant architecture and individual organ development over time during the entire growth cycle. PhenoTrack3D improves a former method limited to 3D reconstruction at a single time point [Artzet et al ., 2019] by (i) a novel stem detection method based on deep-learning and (ii) a new and original multiple sequence alignment method to perform the temporal tracking of ligulated leaves. Our method exploits both the consistent geometry of ligulated leaves over time and the unambiguous topology of the stem axis. Growing leaves are tracked afterwards with a distance-based approach. This pipeline is validated on a challenging dataset of 60 maize hybrids imaged daily from emergence to maturity in the PhenoArch platform (ca. 250,000 images). Stem tip was precisely detected over time (RMSE < 2.1cm). 97.7% and 85.3% of ligulated and growing leaves respectively were assigned to the correct rank after tracking, on 30 plants x 43 dates. The pipeline allowed to extract various development and architecture traits at organ level, with good correlation to manual observations overall, on random subsets of 10 to 355 plants. Conclusions We developed a novel phenotyping method based on sequence alignment and deep-learning. It allows to characterise automatically and at a high-throughput the development of maize architecture at organ level. It has been validated for hundreds of plants during the entire development cycle, showing its applicability to the GxE analyses of large maize datasets.
Why it matches plant phenotyping methodsトウモロコシ器官の3D再構成・時系列追跡による表現型抽出パイプラインを開発し、大規模データセットで技術検証しており、方法が研究の中心である。
abstractWe propose PhenoTrack3D, a new pipeline to extract a 3D+t reconstruction of maize at organ level from plant images.
Reproduction assets foundThe paper explicitly states that the PhenoTrack3D pipeline source code and examples are publicly available on GitHub under an Open Source licence (Cecill-C). This is the authors' analysis code for the paper's maize phenotyping pipeline. No public phenotype dataset or trained model checkpoint URL is stated in the blocksCode · publicThe source code and examples are available on Github
(https://github.com/openalea/phenotrack3d) under an Open Source licence (Cecill-C).Open asset ↗openalea/phenotrack3dpdf-page:28 lines:1-62Code / dataset availability confirmedEurope PMC · Crossref · checked 15 Sept 2026
Background Unmanned aerial vehicle (UAV)-based image retrieval in modern agriculture enables gathering large amounts of spatially referenced crop image data. In large-scale experiments, however, UAV images suffer from containing a multitudinous amount of crops in a complex canopy architecture. Especially for the observation of temporal effects, this complicates the recognition of individual plants over several images and the extraction of relevant information tremendously. Results In this work, we present a hands-on workflow for the automatized temporal and spatial identification and individualization of crop images from UAVs abbreviated as "cataloging" based on comprehensible computer vision methods. We evaluate the workflow on 2 real-world datasets. One dataset is recorded for observation of Cercospora leaf spot-a fungal disease-in sugar beet over an entire growing cycle. The other one deals with harvest prediction of cauliflower plants. The plant catalog is utilized for the extraction of single plant images seen over multiple time points. This gathers a large-scale spatiotemporal image dataset that in turn can be applied to train further machine learning models including various data layers. Conclusion The presented approach improves analysis and interpretation of UAV data in agriculture significantly. By validation with some reference data, our method shows an accuracy that is similar to more complex deep learning-based recognition techniques. Our workflow is able to automatize plant cataloging and training image extraction, especially for large datasets.
Why it matches plant phenotyping methodsUAV画像から個体を時空間的に同定・個別化し、植物画像データセットを抽出するコンピュータビジョン手法が研究の中心であり、精度検証も行っている。
abstractwe present a hands-on workflow for the automatized temporal and spatial identification and individualization of crop images from UAVs
Reproduction assets foundThe paper's authors publicly released their plant cataloging workflow code on GitHub and deposited a supporting subset of the sugar beet UAV image data with code snapshots in GigaDB (10.5524/102225). The GitHub repository URL is in the allowed list; the GigaDB DOI is not, so only the code asset is listed with an exact-Code · publicponding data. By automatizing the plant cataloging and providing a data framework, our work helps to exploit the full potential of UAV imaging in agricultural contexts.
Availability of Source Code
The source code of our workflow is available in the following repository:
Project name: Plant Cataloging Workflow
GitHub repository: https://github.com/mrcgndr/plant_cataloging_workflow
RRID: SCR_022276
Operating system(s): Platform independent (with conda), Linux (with Docker)
Programming language: Python (3.9 or higher)
License: Apache License 2.0
Data Availability
A subset of the sugar beet data is available in order to run the workflow and reproduce our results. The data have been uploaded to tOpen asset ↗https://github.com/mrcgndr/plant_cataloging_workflowlines:172-190Code / dataset availability confirmedOpenAlex · Europe PMC · checked 14 Sept 2026
BACKGROUND: Although quantitative single-cell analysis is frequently applied in animal systems, e.g. to identify novel drugs, similar applications on plant single cells are largely missing. We have exploited the applicability of high-throughput microscopic image analysis on plant single cells using tobacco leaf protoplasts, cell-wall free single cells isolated by lytic digestion. Protoplasts regenerate their cell wall within several days after isolation and have the potential to expand and proliferate, generating microcalli and finally whole plants after the application of suitable regeneration conditions. RESULTS: High-throughput automated microscopy coupled with the development of image processing pipelines allowed to quantify various developmental properties of thousands of protoplasts during the initial days following cultivation by immobilization in multi-well-plates. The focus on early protoplast responses allowed to study cell expansion prior to the initiation of proliferation and without the effects of shape-compromising cell walls. We compared growth parameters of wild-type tobacco cells with cells expressing the antiapoptotic protein Bcl2-associated athanogene 4 from Arabidopsis (AtBAG4). CONCLUSIONS: AtBAG4-expressing protoplasts showed a higher proportion of cells responding with positive area increases than the wild type and showed increased growth rates as well as increased proliferation rates upon continued cultivation. These features are associated with reported observations on a BAG4-mediated increased resilience to various stress responses and improved cellular survival rates following transformation approaches. Moreover, our single-cell expansion results suggest a BAG4-mediated, cell-independent increase of potassium channel abundance which was hitherto reported for guard cells only. The possibility to explain plant phenotypes with single-cell properties, extracted with the single-cell processing and analysis pipeline developed, allows to envision novel biotechnological screening strategies able to determine improved plant properties via single-cell analysis.
Why it matches plant phenotyping methods植物プロトプラストの成長・増殖特性を大量画像から抽出する自動顕微鏡解析と画像処理パイプラインを開発・適用しており、表現型取得手法が研究の中心である。
abstractHigh-throughput automated microscopy coupled with the development of image processing pipelines allowed to quantify various developmental properties of thousands of protoplasts
Reproduction assets found保存済みの本文根拠を更新済みルールで再検証し、公開資産1件を確認しました。Code · publicAll the scripts and codes used in the analysis pipeline, additional downloaded plugins used in processing the images as well as sample data can be found in our Github page https://github.com/jodawson/cell_seg_tracking_analysis .Open asset ↗jodawson/cell_seg_tracking_analysislines:143-159Code / dataset availability confirmedbioRxiv · Europe PMC · checked 15 Sept 2026
BackgroundMethods to accurately quantify disease severity are fundamental to plant pathogen interaction studies. Commonly used methods include visual scoring of disease symptoms, tracking pathogen growth in planta over time, and various assays that detect plant defense responses. Several image-based methods for phenotyping of plant disease symptoms have also been developed. Each of these methods has different advantages and limitations which should be carefully considered when choosing an approach and interpreting the results. ResultsIn this paper, we developed two image analysis methods and tested their ability to quantify different aspects of disease lesions in the cassava-Xanthomonas pathosystem. The first method uses ImageJ, an open-source platform widely used in the biological sciences. The second method is a few-shot support vector machine learning tool that uses a classifier file trained with five representative infected leaf images for lesion recognition. Cassava leaves were syringe infiltrated with wildtype Xanthomonas, a Xanthomonas mutant with decreased virulence, and mock treatments. Digital images of infected leaves were captured overtime using a Raspberry Pi camera. The image analysis methods were analyzed and compared for the ability to segment the lesion from the background and accurately capture and measure differences between the treatment types. ConclusionsBoth image analysis methods presented in this paper allow for accurate segmentation of disease lesions from the non-infected plant. Specifically, at 4-, 6-, and 9-days post inoculation (DPI), both methods provided quantitative differences in disease symptoms between different treatment types. Thus, either method could be applied to extract information about disease severity. Strengths and weaknesses of each approach are discussed.
Why it matches plant phenotyping methodsカシャバの病斑を画像から分割・定量する2手法を開発し、処理間の病徴・病害重症度の測定性能を比較検証しており、植物表現型取得が中心である。
abstractIn this paper, we developed two image analysis methods and tested their ability to quantify different aspects of disease lesions in the cassava-Xanthomonas pathosystem.
Reproduction assets foundThe paper deposits its phenotype measurement datasets and custom R analysis scripts on figshare, and documents the machine learning phenotyping workflow (PhenotyperCV) with a public GitHub wiki URL containing the workflow and software download instructions.Dataset · public9
● CSV: Comma separated plain text file
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Declarations:
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Ethics approval and consent to participate: Not applicable
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Consent for publication: Not applicable
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Availability of data and materials:
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The datasets and custom R scripts generated and/or analyzed in this study are
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available in the figshare repository, https://figshare.com/s/0148e5e4fc7f220ac4c3
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Competing interests: The authors declare that they have no competing interests
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Funding:
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National Science Foundation GRFP DGE-2139839 and DGE-1745038 (KE)
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Bill and Melinda Gates Foundation OPP1125410 (RBS)
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Authors' contributions
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.
CC-BY-NC-ND 4.0 International license
available under a
was not certifieOpen asset ↗figsharepdf-raw-page:19 lines:1-45Code · publicned leaf image was converted to a binary mask and referred to as the
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“labeled image”. The machine learning image analysis tool is part of PhenotyperCV, a
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C++11 header-only library designed for image-based plant phenotyping. The machine
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learning workflow and software download instructions are available on GitHub
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(https://github.com/jberry47/ddpsc_phenotypercv/wiki/Machine-Learning-Workflow).369
All steps of the machine learning workflow were run on the Mac terminal command line.
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The labeled leaf mask image and original combined leaf graphic were used to create a
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support vector machine learning classifier or YAML file. Individual images of inoculated
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cassava leaves Open asset ↗github · jberry47/ddpsc_phenotypercvpdf-raw-page:17 lines:1-55Code / dataset availability confirmedbioRxiv · OpenAlex · Europe PMC · checked 8 Sept 2026
The first draft of the Arabidopsis genome was released more than 20 years ago and despite intensive molecular research, more than 30% of Arabidopsis genes remained uncharacterized or without an assigned function. This is in part due to gene redundancy within gene families or the essential nature of genes, where their deletion results in lethality (i.e., the dark genome). High-throughput plant phenotyping (HTPP) offers an automated and unbiased approach to characterize subtle or transient phenotypes resulting from gene redundancy or inducible gene silencing; however, commercial HTPP platforms remain unaffordable. Here we describe the design and implementation of OPEN leaf, an open-source HTPP system with cloud connectivity and remote bilateral communication to facilitate data collection, sharing and processing. OPEN leaf, coupled with the SMART imaging processing package was able to consistently document and quantify dynamic morphological changes over time at the whole rosette level and also at leaf-specific resolution when plants experienced changes in nutrient availability. The modular design of OPEN leaf allows for additional sensor integration. Notably, our data demonstrate that VIS sensors remain underutilized and can be used in high-throughput screens to identify characterize previously unidentified phenotypes in a leaf-specific manner. Significance StatementMany bottlenecks exist in high-throughput phenotyping involving computing power for processing and a lack of focus on abiotic stresses that has prevented an advancement in phenotyping on par with genotyping. Therefore, we create an automated HTP system that performs nutrient studies on Arabidopsis thaliana with cloud-based image processing that quantifies plant traits at a whole and leaf-level.
Why it matches plant phenotyping methodsOPEN leafは、葉単位の形態形質を画像から自動取得・定量するオープンソース高スループット表現型解析システムの設計・実装が中心であり、明確に収載対象です。
abstractHere we describe the design and implementation of OPEN leaf, an open-source HTPP system with cloud connectivity and remote bilateral communication to facilitate data collection, sharing and processing.
Reproduction assets foundThe paper's image-analysis pipeline (SMART) used for rosette and leaf-specific phenotyping is explicitly released as public source code on GitHub and as a prepackaged Docker container. Phenotype data tables are only in supplementary material without a public URL, and other code repos (OPEN Controller, OPEN-leaf-cloud)'Code · public209 available as source code on GitHub (https://github.com/Computational-Open asset ↗pdf-page:8 lines:1-41Code / dataset availability confirmedEurope PMC · checked 14 Sept 2026
ArabidopsisLaboratory / benchtopRootMorphology / geometry measurementTrackingGrowth / development / phenologyRoot system architecture
Directional root growth control is crucial for plant fitness. The degree of root growth deviation depends on several factors, whereby exogenous growth conditions have a profound impact. The perception of mechanical impedance by wild-type roots results in the modulation of root growth traits, and it is known that gravitropic stimulus influences distinct root movement patterns in concert with mechanoadaptation. Mutants with reduced shootward auxin transport are described as being numb towards mechanostimulus and gravistimulus, whereby different growth conditions on agar-supplemented medium have a profound effect on how much directional root growth and root movement patterns differ between wild types and mutants. To reduce the impact of unilateral mechanostimulus on roots grown along agar-supplemented medium, we compared the root movement of Col-0 and auxin resistant 1-7 in a root penetration assay to test how both lines adjust the growth patterns of evenly mechanostimulated roots. We combined the assay with the D-root system to reduce light-induced growth deviation. Moreover, the impact of sucrose supplementation in the growth medium was investigated because exogenous sugar enhances root growth deviation in the vertical direction. Overall, we observed a more regular growth pattern for Col-0 but evaluated a higher level of skewing of aux1-7 compared to the wild type than known from published data. Finally, the tracking of the growth rate of the gravistimulated roots revealed that Col-0 has a throttling elongation rate during the bending process, but aux1-7 does not.
Why it matches plant phenotyping methodsD-rootシステムと根貫通アッセイを組み合わせ、根の成長パターン・伸長速度を追跡して評価する測定ワークフローが研究の中心であるため。
abstractWe combined the assay with the D-root system to reduce light-induced growth deviation.
Reproduction assets found保存済みの本文根拠を更新済みルールで再検証し、公開資産1件を確認しました。Supplement · publicThe following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/plants11050650/s1 , Table S1: raw data.Open asset ↗lines:48-103Code / dataset availability confirmedCrossref · Europe PMC · checked 8 Sept 2026
Abstract Background In recent years, there has been an increase of interest in plant behaviour as represented by growth-driven responses. These are generally classified into nastic (internally driven) and tropic (environmentally driven) movements. Nastic movements include circumnutations, a circular movement of plant organs commonly associated with search and exploration, while tropisms refer to the directed growth of plant organs toward or away from environmental stimuli, such as light and gravity. Tracking these movements is therefore fundamental for the study of plant behaviour. Convolutional neural networks, as used for human and animal pose estimation, offer an interesting avenue for plant tracking. Here we adopted the Social LEAP Estimates Animal Poses (SLEAP) framework for plant tracking. We evaluated it on time-lapse videos of cases spanning a variety of parameters, such as: (i) organ types and imaging angles (e.g., top-view crown leaves vs. side-view shoots and roots), (ii) lighting conditions (full spectrum vs. IR), (iii) plant morphologies and scales (100 μm-scale Arabidopsis seedlings vs. cm-scale sunflowers and beans), and (iv) movement types (circumnutations, tropisms and twining). Results Overall, we found SLEAP to be accurate in tracking side views of shoots and roots, requiring only a low number of user-labelled frames for training. Top views of plant crowns made up of multiple leaves were found to be more challenging, due to the changing 2D morphology of leaves, and the occlusions of overlapping leaves. This required a larger number of labelled frames, and the choice of labelling “skeleton” had great impact on prediction accuracy, i.e., a more complex skeleton with fewer individuals (tracking individual plants) provided better results than a simpler skeleton with more individuals (tracking individual leaves). Conclusions In all, these results suggest SLEAP is a robust and versatile tool for high-throughput automated tracking of plants, presenting a new avenue for research focusing on plant dynamics.
Why it matches plant phenotyping methods植物の成長運動を抽出するため、SLEAPを植物追跡へ適応し、多様な器官・撮像条件・形態・運動で精度を評価している。植物表現型取得手法が中心である。
abstractHere we adopted the Social LEAP Estimates Animal Poses (SLEAP) framework for plant tracking.
Reproduction assets foundThe paper's Availability of data and materials statement points to a public Zenodo deposit containing the paper-specific time-lapse videos, SLEAP .slp labelled training files, and predicted output analysis files used in this study.Dataset · publicThe datasets during and/or analysed during the current study available at: https://zenodo.org/record/5764169#.YbCK0_FBxqt , https://doi.org/10.5281/zenodo.5764169 , which includes: (1) raw videos of the timelapse for each analysis. (2) The.slp files for each video analysis, which can be loaded into SLEAP and contain the 5, 10 or 20 labelled training frames.Open asset ↗zenodo · 10.5281/zenodo.5764169lines:134-177Code / dataset availability confirmedEurope PMC · checked 14 Sept 2026
Fruit yield estimation is crucial for establishing fruit harvest and marketing strategies. Recently, computer vision and deep learning techniques have been used to estimate citrus fruit yield and have exhibited notable fruit detection ability. However, computer-vision-based citrus fruit counting has two key limitations: inconsistent fruit detection accuracy and double-counting of the same fruit. Using oranges as the experimental material, this paper proposes a deep-learning-based orange counting algorithm using video sequences to help overcome these problems. The algorithm consists of two sub-algorithms, OrangeYolo for fruit detection and OrangeSort for fruit tracking. The OrangeYolo backbone network is partially based on the YOLOv3 algorithm, which has been improved upon to detect small objects (fruits) at multiple scales. The network structure was adjusted to detect small-scale targets while enabling multiscale target detection. A channel attention and spatial attention multiscale fusion module was introduced to fuse the semantic features of the deep network with the shallow textural detail features. OrangeYolo can achieve mean Average Precision (mAP) values of 0.957 in the citrus dataset, higher than the 0.905, 0.911, and 0.917 achieved with the YOLOv3, YOLOv4, and YOLOv5 algorithms. OrangeSort was designed to alleviate the double-counting problem associated with occluded fruits. A specific tracking region counting strategy and tracking algorithm based on motion displacement estimation were established. Six video sequences taken from two fields containing 22 trees were used as the validation dataset. The proposed method showed better performance (Mean Absolute Error (MAE) = 0.081, Standard Deviation (SD) = 0.08) than video-based manual counting and produced more accurate results than the existing standards Sort and DeepSort (MAE = 0.45 and 1.212; SD = 0.4741 and 1.3975).
Why it matches plant phenotyping methods柑橘果実の検出・追跡により樹上果実数（収量推定に使う形質）を定量する画像解析手法を開発し、既存手法および手動計数と検証・比較しており、植物フェノタイピング手法が中心です。
abstractthis paper proposes a deep-learning-based orange counting algorithm using video sequences to help overcome these problems.
Reproduction assets foundThe authors publicly released the annotated orange fruit image dataset used to train and test OrangeYolo on GitHub, with explicit data availability statement. A supplementary tracking example video is also on YouTube. No analysis code release is stated.Dataset · publicW.W., and Y. S. collected field data with self-designed field rover. All authors discussed, wrote the manuscript, and gave final approval for publication.
Data availability
The dataset used during this study is available in a repository in accordance with funder data retention policies. We have published the dataset at GitHub ( https://github.com/I3-Laboratory/orange-dataset ).
Conflict of interest statement
The authors declare that they have no conflicts of interest.
Supplementary data
Supplementary data is available at Horticulture Research online.
Supplementary Material
Web_Material_uhac003
Click here for additional data file.
Reference
1.
Anderson
NT
, Walsh KB , Wulfsohn D
.
TechnologieOpen asset ↗GitHub · I3-Laboratory/orange-datasetlines:986-1102Code / dataset availability confirmedEurope PMC · Crossref · checked 13 Sept 2026
Arabidopsis (Arabidopsis thaliana) primary and lateral roots (LRs) are well suited for 3D and 4D microscopy, and their development provides an ideal system for studying morphogenesis and cell proliferation dynamics. With fast-advancing microscopy techniques used for live-imaging, whole tissue data are increasingly available, yet present the great challenge of analyzing complex interactions within cell populations. We developed a plugin "Live Plant Cell Tracking" (LiPlaCeT) coupled to the publicly available ImageJ image analysis program and generated a pipeline that allows, with the aid of LiPlaCeT, 4D cell tracking and lineage analysis of populations of dividing and growing cells. The LiPlaCeT plugin contains ad hoc ergonomic curating tools, making it very simple to use for manual cell tracking, especially when the signal-to-noise ratio of images is low or variable in time or 3D space and when automated methods may fail. Performing time-lapse experiments and using cell-tracking data extracted with the assistance of LiPlaCeT, we accomplished deep analyses of cell proliferation and clonal relations in the whole developing LR primordia and constructed genealogical trees. We also used cell-tracking data for endodermis cells of the root apical meristem (RAM) and performed automated analyses of cell population dynamics using ParaView software (also publicly available). Using the RAM as an example, we also showed how LiPlaCeT can be used to generate information at the whole-tissue level regarding cell length, cell position, cell growth rate, cell displacement rate, and proliferation activity. The pipeline will be useful in live-imaging studies of roots and other plant organs to understand complex interactions within proliferating and growing cell populations. The plugin includes a step-by-step user manual and a dataset example that are available at https://www.ibt.unam.mx/documentos/diversos/LiPlaCeT.zip.
Why it matches plant phenotyping methods植物の4Dライブイメージングから細胞系譜・位置・長さ・成長率などの形態・成長表現型を抽出する解析プラグインとパイプラインの開発が中心である。
abstractWe developed a plugin "Live Plant Cell Tracking" (LiPlaCeT) coupled to the publicly available ImageJ image analysis program and generated a pipeline that allows, with the aid of LiPlaCeT, 4D cell tracking and lineage analysis of populations of dividing and growing cells.
Reproduction assets foundThe paper's LiPlaCeT Fiji plugin for 4D plant cell tracking is publicly available: source code on GitHub and an ImageJ plugin package including a dataset example and user manual on the authors' IBT-UNAM site.Code · publicThe source code is freely available at https://github.com/paul-hernandez-herrera/LiPlaCeT and the ImageJ plugin including a dataset example and the User Manual can be downloaded from https://www.ibt.unam.mx/documentos/diversos/LiPlaCeT.zip .Open asset ↗paul-hernandez-herrera/LiPlaCeTlines:203-225Code / dataset availability confirmedEurope PMC · checked 15 Sept 2026
In plants, secretion of cell wall components and membrane proteins plays a fundamental role in growth and development as well as survival in diverse environments. Exocytosis, as the last step of the secretory trafficking pathway, is a highly ordered and precisely controlled process involving tethering, docking, and fusion of vesicles at the plasma membrane (PM) for cargo delivery. Although the exocytic process and machinery are well characterized in yeast and animal models, the molecular players and specific molecular events that underpin late stages of exocytosis in plant cells remain largely unknown. Here, by using the delivery of functional, fluorescent-tagged cellulose synthase (CESA) complexes (CSCs) to the PM as a model system for secretion, as well as single-particle tracking in living cells, we describe a quantitative approach for measuring the frequency of vesicle tethering events. Genetic and pharmacological inhibition of cytoskeletal function, reveal that the initial vesicle tethering step of exocytosis is dependent on actin and myosin XI. In contrast, treatments with the microtubule inhibitor, oryzalin, did not significantly affect vesicle tethering or fusion during CSC exocytosis but caused a minor increase in transient or aborted tethering events. With data from this new quantitative approach and improved spatiotemporal resolution of single particle events during secretion, we generate a revised model for the role of the cortical cytoskeleton in CSC trafficking.
Why it matches plant phenotyping methods植物細胞内の小胞テザリング頻度を単一粒子追跡で定量する新しい測定手法を開発・適用しており、植物状態の取得・定量が研究の中心である。
abstractsingle-particle tracking in living cells, we describe a quantitative approach for measuring the frequency of vesicle tethering events.
Reproduction assets foundThe paper's supplementary materials include Video S1, a live-cell imaging movie of a CSC particle insertion event next to a cortical microtubule, which directly reproduces the paper's plant phenotyping (single-particle CSC trafficking) measurements. No author analysis code or datasets with explicit deposit language areSupplement · publicnt care and maintenance of plant materials and to all members of the Staiger laboratory for helpful discussions and input.
Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Supplementary Materials
The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/ijms23010317/s1 , Video S1: A CSC particle is inserted next to a cortical microtubule and translocates on the microtubule during the steady movement phase.
Click here for additional data file.
Author Contributions
W.Z. and C.J.S. designed the research. W.Z. performed the experiments and analyzed the data. W.Z. and C.J.S. wrote the Open asset ↗lines:64-115Code / dataset availability confirmedCrossref · checked 9 Sept 2026
Significance The lack of suitable approaches for studying root–microbe interactions, live and in situ, has severely limited our ability to understand the rhizosphere. In this study, we overcome this major limitation with an imaging system that combines transparent soils with cutting edge light sheet microscopy. The study revealed that the root cap is a point of first contact for microbes before establishment and reveals how the pore structure influences the patterns of interactions between the microbe and the plant. With the combined use of light sheet microscopy and transparent soils, we shed light on previously unseen interaction phenomena and accelerate the understanding of how rhizospheres are formed.
Why it matches plant phenotyping methods透明土壌とライトシート顕微鏡を組み合わせたライブ・インサイチュ画像システムの開発が研究の中心で、根と微生物の相互作用という植物状態を可視化している。
abstractwe overcome this major limitation with an imaging system that combines transparent soils with cutting edge light sheet microscopy.
Reproduction assets foundThe paper deposits its phenotyping data (light-sheet microscopy volumes of root–soil–bacteria interactions) on Zenodo, makes its image analysis software (MATLAB/MeVisLab segmentation and quantification pipeline) publicly available on GitHub, and hosts supplementary materials on PNAS.Dataset · publicThe data in this study is available at https://doi.org/10.5281/zenodo.5650962 .Open asset ↗zenodo · 10.5281/zenodo.5650962lines:90-123Code · publicImage processing methods were programmed using MATLAB using the Image Processing Toolbox (MathWorks). Segmentation and extraction of geometrical features were performed using MeVisLab (MeVis Medical Solutions AG). All software is freely available from https://github.com/LionelDupuy/SENSOIL .Open asset ↗github · LionelDupuy/SENSOILlines:80-89Code / dataset availability confirmedOpenAlex · Europe PMC · checked 14 Sept 2026
BACKGROUND: Cellular components are controlled by genetic and physiological factors that define their shape and size. However, quantitively capturing the morphological characteristics and movement of cellular organelles from micrograph images is challenging, because the analysis deals with complexities of images that frequently lead to inaccuracy in the estimation of the features. Here we show a unique quantitative method to overcome biases and inaccuracy of biological samples from confocal micrographs. RESULTS: We generated 2D images of cell walls and spindle-shaped cellular organelles, namely ER bodies, with a maximum contrast projection of 3D confocal fluorescent microscope images. The projected images were further processed and segmented by adaptive thresholding of the fluorescent levels in the cell walls. Micrographs are composed of pixels, which have information on position and intensity. From the pixel information we calculated three types of features (spatial, intensity and Haralick) in ER bodies corresponding to segmented cells. The spatial features include basic information on shape, e.g., surface area and perimeter. The intensity features include information on mean, standard deviation and quantile of fluorescence intensities within an ER body. Haralick features describe the texture features, which can be calculated mathematically from the interrelationship between the pixel information. Together these parameters were subjected to multivariate analysis to estimate the morphological diversity. Additionally, we calculated the displacement of the ER bodies using the positional information in time-lapse images. We captured similar morphological diversity and movement within ER body phenotypes in several microscopy experiments performed in different settings and scanned under different objectives. We then described differences in morphology and movement of ER bodies between A. thaliana wild type and mutants deficient in ER body-related genes. CONCLUSIONS: The findings unexpectedly revealed multiple genetic factors that are involved in the shape and size of ER bodies in A. thaliana. This is the first report showing morphological characteristics in addition to the movement of cellular components and it quantitatively summarises plant phenotypic differences even in plants that show similar cellular components. The estimation of morphological diversity was independent of the cell staining method and the objective lens used in the microscopy. Hence, our study enables a robust estimation of plant phenotypes by recognizing small differences in complex cell organelle shapes and their movement, which is beneficial in a comprehensive analysis of the molecular mechanism for cell organelle formation that is independent of technical variations.
Why it matches plant phenotyping methods植物細胞小器官の形態・移動を画像から抽出する特徴量計算法を開発し、異なる撮像条件で頑健性を検証しているため、表現型取得法が研究の中心です。
abstractHere we show a unique quantitative method to overcome biases and inaccuracy of biological samples from confocal micrographs.
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 z-stack images were merged using specific criteria for the MaxContrastProjection package ( https://github.com/arpankbasak/ERB_DynaMo ).Open asset ↗arpankbasak/ERB_DynaMolines:96-99Code / dataset availability confirmedEurope PMC · bioRxiv · checked 15 Sept 2026
Summary Mitochondria form highly dynamic populations in the cells of plants (and all eukaryotes). The characteristics of this collective behaviour, and how it is influenced by nuclear features, remain to be fully elucidated. Here, we use a recently-developed quantitative approach to reveal and analyse the physical and collective “social” dynamics of mitochondria in an Arabidopsis msh1 mutant where organelle DNA maintenance machinery is compromised. We use a newly-created line combining the msh1 mutant with mitochondrially-targeted GFP, and characterise mitochondrial dynamics with a combination of single-cell timelapse microscopy, computational tracking and network analysis. The collective physical behaviour of msh1 mitochondria is altered from wildtype in several ways: mitochondria become less evenly spread, and networks of inter-mitochondrial encounters become more connected with greater potential efficiency for inter-organelle exchange. We find that these changes are similar to those observed in friendly , where mitochondrial dynamics are altered by a physical perturbation, suggesting that this shift to higher connectivity may reflect a general response to mitochondrial challenges.
Why it matches plant phenotyping methods単なる生物学的測定ではなく、タイムラプス顕微鏡、計算追跡、ネットワーク解析を組み合わせて植物細胞内ミトコンドリアの動態状態を定量化する手法の実質的な適用である。
abstractwe use a recently-developed quantitative approach to reveal and analyse the physical and collective “social” dynamics of mitochondria
Reproduction assets foundThe paper explicitly states that all analysis code and data are available on the authors' GitHub repository, and a supplementary time-lapse microscopy video (phenotyping input) is hosted publicly. The Arabidopsis msh1 seed stock (N3372) used for the phenotyping is also publicly available from the NASC stock centre.Code · public14
average number of shortest paths crossing each node in the network. The mean connected
414
component number is the average number of disconnected subgraphs within the network.
415
416
Accession numbers
417
All analysis code and data is available from Github at
418
https://github.com/StochasticBiology/plant-mito-dynamics
419
420
Acknowledgments
421
422
J.M.C. is supported by the BBSRC and University of Birmingham via the MIBTP doctoral
423
training scheme (grant number BB/M01116X/1). This project has received funding from the
424
European Research Council (ERC) under the European Union’s Horizon 2020 research and
425
innovation programme (grantOpen asset ↗StochasticBiology/plant-mito-dynamicspdf-raw-page:14 lines:1-67Code / dataset availability confirmedOpenAlex · Europe PMC · checked 15 Sept 2026
Abstract Background: Cellular components are controlled by genetic and physiological factors that define their shape and size. However, quantitively capturing the morphological characteristics and movement of cellular organelles from micrograph images is challenging, because the analysis deals with complexities of images that frequently lead to inaccuracy in the estimation of the features. Here we show a unique quantitative method to overcome biases and inaccuracy of biological samples from confocal micrographs. Results: We generated 2D images of cell walls and spindle-shaped cellular organelles, namely ER bodies, with a maximum contrast projection of 3D confocal fluorescent microscope images. The projected images were further processed and segmented by adaptive thresholding of the fluorescent levels in the cell walls. Micrographs are composed of pixels, which have information on position and intensity. From the pixel information we calculated three types of features (spatial, intensity and Haralick) in ER bodies corresponding to segmented cells. The spatial features include basic information on shape, e.g., surface area and perimeter. The intensity features include information on mean, standard deviation and quantile of fluorescence intensities within an ER body. Haralick features describe the texture features, which can be calculated mathematically from the interrelationship between the pixel information. Together these parameters were subjected to multivariate analysis to estimate the morphological diversity. Additionally, we calculated the displacement of the ER bodies using the positional information in a time-lapse image. We captured similar morphological diversity and movement within ER body phenotypes on several microscopy experiments performed in different settings and scanned under different objectives. We then described differences in morphology and movement of ER bodies between A. thaliana wild type and mutants deficient in ER body-related genes. Conclusions: The findings unexpectedly revealed multiple genetic factors that are involved in the shape and size of ER bodies in A. thaliana . This is the first report showing morphological characteristics in addition to the movement of cellular components and quantitatively summarises plant phenotypic differences even in plants that show similar cellular components. The estimation of morphological diversity was independent of the cell staining method and the objective lens used in the microscopy. Hence, our study enables a robust estimation of plant phenotypes by recognizing small differences of complex cell organelle shapes and their movement, which is beneficial in a comprehensive analysis of the molecular mechanism for cell organelle formation that is independent of technical variations.
Why it matches plant phenotyping methods顕微鏡画像からERボディの形態・テクスチャ・移動を抽出し、異なる実験条件で頑健性を検証する植物表現型解析手法が中心である。
abstractHere we show a unique quantitative method to overcome biases and inaccuracy of biological samples from confocal micrographs.
Reproduction assets foundThe paper's authors publicly release their phenotyping analysis scripts (segmentation, feature extraction, dynamics, clustering) on GitHub and the conda analysis environment on Anaconda Cloud, both with explicit availability statements. Microscope images are said to be in a 'Bioimage database' but no URL is given, so aCode · publicell as the institutional core support by Małopolska Centre of Biotechnology, Jagiellonian University.
Availability of data and materials
The scripts used can be found in the GitHub repository, images can be found in the Bioimage database. Scripts used for pre-processing and analysis along with supplementary data can be found in http://www.github.com/arpankbasak/ERB_DynaMo The analysis environment erb_dynamo with all necessary dependencies have been uploaded to https://anaconda.org/arpankbasak/erb_dynamo/files . The plant materials are available from the Arabidopsis Biological Resource Center (ABRC) and Nottingham Arabidopsis Stock Center (NASC).
Ethics approval and consent to participate
NotOpen asset ↗arpankbasak/ERB_DynaMolines:342-369Code · publics
The scripts used can be found in the GitHub repository, images can be found in the Bioimage database. Scripts used for pre-processing and analysis along with supplementary data can be found in http://www.github.com/arpankbasak/ERB_DynaMo The analysis environment erb_dynamo with all necessary dependencies have been uploaded to https://anaconda.org/arpankbasak/erb_dynamo/files . The plant materials are available from the Arabidopsis Biological Resource Center (ABRC) and Nottingham Arabidopsis Stock Center (NASC).
Ethics approval and consent to participate
Not applicable.
Consent for publication
Consent and approval for publication from all the authors was obtained.
Competing Interests
The auOpen asset ↗arpankbasak/erb_dynamolines:342-369Code / dataset availability confirmedbioRxiv · Europe PMC · checked 8 Sept 2026
Land surface phenology (LSP) enables global scale tracking of ecosystem processes, but its utility is limited in drylands due to low vegetation cover and resulting low annual amplitudes of vegetation indices (VIs). Due to the importance of drylands for biodiversity, food security, and the carbon cycle it is necessary to understand limitations in measuring dryland dynamics. Here, using simulated data and multi-temporal unmanned aerial vehicle (UAV) imagery of a desert shrubland, we explore the feasibility of detecting LSP with respect to fractional vegetation cover, plant functional types, VI uncertainty, and two different detection algorithms. Using simulated data we found that plants with distinct VI signals, such as deciduous shrubs, can require up to 60% fractional cover to consistently detect LSP. Evergreen plants, with lower seasonal VI amplitude, require considerably higher cover and can have undetectable phenology even with 100% vegetation cover. Our evaluation of two algorithms showed that neither performed the best in all cases. Even with adequate cover, biases in phenological metrics can still exceed 20 days, and can never be 100% accurate due to VI uncertainty from shadows, sensor view angle, and atmospheric interference. We showed how high-resolution UAV imagery enables LSP studies in drylands, and highlighted important scale effects driven by within canopy VI variation. With high-resolution imagery the open canopies of drylands are beneficial as they allow for straightforward identification of individual plants, enabling the tracking of phenology at the individual level. Drylands thus have the potential to become an exemplary environment for future LSP research.
Why it matches plant phenotyping methodsUAV画像とシミュレーションを用いて、乾燥地の植物フェノロジー検出アルゴリズムの実現可能性・精度・バイアスを評価しており、植物状態の測定法が中心です。
abstractusing simulated data and multi-temporal unmanned aerial vehicle (UAV) imagery of a desert shrubland, we explore the feasibility of detecting LSP
Reproduction assets foundThe paper's Data Availability statement explicitly deposits all simulated VI data, simulated UAV pixel time series, and reproduction code in a public Zenodo repository (DOI 10.5281/zenodo.4777207), which directly reproduces this paper's phenotyping analysis.Code · publicAll simulated VI data, simulated pixel time series from UAV imagery, and code for reproducing this
analysis, is available in the Zenodo data repository (https://doi.org/10.5281/zenodo.4777207).Open asset ↗Zenodo · 10.5281/zenodo.4777207pdf-page:14 lines:1-51Code / dataset availability confirmedEurope PMC · checked 14 Sept 2026
Patchy stomata are a common and characteristic phenomenon in plants. Understanding and studying the regulation mechanism of patchy stomata are of great significance to further supplement and improve the stomatal theory. Currently, the common methods for stomatal behavior observation are based on static images, which makes it difficult to reflect dynamic changes of stomata. With the rapid development of portable microscopes and computer vision algorithms, it brings new chances for stomatal movement observation. In this study, a stomatal behavior observation system (SBOS) was proposed for real-time observation and automatic analysis of each single stoma in wheat leaf using object tracking and semantic segmentation methods. The SBOS includes two modules: the real-time observation module and the automatic analysis module. The real-time observation module can shoot videos of stomatal dynamic changes. In the automatic analysis module, object tracking locates every single stoma accurately to obtain stomatal pictures arranged in time-series; semantic segmentation can precisely quantify the stomatal opening area (SOA), with a mean pixel accuracy (MPA) of 0.8305 and a mean intersection over union (MIoU) of 0.5590 in the testing set. Moreover, we designed a graphical user interface (GUI) so that researchers could use this automatic analysis module smoothly. To verify the performance of the SBOS, the dynamic changes of stomata were observed and analyzed under chilling. Finally, we analyzed the correlation between gas exchange and SOA under drought stress, and the correlation coefficients between mean SOA and net photosynthetic rate (Pn), intercellular CO 2 concentration (Ci), stomatal conductance (Gs), and transpiration rate (Tr) are 0.93, 0.96, 0.96, and 0.97.
Why it matches plant phenotyping methods顕微鏡動画から個々の気孔を追跡し、セグメンテーションで気孔開口面積を定量化する観測・解析システムを開発しており、植物表現型取得が研究の中心です。
abstracta stomatal behavior observation system (SBOS) was proposed for real-time observation and automatic analysis of each single stoma in wheat leaf using object tracking and semantic segmentation methods.
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 · public(2) The module is easy to install with the aid of an executable program (EXE) ( https://github.com/shem123456/Stomata-segmentation-with-GUI ).Open asset ↗https://github.com/shem123456/Stomata-segmentation-with-GUIlines:61-68Code / dataset availability confirmedEurope PMC · checked 9 Sept 2026
Arbuscular mycorrhizal fungi function as conduits for underground nutrient transport. While the fungal partner is dependent on the plant host for its carbon (C) needs, the amount of nutrients that the fungus allocates to hosts can vary with context. Because fungal allocation patterns to hosts can change over time, they have historically been difficult to quantify accurately. We developed a technique to tag rock phosphorus (P) apatite with fluorescent quantum-dot (QD) nanoparticles of three different colors, allowing us to study nutrient transfer in an in vitro fungal network formed between two host roots of different ages and different P demands over a 3-week period. Using confocal microscopy and raster image correlation spectroscopy, we could distinguish between P transfer from the hyphae to the roots and P retention in the hyphae. By tracking QD-apatite from its point of origin, we found that the P demands of the younger root influenced both: (1) how the fungus distributed nutrients among different root hosts and (2) the storage patterns in the fungus itself. Our work highlights that fungal trade strategies are highly dynamic over time to local conditions, and stresses the need for precise measurements of symbiotic nutrient transfer across both space and time.
Why it matches plant phenotyping methods量子ドット標識と共焦点画像解析を開発し、植物根へのリン移行および菌根内保持を時空間的に定量する手法が研究の中心であるため、植物の栄養生理状態を測定するフェノタイピング手法として含める。
abstractWe developed a technique to tag rock phosphorus (P) apatite with fluorescent quantum-dot (QD) nanoparticles of three different colors
Reproduction assets foundThe paper's authors publicly deposited all data, scripts, and analysis for this study in a GitHub repository, explicitly stated in the Methods. This is a paper-specific, publicly actionable code/data asset reproducing the paper's QD-apatite phenotyping measurements and statistical analysis.Code · publicWe performed all statistical analysis in R version 3.6.1 [ 48 ]. All data, scripts, and analysis are available at: https://github.com/anoukvantpadje/Two_roots .Open asset ↗anoukvantpadje/Two_rootslines:57-192Code / dataset availability confirmedbioRxiv · checked 15 Sept 2026
Recent years have witnessed a convergence of data and methods that allow us to approximate the shape, size, and functional attributes of biological organisms. This is not only limited to traditional model species: given the ability to culture and visualize a specific organism, we can capture both its structural and functional attributes. We present a quantitative model for the colonial diatom Bacillaria paradoxa, an organism that presents a number of unique attributes in terms of form and function. To acquire a digital model of B. paradoxa, we extract a series of quantitative parameters from microscopy videos from both primary and secondary sources. These data are then analyzed using a variety of techniques, including two rival deep learning approaches. We provide an overview of neural networks for non-specialists as well as present a series of analysis on Bacillaria phenotype data. The application of deep learning networks allows for two analytical purposes. Application of the DeepLabv3 pre-trained model extracts phenotypic parameters describing the shape of cells constituting Bacillaria colonies. Application of a semantic model trained on nematode embryogenesis data (OpenDevoCell) provides a means to analyze masked images of potential intracellular features. We also advance the analysis of Bacillaria colony movement dynamics by using templating techniques and biomechanical analysis to better understand the movement of individual cells relative to an entire colony. The broader implications of these results are presented, with an eye towards future applications to both hypothesis-driven studies and theoretical advancements in understanding the dynamic morphology of Bacillaria.
Why it matches plant phenotyping methods珪藻の顕微鏡動画から形態・細胞内特徴・群体運動を抽出する画像処理および深層学習手法が研究の中心であり、植物表現型解析手法の開発に該当する。
abstractTo acquire a digital model of B. paradoxa, we extract a series of quantitative parameters from microscopy videos from both primary and secondary sources.
Reproduction assets foundThe paper's Bacillaria phenotyping data and analysis assets are publicly available: raw data, processed numeric/image data, and code in the authors' Digital-Bacillaria GitHub repository; skeleton-creation scripts in the Image-Skeletons subrepository; the OpenDevoCell segmentation platform (GitHub and web app); and a GfDataset · publicstrains contained in our primary data (videos) have been harvested from the Neckar
river in Germany (49°04'41.8"N 9°09'17.9"E). Samples were collected on September 14, 2019.
The average size of each cell (filament) is approximately 81µm.
Data Availability
Select unprocessed (raw) data are available at our Github repository
(https://github.com/devoworm/Digital-Bacillaria), processed numeric and image data (numeric
tables and skeletonized images), and select video files are available on the Open Science
Framework (DOI 10.17605/OSF.IO/AR8C3).
17Open asset ↗devoworm/Digital-Bacillariapdf-layout-page:17 lines:1-49Code · publicckground
color (select the background by color) to RGB value 0,0,0. To create a thick skeleton from a thin
skeleton, select the thin skeleton by color and then select the border function. The border width
should be set to 4, hard border, and filled with RGB value 0,217,0. The pseudo-code for GIMP
script-fu is located on Github (https://github.com/devoworm/Digital-Bacillaria/tree/master/Image-Skeletons).Image Tracking for Movement. We also employ image tracking for the primary microscopy
data. The tracking of a partial image (template) of a diatom can be used under certain conditions
to obtain its trajectory. In particular, a movement of the diatoms in a plane perpendicular to the
optical axiOpen asset ↗devoworm/Digital-Bacillariapdf-raw-page:10 lines:1-44Code · publicn-source software with a web interface called OpenDevoCell (based on DeepLearning 4J).
DeepLabv3 (Google, MountainView, California, USA) is a package for TensorFlow, and Deep
Learning 4J (Eclipse Foundation, Ottawa, Canada), a Java-based library that works with
TensorFlow. OpenDevoCell is open-source software located on Github
(https://github.com/devoworm/GSOC-2019/tree/master/OpenDevoCell) and as a web-based
application (https://open-devo-cell.herokuapp.com).11
.
CC-BY 4.0 International license
available under a
was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made
The copyright holder for this preprint (wOpen asset ↗devoworm/GSOC-2019pdf-raw-page:11 lines:1-35Code / dataset availability confirmedCrossref · Europe PMC · checked 9 Sept 2026
Abstract Background Plant population density is an important factor for agricultural production systems due to its substantial influence on crop yield and quality. Traditionally, plant population density is estimated by using either field assessment or a germination-test-based approach. These approaches can be laborious and inaccurate. Recent advances in deep learning provide new tools to solve challenging computer vision tasks such as object detection, which can be used for detecting and counting plant seedlings in the field. The goal of this study was to develop a deep-learning-based approach to count plant seedlings in the field. Results Overall, the final detection model achieved F1 scores of 0.727 (at $$IOU_{all}$$ I O U all ) and 0.969 (at $$IOU_{0.5}$$ I O U 0.5 ) on the $$Seedling_{All}$$ S e e d l i n g All testing set in which images had large variations, indicating the efficacy of the Faster RCNN model with the Inception ResNet v2 feature extractor for seedling detection. Ablation experiments showed that training data complexity substantially affected model generalizability, transfer learning efficiency, and detection performance improvements due to increased training sample size. Generally, the seedling counts by the developed method were highly correlated ( $$R^2$$ R 2 = 0.98) with that found through human field assessment for 75 test videos collected in multiple locations during multiple years, indicating the accuracy of the developed approach. Further experiments showed that the counting accuracy was largely affected by the detection accuracy: the developed approach provided good counting performance for unknown datasets as long as detection models were well generalized to those datasets. Conclusion The developed deep-learning-based approach can accurately count plant seedlings in the field. Seedling detection models trained in this study and the annotated images can be used by the research community and the cotton industry to further the development of solutions for seedling detection and counting.
Why it matches plant phenotyping methods圃場画像から植物個体数（苗立ち密度）を検出・計数する深層学習手法を開発し、人手評価および複数年・地点のデータで検証しており、植物表現型取得が中心である。
abstractThe goal of this study was to develop a deep-learning-based approach to count plant seedlings in the field.
Reproduction assets foundThe paper's availability statement explicitly archives original images and annotations, source code, and testing videos in a public GitHub repository, along with pretrained models for seedling detection and counting — directly reproducing this paper's phenotyping measurements and analysis.Code · publicOriginal images and annotations, source code, and testing videos are archived in a GitHub repository ( https://github.com/UGA-BSAIL/deepseedling ) along with the instruction to run pretrained models for seedling detection and counting.Open asset ↗UGA-BSAIL/deepseedlinglines:190-267Code / 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
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Sci-kit learn in Python (Pedregosa et al., 2011). The script used for data analysis in R is
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publicly available as an R-notebook (http://doi.org/10.5281/zenodo.3534239), as well as
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the Jupyter notebook containing the command lines used for machine learning
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(http://doi.org/10.5281/zenodo.3534148).5
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3. Results
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3.1 Extended exposure to heat stress results in a proportional decrease of the rosette
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size and photosynthetic efficiency
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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
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Sci-kit learn in Python (Pedregosa et al., 2011). The script used for data analysis in R is
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publicly available as an R-notebook (http://doi.org/10.5281/zenodo.3534239), as well as
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the Jupyter notebook containing the command lines used for machine learning
4
(http://doi.org/10.5281/zenodo.3534148).5
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3. Results
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3.1 Extended exposure to heat stress results in a proportional decrease of the rosette
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size and photosynthetic efficiency
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To assess whether high-throughput phenotyping can capture significant alterations in plant
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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
: Pre-harvest fruit yield estimation is useful to guide harvesting and marketing resourcing, but machine vision estimates based on a single view from each side of the tree ("dual-view") underestimates the fruit yield as fruit can be hidden from view. A method is proposed involving deep learning, Kalman filter, and Hungarian algorithm for on-tree mango fruit detection, tracking, and counting from 10 frame-per-second videos captured of trees from a platform moving along the inter row at 5 km/h. The deep learning based mango fruit detection algorithm, MangoYOLO, was used to detect fruit in each frame. The Hungarian algorithm was used to correlate fruit between neighbouring frames, with the improvement of enabling multiple-to-one assignment. The Kalman filter was used to predict the position of fruit in following frames, to avoid multiple counts of a single fruit that is obscured or otherwise not detected with a frame series. A "borrow" concept was added to the Kalman filter to predict fruit position when its precise prediction model was absent, by borrowing the horizontal and vertical speed from neighbouring fruit. By comparison with human count for a video with 110 frames and 192 (human count) fruit, the method produced 9.9% double counts and 7.3% missing count errors, resulting in around 2.6% over count. In another test, a video (of 1162 frames, with 42 images centred on the tree trunk) was acquired of both sides of a row of 21 trees, for which the harvest fruit count was 3286 (i.e., average of 156 fruit/tree). The trees had thick canopies, such that the proportion of fruit hidden from view from any given perspective was high. The proposed method recorded 2050 fruit (62% of harvest) with a bias corrected Root Mean Square Error (RMSE) = 18.0 fruit/tree while the dual-view image method (also using MangoYOLO) recorded 1322 fruit (40%) with a bias corrected RMSE = 21.7 fruit/tree. The video tracking system is recommended over the dual-view imaging system for mango orchard fruit count.
Why it matches plant phenotyping methods動画画像と深層学習・追跡アルゴリズムを組み合わせ、樹上マンゴー果実数（収量関連形質）を推定する手法の開発・比較検証が中心である。
abstractA method is proposed involving deep learning, Kalman filter, and Hungarian algorithm for on-tree mango fruit detection, tracking, and counting
Reproduction assets foundThe paper states that the tree images and video used for the MangoYOLO–Kalman–Hungarian fruit tracking/counting analysis are available as a supplementary data file, and the Supplementary Materials section lists Video S1 (Fruit Tracking Count) at the MDPI supplementary URL. This is a paper-specific, publicly accessible,Supplement · publicThe images and video are available as a supplementary data file to this manuscript.Open asset ↗lines:34-41Code / dataset availability confirmedEurope PMC · checked 15 Sept 2026
MilletFlowerClassificationTrackingGrowth / development / phenology
In many plant species, the time of day at which flowers open to permit pollination is tightly regulated. Proper time of flower opening, or Time of Day of Anther Appearance ( TAA ), may coordinate flowering opening with pollinator activity or may shift temperature sensitive developmental processes to cooler times of the day. The genetic mechanisms that regulate the timing of this process in cereal crops are unknown. To address this knowledge gap, it is necessary to establish a monocot model system that exhibits variation in TAA. Here, we examine the suitability of Setaria viridis , the model for C4 photosynthesis, for such a role. We developed an imaging system to monitor the temporal regulation of growth, flower opening time, and other physiological characteristics in Setaria. This system enabled us to compare Setaria varieties Ames 32254, Ames 32276, and PI 669942 variation in growth and daily flower opening time. We observed that TAA occurs primarily at night in these three Setaria accessions. However, significant variation between the accessions was observed for both the ratio of flowers that open in the day vs. night and the specific time of day where the rate is maximal. Characterizing this physiological variation is a requisite step toward uncovering the molecular mechanisms regulating TAA. Leveraging the regulation of TAA could provide researchers with a genetic tool to improve crop productivity in new environments.
Why it matches plant phenotyping methodsSetariaの花開時刻や成長を時系列画像とニューラルネットワーク・運動解析で取得するイメージングシステムを開発しており、植物形質の取得手法が研究の中心です。
titleNeural Net Classification Combined With Movement Analysis to Evaluate Setaria viridis as a Model System for Time of Day of Anther Appearance.
Reproduction assets foundThe paper's phenotyping analysis code (camera optimization loop, day-time classification function and trained model, bristle density and growth scripts) is publicly available in the authors' DohertyLab GitHub repository, and a sample A10 day/night image dataset is deposited on Harvard Dataverse. Supplementary material,Code · publiclines never intersect, then the row of the first white pixel not classified as a bristle from the top of the image was used. The bristle density was then calculated for each image in a set. All day time images after panicle emergence were averaged to determine the final bristle density for that panicle. Scripts are available at https://github.com/DohertyLab/Setaria-Flower-Opening-Time .
Quantification of growth
ImageJ Fiji ( https://imagej.net/Fiji/Downloads ) was used to analyze the daily growth pattern of seedlings from 3 to 10 days after emergence. Dawn and dusk images at 8 a.m. and 8 p.m. were imported as an image sequence. In three replicates of each variety the second and/or third leavOpen asset ↗DohertyLab/Setaria-Flower-Opening-Timelines:43-49Code / dataset availability confirmedbioRxiv · Europe PMC · Crossref · checked 14 Sept 2026
ArabidopsisLaboratory / benchtopRootMorphology / geometry measurementObject detectionTrackingRoot system architecture
Root analysis is essential for both academic and agricultural research. Despite the great advances in root phenotyping and imaging however, calculating root length is still performed manually and involves considerable amounts of labor and time. To overcome these limitations, we have developed MyROOT, a novel software for the semi-automatic quantification of root growth of seedlings growing directly in agar plates. Our method automatically determines the scale from the image of the plate, and subsequently measures the root length of the individual plants. To this aim, MyROOT combines a bottom-up root tracking approach with a hypocotyl detection algorithm. At the same time as providing accurate root measurements, MyROOT also significantly minimizes the user intervention required during the process. Using Arabidopsis, we tested MyROOT with seedlings from different growth stages. Upon comparing the data obtained using this software with that of manual root measurements, we found that there are no significant differences (t-test, p-value < 0.05). Thus, MyROOT will be of great aid to the plant science community by permitting high-throughput root length measurements while saving on both labor and time.
Why it matches plant phenotyping methods根長という植物形質の半自動画像計測ソフトウェアを開発し、手動測定との比較で検証しており、フェノタイピング手法が研究の中心です。
abstractwe have developed MyROOT, a novel software for the semi-automatic quantification of root growth of seedlings growing directly in agar plates.
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 · publicMyROOT software is available at https://www.cragenomica.es/research-Open asset ↗pdf-page:3 lines:1-45Code / dataset availability confirmedOpenAlex · Crossref · Europe PMC · checked 5 Sept 2026
Modern phenotyping and plant disease detection methods, based on optical sensors and information technology, provide promising approaches to plant research and precision farming. In particular, hyperspectral imaging have been found to reveal physiological and structural characteristics in plants and to allow for tracking physiological dynamics due to environmental effects. In this work, we present an approach to plant phenotyping that integrates non-invasive sensors, computer vision, as well as data mining techniques and allows for monitoring how plants respond to stress. To uncover latent hyperspectral characteristics of diseased plants reliably and in an easy-to-understand way, we "wordify" the hyperspectral images, i.e., we turn the images into a corpus of text documents. Then, we apply probabilistic topic models, a well-established natural language processing technique that identifies content and topics of documents. Based on recent regularized topic models, we demonstrate that one can track automatically the development of three foliar diseases of barley. We also present a visualization of the topics that provides plant scientists an intuitive tool for hyperspectral imaging. In short, our analysis and visualization of characteristic topics found during symptom development and disease progress reveal the hyperspectral language of plant diseases.
Why it matches plant phenotyping methods非侵襲的ハイパースペクトル画像から植物の病害進展・生理状態を抽出し、トピックモデルで自動追跡する新しい表現・解析手法が研究の中心である。
abstractwe present an approach to plant phenotyping that integrates non-invasive sensors, computer vision, as well as data mining techniques
Reproduction assets foundThe paper's authors publicly released the Python implementation of online regularized LDA used for their hyperspectral plant phenotyping analysis on GitHub. No public phenotype dataset or image deposit is stated; the hyperspectral data itself is only described, not deposited.Code · publicThe Python implementation of online regularized LDA is freely available at https://github.com/mirwaes/sclda .Open asset ↗mirwaes/scldalines:78-87