RiceMultimodalX-ray / CTRootMorphology / geometry measurementPhysiological trait estimationGrowth / time-series analysisGrowth / development / phenologyRoot system architecture
Rhizosphere oxidation is a key adaptive mechanism in reductive soil environments, in which oxygen released from roots alters rhizosphere redox conditions and regulates biogeochemical processes. Rice plants possess an internal oxygen transport system, and radial oxygen loss (ROL) from roots is closely associated with root development. However, the spatial patterns of ROL in soil and their relationships with root traits remain poorly characterized. In this study, we developed a multimodal imaging system that integrates planar oxygen optodes with X-ray computed tomography to simultaneously visualize rhizosphere oxidation and root development in rice. Daily time-course tracking of individual crown roots revealed dynamic changes in the spatial distribution and magnitude of rhizosphere oxygen in relation to root elongation and aging. Root thickness was positively correlated with dissolved oxygen levels near root tips. Genotypic comparisons further identified a cultivar with reduced rhizosphere oxidation despite possessing thicker roots among the tested genotypes, thereby indicating the involvement of additional physiological processes. Overall, these findings demonstrate that rhizosphere oxidation is regulated by root growth stage and thickness and dynamically modulated during root development.
Why it matches plant phenotyping methods平面酸素オプトードとX線CTを統合したマルチモーダル画像システムを開発し、根の発達と根圏酸化を時系列・個体別に定量化しており、表現型取得手法が研究の中心である。
abstractwe developed a multimodal imaging system that integrates planar oxygen optodes with X-ray computed tomography to simultaneously visualize rhizosphere oxidation and root development in rice.
Reproduction assets foundThe paper's Data availability statement explicitly deposits the authors' RG2DO-Root analysis program together with sample optode and CT images (the paper's phenotyping inputs) in a public GitHub repository, matching the allowed URL.Code · publicing
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This work was supported by project JPNP18016, commissioned by the New Energy and
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Industrial Technology Development Organization (NEDO), JST CREST (JPMJCR17O1),
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and JST ALCA-Next (JPMJAN23D3).
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Data availability
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The source code and sample data (optode and CT images) are available from the
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GitHub repository (https://github.com/tsubasa-kawai28/RG2DO-Root).15
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References
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Aguilar EA et al. 2003. Oxygen distribution and movement, respiration and nutrient
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loading in banana roots (Musa spp. L.) subjected to aerated and oxygen-depleted
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environments. Plant Soil. 253:91–102. https://doi.org/10.1023/A:1024598319404.20
Armstrong W, Wright EJ. 1975. Radial oxygen loss fromOpen asset ↗https://github.com/tsubasa-kawai28/RG2DO-Root · RG2DO-Rootpdf-raw-page:19 lines:1-82Code / dataset availability confirmedEurope PMC · Crossref · checked 15 Sept 2026
Published3 Sept 2026Methods in ecology and evolution
Growth chamberMultimodalStereoRootStem / branchTrackingGrowth / development / phenology
Understanding plant behaviour requires the integration of multiple phenotypic and physiological signals measured over time under controlled conditions. However, different plant signals are typically studied using separate experimental setups, limiting temporal alignment and integrative analyses.We present Mind(the)Plant, a modular experimental facility designed for the synchronized, long-term acquisition of multimodal plant data, including three-dimensional shoot kinematics, above- and below-ground volatile organic compounds (VOCs) and root imaging. Its modular architecture is designed to accommodate additional acquisition modules, such as electrophysiological signalling, as future extensions. The platform integrates a controlled growth environment with stereovision imaging, high-resolution time-of-flight mass spectrometry and custom rhizocameras. These components are connected through a unified network infrastructure that ensures synchronized acquisition and centralized data handling.We validate the performance of each acquisition module through multi-week recordings, demonstrating high-temporal stability, reliable stereovision synchronization, effective isolation of VOCs signals and robust operation of below-ground imaging. We further illustrate the analytical potential of the platform using a one-day continuous multimodal acquisition combining shoot kinematics, above-ground VOC emissions, rhizocameras observations and environmental data.Mind(the)Plant provides a novel methodological framework for studying plant behaviour, signalling and phenotypic plasticity in ecological and evolutionary research. By enabling coordinated measurements of multiple plant response modalities, the platform supports investigations of dynamic plant-environment and plant-plant interactions from a behavioural perspective.
Why it matches plant phenotyping methods植物の複数の表現型・生理シグナルを同期取得する施設を開発し、各取得モジュールの性能を検証しているため、表現型計測プラットフォームが研究の中心です。
abstractWe present Mind(the)Plant, a modular experimental facility designed for the synchronized, long-term acquisition of multimodal plant data, including three-dimensional shoot kinematics, above- and below-ground volatile organic compounds (VOCs) and root imaging.
Reproduction assets foundThe paper's data availability statement explicitly deposits data, code and processing pipelines (supporting the multimodal plant phenotyping measurements and analysis) in a public Zenodo archive with an authors' URL matching an allowed URL.Code · publicf Interest Statement
The authors have no conflicts of interest to declare.
Peer Review
The peer review history for this article is available at https://www.webofscience.com/api/gateway/wos/peer-review/10.1111/2041-210x.70411 .
Data availability Statement
Data, code and processing pipelines supporting this study are available at https://doi.org/10.5281/zenodo.22095454 ( Simonetti & Castiello, 2026 ).
References
Avesani S, Bonato B, Simonetti V, Guerra S, Ravazzolo L, Gjinaj G, Dadda M, Castiello U. Comparing proton transfer reaction (PTR) and adduct ionization mechanism (AIM) for the study of volatile organic compounds. Molecules. 2026;31(3):402. doi: 10.3390/molecules31030402.
Baluška F, LeOpen asset ↗zenodo · 10.5281/zenodo.22095454lines:482-508Code / dataset availability confirmedEurope PMC · checked 15 Sept 2026
Background Olive ( Olea europaea L.) breeding initiatives rely heavily on the extensive and correct characterisation of genetic resources to successfully achieve their goals, such as addressing climate change and emerging diseases challenges. However, the historical lack of standardised phenotyping protocols across multi-environment trials has severely hindered data interoperability and large-scale comparative analyses. Methods Within the Horizon 2020 GEN4OLIVE project, five international olive germplasm banks established a consensus-based methodological framework to systematically evaluate over 500 olive cultivars. We harmonised 14 evaluation protocols covering six fundamental dimensions: phenological and agronomic traits, abiotic stress resilience, biotic stress resilience, olive oil yield and chemical quality, table olive quality assessment, and morphological characterisation and photography. While most protocols were adapted from previously published literature to ensure ease of implementation across different facilities, novel methodologies for frost tolerance and standardised photography were developed de novo. Results The implementation of these consensus methods across five countries proved highly successful. This methodological framework enabled the generation of the largest harmonised, publicly available dataset on olive genetic resources to date, effectively making possible the correct comparation and ranking of the olive cultivars based on their specific characteristics. Conclusions This compendium of methods provides a robust, highly replicable reference point for the standardisation of olive germplasm characterisation and use of shared benchmark cultivars as an effective way for data normalization and comparation. It facilitates future global pre-breeding efforts, ensures international data interoperability, and supports the discovery of resilient cultivars to secure the future of the olive sector.
Why it matches plant phenotyping methodsオリーブ遺伝資源の標準化フェノタイピングプロトコルを体系化し、複数機関で実装・検証して大規模データセットを生成した方法論中心の研究である。
abstractthe historical lack of standardised phenotyping protocols across multi-environment trials has severely hindered data interoperability and large-scale comparative analyses.
Reproduction assets foundThe article declares two paper-specific public assets: the GEN4OLIVE phenotypic dataset from evaluating over 500 olive accessions across five germplasm banks, hosted on the project's Olive Varieties Database, and a Zenodo-deposited methodological handbook (Extended Data) containing the 14 protocols, visual assessment, Dataset · publicData and software availability
The phenotypic dataset generated from the evaluation of over 500 olive varieties across the five Mediterranean
germplasm banks using this compendium of protocols and methodologies, is publicly available via the GEN4OLIVE
project repository.
• Repository: GEN4OLIVE Olive Varieties Database.
• Link: https://www.uco.es/ucolivo/gen4olive/olivevarieties (GEN4OLIVE Database, 2025).
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Open Research Europe 2026, 6:322 Last updated: 14 SEP 2026Open asset ↗GEN4OLIVE Olive Varieties Databasepdf-raw-page:8 lines:1-44Code / dataset availability confirmedOpenAlex · Europe PMC · bioRxiv · checked 14 Sept 2026
Abstract Background and aims Plant volatile organic compounds (VOCs) change dynamically with plant development and in response to environmental conditions. However, their potential as non-invasive indicators of phenological progression remains poorly explored. In this study, we developed a framework integrating automated VOC sampling, time-resolved VOC profiling, and machine-learning analysis for the non-invasive assessment of plant phenology. Using soybean ( Glycine max (L.) Merr.), we investigated whether development-associated temporal variation in VOC emissions could delineate and predict developmental phases. Methods We collected VOCs daily under controlled environmental conditions from 16 to 43 days after sowing, spanning the transition from vegetative to reproductive stages, using an automated sampling system coupled with thermal desorption-gas chromatograph-mass spectrometer (TD- GC-MS). To characterise temporal changes in VOC profiles associated with phenological progression, we analysed the daily VOC data using a multi-step pipeline combining statistical filtering and similarity-based network analysis. We defined VOC-derived developmental phases from similarity patterns in the VOC profiles, then developed and evaluated machine-learning models to predict these phases. Key results Seven VOCs exhibited distinct phase-dependent dynamics, including green leaf volatiles and monoterpenes showing characteristic temporal changes during phenological progression. Network-based clustering of VOC profiles resolved five developmental phases closely aligned with conventional developmental stages. A machine-learning model predicted these phases from the VOC profiles with high predictive accuracy on independent test data, demonstrating that phenological progression could be quantitatively inferred from VOC emission patterns. Conclusions Our findings support VOC profiling as a reliable and non-invasive approach for assessing phenological progression in soybean. By extracting temporally structured VOC signals, this framework captures developmental information that may be difficult to obtain through visual observation alone, particularly after canopy closure. VOC profiling offers a practical tool for monitoring crop developmental dynamics and has broader potential for plant phenotyping and precision crop management.
Why it matches plant phenotyping methods自動VOCサンプリング、時系列VOCプロファイリング、機械学習を統合し、VOCから植物の発育段階を非破壊推定する方法を開発・評価しており、フェノタイピング手法が中心である。
abstractwe developed a framework integrating automated VOC sampling, time-resolved VOC profiling, and machine-learning analysis for the non-invasive assessment of plant phenology.
Reproduction assets found保存済みの本文根拠を更新済みルールで再検証し、公開資産1件を確認しました。Supplement · publicThe peak area matrix obtained from the MS- DIAL analysis (Supplementary Dataset S1) was filtered to remove unreliable features.Open asset ↗lines:66-69Code / dataset availability confirmedbioRxiv · checked 5 Sept 2026
Biomolecular condensates that persist through cell division must be reorganized and inherited, yet it remains unclear whether subtle defects before division are associated with later organelle or growth phenotypes. We examined the Chlamydomonas reinhardtii pyrenoid, a liquid-like condensate that concentrates ribulose-1,5-bisphosphate carboxylase/oxygenase (Rubisco), the photosynthetic CO2-fixing enzyme. As part of the algal CO2-concentrating mechanism, the pyrenoid raises CO2 availability around Rubisco. We generated an RBCS1-mGold Rubisco reporter and developed an unsupervised image-analysis pipeline combining a convolutional autoencoder and a one-class support vector machine. Using 4,905 wild-type single-cell images, augmented 22-fold to 107,910 image instances, we defined the range of normal pyrenoid morphology. A combined machine-learning and visual screen of approximately 21,000 insertional mutants yielded 17 pyrenoid integrity mutants (pim1-pim17). Differential reconstruction-error maps highlighted local deviations from the wild-type reference, including phenotypes difficult to classify by eye. Four-dimensional live imaging showed defects in matrix dispersal, partitioning of Rubisco-containing foci, or pyrenoid recondensation in multiple pim strains. Growth assays identified broad defects and phenotypes that became more apparent as CO2 supply decreased. Insertion-site mapping nominated candidate loci, including STT7, which encodes a chloroplast kinase best known for regulating photosynthetic light harvesting. Independent STT7-edited lines lacked detectable STT7 accumulation and showed pyrenoid-region reconstruction-error patterns, supporting an association between impaired STT7 function and altered pyrenoid morphology. These findings show that unsupervised image screening can extend forward genetics to subtle pyrenoid phenotypes accompanied by mitotic remodeling or growth defects.
Why it matches plant phenotyping methods藻類細胞のピレノイド形態を対象に、画像解析と教師なし機械学習パイプラインを開発し、正常範囲の定義・変異体スクリーニング・検出性能の実証を行っており、表現型取得手法が研究の中心である。
abstractdeveloped an unsupervised image-analysis pipeline combining a convolutional autoencoder and a one-class support vector machine
Reproduction assets foundThe paper's custom machine-learning analysis scripts (CAE–OC-SVM pyrenoid screening pipeline) are explicitly stated to be publicly available on the authors' GitHub repository. Other data (microscopy files, anomaly scores) are only available upon request, so they do not qualify as public assets.Code · publicCustom scripts used for the machine-learning analyses are publicly available at https://github.com/Yamano-Lab/2025_Machine_Learning-based_screening .Open asset ↗Yamano-Lab/2025_Machine_Learning-based_screeninglines:103-119Code / dataset availability confirmedOpenAlex · checked 5 Sept 2026
Published18 Aug 2026Journal of King Saud University - Computer and Information SciencesCited by 0 · OpenAlex ↗
Plant phenotyping is essential for modern crop breeding, yet traditional static image analysis fails to capture the nonlinear dynamics of plant growth. Existing time-series forecasting models exhibit notable limitations when processing multimodal data: global pooling operations may compress local 2D spatial topology of plants, and shallow feature concatenation may be insufficient for effective cross-modal semantic alignment. Moreover, current methods typically regress absolute morphological states, which may contribute to temporal lag during nonlinear growth spurts. In this paper, we propose ST-CrossGro-Former, a cross-modal residual forecasting framework for plant dynamic growth. The network removes the final global pooling and classification layers to preserve spatial topology and incorporates a scalar-guided cross-modal attention module based on the standard query-key-value formulation. This module utilizes 1D morphological features as queries to dynamically weight local visual regions, promoting multimodal feature alignment. Concurrently, a residual incremental forecasting strategy is introduced to predict short-term growth increments rather than absolute states, aiming to improve tracking sensitivity to sudden growth events. Evaluations on the UNL-CPPD maize dataset and supplementary validation on the FIP1 wheat field dataset show that the proposed model achieves competitive single-step forecasting accuracy and favorable temporal trajectory alignment compared with adapted spatiotemporal attention, graph-based, and physics-informed baselines under the evaluated settings. In particular, the FIP1 results suggest that ST-CrossGro-Former can maintain favorable height trajectory alignment under a field-acquired wheat setting, indicating its potential for helping mitigate temporal misalignment in dynamic growth forecasting.
Why it matches plant phenotyping methods植物の動的形態成長を予測する新規クロスモーダル手法を開発し、トウモロコシ・コムギデータセットで評価しており、表現型の抽出・予測手法が中心である。
abstractwe propose ST-CrossGro-Former, a cross-modal residual forecasting framework for plant dynamic growth.
Reproduction assets foundThe paper evaluates its ST-CrossGro-Former model on two public plant phenotyping datasets: the UNL-CPPD maize dataset (explicitly stated as publicly available with a repository URL) and the FIP1 wheat field dataset (public dataset from ETH Zürich, with its GigaScience dataset publication DOI). No author analysis code, Dataset · publicThe UNL-CPPD dataset used in this research was acquired from the UNL Plant Phenotyping Datasets repository, accessible at https://plantvision.unl.edu/datasets.Open asset ↗UNL Plant Phenotyping Datasets · UNL-CPPDlines:266-273Code / dataset availability confirmedOpenAlex · Europe PMC · checked 15 Sept 2026
Boll-opening concentration is critical for mechanical cotton harvesting, yet it is still assessed mainly by manual records and single time-point indicators that miss temporal dynamics. To bridge the lack of a unified workflow linking vision foundation models, multi-temporal boll-opening monitoring, and harvest decision-making, we developed a cross-scale UAV high-throughput phenotyping framework centered on DINO-BollGX. DINO-BollGX couples a DINO v3 backbone, a RetinaNet detection head, and an adaptive refinement-and-suppression module for robust open-boll detection under complex field conditions. Using multi-temporal UAV imagery collected over two years for 383 cultivars, we reconstructed plot-scale time series of open-boll counts, derived dynamic features describing progression and intensity changes, and proposed a Cotton Boll-Opening Temporal Stability Index (CTSI) to quantify boll-opening rhythm and concentration; CTSI was further integrated with a time-based risk function to generate harvest decision curves. Under unified data and training settings, DINO-BollGX achieved precision = 0.91, F1 = 0.88, and AP@0.50 = 0.80, and provided accurate boll-count estimation (R 2 =0.98; MAE=3.10), outperforming YOLOv11, YOLOv12, YOLOv13, and RT-DETR. On an independent cross-year dataset acquired at 5 m altitude, it obtained precision = 0.98 and F1 = 0.87. An internal consistency analysis showed that CTSI had the expected negative association with Window_days (r = −0.83) and positive associations with Max_count (r = 0.78) and the boll-opening efficiency index (r = 0.92), reflecting the co-occurrence of temporal compactness and main-phase opening intensity in the cultivar population. CTSI ranged from −2.72 to 4.69 across cultivars, enabling identification of highly synchronized boll-opening. Harvest decision curves indicated that the relative net income index peaked at day 67 after the first observation and a compact optimal harvest window near the end of monitoring; on a fixed harvest date, Kuche 130292 (CTSI=4.69) produced 486 open bolls versus 182 for Xinluzao 36 (CTSI=0.53) and 90 for Andizhan-60 (CTSI=-2.72). Overall, the framework integrates dynamic boll-opening phenotyping with harvest timing optimization, supporting scalable cultivar screening and mechanization-ready deployment, with potential extension to harvest decision scenarios in other crops.
Why it matches plant phenotyping methodsUAV画像と基盤モデルを用いて綿花の開絮を検出・定量し、時系列表現型指標を構築・検証する方法が研究の中心であるため。
abstractwe developed a cross-scale UAV high-throughput phenotyping framework centered on DINO-BollGX.
Reproduction assets foundThe paper publicly releases its cotton boll-opening UAV image dataset (3638 patches, 94,774 YOLO-format bounding-box annotations) on GitHub, directly supporting the paper's phenotyping analysis. No author analysis code or trained model checkpoints are explicitly deposited.Dataset · publicentary information for evaluating cross-scale detection performance and characterizing macroscopic spatial patterns. The 5 m imagery acquired on 18 Sept 2024 is used exclusively for cross-year generalization assessment. All cropped images and the corresponding YOLO-format annotation files have been publicly released on GitHub ( https://github.com/mianchen0529/cotton-boll-dataset/tree/main ) to facilitate further research on cotton phenotyping, agricultural remote sensing, and intelligent analytics.
2.3.
Model construction
2.3.1.
Overall architecture of the DINO-BollGX network
The proposed DINO-BollGX network consists of four stages: image preprocessing, feature extraction, object prediction,Open asset ↗mianchen0529/cotton-boll-datasetlines:63-74Code / 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 · checked 5 Sept 2026
The simultaneous diagnosis of diseases and evaluation of age quality grades in tea leaves are critical for precision agriculture and the economic valuation of tea products. Although deep learning has shown promise in agricultural vision tasks, current multi-task models often suffer from performance degradation due to feature conflicts: tea leaf disease recognition relies heavily on macro-structural lesions, whereas tea leaf-age quality grading depends on micro-textural features such as trichome density and color uniformity. To address this discrepancy, we propose a novel dual branch fusion network. Our architecture fundamentally decouples the feature extraction process by utilizing a dual branch mechanism. The first branch employs global average pooling to capture first-order spatial statistics; it can retain the global structural layout necessary for macro-lesion detection. The second branch introduces a dimensionality-reduced self-bilinear pooling module to compute second-order covariance matrices; it can effectively capture the fine-grained textural patterns essential for micro-grade classification. These decoupled features are subsequently fused and optimized through a weighted multi-task loss function. Experimental results on a comprehensive tea leaf dataset demonstrate that the proposed dual fusion framework significantly outperforms baseline models. The proposed network can rescue the disease classification accuracy drop observed in standard bilinear models while maintaining exceptional grading performance. Furthermore, the proposed network maintains a compact parameter footprint and low computational complexity. This balance renders it suitable for deployment on agricultural Internet of Things edge devices where inference speed is critical.
Why it matches plant phenotyping methods茶葉の病害状態と葉齢品質を画像から推定する深層学習手法を開発し、データセット上でベースラインと比較評価しており、表現型取得・推定法が中心である。
abstractwe propose a novel dual branch fusion network
Reproduction assets foundThe paper's Data Availability Statement publicly releases the two tea leaf image datasets used for its phenotyping tasks (disease recognition and leaf-age quality grading) via Mendeley Data. The authors' analysis code and trained models are only promised 'upon acceptance' with no public URL, so they do not qualify.Dataset · publicThe tea leaf disease recognition dataset analyzed in this study is available from https://data.mendeley.com/datasets/744vznw5k2/3 (accessed on 11 February 2026)Open asset ↗744vznw5k2/3lines:514-565Dataset · publicthe tea leaf grading dataset is available from https://data.mendeley.com/datasets/7t964jmmy3/1 (accessed on 11 February 2026)Open asset ↗7t964jmmy3/1lines:514-565Code / dataset availability confirmedOpenAlex · checked 14 Sept 2026
Background: Tropism, an adaptive growth mechanism often completely overlooked in tree phenotyping studies, is a crucial aspect of tree growth that allows them to reconfigure geometrically in relation to their immediate environment. This study introduces an integrated method to quantify tropic behaviour in plant phenotyping studies. Methods: The methodology combines cost-effective three-dimensional (3D) photogrammetric data capture from video, stem delineation techniques and 3D mathematical modelling of posture control for model-assisted identification of tropism traits. The proposed method was tested on a Pinus radiata D.Don. seedling subjected to a gravitational stimulus for 75 days. Stem posture was repeatedly measured using both 3D photogrammetry and fixed photography to create multitemporal 3D datasets and two-dimensional (2D) reference curves. Results: Individual 3D stem curves reconstructed with the proposed methodology introduced an error on spatial coordinates with a normalised RMSD ranging from 1.6 to 4.3% depending on time of capture, when compared with the 2D reference. The error for local tilt angle was higher than the error on spatial coordinates, with RMSD ranging between 5.6–12.2°, as expected for a first-order derivative. The gravitropic coefficient, capturing the sensing of and the reaction to local inclination by the plant, was underestimated by 2% if compared to the reference methodology. No contribution of autotropism (tendency to remain straight) was identified using the new methodology, but that contribution was found to be small using the 2D-approach and likely a key aspect of the gravitropic signature in the studied species. The major challenge with the proposed point cloud-based methodology arose from automated stem delineation. With dedicated algorithm enhancements to address stem occlusion in juvenile conifers and with more regular captures during plant motion, the proposed method could, however, perform identically to the 2D reference methodology. Overall, recovery of tropism traits performed equivalently whether using 2D or 3D data to fit the model of posture control. The minor discrepancies with experimental behaviour originated from fitting a simple kinematic model to complex real-world behaviour rather than data capture and digitising procedures. Conclusions: Overall, the proposed methodology, in its current form, offers a viable alternative to traditional 2D imagery methods at the cost of a small reduction in accuracy and capture time. The advantage of the 3D methodology is that it has the potential to track motion in multiple planes, whilst also measuring plant structure. With refinement, this methodology could be streamlined and adapted for deployment in field and operational environments at scale for phenotyping studies.
Why it matches plant phenotyping methods3Dフォトグラメトリ、茎の自動抽出、点群解析、姿勢モデルを統合し、植物の屈性形質を定量化・検証する方法が研究の中心である。
abstractThis study introduces an integrated method to quantify tropic behaviour in plant phenotyping studies.
Reproduction assets foundThe paper's data availability statement explicitly deposits the raw photogrammetric point clouds and derived stem curves on Figshare and the R stem-extraction pipeline code on GitHub, both with public URLs.Dataset · publicthe
Ministry of Business Innovation & Employment (MBIE)
New Zealand as part of the Tree Interactions Programme
(Catalyst Fund C09X1923).
Supplementary materials and data availability
The raw photogrammetric point clouds and the stem
curves derived from both photogrammetry and 2D
imagery can be found at the following repository:
https://doi.org/10.6084/m9.figshare.32248617. The
R code for the stem extraction pipeline is available
at https://github.com/Robin-hartley/tropism-stem-curves-3d
Hartley et al. New Zealand Journal of Forestry Science (2026) 56:11 Page 14Open asset ↗figshare · 10.6084/m9.figshare.32248617pdf-raw-page:14 lines:97-113Code · publicamme
(Catalyst Fund C09X1923).
Supplementary materials and data availability
The raw photogrammetric point clouds and the stem
curves derived from both photogrammetry and 2D
imagery can be found at the following repository:
https://doi.org/10.6084/m9.figshare.32248617. The
R code for the stem extraction pipeline is available
at https://github.com/Robin-hartley/tropism-stem-curves-3d
Hartley et al. New Zealand Journal of Forestry Science (2026) 56:11 Page 14Open asset ↗github · Robin-hartley/tropism-stem-curves-3dpdf-raw-page:14 lines:97-113Code / dataset availability confirmedEurope PMC · checked 5 Sept 2026
Background Covered smut in barley caused by Ustilago hordei leads to yield reduction and quality loss of stored grains and is especially challenging in organic production. However, screening for resistance remains challenging. The goal of our research was to evaluate protocols for screening covered smut in barley under normal and speed breeding conditions that could be scaled up for breeding purposes. We considered favorable pathogen growth conditions, a sufficient sample size to detect differences among genotypes through a power analysis, sources of disease escape or avoidance, and the infection effect on agronomic traits. Results In the first experiment, twenty genotypes treated with various inoculum concentrations were screened for disease incidence under a speed breeding system. Generally, low infection levels were found, likely due to disease escape or avoidance. Based on a power analysis, we modified the protocol to include more plants and improved pathogen growth conditions under a normal greenhouse system. With the modified protocol, the incidence of covered smut was significantly different among genotypes. The protocol also reduced the number of plants required to detect at least one infected plant. Artificial inoculation significantly decreased germination rates while head emergence, days to heading, and plant height were affected by disease infection in the most susceptible genotypes. We also found that covered smut incidence varied with tiller emergence order. The genotypes 'DH160779' (RES check), PI 270630', 'CIho15270', and 'MTV-color-158' presented potential resistance to covered smut. Conclusion The protocol has a high power to differentiate moderately resistant barley genotypes and we confirmed that specific agronomic traits were affected by disease incidence in susceptible genotypes.
Why it matches plant phenotyping methodsオオムギ病害の抵抗性スクリーニングプロトコルを評価・改良し、検出力と遺伝子型間の識別性能を検証しているため、植物病害表現型の取得法が研究の中心です。
abstractThe goal of our research was to evaluate protocols for screening covered smut in barley under normal and speed breeding conditions that could be scaled up for breeding purposes.
Reproduction assets foundThe paper's disease-screening and agronomic-trait measurement data are publicly deposited on Zenodo, as stated in the Availability of data and materials section. No author analysis code or trained models are explicitly deposited.Dataset · publicThe data used and/or analyzed in the current study are available through the Zenodo, which is available at Gopinathan, G. (2025). Optimization of a protocol for covered smut in barley [Dataset]. Zenodo. [ 47 ] (https:/doi.org/ https://doi.org/10.5281/zenodo.17906264 ).Open asset ↗Zenodo · 10.5281/zenodo.17906264lines:190-223Code / dataset availability confirmedEurope PMC · checked 5 Sept 2026
This study compiles and releases the first standardized rapeseed phenology observation dataset spanning forty-four years (1981-2024) over the core winter rapeseed production region of the Middle and Lower Yangtze River Plain in China. The data originate from systematic observations at 50 national-level agrometeorological stations across six provinces: Jiangsu, Zhejiang, Anhui, Jiangxi, Hubei, and Hunan. The dataset provides complete records of the specific dates for each phenology stage from sowing to maturity, including eight key phenology periods: Sowing (SO), Emergence (EM), Five-leaf (FV), Bud Formation (BF), Stem Elongation (SE), Flowering (FL), Green Ripening (GR), and Maturity (MA), along with the calculated durations of six distinct growth lengths. We implemented a multi-level quality control protocol encompassing internal logical checks, statistical outlier detection, climatological validation, time series homogenization, and expert arbitration. This protocol effectively constrained data uncertainty and corrected non-climatic discontinuities. Univariate linear regression was further employed to quantify the decadal change trends of each phenology period and growth length, supplemented by Kernel Density Estimation (KDE) to characterize their probability distribution features. The final dataset is presented as structured tables (in xlsx format) and high-resolution diagnostic plots (including trend and density plots), with a total volume of approximately 470 MB, systematically organized by province and station. This dataset fills a critical gap in long-term, standardized rapeseed phenology data for the region. The integrated analysis of phenology dates, growth stage durations, and their trends across the entire network provides an indispensable, high-quality empirical foundation. It is designed to support in-depth investigations into the nonlinear response mechanisms of overwintering crops to climate warming, improve crop model parameterization and validation, and inform regional adaptive management strategies.
Why it matches plant phenotyping methods44年間のナタネの生育段階日を標準化・品質管理して公開するデータセット研究であり、植物状態（フェノロジー）の測定データ整備が中心です。
abstractThis study compiles and releases the first standardized rapeseed phenology observation dataset spanning forty-four years (1981-2024)
Reproduction assets foundThe paper's rapeseed phenology dataset (1981–2024, 50 stations) is openly deposited in Science Data Bank under DOI 10.57760/sciencedb.34086, containing structured xlsx tables and diagnostic plots. No custom code was created per the authors.Dataset · publicThe dataset described in this work has been deposited in the Science Data Bank (ScienceDB) under
accession code https://doi.org/10.57760/sciencedb.34086 [27].Open asset ↗Science Data Bank · 10.57760/sciencedb.34086pdf-page:12 lines:1-68Code / dataset availability confirmedEurope PMC · Crossref · checked 5 Sept 2026
Abstract Biotic stress is a major, yet under-quantified, driver of global soybean yield losses, and field-based phenotyping under pest pressure remains a critical bottleneck for crop improvement. Using multi-temporal data from soybean genotypes grown under insecticide-protected and unprotected conditions in Brazil, we present a UAV-based, large-scale and non-invasive framework for evaluating genotype performance under natural pest pressure. We introduce a three-dimensional metric that jointly captures productivity, feature-level similarity as a proxy for tolerance, and phenological response through days to maturity. This unified formulation enables field-based quantification of pest resilience and replaces labor-intensive and often unreliable direct pest collection and counting. To operationalize this framework, we integrate vegetation indices and self-supervised visual embeddings into a common representation space linking feature stability, performance response and phenological development. This approach enables robust identification of genotypes that maintain feature integrity, minimize developmental delay and sustain yield under pest pressure, with genotypic differences peaking during the pod-fill (R3–R4) and grain-fill (R5.1–R5.5) stages. Overall, this work establishes a scalable, field-ready paradigm for quantifying crop resilience to biotic stress and provides a practical pathway to accelerate breeding for stable yields under real-world agricultural conditions.
Why it matches plant phenotyping methodsUAVによる大規模な圃場フェノタイピング基盤と、植生指数・視覚埋め込みを統合した新しい耐虫性表現型の定量手法が研究の中心である。
abstractwe present a UAV-based, large-scale and non-invasive framework for evaluating genotype performance under natural pest pressure
Reproduction assets foundThe paper explicitly states that the analysis code is publicly available in the authors' GitHub repository (jianglong26/soybean-insect-resistance), which directly reproduces this paper's phenotyping pipeline (orthomosaic processing, VI/DINOv3 feature extraction, similarity analysis, genotype ranking). The paper also声明sCode · public540 The code used for analysis is available at https://github.com/jianglong26/Open asset ↗pdf-page:16 lines:1-45Code / dataset availability confirmedOpenAlex · checked 14 Sept 2026
Early crop establishment strongly influences plant performance and yield, making seedling emergence an important trait in crop phenotyping, breeding, and stress physiology studies. However, emergence monitoring is still commonly performed manually and typically records only the final emergence percentage, limiting the analysis to other dynamic observations. Automated image-based approaches are promising but remain challenging due to the small size of plant structures, heterogeneous soil backgrounds, and variability across imaging systems. Here, we present SPROUT (AI- based S eedling PR edicti O n and trait extraction U sing RGB T ime-series), a low-cost RGB imaging pipeline for automated prediction of crop emergence dynamics and trait extraction. The system integrates instance segmentation, object-detection–based data reduction, and temporal deep learning to estimate the emergence time of individual seedlings from RGB image sequences. The pipeline then automatically reconstructs emergence curves and extracts associated traits, including final emergence percentage, EC50, and emergence synchronicity. SPROUT was developed and evaluated using barley and wheat datasets acquired with different RGB cameras under controlled growth-chamber conditions. In the development and retraining settings, the best-performing TCN model achieved 90.0% per-well accuracy with a ± 2h tolerance, supporting accurate emergence curve reconstruction. In an independent inference-only dataset, the model still captured approximate emergence dynamics, although accuracy decreased to 59.3%, indicating that SPROUT is best used as a modular pipeline that can be retrained or fine-tuned for new crop, camera, or experimental domains. A cadmium-stress case study in two contrasting wheat genotypes showed that SPROUT-derived traits captured genotype-specific establishment strategies associated with growth and metabolic responses.
Why it matches plant phenotyping methodsRGB時系列画像から出芽動態を推定し、出芽率・EC50・同時性などの形質を抽出するパイプラインを開発・評価しており、植物フェノタイピング手法が中心である。
abstractHere, we present SPROUT (AI- based S eedling PR edicti O n and trait extraction U sing RGB T ime-series), a low-cost RGB imaging pipeline for automated prediction of crop emergence dynamics and trait extraction.
Reproduction assets foundThe paper's SPROUT emergence-prediction pipeline code is publicly available on GitHub with explicit availability language, and raw images plus morphology/metabolic data are deposited on Zenodo (10.5281/zenodo.18889863). The GitHub URL is in allowed_urls; the Zenodo DOI is not, so only the code asset is listed as an ad-Code · publiccan be found online at https://doi.org/10.1016/j.compag.2026.112184.Data availability
The raw images and raw data for the morphology and metabolic
profiling on the case study are available in ZENODO (10.5281/zen
odo.18889863), and the code for the machine learning pipeline and
emergence curve analysis are available on GitHub (https://github.com/kit-pef-czu-cz/sprout-emergence-prediction).References
Albarenque, S., Basso, B., Davidson, O., Maestrini, B., Melchiori, R., 2023. Plant
emergence and maize (Zea mays L.) yield across multiple farmers’ fields. Field Crops
Res. 302. https://doi.org/10.1016/j.fcr.2023.109090.Arsovski, A.A., Galstyan, A., Guseman, J.M., Nemhauser, J.L., 2012.
PhotomorpOpen asset ↗kit-pef-czu-cz/sprout-emergence-predictionpdf-raw-page:13 lines:78-112Code / dataset availability confirmedOpenAlex · Europe PMC · checked 5 Sept 2026
Orthoimagery derived from unmanned aerial vehicles (UAVs) has become a valuable data source for crop-growth monitoring. Individual plant-level (IPL) information enables high-throughput analyses by capturing plant-to-plant variability within fields. However, reliable IPL-based analysis requires accurate extraction of plant-specific regions, which remains challenging in soybean cultivation due to weed interference and canopy overlap. This study proposed an automatic preprocessing framework for IPL soybean growth monitoring that integrates deep-learning-based semantic segmentation with a furrow-guided region of interest (ROI) generation strategy using UAV imagery. A segmentation model was developed using combinations of RGB and multispectral orthoimagery, and a furrow line detection algorithm was designed to generate IPL ROIs aligned with crop rows. The ensemble model combining U-Net, DeepLabV3+, and SegFormer achieved the most stable performance (F1-score up to 0.94 and IoU up to 0.89). The furrow-guided ROI generation algorithm also accurately estimated crop counts, showing strong agreement with manual observations (R² = 0.90 and RMSE = 6.35). The generated IPL ROIs enabled accurate quantification of growth-related features, with strong agreement between automatically generated and manually delineated ROIs (R² > 0.90). Overall, the proposed preprocessing framework provides a practical and scalable solution for UAV-based high-throughput phenotyping in soybean and other ridge-based cropping systems.
Why it matches plant phenotyping methodsUAV画像から個体単位の植物領域を抽出し、成長形質を定量化する前処理・セグメンテーション手法が研究の中心であるため。
abstractThis study proposed an automatic preprocessing framework for IPL soybean growth monitoring that integrates deep-learning-based semantic segmentation with a furrow-guided region of interest (ROI) generation strategy using UAV imagery.
Reproduction assets foundThe authors state that the complete implementation of their IPL soybean preprocessing pipeline (semantic segmentation + furrow line detection) is publicly available on Zenodo. The annotated sample dataset, however, is only available upon request from the corresponding author, so it is not a public asset.Code · publicThe complete implementation of
this pipeline is publicly available at https://doi.org/10.5281/zenodo.21095307.Open asset ↗zenodo · 10.5281/zenodo.21095307pdf-page:7 lines:1-62Code / dataset availability confirmedEurope PMC · checked 5 Sept 2026
Spatio-Temporal Fusion (STF) has been widely used across various remote sensing applications, including environmental monitoring, land cover change detection, and water resource management by integrating multi-sensor data with different spatial and temporal resolutions. The objective of this study was to generate spatially and temporally fine-resolution imagery and to evaluate the performance of multiple STF algorithms and Consistent Adjustment of the Climatology to Actual Observations (CACAO) post-processing for parcel-level crop monitoring. The study was conducted in a soybean field located in Anseong, South Korea, covering the entire soybean growing season from late June to early November. Near-daily Planet SuperDove imagery with 3 m resolution was used to temporally enhance UAV images, which were acquired at 0.05 m resolution but only at weekly to monthly intervals. Through the downscaling process, the UAV data were converted into a daily dataset with a target spatial resolution of 0.5 m. Relative radiometric normalization was applied, followed by the implementation and comparison of four STF algorithms- Enhanced Spatial and Temporal Adaptive Reflectance Fusion Model (ESTARFM), Fitting, spatial Filtering and residual Compensation (Fit-FC), Flexible Spatiotemporal Data Fusion (FSDAF), and Variation-based Spatiotemporal Data Fusion (VSDF)-within a 4-fold cross-validation framework. CACAO post-processing was then employed to reconstruct temporally continuous Normalized Difference Vegetation Index (NDVI) and Enhanced Vegetation Index (EVI) trajectories, from which the NDVI-based Vegetation Growth Metrics (VGM)85 and the EVI-based VGMmax were derived. The validation results indicated that ESTARFM achieved the highest NDVI performance among the evaluated algorithms, with a Root Mean Square Error (RMSE) of 0.113 and the Universal Image Quality Index (UIQI) of 0.697. CACAO post-processing further improved these results, with CA-ESTARFM achieving an RMSE of 0.108 and a UIQI of 0.740, corresponding to a 4.4% reduction in RMSE and a 6.2% improvement in UIQI relative to the baseline ESTARFM. NDVI histogram and spatial analyses demonstrated that CA-ESTARFM achieved the most consistent agreement with UAV observations while preserving fine-scale spatial heterogeneity. In addition, intra-field vegetation assessment using NDVI-based VGM85 and EVI-based VGMmax showed that CA-ESTARFM remained consistent with simple linear interpolation of UAV observations while retaining finer spatial structure and reducing localized noise in the derived growth metrics. The proposed framework demonstrates strong potential for applications in comprehensive crop monitoring, precision agriculture management, and yield forecasting.
Why it matches plant phenotyping methodsUAV・衛星画像の時空間融合とCACAO処理により、NDVI/EVIおよび植生成長指標を抽出するワークフローを開発・比較検証しており、植物状態の取得手法が中心である。
abstractThe objective of this study was to generate spatially and temporally fine-resolution imagery and to evaluate the performance of multiple STF algorithms and Consistent Adjustment of the Climatology to Actual Observations (CACAO) post-processing for parcel-level crop monitoring.
Reproduction assets foundThe paper's STF/CACAO analysis code is openly available on Zenodo. The underlying Planet/UAV imagery data are only available from the corresponding author upon request, so they qualify as request_only.Code · publicThe code supporting this study is openly available at Zenodo
(https://doi.org/10.5281/zenodo.20923823).Open asset ↗Zenodo · 10.5281/zenodo.20923823pdf-page:20 lines:1-70Code / dataset availability confirmedEurope PMC · checked 5 Sept 2026
Objective This study presents an integrated, multi-scale approach for the non-destructive estimation of phenological stages and carotenoid content in carrots by combining spectral techniques, colorimetry, and artificial intelligence. Methods Six commercial varieties, including orange, yellow, white, and purple genotypes, were evaluated under field and laboratory conditions using multispectral drone imagery, high-resolution spectroradiometric signatures, red green blue (RGB) images, and CIELAB color measurements. A hierarchical modeling framework was developed across two phases: (i) spectral modeling using uncrewed aerial vehicle (UAV)-based multispectral indices, textural and geometric metrics, and laboratory-generated hyperspectral signatures; and (ii) a colorimetric index from RGB images. Results Using UAV-based multispectral field data, phenological prediction indices achieved high classification performance (F1-scores > 0.90) when modeled with a Random Forest classifier, supported by distinct spectral signatures associated with canopy development and senescence. In parallel, carotenoid content estimation using a Random Forest regression model demonstrated strong predictive accuracy ( R 2 = 0.897; RMSE = 0.584), with the Plant Senescence Reflectance Index (PSRI) and Carotenoid Reflectance Index (CRI) identified as the most influential predictors. A complementary laboratory-based Random Forest regression model using high-resolution spectral signatures achieved near-perfect predictive performance ( R 2 = 0.987). SHapley Additive exPlanations (SHAP) analysis identified physiologically relevant wavelengths in the green (540-550 nm) and red-edge (∼700 nm) regions as the primary drivers of carotenoid concentration. Likewise, a novel colorimetric index (ICarot), derived from CIELAB parameters, enabled accurate image-based carotenoid estimation ( R 2 = 0.85). Conclusion This study introduces an innovative multi-sensor framework for precision agriculture and automated postharvest quality control, enabling rapid, objective, and scalable phenotyping in carrot production systems. Through the integration of spectral, colorimetric, and AI-based approaches, the proposed methodology effectively captures both internal nutritional attributes and external quality traits within a unified, non-destructive assessment pipeline.
Why it matches plant phenotyping methods複数センサー画像・分光計測とAIを統合し、ニンジンの生育段階およびカロテノイド含量を非破壊推定する手法を開発・評価しており、表現型取得が研究の中心である。
abstractThis study presents an integrated, multi-scale approach for the non-destructive estimation of phenological stages and carotenoid content in carrots by combining spectral techniques, colorimetry, and artificial intelligence.
Reproduction assets foundThe paper's Data Availability section explicitly deposits the study's data (and project materials) on GitHub and Zenodo, both with authors' public URLs matching allowed_urls. These qualify as paper-specific public assets for the carrot phenotyping measurements and analysis.Dataset · publicThe data is available at GitHub and Zenodo:
- https://github.com/agrocompuepidemlab/Carrot-value-chain-proyect/tree/mainOpen asset ↗github.com/agrocompuepidemlab/Carrot-value-chain-proyectlines:184-307Code / 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 confirmedEurope PMC · Crossref · checked 6 Sept 2026
Abstract Satellite-based prediction of grain protein concentration (GPC) in wheat typically relies on spectral observations composited over fixed calendar windows, implicitly assuming phenological synchrony across fields. This study tests whether aligning multi-source remote sensing time series to field-specific phenology-based windows improves field-level GPC prediction. We integrated Sentinel-2 multispectral imagery (32 vegetation indices, 10 spectral bands), ERA5-Land meteorological reanalysis, gSSURGO soil properties, and USGS 3DEP topographic data, and systematically compared six temporal strategies, the factorial combination of two normalization approaches (peak-relative vs.\calendar) and three resolutions (monthly, biweekly, growth stages), across 228 commercial winter wheat fields in western Kansas (2024--2025). Three ensemble tree models (Random Forest, XGBoost, LightGBM) were trained under nested cross-validation with Boruta feature selection. Peak-relative monthly normalization achieved the highest accuracy (\((R^2 = 0.304 \pm 0.051)\), RMSE \((= 1.11)\)%), explaining an additional 5.1% of variance compared with the best calendar strategy (\((R^2 = 0.253)\)). A single 30-day post-peak window (M\((+)\)1, \((\sim)\)15--45 days after maximum canopy greenness) carried more predictive information than any broader aggregation. SHAP analysis identified topsoil organic matter, SWIR-based senescence indices (NBR2, MIRBI), and grain-filling temperature as the most influential predictors. Three-class quality classification reached 47--49% accuracy (versus 33.3% by chance), indicating practical utility for early grain segregation. While demonstrated for wheat GPC, the framework is transferable to other crop traits with temporally concentrated satellite signals, particularly those tied to specific developmental stages. The results highlight phenological alignment as a generalizable strategy for trait prediction from Earth observation data.
Why it matches plant phenotyping methods衛星リモートセンシング時系列を用いた小麦粒タンパク質濃度予測のため、フェノロジー整列と複数の時間集約戦略を体系的に比較・検証しており、植物形質推定手法が研究の中心である。
abstractThis study tests whether aligning multi-source remote sensing time series to field-specific phenology-based windows improves field-level GPC prediction.
Reproduction assets foundThe paper's data availability statement releases a de-identified field-level GPC dataset alongside a public authors' code repository (Ciampitti-Lab WheatGPCPipeline) implementing the data-acquisition, feature-engineering, and modeling pipeline. Both are paper-specific, public, and actionable.Code · publicthe figure-generation scripts is available at https://github.com/Ciampitti-Lab/Open asset ↗pdf-page:48 lines:1-55Code / dataset availability confirmedOpenAlex · Europe PMC · checked 15 Sept 2026
Plant phenotyping based on unmanned aerial vehicles still faces challenges regarding the direct correlation between spectral information with field-collected variables, due to the influence of environmental factors and the considerable variation among maize phenological stages. Therefore, the objectives of this research were: I) to evaluate the interaction of nitrogen doses and evaluation environments (phenological stages and growing seasons) and variance components for field variables and vegetation indices; II) to identify the most suitable indices according to the evaluation environments; and III) to predict field variables based on relevant vegetation indices identified through the proposed methodology. The study was conducted using a randomized complete block design with four repetitions, in which treatments consisted of six nitrogen (N) topdressing doses (0, 50, 100, 200, 300, and 400 kg ha−1) during the 2022/2023 and 2023/2024 growing seasons. Evaluations of agronomic variables and image acquisition were performed in five distinct phenological stages throughout the maize crop cycle. The data were analyzed using deviance analysis and variance components, principal component analysis (PCA), and multivariate linear modeling for the prediction of field variables. Our results demonstrated that all indices were affected by the interaction between N doses and evaluation environments (phenological stages and growing seasons). Additionally, the most reliable were EXGRaw, TGI, GNDVI, NDRE, CIRE, GVI, CVI, BNDVI, PanNDVI, SRNIRRe, SFDVI, RGBindex, NDVI, SAVI, MSAVI, and OSAVI, which showed clustering patterns according to growing season condition and phenological stage. Finally, the variables predicted using the proposed methodology achieved coefficients of determination above 0.80, except for shoot biomass and 100-grain weight. Therefore, it can be concluded that vegetation indices are influenced by the evaluated environment; however, the proposed framework based on the deduction of fixed and random effects enables the prediction of field variables with high accuracy using relatively simple models.
Why it matches plant phenotyping methodsUAVマルチスペクトル画像から植生指数を選定し、農業形質を予測する方法論の開発・評価が研究の中心であり、植物形質の取得・推定に直接関与している。
titleMethodology for Selecting Stable UAV-Based Vegetation Indices for Prediction of Agronomic Variables in Maize Using a Multispectral Sensor.
Reproduction assets foundThe paper's supplementary file contains the REML-BLUP adjusted values for all vegetation indices and field variables, which directly reproduce the paper's phenotyping measurements and underpin its computational analysis. The raw UAV imagery and field data are only available on request, and the EstimateBreed R package (Dataset · publicdual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.
Supplementary Materials
The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/plants15121782/s1 , Table_Supplementary_1. This table contains all vegetation indices and field variables with values adjusted using the RELM-BLUP methodology.
Author Contributions
C.d.S.L.: Conceptualization, methodology, validation, visualization, writing—original draft, writing—review and editing. A.J.T.S.: Data collection and iOpen asset ↗lines:76-146Code / dataset availability confirmedEurope PMC · checked 15 Sept 2026
WheatGrowth chamberLeafGrowth / time-series analysisGrowth / development / phenology
Disentangling genotype × environment (G×E) controls of flowering time requires phenotypes that link molecular regulation, developmental physiology and environment. Here, we integrated time-resolved measurements of apical development, final leaf number (FLN), and expression of the flowering-time genes VRN1, VRN2 and VRN3 across contrasting temperature and photoperiod regimes in six wheat genotypes spanning a wide range of developmental sensitivities. By combining controlled-environment phenotyping with concurrent gene-expression profiling, we show that environmentally driven variation in FLN is coherently explained by shifts in the timing of key apical transitions and associated VRN gene-expression dynamics. These integrated datasets were used to parameterise and interrogate the Cereal Anthesis Molecular Phenology (CAMP) model, enabling direct comparison between observed foliar gene-expression time courses and modelled gene activity. While overall developmental responses were well captured by the model, systematic differences between observed and modelled gene-expression patterns highlight the importance of distinguishing foliar expression from apical regulatory activity, as well as differences in temporal scaling. Building on this framework, we present a phenotyping protocol based on FLN responses to defined temperature and photoperiod treatments that delivers unconfounded developmental phenotypes explicitly linked to underlying genetic regulation.
Why it matches plant phenotyping methodsFLN応答に基づくフェノタイピングプロトコルを提示し、温度・光周期処理下で遺伝的に解釈可能な発育表現型を取得する方法が中心的に扱われている。
abstractBuilding on this framework, we present a phenotyping protocol based on FLN responses to defined temperature and photoperiod treatments that delivers unconfounded developmental phenotypes explicitly linked to underlying genetic regulation.
Reproduction assets foundThe paper's CAMP model code and the analysis scripts producing its figures are explicitly stated as publicly available on the authors' GitHub repository, directly reproducing this paper's phenotyping analysis.Code · publicwere also validated and the best-performing sets selected. A
348 description of each of the primers used in this study is given in the supplementary material
349 (Table SA1).
350 2.9 Verification of CAMP predictions
351 2.9.1 Model set-up and operation.
352 The CAMP model was coded into a Python script which is available at
353 https://github.com/HamishBrownPFR/CAMP/blob/master/CAMP.ipynb. A formal
354 description of the code and parameterisation scheme is given in the supplementary material.
355 The FLN developmental phenotypes measured for each genotype (Section 3.1) were used to
356 derive the Vrn expression parameters needed for CAMP. Each of the treatments was
357 simulated using CAMP wOpen asset ↗https://github.com/HamishBrownPFR/CAMP/pdf-layout-page:14 lines:1-49Code · publicpression parameters needed for CAMP. Each of the treatments was
357 simulated using CAMP with its corresponding daily temperature and Pp, so its predictions of
358 Vrn gene expression could be compared with those observed. The script running the CAMP
359 code and producing the graphs displayed in this paper can be viewed at
360 https://github.com/HamishBrownPFR/CAMP/blob/master/Tests/CAMPCETests.py.
14
UNOFFICIALOpen asset ↗https://github.com/HamishBrownPFR/CAMP/pdf-layout-page:14 lines:1-49Code · publicnd testing of the model in
690 broader contexts. EW contributed substantially to the improvement of model concepts and the
691 manuscript and all authors provided final checking.
692 8. Data Availability
693 All the data and scripts used to analyse data and produce graphs as well as CAMP model code are
694 publicly available at https://github.com/HamishBrownPFR/CAMP/
695 9. References
696 Allard V, Otto V, Bela K, Rousset M, Le Gouis J, Martre P. 2012. The quantitative
697 response of wheat vernalization to environmental variables indicates that vernalization is not
698 a response to cold temperature. Journal of Experimental Botany 63: 847–857.
699 Baumont M, Parent B, Manceau L, Brown HE,Open asset ↗https://github.com/HamishBrownPFR/CAMP/pdf-layout-page:31 lines:1-60Code / dataset availability confirmedEurope PMC · Crossref · checked 5 Sept 2026
This study suggests a novel extraction pipeline based on terrestrial laser scanning across multiple growth stages to address the current deficiency of three-dimensional (3D) phenotypic traits for wheat populations derived from 3D point clouds. This study presents 3D Wheat Point-seg Net (3D WP-seg Net), a novel 3D point cloud segmentation network that incorporates an SA-CrossAttention module to address the difficulties presented by complex structures, background noise, non-uniform point distributions, and scale variations in plot-level wheat point cloud data. Plot height, canopy area, and volume are examples of common phenotypic parameters that are successfully extracted using this technique. Additionally, two new phenotypic parameters: plot extension distance and lodging angle are suggested by fusing the centroid and slice-skeletonization algorithms. A software platform called 3D Trait Analysis was created to facilitate multi-sensor 3D data processing and trait extraction. A genome-wide association study (GWAS) was then conducted using the extracted population-level traits to find potential genes linked to these new phenotypes. While the segmentation accuracies of 3D WP-seg Net achieved 93.1%, 88.3%, and 92.5% under various sensor systems, the results showed a strong correlation between the predicted and measured plot heights (R 2 = 0.954). Furthermore, four candidate genes linked to extension distance were found on chromosomes 1A, 2A, and 4A, and five putative genes controlling plot lodging angle were found on chromosomes 2D, 3A, and 7A. The multi-stage 3D phenotyping and analysis framework for wheat populations established by this study improves the accuracy of point cloud segmentation and trait quantification while offering a new and efficient method for the genetic analysis of important population-level traits.
Why it matches plant phenotyping methodsLiDAR点群の分割、3D形質抽出、検証、ソフトウェア基盤の開発が研究の中心であり、コムギの形態・倒伏関連形質を定量化しているため。
abstractThis study presents 3D Wheat Point-seg Net (3D WP-seg Net), a novel 3D point cloud segmentation network
Reproduction assets foundThe paper's data availability statement explicitly deposits the authors' source code (3D WP-seg Net segmentation pipeline and 3D Trait Analysis software), testing data, and supporting datasets in a public GitHub repository, directly supporting this paper's wheat 3D phenotyping and segmentation analysis.Code · publicThe source code, testing data, and other datasets supporting the results presented here are available at https://github.com/AI-PhenoLab/3D-WP-seg-Net .Open asset ↗AI-PhenoLab/3D-WP-seg-Netlines:511-575Code / 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 confirmedCrossref · OpenAlex · Europe PMC · checked 14 Sept 2026
Modern plant phenotyping faces the challenge of interpreting complex, high-dimensional data. Traditional analytical tools often fail to capture the non-linear, hierarchical, and temporal relationships that define plant responses under multifactorial conditions. We present the Hyperbolic Topological Data Analysis Mapper (HTDA-Mapper), a novel algorithm designed to overcome these limitations by embedding data in Poincaré ball space. Unlike conventional Euclidean approaches, HTDA-Mapper preserves the hierarchical structure of phenotypic traits, improves cluster resolution, and reveals hidden growth trajectories across treatments and time, offering a powerful means to explore latent phenoms. The pipeline supports both quantitative data and images. When integrated with unsupervised contrastive learning, HTDA-Mapper identifies similarities and differences in raw image data without requiring manual labelling or post hoc processing. We applied this framework to a high-throughput phenotyping (HTP) dataset of over 27,000 images of Arabidopsis thaliana seedlings exposed to varying nutrient levels and priming agents at different concentrations over seven days. Using cubical complexes, HTDA-Mapper mapped relationships between treatment variables, compound concentrations, and phenotypic outcomes. Furthermore, it reliably detected compound-specific effects, uncovered dynamic trait–environment interactions, revealed phenotypic trajectories not captured by conventional methods, and facilitated biologically meaningful interpretation of the complex dataset. By preserving the geometry and temporal evolution of plant development, HTDA-Mapper sets a new standard for HTP analysis. Beyond phenomics, it is a versatile tool for other omics, such as transcriptomics and metabolomics, where structured, high-dimensional data is prevalent. HTDA-Mapper can accelerate data-driven crop improvement by uncovering effective compounds, robust genotypes, and adaptive growth strategies that enhance plant resilience.
Why it matches plant phenotyping methods植物フェノミクスの高次元画像・形質データを解析するHTDA-Mapperアルゴリズムを開発し、27,000枚超の植物画像データで適用・評価しているため、解析手法が中心的である。
abstractWe present the Hyperbolic Topological Data Analysis Mapper (HTDA-Mapper), a novel algorithm designed to overcome these limitations by embedding data in Poincaré ball space.
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 · publicUpon acceptance, the codes and all material used in this research will be freely available at HYPERLINK: https://github.com/JZdrazilX/MML and data at ZENODO: 10.5281/zenodo.17952279.Open asset ↗JZdrazilX/MMLhtml-lines:222-260Code / dataset availability confirmedEurope PMC · checked 5 Sept 2026
The increasing global food insecurity driven by climate-induced natural hazards and soil degradation has made the resilience of alternative agricultural systems a critical focus in risk management. This study presents a geospatially integrated monitoring framework, the Optimized Multi-Scale Adaptive Graph Neural Network (OMSA-GNN), designed to mitigate risks associated with nutrient instability in hydroponic and aeroponic environments. The proposed system leverages a Raspberry Pi-based IoT network to monitor complex interactions among microclimatic variables, plant physiological health, and nutrient concentrations, treating them as localized geospatial data points. To enhance decision-making under environmental uncertainty, an Improved Sparrow Search Algorithm (ISSA) is employed to optimize the predictive performance of the GNN. The OMSA-GNN model incorporates visual plant indices as a proximal remote sensing approach to enable early detection of physiological stress that may lead to crop failure. Evaluated using a lettuce growth dataset, the framework demonstrates superior performance in forecasting growth trajectories and managing resource-related risks compared to conventional static models. The results highlight a scalable approach for improving the reliability of urban food systems, where traditional land-based agriculture is increasingly vulnerable to natural hazards.
Why it matches plant phenotyping methods植物の生理的ストレスと成長軌跡を、視覚的植物指数およびIoTセンサーデータから推定するGNNベースの監視・解析手法が研究の中心であり、植物表現型取得と予測に該当する。
abstractThe OMSA-GNN model incorporates visual plant indices as a proximal remote sensing approach to enable early detection of physiological stress that may lead to crop failure.
Reproduction assets foundThe paper's Data Availability statement points to a public Kaggle lettuce growth dataset used for evaluation, matching an allowed URL. No author code or model checkpoints are disclosed.Dataset · publicThe datasets used and/or analyzed during the current study are available in the Kaggle repository, https://www.kaggle.com/datasets/jurijsruko/lettuce/data.Open asset ↗Kaggle · jurijsruko/lettucehtml-lines:469-500Code / dataset availability confirmedEurope PMC · checked 8 Sept 2026
Field / plotMultimodalWhole plant / canopy / plot / fieldClassificationCountingGrowth / development / phenology
Phenological monitoring of Actinidia chinensis is critical for optimising operational costs and yield prediction. However, current manual assessment methods are time-consuming, making them impractical for large-scale precision agriculture applications. Most existing phenological datasets focus exclusively on image data without spatial validation. The Multi-Modal Actinidia chinensis Phenology Dataset is composed of (i) 1 665 annotated images of phenological stages from bud to fruit set and (ii) georeferenced videos with systematic manual ground truth of spatial stage distributions. The dataset employs an adapted 17-class BBCH system that consolidates visually similar stages, excludes problematic categories, and introduces generic structural classes to address practical annotation difficulties. Additionally, the data is organised hierarchically across various plant structures, genders, and phenological stages. The annotated images offer versatility for a range of applications, including training data for computer vision models to detect phenological stages. Furthermore, the georeferenced videos facilitate the validation of automated counting algorithms. This combined approach enables plant-level detection accuracy and provides an illustrative methodology for spatial validation that users can extend to additional orchards, promoting the development and benchmarking of automated phenological monitoring systems for precision agriculture applications in kiwifruit production.
Why it matches plant phenotyping methodsキウイフルーツの生育段階を対象とした注釈画像・地理参照動画データセットであり、自動フェノロジー検出と空間検証のためのベンチマーク基盤が中心である。
abstractThe Multi-Modal Actinidia chinensis Phenology Dataset is composed of (i) 1 665 annotated images of phenological stages from bud to fruit set and (ii) georeferenced videos with systematic manual ground truth of spatial stage distributions.
Reproduction assets foundThe paper describes a public multi-modal Actinidia chinensis phenology dataset (annotated images, georeferenced videos, ground-truth counts) deposited on Zenodo, plus authors' MIT-licensed preprocessing scripts on GitHub. CVAT and FiftyOne are generic third-party tools and excluded.Dataset · publicThe Multi-Modal Actinidia chinensis Phenology Dataset described in this Data Descriptor is publicly available
at Zenodo: https://doi.org/10.5281/zenodo.17371025.Open asset ↗Zenodo · 10.5281/zenodo.17371025pdf-page:12 lines:1-92Code · publicCustom scripts for dataset preparation are publicly available under the MIT License at https://github.com/Open asset ↗GitHubpdf-page:12 lines:1-92Code / dataset availability confirmedEurope PMC · checked 14 Sept 2026
Understanding cellular growth dynamics in plants requires precise, long-term imaging of developing tissues. Cauline leaves are produced during the transition from vegetative to reproductive development and provide a useful system for studying how laminar organs diversify in form and function. While other laminar organs, such as rosette leaves and sepals, have been extensively studied, early cauline leaf development remains technically challenging to capture due to their concealed position, curved morphology, and the presence of dense trichomes. Here, we provide a complete pipeline for the dissection, confocal imaging, 2.5D segmentation, and image analysis of initiating cauline leaves in Arabidopsis thaliana . This method enables reproducible, high-resolution imaging of cauline leaves, supporting robust quantitative analysis of growth across developmental stages at cellular scale resolution. Key features • Fine dissection method for exposing initiating cauline leaves in Arabidopsis thaliana . • Long-term confocal live imaging of cauline leaf development at cellular resolution. • Optimized imaging parameters for high-fidelity 2.5D segmentation and growth analysis in MorphoGraphX.
Why it matches plant phenotyping methodsカウリン葉の成長を細胞レベルで定量化するための解剖、共焦点イメージング、2.5Dセグメンテーション、画像解析パイプラインが中心的に開発・提示されている。
abstractHere, we provide a complete pipeline for the dissection, confocal imaging, 2.5D segmentation, and image analysis of initiating cauline leaves in Arabidopsis thaliana .
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 · public2. MorphoGraphX 2.0.1 ( https://morphographx.org/software/ ) (access date, 2026-02-26) [10–11]
3. All codes have been deposited to OSF: https://osf.io/uth78/ (access date, 2026-02-26)
Procedure
A. Plant growth
1. Sow the seeds in pots filled with moist, room-temperature soil. Add a layer of water to the bottom of the tray and cover with a lid to maintain high humidity.
Note: Space seeds sufficiently to avoid contact between the developing plants and to prevent leaf damage; typicallyOpen asset ↗OSFlines:109-143Code / dataset availability confirmedEurope PMC · checked 5 Sept 2026
Genomic selection (GS) can accelerate crop breeding and enhance selection efficiency. However, accurately predicting genomic estimated breeding values (GEBVs) for complex traits and applying GS in diverse environments remains challenging. To address these issues, we developed a novel hybrid method capable of modelling gene-gene and gene-environment interactions. This method offers precise predictions of phenotypic performance for complex traits, identifies haplotypes associated with desirable phenotypes, and enables prediction of optimal haplotypes tailored to specific environments. We evaluated the approach using a dataset of 855 barley lines, with phenotypic data for grain yield and flowering time collected across multiple environments. The model incorporated 30,543 SNPs, nine soil parameters, and six daily environmental variables, achieving high prediction accuracies, with correlation coefficients of 0.93 for flowering time and 0.82 for grain yield. Our method identified 10 haplotype blocks significantly associated with flowering time and 13 blocks with grain yield, collectively accounting for over 90% of the total genetic variance. Additionally, we predicted the phenotypic effects of each haplotype and identified elite varieties carrying the most favourable haplotypes for crossing design and selection. The method also allows prediction of untested genotype × environment combinations, enabling selection of optimal genotypes for targeted environments. To facilitate its application, we developed a web-based interface (accessible at [https://penghaowang.shinyapps.io/shinygui/]), which enables breeders to identify optimal haplotypes and the varieties that carry them, streamlining the process of haplotype-based, environment-informed breeding. We note that the reverse prediction framework is currently applied on a single-trait basis and does not resolve multi-trait trade-offs such as between flowering time and yield, which remains a topic for future extensions.
Why it matches plant phenotyping methods複雑形質の表現型性能を遺伝子型・環境情報から予測する新規計算手法を開発し、オオムギの収量・開花期で評価している。ウェブインターフェースも提供され、形質推定ワークフローが中心である。
abstractwe developed a novel hybrid method capable of modelling gene-gene and gene-environment interactions.
Reproduction assets foundThe paper deposits its barley genotype, phenotype, and environmental datasets at three DOI repositories, and its analysis source code on GitHub, plus a public Shiny web tool.Dataset · publicDetailed information on all experimental lines, including their genotypes, phenotypic, and environmental data, is available at https://doi.org/10.60867/00000010 , https://doi.org/10.60867/00000003 , and https://doi.org/10.60867/00000011 , respectively.Open asset ↗10.60867 · 10.60867/00000010lines:31-42Dataset · publicDetailed information on all experimental lines, including their genotypes, phenotypic, and environmental data, is available at https://doi.org/10.60867/00000010 , https://doi.org/10.60867/00000003 , and https://doi.org/10.60867/00000011 , respectively.Open asset ↗10.60867 · 10.60867/00000003lines:31-42Dataset · publicDetailed information on all experimental lines, including their genotypes, phenotypic, and environmental data, is available at https://doi.org/10.60867/00000010 , https://doi.org/10.60867/00000003 , and https://doi.org/10.60867/00000011 , respectively.Open asset ↗10.60867 · 10.60867/00000011lines:31-42Code · publicAll the data and source codes have been uploaded to GitHub and can be accessed under the GNU Open License at: https://github.com/pwang2019/GxE_Model .Open asset ↗github.com/pwang2019/GxE_Modellines:196-205Code / dataset availability confirmedEurope PMC · checked 5 Sept 2026
AppleField / plotRGB / grayscaleFruitClassificationObject detectionSegmentationGrowth / development / phenology
This article presents a comprehensive dataset of 1406 RGB images of apples ( Malus domestica ), covering three key growth stages-immature (green), semi-mature (color transition), and mature (red). The dataset serves as a resource for detecting and segmenting apples across different developmental phases. Each image includes pixel-level instance segmentation masks annotated in JSON format using the VGG Image Annotator (VIA), ensuring compatibility with deep learning frameworks. The dataset's real-world variability-spanning lighting conditions, occlusions, and clustered fruit arrangements-enhances its utility for training generalizable computer vision models in precision agriculture. It supports tasks such as fruit detection, segmentation and growth-stage classification, addressing the scarcity of annotated data for transitional maturity phases. With 2574 annotated apple instances, this dataset facilitates research on maturity grading and transfer learning for agricultural robotics. By standardizing annotations and incorporating diverse field conditions, this dataset reduces preprocessing overhead and accelerates the development of deployable AI solutions for orchard management. It is particularly valuable for improving model robustness in heterogeneous environments, thereby advancing data-driven horticultural practices.
Why it matches plant phenotyping methodsリンゴ果実の発育段階・成熟度という植物器官の状態を対象に、画素単位アノテーション付き画像データセットを構築しており、観測・抽出手法の再利用可能な基盤が中心である。
abstractThis article presents a comprehensive dataset of 1406 RGB images of apples ( Malus domestica ), covering three key growth stages-immature (green), semi-mature (color transition), and mature (red).
Reproduction assets foundThe paper is a data descriptor for a public apple image dataset (1406 RGB images, pixel-level instance segmentation masks in JSON) deposited on Mendeley Data with a direct URL and DOI, matching the allowed URL exactly.Dataset · publicRepository name: Wang, Dandan; Wang, Bo (2026), “A Multi-Stage, Pixel-Level Annotated Apple Dataset for Precision Agriculture Research”, Mendeley Data, V4
Data identification number: 10.17632/gfcmdbvw65.4
Direct URL to data:https://data.mendeley.com/datasets/gfcmdbvw65/4Open asset ↗Mendeley Data · 10.17632/gfcmdbvw65.4html-lines:1-97Code / dataset availability confirmedCrossref · Europe PMC · checked 14 Sept 2026
LiDAR / point cloudFlowerLeafStem / branchSegmentationGrowth / development / phenology
The segmentation of 3D point clouds of plant organs, such as leaves and stems, helps to monitor plant growth and is a key step in plant growth phenotype analysis. Compared to point cloud segmentation tasks in other fields, plant point cloud segmentation is more challenging due to the interwoven distribution of various parts such as stems, leaves, and flowers. In this paper, we propose a universal point cloud segmentation network PlantEFRSegnet that can be used for multi-species of plants. The proposed PlantEFRSegnet utilizes a newly designed edge point preservation downsampling module to identify and preserve the points at the edges of plant organs during the downsampling process, in order to assist the segmentation network in learning the contours of various plant organs. PlantEFRSegnet performs supervised feature repair on the point cloud features obtained through downsampling to mitigate the impact of feature loss on segmentation performance during feature embedding. The encoder of the segmentation network is composed of four local feature extraction modules. These four modules can not only extract features but also enhance the features corresponding to points with high contributions in local regions based on point attention mechanism. We evaluated the proposed PlantEFRSegnet on a laser-scanned plant point cloud dataset. Compared with the state-of-the-art approaches, the proposed PlantEFRSegnet achieved better segmentation results.
Why it matches plant phenotyping methods植物器官の3D点群を対象に、器官分割と植物成長フェノタイプ解析を行う新規ネットワークを開発・評価しており、フェノタイプ取得の計算手法が中心である。
abstractThe segmentation of 3D point clouds of plant organs, such as leaves and stems, helps to monitor plant growth and is a key step in plant growth phenotype analysis.
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 experimental dataset used in this paper can be obtained through the following link: https://github.com/dllab23/PlantPointCloud (accessed on 11 May 2026).Open asset ↗dllab23/PlantPointCloudhtml-lines:785-806Code / dataset availability confirmedCrossref · checked 15 Sept 2026
Plant disease is a serious threat to agricultural productivity and food security worldwide. Traditional diagnostic methods such as manual observation and laboratory testing are time-consuming, labor-intensive and error prone. The emergence of artificial intelligence (AI) and deep learning (DL) offer scalable solutions for precision agriculture in plant disease detection using advanced computational techniques to process large datasets. Hybrid deep learning architecture integrates Convolutional Neural Networks (CNNs) along with Artificial Neural Networks (ANNs) can leverage both visual and contextual data to improve detection performance. The hybrid CNN-ANN model was developed to analyze visual data (plant images) and contextual data (environmental and soil metrics). The CNN module extracted spatial and textural features from plant images, while the ANN module processed environmental parameters. These outputs were fused into a unified feature vector for disease classification. A total of 15 plant species and their associated diseases were analyzed using 200-270 training samples and 150-190 testing samples for each disease across a total of 1000 images. The model was judged by metrics such as detection accuracy, AUC, sensitivity etc. Data augmentation, pre-trained architectures (e.g., ResNet50) and early stopping techniques were utilized to improvise model performance. The hybrid model saliently achieved detection accuracy consistently above 87% with majority of diseases surpassing 90%. Highperforming cases like Rice Blast (92.5%), Tomato Early Blight (93.8%), and Coffee Rust (93.0%), with AUC values of 0.93 or higher, sensitivity exceeding 94% and specifically above 90%. Diseases of Sugarcane Red Rot and Tea Blister Blight exhibited sensitivities of 92.4% and 92.1% and specificities of 91.1% and 90.5% respectively. Moderate accuracy for Coconut Bud Rot (87.5%) and Mustard Alternaria Blight (87.8%) was due to smaller training sample sizes.
Why it matches plant phenotyping methods植物画像から病害状態を分類するハイブリッドCNN-ANN手法を開発し、精度・AUC・感度などで評価しており、病害フェノタイピング手法が研究の中心である。
abstractThe hybrid CNN-ANN model was developed to analyze visual data (plant images) and contextual data (environmental and soil metrics).
Reproduction assets foundThe paper's plant disease detection model was trained on public plant image datasets: the PlantSeg dataset (Zenodo record 13958858, DOI 10.5281/zenodo.13293891) and the UCI Machine Learning Repository Plants dataset. Both are cited in Materials and Methods as sources of the visual data used for the CNN module. No code,Dataset · publicebao (ZiranKexueBan)/Journal of
Huazhong University of Science and Technology
(Natural Science Edition). 2021;49(8).
37. Ma C, Mu X, Sha D. Multi-Layers Feature Fusion of
Convolutional Neural Network for Scene
Classification of Remote Sensing. IEEE Access.
2019;7.
38. Hämäläinen W.Plants Dataset[Internet]. 2024.
Available from:
https://archive.ics.uci.edu/dataset/180/plants
39. WeiT. PlantSeg: A Large-Scale In-the-wild Dataset
for Plant Disease Segmentation [Internet]. 2018.
Available from: https://zenodo.org/records/13958858Open asset ↗180pdf-raw-page:15 lines:1-48Code / dataset availability confirmedOpenAlex · Europe PMC · checked 5 Sept 2026
Crop phenology is a critical determinant for yield prediction and germplasm evaluation. However, precise phenological monitoring in large-scale rice breeding trials faces significant challenges due to the inherent phenological asynchrony among hundreds of cultivars and the trade-off between spatial resolution and temporal continuity in unmanned aerial vehicle (UAV) remote sensing. To address these issues, this study proposes a multi-scale temporal deep learning framework that integrates high-frequency medium-resolution (MR) images as temporal anchor with sparse high-resolution (HR) images as spatial enhancement. We introduce a Missing Aware Gated Fusion (MAGF) mechanism to dynamically integrate multi-resolution features on non-aligned timelines, enabling robust modeling under irregular sampling conditions. Validated on a massive dataset covering approximately 500 rice cultivars and over 100,000 images across 2023 and 2024 growing seasons, the proposed method significantly outperformed single-temporal-scale baselines despite multiple growth stages coexisting within the same dates. The integration of multi-spatial-scale fusion with LSTM temporal modeling yielded superior performance considering efficiency, achieving an Overall Accuracy (OA) and F1-score of 0.873, with a Kappa coefficient of 0.84. A hybrid sampling strategy (daily MR image combined with weekly HR image) demonstrates that weekly flight time can be reduced from 28 h to approximately 6 h while maintaining high accuracy. Notably, even when HR acquisition was reduced to a once every 14 days frequency, the fusion performance remained significantly superior to that of daily MR monitoring alone. The model exhibited strong generalization capabilities. When directly applying the model trained on 2024 data to the 2023 dataset, it maintained an OA of 0.774 and an F1-score of 0.738 under a 3-day error tolerance, with recall for the maturity stage consistently exceeding 0.96. This framework offers a flexible, scalable, and cost-effective solution for high-throughput phenotyping in precision breeding.
Why it matches plant phenotyping methodsUAVリモートセンシング画像と深層学習によるイネの生育ステージ（フェノロジー）推定手法を開発・検証し、大規模育種データで性能評価しているため、フェノタイピング手法が中心である。
abstractTo address these issues, this study proposes a multi-scale temporal deep learning framework that integrates high-frequency medium-resolution (MR) images as temporal anchor with sparse high-resolution (HR) images as spatial enhancement.
Reproduction assets foundThe article explicitly states that the authors' source code and test samples for the rice phenology identification framework are publicly available on GitHub, matching an allowed URL. No public dataset deposit is stated; additional data is only on request.Code · publicThe source code and test samples used in this study are publicly available at: https://github.com/gfjiyue/Rice-phenology-identification-by-UAV . Additional data can be made available upon reasonable request.Open asset ↗https://github.com/gfjiyue/Rice-phenology-identification-by-UAVlines:601-709Code / dataset availability confirmedOpenAlex · checked 14 Sept 2026
ABSTRACT Lodging is a major contributor to decreased yield in tef, a staple cereal crop in Ethiopia. Semidwarf varieties have been developed with a goal to increase yield through reduced lodging, but studying lodging susceptibility currently requires a labor‐intensive, imprecise, manual scoring method. Here we present workflows for analyzing tef stand height from UAS sensors across time to both predict lodging later in the season with early height and to measure the severity of lodging after a storm event. We compare 3D point clouds generated by photogrammetry from RGB images with those generated from LiDAR to estimate height, demonstrating that they produce similar results, despite differences in cost. Stand height and lodging can both be accurately measured with low‐cost UAS, reducing the need for manual measurements and increasing precision and temporal resolution in plant breeding programs.
Why it matches plant phenotyping methodsUAS画像・LiDARによるテフの草高と倒伏程度の推定ワークフローを開発・比較し、育種での測定精度向上を示す中心的な表現型計測研究。
abstractHere we present workflows for analyzing tef stand height from UAS sensors across time to both predict lodging later in the season with early height and to measure the severity of lodging after a storm event.
Reproduction assets foundThe paper's Data Availability Statement and Methods sections point to a public GitHub repository containing the authors' analysis code and associated data (including PheNode sensor data), plus the PlantCV-Geospatial package used for the RGB/LiDAR height and lodging analysis.Code · publicthe USDA NIFA AFRI (Grant Number
2022-
67021-
36467 to N.F.), and by the Bellwether Foundation.
Conflicts of Interest
Getu Beyene has patent “Lodging resistance in Eragrostis tef” pending
to Donald Danforth Plant Science Center.
Data Availability Statement
Code and data associated with this manuscript are available on GitHub
(https://github.com/danforthcenter/teff-manuscript).References
Abebe, Y., A. Bogale, K. Michael Hambidge, B. J. Stoecker, and R. S.
Gibson. 2007. “Phytate, Zinc, Iron and Calcium Content of Selected Raw
and Prepared Foods Consumed in Rural Sidama, Southern Ethiopia,
and Implications for Bioavailability.” Journal of Food Composition and
Analysis 20, no. 3: 161–168.
AssOpen asset ↗danforthcenter/teff-manuscriptpdf-raw-page:8 lines:1-98Code · publicyzing images of plants (Gehan
et al. 2017; Schuhl et al. 2026) that provides a framework for
measuring and storing observations extracted per object within
each image. All code associated with these analyses is available
on GitHub (https://github.com/danforthcenter/teff-manuscript),
as well as the PlantCV-
Geospatial package (https://github.com/danforthcenter/plantcv-geospatial). As observed in the ortho-
mosaic (Figure 1A), tef plots were planted under power lines in
the field, which could not be flown under due to UAS safety re-
strictions. Pixels belonging to powerlines needed to be removed
to measure plot heights. During import, PlantCV-
Geospatial
was used with a height percentile tOpen asset ↗danforthcenter/plantcv-geospatialpdf-raw-page:4 lines:1-107Code / dataset availability confirmedEurope PMC · checked 5 Sept 2026
Monitoring the growth dynamics in field-grown cabbage is critically important for ensuring stable vegetable production and advancing precision agricultural management. However, conventional two-dimensional (2D) image-based monitoring approaches are limited to planar projection information and lack representations of spatial structural characteristics, rendering them inadequate for supporting high-precision, full-cycle phenotypic monitoring of cabbage under open-field conditions. In this study, a high-precision three-dimensional (3D) point cloud dataset covering the period from the seedling stage to maturity was constructed using depth cameras in conjunction with multi-view spatial registration techniques. Building on this dataset, an adaptive point cloud segmentation network designed for the whole-cycle growth monitoring was proposed, incorporating a Head Refinement Module (HRM), a Leaf Instance Segmentation Module (LISM), and Cross Module Interaction (CMI) to address leaf adhesion and head boundary delineation. Experimental results demonstrated that the proposed method consistently outperformed state-of-the-art models in both semantic and instance segmentation tasks. For semantic segmentation, the mean Intersection over Union (mIoU) reached 0.767, with a point classification accuracy of 94.8%. The model comprises 54.25 million parameters and achieves an average response time of 0.76 s. For instance segmentation, the Average Precision (AP) improved by 2.3% for cabbage heads and 3.8% for leaves, while the Average Recall (AR) increased by 6.9%. Growth parameters, including plant height and canopy spread, extracted from the segmentation results showed strong agreement with ground-truth measurements, with correlation of coefficients (R 2 ) exceeding 0.9 for plant height, canopy length, and canopy width. Leveraging these multidimensional phenotypic descriptors, the temporal dynamics of cabbage growth throughout the entire growth cycle were systematically characterized. Overall, this study enables dynamic monitoring of cabbage phenotypes across the full growth cycle, providing a novel technical pathway for extending 3D phenotyping from controlled environments to open-field applications and offering important support for precise crop monitoring and the development of digital twin agriculture.
Why it matches plant phenotyping methods3D点群データセット、セグメンテーションネットワーク、形質抽出を開発・検証し、圃場キャベツの草高や冠幅を定量化する植物フェノタイピング手法が研究の中心である。
abstracta high-precision three-dimensional (3D) point cloud dataset covering the period from the seedling stage to maturity was constructed using depth cameras in conjunction with multi-view spatial registration techniques.
Reproduction assets foundThe paper's authors publicly release their improved OneFormer3D point cloud segmentation code on GitHub; the cabbage 3D point cloud dataset is only available upon request.Code · publicThe code is available at https://github.com/PandaDalin/improve_oneformer3d. The data of this study are available from the corresponding author upon request.Open asset ↗PandaDalin/improve_oneformer3dhtml-lines:449-475Code / dataset availability confirmedOpenAlex · Crossref · checked 5 Sept 2026
Abstract 3D models are used in plant phenotyping for non-destructive quantification and analysis of morphological characteristics. Analyzing plant structure allows breeders to select for desirable traits, associated with e.g. drought tolerance or increased productivity. In sugar beet, morphological parameters depict an essential element of the variety approval for distinguishing between genotypes. However, only a limited number of measured or scored parameters are considered at a single time point. In contrast, 4D data adds a temporal component and can depict the dynamic development of 3D parameters. To explore the potential of spatio-temporal 4D phenotyping for automated crop genotype differentiation, a greenhouse experiment was conducted by us covering twelve sugar beet genotypes. High-resolution 3D models were generated twice a week over the course of two months and both common and novel 3D morphological parameters were extracted. The importance of these parameters was assessed by us, and the dataset was analyzed using unsupervised pointwise clustering and time series clustering. Varying importance of parameters depending on the time point and significantly higher importance of plant parameters compared to leaf parameters are demonstrated by our results. Moreover, increased and more stable genotype differentiation is archived using time series clustering compared to pointwise clustering. Furthermore, taproot formation of sugar beet was found to have a crucial impact on morphological development. Substantial variations in the dynamic development of 3D morphological parameters underline the importance of 4D data for plant genotype differentiation. Thus, a novel foundation for genotype differentiation in plant phenotyping is provided by our findings.
Why it matches plant phenotyping methods3Dモデルから植物形態形質を抽出し、時系列クラスタリングで遺伝型識別を評価する4Dフェノタイピング手法が研究の中心である。
titleSpatio-temporal 4D phenotyping for automated morphological genotype differentiation of sugar beet
Reproduction assets foundThe paper publicly deposits its generated sugar beet point cloud dataset under CC BY 4.0 at a Dataverse DOI, directly reproducing the paper's phenotyping measurements. Supplementary Python codes and extracted parameter values are stated to be included with the article, but no authors' public URL for the code is presentDataset · publicThe generated point cloud dataset is available at https://doi.org/10.60507/FK2/IS8YBZ under CC BY 4.0 license.Open asset ↗10.60507/FK2/IS8YBZlines:277-363Code / dataset availability confirmedEurope PMC · checked 5 Sept 2026
The quality of forest reproductive material is crucial for successful reforestation and afforestation. While physical seed properties like mass are known indicators of quality, the potential of non-destructive, rapid color analysis for predicting germination in coniferous species requires further exploration. This study investigates the relationship between the seed coat color of individual Pinus sylvestris seeds, quantified in RGB (Red, Green, Blue) space using a flatbed scanner, and their subsequent germination in container nurseries. The resulting images were processed using ImageJ software to measure the mean pixel intensity (0–255) for the Red (R), Green (G), and Blue (B) channels from the segmented seed area, following the «seed–culture» passport methodology [Forestry Engineering Journal 14 | 55 (2024), 37–60]. From a population of individually tracked seeds, we compared the RGB values of germinated (N = 942) and non-germinated (N = 258) seeds after 30 days. Results from the Kolmogorov-Smirnov test showed that non-germinated seeds had significantly lower individual mass (p = 0.0045) and significantly higher pixel brightness values in the R-, G-, and B-channels (p < 0.0001) compared to germinated seeds. Normalized RGB indices also showed significant differences between groups. Our findings demonstrate that seeds with a lighter, more reflective epidermis – indicative of higher RGB brightness – are statistically associated with a lower probability of successful germination under container nursery conditions. This non-destructive, low-cost method shows significant promise for the rapid pre-sorting of Scots pine seeds. It offers a practical tool to improve the efficiency and predictability of seedling production in forest nurseries by increasing the proportion of viable seeds in sowing batches.
Why it matches plant phenotyping methods個別種子のRGB画像から種皮色を定量抽出し、発芽予測・事前選別に用いる非破壊的な表現型計測法が研究の中心である。
abstractthe potential of non-destructive, rapid color analysis for predicting germination in coniferous species requires further exploration
Reproduction assets foundThe paper openly deposits its three core phenotyping datasets in Mendeley Data: morphometric seed data (Dataset 1), the raw VIS/RGB scanner images of individual Pinus sylvestris seeds (Dataset 2), and germination outcome data (Dataset 3). All three DOIs are listed in the Data Availability statement and match allowed URDataset · publicThe original morphometric data—Dataset 1—of Pinus sylvestris L. are openly available in Mendeley Data at DOI: https://doi.org/10.17632/8g258nbgmf.1Open asset ↗Mendeley Data · 10.17632/8g258nbgmf.1lines:133-160Dataset · publicThe original VIS image data of Pinus sylvestris L. are openly available in Mendeley Data at DOI: https://doi.org/10.17632/dt78jhyw2j.2Open asset ↗Mendeley Data · 10.17632/dt78jhyw2j.2lines:133-160Dataset · publicThe original germination data—Dataset 3—are openly available in Mendeley Data at DOI : https://doi.org/10.17632/hrs3fgc8tt.1Open asset ↗Mendeley Data · 10.17632/hrs3fgc8tt.1lines:133-160Code / dataset availability confirmedEurope PMC · checked 5 Sept 2026
FruitClassificationGrowth / development / phenology
Introduction Cashew apple is a nutrient-rich fruit containing abundant minerals, vitamins, and energy. However, its fleshy texture and delicate skin significantly limit its storage life and market value. Accurate maturity grading is therefore essential for improving post-harvest management and transportation efficiency. Methods This study proposes a lightweight vision transformer (ViT) student model trained using multi-granular knowledge distillation (KD) from a stronger data-efficient image transformer (DeiT)-Base teacher. The distillation framework integrates response-based soft-label supervision, attention transfer, and token-level feature regression to enhance representation learning under limited data conditions. Auxiliary lightweight architectures, including MobileNet, ConvNeXt, and EdgeNeXt, were trained independently to provide complementary predictions, and a weighted fusion strategy was employed for ensemble evaluation. Results The proposed ensemble ViT-KD with EdgeNeXt achieved 90% accuracy under the evaluated test split. To ensure statistical reliability and address potential partition bias, a stratified fivefold cross-validation was conducted on the dataset, yielding a mean accuracy of 86.89% ± 2.89% with consistent F1 scores and recall. The relatively low variance across the folds indicates stable internal generalization. Comparative experiments with conventional convolutional neural network (CNN) baselines and lightweight CNN baselines such as MobileViT-S and ShuffleNetV2 were performed, with the proposed ensemble framework achieving improved accuracy while maintaining computational efficiency. Computational analysis indicates that the stand-alone distilled ViT maintains a real-time inference capability of 8.79 ms per image, which supports suitability for edge-oriented agricultural applications. Discussion These results highlight the effectiveness of knowledge-distilled lightweight transformers for data-efficient maturity grading of cashew apples.
Why it matches plant phenotyping methodsカシューナッツ果実の成熟度という植物器官の状態を画像から推定する手法を開発し、交差検証・比較実験・推論速度評価まで行っており、フェノタイピング手法が中心である。
abstractThis study proposes a lightweight vision transformer (ViT) student model trained using multi-granular knowledge distillation (KD) from a stronger data-efficient image transformer (DeiT)-Base teacher.
Reproduction assets foundThe paper's cashew apple maturity grading experiments use a public image dataset from IEEE Dataport (Sawant, 2025), explicitly linked in the data availability statement. No author code or models are shared.Dataset · publicPublicly available datasets were analyzed in this study. This data can be found here: https://ieee-dataport.org/documents/goa-cashew-apple-maturity-grading .Open asset ↗goa-cashew-apple-maturity-gradinglines:837-851Code / dataset availability confirmedOpenAlex · Europe PMC · checked 14 Sept 2026
BACKGROUND: Leaf inclination angle (LIA) is a key trait affecting crop canopy structure and photosynthetic efficiency, but its accurate measurement is challenging due to complex leaf geometry, especially in narrow, curved rice leaves. As the flag leaf serves as the primary photosynthetic organ in rice, the precise spatial parsing of its architecture is crucial for optimizing canopy light interception and yield potential. With the rapid development of high-throughput phenotyping technologies, an increasing number of studies have focused on the fine-grained characterization of 3D crop architecture. However, accurate methodologies for extracting the flag leaf inclination angle (FLIA) in rice, as well as systematic investigations into its spatiotemporal variation patterns, remain largely unexplored. RESULTS: In this study, we systematically evaluated multiple plane-fitting strategies based on SfM-MVS point clouds, finding that voxel-based piecewise analysis outperformed traditional global approaches. To further improve accuracy, skeleton extraction methods were innovatively extended to LIA estimation. A proposed multi-method ensemble, based on the median of eight skeleton extraction combinations, yielded high robustness (R2 = 0.923, RMSE = 2.072°) against photographic ground truth. By applying the proposed framework to both field- and pot-grown rice, we observed no significant FLIA differences between varieties or nitrogen treatments under field-grown conditions, likely due to phenotypic plasticity regulated by population effects. However, pot-grown plants, experiencing reduced interplant competition, exhibited significant varietal differences in FLIA. Across growth environments, varieties, and nitrogen treatments, FLIA at maturity was significantly lower than at anthesis and grain filling stages due to leaf senescence. CONCLUSIONS: This study establishes a robust and accurate measurement framework for LIA based on 3D point clouds, improving estimation performance through piecewise analysis, voxelization, and ensemble strategies. The proposed approach is demonstrated to be an effective tool for the precise quantification of rice leaf phenotypes.
Why it matches plant phenotyping methodsSfM-MVS点群からイネ葉の傾斜角を抽出する手法を開発・検証し、圃場および鉢植えで適用しているため、植物フェノタイピング手法が研究の中心である。
abstractA proposed multi-method ensemble, based on the median of eight skeleton extraction combinations, yielded high robustness (R2 = 0.923, RMSE = 2.072°) against photographic ground truth.
Reproduction assets found保存済みの本文根拠を更新済みルールで再検証し、公開資産1件を確認しました。Code · publicThe python program, complete dataset, including the original two-dimensional images and corresponding piecewise measurement trajectories, is publicly available at https://github.com/Interstingsun/LIA (accessed on 6 February, 2026).Open asset ↗Interstingsun/LIAlines:77-83Code / dataset availability confirmedEurope PMC · checked 5 Sept 2026
Rice ( Oryza sativa ) production underpins food security in many rice-consuming nations. As a critical developmental transition that directly determines yield and grain quality, flowering dates and timing are genetically complex and highly sensitive to environmental fluctuations. This complexity requires new methods to quantify diurnal floral characteristics, which are essential to hybrid breeding in cereals. Here, we present BloomSight, an ultra-high-frequency and deep-learning (DL) powered framework for phenotyping and measuring minute-level flowering dynamics in japonica and indica rice. After monitoring 172 rice accessions selected from the Chinese Rice Mini-Core Collection using cost-effective time-lapse imaging platforms for 16 days, we acquired over 530,000 accession-level images and established the Open Rice Flowering Training (ORFT) dataset, with over 39,000 panicles and 350,000 anthers annotated. Next, a two-stage customised DL model (i.e. YOLACT-Panicle for panicle segmentation and UNet-Anther for anther identification) was trained using the ORFT set, enabling ultra-high-frequency measures of anther extrusion at the minute level. Based on trait analysis, we further fitted curves to dynamically identify diurnal flowering patterns, including key timepoints such as the initial flowering timepoint ( T Ini. ), quickest flowering timepoint ( T Qck. ), and peak flowering time ( T Peak ), and novel traits such as the duration of rapid flowering phase ( P Rpd. ) and flowering density across key phases. After validating BloomSight-derived traits against manual observations, we classified the japonica and indica accessions into three patterns: Slow, Moderate, and Fast, all of which had distinct flowering windows. These analyses helped us integrate phenotypic variations into a genome-wide association study (GWAS), revealing many significant single nucleotide polymorphisms (SNPs) associated with known (e.g. EMF1 , OsMYB8 , and PME42 ) and several repeatedly identified unknown loci (one of these loci has been recently verified by other groups), demonstrating the value of the BloomSight framework. Taken together, we believe that BloomSight provides an ultra-high-frequency framework for diurnal flowering phenotyping, enabling the measurement of biological meaningful floral traits with minute-level resolution that can enable flowering-related developmental studies and hybrid-breeding applications in rice and more broadly benefit the plant and crop research community.
Why it matches plant phenotyping methodsイネの開花動態を高頻度画像と深層学習で抽出するフェノタイピング基盤を開発し、データセット構築と手動観測による検証も行っているため、方法が研究の中心である。
abstractwe present BloomSight, an ultra-high-frequency and deep-learning (DL) powered framework for phenotyping and measuring minute-level flowering dynamics in japonica and indica rice
Reproduction assets foundThe paper's Data and code availability statement explicitly provides public access to the ORFT annotated image dataset (BioStudies S-BSST2157), Python source code for floral trait analysis (GitHub The-Zhou-Lab/BloomSight), and trained DL models (GitHub releases). SRA accessions are molecular sequencing data, not phenotCode · publicPython-based source codes for automating floral trait analysis using the above data are accessible via our GitHub repository ( https://github.com/The-Zhou-Lab/BloomSight ).Open asset ↗The-Zhou-Lab/BloomSightlines:336-349Code / dataset availability confirmedEurope PMC · checked 5 Sept 2026
Growth chamberRootMorphology / geometry measurementSegmentationGrowth / time-series analysisGrowth / development / phenologyRoot system architecture
Abstract Root system architecture and root hairs highly influence plant resource uptake, yet their simultaneous quantification at the whole-plant scale remains challenging due to the conflicting requirements of high-resolution imaging and non-destructive, repeated measurements. Here, we present a plant growth and imaging system based on A3 sized flatbed rhizotrons, combined with a non–machine-learning image analysis workflow implemented in R, that enables repeated, in situ quantification of cereal root system architecture and root hair area over three weeks of growth. The system produces integrated outputs highlighting both whole-root architecture and the spatial distribution of surrounding root hair area from single images. Validation of the image analysis algorithm showed good segmentation performance, with average Matthews Correlation Coefficient values of 0.68 for root area and 0.65 for root hair area. To demonstrate its experimental applicability, the system was used to assess root growth and root hair responses under controlled environmental conditions, combining three irrigation regimes (2, 4, and 6 irrigation events per day) with three dry bulk density levels (1.4, 1.5, and 1.6 g cm⁻³). In addition to whole-system metrics, the approach enables analysis of root hair expansion at individual root tips. This methodology provides a rapid, scalable, and training-free method for integrated analysis of root architecture and root hairs under controlled physical conditions similar to soil, facilitating studies of root–soil interactions that require both spatial resolution and temporal continuity.
Why it matches plant phenotyping methods根系画像解析システムとRベースの画像分析ワークフローを開発し、根系構造と根毛面積の定量化およびアルゴリズム性能検証を中心に扱っているため、植物フェノタイピング手法として適格です。
abstractwe present a plant growth and imaging system based on A3 sized flatbed rhizotrons, combined with a non–machine-learning image analysis workflow implemented in R, that enables repeated, in situ quantification of cereal root system architecture and root hair area over three weeks of growth.
Reproduction assets foundThe paper's authors state that the RootHairFinder C++ file, R script, and example rhizotron data will be available via a GitHub repository and Zenodo upload, and the preprint's supplementary files already include RootHairFinder.cpp and example rhizotron images (Supplimentaryfile4.tif, Supplimentaryfile5.tif). This is aCode · publicThe RootHairFinder cpp file, R script and example data files for rhizotron analysis will be made available through github https://github.com/TracyValentine/RootHairFinder and https://zenodo.org/uploads/19288922Open asset ↗TracyValentine/RootHairFinderlines:236-263Code / dataset availability confirmedEurope PMC · checked 5 Sept 2026
RGB / grayscaleFlowerClassificationGrowth / development / phenology
This study presents a comprehensive BDFlower growth stage dataset designed to support research in precision agriculture and floriculture. The dataset encompasses eight common flower species found in Bangladesh: Bush Allamanda, Red Hibiscus, Yellow Bell, Pinwheel Flower, Pink Periwinkle, White Madagascar Periwinkle, Marvel of Peru, and White Hibiscus. Each species is represented across three growth stages-Early, Mid, and Full-resulting in 24 distinct classes. A total of 23,334 colour images are included, comprising 3889 original photographs and 19,445 augmented samples generated with five augmentation techniques. Bush Allamanda contains 499 images, Red Hibiscus contains 489 images, Yellow Bell contains 483 images, Pinwheel Flower contains 497 images, Pink Periwinkle contains 452 images, White Madagascar Periwinkle contains 472 images, Marvel of Peru contains 468 images and White Hibiscus contains 529 images. Each image was collected using smartphone camera at three-time intervals per day, spaced eight hours apart, to capture natural variations in lighting and appearance. The dataset is further organized into training, validation, and testing splits, enabling direct application to machine learning workflows. This is a publicly available dataset specifically curated for flower growth stage classification. In addition to dataset collection, we also conducted a simple experiment using a CNN model to evaluate its performance on this dataset. It is intended to facilitate the development of robust computer vision models that can monitor flower development, with potential applications in automated plant phenotyping, crop monitoring, and digital floriculture systems.
Why it matches plant phenotyping methods花の生育段階を画像で分類する公開データセットを構築し、CNN評価も行っており、植物表現型取得・解析が研究の中心である。
abstractThis is a publicly available dataset specifically curated for flower growth stage classification.
Reproduction assets foundThe paper's own flower growth-stage image dataset (BDFlower) is publicly deposited on Mendeley Data with an explicit direct URL and DOI, directly reproducing the paper's phenotyping (flower growth stage) image measurements. No author analysis code or trained model checkpoints are explicitly deposited.Dataset · publicRepository name: Data Mendeley
Data identification number: 10.17632/m8g2wynwyr.2
Direct URL to data: https://data.mendeley.com/datasets/m8g2wynwyr/2Open asset ↗10.17632/m8g2wynwyr.2html-lines:94-129Code / dataset availability confirmedCrossref · Europe PMC · checked 5 Sept 2026
Introduction Accurate plant disease identification is of great importance for ensuring agricultural productivity and food security. However, complex illumination variations, leaf occlusion, and diverse disease spot scales throughout plant growth stages significantly increase the difficulty of real-time detection, leading to limited accuracy and robustness in existing approaches. Methods To address these challenges, we propose an improved RT-DETRv2-based plant disease detection model, termed AF-RT-DETR. A Bidirectional Cross Gate (BCG) module is introduced in the feature extraction stage to reduce channel redundancy and enhance discriminative feature representation through multi-level feature interactions. The original RepVGG structure is replaced with a Dynamic Channel Shift (DCS) module, effectively enlarging the receptive field and strengthening contextual feature fusion without additional computational overhead. Additionally, an improved Scale-aware Multi-level Loss (SML) emphasizes low-quality feature maps to improve detector robustness. Results The model achieves mAP50 and mAP50:95 of 93.6% and 67.2% on the Plant-Disease dataset, surpassing the baseline by 5.1% and 4.5%. Furthermore, the model was evaluated on multiple crops and growth stages under diverse field conditions, demonstrating robust performance and adaptability. Discussion These results indicate that AF-RT-DETR effectively enables real-time plant disease detection in complex field environments.
Why it matches plant phenotyping methods植物の病徴を画像から検出するモデルの開発と、複数作物・生育段階・圃場条件での性能評価が中心であり、植物病害状態の表現型計測手法に該当する。
Reproduction assets foundThe paper evaluates AF-RT-DETR on three public Roboflow plant-disease image datasets, each cited with an explicit public URL. No author analysis code or trained model release is mentioned. The Ultralytics YOLOv8 repository is a generic third-party library, not a paper-specific asset.Dataset · publicRoboflow Detecting rice crop diseases object detection dataset . Available online at: https://universe.roboflow.com/crop-diseases-l2qhk/detecting-rice-crop-diseases/dataset/21Open asset ↗lines:816-932Dataset · publicRoboflow Disease detection object detection dataset . Available online at: https://universe.roboflow.com/projects-h0apg/disease-detection-0slunOpen asset ↗lines:816-932Dataset · publicRoboflow Plant Disease v2 512×512 . Available online at: https://universe.roboflow.com/sangeeth-mathew-john-nl43i/plant-disease-czcfe/dataset/2Open asset ↗lines:933-1045Code / dataset availability confirmedEurope PMC · checked 5 Sept 2026
Metabolic processes are essential for regulating and maintaining developmental transitions. However, the distinct metabolite-driven mechanisms that are crucial for development remain poorly characterized due to inherent challenges in measuring their localization and function in situ. We applied desorption electrospray ionization mass spectrometry imaging (DESI-MSI) to generate near single-cell resolution (50-80 µm) images of metabolites in the maize root tip, which has a well-characterized longitudinal developmental gradient. We developed a new computational tool, called Developmental Imaging Mass Spectrometry Pipeline for Linear Evaluation (DIMPLE), which processes mass signatures along linear gradients and clusters metabolites based on their developmental enrichment patterns. We employed this method to compare developmental enrichment of metabolites in Oaxacan Green, a salt-resilient maize variety, to B73, which is salt sensitive. DIMPLE uncovers specific differences in individual mass signatures and overall enrichment patterns between these varieties. Further characterization of these differences revealed meristem enrichment of D-erythrose, a metabolite that can improve stress tolerance in maize. Overall, DIMPLE enables comprehensive and rapid analysis of metabolite patterns along a linear gradient, informing biological hypotheses related to plant growth and stress response.
Why it matches plant phenotyping methods植物根端の発達勾配に沿った代謝物分布を画像化・解析する計算ツールを開発しており、植物の発達状態やストレス応答に関わる表現型抽出が研究の中心である。
abstractWe developed a new computational tool, called Developmental Imaging Mass Spectrometry Pipeline for Linear Evaluation (DIMPLE), which processes mass signatures along linear gradients and clusters metabolites based on their developmental enrichment patterns.
Reproduction assets foundThe paper's authors publicly deposited the DIMPLE analysis code and raw DESI-MSI data on the Dickinson Lab GitHub and Zenodo, as stated in the Technical aspects and Data availability sections.Code · publicThe full R code analysis can be found in the Dickinson Lab Github at https://github.com/dickinsonlab.Open asset ↗dickinsonlabhtml-lines:198-204Code · publicSource code and raw data for DIMPLE are available on the Dickinson Lab GitHub (https://github.com/dickinsonlab) and at https://zenodo.org/records/17187822.Open asset ↗17187822html-lines:198-204Code / dataset availability confirmedCrossref · checked 14 Sept 2026
Published31 Mar 2026Journal of Wireless Mobile Networks, Ubiquitous Computing, and Dependable ApplicationsCited by 0 · OpenAlex ↗
The aspect of crop monitoring takes into consideration the timely detection of crop stages, leaf disorders, and deficiencies to enhance crop yield and decrease losses in agriculture. However, most of the current methods are limited to either disease detection or nutrient evaluation and do not examine the conditions of crops at various stages of growth, even though several AI -IoT-based solutions have been suggested to be applied to crop health monitoring. In addition, the estimation of the severity of the diseases is neglected, and this restricts decision-making in favor of the farmers. To address these constraints, the paper presents a Parametrized Elliptical Cauchy Fuzzy Inference System (PECFIS) combined with a Deep Glorot Bessel Elliott Softplus Convolutional Neural Network (DGBESCNN), proposed as an AI-based solution for crop monitoring and IoT support. The IoT devices in the form of drones are used to get real-time field images, and they are preprocessed in terms of noise reduction, contrast enhancement by LHM-CLAHE, conversion to HSV color space, and feature discrimination by vegetation indexing, as well as C3MEK-Means. PECFIS is used to determine eight key stages of rice growth and the severity of leaf diseases, whereas DGBESCNN provides proper classification of leaf diseases and nutrient deficiencies at each growth stage. The evaluation of the proposed framework was conducted using publicly available datasets on rice leaf disease and nutrient deficiency. The results of the experiments show that the system achieves high classification performance, with an accuracy of 98.82, a precision of 98.65, a recall of 98.73, an F1-score of 98.59, and low error rates (MSE = 0.0135, RMSE = 0.116). The findings show that the developed AI-IoT system is superior to available approaches and can serve as a dependable, real-time, and scalable solution in precision agriculture and intelligent crop monitoring.
Why it matches plant phenotyping methodsドローン画像からイネの生育段階と葉病害の重症度を推定・分類するAI-IoT手法が研究の中心であり、植物状態の取得・抽出方法を技術的に評価している。
abstractThe IoT devices in the form of drones are used to get real-time field images
Reproduction assets foundThe paper evaluates its PECFIS-DGBESCNN crop monitoring framework on two publicly available Kaggle datasets (Nutrient Deficiency Symptoms in Rice, 1,156 images; Rice Leaf Diseases, 120 images), with explicit dataset links provided by the authors. No author code, models, or other paper-specific assets are shared.Dataset · publicn of the low-cost ground-based IoT and weather sensors and enhanced robustness in the
current unfavorable environmental conditions.
Future Enhancement
In the future, enhanced techniques will be developed to classify the numerous types of nutrient
deficiencies in rice crops for improved productivity in agriculture.
Dataset link: https://www.kaggle.com/datasets/guy007/nutrientdeficiencysymptomsinrice
https://www.kaggle.com/datasets/vbookshelf/rice-leaf-diseases/data
References
[1] Aggarwal, M., Khullar, V., Goyal, N., Alammari, A., Albahar, M. A., & Singh, A. (2023).
Lightweight federated learning for rice leaf disease classification using non independent and
identically distributed images. SuOpen asset ↗Kaggle · guy007/nutrientdeficiencysymptomsinricepdf-raw-page:21 lines:1-50Dataset · publicstness in the
current unfavorable environmental conditions.
Future Enhancement
In the future, enhanced techniques will be developed to classify the numerous types of nutrient
deficiencies in rice crops for improved productivity in agriculture.
Dataset link: https://www.kaggle.com/datasets/guy007/nutrientdeficiencysymptomsinrice
https://www.kaggle.com/datasets/vbookshelf/rice-leaf-diseases/data
References
[1] Aggarwal, M., Khullar, V., Goyal, N., Alammari, A., Albahar, M. A., & Singh, A. (2023).
Lightweight federated learning for rice leaf disease classification using non independent and
identically distributed images. Sustainability, 15(16), 12149.
https://doi.org/10.3390/su151612149
[2] AlfOpen asset ↗Kaggle · vbookshelf/rice-leaf-diseasespdf-raw-page:21 lines:1-50Code / dataset availability confirmedEurope PMC · checked 5 Sept 2026
CoffeeField / plotFruitObject detectionGrowth / development / phenology
Introduction Coffee cherry ripeness assessment is critical for harvesting efficiency and product quality, yet traditional manual inspection methods suffer from subjectivity and low efficiency. Methods To address the challenges of detecting small, occluded, and densely distributed coffee cherries in complex field environments, this study proposes an Occlusion and Density Aware Network (ODANet). Built upon the YOLOv8 framework, ODANet integrates three innovative modules: (1) Condition-Guided Windowed Attention (CGWA), which incorporates occlusion and density maps as auxiliary guidance signals for efficient feature enhancement; (2) Attention-guided Space-Preserving Convolution (ASPC), which employs space-to-depth transformation with cascaded attention to preserve spatial information during downsampling; and (3) Dual-Adaptive Dynamic Upsampling (DADU), which achieves content-adaptive feature reconstruction through dual-branch offset prediction with learnable fusion weights. Results Comprehensive evaluation on a publicly available dataset demonstrates that ODANet achieves state-of-the-art performance among 17 diverse detection architectures, attaining 76.7% mAP@0.5 with a 6.3 percentage point improvement over baseline YOLOv8, while maintaining computational efficiency (8.1 GFLOPs, 30.4M parameters) suitable for real-time deployment. Ablation studies validate the contributions of each module: ASPC improves performance by 2.2%, DADU by 0.6%, and CGWA by 3.5%. Discussion The model demonstrates robust performance across varying lighting conditions, occlusion levels, and growth stages, making it particularly suitable for practical agricultural deployment. This research provides an efficient solution for small object detection in precision agriculture.
Why it matches plant phenotyping methodsコーヒーチェリーの成熟度という植物器官の状態を画像から推定する検出手法を開発し、複数モデル比較・アブレーションで技術的に検証しているため、植物フェノタイピング手法が中心である。
abstractthis study proposes an Occlusion and Density Aware Network (ODANet)
Reproduction assets foundThe paper analyzes a publicly available coffee cherry dataset hosted on Kaggle, explicitly linked in the data availability statement. This is the paper-specific image dataset used for its coffee cherry ripeness detection experiments. No author code or model checkpoints are stated as available.Dataset · publicPublicly available datasets were analyzed in this study. This data can be found here: https://www.kaggle.com/datasets/harisyunanda/dataset-coffee-cherry/data .Open asset ↗Kaggle · harisyunanda/dataset-coffee-cherrylines:682-758Code / dataset availability confirmedOpenAlex · Crossref · Europe PMC · checked 5 Sept 2026
Precision greenhouse agriculture enhances plant health and crop yields by continuously monitoring key plant parameters. Stem diameter is such a parameter and is monitored to support decisions on plant care. However, traditional contact-based methods induce thigmomorphogenic effects that impact plant growth. Here, we introduce the Optical Caliper (OC), a novel contactless device for precise, non-invasive stem diameter measurement. The OC operates by projecting a collimated light beam to cast a shadow of the stem onto a high-resolution image sensor. The shadow size is a measure for the stem diameter. Controlled laboratory tests show the OC offers an accuracy comparable to that of a Digital Caliper (DC). Field trials on irregular tomato and cucumber stems demonstrate a repeatability of 0.1-0.2 mm. The OC's non-invasive design and high repeatability exceed the performance of a DC, making it particularly suited for accurately monitoring soft, variable plant structures. Bringing the advantage of avoiding thigmomophogenic effects and thus optimizing crop yield, the OC is a promising tool for high-throughput plant phenotyping and precision agriculture applications.
Why it matches plant phenotyping methods植物の茎径を非接触・高精度に測定する光学デバイスを開発し、実験室および圃場で精度・再現性を検証しており、植物表現型取得法が研究の中心です。
abstractHere, we introduce the Optical Caliper (OC), a novel contactless device for precise, non-invasive stem diameter measurement.
Reproduction assets foundThe paper's measurement data (optical caliper, digital caliper, and micrometer readings on reference cylinders and tomato/cucumber stems) is openly deposited on the SURF data repository of The Hague University of Applied Sciences. No author analysis code or trained models are explicitly deposited; other allowed URLs (DDataset · publicThe data gathered during this study is openly available via https://hhs.data.surf.nl/s/nqnFYBf42KPA75P (accessed on 10 February 2026).Open asset ↗hhs.data.surf.nlhtml-lines:282-314Code / dataset availability confirmedEurope PMC · checked 5 Sept 2026
Accurate delineation of crop growth stages under real-world field conditions remains a long-standing challenge in computational phenotyping, particularly for wheat whose developmental phases are characterized by subtle, continuous morphological transitions and environmental noise. In this study, we propose AMFR-Net, an Adaptive Multi-Scale Feature Refinement Network tailored for fine-grained wheat stage identification using ground-level RGB imagery. Unlike conventional architectures that struggle with ambiguous inter-stage boundaries and rigid receptive structures, AMFR-Net leverages a ResNet-101 backbone augmented by a novel Adaptive Multi-Scale Attention Fusion (AMSAF) module-comprising cross-scale interaction blocks and confidence-weighted feature aggregation-to hierarchically recalibrate spatial-semantic representations. This design enables the network to adaptively amplify phenologically salient cues while suppressing irrelevant context, ensuring robust generalization under constrained annotation and deployment conditions. Evaluated on the expert-labeled CGIAR benchmark, AMFR-Net achieves state-of-the-art performance across all major metrics (Top-1 Accuracy: 89.10%; Macro-F1: 89.10%; AUC: 97.88%) and demonstrates superior discriminability in phenologically adjacent stages compared to lightweight and deep CNN baselines. Ablation studies validate the synergistic effect of multi-level attention and scale-aware refinement. The proposed framework offers a scalable, interpretable, and field-deployable solution for in-situ phenology monitoring, and sets a foundation for future integration of multimodal sensing, weak supervision, and cross-seasonal adaptation.
Why it matches plant phenotyping methods小麦の生育ステージを地上RGB画像から推定する新規深層学習手法を開発し、ベンチマーク、比較、アブレーションで検証しており、植物フェノタイピング手法が研究の中心です。
abstractwe propose AMFR-Net, an Adaptive Multi-Scale Feature Refinement Network tailored for fine-grained wheat stage identification using ground-level RGB imagery.
Reproduction assets foundThe paper's phenotyping analysis is built on the public CGIAR Wheat Growth Stage Challenge dataset (ground-level RGB wheat images with growth-stage labels), which the authors explicitly state is publicly available on Zindi with a direct link. No author analysis code, trained model checkpoints, or supplementary code/deeDataset · publicPublicly available datasets were analyzed in this study. This data can be found here: Dataset name: CGIAR Wheat Growth Stage Challenge Primary repository: Zindi (official competition page) Direct link: https://zindi.africa/competitions/cgiar-wheat-growth-stage-challengeAccession/Open asset ↗Zindilines:808-824Code / dataset availability confirmedEurope PMC · bioRxiv · Crossref · checked 14 Sept 2026
Lettuce ( Lactuca sativa ) is an important field crop, but our understanding of its phenotypic variation and underlying genetics under natural field conditions remains limited, posing challenges for identifying effective crop breeding targets. Longitudinal hyperspectral phenotyping allows for non-invasive monitoring of crop performance under diverse agricultural conditions. In this study, we used hyperspectral imaging to assess the phenotypic variation of almost 200 different field-grown lettuce varieties, following the same plants from just after seedling- to flowering-stage. With automated image processing, we extracted a wide range of spectral phenotypes related to metabolite content, growth efficiency, and environmental stress responses, creating a multi-dimensional time-resolved data set. Principal component analysis (PCA) revealed the major axes of spectral variation over time, and highlighted differences in spectral patterns among lettuce genotypes. Integrating on-site weather data, we modelled G×E interactions of reflectance, revealing regions of the lettuce vegetation spectrum that are primarily shaped by genotype and/or environment. We estimated phenotypic plasticity in response to time, temperature and rainfall using best linear unbiased predictions (BLUPs), capturing genotype-specific developmental trajectories and responses to the environment. We used genome-wide association studies (GWAS) to identify quantitative trait loci (QTLs) of PC-based, single and BLUP-based phenotypes, disentangling the genetic architecture of spectral lettuce phenotypes from major axes of variation down to single wavelength spectral plasticity. These findings provide new insights into the genome-wide genetic regulation and dynamics of spectral phenotypes in field grown lettuce.
Why it matches plant phenotyping methods圃場レタスを対象に、縦断ハイパースペクトル画像と自動画像処理でスペクトル形質を抽出するフェノタイピング手法・データセットが研究の中心である。
abstractLongitudinal hyperspectral phenotyping allows for non-invasive monitoring of crop performance under diverse agricultural conditions.
Reproduction assets found保存済みの本文根拠を更新済みルールで再検証し、公開資産1件を確認しました。Code · publicScripts used in this study can be found at https://github.com/SnoekLab/Hyperspec_Mehrem_etal_2025.Open asset ↗SnoekLab/Hyperspec_Mehrem_etal_2025pdf-page:9 lines:1-31Code / dataset availability confirmedOpenAlex · Europe PMC · checked 14 Sept 2026
A detailed characterization of root system architecture (RSA) and growth dynamics is key to develop stress-resilient maize varieties. We evaluated sixty-five Mediterranean maize inbred lines using automated high-throughput phenotyping under controlled conditions. Shoot and root traits were extracted from imaging data during early vegetative development, revealing significant genotype-specific variation in root biomass-related traits (total root length, total root volume), root architecture (root angle, root system depth, root system width), and relative growth rates. Notably, lines previously classified as heat and drought stress-resilient or stress-sensitive based on above-ground development did not group according to particular root traits, indicating that multiple strategies may underlie tolerance to combined stress. We identified lines with contrasting RSA, including deeper roots, shallower roots, or overall larger root systems, that offer new opportunities for resilience breeding. Our results underscore root traits as critical yet underexploited targets for improving stress resilience and resource efficiency.
Why it matches plant phenotyping methods自動化ハイスループット画像解析により根系形態・成長形質を抽出する表現型取得が研究の主要手段であり、根系構造の実質的な応用解析に該当する。
abstractWe evaluated sixty-five Mediterranean maize inbred lines using automated high-throughput phenotyping under controlled conditions.
Reproduction assets found保存済みの本文根拠を更新済みルールで再検証し、公開資産1件を確認しました。Supplement · publicSupplementary Materials
The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/plants15060935/s1 , Figure S1: Repeatability of image-derived shoot (a) and root traits (b) of the tested 65 maize inbred lines over time.Open asset ↗lines:68-215Code / dataset availability confirmedEurope PMC · bioRxiv · Crossref · checked 15 Sept 2026
Quantitative studies of plant growth and environmental responses increasingly rely on time-series imaging, yet automated segmentation remains challenging due to continuous growth, large non-rigid morphological change, and frequent self-occlusion. Traditional image-processing pipelines and task-specific deep learning models often require extensive annotated datasets and retraining, limiting portability across species, developmental stages, and imaging conditions. Here we present SAP (Segment Any Plant), a plant-focused framework that leverages the pretrained Segment Anything Model 2 (SAM2) to enable few-shot, training-free segmentation of plant time-series imagery. SAP integrates interactive prompting, automated temporal mask propagation, and centerline extraction within a web-based interface, allowing users to move from raw images to quantitative descriptors of organ shape and dynamics without programming expertise. Across multiple systems, including Arabidopsis thaliana rosette development, root growth, sunflower gravitropism, and confocal root microscopy, SAP achieves high segmentation accuracy (mean IoU 0.89–0.93) and sub-pixel centerline precision from single-frame prompting. By reducing the need for task-specific retraining, SAP provides a transferable framework for reproducible time-series phenotyping across diverse experimental contexts.
Why it matches plant phenotyping methods植物の時系列画像から器官形状・動態を抽出するセグメンテーション手法とWeb基盤を開発し、複数系で精度検証しているため、植物フェノタイピング手法が中心である。
abstractHere we present SAP (Segment Any Plant), a plant-focused framework that leverages the pretrained Segment Anything Model 2 (SAM2) to enable few-shot, training-free segmentation of plant time-series imagery.
Reproduction assets foundThe paper's authors publicly release both the SAP analysis code (GitHub repository) and the datasets generated/analyzed in the study (Zenodo), including raw images, ground-truth and SAP-generated segmentation masks, centerline validation data, and supplementary videos. Both are paper-specific, public, and directly cit.Dataset · publicCode Availability. The code is available at
https://github.com/merozlab/plant-segmentation-app.Data Availability. The datasets generated and an-
alyzed during this study are available on Zenodo at
https://doi.org/10.5281/zenodo.18732705. This includes
raw images and segmentation masks for the sunflower
gravitropism and Arabidopsis root growth experiments,
SAP-generated masks for the Lee et al. (9) and Strauss
et al. (13) datasets, centerline validation data, and supple-
mentary videos.
Funding. Y.M. acknowledges support from the Israel Sci-
ence Foundation ResOpen asset ↗zenodo · 10.5281/zenodo.18732705pdf-raw-page:9 lines:1-74Code / dataset availability confirmedEurope PMC · checked 5 Sept 2026
Field / plotMultimodalWhole plant / canopy / plot / fieldClassificationGrowth / development / phenology
Abstract Phenological monitoring of Actinidia chinensis is critical for optimising operational costs and yield prediction. However, current manual assessment methods are time-consuming, making them impractical for large-scale precision agriculture applications. Most existing phenological datasets focus exclusively on image data without spatial validation. The Multi-Modal Actinidia chinensis Phenology Dataset is composed of (i) 1 665 annotated images of phenological stages from bud to fruit set and (ii) georeferenced videos with systematic manual ground truth of spatial stage distributions. The dataset employs an adapted 17-class BBCH system that consolidates visually similar stages, excludes problematic categories, and introduces generic structural classes to address practical annotation difficulties. Additionally, the data is organised hierarchically across various plant structures, genders, and phenological stages. The annotated images offer versatility for a range of applications, including training data for computer vision models to detect phenological stages. Furthermore, the georeferenced videos facilitate the validation of automated counting algorithms. This combined approach enables plant-level detection accuracy and provides an illustrative methodology for spatial validation that users can extend to additional orchards, promoting the development and benchmarking of automated phenological monitoring systems for precision agriculture applications in kiwifruit production.
Why it matches plant phenotyping methodsキウイフルーツの生育段階を対象とした注釈画像・地理参照動画のデータセットで、植物フェノロジー自動検出の訓練、検証、ベンチマークを目的とする方法論的成果である。
titleA Multi-Modal Dataset for Automated Phenological Stage Mapping in Actinidia chinensis
Reproduction assets foundThe paper is a Data Note describing the Multi-Modal Actinidia chinensis Phenology Dataset, which is explicitly stated to be publicly available on Zenodo with a DOI matching an allowed URL. The dataset contains the paper's own phenotyping assets: 1,665 annotated images with bounding-box phenological labels, georeferenedDataset · publicThe Multi-Modal Actinidia chinensis Phenology Dataset described in this Data Descriptor is publicly available
at Zenodo: https://doi.org/10.5281/zenodo.17371025. This dataset comprises two components: (1) 1 665
JPEG images (1 024 × 1 024 pixels) with corresponding Pascal VOC XML annotation files containing bounding
box coordinates and phenological class labels, and (2) 24 MP4 video files (3 840 × 2 160 pixels) with corresponding
GPX coordinate files and Excel validation files containing manual ground truth counts.Open asset ↗Zenodo · 10.5281/zenodo.17371025pdf-page:13 lines:1-62Code / dataset availability confirmedCrossref · checked 5 Sept 2026
Abstract Background Apoplastic pH is a central regulator of plant growth, development, and environmental adaptation, influencing cell expansion, nutrient uptake, and extracellular signaling. Many studies have successfully used HPTS to monitor relative changes in apoplastic pH in plants. At the same time, research increasingly targets pH-dependent biochemical and biophysical processes. Many enzymatic activities, ion binding events, and receptor–ligand interactions depend on defined proton concentrations. Accordingly, the development of reliable approaches to measure absolute pH in living tissues is gaining importance. Methods A calibration-based workflow was developed to enable quantitative assessment of absolute apoplastic pH using ratiometric HPTS imaging. The approach integrates a simplified two-point normalization strategy with an in-vitro derived sigmoidal calibration model, thereby minimizing the need for extensive in-vivo calibration curves. Confocal imaging was performed using HPTS excited at two wavelengths followed by ratiometric image processing. Data analysis is supported by a custom Fiji plugin, Ratio2pH, which converts ratiometric images into pixel-resolved maps of absolute pH. Results In vitro characterization revealed a robust, non-linear relationship between normalized HPTS ratios and pH, enabling accurate pH estimation within the physiologically relevant range of pH 5.0–7.0. When applied in-vivo to Arabidopsis thaliana roots, the workflow yielded extracellular pH estimates consistent with the pH of the incubation medium and detected reproducible pH shifts in response to pharmacological treatments. Conclusions This workflow enables reproducible, spatially resolved measurement of absolute apoplastic pH in living plant tissues. By combining a simplified calibration strategy with accessible image analysis tools, it facilitates quantitative extracellular pH measurements and their integration into biochemical and biophysical analyses.
Why it matches plant phenotyping methods生きた植物組織の絶対アポプラストpHを画像から定量する校正ワークフローを開発・検証し、Fijiプラグインも提供しているため、植物状態の取得法が中心である。
abstractA calibration-based workflow was developed to enable quantitative assessment of absolute apoplastic pH using ratiometric HPTS imaging.
Reproduction assets foundThe paper deposits its authors' analysis code and data publicly: the Ratio2pH Fiji plugin (Zenodo 10.5281/zenodo.15599805), a Python script for sigmoidal calibration curve fitting (Zenodo 10.5281/zenodo.17303477), and source data files and raw confocal images (Freidata 10.60493/t29wb-7my86). The Zenodo 15658668 ratiom�Code · publicThe Python Script for generating a user-defined sigmoidal calibration curve is available at Zenodo: https://doi.org/10.5281/zenodo.17303477Open asset ↗Zenodo · 10.5281/zenodo.17303477lines:175-235Dataset · publicSource data files and raw images are uploaded at Freidata, the data server of the University of Freiburg, available under https://doi.org/10.60493/t29wb-7my86Open asset ↗Freidata · 10.60493/t29wb-7my86lines:175-235Code / 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 confirmedOpenAlex · arXiv · checked 15 Sept 2026
Modeling plant growth dynamics plays a central role in modern agricultural research. However, learning robust predictors from multi-view plant imagery remains challenging due to strong viewpoint redundancy and viewpoint-dependent appearance changes. We propose a level-aware vision language framework that jointly predicts plant age and leaf count using a single multi-task model built on CLIP embeddings. Our method aggregates rotational views into angle-invariant representations and conditions visual features on lightweight text priors encoding viewpoint level for stable prediction under incomplete or unordered inputs. On the GroMo25 benchmark, our approach reduces mean age MAE from 7.74 to 3.91 and mean leaf-count MAE from 5.52 to 3.08 compared to the GroMo baseline, corresponding to improvements of 49.5% and 44.2%, respectively. The unified formulation simplifies the pipeline by replacing the conventional dual-model setup while improving robustness to missing views. The models and code is available at: https://github.com/SimonWarmers/CLIP-MVP
Why it matches plant phenotyping methods植物画像から葉数・植物齢を推定するマルチビュー表現学習手法を開発し、ベンチマークで性能評価しており、表現型取得・推定が研究の中心である。
abstractWe propose a level-aware vision language framework that jointly predicts plant age and leaf count using a single multi-task model built on CLIP embeddings.
Reproduction assets foundThe paper explicitly states that the model and code are publicly available at the authors' GitHub repository, which qualifies as a paper-specific public code asset.Code · publicm 7.74 to 3.91 and mean leaf-count MAE from 5.52 to 3.08 compared to the GroMo baseline, corresponding to improvements of 49.5% and 44.2%, respectively. The unified formulation simplifies the pipeline by replacing the conventional dual-model setup while improving robustness to missing views. The modela and code is available at: https://github.com/SimonWarmers/CLIP-MVP
Index Terms:
Plant phenotyping, Multi-view learning, Multi-task regression, Precision agriculture
† † address: † Computer Vision Lab, CAIDAS, IFI, University of Würzburg, Germany
‡ Technological University Dublin, Ireland
1 Introduction
Plant phenotyping from multiview imagery is crucial for precision agriculture, enabling non-Open asset ↗SimonWarmers/CLIP-MVPlines:1-53Code / dataset availability confirmedEurope PMC · checked 5 Sept 2026
The wheat canopy genome harbors abundant yet untapped genetic variation that could be harnessed to enhance yield potential. The green area index (GAI) is a structural metric that reflects the photosynthetically active canopy surface and is closely linked to final grain yield. Current image-based GAI retrieval methods often suffer from signal saturation and coarse structural depiction, constraining downstream genetic analyses. To address this limitation, we constructed a comprehensive image dataset spanning eight field experiments across China and France, encompassing approximately 600 genotypes under six distinct management regimes. Leveraging this diverse data, we developed a multimodal deep-learning framework augmented by simulated-to-realistic (sim2real) synthetic data transfer. This framework fuses nadir and oblique RGB images with accumulated thermal time to produce high-precision, time-series GAI estimates. Validated on independent testing datasets from both China and France, the multimodal approach demonstrated robust performance with an accuracy of R 2 = 0.88 and an RMSE of 0.49 m 2 m -2 , representing an improvement of about 22% over the traditional gap fraction method. In three site-year field experiments involving 565 genotypes, the GAI dynamics derived from the multimodal approach showed higher broad-sense heritability (0.20-0.48) than those from the gap fraction approach (0.02-0.13) and stronger genotypic correlations with yield (0.19-0.40 versus 0.09-0.31). Furthermore, genetic analysis confirmed the biological fidelity of the estimated traits, identifying loci that co-localize with known architectural regulators such as Rht-D1 , TaTB1-4D , and TaBGC1-4D . Consistently, the multimodal-derived phenotypes were specifically enriched in cell-wall remodeling and hormonal signaling pathways (e.g., brassinosteroid) that directly regulate canopy expansion. Overall, the proposed method offers a powerful tool for unlocking genetic gain in canopy architecture and accelerating canopy-targeted wheat improvement.
Why it matches plant phenotyping methodsデュアルアングルRGB画像と熱時間を統合してGAIを推定する深層学習法を開発し、独立データで検証しているため、植物形質取得法が研究の中心です。
abstractwe constructed a comprehensive image dataset spanning eight field experiments across China and France
Reproduction assets foundThe paper publicly releases its pre-trained multimodal GAI-estimation model weights and inference code on Hugging Face, directly reproducing this paper's phenotyping analysis. The raw image and phenology datasets are not public and require contacting the authors.Code · publicThe pre-trained model weights, inference code, and usage instructions are publicly available in the Hugging Face repository at https://huggingface.co/PheniX-Lab/GAI-Estimation/tree/main .Open asset ↗PheniX-Lab/GAI-Estimationlines:259-277Code / dataset availability confirmedOpenAlex · Crossref · Europe PMC · checked 5 Sept 2026
Abstract Observer bias and inconsistencies in traditional plant phenotyping methods limit the accuracy and reproducibility of fine-grained plant analysis. To address these limitations, TomatoMAP is introduced as a comprehensive dataset for Solanum lycopersicum . The dataset contains 68,080 RGB images: 3,616 high-resolution macrophotographs (3648 × 5472) with semantic annotations, and 64,464 moderate-resolution images (1080 × 1440) captured from 12 plant poses at four camera elevations. Each image is accompanied by manually annotated bounding boxes for seven regions of interest (leaves, panicle, flower clusters, fruit clusters, axillary shoot, shoot, and whole-plant area) and by labels spanning 50 BBCH classes representing phenologically growth stages. A general cascading structure is proposed. For real-time applicability, models emphasizing the accuracy-efficiency trade-off (MobileNetv3, YOLOv11, and Mask R-CNN) are prioritized and benchmarked against multiple state-of-the-art models. Performance is assessed using accuracy, mAP, inference FPS, and normalized confusion matrices. In a study involving five domain experts, AI models trained on TomatoMAP achieves comparable accuracy levels. Reliability of automated fine-grained phenotyping is supported by Cohen’s Kappa statistics and inter-rater agreement heatmaps.
Why it matches plant phenotyping methodsトマトの多視点画像、器官領域・生育ステージ注釈を備えたデータセットを構築し、画像モデルの精度・効率・専門家一致度をベンチマークしており、植物フェノタイピング手法が中心である。
titleTomato Multi-Angle Multi-Pose Dataset for Fine-Grained Phenotyping.
Reproduction assets foundThe paper's authors publicly release their analysis code (dataset construction scripts for TomatoMAP-Cls/Det and model training/evaluation code) on GitHub. The TomatoMAP phenotype image dataset itself is deposited at e!DAL (10.5447/ipk/2025/14), but no matching URL is present in the allowed list, so only the code assetCode · publicThe scripts for constructing TomatoMAP-Cls and TomatoMAP-Det, as well as the code used for model evaluation, are available at: https://github.com/0YJ/TomatoMAP.Open asset ↗https://github.com/0YJ/TomatoMAPhtml-lines:423-479Code / dataset availability confirmedOpenAlex · checked 15 Sept 2026
Post-disturbance recovery is a central element of forest resilience against intensifying disturbance regimes. Although recovery signals are strong across Central European forests, the relative roles of different factors contributing to recovery remain incompletely understood. As climate change increasingly challenges recovery, elucidating these processes is essential to adapt forest management to changing climate and disturbance regimes. We extended and applied a biologically grounded model of forest growth to remote sensing data to quantify how management shapes two key drivers of canopy recovery—disturbance legacies and post-disturbance height growth—across Bavaria, Germany. We combined 23,036 ha of quality-filtered photogrammetric canopy height model data with a Landsat-based disturbance map, a forest ownership map and environmental covariates in a Bayesian modelling framework. Post-disturbance growth rates were governed primarily by forest type and site conditions, whereas management strongly influenced disturbance legacies, i.e. the remaining post-disturbance vegetation height structure on site. Legacies varied widely across management types: Federal and set-aside forests retained the highest level of disturbance legacies, while private forests had the lowest legacy levels. Despite marginally lower growth rates, set-aside areas had recovery trajectories that were comparable to managed forests. The median recovery time to 5 m mean canopy height was 14.3 years over all forest and management types. Set-aside areas exhibited the greatest variation in recovery trajectories. We here show that (i) management affects disturbance legacies more strongly than post-disturbance tree growth, (ii) set-aside areas do not differ in recovery speed from managed areas, and (iii) legacies are diversifying forest recovery trajectories, with potential implications for future forest resilience. Our results underline that the post-disturbance reorganization window is a crucial period for management to influence long-term forest development. The framework presented here provides a scalable approach to monitor structural recovery and guide adaptive forest policy and management under increasing disturbance. • Forest management in Central Europe affects post-disturbance recovery more via legacies than tree growth rates. • Set-aside forests recover their canopy height equally fast as managed forests in Central Europe. • Homogenizing and removing disturbance legacies can reduce forest canopy variation across forest stand development. • We combined a biological growth model with remote sensing data to assess forest canopy recovery.
Why it matches plant phenotyping methodsリモートセンシングによる林冠高構造の定量と生物学的成長モデルを組み合わせ、森林の構造回復をスケーラブルにモニタリングする枠組みが研究の中心である。
abstractWe extended and applied a biologically grounded model of forest growth to remote sensing data to quantify how management shapes two key drivers of canopy recovery—disturbance legacies and post-disturbance height growth—across Bavaria, Germany.
Reproduction assets foundThe paper's Data availability statement explicitly deposits the analysis data and code on Zenodo with a public DOI, which is a paper-specific, publicly actionable asset for reproducing the forest recovery analysis.Code · publicthank three anonymous reviewers for providing helpful
suggestions on an earlier version of the work.
Appendix A. Supporting information
Supplementary data associated with this article can be found in the
online version at doi:10.1016/j.foreco.2026.123616.
Data availability
Data and code of the analysis are available at Zenodo: https://doi.org/10.5281/zenodo.17804070.References
Anderson-Teixeira, Kristina J., Miller, Adam D., Mohan, Jacqueline E., Hudiburg, Tara
W., Duval, Benjamin D., DeLucia, Evan H., 2013. Altered Dynamics of Forest
Recovery under a Changing Climate. Glob. Change Biol. 19 (7), 2001–2021. https://
doi.org/10.1111/gcb.12194.
Arano, Kathryn G., Munn, Ian A., 2006. Evaluating Open asset ↗Zenodo · 10.5281/zenodo.17804070pdf-raw-page:10 lines:1-55Code / dataset availability confirmedEurope PMC · checked 15 Sept 2026
Grapevines ( Vitis vinifera L.) undergo structural and physiological changes throughout the growing season, progressing through distinct phenological stages that require regular monitoring. This dataset consists of high-resolution point cloud data acquired with a stationary terrestrial laser scanner (TLS) to document grapevine development from early leaf development to dormancy. Georeferenced point clouds were generated from 15 TLS scans along two vineyard rows at nine phenological stages. The dataset also includes multispectral and RGB photogrammetric point clouds and orthorectified raster products from an unmanned aerial vehicle survey conducted before harvest. Ground-truth measurements leaf area index, grape production, and pruning wood biomass were collected for each monitored grapevine. As a result, the dataset provides multi-temporal TLS observations that support grapevine structural analysis and development, phenological monitoring, and can be used for the development of AI-based models for precision viticulture.
Why it matches plant phenotyping methodsブドウの生育・構造・フェノロジーを対象とするTLS点群および関連画像データセットであり、植物フェノタイピング用の再利用可能なデータ基盤として中心的です。
titleTLS-grapevine2024: A terrestrial laser scanner point cloud dataset of grapevines at different phenological stages.
Reproduction assets foundThe paper is a Data in Brief article describing the TLS-grapevine2024 dataset itself, publicly deposited on Zenodo with DOI 10.5281/zenodo.16751663. This is a paper-specific, openly available asset containing the TLS point clouds, UAV imagery/rasters, and ground-truth agronomic measurements (LAI, grape production, prunDataset · publicditions: clear sky.
Data source location
Institution: University of Trás-os-Montes e Alto Douro
City/Town/Region: Arroios, Vila Real, Norte
Country: Portugal
Coordinates: 41°17′28.83″N 7°43′17.90″W,
Altitude: 435 m
Data accessibility
Repository name: Zenodo
Data identification number: 10.5281/zenodo.16751663
Direct URL to data: https://doi.org/10.5281/zenodo.16751663
Related research article
None
1.
Value of the Data
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This dataset covers nine phenological stages of grapevine growth from April 2024 to January 2025, providing multi-temporal terrestrial laser scanner (TLS) observations for structural and phenological analysis.
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It includes TLS point clouds collected at multiple stages and muOpen asset ↗Zenodo · 10.5281/zenodo.16751663lines:1-50Code / dataset availability confirmedCrossref · Europe PMC · checked 5 Sept 2026
Abstract Soybean growth is determined by the interaction of genetic, environmental, and management factors. In the context of future climate and climate extremes, understanding genotype by environment interaction (GxE) will be crucial for selecting resilient breeding lines and optimizing management practices to minimize stress. This requires an in depth elucidation of stressful weather conditions and differing temporal responses of genotypes to those conditions. In field studies, however, the environment is often treated as a static factor, and the specific effects of weather variability on crop growth remain poorly understood. Here, we present a longitudinal dataset comprising 17,247 high-resolution RGB images of soybean breeding lines collected throughout eight years in Eschikon, Switzerland. Top-of-canopy images were acquired throughout the entire growing seasons and complemented by hourly weather data, enabling a comprehensive analysis of soybean growth dynamics under varying field conditions. High spatio-temporal image resolution allows detailed analysis of growth dynamics and GxE, supporting identification of stress-tolerant genotypes to improve yield prediction and yield stability.
Why it matches plant phenotyping methods8年間の高スループット画像フェノタイピングによる大規模データセットを提示し、画像取得基盤と作物成長動態の解析を中心に扱っているため、方法論文として適格です。
titleFIP 1.0 soybean data: Insights on soybean growth from eight years of high-throughput image field phenotyping
Reproduction assets foundThe paper's canopy cover analysis code is publicly available on the authors' ETH GitLab repository. The FIP 1.0 soybean image/trait dataset itself is deposited in the ETH Research Collection and Hugging Face, but those URLs are not among the allowed URLs, so only the code asset qualifies.Code · publicCode availability
The code is available on: https://gitlab.ethz.ch/crop_phenotyping/fip-soybean-canopycover. Users with similar data can use the implemented workflow to get canopy cover from their experiments.Open asset ↗gitlab.ethz.ch/crop_phenotyping/fip-soybean-canopycoverhtml-lines:207-226Code / 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 confirmedEurope PMC · checked 5 Sept 2026
Wheat emergence rate and emergence uniformity are key indicators for evaluating seed vigor and sowing quality, and they play an important role in wheat growth and yield formation. Traditional methods for measuring emergence rate and evaluating emergence uniformity rely on manual assessment, which is inefficient, highly subjective, and unable to meet the demand for large scale, high efficiency, and precise acquisition of wheat emergence data. In this study, RGB images and a two-stage deep learning algorithm were used to extract and analyze seedling traits of 420 wheat varieties under two nitrogen levels, and the results were applied to genome wide association studies to elucidate the genetic basis. The two-stage algorithm integrates a Bidirectional Feature Pyramid Network, small object detection layer, large size image input, and FasterNet to improve detection and instance segmentation speed and accuracy. The proposed method achieved an emergence rate accuracy of 0.929, with R 2 = 0.914 and RMSE = 2.448 compared to manual measurements, and required less than 0.2 s per image for analysis. By employing this two-stage algorithm for processing and analysis, varieties (e.g., Gao8901 and ShiYou20) that consistently exhibited high emergence rates and uniformity under multiple nitrogen treatments were identified. Furthermore, genome-wide association study identified the major loci qEmergence rate-3A and qUniformity-6B governing seedling emergence rate and uniformity, which likely enhance wheat seedling traits by modulating energy supply or related signaling molecules. The emergence-rate and uniformity data generated by the two-stage algorithm significantly accelerated the discovery of relevant genes and enabled the identification of wheat varieties with high emergence rate and uniformity, providing valuable insights and practical references for high-quality breeding and gene mining.
Why it matches plant phenotyping methodsRGB画像と二段階深層学習による出芽率・均一性の自動取得手法を開発し、手動測定との精度比較および大規模品種適用を行っており、表現型取得法が研究の中心である。
abstractTraditional methods for measuring emergence rate and evaluating emergence uniformity rely on manual assessment, which is inefficient, highly subjective, and unable to meet the demand for large scale, high efficiency, and precise acquisition of wheat emergence data.
Reproduction assets foundThe authors openly provide test code, base models, and sample test data for the WS-YOLO two-stage wheat seedling phenotyping pipeline in a public GitHub repository. Raw phenotype datasets are only available upon request, so they do not qualify as public assets.Code · publicThe test code, base models, and sample test data are openly available in the GitHub repository: https://github.com/AIWheatLab/WheatSeedling.Open asset ↗AIWheatLab/WheatSeedlinghtml-lines:375-402Code / 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 confirmedEurope PMC · Crossref · checked 15 Sept 2026
Common beanStem / branchObject detectionPhysiological trait estimationGrowth / development / phenology
Growing plants are remarkable at negotiating obstacles in their unstructured and changing environments. Measuring the mechanical interactions of growing plants with surrounding objects is a critical step towards deciphering thigmotropic responses underpinning complex growth strategies. Yet, available force measurement systems have limited capacity to capture weak forces in freely moving plant organs-such as the forces applied by a growing shoot pushing at an obstacle. We developed a measurement system based on the deflection of a pendulum by a freely moving shoot. Unlike many force measurement systems, the organ is not tethered to the device. Moreover, force is measured along two axes, as opposed to one axis in commonly used methods. Orthogonal cameras track the 3D position of the rod and shoot, yielding the rod deflection angle and, using a mechanical torque equilibrium equation, allowing extraction of the force applied by the plant over time. This system is relevant for measuring weak forces in macro-sized systems (e.g. growth or turgor pressures), and the force detection range can be tuned by altering rod mass and length. We demonstrate the system with Phaseolus vulgaris shoots, measuring the forces they apply on a candidate support during inherent circumnutation movements, prior to twining. Such measurements lay the foundations for deciphering how climbing plants assess whether to twine or not- an open question since Darwin's first observations.
Why it matches plant phenotyping methods自由に動く植物器官が発生する微弱な力を、カメラ追跡と力抽出により定量する測定システムを開発・実証しており、植物表現型の取得方法が研究の中心である。
abstractWe developed a measurement system based on the deflection of a pendulum by a freely moving shoot.
Reproduction assets foundThe authors deposited the full analysis workflow (data and code) for five example force-measurement trajectories on Zenodo, publicly accessible via DOI 10.5281/zenodo.15545548. This directly reproduces the paper's camera-based plant force phenotyping measurements and computational analysis. Other experimental data are仅Dataset · publicof interest
None declared.
Funding
YM acknowledges support from the Israel Science Foundation Research
Grant (ISF) no. 2307/22, and ERC grant GROWsmart 101165101. AO
acknowledges support from the Colton Foundation scholarship.
Data availability
We have put the full workflow for five example trajectories on a Zenodo
repository (https://doi.org/10.5281/zenodo.15545548; Ohad and
Meroz, 2025). Other experimental data are available upon request.
References
Autumn K, Liang YA, Tonia Hsieh S, Zesch W, Chan WP, Kenny TW,
Fearing R, Full RJ. 2000. Adhesive force of a single gecko foot-hair.
Nature 405, 681–685.
Backholm M, Bäumchen O. 2019. Micropipette force sensors for in vivo
force measurementsOpen asset ↗Zenodo · 10.5281/zenodo.15545548pdf-raw-page:9 lines:1-95Code / dataset availability confirmedOpenAlex · Europe PMC · checked 5 Sept 2026
BACKGROUND: Phenotypic diversity arises from the process of development and is shaped by genomic variation in plants. However, the genetic basis of growth dynamics remains poorly understood in maize. RESULTS: Here, we analyze 679 maize inbred lines derived from a synthetic CUBIC population with approximately 2.8 million SNPs, leveraging high-throughput phenotyping to capture 1,002,240 RGB images across 18 growth stages. We quantify 67 image-based traits (i-traits), revealing distinct dynamic patterns throughout development. Genome-wide association studies identify 857 quantitative trait loci (QTLs) influencing growth variation, with 88.6% classified as period-specific dynamic QTLs exhibiting modest effects, and 11.4% as conservative QTLs with sustained effects. Notably, 1.5% of cryptic pleiotropic QTLs spanning different growth stages suggest genetic relocations during development. These QTLs enhance heritability estimates for mature traits by an average of 6.2%. We further characterize the novel function of key genes linked with these QTLs, including BRD1 with the pleiotropic effects on plant height and perimeter of convex hull and ZmGalOx1 with the broad-spectrum regulation of plant architecture. Developmental rewiring of epistatic networks shapes maize growth, underscoring the vitality of temporal genetic regulation. Trajectory modeling of i-traits across periods decodes the growth variation patterns, supporting the ontogenic hypothesis driven predictive breeding strategies. CONCLUSION: The findings elucidate the genetic architecture underlying growth dynamics from a spatial-temporal perspective, offering novel insights for maize improvement.
Why it matches plant phenotyping methods大規模RGB画像から67の画像形質を抽出し、発育段階ごとのトレイト動態を解析する高スループット植物表現型解析が研究の中核であるため、方法応用として収録する。
abstractleveraging high-throughput phenotyping to capture 1,002,240 RGB images across 18 growth stages
Reproduction assets foundThe paper's own phenotyping assets are publicly available: selected RGB plant images on Zenodo (record 18150504), and the image-analysis/i-trait extraction pipeline code on GitHub with a Zenodo mirror (record 18151471). The NCBI BioProject and MaizeGDB are prior-study/generic resources, not paper-specific.Code · publicThe image analysis and i-trait extraction pipeline and codes followed the previous procedure [ 18 ] without any modifications and has been publicly released at Github [ 49 ] and Zenodo [ 50 ] platform, all code in the repository are released under the MIT License.Open asset ↗GitHublines:195-202Code · publichas been publicly released at Github [ 49 ] and Zenodo [ 50 ] platform, all code in the repository are released under the MIT License.Open asset ↗Zenodolines:195-202Code / dataset availability confirmedEurope PMC · bioRxiv · checked 14 Sept 2026
Quinoa ( Chenopodium quinoa Willd.) is a genetically diverse Andean crop valued for its nutrition and adaptability to varied agro-climatic conditions with potential for cultivation in European and Mediterranean, particularly on marginal lands. Low temperatures during early sowing can impair germination, while delayed sowing increases the risk of poor maturation due to unfavorable autumn weather. To assess the adaptation of quinoa to low temperature conditions, that reflect cold stress, we evaluated germination and phenotypic variation in 60 accessions from highland and coastal ecotypes across three sowing dates in South-Western Germany: late winter (S1), early spring (S2), and spring (S3). Early sowing under low temperature conditions in S1 delayed seedling-emergence and reduced emergence percentages, yet these plants produced the highest average seed yield per plot (64 g) compared to S2 (46 g) and S3 (35 g). Highland accessions showed earlier seedling-emergence and with higher emergence percentages, while coastal types matured earlier and gave higher yields across sowing dates. A complementary laboratory experiment assessed germination under cold (4.4 °C) and control (18.3 °C) conditions, using both manual scoring and image analysis via a Mask R Convolutional Neural Network, to track seedling growth. This confirmed the beneficial germination performance of highland accessions under low temperature conditions, with strong agreement between manual and automated scoring. Our findings suggest that quinoa demonstrates resilience to cold stress with highland quinoa exhibiting superior germination traits, and early sowing, despite reduced emergence, can lead to higher yields. We conclude that combining favorable traits such as faster maturity and higher yield of coastal ecotypes with superior germination traits of highland accessions is a promising avenue for breeding improved quinoa varieties for cold climatic regions.
Why it matches plant phenotyping methodsMask R-CNNによる発芽・幼植物成長の画像解析を手動評価と比較し、強い一致を検証しており、植物表現型取得法の技術的検証を含む。
abstractA complementary laboratory experiment assessed germination under cold (4.4 °C) and control (18.3 °C) conditions, using both manual scoring and image analysis via a Mask R Convolutional Neural Network, to track seedling growth.
Reproduction assets foundThe paper states that all phenotypic data and R analysis scripts are available as supplementary material (publicly hosted with the bioRxiv preprint), while raw seed germination images are only available upon request. No separate repository or trained model checkpoint is named.Dataset · publicData availability: All phenotypic data and R scripts used for the analysis are available
as supplementary material.Open asset ↗pdf-page:1 lines:1-52Code / dataset availability confirmedEurope PMC · OpenAlex · Crossref · checked 5 Sept 2026
Background Rice plant architecture underpins yield and grain quality, yet two obstacles impede accurate field characterization in dense paddies. First, single-plant reconstruction is constrained by severe inter-plant occlusion, cluttered backgrounds, and limited viewpoints. These factors obscure culms, leaves, basal tillers, and the true physical scale of the plant. Active ranging devices are cumbersome in outdoor plots and can lose accuracy, whereas conventional passive photogrammetry performs poorly under such conditions. Second, delineating panicles within a 3D rice model is intrinsically difficult. Panicles are slender, highly branched, and visually similar to surrounding foliage, often interwoven and partially hidden. These factors result in fragmented boundaries and missing details. Direct point-cloud segmentation struggles with such discontinuous geometry and requires costly 3D annotation, whereas generic image segmentation models trained on natural scenes transfer poorly to paddy imagery. These challenges motivate a field-ready workflow that both reconstructs whole plants at high resolution in dense plantings and reliably segments panicles to enable trait extraction. Results A low-cost, in-field, multi-view pipeline for whole-plant three-dimensional reconstruction, termed One Stop 3D Target Reconstruction And segmentation (OSTRA), operates on color images with a reference-board setup. The pipeline builds detailed three-dimensional models of individual rice plants and automatically segments key organs (in this case, panicles), despite dense surrounding vegetation. When applied to 231 diverse rice landraces grown in a crowded field setting, the method produced high-fidelity plant models with clearly delineated panicle structures. From these reconstructions, three architectural traits were derived: plant height, leaf area, and panicle length. Genome-wide association analysis of the measured traits identified strong genotype-phenotype associations tagging known candidate genes. Natural variants at D2 and RFL/APO2 were associated with plant height variation, variants at FLW7 were linked to differences in leaf area, and allelic variation at AAI1 corresponded to panicle length variation. These loci are established regulators of plant growth and morphology, indicating that this three-dimensional phenotyping pipeline attains accuracy sufficient to rediscover meaningful genetic signals. Conclusions This study provides a practical tool for precise rice phenotyping even under dense field planting conditions, overcoming occlusion and structural complexity. By enabling non-destructive, field-based measurement of complete plant architecture and linking these phenotypes to specific genes, the pipeline bridges field phenomics and genomics. The integrated reconstruction and analysis framework advances the study of rice architecture and offers a general route to connect complex traits with their genetic determinants.
Why it matches plant phenotyping methods密植圃場でのイネ全体3D再構築、器官分割、形質抽出を中核とする画像ベース表現型解析手法の開発・実証であり、明確に収載対象。
abstractA low-cost, in-field, multi-view pipeline for whole-plant three-dimensional reconstruction, termed One Stop 3D Target Reconstruction And segmentation (OSTRA), operates on color images with a reference-board setup.
Reproduction assets foundThe paper explicitly states that the 3D rice plant models (231 landraces) are deposited on Zenodo and the OSTRA source code is publicly available on GitHub. Both are paper-specific, public, and actionable.Code · publicThe source code of OSTRA is available on GitHub at [http://github.com/ganlab/ostra] (http:/github.com/ganlab/ostra).Open asset ↗github · ganlab/ostralines:217-246Code / dataset availability confirmedOpenAlex · Crossref · checked 13 Sept 2026
Banana (Musa spp.) fruit morphology is a key determinant of yield and quality, yet modeling its 3D structural dynamics across genotypes remains difficult. To address this challenge, we developed a generic, biomass-driven 3D structural model for banana fruit fingers that quantitatively links growth and morphology. Field experiments were conducted over two growing seasons in Hainan, China, using three representative genotypes. Morphological traits, including outer and inner arc length, circumference, and pedicel length, along with dry (Wd) and fresh weight (Wf), were measured every 10 days after flowering until 110 days. Quantitative relationships between morphological traits and Wf, as well as between Wd and Wf, were fitted using linear or Gompertz functions with genotype-specific parameters. Based on these functions, a parameterized 3D reconstruction method was implemented in Python, combining biomass-driven growth equations, curvature geometry, and cross-sectional interpolation to simulate the fruit’s bending, tapering, and volumetric development. The resulting dynamic 3D models accurately reproduced genotype-specific differences in curvature, length, and shape with average fitting R2 > 0.95. The proposed biomass-driven 3D structural model provides a methodological framework for integrating banana fruit morphology into functional–structural plant models.
Why it matches plant phenotyping methodsバナナ果実の形態形質を推定・再現するバイオマス駆動型3D構造モデルを開発し、遺伝子型間の形状を検証しており、フェノタイピング手法が中心である。
abstractwe developed a generic, biomass-driven 3D structural model for banana fruit fingers that quantitatively links growth and morphology.
Reproduction assets foundThe paper explicitly states that the source code of the Banana Morphology Simulation System and the datasets are publicly available on GitHub at the authors' URL, which matches an allowed URL. This covers the paper's phenotyping datasets and analysis/3D modeling code.Code · publicData analysis was performed using a custom-developed software platform, the Banana
Morphology Simulation System. The source code and datasets are publicly available
on GitHub (https://github.com/Interstingsun/SimBanana, accessed on 4 January 2026).Open asset ↗Interstingsun/SimBananapdf-page:5 lines:1-24Dataset · publicThe source code and datasets are publicly available
on GitHub (https://github.com/Interstingsun/SimBanana, accessed on 4 January 2026).Open asset ↗Interstingsun/SimBananapdf-page:5 lines:1-24Code / dataset availability confirmedEurope PMC · OpenAlex · checked 5 Sept 2026
Three-dimensional (3D) reconstruction technologies for crops are of significant importance in the context of smart breeding and precision agriculture, as they enable accurate characterization of crop spatial architecture and developmental dynamics. Such capabilities provide essential phenotypic information for the rapid selection of breeding materials and informed agronomic decision-making. A critical requirement for the practical application of crop 3D models is high-accuracy organ-level segmentation. However, the absence of a stage-universal segmentation framework capable of operating across complete soybean growth cycle remains a major bottleneck hindering progress in this field. To address this issue, we propose SOY3DSEG-a high-precision framework based on an improved Point Transformer, designed to support the full developmental spectrum of soybean (V1-R7). The framework incorporates a novel down sampling strategy termed Dynamic Multi-Stage Sampling Strategy (DMSS), alongside multi-scale feature enhancement and a local geometry-aware attention mechanism, enhancing segmentation accuracy and efficiency. Performance evaluations across 12 consecutive soybean growth stages (V1 to R7) indicate that SOY3DSEG achieved an average mean Intersection-over-Union (mIoU) of 93.34 % for stem-leaf segmentation-surpassing RandLA-Net, BAAF-Net, PointNet++, and PointConv by over 30 %, and outperforming the baseline Point Transformer by 14.18 %. A moderate accuracy decline appears at R6-R7 due to dense canopies and strong occlusion, yet SOY3DSEG retains clear superiority over the baseline Point Transformer, demonstrating robustness under complex morphology. In cross-crop transfer tests limited to early seedling stages of maize and tomato, the model achieves an mIoU of approximately 99 %, indicating strong early-stage transferability while mature-stage generalization across species remains open for future study. SOY3DSEG thus provides a stage-robust and scalable solution for full-cycle soybean phenotyping and growth monitoring, contributing to precision agricultural practice.
Why it matches plant phenotyping methods大豆の3D点群から器官レベル形態を抽出する分割フレームワークを開発・評価しており、植物表現型取得手法が研究の中心である。
abstractA critical requirement for the practical application of crop 3D models is high-accuracy organ-level segmentation.
Reproduction assets foundThe authors state that the dataset (Soybean-MVS point clouds) and program code used in this study are publicly available at their GitHub repository, which is an allowed URL.Code · publicThe dataset and program code used in this study can be found at the link below: https://github.com/NiuJiarui718/SOY3DSEG .Open asset ↗NiuJiarui718/SOY3DSEGlines:306-323Code / dataset availability confirmedEurope PMC · OpenAlex · checked 15 Sept 2026
Fruit growth has long been described using single- or double-sigmoid curves; however, these temporal models cannot fully capture the spatial heterogeneity that ultimately shapes a fruit. Here, we present a three-dimensional analysis pipeline that non-destructively tracks spatial fruit growth dynamics from field-collected imagery. Surface landmarks were drawn, and video recordings were taken throughout development for three pome fruits, apple ( Malus × domestica ), Japanese pear ( Pyrus pyrifolia ) and European pear ( Pyrus communis ), and two drupe fruits, peach ( Prunus persica ) and Japanese apricot ( Prunus mume ), to track their motion. Using 3D Gaussian splatting, we successfully reconstructed 3D models of the fruits, and the landmark displacement could be measured with high accuracy, with R 2 ≥ 0.98 when compared to manual recordings. We found a common spatial growth gradient in the longitudinal growth shared in the pomes and drupes of the Rosaceae; proximal (stem-end) regions exhibited more pronounced growth than the distal (stylar) end. An exception was found in European pear 'Bartlett,' which showed relatively vigorous growth in the distal region, explaining its distinct shape with expanded distal end. Transverse expansion varied far less than longitudinal expansion, with a possible association with initial fruit morphology. Inter-fruit growth variability peaked in the fastest-growing regions, particularly in the distal area of the European pear, highlighting the link between growth vigor and phenotypic variance. These results provide foundational insights into the developmental dynamics of both pome and drupe fruits of the Rosaceae family, contributing to the optimization of fruit size, shape, and uniformity.
Why it matches plant phenotyping methods3D画像解析パイプラインと3D Gaussian Splattingを用いて果実の空間的成長を非破壊計測し、手動記録との精度比較で検証しているため、植物表現型取得法が中心である。
abstractHere, we present a three-dimensional analysis pipeline that non-destructively tracks spatial fruit growth dynamics from field-collected imagery.
Reproduction assets foundThe authors deposited a subset of the 3DGS-reconstructed fruit models (the paper's phenotyping outputs) on Figshare with a public DOI; additional data only on request. No author analysis code or raw imagery deposit is stated.Dataset · publicFootnotes
Appendix A
Supplementary data to this article can be found online at https://doi.org/10.1016/j.plaphe.2026.100166 .
Appendix A.
Supplementary data
The following is the Supplementary data to this article:
Multimedia component 1
Multimedia component 1
Data availability
A subset of the generated 3D models is available at https://doi.org/10.6084/m9.figshare.30854579 , where the quality of the 3DGS reconstructions and the marking/measurement procedure can be examined. Additional data may be provided upon reasonable request to the corresponding author.
ReferencesOpen asset ↗figshare · 10.6084/m9.figshare.30854579lines:151-171Code / dataset availability confirmedEurope PMC · bioRxiv · OpenAlex · checked 15 Sept 2026
Lodging is a major contributor to decreased yield in tef, a staple cereal crop in Ethiopia. Semidwarf varieties have been developed with a goal to increase yield through reduced lodging, but studying lodging susceptibility currently requires a labor-intensive, imprecise, manual scoring method. Here we present workflows for analyzing tef stand height from UAS sensors across time to both predict lodging later in the season with early height and to measure the severity of lodging after a storm event. We compare 3D point clouds generated by photogrammetry from RGB images with those generated from LiDAR to estimate height, demonstrating that they produce similar results, despite differences in cost. Stand height and lodging can both be accurately measured with low-cost UAS, reducing the need for manual measurements and increasing precision and temporal resolution in plant breeding programs. Significance Statement Extreme weather or heavy grain can cause plant stems to bend, a process called lodging. Lodging significantly reduces crop yields globally, particularly in grain crops such as tef ( Eragrostis tef ). Semidwarf crops have previously been reported to be lodging-resistant, increasing crop yields. Here, we used uncrewed aerial systems (UAS) to measure plant growth, height, and lodging in gene edited semidwarf tef lines, and compared the results to ground-truth data. Using a UAS equipped with a red-green-blue (RGB) camera or LiDAR sensor, we measured plant height and lodging, and found that early-season height measurements could predict future lodging potential. The tools used were contributed to the open-source software PlantCV-Geospatial for community use. This work contributes to a broader understanding of genetic resistance to lodging, providing valuable insights for tef crop improvement and reduces the need for labor-intensive manual measurements.
Why it matches plant phenotyping methodsUASのRGB画像・LiDARから3D点群を生成し、植物の草高と倒伏を定量化・検証するワークフローが研究の中心であるため、植物フェノタイピング手法として含める。
abstractHere we present workflows for analyzing tef stand height from UAS sensors across time to both predict lodging later in the season with early height and to measure the severity of lodging after a storm event.
Reproduction assets foundThe paper states that code and data associated with the manuscript (UAS-based tef height/lodging phenotyping analyses) are publicly available in the authors' GitHub repository danforthcenter/teff-manuscript. The PlantCV-Geospatial package and D2S platform are general-purpose tools/platforms rather than paper-specific,.Code · publicInstitute Block Grant to K.M.M. and
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N.F., the National Science Foundation (grant numbers 2120153 and 2346101 to N.F.),
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the USDA NIFA AFRI (grant number 2022-67021-36467 to N.F.), and by the Bellwether
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Foundation.
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Data Availability
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Code and data associated with this manuscript are available on GitHub
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(https://github.com/danforthcenter/teff-manuscript).477
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CC-BY 4.0 International license
available under a
(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made
The copyright holder for this preprint
this version posted January 7, 2026.
;
https://doi.org/10.64898/2026.01.0Open asset ↗danforthcenter/teff-manuscriptpdf-raw-page:13 lines:1-76Code / 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.
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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.
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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.
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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.
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Gaggion N, Boccardo N A, Bonazzola R, et al. ChronoRoot 2.0 Project Home Page. 2026. https://chronoroot.github.io . Accessed 2Open asset ↗https://huggingface.co/datasets/ngaggion/ChronoRoot2lines:568-618Code · publicical modules, and experimental protocols. We hope that this approach to open science will not only ensure transparency and reproducibility but also allow the system to evolve alongside the changing needs of the plant biology community.
Availability of source code and requirements
Project name: ChronoRoot 2.0.
Project home page: https://chronoroot.github.io .
Main Source Code repository: https://github.com/ChronoRoot/ChronoRoot2 .
Operating system(s): Platform independent.
Programming language: Python.
Other requirements: Conda, Apptainer, or Docker.
License: GNU GPL 3.0.
Additional files
Supplementary Text S1 : Functional PCA. Provides an intuitive explanation of functional princOpen asset ↗lines:439-479Code / dataset availability confirmedEurope PMC · checked 5 Sept 2026
Published1 Jan 2026The Plant journal : for cell and molecular biologyCited by 4 · OpenAlex ↗
Live imaging data analysis often requires an objective, local, and accurate way of quantification of cell dynamics. In the research field of polarized tip-growth, the cell fluctuations and/or fluctuations in tip position and growth direction hamper automated analyses of huge amounts of imaging sequences. The fluctuated nature in data makes it unclear how cell shape and growth are linked to intracellular events that could be the actual driving force of cell growth. To overcome these difficulties, we developed a powerful and user-friendly tool called KymoTip with an available format. In this software, novel functions such as coordinate normalization, tip-bottom detection, and signal kymograph were implemented. We confirmed that not only plasma membrane-labeled fluorescent images, but also images such as bright-field and cortical microtubule markers-so long as the cell contours can be identified-are amenable to KymoTip. Furthermore, by combining markers for cell contours with those that visualize intracellular structures, it becomes possible to quantitatively analyze various intracellular events, such as nuclear migration and calcium wave, in conjunction with cellular growth dynamics. Since KymoTip can be handled by non-specialists, it is expected to promote understanding of what happens at the sub- and cellular level with high-throughput outcomes.
Why it matches plant phenotyping methods植物細胞のライブ画像から細胞形状・先端位置・成長方向・成長動態を定量化する解析ソフトウェアを開発しており、植物表現型取得・抽出が研究の中心である。
abstractwe developed a powerful and user-friendly tool called KymoTip
Reproduction assets foundThe paper's Data Availability Statement explicitly provides public authors' code repositories (KymoTip analysis tool and SAM2 segmentation code) and a figshare deposit of the raw imaging data used for the tip-growth phenotyping measurements.Code · publicThe code for KymoTip is available on GitHub: https://github.com/blues0910/KymoTipOpen asset ↗blues0910/KymoTiphtml-lines:159-244Code · publicthe code for SAM2 segmentation is available at https://github.com/YusukeKimata‐Moo/SAM2‐segmentation/Open asset ↗YusukeKimata‐Moo/SAM2‐segmentationhtml-lines:159-244Dataset · publicThe raw data used in this paper are available on figshare: https://doi.org/10.6084/m9.figshare.30847580Open asset ↗figshare · 10.6084/m9.figshare.30847580html-lines:159-244Code / dataset availability confirmedOpenAlex · Europe PMC · checked 15 Sept 2026
High-throughput plant phenotyping with hyperspectral imaging (HSI) is pivotal for accelerating crop improvement to address global food security. Adoption is limited by a data-processing bottleneck, forcing a trade-off between costly, inflexible commercial software and programming-intensive open-source libraries. To overcome this barrier, we developed PlantSpecLab, an open-source, no-code platform that unifies the HSI workflow from image processing to modeling within a single interactive interface. The platform introduces spectrally guided segmentation strategies (Range Averaging, Difference Enhancement) and a spectral Fractional-Order Differencing (FOD) preprocessor to enhance extraction of subtle, physiologically relevant features. Across diverse in-house and public datasets, FOD-preprocessed spectra improved model performance over conventional pipelines, yielding 87.35% accuracy for tomato maturity and R 2 = 0.878 for fruit firmness. In cross-software benchmarks, PlantSpecLab matched the accuracy of ENVI and code-based Python pipelines while reducing end-to-end workflow time by >90% (>80 min to ∼8 min). PlantSpecLab provides a transparent, efficient analytical environment that lowers the technical barrier to HSI analysis. This enables researchers to prioritize biological interpretation while minimizing computational overhead.
Why it matches plant phenotyping methods植物のハイパースペクトル画像から表現型特徴を抽出・モデル化するオープンソース基盤を開発し、既存ソフトウェアとの性能・処理時間を比較検証しているため、フェノタイピング手法が中心である。
abstractwe developed PlantSpecLab, an open-source, no-code platform that unifies the HSI workflow from image processing to modeling within a single interactive interface.
Reproduction assets foundThe authors explicitly state the PlantSpecLab source code (the platform used for all phenotyping analyses in the paper) is publicly available on GitHub under an MIT license, with a versioned release archived alongside the data.Code · publicsis. Jingye Liu: Data curation. Chu Zhang: Supervision, Writing—review & editing. Wei Xu: Supervision, Funding acquisition, Writing—review & editing.
Data and code availability
All data and code that support the findings of this study will be made publicly available upon publication. The PlantSpecLab source code is available at https://github.com/Another-Train/PlantSpecLab (MIT License), with a versioned release archived alongside the data.
Funding
This work was supported by the National Natural Science Foundation of China (Grant Nos. 62265015 and 32360750), the Xinjiang Uygur Autonomous Region Key R&D Program (Grant No. 2023B02028-3), and the Finance Plan Project of the 8th Division of the Open asset ↗Another-Train/PlantSpecLablines:458-487Code / dataset availability confirmedEurope PMC · bioRxiv · checked 15 Sept 2026
FlowerAnnotation / quality controlObject detectionGrowth / development / phenology
ABSTRACT Herbarium specimens represent critical historical records of plant phenology, yet automating annotation of reproductive structures remains challenging given the diversity of floral morphologies, specimen age and quality, and image quality. Here, we present a machine learning pipeline that uses an ensemble modeling approach to detect flowers on herbarium specimens and deliver these data to the phenology research community. After testing multiple strategies for generating training data, we found in-house expert-curated annotations were essential for producing reliable results. Expert validation found relatively strong accuracy for detecting present floral structures, but still had moderately high false negative rates. Applying the ensemble to our filtered final image dataset of 22 million records resulted in 11.1 million records labeled with flowers present. However, only 2.9 million of these contained complete metadata necessary for downstream phenology research, highlighting the need for full label digitization efforts. Still, this dataset represents a large compilation of historical herbarium-derived phenology records available as a resource for the phenology community. We end by demonstrating how integrating these machine-labeled records into Phenobase, a publicly-available phenology database, expands taxonomic and temporal coverage for large-scale phenological analyses, and discuss remaining challenges and next steps.
Why it matches plant phenotyping methods植物標本画像から花の存在を自動検出し、精度検証と大規模な phenology データセット化を行う機械学習手法が研究の中心であるため、植物フェノタイピング手法として適格です。
abstractwe present a machine learning pipeline that uses an ensemble modeling approach to detect flowers on herbarium specimens and deliver these data to the phenology research community.
Reproduction assets foundThe paper's Data Availability Statement explicitly deposits the ensemble models, training/validation/test images, training data and final ensemble output on Zenodo, and the analysis code on GitHub; machine-labeled records are also served via the public Phenobase portal. All are paper-specific, public, and actionable.Dataset · publicors contributed to drafts and gave final
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The ensemble data models and a corresponding JSON file with model metadata data are
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housed on Zenodo (https://doi.org/10.5281/zenodo.17079402). Images used in training,
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validation, and testing are located here: https://zenodo.org/records/17675089. Code used for
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this project can be found on github (https://github.com/rafelafrance/phenobase/tree/v1.0.0).461
Training data and final ensemble output can be found on Zenodo
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(https://doi.org/10.5281/zenodo.17675089).463
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Supporting Information
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Additional Supporting Information may be found online in the SupportinOpen asset ↗Zenodo · 17675089pdf-raw-page:19 lines:1-55Dataset · publictps://doi.org/10.5281/zenodo.17079402). Images used in training,
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validation, and testing are located here: https://zenodo.org/records/17675089. Code used for
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this project can be found on github (https://github.com/rafelafrance/phenobase/tree/v1.0.0).461
Training data and final ensemble output can be found on Zenodo
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(https://doi.org/10.5281/zenodo.17675089).463
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Supporting Information
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Additional Supporting Information may be found online in the Supporting Information section at
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the end of the article.
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Appendix S1. List of difficult-to-annotate genera and families removed from training and
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downstream data.
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Appendix S2. Table S1. Validation results for held-oOpen asset ↗Zenodo · 10.5281/zenodo.17675089pdf-raw-page:19 lines:1-55Code · publiclity Statement
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The ensemble data models and a corresponding JSON file with model metadata data are
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housed on Zenodo (https://doi.org/10.5281/zenodo.17079402). Images used in training,
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validation, and testing are located here: https://zenodo.org/records/17675089. Code used for
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this project can be found on github (https://github.com/rafelafrance/phenobase/tree/v1.0.0).461
Training data and final ensemble output can be found on Zenodo
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(https://doi.org/10.5281/zenodo.17675089).463
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Additional Supporting Information may be found online in the Supporting Information section at
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the end of the article.
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Appendix S1. List of difficult-to-annotaOpen asset ↗GitHub · rafelafrance/phenobasepdf-raw-page:19 lines:1-55Code / dataset availability confirmedEurope PMC · checked 5 Sept 2026
TomatoGreenhouseMultimodalRGB / grayscaleMultispectral / hyperspectralFruitClassificationSegmentationGrowth / development / phenology
Computer vision and multispectral imaging have increasingly become essential tools in modern precision agriculture. Accurate ripeness assessment is critical for yield optimization, reducing post-harvest losses, and enabling automated harvesting systems. However, traditional RGB-based approaches struggle to differentiate subtle maturity changes, and existing solutions often fail under varying lighting, occlusion, or cultivar-specific conditions. To address these challenges, this study focuses on the integration of complementary spectral cues for reliable tomato ripeness evaluation. The work utilizes a curated RGB-NIR tomato dataset comprising 224 hyperspectral samples, processed into aligned multimodal image pairs with balanced ripeness categories.The proposed TomatoRipen-MMT model employs a multimodal Transformer framework with dual encoders, cross-spectral attention, and a joint decoder to fuse spatial and biochemical cues. The novelty of the methodology lies in the dynamic cross-attention mechanism, which learns inter-modal dependencies between RGB and NIR signals for enhanced ripeness interpretation. Performance metrics including accuracy, precision, recall, F1-score, mIoU, and AUC were used to comprehensively evaluate the system. Experimental results demonstrate that TomatoRipen-MMT significantly outperforms all baseline RGB-only, NIR-only, and fusion methods, achieving 94.8% classification accuracy and 82.6% mIoU. These findings establish the effectiveness of multimodal Transformers for robust, high-precision fruit maturity assessment in controlled and greenhouse environments.
Why it matches plant phenotyping methodsトマト果実の成熟度という植物器官の状態を、RGB・NIR画像融合とTransformerで推定する手法を開発・評価しており、フェノタイピング手法が中心です。
abstractThe proposed TomatoRipen-MMT model employs a multimodal Transformer framework with dual encoders, cross-spectral attention, and a joint decoder to fuse spatial and biochemical cues.
Reproduction assets foundThe paper's phenotyping analysis is built on a publicly available USDA/NAL hyperspectral tomato dataset, explicitly linked in the Data Availability statement with an exact URL match. No author code or model checkpoints are disclosed.Dataset · publicThe dataset analyzed in this study is publicly available at the https://agdatacommons.nal.usda.gov/articles/dataset/Data_from_b_Hyperspectral_Imaging_Analysis_for_Early_Detection_of_Tomato_Bacterial_Leaf_Spot_Disease_b_/26046328.Open asset ↗26046328html-lines:1038-1053Code / dataset availability confirmedOpenAlex · Crossref · checked 6 Sept 2026
Automated three-dimensional plant phenotyping is an essential tool for non-destructive analysis of plant growth and structure. This paper presents a low-cost system based on stereo vision for depth estimation and morphological characterization of maize plants. The system incorporates an automatic detection stage for the object of interest using deep learning techniques to delimit the region of interest (ROI) corresponding to the plant. The Semi-Global Block Matching (SGBM) algorithm is applied to the detected region to compute the disparity map and generate a partial three-dimensional representation of the plant structure. The ROI delimitation restricts the disparity calculation to the plant area, reducing processing of the background and optimizing computational resource use. The deep learning-based detection stage maintains stable foliage identification even under varying lighting conditions and shadowing, ensuring consistent depth data across different experimental conditions. Overall, the proposed system integrates detection and disparity estimation into an efficient processing flow, providing an accessible alternative for automated three-dimensional phenotyping in agricultural environments.
Why it matches plant phenotyping methods植物の3次元形態を取得・特徴づけるステレオビジョンと深度推定システムの開発が中心であり、明確な植物フェノタイピング手法です。
abstractThis paper presents a low-cost system based on stereo vision for depth estimation and morphological characterization of maize plants.
Reproduction assets foundThe paper's Data Availability Statement openly deposits the original study data (the 544 stereo RGB maize images and related phenotyping data) on OSF at a DOI, which is a paper-specific, publicly actionable asset. No author analysis code repository is explicitly stated.Dataset · publicData Availability Statement: The original data presented in the study are openly available in OSF at
https://doi.org/10.17605/OSF.IO/MN6P9.Open asset ↗OSF · 10.17605/OSF.IO/MN6P9pdf-page:18 lines:1-59Code / dataset availability confirmedEurope PMC · checked 6 Sept 2026
Accurate monitoring of wheat phenology is critical for ensuring wheat production. Recent advances in deep learning have enabled the automated detection of wheat phenology in the field. In particular, deep learning models using multi-temporal image series have addressed the challenge of low accuracy in models that only use spatial features by incorporating dynamic aspects of the wheat growth process. However, utilizing multi-temporal image series introduces challenges such as model parameter redundancy, complex inference processes, and difficulties in real-time deployment. To address these issues, this study presents an optimization method for deriving wheat phenology from single-temporal images (WPDSI) that combines knowledge distillation and multi-layer attention transfer. The proposed approach employs knowledge distillation. In this framework, a teacher model extracts spatiotemporal features from multi-temporal image-series and generates soft labels to guide a student model trained on single-temporal images. This reduces model complexity and input data requirements. Multi-layer attention transfer allows the student model to inherit feature representations from multiple layers of the teacher model. This enhances its ability to capture key phenological characteristics and supports interpretability through attention mechanisms. The proposed method achieves an overall accuracy (OA) of 0.927, comparable to models trained on multi-temporal image series. Furthermore, the model demonstrates strong generalization on unseen datasets, enhancing real-time performance and computational efficiency while maintaining high accuracy, providing a practical solution for deriving wheat phenology in the field. The dataset is available at https://github.com/phenology-detection/WPDSI.
Why it matches plant phenotyping methods小麦の生育ステージを単一時点画像から推定する深層学習手法を開発・評価しており、植物フェノタイピング手法が研究の中心です。
abstractthis study presents an optimization method for deriving wheat phenology from single-temporal images (WPDSI)
Reproduction assets foundThe paper's wheat phenology image dataset is explicitly stated as publicly available at the authors' GitHub repository (https://github.com/phenology-detection/WPDSI), matching an allowed URL. No separate code availability is stated beyond this repository, so it is treated as the paper-specific public asset.Dataset · publicData availability
The dataset is publicly available at https://github.com/phenology-detection/WPDSI .Open asset ↗phenology-detection/WPDSIlines:270-275Code / dataset availability confirmedOpenAlex · Crossref · checked 15 Sept 2026
Abstract High‐throughput and noninvasive phenotyping methods are promising technology for improving efficiency in plant research and breeding. In this study, we evaluated the performance of a digital phenotyping system (DPS) based on three‐dimensional (3D) model reconstruction for quantifying key growth traits in rice ( Oryza sativa ). The DPS was used to estimate plant height, biomass, color, leaf morphology, and tiller angle in four rice varieties (Koshihikari, Nipponbare, PL9, and Tachiaoba). The results show high accuracy and correlation between manually measured and DPS‐derived traits. Notably, the 3D volume analysis can quantify biomass accumulation and growth dynamics and revealed distinct differences among varieties. The strong correlation between the green‐red normalized difference index (a red‐green‐blue‐based index) and soil plant analysis development also demonstrated the viability of the system in monitoring leaf color without using a multispectral instrument. The analysis also captured growth patterns over time, including canopy development and senescence, which are often challenging to quantify through manual measurements alone. Furthermore, the tiller angle estimation derived from DPS provided an alternative method to plant architecture evaluation, demonstrating its potential for use in breeding programs aimed to optimize canopy structure. These findings establish DPS as a reliable and scalable tool for a digital phenotyping platform that enables comprehensive trait analysis with reduced labor and increased precision and the capability to continuously monitor plant growth and biomass accumulation. This study shows the potential of this novel digital tool for automating manual measurements, which can increase efficiency and expedite research and breeding in rice and other crops.
Why it matches plant phenotyping methods3Dモデル再構築に基づくデジタル表現型解析システムを開発・評価し、イネの複数形質を手測定と比較検証しているため、方法が研究の中心です。
abstractwe evaluated the performance of a digital phenotyping system (DPS) based on three‐dimensional (3D) model reconstruction for quantifying key growth traits in rice
Reproduction assets foundThe paper's data availability statement explicitly says the analysis code is openly available on GitHub at the authors' repository Rice_VTGa.O, which contains the digital phenotyping/leaf-tracing analysis code for this study. No phenotype dataset or image deposit is stated.Code · publicGrant Number 39 [2023] and 38 [2024]),
and Microbiome and Metabolome Control Project, University
of Miyazaki, Japan.
C O N F L I C T O F I N T E R E S T S TAT E M E N T
The authors declare no conflicts of interest.
DATA AVA I L A B I L I T Y S TAT E M E N T
Codes used for analysis in this study are openly available on
GitHub at https://github.com/sandysan42/Rice_VTGa.O RC I D
SorawichPongpiyapaiboon https://orcid.org/0000-0002-9314-8375
Kenji Aoki https://orcid.org/0000-0001-7003-1994
MasatsuguHashiguchi https://orcid.org/0000-0003-0637-2780
RyoAkashi https://orcid.org/0000-0002-5651-8285
Yuji Kishima https://orcid.org/0000-0002-0942-3371
Hidenori Tanaka https://orcid.org/0000-0002-4237-8154Open asset ↗https://github.com/sandysan42/Rice_VTGa.O · Rice_VTGa.Opdf-raw-page:13 lines:1-84Code / dataset availability confirmedEurope PMC · checked 15 Sept 2026
We present a meticulously curated, long-term (1981-2024) dataset documenting maize phenology dynamics across Northeast China, the nation's most critical commercial grain base. Derived from 61 national agrometeorological stations, it captures the timing of 10 pivotal phenological stages (sowing, emergence, three-leaf, seven-leaf, jointing, tasseling, flowering, silking, milking, maturity) and derives the durations of 4 growth period lengths (sowing-jointing, jointing-silking, silking-maturity, sowing-maturity). The dataset underwent a rigorous, multi-tiered quality control protocol, including automated checks for internal consistency and expert arbitration for ambiguous records, ensuring high integrity. Subsequent analysis employed kernel density estimation to characterize the probability distribution of phenological events and univariate linear regression to quantify decadal trends. The resulting repository is substantial, comprising 976 georeferenced diagnostic plots in JPEG format and two primary data tables in XLSX format, with a total volume of 601.04 MB. Systematically organized by province and station, this dataset serves as a foundational empirical resource for quantifying climate-driven shifts in crop development, enhancing the parameterization and validation of process-based crop models, and informing the development of optimized cultivation practices and regional climate adaptation frameworks.
Why it matches plant phenotyping methodsトウモロコシの複数の生育ステージと生育期間を長期・広域に収録し、品質管理済みデータセットとして構築しているため、植物フェノタイピングデータセットが研究の中心です。
abstractit captures the timing of 10 pivotal phenological stages
Reproduction assets foundThe paper is a data descriptor whose maize phenology dataset (1981–2024, 61 stations, 10 phenological stages, diagnostic plots and XLSX tables) is openly deposited in Science Data Bank. No custom code was created.Dataset · publich stage timing and duration, it empowers farmers and
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agricultural planners to optimize production systems in response to evolving climatic
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conditions, thereby enhancing regional food security resilience.
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Data Availability
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The dataset generated during this study is openly available in the Science Data Bank at
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https://doi.org/10.57760/sciencedb.28709 or https://cstr.cn/31253.11.sciencedb.28709.312
Code availability
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No custom code was created for the production of this dataset.
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References
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1.Cai C, Ding T, Chen W. (2024). Potential yield of world maize under global warming based on ARIMA-TR model.
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Journal of Agrometeorology, 2024, 26(1).
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2.Li M. RetrospectOpen asset ↗Science Data Bank · 10.57760/sciencedb.28709pdf-raw-page:15 lines:1-94Code / dataset availability confirmedEurope PMC · checked 14 Sept 2026
Field / plotMultimodalMultispectral / hyperspectralRaman / spectroscopyWhole plant / canopy / plot / fieldCalibration / preprocessingGrowth / development / phenology
This data article presents a multimodal, non-invasive dataset documenting the physiology and growth stages of Stenocereus queretaroensis (pitayo), a native species from the arid and semi-arid regions of Southern Zacatecas, Mexico. In particular, Stenocereus spp. are important cacti in the region due to its nutritional properties, role as an economic resource, and cultural significance.It is worth emphasising that these cacti traditionally grow wild (i.e., without deliberate cultivation); accordingly, controlled cultivation is uncommon and remains understudied. With the aim of producing a formal, comprehensive analysis and compendium, the data were collected across multiple phenological stages to provide a complete representation of the plant development cycle, from vegetative growth through to fruiting. To achieve this, the collection process combined high-resolution multispectral imaging with field spectrometry in the 400-700 nm range. Standardized acquisition protocols were applied in field conditions to capture consistent reflectance data, and environmental variables such as illumination, temperature, and geographic coordinates were recorded for each session to ensure reproducibility. The dataset integrates several components: (i) multispectral images that provide spatial information on canopy and structural characteristics, (ii) field spectral signatures with detailed reflectance values for each sampled plant, and (iii) metadata describing phenological stage, acquisition date and time, environmental conditions, and equipment settings. For subsequent analysis, data was preprocessed and normalized to enable reliable comparisons between growth stages and across acquisition sessions, resulting in a clean, structured resource ready for computational analysis. In this regard, this dataset has been organized to facilitate its direct application across multiple research and development contexts. Specifically, potential applications include the training and validation of machine learning and computer vision models for automated phenological stage classification, harvest time estimation, and development of species-specific vegetation indices. Moreover, owing to its standardized design, the resource can serve as a benchmark for comparing methods, validating algorithms, and supporting reproducible workflows in precision agriculture and remote sensing. Beyond Stenocereus queretaroensis, the documented acquisition and preprocessing methodology can be replicated or adapted to generate similar multimodal datasets for other climate-resilient crops, particularly those cultivated in arid and semi-arid regions. This could enable comparative analyses across species and provide a reference for extending multimodal sensing approaches to underrepresented plants of ecological and economic importance.
Why it matches plant phenotyping methods植物の生育段階・生理・構造特性を対象に、標準化されたマルチスペクトル画像とフィールド分光データを収集・前処理した再利用可能なデータセットであり、ベンチマークやアルゴリズム検証を目的とするため、フェノタイピング手法が中心です。
abstractThis data article presents a multimodal, non-invasive dataset documenting the physiology and growth stages of Stenocereus queretaroensis (pitayo)
Reproduction assets foundThe paper's own multimodal phenotyping dataset (multispectral/RGB images, spectral signatures, NDVI products, metadata, and example MATLAB scripts) is publicly deposited on Mendeley Data with explicit direct URL and DOI.Dataset · public) at ∼1750 m a.s.l., under semi-arid temperate conditions with spring temperatures ranging 20–33°C. The data were collected from the Unit Academic of Electrical Engineering Plantel Jalpa.
Data accessibility
Repository name: Multimodal_Cactaceae_Dataset_25
Data identification number: doi:10.17632/skw8tjc82f.1
Direct URL to data: https://data.mendeley.com/datasets/skw8tjc82f/1
Instructions for accessing these data: click on the direct URL to obtain the multimodal data from Mendeley Dataset Repository.
Related research article
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Value of the Data
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These data provide a unique, non-invasive resource for studying Stenocereus spp. physiology. The integrated collection of high-resolution multOpen asset ↗Mendeley Data · doi:10.17632/skw8tjc82f.1lines:32-58Code / dataset availability confirmedEurope PMC · OpenAlex · checked 6 Sept 2026
TomatoMultimodalStereoFruitObject detectionVisualization / data managementGrowth / development / phenologyFruit / seed / panicle traitsYield / yield components
Introduction The advancement of smart agriculture has witnessed increasing applications of computer vision in crop monitoring and management. However, existing approaches remain challenged by high computational complexity, limited real-time capability, and poor multi-task coordination in tomato cultivation scenarios. Methods To address these limitations, an intelligent tomato management system is proposed based on the Ghost-based Adaptive Efficient You Only Look Once (GAE-YOLO) algorithm. The lightweight architecture of the GAE-YOLO framework is achieved through the replacement of standard convolutional layers with Ghost Convolution (GhostConv) modules, while detection accuracy is significantly improved by the integration of both AReLU activation functions and Effective Intersection over Union (E-IoU) loss optimization. The system, implemented on a Jetson TX2 embedded platform, also incorporates ZED stereo vision for 3D localization and a PyQt6-based visualization platform. Results When implemented on Jetson TX2, the system achieving 93.5% mean Average Precision at 50% intersection over union (mAP@50) at 10.2 frames per second (FPS), which can be optimized to 27 FPS by employing TensorRT acceleration and 720p resolution for scenarios demanding higher throughput. Furthermore, it establishes standardized assessment systems for tomato maturity and yield prediction, and offers integrated modules for disease diagnosis and agricultural large language model consultation. Discussion This work establishes a new paradigm for edge computing in agriculture while providing critical technical support for smart farming development.
Why it matches plant phenotyping methodsトマトの成熟度・収量予測および病害診断を含む画像・3Dビジョン基盤を開発し、エッジ環境で性能評価しているため、植物表現型取得が中心的な研究である。
abstractan intelligent tomato management system is proposed based on the Ghost-based Adaptive Efficient You Only Look Once (GAE-YOLO) algorithm
Reproduction assets foundThe paper's data availability statement explicitly states that the data and code supporting the study are publicly available on GitHub at the authors' repository (GAE-YOLO), which matches an allowed URL. This qualifies as a paper-specific public code asset for the tomato detection/phenotyping analysis.Code · publicThe data and code supporting this study are publicly available at GitHub under the following links: https://github.com/NSSCk/GAE-YOLO .Open asset ↗NSSCk/GAE-YOLOlines:756-834Code / dataset availability confirmedEurope PMC · checked 14 Sept 2026
Field / plotChlorophyll fluorescenceLeafPhysiological trait estimationGrowth / development / phenologyPhotosynthesis / fluorescencePigment / colour / senescence
Abstract Accurate assessment of leaf chlorophyll is essential for understanding plant physiological responses to environmental variation. While solvent extraction provides precise chlorophyll concentrations, it is destructive and temporally limited. In contrast, portable optical meters such as the CCM-300 enable rapid, non-destructive measurements of chlorophyll fluorescence ratio (CFR), but their calibration against extracted pigments is often species- and season-specific. This study evaluated the reliability of CCM-300 measurements and reconstructed seasonal chlorophyll dynamics in Acer campestre across two contrasting summers in the United Kingdom.Paired CFR and acetone-extracted chlorophyll data collected in 2023 were used to develop calibration models. Random Forest regression achieved the best predictive accuracy (R² = 0.51, RMSE = 0.51 mg cm⁻²), although a simple linear model was adopted for cross-year projection due to its stability. Applying this calibration to daily 2022 CFR measurements generated a “virtual acetone” chlorophyll time series, allowing comparison with weekly destructive extractions in 2023. Both years exhibited mid-season chlorophyll plateaus followed by late-summer declines, but senescence occurred approximately ten days earlier in the warmer, drier 2022 season.Mixed-effects modelling of the 2022 data indicated positive effects of temperature (β = 0.0029 ± 0.0012 SE) and wind speed (β = 0.0053 ± 0.0021 SE) on CFR, whereas day of year and precipitation were not significant. A generalised additive model for 2023 explained 90% of deviance (adj. R² = 0.89) and revealed significant nonlinear effects of temperature, rainfall, and wind speed. Together, these results demonstrate that the CCM-300 can provide a robust non-destructive proxy for total chlorophyll when properly calibrated, and that Acer campestre chlorophyll dynamics are highly sensitive to interannual climatic variability.
Why it matches plant phenotyping methodsCCM-300による葉クロロフィル測定を抽出クロロフィルと比較・較正し、季節時系列へ適用して信頼性を評価しているため、植物フェノタイピング手法が中心である。
abstractThis study evaluated the reliability of CCM-300 measurements and reconstructed seasonal chlorophyll dynamics in Acer campestre across two contrasting summers in the United Kingdom.
Reproduction assets found保存済みの本文根拠を更新済みルールで再検証し、公開資産1件を確認しました。Dataset · publicData Availability: Data used in the study can be accessed via https://zenodo.org/records/17475985.Open asset ↗zenodo · 17475985pdf-page:11 lines:1-44Code / dataset availability confirmedEurope PMC · checked 6 Sept 2026
Tea flowers play a crucial role in taxonomic research and hybrid breeding of tea plants. As traditional methods of observing tea flower traits are labor-intensive and inaccurate, TflosYOLO and Tea Flowering Stage Classification (TFSC) models were proposed for tea flowering quantification, which enable the estimation of flower count and flowering period. In this study, a highly representative and diverse dataset was constructed by collecting flower images from 29 tea accessions in 2 years. Based on this dataset, the TflosYOLO model was built on the YOLOv5 architecture and enhanced with the Squeeze-and-Excitation (SE) network, Adaptive Rectangular Convolution, and Attention Free Transformer, which is the first model to offer a viable solution for detecting and counting tea flowers. The TflosYOLO model achieved a mean Average Precision at 50% IoU (mAP50) of 0.844, outperforming YOLOv5, YOLOv7, and YOLOv8. Furthermore, the TflosYOLO model was tested on 31 datasets encompassing 26 tea accessions and five flowering stages, demonstrating high generalization and robustness. The correlation coefficient (R 2 ) between the predicted and actual flower counts was 0.964. Additionally, the TFSC model-a seven-layer neural network-was designed for the automatic classification of the flowering period. The TFSC model was evaluated for 2 years and achieved an accuracy of 0.738 and 0.899. Using the TflosYOLO+TFSC model, the tea flowering dynamics were monitored, and the changes in flowering stages were tracked across various tea accessions. The framework provides crucial support for tea plant breeding programs and the phenotypic analysis of germplasm resources.
Why it matches plant phenotyping methods茶花画像から花数と開花期を推定するモデルを開発・検証しており、植物表現型の取得・抽出手法が研究の中心である。
abstractTflosYOLO and Tea Flowering Stage Classification (TFSC) models were proposed for tea flowering quantification, which enable the estimation of flower count and flowering period.
Reproduction assets foundThe paper's data availability statement explicitly deposits the tea flower datasets and models in a public GitHub repository (sufie-mi/tea-flower-model), which directly supports this paper's tea flower phenotyping measurements and models. The labelImg repository is a generic third-party annotation tool, not a paper-ownDataset · publicThe datasets presented in this study can be found in online repositories. The names of the repository/repositories and accession number(s) can be found below: https://github.com/sufie-mi/tea-flower-model .Open asset ↗https://github.com/sufie-mi/tea-flower-model · tea-flower-modellines:764-781Code / dataset availability confirmedOpenAlex · Crossref · checked 14 Sept 2026
Abstract Unoccupied aerial vehicle (UAV)‐based high‐throughput phenotyping provides scalable and cost‐effective access to phenotypic information for crop improvement, yet its application in minor crops such as lentil ( Lens culinaris Medik.) remains limited. This study applied UAV‐derived canopy traits and crop growth regression modeling to a nested association mapping population developed from CDC Redberry crossed with 32 diverse founder lines. UAV imagery collected across four site‐years was used to capture canopy height, crop area, and crop volume per plot basis at multiple time points. Crop growth regression models were fitted to derive crop growth parameters, maximum canopy size, growth rate, and cumulative growth anchored to phenological stages. These static and time‐series traits were evaluated for seed yield prediction using partial least squares regression with a 70:20:10 data split and 10‐fold cross‐validation. Static traits such as maximum crop volume and maximum crop area were consistently associated with yield. Dynamic trait‐based models improved prediction accuracy and identified the swollen pod stage (R5–R6) as the most informative forecasting window. External validation using an independent trial confirmed the generalizability of the approach. This study presents a UAV phenotyping framework that supports crop growth dissection and early yield prediction and downstream trait discovery in lentil.
Why it matches plant phenotyping methodsUAV画像から作物の形態・成長形質を抽出し、時系列成長モデルと収量予測を検証するフレームワークが研究の中心であるため。
abstractUAV imagery collected across four site‐years was used to capture canopy height, crop area, and crop volume per plot basis at multiple time points.
Reproduction assets foundThe paper's data availability statement points to a public KnowPulse experiment page hosting the study's UAV-derived phenotyping and yield data; no author analysis code repository is stated.Dataset · publicof S).
We thank Dr. Ana Vargas at the Crop Development Center, U
of S for generously providing yield data from the independent
field trial.
C O N F L I C T O F I N T E R E S T S TAT E M E N T
The authors declare no conflicts of interest.
DATA AVA I L A B I L I T Y S TAT E M E N T
The data supporting this study are available at:
https://knowpulse.usask.ca/experiment/AGILE-NAM-UAV-growth-modelling or from the authors upon request.
O RC I D
SandeshNeupane https://orcid.org/0000-0003-3679-1046
KirstinE. Bett https://orcid.org/0000-0001-7959-6959
SteveJ. Shirtliffe https://orcid.org/0000-0002-3603-7417
R E F E R E N C E S
Araus, J. L., Kefauver, S. C., Zaman-Allah, M., Olsen, M. S., & Cairns, J.Open asset ↗KnowPulse · AGILE-NAM-UAV-growth-modellingpdf-raw-page:15 lines:1-90Code / dataset availability confirmedEurope PMC · checked 14 Sept 2026
High-throughput phenotyping of growth kinetics and organ size in the model plant Arabidopsis thaliana requires rapid and precise methods for trait estimation. To address this need, we developed the Arabidopsis Phenotypic Trait Estimation System, APTES, an open-access, high-throughput program that uses computer vision and deep learning to extract 64 leaf traits and 64 silique traits from photographs. The enhanced segmentation model Cascade Mask Region-based Convolutional Neural Network (Mask R-CNN) achieved precision (measure of positive prediction accuracy), recall (sensitivity in detection), and F1 score values (harmonic mean of precision and recall) of 0.965, 0.958, and 0.961, respectively, for individual leaf segmentation. These metrics demonstrated a consistent improvement of approximately 1 percentage point over the baseline model. For silique segmentation, our enhanced DetectoRS model for silique segmentation attained precision, recall, and F1 scores of 0.954, 0.930, and 0.942, respectively. Notably, precision increased by 1%, while the F1 score improved by 2 percentage points. Trait parameters were automatically calculated with coefficient of determination values for leaf and silique traits ranging from 0.776 to 0.976 and mean absolute percentage error values from 1.89% to 7.90%. We phenotyped 166 Arabidopsis accessions, using APTES, and subjected the resulting values to a genome-wide association study (GWAS), revealing 1,042 single-nucleotide polymorphisms (SNPs) as being significantly associated with 18 leaf and silique traits, and one significant SNP on chromosome 3 linked to silique number. Furthermore, we validated APTES across other public Arabidopsis databases and other plant species, with segmentation results demonstrating its applicability across diverse datasets. In conclusion, APTES is a valuable automated tool for leaf and silique segmentation and trait estimation, which should offer benefits to the broader plant science community. Supplementary information The online version contains supplementary material available at 10.1007/s42994-025-00239-y.
Why it matches plant phenotyping methods植物の葉・莢の形質を画像から抽出する深層学習システムを開発し、性能検証・他データセットでの妥当性確認まで行っており、フェノタイピング手法が研究の中心である。
abstractwe developed the Arabidopsis Phenotypic Trait Estimation System, APTES, an open-access, high-throughput program that uses computer vision and deep learning to extract 64 leaf traits and 64 silique traits from photographs.
Reproduction assets found保存済みの本文根拠を更新済みルールで再検証し、公開資産2件を確認しました。Code · publicThe executable tool and software packages are available at https://drive.google.com/drive/folders/1i9IariiIrxuFtVIaRiaIzqvb8Gfg3xTc or http://plantphenomics.hzau.edu.cn/usercrop/Rice/download .Open asset ↗lines:292-292Code · publicThe executable tool and software packages are available at https://drive.google.com/drive/folders/1i9IariiIrxuFtVIaRiaIzqvb8Gfg3xTc or http://plantphenomics.hzau.edu.cn/usercrop/Rice/download .Open asset ↗lines:292-292Code / dataset availability confirmedEurope PMC · checked 14 Sept 2026
Large-scale rainfed cropping systems (broadacre agriculture) face intensifying climate and resource stresses that undermine yield stability and farm livelihoods. Remote sensing (RS) offers critical tools for improving resilience by monitoring crop performance—productivity, phenology, and environmental stress—across large areas and timeframes. This review aims to synthesize methodological advances over the past two decades in applying RS for broadacre crop monitoring and to identify key challenges and integration opportunities. Peer-reviewed studies across diverse crops and regions were systematically examined to evaluate the strengths, limitations, and emerging trends across the three RS application themes. The review finds that (1) RS enables spatially explicit yield estimation from regional to paddock scales, with vegetation indices (VIs) and phenology-adjusted metrics closely correlated with yield. (2) Time-series analyses of RS data effectively capture phenological transitions critical for forecasting, supported by advances in curve fitting, sensor fusion, and machine learning. (3) Thermal and multispectral indices support early detection of abiotic (drought, heat, salinity) and biotic (pests, disease) stresses, though specificity remains limited. Across themes, methodological silos and sensor integration barriers hinder holistic application. Emerging approaches—such as multi-sensor/scale fusion, RS–crop model data assimilation, and operational and big data integration—provide promising pathways toward resilience-focused decision support. Future research should define quantifiable resilience metrics and cross-theme predictive integration to guide climate adaptation.
Why it matches plant phenotyping methods作物の生産性、フェノロジー、環境ストレスをリモートセンシングで測定・推定する方法論レビューであり、植物形質・状態の取得手法が中心である。
abstractThis review aims to synthesize methodological advances over the past two decades in applying RS for broadacre crop monitoring
Reproduction assets foundThis methodological review includes a case study (Figure 2) using MODIS NDVI composites, SILO gridded climate data, and ABARES historical winter crop yield data. The authors explicitly state the case-study datasets are publicly accessible via official portals; the SILO and ABARES portals are paper-specific public data-Dataset · publiclies, and observed productivity. Note: This figure is derived from
the authors’ ongoing study. The monthly NDVI composites (MOD13C2) were generated post-season, which limits their
utility for in-season forecasting. The gridded Climate data were obtained from the Australian Scientific Information for
Land Owners (SILO) database (https://www.longpaddock.qld.gov.au/silo/), and historical winter crop yield data were
sourced from the Australian Bureau of Agricultural and Resource Economics and Sciences (ABARES)
(https://www.agriculture.gov.au/abares/data).
However, Figure 2 also illustrates key limitations of NDVI-based monitoring. First, the complete
seasonal NDVI composite becomes available onlOpen asset ↗SILOpdf-layout-page:8 lines:1-53Dataset · publiclity for in-season forecasting. The gridded Climate data were obtained from the Australian Scientific Information for
Land Owners (SILO) database (https://www.longpaddock.qld.gov.au/silo/), and historical winter crop yield data were
sourced from the Australian Bureau of Agricultural and Resource Economics and Sciences (ABARES)
(https://www.agriculture.gov.au/abares/data).
However, Figure 2 also illustrates key limitations of NDVI-based monitoring. First, the complete
seasonal NDVI composite becomes available only after crop harvest, limiting its usefulness for in-
season yield forecasting or early drought warning. In other words, detailed phenological curves and
productivity metrics can onlyOpen asset ↗ABARESpdf-layout-page:8 lines:1-53Code / dataset availability confirmedCrossref · checked 15 Sept 2026
Crop monitoring over large areas with high accuracy is of great significance in precision agriculture. The study is an attempt to assess the potential of open-source high-resolution satellite datasets and open-source digital platforms in combination with AI/ML algorithms for near-real-time crop monitoring and yield estimation at the farm level. In this research, we used Sentinel-1 and Sentinel-2 datasets, by processing them on the Google Earth Engine platform and developed several crop-based indicators to assess crop phenology as well as the distinction between a well-managed field (demo plots) vs a normal farmers' practice-managed crop (control plots) using Sentinel-1 satellite data. Further, crop yields were estimated before the harvesting of the crop by using Sentinel-1 and Sentinel-2 data with machine learning algorithms. The findings demonstrate that the effect of an improved package of practices on rice was significantly different from the farmer's practice. Among the statistical yield models developed for yield estimation, the gradient tree boosting model performed better than other models. This study proposes a novel method of near-real-time remote crop monitoring right from sowing to harvest time to estimate crop yields with an accuracy of 77 percent. There is potential in using open-source satellite data for monitoring farm fields in the future.
Why it matches plant phenotyping methods衛星データとAI/MLを用いて水稲の生育状態（phenology）と収量を圃場レベルで推定する手法を開発・評価しており、植物形質の取得・推定が研究の中心である。
abstractdeveloped several crop-based indicators to assess crop phenology
Reproduction assets foundThe paper's Data Availability Statement points to a public Figshare deposit (DOI 10.6084/m9.figshare.29858924) containing the data supporting the study's rice monitoring findings, which qualifies as a paper-specific public asset.Dataset · publicuddin Shaik: Software, Visualization, Writing –
original draft
Suman Saraswathibatla: Investigation, Project admin-
istration, Supervision
Mukund Patil: Validation, Writing – review & editing
Data Availability Statement
The data that support the findings of this study can be found
at https://figshare.com/s/b611c04368825e6a028b
(https://doi.org/10.6084/m9.figshare.29858924).Conflicts of Interest
The authors declare no conflicts of interest.
References
[1] Mandapati, R., Gumma, M. K., Metuku, D. R., Bel-lam, P. K.,
Panjala, P., Maitra, S., Maila, N. Crop yield assessment using
field-based data and crop models at the village level: A case
study on a homogeneous rice area in Telangana, India.
AgOpen asset ↗figshare · 10.6084/m9.figshare.29858924pdf-raw-page:21 lines:1-101Code / dataset availability confirmedEurope PMC · checked 14 Sept 2026
Field / plotWhole plant / canopy / plot / fieldPhysiological trait estimationGrowth / development / phenology
Accurate estimation of chilling requirements (CR) is essential for breeding and selecting temperate fruit trees adapted to specific agroclimatic conditions, particularly under global warming scenarios. Among the available methodologies to determine CR, the Partial Least Squares (PLS) regression procedure, based on long-term phenological and temperature records, offers a suitable approach to delineate the effective chill accumulation period. In this study, we present an R-based workflow developed using the agroclimatic functions integrated into the chillR package for R to determine the genotype-specific CR of 282 apricot ( Prunus armeniaca L.) seedlings from two progenies grown in southwestern Spain. The pipeline generates standardized CR datasets suitable for downstream applications, including QTL mapping and the selection of promising genotypes for breeding purposes. This tool streamlines the estimation process, reducing the technical expertise and time required for CR estimation, thereby supporting efficient phenotypic selection and accelerating genetic research in temperate fruit trees. The complete code and associated datasets are freely available in a public repository (https://github.com/CEBASFruitBreed/R-workflow-ChillPLS), promoting the use across a range of temperate fruit species.•Uses long-term flowering observations and temperature records to determine genotype-specific chilling requirements.•Integrates PLS regression procedure within an R-based workflow to estimate chilling requirements from datasets comprising multiple genotypes.•Generates standardized outputs suitable to support genetic analysis and informed breeding decisions.
Why it matches plant phenotyping methods複数のアンズ遺伝子型の低温要求量という植物生理形質を、PLS回帰とRワークフローで推定する方法自体が中心であり、再利用可能なコードとデータも提供している。
abstractwe present an R-based workflow developed using the agroclimatic functions integrated into the chillR package for R to determine the genotype-specific CR of 282 apricot ( Prunus armeniaca L.) seedlings
Reproduction assets foundThe authors explicitly state that the complete R code (workflow for PLS-based chilling requirement estimation) and the associated datasets (flowering records and temperature data) are freely available in a public GitHub repository.Code · publicThe complete code and associated datasets are freely available in a public repository ( https://github.com/CEBASFruitBreed/R-workflow-ChillPLS )Open asset ↗CEBASFruitBreed/R-workflow-ChillPLSlines:1-47Code / dataset availability confirmedEurope PMC · checked 6 Sept 2026
Rapid prediction and control of flowering time is essential for breeding crops resilient to changing climates. Current models often fail to predict flowering time in new cultivars because molecular models lack integration of environmental signals, while physiological models inadequately capture the interactions of vernalization, photoperiod and temperature. This leads to mischaracterized genotypes and inaccurate forecasts. A new Cereal Anthesis Molecular Phenology (CAMP) model was developed for wheat. It explicitly integrates the regulatory roles of three major 'virtual' flowering genes (Vrn1, Vrn2, and Vrn3) with environmental cues. A novel phenotyping strategy based on main stem leaf number was introduced to shorten the time required for data collection and model calibration. CAMP predicted flowering time within 4-7 d across 64 genetically diverse wheat cultivars grown under contrasting environments. The leaf-number phenotyping method reduced phenotyping time by more than 80%, offering a practical alternative to resource-intensive field trials. Together, these advances enable accurate cultivar characterization and scalable prediction of flowering behaviour. CAMP enables the ability to predict flowering time directly from genotypic data (e.g. SNPs), eliminating the need for costly controlled-environment experiments. This represents a step change in molecular-physiological modelling, supporting faster deployment of new cultivars and more effective design of wheat for future climates.
Why it matches plant phenotyping methods主茎葉数に基づく新規フェノタイピング手法とCAMPモデルを開発し、多様なコムギ品種・環境で開花期予測を検証している。表現型取得の効率化が中心的貢献である。
abstractA novel phenotyping strategy based on main stem leaf number was introduced to shorten the time required for data collection and model calibration.
Reproduction assets foundThe paper's Data availability statement explicitly provides public repositories containing the CAMP model source code and analysis scripts used for the phenotyping data analysis and flowering-time prediction: the APSIM Next Generation framework repository, the standalone Python CAMP model and analysis scripts, and theCCode · publicAll the data and the source code of the model are freely accessible for research use through the APSIM General Use License at: https://github.com/apsimInitiative/apsimxOpen asset ↗apsimInitiative/apsimxlines:295-475Code · publicPython code and analysis scripts can be found at https://github.com/HamishBrownPFR/CAMPOpen asset ↗HamishBrownPFR/CAMPlines:295-475Code · publicC# implementation is available at https://github.com/APSIMInitiative/ApsimX/tree/master/Models/PMF/Phenology/CAMPOpen asset ↗APSIMInitiative/ApsimXlines:295-475Code / dataset availability confirmedEurope PMC · checked 6 Sept 2026
Introduction The germination rate of rice seed is a critical indicator in agricultural research and production, directly influencing crop yield and quality. Traditional assessment methods based on manual visual inspection are often time-consuming, labor-intensive, and prone to subjectivity. Existing automated approaches, while helpful, typically suffer from limitations such as rigid germination standards, strict imaging requirements, and difficulties in handling the small size, dense arrangement, and variable radicle lengths of rice seeds. Methods To address these challenges, we present SeedRuler, a versatile, web-based application designed to improve the accuracy, efficiency, and usability of rice seed germination analysis. SeedRuler integrates three core components: SeedRuler-IP, a traditional image processing-based module; SeedRuler-YOLO, a deep learning model built on YOLOv5 for high-precision object detection; and SeedRuler-SAM, which leverages the Segment Anything Model (SAM) for fine-grained seed segmentation. A dataset of 1,200 rice seed images was collected and manually annotated to train and evaluate the system. An interactive module enables users to flexibly define germination standards based on specific experimental needs. Results SeedRuler-YOLO achieved a mean average precision (mAP) of 0.955 and a mean absolute error (MAE) of 0.110, demonstrating strong detection accuracy. Both SeedRuler-IP and SeedRuler-SAM support interactive germination standard customization, enhancing adaptability across diverse use cases. In addition, SeedRuler incorporates an automated seed size measurement function developed in our prior work, enabling efficient extraction of seed length and width from each image. The entire analysis pipeline is optimized for speed, delivering germination results in under 30 seconds per image. Conclusions SeedRuler overcomes key limitations of existing methods by combining classical image processing with advanced deep learning models, offering accurate, scalable, and user-friendly germination analysis. Its flexible standard-setting and automated measurement features further enhance usability for both researchers and agricultural practitioners. SeedRuler represents a significant advancement in rice seed phenotyping, supporting more informed decision-making in seed selection, breeding, and crop management.
Why it matches plant phenotyping methodsイネ種子の発芽状態と種子サイズを画像から抽出するウェブ型フェノタイピング手法を開発し、データセットで性能評価しているため、方法が研究の中心である。
abstractwe present SeedRuler, a versatile, web-based application designed to improve the accuracy, efficiency, and usability of rice seed germination analysis.
Reproduction assets foundThe paper's rice seed germination image dataset (1,200 annotated images) is publicly deposited on Kaggle, and the SeedRuler platform (web tool plus offline software package with user manual) is freely available at the authors' lab site.Dataset · publicThe datasets presented in this study can be found in online repositories. The names of the repository/repositories and accession number(s) can be found below: https://www.kaggle.com/jinfengzhao/riceseedgermination .Open asset ↗Kaggle · jinfengzhao/riceseedgerminationlines:744-763Code / dataset availability confirmedCrossref · Europe PMC · checked 6 Sept 2026
This dataset was generated to characterize the physiological and morphological mechanisms underlying tolerance and resilience to combined drought and heat stress using a panel of 106 Mediterranean maize inbred lines. To achieve this, high-throughput non-invasive phenotyping combined with genome-wide association analysis was applied to accurately capture the dynamic responses of the maize lines to stress and to dissect the genetic basis of maize tolerance and resilience. Two experiments were conducted under control (25/20 °C, 70 % field capacity (FC)) and stress conditions (35/25 °C, 30 % FC). Stress was applied from 18 to 32 DAS (days after sowing), followed by a recovery period under control conditions. Plants were grown under controlled air temperature and soil water content, and were harvested at 45 DAS. Throughout the cultivation period, multiple camera sensors captured images daily, allowing agronomic traits to be extracted for analysis. The dataset includes raw and processed images, phenotypic data obtained from these images, results of two photosynthesis related parameters, Genome-Wide Association Study (GWAS) results from one parameter as an example, and scripts used for data analysis. Additionally, metadata and a detailed description of the experimental setup are provided. This resource is suitable for researchers interested in stress phenotyping and quantitative genetics. It allows further exploration of genotype-by-environment interactions and integration with other omics datasets. The dataset provides a valuable foundation for studies aiming to understand and improve crop resilience to climate-related abiotic stresses.
Why it matches plant phenotyping methods植物の高スループット表現型取得を中心とするデータセットで、画像から農業形質を抽出するセンサー基盤、処理画像、表現型データ、解析スクリプトを提供しているため。
abstracthigh-throughput non-invasive phenotyping combined with genome-wide association analysis was applied to accurately capture the dynamic responses of the maize lines to stress
Reproduction assets foundThe authors deposited the paper's raw/processed phenotyping images, phenotypic and photosynthesis data, GWAS inputs/results, and R analysis scripts in the public e!DAL repository (DOI 10.5447/ipk/2025/8) in ISA-Tab/MIAPPE format.Dataset · publicThe produced raw datasets and source code were uploaded to the e!DAL repository in ISA-Tab format (http://dx.doi.org/10.5447/ipk/2025/8) according to the MIAPPE standard.Open asset ↗e!DAL · 10.5447/ipk/2025/8html-lines:126-157Code / dataset availability confirmedEurope PMC · checked 15 Sept 2026
GrapevineField / plotRGB / grayscaleObject detectionGrowth / development / phenology
Bud break is a critical phenological stage in muscadine grapevines, marking the start of the growing season and the increasing need for irrigation management. Real-time bud detection enables irrigation to match muscadine grape phenology, conserving water and enhancing performance. This study presents BudCAM, a low-cost, solar-powered, edge-computing camera system based on Raspberry Pi 5 and integrated with LoRa radio board, developed for real-time bud detection. Nine BudCAMs were deployed at Florida A&M University Center for Viticulture and Samll Fruit Research from mid February to mid March, 2024, monitoring three wine cultivars (A-27, noble, and Floriana) with three replicates each. Muscadine grape canopy images were captured every 20 minutes between 7:00 to 19:00, generating 2656 high-resolution (4656×3456 pixels) bud break images as database for bud detection algorithm development. The dataset was divided into 70% training, 15% validation, and 15% test. YOLOv11 models were trained using two primary strategies: a direct single-stage detector on tiled raw images and a refined two-stage pipeline that first identifies the grapevine cordon. Extensive evaluation of multiple model configurations identified top performers for both the single-stage (mAP@0.5=86.0%) and two-stage (mAP@0.5=85.0%) approaches. Further analysis revealed that preserving image scale via tiling was superior to alternative inference strategies like resizing or slicing. Field evaluations during the 2025 growing season confirmed the system’s effectiveness, with the two-stage model showing greater robustness to environmental noise like lens fog. A time-series filter smooths the raw daily counts to reveal a clear phenological trend for visualization. In its final deployment, the autonomous BudCAM system captures an image, runs inference on-device, and transmits the bud count in under three minutes, demonstrating a complete, field-ready solution for precision vineyard management.
Why it matches plant phenotyping methodsブドウの芽数・芽吹きという植物の表現型を、エッジカメラ、画像データセット、検出アルゴリズム、時系列処理で取得・推定するシステムを開発・評価しており、方法が研究の中心である。
abstractThis study presents BudCAM, a low-cost, solar-powered, edge-computing camera system based on Raspberry Pi 5 and integrated with LoRa radio board, developed for real-time bud detection.
Three-dimensional(3D) seedling reconstruction technology can provide critical technical support for monitoring plant growth, phenotyping high-throughput plants, and conducting precision agriculture. However, multiview image-based reconstruction methods, which rely on image registration and feature matching, are susceptible to issues such as similar textures and viewpoint differences, leading to matching errors and the loss of key structural information. This can result in local deficiencies and reduced accuracy in the reconstructed models. Therefore, to attain improved reconstruction accuracy under low-cost constraints, deep learning-based feature extraction and matching methods are employed in this study, the SuperPoint network is utilized to increase the robustness of the feature point detection and description processes, and the LightGlue algorithm is introduced to improve the accuracy and stability of matching. Additionally, to reduce the impact of shooting and platform jitter on image quality, a dedicated plant 3D reconstruction platform is designed and constructed, and a dataset of densely planted rice seedlings under light stress conditions is collected, comprising three factors (light quality, light quantity, and the photoperiod) × three levels, totaling nine groups. Experimental results show that the proposed method achieves optimal performance in terms of its point cloud completeness and reprojection error. The phenotypic parameters (e.g., plant height) extracted from the reconstruction data are strongly correlated with the actual measurements (R 2 = 0.989, RMSE = 4.54 mm), validating the potential of the proposed method for applications related to simulating plant growth processes, analyzing the effects of environmental factors (e.g., light), and optimizing crop cultivation schemes.
Why it matches plant phenotyping methodsマルチビュー画像によるイネ幼苗の3D再構成プラットフォームとデータセットを開発し、再構成精度および抽出形質を実測値と検証しており、表現型取得手法が研究の中心である。
abstractdeep learning-based feature extraction and matching methods are employed in this study
Reproduction assets foundThe paper's authors explicitly state their analysis code is publicly available on GitHub; the phenotype/image dataset is only available upon request, so it does not qualify as a public asset.Code · publicThe code used in this study is available at https://github.com/Terrywewee/3D-reconstruction-of-densely-planted-rice-seedlings---superpoint-lightglue.git .Open asset ↗https://github.com/Terrywewee/3D-reconstruction-of-densely-planted-rice-seedlings---superpoint-lightglue.gitlines:433-485Code / dataset availability confirmedEurope PMC · bioRxiv · checked 6 Sept 2026
RGB / grayscaleLeafAnnotation / quality controlClassificationGrowth / development / phenology
ABSTRACT Plant phenology dictates many aspects of community function and ecosystem dynamics. Yet, global phenology data are still limited, especially in areas lacking monitoring programs. Here we present a new data resource, PhenoVision–Leaf, which extends a computer-vision pipeline utilizing iNaturalist digital image vouchers to produce global-scale leaf phenophase data for deciduous, woody genera. We first discuss our implementation of a new human annotation framework for leaf phenology on iNaturalist, aligning with phenophase definitions used by the larger phenology community. We then showcase the use of 165,988 crowdsourced annotated records to train a Vision Transformer model with a two-stage regime to maximize accuracy across single- and multi-image records. This approach extends Phenovision from scoring individual images to aggregating at the iNaturalist record level, better aligning with human annotation processes. Post-hoc validation showed high performance for detecting present green and colored leaves (>98% accuracy), and reasonable accuracy for breaking leaf buds (>87% accuracy). Applying PhenoVision–Leaf to over 26 million iNaturalist records yielded 5.6 million record-level phenology observations across 6,500 species and 57 families, filling geographic and taxonomic gaps. These data, now accessible through the Phenobase portal, establish a foundation for near real-time monitoring of leaf phenology, supporting global-scale synthesis analyses.
Why it matches plant phenotyping methods葉のフェノロジー状態を画像から推定するコンピュータビジョン手法と、注釈・学習・検証・大規模データ生成基盤が研究の中心であるため。
abstractwe present a new data resource, PhenoVision–Leaf, which extends a computer-vision pipeline utilizing iNaturalist digital image vouchers to produce global-scale leaf phenophase data
Reproduction assets foundThe paper's PhenoVision–Leaf record-level leaf phenology dataset (5.6M machine-labeled observations) is publicly available via the Phenobase portal, and the underlying iNaturalist images used for training and machine labeling are available through the iNaturalist open data repository on AWS. No author analysis code or Dataset · publicAll images associated with these records were
downloaded using iNaturalist’s open data repository on AWS
(https://registry.opendata.aws/inaturalist-open-data/).Open asset ↗pdf-page:4 lines:1-44Code / dataset availability confirmedEurope PMC · checked 15 Sept 2026
Abstract Accurate yield prediction before maize harvest is crucial for advancing agricultural management and ensuring food security. Unlike conventional approaches that rely on phenotypes from a single growth stage, this study models multiple traits across different developmental stages, all targeting final yield, thereby uncovering their dynamic and cumulative contributions. We introduce Yield-Graph, an innovative framework that integrates multi-stage phenotypic data for yield prediction. The method employs a bipartite graph structure to impute missing trait values at each stage and leverages a hypergraph attention mechanism to capture high-order sample relationships. Comprehensive benchmark experiments demonstrate that Yield-Graph consistently outperforms traditional machine learning and graph-based models in both trait completion and yield prediction. Moreover, the framework exhibits strong robustness across growth stages, high adaptability to regional variations, and effective generalization across datasets. These findings highlight the potential of graph-enhanced multi-stage modeling for early-stage yield prediction, offering a scalable solution for precision agriculture and intelligent crop management.
Why it matches plant phenotyping methods多段階の植物形質を補完・統合し、収量という植物形質を予測するグラフ手法を開発・ベンチマークしており、形質取得・推定ワークフローが中心です。
abstractWe introduce Yield-Graph, an innovative framework that integrates multi-stage phenotypic data for yield prediction.
Reproduction assets foundThe paper's authors publicly release their Yield-Graph analysis code on GitHub; the phenotype datasets themselves are only available on request.Code · publicthe
manuscript. All authors read and approved the final manuscript.
Data availability
The datasets generated during and/or analysed during the current study are available from the corresponding author on reasonable
request.
Code availability
The code developed to generate the results and analysis in this article is available at https://github.com/wjhhh2928/Yield-GraphOpen asset ↗https://github.com/wjhhh2928/Yield-Graphpdf-raw-page:14 lines:1-38Code / dataset availability confirmedEurope PMC · bioRxiv · checked 15 Sept 2026
Significance Statement Early studies noting uneven spatial distribution of progeny genotypes after pollination support a hypothesis where differences in pollen tube growth rate can bias inheritance. We used computer vision and statistical analysis to show alleles reducing maize pollen fitness are likely to produce statistically significant increasing, decreasing, or curvilinear spatial patterns from the apex of the inflorescence to the base, suggesting that differential pollen tube growth is not the only mechanism at play. Summary Often, more pollen grains land on recipient flowers than there are ovules to fertilize. Consequently, the haploid male gametophyte engages in post-pollination competition, one way that pollen genotype can influence inheritance. The maize ( Zea mays subsp. mays L.) inflorescence (ear), with its elongated stigma and style structures (silks), has a conspicuous spatial heterogeneity, with longer silks at the base of the ear than those at the apex. To evaluate the hypothesis that alleles with reduced pollen fitness influence the spatial distribution of progeny genotypes along the ear, we developed an updated phenotyping platform that maps mutant Ds-GFP kernel phenotypes on the ear via an implementation of the Faster R-CNN machine vision model (EarVision.v2) and a statistical pipeline that evaluates the relationship between kernel position and transmission ratio (EarScape). In our dataset (1384 ears) representing 58 Ds-GFP alleles, none with Mendelian inheritance (0/48) showed any significant pollen-conditioned spatial trend. In contrast, 50% of alleles with a pollen-specific transmission defect (5/10) exhibited significant spatial effects. An insertion into a gene encoding a putative actin-binding protein, base-to-apex gradient1* ( bag1* ), conditions increased mutant transmission at the ear apex relative to the base. Surprisingly, mutant alleles of two other pollen-expressed genes can generate the opposite pattern, decreased mutant transmission toward the ear apex; and two mutant alleles of the sperm-cell attachment factor, gamete expressed2 ( gex2 ), can produce ears with transmission highest at both base and apex. We conclude that pollen fitness mutants have relatively common but heterogenous effects on the spatial distribution of progeny genotypes.
Why it matches plant phenotyping methodsトウモロコシ穂上のカーネル表現型を画像認識でマッピングし、空間的位置と遺伝子型伝達比を解析するプラットフォームおよび統計パイプラインを開発しており、表現型取得・抽出法が研究の中心である。
abstractwe developed an updated phenotyping platform that maps mutant Ds-GFP kernel phenotypes on the ear via an implementation of the Faster R-CNN machine vision model (EarVision.v2) and a statistical pipeline that evaluates the relationship between kernel position and transmission ratio (EarScape).
Reproduction assets foundThe paper's maize ear phenotyping assets are publicly available: the EarVision.v2 repo contains the training images with bounding-box annotations and the trained Faster R-CNN model, and the EarScannerUtilities repo contains the ear-scanning/projection code. The EarScape spatial-analysis repo (with coordinate .xml filesCode · publica license to display the preprint in perpetuity. It is made
available under a CC-BY 4.0 International license.
540 Varifocal Lens 1080P USB Camera with H.264 High DeYinition Sony IMX323 Webcam. The
541 code for scanning ears, generating projections, and uploading those into cloud storage was
542 also updated and is available at https://github.com/fowler-lab-osu/EarScannerUtilities.
543 The set of ear projections used for the training set included 409 examples from the
544 summer Yield seasons of 2018, 2019 and 2022, encompassing images generated from three
545 different digital cameras and two different versions of the MES. For this training set,
546 projections were manually annotated usingOpen asset ↗fowler-lab-osu/EarScannerUtilitiespdf-layout-page:20 lines:1-56Code / dataset availability confirmedEurope PMC · checked 15 Sept 2026
WheatGrowth chamberLeafPhysiological trait estimationGrowth / development / phenologyLeaf traits
Summary Disentangling genotype × environment (G×E) effects is critical to understand the performance of wheat across different environments. A framework for doing this was previously presented in a model that integrated knowledge of crop physiology and the Vrn gene feedback loop to explain and predict the time of anthesis. The aims of this study were: 1) provide an updated description of the Cereal Anthesis Molecular Phenology (CAMP) model; 2) to verify the model’s assumptions regarding the relationship between Vrn gene expression and the timing of phenological stages in a set of diverse genotypes and environments; 3) to use the CAMP model to establish a phenotyping strategy for use in genetic studies and model parameterisation. Six wheat genotypes with a range of cool temperature and photoperiod sensitivities were evaluated. Apical development, final leaf number (FLN) and temporal expression of Vrn1, Vrn2 and Vrn3 were compared with model predictions. There was a clear relationship between FLN responses to cool temperature and photoperiod, the timing of phenological events and the patterns of Vrn gene expression for all genotypes. There was general agreement between the temporal patterns of foliar gene expression observed with those assumed by CAMP, but some obvious discrepancies. These may be related to differences between gene expression in foliar (observed) and apical (assumed by the model) parts of the plant, or differences in the way observed and modelled gene expression are scaled. Overall, the model described all the observed development responses to environment and provides a basis for building quantitative predictions of field-based development from genotypic and environmental data. A protocol is presented for phenotyping wheat using FLN measured in specific combinations of temperature and photoperiod. It allows easy and unconfounded measure of key developmental phenotypes that clearly relate to the genetic make-up of the plants and underlying gene expression profiles.
Why it matches plant phenotyping methodsCAMPモデルの更新・検証と、FLNを用いた小麦発育形質のフェノタイピングプロトコル提示が研究の中心であり、単なる生物学的測定ではない。
abstractto use the CAMP model to establish a phenotyping strategy for use in genetic studies and model parameterisation.
Reproduction assets foundThe paper's CAMP model code, analysis scripts, and data are explicitly stated as publicly available on the authors' GitHub repository, with specific URLs for the model notebook and the test/plotting script.Code · publicwere also validated and the best-performing sets selected. A
347
description of each of the primers used in this study is given in the supplementary material
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(Table SA1).
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2.9 Verification of CAMP predictions
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2.9.1 Model set-up and operation.
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The CAMP model was coded into a Python script which is available at
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https://github.com/HamishBrownPFR/CAMP/blob/master/CAMP.ipynb. A formal
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description of the code and parameterisation scheme is given in the supplementary material.
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The FLN developmental phenotypes measured for each genotype (Section 3.1) were used to
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derive the Vrn expression parameters needed for CAMP. Each of the treatments was
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simulated using CAMP wOpen asset ↗https://github.com/HamishBrownPFR/CAMP/pdf-raw-page:14 lines:1-70Code · publicpression parameters needed for CAMP. Each of the treatments was
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simulated using CAMP with its corresponding daily temperature and Pp, so its predictions of
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Vrn gene expression could be compared with those observed. The script running the CAMP
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code and producing the graphs displayed in this paper can be viewed at
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https://github.com/HamishBrownPFR/CAMP/blob/master/Tests/CAMPCETests.py.360
.
CC-BY-NC 4.0 International license
available under a
(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made
The copyright holder for this preprint
this version posted September 12, 2025.
;
https://doiOpen asset ↗https://github.com/HamishBrownPFR/CAMP/pdf-raw-page:14 lines:1-70Code · publicnd testing of the model in
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broader contexts. EW contributed substantially to the improvement of model concepts and the
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manuscript and all authors provided final checking.
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8. Data Availability
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All the data and scripts used to analyse data and produce graphs as well as CAMP model code are
693
publicly available at https://github.com/HamishBrownPFR/CAMP/694
9. References
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Allard V, Otto V, Bela K, Rousset M, Le Gouis J, Martre P. 2012. The quantitative
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response of wheat vernalization to environmental variables indicates that vernalization is not
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a response to cold temperature. Journal of Experimental Botany 63: 847–857.
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Baumont M, Parent B, Manceau L, Brown HE, DOpen asset ↗https://github.com/HamishBrownPFR/CAMP/pdf-raw-page:31 lines:1-68Code / dataset availability confirmedEurope PMC · checked 15 Sept 2026
Field / plotRGB / grayscaleWhole plant / canopy / plot / fieldObject detectionGrowth / time-series analysisGrowth / development / phenology
1. Ecosystems are rapidly degrading. Widely used approaches to monitor ecosystems to manage them effectively are both expensive and time consuming. The recent proliferation of publicly available imagery from satellites, Google Earth, and citizen-science platforms holds the promise to revolutionising ecological monitoring and optimising their efficiency. However, the potential of these platforms to detect species and track their population dynamics remains under-explored. 2. We introduce a fast, inexpensive method for retrospective image analysis combining current ground-truth data with historical RGB imagery from Google Earth to extract long-term demographic data. We apply this method to three case studies involving two major Mediterranean invasive plant taxa with contrasting growth forms. Specifically, we: (1) utilise deep learning to automatically detect individuals of prickly pear ( Opuntia sp.) across various Mediterranean habitats and image resolutions; (2) reconstruct 10 years of spatially explicit recruitment rates for Opuntia along a climatic gradient; and (3) quantify nearly 20 years of growth dynamics for the clonal invader Carpobrotus sp. in two contrasting environments. 3. Our object detection model, trained with Google Earth imagery, achieves 60-80% success in identifying individuals of Opuntia , regardless of habitat type. Model performance increases with target species colour consistency and contrast, as well with the usage of basic data augmentation techniques. Detection is constrained by individual area (<4 m 2 ) but captures 80% of the examined population. 4. Beyond detection, our time-series analysis of publicly available imagery enables detailed population monitoring. With 10-year image series available for Spain, Greece, and the UK, and 20 years for Portugal, we successfully estimate annual recruitment and growth rates and their climatic sensitivity, identify productive and unproductive years, estimate individual age, characterise population structure, model size-age relationships, and identify recruitment hotspots for targeted management. 5. Our pipeline opens new avenues for cost-effective, large-scale demographic monitoring by retrospectively harnessing open-access imagery. While demonstrated here with invasive plants, we discuss the broad applicability of our approach across taxa and ecosystems. The use of retrospective image analysis for long-term demography with Google Earth imagery has the potential to expedite conservation decisions, support effective restoration, and enable robust ecological forecasting in the Anthropocene.
Why it matches plant phenotyping methodsGoogle Earth画像と深層学習を用いて植物個体の検出、成長・加入率・年齢などの形態・動態形質を抽出する再利用可能な解析パイプラインを開発・評価しており、植物フェノタイピング手法が中心である。
abstractWe introduce a fast, inexpensive method for retrospective image analysis combining current ground-truth data with historical RGB imagery from Google Earth to extract long-term demographic data.
Reproduction assets foundThe paper's Data Availability Statement explicitly deposits all data and code (Google Earth imagery-based demographic data, segmentation/training data, and analysis code) on FigShare with a DOI, and provides author video-tutorials of the phenotyping/demography pipeline on a YouTube playlist. Both are paper-specific,公开,Dataset · public9
Google Earth
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Author Contributions: EF: Conceptualisation, field data collection, data analysis, first draft
12
writing. GC: First approach on part of the analysis. RS-G: Supervision, support in
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conceptualisation, writing-feedback.
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Data Availability Statement: All data and code can be found in FigShare: DOI:
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https://doi.org/10.6084/m9.figshare.30024679.v1. Video-tutorials can also be found in this
17
YouTube playlist: https://www.youtube.com/playlist?list=PL_LKE-18
yTi9kBXfw_qDdJCQ3Sxu2fjGvDD, of EF’s account: @environmentaldatascientist.
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Acknowledgments: We thank C. Ribalta-Pizarro for her assistance geolocalising individuals
21
on the field and collecting UAV data.Open asset ↗FigShare · 10.6084/m9.figshare.30024679.v1pdf-raw-page:1 lines:1-62Code / dataset availability confirmedCrossref · checked 15 Sept 2026
Rice is a staple crop for over half the world’s population, and accurate, timely information on its planted area and production is crucial for food security and agricultural policy, particularly in developing nations like Sri Lanka. However, reliable rice monitoring in regions like Sri Lanka faces significant challenges due to frequent cloud cover and the fragmented nature of smallholder farms. This research introduces a novel, cost-effective method for mapping rice-planted area and yield at field scales in Sri Lanka using optical satellite data. The rice-planted fields were identified and mapped using a phenologically tuned image classification algorithm that highlights rice presence by observing water occurrence during transplanting and vegetation activity during subsequent crop growth. To estimate yields, a random forest regression model was trained at the district level by incorporating a satellite-derived chlorophyll index and environmental variables and subsequently applied at the field level. The approach has enabled the creation of two decades (2000–2022) of reliable, field-scale rice area and yield estimates, achieving map accuracies between 70% and over 90% and yield estimates with less than 20% error. These highly granular results, which are not available through traditional surveys, show a strong correlation with government statistics. They also demonstrate the advantages of a rule-based, phenology-driven classification over purely statistical machine learning models for long-term consistency in dynamic agricultural environments. This work highlights the significant potential of remote sensing to provide accurate and detailed insights into rice cultivation, supporting policy decisions and enhancing food security in Sri Lanka and other cloud-prone regions.
Why it matches plant phenotyping methods衛星画像から圃場レベルのイネ作付面積・収量を推定する分類および回帰手法が研究の中心であり、精度評価も実施しているため、植物形質推定の方法論として適格。
abstractThis research introduces a novel, cost-effective method for mapping rice-planted area and yield at field scales in Sri Lanka using optical satellite data.
Reproduction assets foundThe paper's Data Availability Statement explicitly states that all data and code to reproduce the rice area and yield maps are publicly available in the authors' GitHub repository (ozdogan15/srilanka), which directly reproduces this paper's rice mapping and yield estimation analysis.Code · publicbrella Facility for Trade trust fund
(financed by the governments of the Netherlands, Norway, Sweden, Switzerland, and the United
Kingdom) and the World Bank’s Research Support Budget for financial support.
Data Availability Statement: All data and code to reproduce rice and yield maps are publicly
available at this repository: https://github.com/ozdogan15/srilanka#.
Acknowledgments: The authors acknowledge funding from the World Bank Whole of Economy
Program. We also thank the reviewers. The findings, interpretations, and conclusions expressed in
this paper are solely those of the authors and do not necessarily represent the views of the World
Bank, its affiliated organizations, or the ExOpen asset ↗ozdogan15/srilankapdf-layout-page:23 lines:1-59Code / dataset availability confirmedEurope PMC · bioRxiv · OpenAlex · checked 15 Sept 2026
Fruit growth has long been described using single- or double-sigmoid curves; however, these temporal models cannot fully capture the spatial heterogeneity that ultimately shapes a fruit. Here, we present a three-dimensional analysis pipeline that non-destructively tracks spatial fruit growth dynamics from field-collected imaginary. Surface landmarks were drawn, and video recordings were taken throughout development for three pome fruits, apple ( Malus × domestica ), Japanese pear ( Pyrus pyrifolia ) and European pear ( Pyrus communis ), and two drupe fruits, peach ( Prunus persica ) and Japanese apricot ( Prunus mume ), to track their motion. Using 3D Gaussian Splatting, we successfully reconstructed 3D models of the fruits, and the landmark displacement could be measured with high accuracy, with R 2 ≥ 0.98 when compared to manual recordings. We found a common spatial growth gradient in the longitudinal growth shared in the pomes and drupes of the Rosaceae; proximal (stem-end) regions exhibited more pronounced growth than the distal (stylar) end. An exception was found in European pear ‘Bartlett,’ which showed relatively vigorous growth in the distal region, explaining its distinct shape with expanded distal end. Transverse expansion varied far less than longitudinal expansion, with a possible association with initial fruit morphology. Inter-fruit growth variability peaked in the fastest-growing regions, particularly in the distal area of the European pear, highlighting the link between growth vigor and phenotypic variance. These results provide foundational insights into the developmental dynamics of both pome and drupe fruits of the Rosaceae family, contributing to the optimization of fruit size, shape, and uniformity.
Why it matches plant phenotyping methods3D Gaussian Splattingを用いて果実の3D再構成と空間的成長形質の非破壊計測パイプラインを開発し、手動記録との精度比較で検証しているため、フェノタイピング手法が中心である。
abstractHere, we present a three-dimensional analysis pipeline that non-destructively tracks spatial fruit growth dynamics
Reproduction assets foundThe paper's data availability statement deposits a subset of the generated 3DGS fruit reconstruction models (the paper's phenotyping outputs) on Figshare with a public DOI; additional data is request-only. No author analysis code is explicitly deposited.Dataset · publicFootnotes
Appendix A
Supplementary data to this article can be found online at https://doi.org/10.1016/j.plaphe.2026.100166 .
Appendix A.
Supplementary data
The following is the Supplementary data to this article:
Multimedia component 1
Multimedia component 1
Data availability
A subset of the generated 3D models is available at https://doi.org/10.6084/m9.figshare.30854579 , where the quality of the 3DGS reconstructions and the marking/measurement procedure can be examined. Additional data may be provided upon reasonable request to the corresponding author.
ReferencesOpen asset ↗figshare · 10.6084/m9.figshare.30854579lines:151-171Code / dataset availability confirmedEurope PMC · checked 6 Sept 2026
FlowerClassificationObject detectionGrowth / development / phenology
As an ornamental plant, Alstroemeria Genus Morado holds great significance in precision agriculture for the automatic detection and classification of its flower maturity. However, due to its diverse morphologies, complex growth environments, and factors such as occlusion and lighting changes, related tasks face numerous challenges, and research in this area is relatively scarce. This study proposes a deep - learning - based object detection framework, the Morado Flower Detection Network (MFDN), which consists of two parts: a backbone network and a head network. Novel modules such as C3k2_PPA are introduced. Through multi - branch fusion and the attention mechanism, the ability to detect small targets is enhanced. The head network uses the CARAFE module for upsampling, combines features through Concat, accelerates processing with the optimized C2f module, and finally achieves precise detection and classification through the Detect module. In the comparative experiment on the morado_5may dataset, MFDN performs outstandingly in indicators such as Precision, Recall, and F1 - score. The mean Average Precision (mAP) of MFDN is 1.3% - 5.8% higher than that of YOLO - series models. It has strong generalization ability and is expected to contribute to improving the efficiency and automation level of agricultural production.
Why it matches plant phenotyping methodsアルストロメリア花の成熟度を画像から検出・分類する深層学習手法を開発し、データセット上で比較評価しており、植物表現型取得が中心である。
abstractThis study proposes a deep - learning - based object detection framework, the Morado Flower Detection Network (MFDN)
Reproduction assets foundThe paper's core phenotyping measurements (flower maturity detection/classification) are performed on the publicly released morado_5may dataset of 414 annotated Alstroemeria Morado flower images (5,439 bounding-box labels, raw/ripe classes), which the authors state is publicly available on Kaggle. The YOLOv5/ultralytcsDataset · publicLentsch T. (2021). Available online at: https://www.kaggle.com/datasets/teddevrieslentsch/morado-5may (Accessed July 20, 2021)Open asset ↗Kaggle · teddevrieslentsch/morado-5mayhtml-lines:544-586Dataset · publicthis study selected the publicly available morado_5may dataset (Lentsch, 2021) for experimentsOpen asset ↗morado_5mayhtml-lines:97-149Code / dataset availability confirmedOpenAlex · checked 14 Sept 2026
Abstract The development of high‐throughput phenotyping platforms to capture time‐series data on large, diverse populations holds promise for crop researchers and breeders investigating growth‐related traits. We used imagery from unoccupied aerial vehicles (UAVs) with red/green/blue (RGB) and multispectral cameras flown over multiple site‐years in Saskatchewan, Canada, and Metaponto, Italy, to gather data for crop height, area, and volume in a lentil diversity panel (324 genotypes). The temporal nature of the UAV image‐derived data enabled the modeling of growth curves for volume, height, and area, something that would be impractical under traditional phenotyping procedures in such a large population grown in multiple environments. A principal component analysis and hierarchical clustering revealed differential growth patterns across contrasting environments, with large variations in temperature and photoperiod, within our lentil diversity panel. Combining this analysis with genome‐wide genotyping data, we identified markers, from an exome capture array (267,845 single nucleotide polymorphisms), associated with crop growth that could be used for marker‐assisted selection. Our study demonstrates the potential for UAV‐based imaging to obtain large‐scale time‐series data across multiple environments to model growth curves and investigate genotype‐by‐environment interactions. In addition, we can now use phenotypic traits that were once impractical to collect and derive novel phenotypes to improve our understanding of crop growth and the genetics underlying adaptation in lentil, approaches that will be useful for both researchers and breeders.
Why it matches plant phenotyping methodsUAV画像からレンティルの高さ・面積・体積を時系列推定し、大規模集団で成長曲線をモデル化するフェノタイピング手法の実質的な適用・評価が中心である。
abstractThe development of high‐throughput phenotyping platforms to capture time‐series data on large, diverse populations holds promise for crop researchers and breeders investigating growth‐related traits.
Reproduction assets foundThe paper's UAV-derived lentil growth phenotypes are publicly available on KnowPulse, and the authors' full analysis code/workflow is public on GitHub with a rendered vignette. Both are explicitly stated in the data availability statement and methods.Dataset · publiciluppo e di Innovazione
in Agricoltura) in Metaponto, Italy. Special thanks to Laura
Jardine for help with editing.
C O N F L I C T O F I N T E R E S T S TAT E M E N T
The authors declare no conflicts of interest.
DATA AVA I L A B I L I T Y S TAT E M E N T
The data that support the findings of this study are
available online at https://knowpulse.usask.ca/research-experiment/AGILE-UAV and https://github.com/derekmichaelwright/AGILE_LDP_UAV or from the authors
upon request.
O RC I D
DerekM. Wright https://orcid.org/0000-0002-9639-7596
SandeshNeupane https://orcid.org/0000-0003-3679-1046
Tania Gioia https://orcid.org/0000-0001-8980-3034
Giuseppina Logozzo https://orcid.org/0000-0002-7951-2425
SOpen asset ↗knowpulse.usask.ca · AGILE-UAVpdf-raw-page:11 lines:1-84Code · publical user-
calculated traits as described in Figure 2. G × E analysis was
done with “lme4” using linear mixed models (Bates et al.,
2015). Principal component analysis (PCA) and hierarchical
k-means clustering were performed using the “FactoMineR”
R package (Lê et al., 2008). The source code for all data
analyses is available at: https://derekmichaelwright.github.io/AGILE_LDP_UAV/LDP_UAV_Vignette.html.25782703,
2025,
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(https://onlinelibrary.wiley.com/terms-and-conditions)
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WileyOpen asset ↗derekmichaelwright.github.io/AGILE_LDP_UAV · LDP_UAV_Vignettepdf-raw-page:3 lines:1-106Code / dataset availability confirmedEurope PMC · checked 6 Sept 2026
Global climate change and urbanization have posed challenges to sustainable food production and resource management in agriculture. Vertical farming, in particular, allows for high-density cultivation on limited land but requires precise control of crop height to suit vertical farming systems. Tomato, a globally significant vegetable crop, urgently requires mutant varieties that suppress indeterminate growth for effective cultivation in vertical farming systems. In this study, we utilized the CRISPR-Cas9 system to develop a new tomato cultivar optimized for vertical farming by editing the Gibberellin 20-oxidase ( SlGA20ox ) genes, which are well known for their roles in the "Green Revolution". Additionally, we proposed a volumetric model to effectively identify mutants through non-destructive analysis of chlorophyll fluorescence. The proposed model achieved over 84 % classification accuracy in distinguishing triple-determinate and slga20ox gene-edited plants, outperforming traditional machine learning methods and 1D-CNN approaches. Unlike previous studies that primarily relied on manual feature extraction from chlorophyll fluorescence data, this research introduced a deep learning framework capable of automating feature extraction in three dimensions while learning the temporal characteristics of chlorophyll fluorescence imaging data. The study demonstrated the potential to classify tomato plants customized for vertical farming, leveraging advanced phenotypic analysis methods. Our approach explores new analytical methods for chlorophyll fluorescence imaging data within AI-based phenotyping and can be extended to other crops and traits, accelerating breeding programs and enhancing the efficiency of genetic resource management.
Why it matches plant phenotyping methodsトマトのクロロフィル蛍光画像から遺伝子編集植物を識別する3次元深層学習モデルを提案し、既存手法と比較評価しており、表現型取得・抽出法が研究の中心である。
abstractAdditionally, we proposed a volumetric model to effectively identify mutants through non-destructive analysis of chlorophyll fluorescence.
Reproduction assets foundThe authors state that the dataset and source code used in this study are publicly available on GitHub, which qualifies as a paper-specific public code asset for the CF 3D-CNN phenotyping analysis.Code · publicThe dataset and source code used in this study are available at https://github.com/youzh-all/CF_3D-CNN .Open asset ↗https://github.com/youzh-all/CF_3D-CNN · CF_3D-CNNlines:358-392Code / dataset availability confirmedEurope PMC · checked 15 Sept 2026
Laboratory / benchtopRGB / grayscaleFruitClassificationGrowth / development / phenology
Okra is a highly nutritious farming product that combats malnutrition issues while supporting sustainable agricultural methods. In order to keep its quality and versatility in preparation applications, it is important to classify its maturity stages into under-mature, mature, and over-mature categories. Classification is vital to identify the best time to harvest, satisfy market demands, minimize post-harvest losses, and optimize cooking uses. This data in brief uses a non-destructive approach to okra maturity classification based on a dataset of images taken under controlled illumination using a standard RGB camera. The dataset contains okra samples that were sourced from various farms and vegetable markets, ensuring that it encapsulates the natural variability found in real-world farm and market environments. The availability of such a large dataset enables the creation of precise classification models that can assist farmers in optimizing the time of harvest, fulfilling consumers' requirements, and improving market results. The research has great relevance to promoting agricultural quality evaluation and boosting market readiness using non-invasive techniques.
Why it matches plant phenotyping methodsRGB画像データセットによるオクラ果実の成熟段階分類が研究の中心であり、植物器官の状態を画像から推定する再利用可能なフェノタイピング資源に該当する。
titleRGB image dataset for okra maturity classification to enhance agricultural quality and market readiness.
Reproduction assets foundThe paper is a Data in Brief article whose core contribution is a public RGB okra image dataset (364 images across three maturity classes) deposited on Mendeley Data, directly serving as the paper's phenotyping image asset. No separate analysis code repository is described.Dataset · publicVellore Institute of Technology - Chennai Campus.
City/Country: Chennai, India.
Latitude and longitude for collected samples/data: (12.8406° N, 80.1534° E), Vellore Institute of Technology - Chennai.
Data accessibility
Repository name: Okra Image Dataset
Data identification number: DOI: 10.17632/jmhz4826f2.1
Direct URL to data: https://data.mendeley.com/datasets/jmhz4826f2/1
Related research article
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1
Value of the Data
•
Agricultural quality assessment is advancing through non-invasive methods that include the use of RGB image analysis for effective determination of okra maturity stages.
•
Utilization of ML and DL methods in recognizing visual characteristics, i.e., color, texture, andOpen asset ↗10.17632/jmhz4826f2.1lines:1-56Code / dataset availability confirmedEurope PMC · checked 6 Sept 2026
The vigor of potato plants is of crucial importance for potato seed producers, who are interested in predicting it at scale by exploiting the dependence of plant growth and development on the origin and physiological state of the seed tuber. In this article we present the results of a three-year long experiment in which we studied six potato varieties in three test fields. We identify a 73-[Formula: see text] overall correlation in the vigor of plants from the same seedlot grown in different test fields. Similarly, the biochemical tuber data produce plant vigor predictions that correlate up to 70-[Formula: see text] with the measurements. However, these relatively large data and prediction correlations are mostly due to the strong dependence of the seedlot vigor on the tuber genotype. For five out of six studied varieties, variety-specific cross-field and cross-year vigor predictions produce negligible or even negative correlations when the seed tubers and young plants experience environmental stress. At the same time, for the variety that appeared to be less sensitive to environmental stresses, we obtained cross-field and cross-year vigor predictions correlating up to [Formula: see text] with the measurements. Analysis of individual predictor variables, such as the abundance of a particular metabolite, indicates that the vigor-enhancing properties of the seed tubers are also variety-specific and that the FTIR spectroscopy data is the most reliable predictor.
Why it matches plant phenotyping methodsFTIR・生化学データからジャガイモ植物の vigor を予測し、圃場間・年次間で予測性能を検証しており、植物形質の取得・推定法が中心です。
abstractinterested in predicting it at scale
Reproduction assets foundThe paper's Data Availability statement points to a public 4TU.ResearchData deposit containing the seed tuber and plant canopy (drone-derived vigor) datasets plus the Python code needed to reproduce the regression results — a paper-specific, publicly actionable asset.Dataset · publicBoth the seed tuber and plant canopy datasets are available at http://doi.org/10.4121/3a97fa0c-8c7d-451a-b8fe-d521f1cec55e . The data also includes the Python code necessary to reproduce the results of regression.Open asset ↗10.4121/3a97fa0c-8c7d-451a-b8fe-d521f1cec55elines:267-336Code / dataset availability confirmedOpenAlex · Europe PMC · checked 15 Sept 2026
Abstract Soybean growth is determined by the interaction of genetic, environmental, and management factors. In the context of future climate and climate extremes, understanding genotype by environment interaction (GxE) will be crucial for selecting resilient breeding lines and optimizing management practices to minimize stress. As stress periods occur periodically in a season, in depth knowledge, about causing weather variables and differing responses of genotypes over time is required. In field studies, however, the environment is often treated as a static factor, and the specific effects of weather variability on growth remain poorly understood. Here, we present a longitudinal dataset comprising 17,247 high-resolution RGB images of soybean breeding line collected over eight years in Eschikon, Switzerland. Top of canopy images were acquired throughout the entire growing seasons and complemented by hourly weather data, enabling a comprehensive analysis of soybean growth dynamics under varying field conditions. High spatio-temporal image resolution enables detailed analysis of growth dynamics and GxE, supporting identification of stress-tolerant genotypes to improve yield prediction and yield stability.
Why it matches plant phenotyping methods8年間の高解像度RGB画像による圃場フェノタイピングデータセットを提示し、作物生育動態とG×E解析を可能にする方法・データ基盤が中心である。
titleFIP 1.0 Soybean data: Insights on soybean growth from eight years of high-throughput image field phenotyping
Reproduction assets foundThe paper is a data note whose core contribution is a public soybean phenotyping dataset (raw FIP images, segmentation masks, canopy cover data, BLUEs, weather, reference traits) deposited at ETH Research Collection, plus the authors' canopy cover extraction workflow code on GitLab. Both are paper-specific, public, andDataset · publicason, therefore,
from 2020 to 2022, photosynthetic photon fluence rate (PPFR) was taken from a LI-COR sensor placed next to the
field. The factor to convert radiation in MJ m−
2 to PPFR was 2.04 according to [26].
4.1 Data Files and Structure
The dataset presented in this study is available at ETH Research Collection under DOI: https://doi.org/10.3929/ethz-b-000742401. The dataset is structured into directories that align with the described data processing
pipeline used for extracting and analyzing canopy cover traits from field images. All files are provided in interoperable
and widely-used ‘.csv‘ and ‘.png‘ format.
• data/Design 2015 2022 Eschikon.csv: Experimental design file, including pOpen asset ↗ETH Research Collection · 10.3929/ethz-b-000742401pdf-raw-page:6 lines:1-47Code · publicCollection (https://doi.org/10.3929/ethz-b-000742401) and as Hugging Face data set card (doi.org/10.57967/hf/6052) allowing
interoperability and standardization with other datasets.
9 Code availability
Users with similar data can use the implemented workflow to get canopy cover from their experiments. The code
is available on: https://gitlab.ethz.ch/crop_phenotyping/fip-soybean-canopycover
10 Author contributions
BK: Developed algorithm, analyzed data and drafted manuscript; NK, LR, AH, AM: FIP development, BK, NK,
CO, LK, LR, OZ, SC, FL, HA, NS, FT, HZ, CAB, CB, AH: Collected and prepared data; Experimental design:
BK, LK, LR, AH; all authors improved and approved the manuscript
5Open asset ↗gitlab.ethz.ch · crop_phenotyping/fip-soybean-canopycoverpdf-raw-page:7 lines:1-46Code / dataset availability confirmedEurope PMC · OpenAlex · checked 13 Sept 2026
Abstract To watch the growth of 1200 P. sylvestris cv. Negorelskaya trees from seeds to young or even old stage is a big grant project. We want to make a «seed–culture» passport. Each individual seed (N = 1200) was weighed, and image acquisition via a flatbed scanner in the VIS wavelength region and seeded into an individual 120 cm 3 cell of a 40-cell container. On day 30, container-grown germination was evaluated according to the following dichotomous criterion: 1 – germinated (n 1 = 942), 0 – did not germinate (n 0 = 258), and 0-group and 1-group datasets were formed. The RGB space color of the individual seed epidermis between the 0- and the 1-group were compared via the Kolmogorov‒Smirnov criterion D. The lower individual weight of the seed in the 0-group compared with the 1-group was not accidental (p = 0.0045). Additionally, in the 0 group, the median values of R, G, and B brightness of pixels from individual seeds are not accidental (p = 0.0000381) compared with those of the 1 group. Therefore, in this experiment, seeds that reflected most of the light from the epidermis showed a lower germination when placed in the container.
Why it matches plant phenotyping methods個体種子を対象にスキャナ画像からRGB形質を抽出し、発芽との関連を評価する画像ベースの表現型取得が研究の中心である。
abstractimage acquisition via a flatbed scanner in the VIS wavelength region
Reproduction assets foundThe paper's data availability statement openly deposits all three paper-specific phenotyping assets in Mendeley Data: Dataset 1 (individual seed morphometric/weight data, N=1200), Dataset 2 (original VIS flatbed-scanner seed images, N=1200), and Dataset 3 (individual container germination data, N=1200). These directly供Dataset · publicThe original morphometric data—Dataset 1—of Pinus sylvestris L. сv. The individual Negorelskaya seeds (N = 1200) presented in the study are openly available in Mendeley Data at DOI : https://doi.org/10.17632/8g258nbgmf.1.Open asset ↗Mendeley Data · 10.17632/8g258nbgmf.1lines:152-174Dataset · publicThe original VIS image data of Pinus sylvestris L. сv. The individual Negorelskaya seeds (N = 1200) presented in the study are openly available in Mendeley Data at DOI : https://doi.org/10.17632/dt78jhyw2j.2.Open asset ↗Mendeley Data · 10.17632/dt78jhyw2j.2lines:152-174Dataset · publicThe original germination data—Dataset 3—of Pinus sylvestris L. cv. The individual Negorelskaya seeds (N = 1200) presented in the study are openly available in Mendeley Data at DOI : https://doi.org/10.17632/hrs3fgc8tt.1.Open asset ↗Mendeley Data · 10.17632/hrs3fgc8tt.1lines:152-174Code / dataset availability confirmedCrossref · checked 6 Sept 2026
The detection of plant constituents is a crucial issue in precision agriculture, as monitoring these enables the automatic analysis of factors such as growth rate, health status, and crop yield. Tomatoes (Solanum sp.) are an economically and nutritionally important crop in Mexico and worldwide, which is why automatic monitoring of these plants is of great interest. Detecting leaves on images of outdoor tomato plants is challenging due to the significant variability in the visual appearance of leaves. Factors like overlapping leaves, variations in lighting, and environmental conditions further complicate the task of detection. This paper proposes modifications to the Yolov11n architecture to improve the detection of tomato leaves in images of complex outdoor environments by incorporating attention modules, transformers, and WIoUv3 loss for bounding box regression. The results show that our proposal led to a 26.75% decrease in the number of parameters and a 7.94% decrease in the number of FLOPs compared with the original version of Yolov11n. Our proposed model outperformed Yolov11n and Yolov12n architectures in recall, F1-measure, and mAP@50 metrics.
Why it matches plant phenotyping methodsトマト葉の画像検出を改善する深層学習アーキテクチャ自体が中心的な技術貢献であり、植物器官の画像ベース取得・推定手法に該当する。
abstractThis paper proposes modifications to the Yolov11n architecture to improve the detection of tomato leaves in images of complex outdoor environments
Reproduction assets foundThe authors explicitly state that the data (custom tomato leaf detection dataset with ground-truth annotations) and code used in this paper are publicly available in their GitHub repository andros1206/Leaf-Detection. This is a paper-specific, publicly actionable asset reproducing the paper's phenotyping images/labels (Code · publicData Availability Statement: We make the data and code used available at https://github.com/
andros1206/Leaf-DetectionOpen asset ↗andros1206/Leaf-Detectionpdf-page:20 lines:1-58Code / dataset availability confirmedarXiv · checked 6 Sept 2026
Observer bias and inconsistencies in traditional plant phenotyping methods limit the accuracy and reproducibility of fine-grained plant analysis. To overcome these challenges, we developed TomatoMAP, a comprehensive dataset for Solanum lycopersicum using an Internet of Things (IoT) based imaging system with standardized data acquisition protocols. Our dataset contains 64,464 RGB images that capture 12 different plant poses from four camera elevation angles. Each image includes manually annotated bounding boxes for seven regions of interest (ROIs), including leaves, panicle, batch of flowers, batch of fruits, axillary shoot, shoot and whole plant area, along with 50 fine-grained growth stage classifications based on the BBCH scale. Additionally, we provide 3,616 high-resolution image subset with pixel-wise semantic and instance segmentation annotations for fine-grained phenotyping. We validated our dataset using a cascading model deep learning framework combining MobileNetv3 for classification, YOLOv11 for object detection, and MaskRCNN for segmentation. Through AI vs. Human analysis involving five domain experts, we demonstrate that the models trained on our dataset achieve accuracy and speed comparable to the experts. Cohen's Kappa and inter-rater agreement heatmap confirm the reliability of automated fine-grained phenotyping using our approach.
Why it matches plant phenotyping methods植物の多視点画像取得、アノテーション付きデータセット、深層学習による分類・検出・セグメンテーションを中心に開発・検証した、明確な植物フェノタイピング手法研究です。
abstractwe developed TomatoMAP, a comprehensive dataset for Solanum lycopersicum using an Internet of Things (IoT) based imaging system with standardized data acquisition protocols.
Reproduction assets foundThe paper's TomatoMAP dataset (images, annotations) is publicly deposited in e!DAL at IPK with an explicit DOI URL given in the Data Records section.Dataset · publicDataset is deposited in e!DAL (electronic data archive library) of IPK (Leibniz Institute of Plant Genetics
and Crop Plant Research): https://doi.ipk-gatersleben.de/DOI/10bb9f14-ce90-4747-836f-cf61dfb5eea1/Open asset ↗e!DAL · 10bb9f14-ce90-4747-836f-cf61dfb5eea1pdf-page:7 lines:1-73Code / dataset availability confirmedEurope PMC · checked 15 Sept 2026
This study presents a comprehensive analysis of winter wheat phenological variations in China's Huang-Huai-Hai Plain (HHHP) from 1981 to 2021, leveraging data from 62 national agrometeorological observation stations. As the world's largest winter wheat production region, the HHHP contributes over 60% of China's total output, playing a pivotal role in national food security. Using kernel density estimation (KDE) and univariate linear regression, the dataset characterizes interannual trends in key phenological stages-sowing, emergence, tillering, jointing, booting, heading, flowering, milking, and maturity-along with growth period durations. Results reveal significant shifts in phenological timings and growth stages under climate change, such as advanced heading stages and altered phase lengths, which correlate with temperature increases and extreme weather events. The dataset, comprising 1,120 figures generated via Origin Lab, is publicly available on ScienceDB, providing critical insights for climate adaptation strategies, cultivation optimization, and yield stability. Technical validation confirms the reliability of the data, sourced from standardized, long-term manual observations by trained professionals under China Meteorological Administration protocols. This work offers a foundational resource for understanding climate-crop interactions and guiding sustainable agricultural practices in a warming world.
Why it matches plant phenotyping methods冬小麦の複数生育ステージという植物形質を長期・標準化観測で収録した公開データセットであり、データの技術的検証も含むため、フェノタイピングデータセットとして中心的です。
abstractthe dataset characterizes interannual trends in key phenological stages-sowing, emergence, tillering, jointing, booting, heading, flowering, milking, and maturity-along with growth period durations
Reproduction assets foundThe paper describes a public dataset of winter wheat phenology (1,120 KDE and linear-trend figures from 62 agrometeorological stations, 1981–2021) deposited on ScienceDB under DOI 10.57760/sciencedb.23011, freely downloadable. No custom analysis code exists ('No custom code was created for the production of this dataetDataset · publicThe Variation of winter wheat phenology dataset in Huang Huai Hai Plain of China from 1981 to 2021 is available at ScienceDB 35 . The dataset is provided in JPG format estimated and plotted by Origin Lab. All the diagrams can be downloaded directly for free.Open asset ↗lines:47-83Code / dataset availability confirmedEurope PMC · checked 15 Sept 2026
AppleLaboratory / benchtopMultispectral / hyperspectralFruitClassificationGrowth / development / phenology
The study created a detailed database for apple quality inspection using a cost-effective, self-designed multi-spectral imaging system. The system was optimized to allow spectral information to be obtained in 8 discrete wavebands, which enabled non-destructive determination of such key fruit components as ripeness, sugar type and cultivar. Stringent environmental conditions were maintained during image acquisition for optimal measurement consistency and experimental repeatability. The detailed dataset encompasses 32,463 multi-spectral images across three distinct classification categories. For sweetness evaluation, 1620 images spanning Brix values from 10 % to 15 % were collected from five apple varieties. Ripeness evaluation includes 29,160 images documenting the complete maturation cycle over 18 days, while variety classification contains 1683 images from three distinct cultivars. Each image was captured under controlled lighting conditions using eight specific wavelengths, ensuring spectral consistency crucial for machine learning applications. These multi-spectral images were concatenated for grading by sweetness, ripeness, and variety, creating a processed dataset of concatenated images optimized for AppleNet processing. The concatenation process combines the eight wavelength channels into unified image representations suitable for deep learning applications. Sample collection included the picking of different apple cultivars at different physiological development phases of fruit from local orchards. Single specimens were imaged sequentially using a multi-spectral technique. Information on sugar content concentration ( % Brix), maturation phase classification and varietal identification was recorded according to standard laboratory procedure. The resulting annotated database includes such quantitative reference points, which can be used to train supervised learning classifiers in computational classification systems. The reuse value of the dataset covers a wide range of applications such as machine learning-based fruit quality evaluation, agricultural automation and food industry examination. This dataset of ours can be used by researchers to develop and test algorithms to classify apples and estimate their ripeness and the presence of diseases. Furthermore, the proposed multi-spectral imaging can be generalized to cover other fruits and agricultural products, extending the application of the method in smart agriculture. This dataset serves as a valuable resource for researchers in computer vision, machine learning, and agricultural technology, fostering advancements in non-destructive fruit quality evaluation methodologies.
Why it matches plant phenotyping methodsリンゴの甘度・成熟度・品種を推定するマルチスペクトル撮像システムと、注釈付き大規模画像データセットを中心に構築しており、植物器官の品質・状態を定量化する再利用可能なフェノタイピング手法に該当する。
abstractThe study created a detailed database for apple quality inspection using a cost-effective, self-designed multi-spectral imaging system.
Reproduction assets foundThe article is a Data in Brief describing a public multi-spectral apple image dataset (sweetness/Brix, ripeness over 18 days, variety) deposited on Mendeley Data with DOI 10.17632/y5h6v8w6ms.2 and a direct URL, explicitly stated as publicly accessible. This is the paper's own phenotyping image dataset. The MATLAB code,Dataset · publicme environment using a custom-built multi-spectral imaging chamber . The imaging conditions were carefully maintained to ensure consistency. The dataset is securely stored for research and study purposes.
Data accessibility
Repository name: Mendeley Data
Data identification number: DOI: 10.17632/y5h6v8w6ms.2
Direct URL to data: https://data.mendeley.com/datasets/y5h6v8w6ms/2
Instructions for accessing these data:
Dataset Title: Dataset of Apples for Grading by Sweetness, Ripeness, and Variety
Public Access: The dataset titled ``Dataset of Apples for Grading by Sweetness, Ripeness, and Variety'' is publicly available on Mendeley Data and can be accessed via the following DOI:
https://doi.org/Open asset ↗Mendeley Data · 10.17632/y5h6v8w6ms.2lines:40-82Code / dataset availability confirmedEurope PMC · bioRxiv · checked 6 Sept 2026
Summary Live imaging data analysis often requires an objective, local, and accurate way of quantification of cell dynamics. In the research field of polarized tip-growth, the cell fluctuations and/or fluctuations in tip position and growth direction hampers automated analyses of huge amounts of imaging sequences. The fluctuated nature in data makes it unclear how cell shape and growth are linked to intracellular events that could be the actual driving force of cell growth. To overcome these difficulties, we developed a powerful and user-friendly tool called KymoTip with an available format. In this software, novel functions such as coordinate normalization, tip-bottom detection, and signal kymograph were implemented. We confirmed that not only plasma membrane-labeled fluorescent images, but also images such as bright-field and cortical microtubule markers —so long as the cell contours can be identified— are amenable to KymoTip. Furthermore, by combining markers for cell contours with those that visualize intracellular structures, it becomes possible to quantitatively analyze various intracellular events, such as nuclear migration and calcium wave, in conjunction with cellular growth dynamics. Since KymoTip can be handled by non-specialist, it is expected to promote understanding of what happens at the sub- and cellular level with high throughput outcomes. Significance statement Faced with fluctuations in cell coordinates and cell tip positions, position correction of live imaging data and accurate detection of tip position are key challenges in plant developmental biology. We solved them with a powerful and user-friendly tool, KymoTip, that can realize cell position correction, cell tip detection with cell centerline, and quantification of intracellular events.
Why it matches plant phenotyping methods植物細胞のライブイメージから細胞形状・先端位置・成長動態を定量化する解析ソフトウェアを開発しており、植物フェノタイピング手法が中心です。
abstractwe developed a powerful and user-friendly tool called KymoTip
Reproduction assets foundThe paper's authors explicitly state that the KymoTip analysis code is publicly available on GitHub at https://github.com/blues0910/KymoTip, which is an allowed URL. This is the authors' own computational tool implementing the paper's tip-growth phenotyping analysis (segmentation, coordinate normalization, tip-bottom, Code · publicThe code for KymoTip is available on GitHub: https://github.com/blues0910/KymoTip.Open asset ↗blues0910/KymoTippdf-page:8 lines:1-44Code / dataset availability confirmedEurope PMC · checked 15 Sept 2026
FruitClassificationGrowth / development / phenology
Over 95% of cashew apples are left to waste and rot on the ground. However, both cashew nuts and the often overlooked cashew apples possess significant nutritional and economic value. The cashew apple constitutes the major part (90%) of the cashew fruit, with the nut forming a modest portion (10%). Cashew nuts can be harvested and processed even after lying on the ground, but cashew apples are more delicate. Assessing the maturity status of these apples still requires human visual observation due to the challenges posed by their moisture content. Timely harvesting is crucial, as the pseudofruit is prone to microbial infections upon hitting the ground, making the process time- and labor-intensive. In this study, a Deep Learning based image classification model is presented, which can be used to automatically identify mature cashew apples. The model achieved an accuracy of 95.58% in classifying the cashew apples (immature vs. mature). Overall, the results highlight the potential of Deep Learning models for the classification of cashew apples and other fruits for precision agriculture purposes. This approach could enhance the harvesting process by enabling the utilization of the entire fruit and reducing the need for manual labor, thereby unlocking the full economic potential of the cashew tree.
Why it matches plant phenotyping methodsカシューナッツ果実の成熟状態という植物器官の状態を画像から自動推定する深層学習手法が研究の中心であり、単なる収穫位置検出ではないため。
abstractIn this study, a Deep Learning based image classification model is presented, which can be used to automatically identify mature cashew apples.
Reproduction assets foundThe paper's Data Availability statement explicitly names the public cashew image dataset used for the maturity classification model, hosted on Mendeley Data (DOI 10.17632/r46c6bpfpf.1), with a related Data in Brief description article. No author analysis code or trained model checkpoint is reported.Dataset · public09952 (Sanya R, Nabiryo AL, Tusubira JF, Murindanyi S, Katumba A, Nakatumba-Nabende J. Coffee and cashew nut dataset: A dataset for detection, classification, and yield estimation for machine learning applications. Data in Brief. 2024 Feb;52:109952). The dataset used in this work is publicly accessible under the following link: https://doi.org/10.17632/r46c6bpfpf.1 .Open asset ↗10.17632/r46c6bpfpf.1lines:1-45Code / dataset availability confirmedOpenAlex · Europe PMC · checked 6 Sept 2026
Seed / grainGrowth / time-series analysisGrowth / development / phenology
Digital phenotyping is rapidly advancing, generating increasing amounts of data, particularly in the case of temporal monitoring. We propose an adaptive sampling method that optimizes sampling, thereby reducing costs associated with data production, processing, and storage. The proposed method is based on Bayesian inference, which utilizes previous measurements, historical data, and an expected model. Five Bayesian methods are assessed in this study: Important sampling (IS), Markov chain Monte-Carlo (MCMC), Gaussian process (GP), Extended Kalman filtering (EKF) and Sampling Importance Resampling particle filtering (SIR-PF). We test these five Bayesian sampling methods for the monitoring of germination rate in terms of compression, distortion and computation cost. The best trade-off is found by the MCMC method, which offers a compression rate of 0.2 with very little distortion. GP offers the most unbiased parameter estimation and the capability to adapt to various germination speeds. It also has reasonable computational times.
Why it matches plant phenotyping methods発芽率の時系列フェノタイピングに対するベイズ適応サンプリング法を開発・比較し、圧縮率、歪み、計算コストで評価しており、表現型取得・監視手法が研究の中心である。
abstractWe propose an adaptive sampling method that optimizes sampling, thereby reducing costs associated with data production, processing, and storage.
Reproduction assets foundThe paper provides two paper-specific public assets: an authors' GitHub repository with the code implementing the five Bayesian adaptive sampling methods, and a public germination kinetics dataset (red clover accessions) deposited at doi.org/10.57745/JECJUI, which is the raw phenotype data analyzed in the study.Code · publicwe provide the codes and data to perform the computation and discuss the convergence of the process: The code is available at the following address: https://github.com/Fatryuk/BayesianAdaptivSampling.gitOpen asset ↗https://github.com/Fatryuk/BayesianAdaptivSampling.gitlines:29-41Dataset · publicData used in the article are table in.csv format containing raw germination along time available at the following repository https://doi.org/10.57745/JECJUIOpen asset ↗https://doi.org/10.57745/JECJUI · 10.57745/JECJUIlines:297-334Code / dataset availability confirmedEurope PMC · checked 6 Sept 2026
RiceFlowerObject detectionGrowth / time-series analysisGrowth / development / phenology
Flower opening time (FOT) is a key trait for successful reproduction and reproductive isolation. In crop science, FOT is critical for stress avoidance and efficient breeding practices. This study developed a system for the automatic detection of rice flower openings and FOT estimation by integrating a low-cost time-lapse camera with machine learning technology. This approach enabled high-resolution monitoring of flowering dynamics in two cultivars: the japonica cultivar Taichung 65 (T65) and the indica cultivar IR24. The system accurately identified regions containing open flowers, and the estimated FOTs varied within a 3-h range, with a root mean square error of approximately 30 min compared to manual detection. A significant difference in estimated FOTs between IR24 and T65 demonstrated the system's potential for genetic screening applications. FOT of both cultivars exhibited a significant negative correlation with daily mean temperature. Notably, a temperature-sensitive period was identified in the morning, suggesting that temperature influences not only flower opening but also preceding physiological processes such as panicle and spikelet development. This study presents a novel approach to investigating FOT dynamics in rice and provides insights into the interaction between environmental factors and internal regulatory mechanisms governing this critical reproductive trait.
Why it matches plant phenotyping methods低コストタイムラプスカメラと機械学習によるイネの開花時刻という植物形質の自動検出・推定システムを開発し、手動検出との誤差で検証しているため、方法が中心的です。
abstractThis study developed a system for the automatic detection of rice flower openings and FOT estimation by integrating a low-cost time-lapse camera with machine learning technology.
Reproduction assets foundThe authors explicitly state that the Python scripts, training dataset (annotated time-lapse rice flower images), and trained YOLOX model used for flower-opening detection are publicly available on their GitHub repository (mwbotan/FLpanicle). This is a paper-specific, public, actionable asset directly reproducing the FCode · publicThe Python scripts, training dataset, and trained model used in this analysis are available on GitHub ( https://github.com/mwbotan/FLpanicle ).Open asset ↗mwbotan/FLpaniclelines:73-84Code / dataset availability confirmedEurope PMC · checked 14 Sept 2026
Dendrometry is the main non-invasive macroscopic technique commonly used in plant physiology and ecophysysiology studies. Over the years several types of dendrometric techniques have been developed, each with their respective strengths and drawbacks. Automatic and continuous monitoring solutions are being developed, but are still limited, particularly for non-invasive monitoring of large-diameter trunks. In this study, we propose a new type of automated dendrometer based on distributed fibre-optic sensing that continuously measures the change in stem circumference, is non-invasive and has no upper limit on the trunk diameter on which it can be installed. We performed a 3-month validation experiment during which we deployed a fibre-optic cable at three localities around the trunks of two specimens of Brachychiton. We verified the accuracy of this new method through comparison with a conventional point-dendrometer, and we observed a consistent time lag between the various measurement locations that varies with the meteorological conditions. Finally, we discuss the feasibility of the fibre-based dendrometer in the context of existing dendrometric techniques and practical experimental considerations.
Why it matches plant phenotyping methods植物幹の周囲長変化を連続測定する新規ファイバー光学式デンドロメータを開発し、従来法との比較で精度を検証しており、植物表現型取得法が研究の中心です。
abstractwe propose a new type of automated dendrometer based on distributed fibre-optic sensing that continuously measures the change in stem circumference
Reproduction assets foundThe authors explicitly state that all data (DSTS strain recordings, dendrometer time series) and analysis scripts needed to reproduce the paper's figures are publicly deposited on Figshare.Dataset · publicnuscript. For the analysis we made use of the following Python libraries: Matplotlib 3.7.2 332
[Hunter, 2007], NumPy 1.25.1 [Harris et al., 2020], Pandas 2.0.3 [Pandas Development Team, 2023], 333
SciPy 1.11.1 [Virtanen et al., 2020]. All the data and scripts needed to reproduce the figures in this 334
study are available here: https://doi.org/10.6084/m9.figshare.25773432. 335
References
T. Ameglio and P. Cruiziat. Daily Variations of Stem and Branch Diameter: Short Overview from a Developed Example. In
T. K. Karalis, editor, Mechanics of Swelling, NATO ASI Series, pages 193–204, Berlin, Heidelberg, 1992. Springer. ISBN
978-3-642-84619-9. doi: 10.1007/978-3-642-84619-9 9.
T. Ameglio, H. CochOpen asset ↗figshare · 10.6084/m9.figshare.25773432pdf-raw-page:14 lines:1-45Code / dataset availability confirmedEurope PMC · checked 14 Sept 2026
Global biodiversity is changing at unprecedented rates during the Anthropocene. Whereas current biodiversity patterns can be observed directly, information from the recent past is far less easily retrieved yet urgently needed to understand present observations and predict future developments. For plants, herbaria offer such a unique glimpse into the past. Evaluation of plant specimens allows determining a wide range of attributes like species identity, morphological and phenological traits and even signs of biotic interactions. Specimen’s labels convey data such as species identity (and identification history), date and locality of collection, as well as the surrounding biotic and abiotic environment. Current methodological developments in sensor technology and computer vision increasingly enable us to extract this information in a high throughput and automated way. Equally vast developments in data science allow to integrate data from other sources for much more comprehensive analyses than before. With millions of specimens already digitized and digitization schemes running in many institutions, we will be increasingly able to determine characteristics of species and link them via distribution records to large-scale climate change scenarios. This allows us to better predict species’ threat levels, and to develop scenarios on the consequences of biodiversity change for ecosystem functioning. The present contribution reviews recent herbaria research and describes potential avenues with respect to Museomics and the Extended Specimen concept, and we propose Collectomics as a new framework to unravel, understand, and cope with the Anthropocene biodiversity change.
Why it matches plant phenotyping methods植物標本から形態・季節形質をセンサー技術とコンピュータビジョンで高スループット抽出する方法を扱うレビューであり、植物形質取得手法が実質的に含まれる。
abstractEvaluation of plant specimens allows determining a wide range of attributes like species identity, morphological and phenological traits and even signs of biotic interactions.
Reproduction assets foundThe authors openly publish the paper-specific dataset underlying their phenotyping analysis (Figure 3): an RO-Crate containing 100 annotated, digitized herbarium specimens with processed images, organ segmentation annotations, and computed surface-area measurements, semantically mapped to the Flora Phenotype Ontology.Dataset · publicure.
274
Comments of two anonymous reviewers greatly helped us to sharpen our ideas.
275
276
7. Data availability statement
277
The dataset consisting of 100 annotated, digitized herbarium specimens, which is referenced in
278
section 4, is available as an RO-Crate under a Creative Commons license and openly published under
279
https://doi.org/10.12761/w2c1-x551.280Open asset ↗10.12761/w2c1-x551.280pdf-raw-page:10 lines:1-68Code / dataset availability confirmedEurope PMC · checked 14 Sept 2026
MaizeSeed / grainObject detectionGrowth / development / phenology
The germination potential of corn seeds, a key index for assessing their quality and directly associated with the ultimate corn yield, is currently defined in a way that cannot effectively portray the seed germination rate, and the prevalent measurement methods are traditional, consuming substantial process resources. To tackle these issues, this paper employs a public corn seed germination dataset, adds noise to it to simulate real - world production conditions, and ultimately acquires a dataset comprising 8148 images. It then proposes an enhanced YOLOv8 target detection model, EBS - YOLOv8, for detecting corn seed germination. Specifically, the ECA lightweight attention mechanism is introduced to decrease small - target feature loss, assist in accurate target recognition, and remove redundant features; simultaneously, the P2BiFPN multiscale feature fusion technique is utilized to boost the detection ability for small targets; furthermore, the ScConv convolution is adopted to enhance the feature - extraction capacity and improve detection accuracy. Combined with the improved model, this paper also proposed a mathematical modeling algorithmnew method for measuring seed germination potential and observing seed germination rate. The results indicate that the proposed model attains a mean average precision at 50% Intersection over Union (mAP50) value of 98.9%, a mean average precision in the range of 50% - 95% Intersection over Union (mAP50 - 95) value of 95.8%, an accuracy of 96.7%, and a recall of 96.3%. In comparison with the original model, the mAP50 has increased by 0.9% and the mAP50 - 95 value has witnessed a 3.7% increment. The experiments have demonstrated that the research method for germination potential put forward in this paper can effectively depict the rate variation of seeds during the germination process, thus offering a novel perspective for future research on seed germination potential.
Why it matches plant phenotyping methodsトウモロコシ種子の発芽状態・発芽率を画像から検出・定量する改良YOLOv8モデルと数理測定法を開発し、データセット上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。
abstractIt then proposes an enhanced YOLOv8 target detection model, EBS - YOLOv8, for detecting corn seed germination.
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 used in this experiment can be accessed at ‘ http://dx.doi.org/10.17332/4wkt6thgp6.2 ’.Open asset ↗10.17332/4wkt6thgp6.2lines:333-347Code / dataset availability confirmedEurope PMC · checked 6 Sept 2026
Multispectral optical data significantly enhances cereal crop monitoring by enabling precise tracking of growth stages, early detection of germination issues, and assessment of plant health. This study evaluates the potential of integrating UAV multispectral sensor with the handheld Plant-O-Meter device for high-precision crop monitoring. The aim was to determine the optimal UAV imaging timing that aligns with proximal sensor measurements to improve growth stage assessments. Experiments were conducted on 41 cereal genotypes, including ancient and modern varieties, under two nitrogen top-dress dosages across 130 plots. The top ten performing genotypes were analyzed to identify resilient varieties adaptable to climate change and evolving field conditions. Our results demonstrate that vegetation indices during booting and spike emergence stages consistently predict yield potential, offering a robust framework for early-stage yield estimation. Additionally, we provide a comparative analysis of UAV and handheld sensor data, highlighting their respective strengths and limitations. Three vegetation indices, GRDVI, NDVI and SAVI demonstrated a very strong average positive correlation: 0.957, 0.954 and 0.944 across the selected genotypes from different performance levels. The combined dataset supports improved fertilization strategies, optimized seeding cycles, and identification of genotypes with stable agronomic traits. This study underscores the synergistic potential of aerial and proximal sensing technologies for next-generation cereal crop management and precision agriculture.
Why it matches plant phenotyping methodsUAVマルチスペクトル画像と近接センサーを統合し、画像取得時期、センサーデータの比較、植物生育段階・収量予測を評価しており、植物形質取得手法が研究の中心である。
abstractThis study evaluates the potential of integrating UAV multispectral sensor with the handheld Plant-O-Meter device for high-precision crop monitoring.
Reproduction assets foundThe paper's Data Availability statement explicitly deposits the study's dataset (UAV multispectral and Plant-O-Meter phenotyping measurements) on Zenodo with a public DOI, matching an allowed URL. No separate analysis code repository is stated.Dataset · publicWe have made the dataset publicly available, and it can be accessed through the following reference: Grbović Ž, Ivošević B, Buden M, Waqar R, Pajević N, Ljubičić N, et al. (2025) Integrating UAV multispectral imaging and proximal sensing for high-precision cereal crop monitoring [Data set]. Zenodo. https://doi.org/10.5281/zenodo.15133473 .Open asset ↗Zenodo · 10.5281/zenodo.15133473lines:281-306Code / dataset availability confirmedEurope PMC · checked 14 Sept 2026
AvocadoStrawberryFruitClassificationObject detectionGrowth / development / phenology
This paper presents a novel and innovative 14,630 fruit images dataset, consisting of 1333 original images and the remaining augmented images for strawberry and avocado fruits. The dataset records the growth of strawberries and avocados in four different stages: unripe, partially ripe, ripe, and rotten. Though the fruit ripening process is commonly known, a lack of systematic datasets to show the fruit changing from an unripe state to a rotting state was prevalent for the two fruits in question. Over two months, the dataset was collected through rigorous tracking to effectively provide a measure of each of the fruits' conditions. The fruits were obtained from Mahabaleshwar farms in Maharashtra, India, as well as from local markets in Maharashtra and Pune. The fruits were monitored continuously from the time of harvesting, and all observed changes were carefully recorded. The uniqueness of this dataset is that it covers both strawberries and avocados, which have different patterns of ripening and are highly commercially valuable. The images were annotated using the online annotation tool - makesense.ai, with a total of 1499 bounding boxes for each fruit. By encompassing these two diverse fruit types, the dataset provides a valuable resource for researchers, agriculturalists, and food scientists to investigate and compare the ripening behaviours of different fruit species.
Why it matches plant phenotyping methodsイチゴとアボカドの果実画像を用いて、未熟から腐敗までの可視的な成熟・状態を体系的に記録し、注釈付きデータセットとして提供しているため、植物器官の状態を対象とする画像ベースのフェノタイピングデータセットに該当する。
abstractThis paper presents a novel and innovative 14,630 fruit images dataset, consisting of 1333 original images and the remaining augmented images for strawberry and avocado fruits.
Reproduction assets foundThe paper is a Data in Brief article describing a public Mendeley Data repository containing the authors' own fruit image dataset (14,630 strawberry/avocado images with YOLO bounding-box annotations across ripening stages), which directly constitutes the paper's phenotyping measurements. No analysis code or trained模型s是Dataset · publicset up using a white background for enabling consistent and uniform image acquisition..
Data source location
Dataset was collected from (i) Mahabaleshwar, Maharashtra, India; and (ii) Pune, Maharashtra, India.
Data accessibility
Repository name: mendeley.com
Data identification number: 10.17632/zysvgmxcyz.1
Direct URL to data: https://data.mendeley.com/datasets/zysvgmxcyz/1
Related research article
1.
Value of the Data
•
This dataset is a useful resource for machine learning solutions in fruit maturity detection and can contribute to the design of automated sorting and classification systems by ripeness stages.
•
Food processing companies and agricultural scientists may utilize this data to Open asset ↗10.17632/zysvgmxcyz.1lines:1-51Code / dataset availability confirmedEurope PMC · checked 6 Sept 2026
The improvement of selection accuracy of genomic prediction is a key factor in accelerating genetic gain for crop breeding. Traditionally, efforts have focused on developing superior individual genomic prediction models. However, this approach has limitations due to the absence of a consistently "best" individual genomic prediction model, as suggested by the No Free Lunch Theorem. The No Free Lunch Theorem states that the performance of an individual prediction model is expected to be equivalent to the others when averaged across all prediction scenarios. To address this, we explored an alternative method: combining multiple genomic prediction models into an ensemble. The investigation of ensembles of prediction models is motivated by the Diversity Prediction Theorem, which indicates the prediction error of the many-model ensemble should be less than the average error of the individual models due to the diversity of predictions among the individual models. To investigate the implications of the No Free Lunch and Diversity Prediction Theorems, we developed a naïve ensemble-average model, which equally weights the predicted phenotypes of individual models. We evaluated this model using 2 traits influencing crop yield-days to anthesis and tiller number per plant-in the teosinte nested association mapping dataset. The results show that the ensemble approach increased prediction accuracies and reduced prediction errors over individual genomic prediction models. The advantage of the ensemble was derived from the diverse predictions among the individual models, suggesting the ensemble captures a more comprehensive view of the genomic architecture of these complex traits. These results are in accordance with the expectations of the Diversity Prediction Theorem and suggest that ensemble approaches can enhance genomic prediction performance and accelerate genetic gain in crop breeding programs.
Why it matches plant phenotyping methods作物の表現型形質を予測するアンサンブル計算法の開発・評価が中心であり、単なる育種実験ではないため、計算的な表現型推定手法として収載する。
abstractwe developed a naïve ensemble-average model, which equally weights the predicted phenotypes of individual models.
Reproduction assets foundThe paper's Data availability statement explicitly deposits the authors' analysis code on GitHub and the data and code on Zenodo. The phenotype/genotype data itself (TeoNAM) was collected by Chen et al. (2019) and is cited as publicly available, but the Zenodo record contains the data and code used in this study.Code · publicThe code generated for this experiment is shared at https://github.com/ShunichiroT/ensemble .Open asset ↗https://github.com/ShunichiroT/ensemblelines:323-356Dataset · publicThe data and code used in this study were also uploaded at https://zenodo.org/records/14776591 .Open asset ↗https://zenodo.org/records/14776591lines:323-356Code / 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 confirmedEurope PMC · checked 14 Sept 2026
Field / plotRGB / grayscalePanicle / ear / spikeClassificationObject detectionGrowth / development / phenology
This dataset is an expanded version of a previously published collection of high-resolution RGB images of Urochloa spp. genotypes, initially designed to facilitate automated classification of phenological stages and raceme identification in forage breeding trials. The original dataset included 2400 images of 200 genotypes captured under controlled conditions, supporting the development of computer vision models for High-Throughput Phenotyping (HTP). In this updated release, 139 additional images and 24,983 new annotations have been added, bringing the dataset to a total of 2539 images and 47,323 raceme annotations. This version introduces increased diversity in image-capture conditions, with data collected from two geographic locations (Palmira, Colombia, and Ocozocoautla de Espinosa, Mexico) and a range of image-capture devices, including smartphones (e.g. Realme C53 and Oppo Reno 11), a Nikon D5600 camera, and a Phantom 4 Pro V2 drone. Images now vary in perspective (nadir, high-angle, and frontal) and capture distance (1-3 meters), enhancing the dataset applicability for robust Deep Learning (DL) models. Compared to the original dataset, raceme density per plant has nearly doubled in some samples, offering higher raceme overlap for advanced instance segmentation tasks. This expanded dataset supports deeper exploration of phenotypic variation in Urochloa spp. and offers greater potential for developing adaptable models in crop phenotyping.
Why it matches plant phenotyping methods植物の生育ステージ分類と総状花序の同定を目的とする画像データセットで、注釈付き画像の拡張、撮影条件の多様化、インスタンスセグメンテーション用途が中心であり、再利用可能な表現型解析基盤に該当する。
abstractThis dataset is an expanded version of a previously published collection of high-resolution RGB images of Urochloa spp. genotypes, initially designed to facilitate automated classification of phenological stages and raceme identification in forage breeding trials.
Reproduction assets foundThe paper is a Data in Brief describing a public Harvard Dataverse deposit of the paper's own Urochloa spp. hybrid RGB images and COCO raceme annotations, with a direct DOI URL listed in allowed_urls.Dataset · publicupo Papalotla
City 1: Palmira, Valle del Cauca.
City 2: Ocozocoautla de Espinosa, Chiapas.
Country 1: Colombia.
Country 2: Mexico.
Geolocalization 1: 3°29’N, 76°21’W
Geolocalization 2: 16°45′N 93°28′W
Data accessibility
Repository name: Harvard Dataverse
Data identification number: doi.org/10.7910/DVN/X4LM19
Direct URL to data: https://doi.org/10.7910/DVN/X4LM19Open asset ↗Harvard Dataverse · 10.7910/DVN/X4LM19lines:1-51Code / dataset availability confirmedCrossref · checked 15 Sept 2026
Published22 Apr 2025Eastern-European Journal of Enterprise TechnologiesCited by 0 · OpenAlex ↗
This study focuses on tomato leaf disease classification using an optimized deep learning architecture. This study proposes an improved architecture called DenseNet-SEGR, which integrates a novel Squeeze-and-Excitation (SE) block with a customized growth rate of 48 to improve feature selection and classification accuracy. Unlike standard methods, this model replaces Global Average Pooling (GAP) with an integral-based squeeze method, thus enabling a more continuous and accurate feature representation. The use of SE blocks dynamically recalibrates the importance of features such as texture, color, and tissue patterns, thereby increasing sensitivity to disease symptoms. The model was trained using the PlantVillage dataset, which includes 12,246 images spanning 10 tomato leaf disease categories, such as bacterial spot, early blight, late blight, mosaic virus, and healthy leaves. Various augmentation techniques, including rotation, scaling, and contrast adjustment, were employed to strengthen generalization and improve robustness against environmental variations. Furthermore, batch normalization and adaptive learning rate scheduling were integrated to enhance model stability and prevent overfitting. As a result, the DenseNet-SEGR architecture is able to achieve a classification accuracy of 98.22 %, outperforming DenseNet-121, DenseNet-201, and MobileNetV2. This result is explained by the integration of adaptive attention mechanisms, sophisticated data augmentation strategies, and optimized architecture. The results can be effectively applied in real-world precision agriculture, especially in edge-based or mobile disease detection systems for early intervention and crop protection
Why it matches plant phenotyping methodsトマト葉の病徴を画像から分類する深層学習アーキテクチャを開発・比較しており、植物の病害状態推定が中心的な方法論的貢献である。
abstractThis study focuses on tomato leaf disease classification using an optimized deep learning architecture.
Reproduction assets foundThe paper's tomato leaf disease classification uses a publicly available PlantVillage-derived Kaggle image dataset, explicitly linked by the authors. No author code or trained model is publicly deposited.Dataset · public. The tomato leaf dataset in this study
consists of 10.639 training samples, 1.607 validation samples
and 3211 samples. The dataset is evaluated using standard
deviation as a reference for dataset stability before model
testing. The dataset in this study uses 10 tomato leaf classes.
Dataset dataset can be accessed from website: https://www.kaggle.com/datasets/emmarex/plantdisease.The proposed classification framework is illustrated
in Fig. 1, which outlines the sequential steps from data pre-
processing to classification.
This flow illustrates the classification process using
the DenseNet-SEGR model. Starting with a dataset, it goes
through a preprocessing stage, which includes normalizationOpen asset ↗Kagglepdf-raw-page:3 lines:1-102Code / 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 confirmedOpenAlex · Europe PMC · bioRxiv · checked 7 Sept 2026
Abstract Complex omics approaches and high-throughput phenotyping generate large, heterogeneous datasets that make linking molecular signatures to plant traits challenging. To address this challenge, here we introduce panomiX, a user-friendly toolbox for multi-omics integration, designed to enable non-experts to apply advanced computational methods with ease. panomiX automates data preprocessing, variance analysis, multi-omics prediction, and interaction modeling through machine learning, revealing meaningful molecular interactions and synergies. We applied panomiX to a tomato heat-stress experiment combining image-based phenotyping, transcriptomics, and Fourier-transform infrared spectroscopy data, with the aim of identification of condition-specific, cross-domain relationships between gene expression, metabolite levels, and phenotypic traits. Our approach identified a network of such connections, with those linking photosynthesis traits with stress-responsive kinases in elevated temperatures among most significant ones. By simplifying complex analyses and improving interpretability, panomiX offers a platform to accelerate the discovery of trait emergence in plants and select specific candidate genes based on multi-omics analyses.
Why it matches plant phenotyping methods植物形質データを含むマルチオミクス統合用ツール panomiX を開発・提示し、画像ベース表現型データを統合解析する再利用可能な計算ワークフローを示しているため、表現型取得そのものより解析ツールが中心的な方法論的貢献である。
abstracthere we introduce panomiX, a user-friendly toolbox for multi-omics integration
Reproduction assets foundThe paper's computational analysis assets are publicly available: the panomiX toolbox source code (GitHub) and its deployed Shiny app, plus the authors' rnaseq-mapper pipeline used to process this study's RNA-seq data. No public deposit of the paper-specific phenotype/FTIR/RNA-seq datasets is stated in the supplied.Code · publicThe source
code for the platform is available on GitHub: https://github.com/NAMlab/panomiX-tool. The
repository contains all the necessary R scripts for data processing, visualization, and machine
learning prediction.Open asset ↗NAMlab/panomiX-toolpdf-page:4 lines:1-42Code · publicThe source code is managed with a GitHub repository connected to
the Shinyapps.io via ‘rsconnect’ [53]: https://szymanskilab.shinyapps.io/panomiX/.Open asset ↗pdf-page:4 lines:1-42Code / dataset availability confirmedCrossref · Europe PMC · checked 15 Sept 2026
Abstract Machine learning (ML) can accelerate biological research. However, the adoption of such tools to facilitate phenotyping based on sensor data has been limited by (i) the need for a large amount of human-annotated training data for each context in which the tool is used and (ii) phenotypes varying across contexts defined in terms of genetics and environment. This is a major bottleneck because acquiring training data is generally costly and time-consuming. This study demonstrates how a ML approach can address these challenges by minimizing the amount of human supervision needed for tool building. A case study was performed to compare ML approaches that examine images collected by an uncrewed aerial vehicle to determine the presence/absence of panicles (i.e. “heading”) across thousands of field plots containing genetically diverse breeding populations of 2 Miscanthus species. Automated analysis of aerial imagery enabled the identification of heading approximately 9 times faster than in-field visual inspection by humans. Leveraging an Efficiently Supervised Generative Adversarial Network (ESGAN) learning strategy reduced the requirement for human-annotated data by 1 to 2 orders of magnitude compared to traditional, fully supervised learning approaches. The ESGAN model learned the salient features of the data set by using thousands of unlabeled images to inform the discriminative ability of a classifier so that it required minimal human-labeled training data. This method can accelerate the phenotyping of heading date as a measure of flowering time in Miscanthus across diverse contexts (e.g. in multistate trials) and opens avenues to promote the broad adoption of ML tools.
Why it matches plant phenotyping methods航空画像とESGANを用いて、ススキの出穂（開花期）を低アノテーションで自動推定する手法を開発・比較しており、植物表現型取得が中心である。
abstractThis study demonstrates how a ML approach can address these challenges by minimizing the amount of human supervision needed for tool building.
Reproduction assets foundThe paper's Data availability statement explicitly deposits the study's datasets (UAV imagery/annotations) in the Illinois Databank and the analysis code in a public GitHub repository, both with author-provided URLs.Dataset · publicons, findings, and conclusions or recommendations expressed in this publication are those of the author(s) and do not necessarily reflect the views of the U.S. Department of Energy.
Data availability
The data sets used and coding implementation during the current study are publicly available from the online Illinois Databank at https://doi.org/10.13012/B2IDB-8462244_V2 and GitHub repository https://github.com/pixelvar79/ESGAN-Flowering-Detection-paper .
References
Ahmad W , Ali H , Shah Z , Azmat S
.
A new generative adversarial network for medical images super resolution
. Sci Rep . 2022 : 12 ( 1 ): 9533 .
10.1038/s41598-022-13658-4
35680968
PMC9184641
Ahmed SF , Alam MDSB , Hassan M , RozbOpen asset ↗Illinois Databank · B2IDB-8462244_V2lines:164-219Code · publichis publication are those of the author(s) and do not necessarily reflect the views of the U.S. Department of Energy.
Data availability
The data sets used and coding implementation during the current study are publicly available from the online Illinois Databank at https://doi.org/10.13012/B2IDB-8462244_V2 and GitHub repository https://github.com/pixelvar79/ESGAN-Flowering-Detection-paper .
References
Ahmad W , Ali H , Shah Z , Azmat S
.
A new generative adversarial network for medical images super resolution
. Sci Rep . 2022 : 12 ( 1 ): 9533 .
10.1038/s41598-022-13658-4
35680968
PMC9184641
Ahmed SF , Alam MDSB , Hassan M , Rozbu MR , Ishtiak T , Rafa N , Mofijur M , Shawkat Ali ABM , GandomOpen asset ↗GitHub · pixelvar79/ESGAN-Flowering-Detection-paperlines:164-219Code / dataset availability confirmedCrossref · Europe PMC · checked 13 Sept 2026
LeafClassificationObject detectionDisease symptoms / severityGrowth / development / phenology
The HydroFarm project presents an innovative IoT-based control and monitoring system for hydroponic plant growth, integrating advanced image processing techniques and mobile applications to enhance urban farming practices. This system addresses critical challenges faced by urban farmers, such as limited space and the need for precise environmental management. By employing a comprehensive approach that combines various sensors (DHT22, DS18B20, pH, TDS) with an ESP32 microcontroller, HydroFarm enables real-time monitoring of essential parameters like temperature, humidity, pH, and nutrient levels. A significant novelty of this project lies in its use of a convolutional neural network (CNN) for plant health assessment through image processing. This technique allows for accurate detection of plant conditions, categorizing leaves as healthy or unhealthy based on visual data captured via a mobile application. The application, developed in Kotlin, not only facilitates user interaction but also provides automated and manual control over nutrient delivery systems based on real-time sensor data. Testing results indicate that the HydroFarm system achieves a high accuracy rate of 96% in detecting plant health conditions, with the sensors providing accurate and consistent data to maintain effective control over hydroponic parameters. The system usability scale (SUS) evaluation yielded an impressive score of 81.875, categorizing the application as excellent and user-friendly. Overall, HydroFarm represents a significant advancement in hydroponic farming technology by integrating IoT capabilities with deep learning for enhanced decision-making and operational efficiency in urban agriculture. The findings underscore the potential for scaling this model to improve food security and promote sustainable agricultural practices in densely populated areas.
Why it matches plant phenotyping methodsCNNによる葉の健康状態推定をシステムの中心的機能として開発・評価しており、植物状態の画像ベース計測が中心的である。
abstractA significant novelty of this project lies in its use of a convolutional neural network (CNN) for plant health assessment through image processing.
Reproduction assets foundThe authors publicly deposited the HydroFarm plant health image dataset (labeled sehat/tidak sehat leaf images for CNN/VGG16 classification) on figshare with an explicit DOI. Supplemental code files exist but only via article supplement links, not an allowed public URL.Dataset · publicThe HydroFarm data is available at figshare: Rofiansyah, Wizman (2025). Dataset HydroFarm. figshare. Dataset. https://doi.org/10.6084/m9.figshare.28340516.v1 .Open asset ↗figshare · 10.6084/m9.figshare.28340516.v1lines:1042-1052Code / dataset availability confirmedEurope PMC · OpenAlex · checked 6 Sept 2026
The need for more sustainable agricultural systems is becoming increasingly apparent. The global demand for agricultural products-food, feed, fuel and fiber-will continue to increase as the global population continues to grow. This challenge is compounded by climate change. Not only does a changing climate make it difficult to maintain stable yields but current agricultural systems are a major source of greenhouse gas emissions and continue to drive the problem further. Therefore, future agricultural systems must not only increase production but also significantly decrease negative environmental impacts. One approach to addressing this is to begin breeding and cultivating new plant species that have fundamental sustainability advantages over our existing crops. The Lemnaceae, commonly known as duckweeds, are one family of plants that have potential to increase output and reduce the negative environmental impacts of agricultural production. Herein we describe the Automated Lab-scale PHenotyping Apparatus, ALPHA, for high-throughput phenotyping of Lemnaceae. ALPHA is being used for selective breeding of one species, Lemna gibba , toward the goal of creating a new crop for use in sustainable agricultural systems. ALPHA can be used on many small aquatic plant species to assess growth rates in different environmental conditions. A proof of principle use case is demonstrated where ALPHA is used to determine saltwater tolerance of six different clones of L. gibba .
Why it matches plant phenotyping methodsALPHAは水生植物の成長率をハイスループットに定量化するための装置・表現型解析プラットフォームとして開発・実証されており、手法が研究の中心である。
abstractHerein we describe the Automated Lab-scale PHenotyping Apparatus, ALPHA, for high-throughput phenotyping of Lemnaceae.
Reproduction assets foundThe authors state that all source code for the phenotyping system and analysis, 3D models, and the data generated for this study (including PlantCV output and barcode map CSVs) are publicly available in the ALPHA GitHub repository.Dataset · publicAll source code used in the phenotyping system, 3D models for printed parts and data generated for this study are available in the ALPHA Github repository .Open asset ↗lines:85-105Code / dataset availability confirmedCrossref · Europe PMC · checked 6 Sept 2026
Automatic plant growth monitoring is an important task in modern agriculture for maintaining high crop yield and boosting the breeding procedure. The advancement of 3D sensing technology has made 3D point clouds to be a better data form on presenting plant growth than images, as the new organs are easier identified in 3D space and the occluded organs in 2D can also be conveniently separated in 3D. Despite the attractive characteristics, analysis on 3D data can be quite challenging. We present 3D-NOD, a framework to detect new organs from time-series 3D plant data by spatiotemporal point cloud deep semantic segmentation. The design of 3D-NOD framework drew inspiration from how a well-experienced human utilizes spatiotemporal information to identify growing buds from a plant at two different growth stages. In the training phase, by introducing the Backward & Forward Labeling, the Registration & Mix-up, and the Humanoid Data Augmentation step, our backbone network can be trained to recognize growth events with organ correlation from both temporal and spatial domains. In testing, 3D-NOD has shown better sensitivity at segmenting new organs against the conventional way of using a network to conduct direct semantic segmentation. On a time-series dataset containing multiple species, Our method reached a mean F1-measure at 88.13 % and a mean IoU at 80.68 % on detecting both new and old organs with the DGCNN backbone.
Why it matches plant phenotyping methods植物の時系列3D点群から新生器官を検出・分割する手法を開発し、複数種データセットで性能評価しており、植物表現型取得が中心である。
abstractWe present 3D-NOD, a framework to detect new organs from time-series 3D plant data by spatiotemporal point cloud deep semantic segmentation.
Reproduction assets foundThe authors explicitly state that both the dataset (labeled time-series plant point clouds for tobacco, tomato, and sorghum) and the analysis code for the 3D-NOD framework are publicly available in a GitHub repository.Dataset · publicOur data and the code are available at: https://github.com/zingersu/3D-New-Organ-Detection-in-Plant-Growth-from-Spatiotemporal-Point-Clouds.Open asset ↗https://github.com/zingersu/3D-New-Organ-Detection-in-Plant-Growth-from-Spatiotemporal-Point-Cloudshtml-lines:481-514Code / dataset availability confirmedEurope PMC · checked 14 Sept 2026
Accurate and automated detection of sunflower plants, along with assessments of their growth stages and health conditions, is crucial for enabling precision agriculture and improving crop management. In this work, we present a drone-based dataset of annotated sunflower images, derived from high-resolution videos captured at two distinct locations in Bangladesh. The original dataset comprises 1649 images extracted from drone footage of the BARI Surjomukhi-3 variety under various orientations, health conditions, and weather scenarios. After meticulous annotation using the Roboflow platform and augmentation with seven distinct techniques, the dataset expanded to 4286 images in Pascal VOC format. Detailed metadata-including geospatial coordinates, timestamped acquisition conditions, and camera settings-accompanies the dataset to support reproducibility and model generalization. By offering a comprehensive suite of annotated and augmented images, this dataset provides a valuable resource for developing and refining computer vision models geared toward sunflower detection, maturity evaluation, and yield prediction, ultimately advancing sustainable farming practices and decision-making tools in agricultural research.
Why it matches plant phenotyping methodsヒマワリ画像を注釈付きデータセットとして構築し、成長段階・健康状態・成熟度などの植物状態推定を支援することが中心であり、再利用可能な画像ベース表現型データセットに該当する。
abstractwe present a drone-based dataset of annotated sunflower images
Reproduction assets foundThis Data in Brief article describes a drone-based annotated sunflower image dataset from Bangladesh, publicly deposited on Mendeley Data (DOI 10.17632/txct4k36ct.1) with a companion Roboflow Universe project for annotation conversion. Both are paper-specific, public, and directly actionable.Dataset · publicrsingdi, and Amjhupi, Meherpur
Country: Bangladesh
Latitude and longitude:
Nagoriakandi, Narsingdi: Latitude 23.906801° N, Longitude 90.710563° E
Amjhupi, Meherpur: Latitude 23.744897° N, Longitude 88.69174° E
Data accessibility
Repository name: Mendeley Data
Data identification number: 10.17632/txct4k36ct.1
Direct URL to data: https://data.mendeley.com/datasets/txct4k36ct/1
1.
Value of the Data
•
Drone-captured, high-resolution images meticulously annotated for sunflower detection, growth stage, and health conditions enable the development of precise computer vision models [ 1 ].
•
Unlike conventional drone images taken from overhead perspectives, the dataset includes images captured at lowOpen asset ↗Mendeley Data · 10.17632/txct4k36ct.1lines:1-51Code / dataset availability confirmedOpenAlex · Europe PMC · checked 14 Sept 2026
In the era of machine learning-driven plant imaging, the production of annotated datasets is a very important contribution. In this data paper, a unique annotated dataset of seedling emergence kinetics is proposed. It is composed of almost 70,000 RGB-depth frames and more than 700,000 plant annotations. The dataset is shown valuable for training deep learning models and performing high-throughput phenotyping by imaging. The ability of such models to generalize to several species and outperform the state-of-the-art owing to the delivered dataset is demonstrated. We also discuss how this dataset raises new questions in plant phenotyping.
Why it matches plant phenotyping methods植物の出芽速度を対象とする大規模RGB深度画像・アノテーションデータセットを提供し、深層学習および高スループット表現型解析への利用性を実証しており、表現型取得基盤が中心である。
abstracta unique annotated dataset of seedling emergence kinetics is proposed
Reproduction assets foundThis is a data paper whose core contribution is a public annotated RGB-depth seedling dataset (~70,000 frames, >700,000 annotations) deposited in DATA INRAE with DOI 10.57745/AMFJTK, explicitly stated as publicly accessible. Other allowed URLs (license, Intel datasheet, Jülich record) are not paper-specific assets.Dataset · publicSynthesis of the full time-lapse and RGB-Depth full frame quantity per species
Species
Pots time-lapse
Labelled pots time-lapse
RGB-depth full frame
Rapeseed
1 760
336
15 218
Tomatoes
1 960
480
33 283
Beans
2 320
400
21 445
Total
6 040
1 216
69 946
The dataset is publicly accessible in the DATA INRAE repository, DOI: https://doi.org/10.57745/AMFJTK . The file tree structure is illustrated in Fig. 4 . The dataset is organized into 11 compressed .zip files, each corresponding to a distinct trial. Within these files, images are sorted chronologically by acquisition start date, then by camera, and stored in .png format within dedicated color and depth folders. Labels are alsoOpen asset ↗DATA INRAE · 10.57745/AMFJTKlines:105-195Code / dataset availability confirmedCrossref · checked 14 Sept 2026
Published1 Feb 2025Indonesian Journal of Electrical Engineering and Computer ScienceCited by 0 · OpenAlex ↗
The widespread occurrence of plant diseases is a major factor in the reduction of agricultural output, affecting both crop quality and quantity. These diseases typically begin on the leaves, influenced by alterations in plant structure and growing techniques, and can eventually spread over the entire plant. This results in a notable decrease in crop variety and yield. Successfully managing these diseases depends on accurately classifying and detecting leaf infections early, which is essential for controlling their spread and ensuring healthy plant growth. To address these challenges, this paper introduces an efficient approach for detecting plant leaf diseases. A concatenation of pre-trained convolutional neural networks (CNN) for enhanced plant leaf disease using transfer learning technique is implemented, with a specific focus on accurate early detection, utilizing the comprehensive new plant diseases dataset. The combined residual network-50 (ResNet-50) with densely connected convolutional network-121 (DenseNet-121) architecture aims to provide an efficient and reliable solution to these critical agricultural concerns. Various evaluation metrics were utilized to evaluate the robustness of the proposed hybrid model. The proposed ResNet-50 with the DenseNet-121 hybrid model achieved a rate of accuracy of 99.66%.
Why it matches plant phenotyping methods植物葉の病徴を画像から分類する深層学習手法を提案し、ハイブリッドCNNの性能評価を行っているため、植物病害状態のフェノタイピング手法が中心である。
abstractA concatenation of pre-trained convolutional neural networks (CNN) for enhanced plant leaf disease using transfer learning technique is implemented
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 · publicclassified into 38 classes of plant diseases categories and can be accessed
at “https://www.kaggle.com/datasets/vipoooool/new-plant-diseases-dataset”Open asset ↗Kaggle · vipoooool/new-plant-diseases-datasetpdf-page:2 lines:54-63Code / dataset availability confirmedCrossref · Europe PMC · checked 14 Sept 2026
Crop field monitoring using unmanned aerial vehicles (UAVs) is one of the most important technologies for plant growth control in modern precision agriculture. One of the important and widely used tasks in field monitoring is plant stand counting. The accurate identification of plants in field images provides estimates of plant number per unit area, detects missing seedlings, and predicts crop yield. Current methods are based on the detection of plants in images obtained from UAVs by means of computer vision algorithms and deep learning neural networks. These approaches depend on image spatial resolution and the quality of plant markup. The performance of automatic plant detection may affect the efficiency of downstream analysis of a field cropping pattern. In the present work, a method is presented for detecting the plants of five species in images acquired via a UAV on the basis of image segmentation by deep learning algorithms (convolutional neural networks). Twelve orthomosaics were collected and marked at several sites in Russia to train and test the neural network algorithms. Additionally, 17 existing datasets of various spatial resolutions and markup quality levels from the Roboflow service were used to extend training image sets. Finally, we compared several texture features between manually evaluated and neural-network-estimated plant masks. It was demonstrated that adding images to the training sample (even those of lower resolution and markup quality) improves plant stand counting significantly. The work indicates how the accuracy of plant detection in field images may affect their cropping pattern evaluation by means of texture characteristics. For some of the characteristics (GLCM mean, GLRM long run, GLRM run ratio) the estimates between images marked manually and automatically are close. For others, the differences are large and may lead to erroneous conclusions about the properties of field cropping patterns. Nonetheless, overall, plant detection algorithms with a higher accuracy show better agreement with the estimates of texture parameters obtained from manually marked images.
Why it matches plant phenotyping methodsUAV画像から植物個体をセグメンテーションし、株数・欠株などの植物状態を推定する画像解析手法を開発・評価しており、手法が研究の中心です。
abstractIn the present work, a method is presented for detecting the plants of five species in images acquired via a UAV on the basis of image segmentation by deep learning algorithms (convolutional neural networks).
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/jimaging11010028/s1 , “Supplementary Material.pdf” contains the following Supplementary Materials: Table S1. The field location of the crop image dataset from Russia (2019–2023); Table S2. Public datasets from Roboflow used for the analysis (accessed on 25 November 2023); Table S3. The row spacing (for different crops) used in the work to mark up images from the additional datasets (not ours); Table S4. Description of the ResNet neural network architectures for models RN18, RN34, and RN50; Table S5. Description of the texture characteristics; Table S6. Estimates of the four texture characteristicsOpen asset ↗lines:344-359Code / dataset availability confirmedCrossref · checked 14 Sept 2026
Abstract Global climate change has driven breeding programs to develop abiotic stress‐resilient plant varieties. Traditionally, assessing drought resilience involves labor‐intensive and time‐consuming processes. This study used an unmanned aerial system (UAS) to predict key phenotyping traits in maize ( Zea mays L.) and monitor plant response to drought during the crop cycle. We grew transgenic maize hybrids in two trials, one irrigated and another subjected to drought stress, and used a drone equipped with red–green–blue (RGB) and multispectral sensors to capture images of the plots over time. Machine learning models and various prediction scenarios revealed significant correlations between vegetation indices over time. Interestingly, the RGB sensor outperformed the multispectral sensor in trait prediction. Prediction accuracy across scenarios with untested genotypes and environments ranged from 0.40 to 0.70 for grain yield, 0.43 to 0.69 for days to anthesis, 0.51 to 0.67 for days to silking, and 0.35 to 0.57 for plant height. Ridge and random forest models consistently delivered the most accurate predictions across traits and environments. The vegetation indices normalized green–red difference index, VARI, and RCC also effectively predicted and captured the plant response to drought. This study highlights the value of UAS phenotyping as a practical tool for assessing abiotic stress due to its straightforward implementation.
Why it matches plant phenotyping methodsUASによるRGB・マルチスペクトル画像と機械学習で、作物形質および干ばつ応答を予測するフェノタイピング手法を、複数環境・遺伝子型で検証しているため。
abstractThis study used an unmanned aerial system (UAS) to predict key phenotyping traits in maize ( Zea mays L.) and monitor plant response to drought during the crop cycle.
Reproduction assets foundThe paper's data availability statement says all codes and datasets (phenomic prediction scripts, folder 'Phenomic prediction', and described datasets) are publicly available at the authors' GCCRC publications page and on Dryad (doi:10.5061/dryad.0zpc8677b).Code · public14 of 16 PEREIRA ET AL.
in this work to perform phenomic prediction for all the eight
models and the four cross-validation scenarios were given as
examples in the folder “Phenomic prediction.” All the codes
and the datasets described are available at https://www.gccrc.unicamp.br/publications/ and https://doi.org/10.5061/dryad.0zpc8677b.O RC I D
HelcioDuartePereira https://orcid.org/0000-0002-2837-9396
Juliana Vieira Almeida Nonato https://orcid.org/0000-0003-4448-4652
Rafaela CarolineRangni MoltocaroDuarte https://orcid.org/0000-0003-2622-3758
Isabel Rodrigues Gerhardt https://orcid.org/0000-0003-1397-0199
RicardoAuOpen asset ↗GCCRCpdf-raw-page:14 lines:1-75Code / dataset availability confirmedOpenAlex · checked 14 Sept 2026
Inorganic nitrogen (N) fertilizer has emerged as one of the key factors driving increased crop yields in the past several decades. However, the overuse of chemical N fertilizer has led to severe ecological and environmental burdens. Understanding how crops respond to N fertilizer has become a central topic in plant science and plant genetics, with the ultimate goal of enhancing N use efficiency (NUE) in crop production. As one of the most essential macronutrients, N significantly influences crop performance across different developmental stages of plants, and phenotypic traits result from the cumulative effects of genetic factors, prevailing environmental conditions (specifically N availability), and their complex interactions. Previous studies have selected a set of genes potentially affecting Sorghum nitrogen responsiveness to be characterized. The knockout mutants of these genes are generated using the CRISPR-Cas9 technique. Using a LemnaTec plant imaging system, this study obtained time series imagery data from 29 to 130 d after sowing (DAS) for these CRISPR-edited mutants under high N and low N greenhouse conditions. After imagery data analysis, temporal pixel count and greenness index traits were extracted as a proxy of plant growth and N responses, which, subsequently, were modeled by mathematical functions, allowing us to estimate seven key parameters from the growth curves. Our findings revealed that the wildtype and the edited sorghum lines exhibited differences in N responses for several of the key growth-related parameters, with the Edit 1 showing especially reduced sensitiveness to use the available N resources. This high-throughput N phenotyping pipeline paves the way for a better understanding of the N responses of edited lines in a dynamic manner and sheds light on further improvements in crop NUE.
Why it matches plant phenotyping methodsLemnaTec画像を用いた時系列の植物表現型取得と、画素数・緑色度および成長曲線パラメータの抽出から成る高スループット表現型パイプラインが研究の中心である。
abstractUsing a LemnaTec plant imaging system, this study obtained time series imagery data from 29 to 130 d after sowing (DAS) for these CRISPR-edited mutants under high N and low N greenhouse conditions.
Reproduction assets foundThe paper's Data availability statement explicitly deposits the raw LemnaTec imagery datasets and the extracted phenotypic data (pixel count and greenness index time series) in a public GitHub repository, which directly reproduces this paper's plant-phenotyping measurements.Dataset · publicJC, Yang J; experimental data generation: Jin H,
Park A, Li G; data analysis and interpretation of results: Jin H,
Sreedasyam A. All authors reviewed the results and approved the final
version of the manuscript.
Data availability
The raw imagery datasets and the extracted phenotypic data are
available in the GitHub repository: https://github.com/JIN-HY/Sorghum-edits-N-Phenotyping.Acknowledgments
This project was supported by the US Department of Energy (Grant No.
DE-SC0023138), and the National Science Foundation under the award
number OIA-1826781.
N responses of sorghum mutants
Page6of8 Jin et al.GenomicsCommunications 2025, 2: e010Open asset ↗Sorghum-edits-N-Phenotypingpdf-raw-page:6 lines:77-121Code / dataset availability confirmedEurope PMC · checked 6 Sept 2026
MaizeRiceClassificationGrowth / development / phenologyStress response / tolerance
Research on the dynamic expression of genes in plants is important for understanding different biological processes. We used the large amounts of transcriptomic data from various plant sample sources that are publicly available to investigate whether the expression levels of a subset of highly variable genes (HVGs) can be used to accurately identify the phenotypes of plants. Using maize ( Zea mays L.) as an example, we built machine learning (ML) models to predict phenotypes using a gene expression dataset of 21 612 bulk RNA sequencing samples. We showed that the ML models achieved excellent prediction accuracy using only the HVGs to identify different phenotypes, including tissue types, developmental stages, cultivars and stress conditions. By ML models, several important functional genes were found to be associated with different phenotypes. We performed a similar analysis in rice ( Orzya sativa L.) and found that the ML models could be generalized across species. However, the models trained from maize did not perform well in rice, probably because of the expression divergence of the conserved HVGs between the two species. Overall, our results provide an ML framework for phenotype prediction using gene expression profiles, which may contribute to precision management of crops in agricultural practices.
Why it matches plant phenotyping methods遺伝子発現データから植物の組織、発育段階、品種、ストレス状態を予測する機械学習フレームワークを開発しており、表現型推定手法が研究の中心である。
abstractwe built machine learning (ML) models to predict phenotypes using a gene expression dataset of 21 612 bulk RNA sequencing samples.
Reproduction assets foundThe paper's maize/rice gene expression datasets are publicly deposited on FigShare, and the authors' analysis source code is available on GitHub with a Zenodo DOI archive. The underlying expression profiles were originally downloaded from the PlantExp database (maize taxonId=4577, rice taxonId=39947).Code · publicSource code is available at https://github.com/Zefeng2018/plant-phenotype-prediction-by-gene-expression and https://doi.org/10.5281/zenodo.14358186 .Open asset ↗GitHub · Zefeng2018/plant-phenotype-prediction-by-gene-expressionlines:100-176Code · publicSource code is available at https://github.com/Zefeng2018/plant-phenotype-prediction-by-gene-expression and https://doi.org/10.5281/zenodo.14358186 .Open asset ↗Zenodo · 10.5281/zenodo.14358186lines:100-176Dataset · publicthe maize gene expression data were downloaded from https://biotec.njau.edu.cn/plantExp/info.php?taxonId=4577Open asset ↗PlantExp · taxonId=4577lines:29-36Dataset · publicthe rice gene expression data were downloaded from https://biotec.njau.edu.cn/plantExp/info.php?taxonId=39947Open asset ↗PlantExp · taxonId=39947lines:29-36Code / dataset availability confirmedEurope PMC · checked 13 Sept 2026
A dataset of aerial photographs acquired with an Unmanned Aerial Vehicle (UAV) DJI Phantom 4 Pro is presented for monitoring a cherry tomato ( Solanum lycopersicum var. cerasiforme ) crop in Navolato, Mexico. Seven photogrammetric flights were carried out to assess the plant growth using a Mapir Survey 3W multispectral camera. Multispectral images with an approximate spatial resolution of 1.83 cm/px were obtained in each photogrammetric flight. These images were acquired every 15 days starting on October 15, 2021, and ending on January 23, 2022. The dataset contains the radiometrically calibrated images of the tomato crop divided into 2 open field parcels. The dataset also includes the processed photogrammetric products (ortho-mosaics) using a binary mask to exclude the soil from the plant area. The dataset was originally acquired to assess plant growth, stress levels, and overall crop health. However, this multispectral imagery dataset can also have various uses, such as creating training datasets with accurate labels or classes which can then be used to develop, train, and/or validate machine learning algorithms for image classification, object detection tasks, or change detection analysis.
Why it matches plant phenotyping methods植物の生育・ストレス・健全性評価を目的とした、放射補正済みマルチスペクトル画像とオルソモザイクを含む再利用可能なデータセットであり、植物表現型取得基盤が中心です。
abstractThe dataset contains the radiometrically calibrated images of the tomato crop divided into 2 open field parcels.
Reproduction assets foundThe paper is itself a data descriptor for a public UAV multispectral cherry tomato phenotyping dataset (calibrated aerial images, manual plant images, orthomosaics, binary masks) deposited in Dryad, with an explicit DOI and direct URL matching an allowed URL.Dataset · publicRepository name: tomatodb
Data identification number: 10.5061/dryad.63xsj3vbd
Direct URL to data: https://datadryad.org/stash/share/Wq_X7QUyGryJ-ZnmgfwRn4MtOCr4VBm_MSnhF40sv_8#readmeOpen asset ↗Dryad · 10.5061/dryad.63xsj3vbdlines:1-42Code / dataset availability confirmedEurope PMC · OpenAlex · checked 6 Sept 2026
The selection and promotion of high-yielding and nitrogen-efficient wheat varieties can reduce nitrogen fertilizer application while ensuring wheat yield and quality and contribute to the sustainable development of agriculture; thus, the mining and localization of nitrogen use efficiency (NUE) genes is particularly important, but the localization of NUE genes requires a large amount of phenotypic data support. In view of this, we propose the use of low-altitude aerial photography to acquire field images at a large scale, generate 3-dimensional (3D) point clouds and multispectral images of wheat plots, propose a wheat 3D plot segmentation dataset, quantify the plot canopy height via combination with PointNet++, and generate 4 nitrogen utilization-related vegetation indices via index calculations. Six height-related and 24 vegetation-index-related dynamic digital phenotypes were extracted from the digital phenotypes collected at different time points and fitted to generate dynamic curves. We applied height-derived dynamic numerical phenotypes to genome-wide association studies of 160 wheat cultivars (660,000 single-nucleotide polymorphisms) and found that we were able to locate reliable loci associated with height and NUE, some of which were consistent with published studies. Finally, dynamic phenotypes derived from plant indices can also be applied to genome-wide association studies and ultimately locate NUE- and growth-related loci. In conclusion, we believe that our work demonstrates valuable advances in 3D digital dynamic phenotyping for locating genes for NUE in wheat and provides breeders with accurate phenotypic data for the selection and breeding of nitrogen-efficient wheat varieties.
Why it matches plant phenotyping methods航空画像・3D点群・マルチスペクトル画像から小麦区画の草冠高と植生指数を抽出するデジタルフェノタイピング手法を開発・適用しており、表現型取得が研究の中心である。
abstractwe propose the use of low-altitude aerial photography to acquire field images at a large scale, generate 3-dimensional (3D) point clouds and multispectral images of wheat plots
Reproduction assets foundThe paper's Data Availability statement explicitly deposits the authors' source code, testing data, and supporting datasets (including the W3DPS 3D plot segmentation dataset and phenotyping/GWAS data) at two public Quark pan links under CC BY 4.0. These are paper-specific, publicly actionable assets. Other allowed URLsCode · publicThe source code, testing data, and other datasets supporting the results presented here are available at https://pan.quark.cn/s/afbf9025b19e and https://pan.quark.cn/s/47e91f9d6c9c .Open asset ↗lines:138-156Dataset · publicThe source code, testing data, and other datasets supporting the results presented here are available at https://pan.quark.cn/s/afbf9025b19e and https://pan.quark.cn/s/47e91f9d6c9c .Open asset ↗lines:138-156Code / dataset availability confirmedEurope PMC · checked 15 Sept 2026
In plant breeding and genetics, predictive models traditionally rely on compact representations of high-dimensional data, often using methods like Principal Component Analysis (PCA) and, more recently, Autoencoders (AE). However, these methods do not separate genotype-specific and environment-specific features, limiting their ability to accurately predict traits influenced by both genetic and environmental factors. We hypothesize that disentangling these representations into genotype-specific and environment-specific components can enhance predictive models. To test this, we developed a compositional autoencoder (CAE) that decomposes high-dimensional data into distinct genotype-specific and environment-specific latent features. Our CAE framework employed a hierarchical architecture within an autoencoder to effectively separate these entangled latent features. Applied to a maize diversity panel dataset, the CAE demonstrated superior modeling of environmental influences and out-performs PCA (principal component analysis), PLSR (Partial Least square regression) and vanilla autoencoders by 7 times for 'Days to Pollen' trait and 10 times improved predictive performance for 'Yield'. By disentangling latent features, the CAE provided a powerful tool for precision breeding and genetic research. This work has significantly enhanced trait prediction models, advancing agricultural and biological sciences.
Why it matches plant phenotyping methods植物形質（開花日数・収量）の予測を目的とする新規オートエンコーダを開発し、既存手法と比較評価しており、計算的な形質推定手法が研究の中心である。
abstractwe developed a compositional autoencoder (CAE) that decomposes high-dimensional data into distinct genotype-specific and environment-specific latent features.
Reproduction assets foundThe paper's data availability statement explicitly deposits the hyperspectral reflectance dataset and trained model weights on Figshare, and the authors' analysis code on a public Bitbucket repository (baskargroup/cae_hyperspectral). Both are paper-specific, public, and actionable.Dataset · publicte the technical advantages of disentanglement, it is not immediately clear how to connect these disentangled features to biological insights.
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/articles/dataset/Hyperspectral_reflectance_data_molecular_and_weights_for_trained_model/24808491/4 ; https://bitbucket.org/baskargroup/cae_hyperspectral/src/main/ .
Author contributions
AP: Formal analysis, Investigation, Methodology, Software, Validation, Visualization, Writing – original draft, Writing – review & editing. TJ: Conceptualization, Open asset ↗figshare · 24808491lines:460-495Code / dataset availability confirmedCrossref · Europe PMC · checked 6 Sept 2026
Background Small object detection via unmanned Aerial vehicle (UAV) is crucial for smart agriculture, enhancing yield and efficiency. Methods This study addresses the issue of missed detections in crowded environments by developing an efficient algorithm tailored for precise, real-time small object detection. The proposed Yield Health Robust Transformer-YOLO (YH-RTYO) model incorporates several key innovations to advance conventional convolutional models. The model features an efficient convolutional expansion module that captures additional feature information through extended branches while maintaining parameter efficiency by consolidating features into a single convolution during validation. It also includes a local feature pyramid module designed to suppress background interference during feature interaction. Furthermore, the loss function is optimized to accommodate various object scales in different scenes by adjusting the regression box size and incorporating angle factors. These enhancements collectively contribute to improved detection performance and address the limitations of traditional methods. Result Compared to YOLOv8-L, the YH-RTYO model achieves superior performance in all key accuracy metrics, with a 13% reduction in the scale of model. Experimental results demonstrate that the YH-RTYO model outperforms others in key detection metrics. The model reduces the number of parameters by 13%, facilitating deployment while maintaining accuracy. On the OilPalmUAV dataset, it achieves a 3.97% improvement in average precision (AP). Additionally, the model shows strong generalization on the RFRB dataset, with AP 50 and AP values exceeding those of the YOLOv8 baseline by 3.8% and 2.7%, respectively.
Why it matches plant phenotyping methods作物の生育異常検出を目的とする画像ベースの物体検出モデルを開発し、複数データセットで性能評価しており、表現型状態の抽出法が中心である。
titleYH-RTYO: an end-to-end object detection method for crop growth anomaly detection in UAV scenarios.
Reproduction assets foundThe paper's Data Availability section explicitly lists public repositories for the authors' code (GitHub and Zenodo) and for the two UAV crop-detection datasets used in the experiments (MOPAD and RFRB). The ultralytics repository is a generic library and is excluded.Code · publicThe code is available at Github and Zenodo:Open asset ↗lines:795-926Dataset · publicThe MOPAD dataset is available at Github and at Zheng et al. (2021):Open asset ↗lines:795-926Dataset · publicThe RFRB dataset is available at Github and is described in Ji et al. (2023):Open asset ↗lines:795-926Code / dataset availability confirmedOpenAlex · Europe PMC · bioRxiv · checked 15 Sept 2026
ABSTRACT Inorganic nitrogen (N) fertilizer has emerged as one of the key factors driving increased crop yields in the past several decades; however, the overuse of chemical N fertilizer has led to severe ecological and environmental burdens. Understanding how crops respond to N fertilizer has become a central topic in plant science and plant genetics, with the ultimate goal of enhancing N use efficiency (NUE) in crop production. As one of the most essential macronutrients, N significantly influences crop performance across different developmental stages of plant, phenotypic traits result from the accumulative effects of genetic factors, prevailing environmental conditions (specifically N availability), and their complex interactions. To characterize the targeting N-responsiveness and growth trajectory, we employed CRISPR-Cas9 technique to generate sorghum mutants using CRISPR technology. Using a LemnaTec plant imaging system, we obtained time series imagery data from 29 to 130 days after sowing (DAS) for these CRISPR-edited mutants under high N and low N greenhouse conditions. After imagery data analysis, we extracted a number of morphological and greenness index traits as a proxy of plant growth and N responses. Subsequently, we employed two different methods to model the temporal N-responsive traits, allowing us to estimate seven key parameters from the growth curve. Our findings revealed that the wildtype and the edited sorghum lines exhibited differences in N responses for several of the key growth-related parameters. The high-throughput N phenotyping pipeline paves the way for a better understanding of the N responses of edited lines in a dynamic manner and sheds light on further improvements in crop NUE.
Why it matches plant phenotyping methodsLemnaTec画像による時系列形質取得と成長曲線モデリングを組み合わせた高スループット表現型解析パイプラインが、研究の主要な技術的要素として記述されています。
abstractUsing a LemnaTec plant imaging system, we obtained time series imagery data from 29 to 130 days after sowing (DAS) for these CRISPR-edited mutants under high N and low N greenhouse conditions.
Reproduction assets foundThe paper's Supporting Information section links four public GitHub-hosted supplementary data files containing the paper-specific phenotypic values (fitted pixel count and ExG curve parameters) and statistical contrasts, directly reproducing this study's sorghum N-response phenotyping measurements and analysis outputs.Supplement · publicSupporting Information
Supporting Tables
Table S1. The phenotypic values calculated from the pixel count curves.
(https://github.com/JIN-HY/Sorghum-edits-N-Phenotyping/blob/main/fitpx.csv)
Table S2. The phenotypic values calculated from the ExG curves.
(https://github.com/JIN-HY/Sorghum-edits-N-Phenotyping/blob/main/fitexg.csv)
Table S3. The contrasts of the phenotypes calculated from the pixel count curves.
(https://github.com/JIN-HY/Sorghum-edits-N-Phenotyping/blob/main/PXcontrasts.xlsx)
Table S4. The contrastOpen asset ↗JIN-HY/Sorghum-edits-N-Phenotyping · fitpx.csvpdf-raw-page:11 lines:1-21Supplement · publicSupporting Information
Supporting Tables
Table S1. The phenotypic values calculated from the pixel count curves.
(https://github.com/JIN-HY/Sorghum-edits-N-Phenotyping/blob/main/fitpx.csv)
Table S2. The phenotypic values calculated from the ExG curves.
(https://github.com/JIN-HY/Sorghum-edits-N-Phenotyping/blob/main/fitexg.csv)
Table S3. The contrasts of the phenotypes calculated from the pixel count curves.
(https://github.com/JIN-HY/Sorghum-edits-N-Phenotyping/blob/main/PXcontrasts.xlsx)
Table S4. The contrasts of the phenotypes calculated from the ExG curves.
(https://github.com/JIN-HY/Sorghum-edits-N-Phenotyping/blob/main/ExGcontrast.xlsx)
11/14Open asset ↗JIN-HY/Sorghum-edits-N-Phenotyping · fitexg.csvpdf-raw-page:11 lines:1-21Code / dataset availability confirmedOpenAlex · Crossref · checked 6 Sept 2026
Time to maturity and yield are important traits for highbush blueberry (Vaccinium corymbosum) breeding. Proper determination of the time to maturity of blueberry varieties and breeding lines informs the harvest window, ensuring that the fruits are harvested at optimum maturity and quality. On the other hand, high-yielding crops bring in high profits per acre of planting. Harvesting and quantifying the yield for each blueberry breeding accession are labor-intensive and impractical. Instead, visual ratings as an estimation of yield are often used as a faster way to quantify the yield, which is categorical and subjective. In this study, we developed and shared a high-throughput phenotyping method using neural networks to predict blueberry time to maturity and to provide a proxy for yield, overcoming the labor constraints of obtaining high-frequency data. We aim to facilitate further research in computer vision and precision agriculture by publishing the labeled image dataset and the trained model. In this research, true-color images of blueberry bushes were collected, annotated, and used to train a deep neural network object detection model [You Only Look Once (YOLOv11)] to detect mature and immature berries. Different versions of YOLOv11 were used, including nano, small, and medium, which had similar performance, while the medium version had slightly higher metrics. The YOLOv11m model shows strong performance for the mature berry class, with a precision of 0.90 and an F1 score of 0.90. The precision and recall for detecting immature berries were 0.81 and 0.79. The model was tested on 10 blueberry bushes by hand harvesting and weighing blueberries. The results showed that the model detects approximately 25% of the berries on the bushes, and the correlation coefficients between model-detected and hand-harvested traits were 0.66, 0.86, and 0.72 for mature fruit count, immature fruit count, and mature ratio, respectively. The model applied to 91 blueberry advance selections and categorized them into groups with diverse levels of maturity and productivity using principal component analysis (PCA). These results inform the harvest window and yield of these breeding lines with precision and objectivity through berry classification and quantification. This model will be helpful for blueberry breeders, enabling more efficient selection, and for growers, helping them accurately estimate optimal harvest windows. This open-source tool can potentially enhance research capabilities and agricultural productivity.
Why it matches plant phenotyping methodsブルーベリーの成熟度・収量 proxy を画像とニューラルネットワークで推定する高スループット表現型計測法を開発・検証し、モデルとラベル付きデータセットを共有しているため、方法が研究の中心である。
abstractwe developed and shared a high-throughput phenotyping method using neural networks to predict blueberry time to maturity and to provide a proxy for yield
Reproduction assets foundThe paper publishes its labeled blueberry image dataset on Zenodo (record 14014858) and its trained YOLOv11-based blueberry fruit counting model/code on GitHub (jeromemaleski/blueberry), both directly supporting the paper's phenotyping measurements and analysis.Dataset · public32. Zhang, J. Blueberry Images and Labels for YOLO Model Training. Zenodo. 2024. Available online: https://zenodo.org/records/Open asset ↗zenodopdf-page:14 lines:1-36Code / dataset availability confirmedEurope PMC · OpenAlex · checked 15 Sept 2026
Common beanAerial / UAVField / plotLiDAR / point cloudRGB / grayscaleWhole plant / canopy / plot / fieldCountingMorphology / geometry measurementGrowth / time-series analysisGrowth / development / phenology
Substantial effort has been made in manually tracking plant maturity and to measure early-stage plant density and crop height in experimental fields. In this study, RGB drone imagery and deep learning (DL) approaches are explored to measure relative maturity (RM), stand count (SC), and plant height (PH), potentially offering higher throughput, accuracy, and cost-effectiveness than traditional methods. A time series of drone images was utilized to estimate dry bean RM employing a hybrid convolutional neural network (CNN) and long short-term memory (LSTM) model. For early-stage SC assessment, Faster RCNN object detection algorithm was evaluated. Flight frequencies, image resolution, and data augmentation techniques were investigated to enhance DL model performance. PH was obtained using a quantile method from digital surface model (DSM) and point cloud (PC) data sources. The CNN-LSTM model showed high accuracy in RM prediction across various conditions, outperforming traditional image preprocessing approaches. The inclusion of growing degree days (GDD) data improved the model's performance under specific environmental stresses. The Faster R-CNN model effectively identified early-stage bean plants, demonstrating superior accuracy over traditional methods and consistency across different flight altitudes. For PH estimation, moderate correlations with ground-truth data were observed across both datasets analyzed. The choice between PC and DSM source data may depend on specific environmental and flight conditions. Overall, the CNN-LSTM and Faster R-CNN models proved more effective than conventional techniques in quantifying RM and SC. The subtraction method proposed for estimating PH without accurate ground elevation data yielded results comparable to the difference-based method. Additionally, the pipeline and open-source software developed hold potential to significantly benefit the phenotyping community.
Why it matches plant phenotyping methodsドローン画像と深層学習を用いて成熟度、株数、草丈を推定する手法を開発・評価し、パイプラインとオープンソースソフトウェアも提示しており、植物表現型取得が研究の中心である。
abstractIn this study, RGB drone imagery and deep learning (DL) approaches are explored to measure relative maturity (RM), stand count (SC), and plant height (PH)
Reproduction assets foundThe paper explicitly states that all R/Python analysis code, apps, and the complete datasets (orthomosaics, shapefiles, ground notes, clipped plots) are publicly available via the authors' GitHub organization and three Zenodo deposits for RM, SC, and PH.Dataset · publicof the manuscript.
Competing interests: The authors declare that they have no competing interests.
Data Availability
Developed software and analysis are available in the GitHub repositories at https://github.com/msudrybeanbreeding and datasets can be download at Zenodo deposit page ( https://zenodo.org/ ) using the links to RM: https://doi.org/10.5281/zenodo.7922565; SC: https://doi.org/10.5281/zenodo.7922584; and PH: https://doi.org/10.5281/zenodo.7922589 .
Supplementary Materials
Supplementary 1
Figs. S1 to S14
Tables S1 and S2
Data files S1 to 21
References
1.
Uebersax
MA , Cichy
KA , Gomez
FE , Porch
TG , Heitholt
J , Osorno
JM , Kamfwa
K , Snapp
SS , Bales
S
.
Dry beans ( Phaseolus vuOpen asset ↗zenodo · 10.5281/zenodo.7922565lines:677-730Dataset · publicauthors declare that they have no competing interests.
Data Availability
Developed software and analysis are available in the GitHub repositories at https://github.com/msudrybeanbreeding and datasets can be download at Zenodo deposit page ( https://zenodo.org/ ) using the links to RM: https://doi.org/10.5281/zenodo.7922565; SC: https://doi.org/10.5281/zenodo.7922584; and PH: https://doi.org/10.5281/zenodo.7922589 .
Supplementary Materials
Supplementary 1
Figs. S1 to S14
Tables S1 and S2
Data files S1 to 21
References
1.
Uebersax
MA , Cichy
KA , Gomez
FE , Porch
TG , Heitholt
J , Osorno
JM , Kamfwa
K , Snapp
SS , Bales
S
.
Dry beans ( Phaseolus vulgaris L.) as a vital component of sustainabOpen asset ↗zenodo · 10.5281/zenodo.7922584lines:677-730Dataset · publicrests.
Data Availability
Developed software and analysis are available in the GitHub repositories at https://github.com/msudrybeanbreeding and datasets can be download at Zenodo deposit page ( https://zenodo.org/ ) using the links to RM: https://doi.org/10.5281/zenodo.7922565; SC: https://doi.org/10.5281/zenodo.7922584; and PH: https://doi.org/10.5281/zenodo.7922589 .
Supplementary Materials
Supplementary 1
Figs. S1 to S14
Tables S1 and S2
Data files S1 to 21
References
1.
Uebersax
MA , Cichy
KA , Gomez
FE , Porch
TG , Heitholt
J , Osorno
JM , Kamfwa
K , Snapp
SS , Bales
S
.
Dry beans ( Phaseolus vulgaris L.) as a vital component of sustainable agriculture and food security—A review
. LeguOpen asset ↗zenodo · 10.5281/zenodo.7922589lines:677-730Code · publics from each individual breeding plot were extracted from the time series of images (6 and 9 flights date), and the RM was estimated using an optimized threshold value of 0.06. To perform the VI extractions from each breeding plot in the field, an open-source Streamlit app in Python was implemented and can be accessed online at: https://msudrybeanbreeding-vegetation-index--vi-extractions-v0-3-9knpzt.streamlit.app/ . Additionally, to accommodate user preferences, an R script is available to perform VI extractions analysis (Data S7 ).
SC DL model
The SC pipeline deployed in this study comprised 6 distinct steps, starting from the raw images and annotations, and ending with the final SC predictiOpen asset ↗lines:139-147Code / dataset availability confirmedOpenAlex · Crossref · Europe PMC · checked 15 Sept 2026
BACKGROUND: graminoid crop cultivated mainly in the arid and semiarid regions of China for more than 7000 years. Its grain highly nutritious and is rich in starch, protein, essential vitamins such as carotenoids, folate, and minerals. To expand the utilisation of foxtail millet, efficient and precise methods for dynamic phenotyping of its growth stages are needed. Traditional foxtail millet monitoring methods have high labour costs and are inefficient and inaccurate, impeding the precise evaluation of foxtail millet genotypic variation. RESULTS: This study introduces a high-throughput imaging system (HIS) with advanced image processing techniques to enhance monitoring efficiency and data quality. The HIS can accurately extract a range of key growth feature parameters, such as plant height (PH), convex hull area (CHA), side projected area (SPA) and colour distribution, from foxtail millet images. Compared with traditional manual measurements, this HIS improved data quality and phenotyping of the key foxtail millet growth traits. High-throughput phenotyping combined with a genome-wide association study (GWAS) revealed genetic loci associated with dynamic growth traits, particularly plant height (PH), in foxtail millet. The loci were linked to genes involved in the gibberellic acid (GA) synthesis pathway related to PH. CONCLUSION: The HIS developed in this study enables the efficient and dynamic monitoring of foxtail millet phenotypic traits. It significantly improves the quality of data obtained for phenotyping key growth traits. The integration of high-throughput phenotyping with GWAS provides new insights into the genetic underpinnings of dynamic growth traits, particularly plant height, by identifying associated genetic loci in the GA synthesis pathway. This methodological advancement opens new avenues for the precise phenotyping and exploration of genetic resources in foxtail millet, potentially enhancing its utilisation.
Why it matches plant phenotyping methodsフォックステールミレットの生育形質を画像から抽出する高スループット画像システムと画像処理手法を開発・評価しており、表現型取得法が研究の中心である。
abstractThis study introduces a high-throughput imaging system (HIS) with advanced image processing techniques to enhance monitoring efficiency and data quality.
Reproduction assets foundThe paper's authors explicitly state that the source code of their foxtail millet image processing program (used to extract phenotypic i-traits such as PH, CHA, SPA, compactness indices, and colour pixel features) is publicly available on GitHub. The MDSi database URL is a general transcriptomic resource, not a paper-Code · publicmation about plant health and physiological responses.
The program was created using the OpenCV library within the Microsoft Visual Studio (C++) environment. The extracted phenotypic data were converted to CSV file format for further analysis and storage. The source code of the program is available for download and reference at https://github.com/ScreenPlant/Foxtail-Millet-Image-Processing .
Analysis of i-traits throughout the entire growth periodOpen asset ↗ScreenPlant/Foxtail-Millet-Image-Processinglines:50-61Code / dataset availability confirmedOpenAlex · checked 14 Sept 2026
Abstract Timeseries data captured by unoccupied aircraft systems (UASs) are increasingly used for agricultural applications requiring accurate prediction of plant phenotypes from remotely sensed imagery. However, prediction models often fail to generalize well from one year to the next or to new environments. Here, we investigate the ability of various machine learning (ML) approaches to improve yield prediction accuracy in new environments from multispectral timeseries imagery acquired on a set of rice (Oryza sativa L.) experiments with different management treatments and varieties. We also trained deep learning models that perform automated feature extraction and compared these against a suite of other approaches. We observed similar performance on a held‐out growing season for a spatiotemporal model (a three‐dimensional convolutional neural network) trained on raw images compared to simpler workflows using dimension reduction of manually extracted features from temporal imagery (i.e., vegetation indices and image texture properties). Manifold learning on raw imagery was better suited for the prediction of phenological traits due to the preservation of local structure in image embeddings at some time points. Together, these results highlight the competitiveness of classical ML approaches for UAS image analysis alongside computationally expensive deep learning models. Along with a new benchmark dataset for rice, our results help extend the toolkit for UAS image analysis, contributing to improved phenotype prediction in plant breeding and precision agriculture applications.
Why it matches plant phenotyping methodsUASマルチスペクトル時系列画像から収量・生育期形質を予測する機械学習手法を比較・評価し、米のベンチマークデータセットも提供しており、表現型取得・推定手法が研究の中心である。
abstractprediction models often fail to generalize well from one year to the next or to new environments.
Reproduction assets foundThe paper's data availability statement explicitly deposits raw and processed UAS imagery, extracted features, and agronomic data on Dryad, and the authors' analysis code on GitHub. Both are paper-specific, public, and actionable.Dataset · publicts complied with the
current laws of the United States, the country in which they
were performed.
C O N F L I C T O F I N T E R E S T S TAT E M E N T
Emily S. Bellis is a full time employee of Avalo, Inc., a crop
improvement company.
DATA AVA I L A B I L I T Y S TAT E M E N T
Raw and processed UAS images are available on Dryad
(https://doi.org/10.5061/dryad.v41ns1s4z) along with
extracted features and agronomic data for the 2021 and 2022
field seasons. Code to reproduce the analyses are available at
https://github.com/FareedFarag/TPPJ-Modeling-Code.O RC I D
FaredFarag https://orcid.org/0000-0002-4659-6781
Trevis D. Huggins https://orcid.org/0000-0002-1937-6687
JeremyD. Edwards https://orcidOpen asset ↗Dryad · 10.5061/dryad.v41ns1s4zpdf-raw-page:16 lines:1-86Code · public61/dryad.v41ns1s4z) along with
approaches for rice trait prediction using UAS imagery as extracted features and agronomic data for the 2021 and 2022
the primary data source. While showcasing the potential of field seasons. Code to reproduce the analyses are available at
various modeling approaches, it also emphasizes the trade- https://github.com/FareedFarag/TPPJ-Modeling-Code.
offs between performance and interpretability for applications
in precision agriculture and plant breeding. Looking for- ORCID
ward, extending the study over multiple years, extending to Fared Farag https://orcid.org/0000-0002-4659-6781
hyperspectral sensors, and exploring additional remotely Trevis D. Huggins https:/Open asset ↗GitHub · FareedFarag/TPPJ-Modeling-Codepdf-layout-page:16 lines:1-54Code / dataset availability confirmedEurope PMC · checked 15 Sept 2026
Field / plotTissuePhysiological trait estimationGrowth / time-series analysisGrowth / development / phenology
Intra-annual variations of carbon stable isotope ratios (δ13C) in different tree compartments could represent valuable indicators of plant carbon source-sink dynamics, at weekly time scale. Despite this significance, the absence of a methodological framework for tracking δ13C values in tree rings persists due to the complexity of tree ring development. To fill this knowledge gap, we developed a method to monitor weekly variability of δ13C in the cambium-xylem continuum of black spruce species [Picea mariana (Mill.) BSP.] during the growing season. We collected and isolated the weekly incremental growth of the cambial region and the developing tree ring from five mature spruce trees over three consecutive growing seasons (2019-21) in Simoncouche and two growing seasons (2020-21) in Bernatchez, both located in the boreal forest of Quebec, Canada. Our method allowed for the creation of intra-annual δ13C series for both the growing cambium (δ13Ccam) and developing xylem cellulose (δ13Cxc) in these two sites. Strong positive correlations were observed between δ13Ccam and δ13Cxc series in almost all study years. These findings suggest that a constant supply of fresh assimilates to the cambium-xylem continuum may be the dominant process feeding secondary growth in the two study sites. On the other hand, rates of carbon isotopic fractionation appeared to be poorly affected by climate variability, at an inter-weekly time scale. Hence, increasing δ13Ccam and δ13Cxc trends highlighted here possibly indicate shifts in carbon allocation strategies, likely fostering frost resistance and reducing water uptake in the late growth season. Additionally, these trends may be related to the black spruce trees' responses to the seasonal decrease in photosynthetically active radiation. Our findings provide new insights into the seasonal carbon dynamics and growth constraints of black spruce in boreal forest ecosystems, offering a novel methodological approach for studying carbon allocation at fine temporal scales.
Why it matches plant phenotyping methods樹木の形成層・木部における週次δ13C変動を追跡する測定法を開発し、複数年・地点で適用して検証しているため、植物の生理状態を取得する方法が中心である。
abstractwe developed a method to monitor weekly variability of δ13C in the cambium-xylem continuum of black spruce species
Reproduction assets foundThe paper's weekly δ13C cambium/xylem measurements are stated to be publicly available via the authors' Quebec-Labrador tree-ring dashboard. A GitHub repository for figure data is mentioned but without a URL and only 'upon publication', so it is not actionable. NOAA GML and the Arizona repository URL are external/citedDataset · publicCanada, 490 de La Couronne, Québec, QC G1K 9A9, Canada.
Conflict of interest
None declared.
Funding
This work was funded by the National Sciences and Engineering Research Council of Canada (NSERC) to É.B. (RGPIN 2021-04216).
Data availability
The weekly carbon isotope measurements published in the study will be available here: https://quebeclabradortr.shinyapps.io/TRdashboard4/ . Additional data used to produce the figures will be available from a GitHub repository, upon publication of the article.
References
Alvarez C, Bégin C, Savard MM, Dinis L, Marion J, Smirnoff A, Bégin Y. (2018). Relevance of using whole-ring stable isotopes of black spruce trees in the perspective of climate reconstrOpen asset ↗TRdashboard4lines:362-389Code / dataset availability confirmedEurope PMC · checked 7 Sept 2026
Multispectral imaging by unoccupied aerial vehicles provides a nondestructive, high-throughput approach to measure biomass accumulation over successive alfalfa (Medicago sativa L. subsp. sativa) harvests. Information from estimated growth curves can be used to infer harvest biomass and to gain insights into the relationship between growth dynamics and forage biomass stability across cuttings and years. In this study, multispectral imaging and several common vegetation indices were used to estimate genetic parameters and model growth of alfalfa cultivars to determine the longitudinal relationship between vegetation indices and forage biomass. Results showed moderate heritability for vegetation indices, with median plot level heritability ranging from 0.11 to 0.64, across multiple cuttings in three trials planted in Ithaca, NY, and Las Cruces, NM. Genetic correlations between the normalized difference vegetation index and forage biomass were moderate to high across trials, cuttings, and the timing of multispectral image capture. To evaluate the relationship between growth parameters and forage biomass stability across cuttings and environmental conditions, random regression modeling approaches were used to estimate the growth parameters of cultivars for each cutting and the variance in growth was compared to the variance in genetic estimates of forage biomass yield across cuttings. These analyses revealed high correspondence between stability in growth parameters and stability of forage yield. The results of this study indicate that vegetation indices are effective at modeling genetic components of biomass accumulation, presenting opportunities for more efficient screening of cultivars and new longitudinal modeling approaches that can provide insights into temporal factors influencing cultivar stability.
Why it matches plant phenotyping methodsUAVマルチスペクトル画像と植生指数を用いてアルファルファのバイオマス蓄積を推定・モデル化し、遺伝パラメータや生育安定性を評価することが研究の中心であるため。
abstractMultispectral imaging by unoccupied aerial vehicles provides a nondestructive, high-throughput approach to measure biomass accumulation over successive alfalfa (Medicago sativa L. subsp. sativa) harvests.
Reproduction assets foundThe paper's authors publicly deposited the R analysis code and input data for the random regression growth-curve modeling and stability analysis in a GitHub repository, explicitly stated in the Data availability section. Phenotype/imagery data themselves are only available upon request (request_only), and Pix4D is a第三方Code · publicAll data is available upon request. R Code and input data are available in the github: https://github.com/rthapa1/FFAR_RandomRegressionModel_growthcurve_modelling_stabilityanalysis_alfalfa .Open asset ↗rthapa1/FFAR_RandomRegressionModel_growthcurve_modelling_stabilityanalysis_alfalfalines:145-180Code / dataset availability confirmedEurope PMC · checked 7 Sept 2026
Wheat ( Triticum aestivum ) is one of the most important staple crops worldwide. To ensure its global supply, the timing and duration of its growth cycle needs to be closely monitored in the field so that necessary crop management activities can be arranged in a timely manner. Also, breeders and plant researchers need to evaluate growth stages (GSs) for tens of thousands of genotypes at the plot level, at different sites and across multiple seasons. These indicate the importance of providing a reliable and scalable toolkit to address the challenge so that the plot-level assessment of GS can be successfully conducted for different objectives in plant research. Here, we present a multimodal deep learning model called GSP-AI, capable of identifying key GSs and predicting the vegetative-to-reproductive transition (i.e., flowering days) in wheat based on drone-collected canopy images and multiseasonal climatic datasets. In the study, we first established an open Wheat Growth Stage Prediction (WGSP) dataset, consisting of 70,410 annotated images collected from 54 varieties cultivated in China, 109 in the United Kingdom, and 100 in the United States together with key climatic factors. Then, we built an effective learning architecture based on Res2Net and long short-term memory (LSTM) to learn canopy-level vision features and patterns of climatic changes between 2018 and 2021 growing seasons. Utilizing the model, we achieved an overall accuracy of 91.2% in identifying key GS and an average root mean square error (RMSE) of 5.6 d for forecasting the flowering days compared with manual scoring. We further tested and improved the GSP-AI model with high-resolution smartphone images collected in the 2021/2022 season in China, through which the accuracy of the model was enhanced to 93.4% for GS and RMSE reduced to 4.7 d for the flowering prediction. As a result, we believe that our work demonstrates a valuable advance to inform breeders and growers regarding the timing and duration of key plant growth and development phases at the plot level, facilitating them to conduct more effective crop selection and make agronomic decisions under complicated field conditions for wheat improvement.
Why it matches plant phenotyping methodsドローン画像と気象データからコムギの生育ステージおよび開花日を推定するモデルを開発・検証し、公開データセットも構築しており、表現型取得・推定手法が研究の中心である。
abstractwe present a multimodal deep learning model called GSP-AI, capable of identifying key GSs and predicting the vegetative-to-reproductive transition (i.e., flowering days) in wheat based on drone-collected canopy images and multiseasonal climatic datasets.
Reproduction assets foundThe paper's Data Availability statement explicitly deposits the authors' GSP-AI source code and the open WGSP dataset (70,410 annotated drone/smartphone canopy images with climatic data) on GitHub, plus the AirMeasurer plot-segmentation platform code. Both are paper-specific, public, and actionable.Code · publicSource code, WGSP, and other datasets supporting the results presented in this article are available at https://Github.com/The-Zhou-Lab/GSP-AI/releases .Open asset ↗The-Zhou-Lab/GSP-AIlines:308-308Code / dataset availability confirmedOpenAlex · Europe PMC · bioRxiv · checked 15 Sept 2026
Abstract Background Understanding genotype-environment interactions of plants is crucial for crop improvement, yet limited by the scarcity of quality phenotyping data. This data note presents the Field Phenotyping Platform 1.0 data set, a comprehensive resource for winter wheat research that combines imaging, trait, environmental, and genetic data. Findings We provide time series data for more than 4,000 wheat plots, including aligned high-resolution image sequences totaling more than 153,000 aligned images across six years. Measurement data for eight key wheat traits is included, namely canopy cover values, plant heights, wheat head counts, senescence ratings, heading date, final plant height, grain yield, and protein content. Genetic marker information and environmental data complement the time series. Data quality is demonstrated through heritability analyses and genomic prediction models, achieving accuracies aligned with previous research. Conclusions This extensive data set offers opportunities for advancing crop modeling and phenotyping techniques, enabling researchers to develop novel approaches for understanding genotype-environment interactions, analyzing growth dynamics, and predicting crop performance. By making this resource publicly available, we aim to accelerate research in climate-adaptive agriculture and foster collaboration between plant science and machine learning communities.
Why it matches plant phenotyping methods高解像度画像時系列と複数の植物形質を含む大規模な公開圃場フェノタイピングデータセットであり、再利用可能なフェノタイピング基盤・ベンチマークとして中心的です。
abstractThis data note presents the Field Phenotyping Platform 1.0 data set, a comprehensive resource for winter wheat research that combines imaging, trait, environmental, and genetic data.
Reproduction assets foundThis data note directly publishes its own phenotyping measurements and image time series: the FIP 1.0 dataset (images, aligned image sequences, eight wheat traits, environmental and marker data) is publicly available on the ETH Research Collection and Hugging Face, and the authors' analysis/processing code is publicly,Dataset · publicn
License: GNU GPL v3
Data Set Compilation
Project name: fip1-dataset
Project home page: https://gitlab.ethz.ch/crop_phenotyping/fip1-dataset
Operating system(s): Platform independent
Programming language: Python
License: GNU GPL v3
Data Availability
• Data Repository: http://doi.org/20.500.11850/697773
• Hugging Face Data set: https://huggingface.co/datasets/mikeboss/FIP1
• Public GABI marker data repository (also integrated in main
Data Repository and Hugging Face Data set): https://doi.org/10.5061/dryad.n02v6wwzc
• Private Agroscope marker data repository: Confidential (Con-
tact: Boulos Chalhoub, boulos.chalhoub@agroscope.admin.ch).
This repository contains marker data (Illumina InfiniumOpen asset ↗mikeboss/FIP1pdf-raw-page:7 lines:1-110Dataset · publicn with FAIR principles [26]:
• Findable: This publication and the Hugging Face data set card
(https://doi.org/10.57967/hf/3191) provide detailed meta-
data and a comprehensive description of the data set’s contents,
making it discoverable to researchers.
• Accessible: The data is hosted on the Research Collection of ETH
Zurich (https://doi.org/20.500.11850/697773), a reliable and
openly accessible data storage.
• Interoperable: The use of the open-source Hugging Face
datasets [27] package makes it easy to use and export to differ-
ent formats. The data is fully MIAPPE v1.1 [28] conform. Given
the shared genotypes the data set can be used to enhance the
data by Gogna et al. [13] by 6 envOpen asset ↗20.500.11850/697773pdf-raw-page:2 lines:84-130Code · publiclly aggregating the derived data into the final
data set using the fip1-dataset repository.
In addition, the data set can be recreated using the fip1-dataset
repository from the derived data that is freely available in the ETH
research collection.
Trait Data Compilation
Project name: FIP 1.0 Data Set - Traits
Project home page: https://gitlab.ethz.ch/crop_phenotyping/fip-1.0-data-set-traits
Operating system(s): Platform independent
Programming language: R, Python
License: GNU GPL v3
Image Data Alignment
Project name: fip1-alignment
Project home page: https://gitlab.ethz.ch/crop_phenotyping/fip1-alignment
Operating system(s): Platform independent
Programming language: Python
License: GNU GPL Open asset ↗fip-1.0-data-set-traitspdf-raw-page:7 lines:1-110Code · publicresearch collection.
Trait Data Compilation
Project name: FIP 1.0 Data Set - Traits
Project home page: https://gitlab.ethz.ch/crop_phenotyping/fip-1.0-data-set-traits
Operating system(s): Platform independent
Programming language: R, Python
License: GNU GPL v3
Image Data Alignment
Project name: fip1-alignment
Project home page: https://gitlab.ethz.ch/crop_phenotyping/fip1-alignment
Operating system(s): Platform independent
Programming language: Python
License: GNU GPL v3
Data Set Compilation
Project name: fip1-dataset
Project home page: https://gitlab.ethz.ch/crop_phenotyping/fip1-dataset
Operating system(s): Platform independent
Programming language: Python
License: GNU GPL v3
Data AvailabiOpen asset ↗fip1-alignmentpdf-raw-page:7 lines:1-110Code · publicramming language: R, Python
License: GNU GPL v3
Image Data Alignment
Project name: fip1-alignment
Project home page: https://gitlab.ethz.ch/crop_phenotyping/fip1-alignment
Operating system(s): Platform independent
Programming language: Python
License: GNU GPL v3
Data Set Compilation
Project name: fip1-dataset
Project home page: https://gitlab.ethz.ch/crop_phenotyping/fip1-dataset
Operating system(s): Platform independent
Programming language: Python
License: GNU GPL v3
Data Availability
• Data Repository: http://doi.org/20.500.11850/697773
• Hugging Face Data set: https://huggingface.co/datasets/mikeboss/FIP1
• Public GABI marker data repository (also integrated in main
Data Repository and HOpen asset ↗fip1-datasetpdf-raw-page:7 lines:1-110Code / dataset availability confirmedCrossref · checked 14 Sept 2026
Moderate-Resolution Imaging Spectroradiometer (MODIS) Nadir Bidirectional Reflectance Distribution Function (BRDF)-Adjusted Reflectance (NBAR) products are being increasingly used for the quantitative remote sensing of vegetation. However, the assumption underlying the MODIS NBAR product’s inversion model—that surface anisotropy remains unchanged over the 16-day retrieval period—may be unreliable, especially since the canopy structure of vegetation undergoes stark changes at the start of season (SOS) and the end of season (EOS). Therefore, to investigate the MODIS NBAR product’s temporal effect on the quantitative remote sensing of crops at different stages of the growing seasons, this study selected typical phenological parameters, namely SOS, EOS, and the intervening stable growth of season (SGOS). The PROBA-V bioGEOphysical product Version 3 (GEOV3) Fractional Vegetation Cover (FVC) served as verification data, and the Pearson correlation coefficient (PCC) was used to compare and analyze the retrieval accuracy of FVC derived from the MODIS NBAR product and MODIS Surface Reflectance product. The Anisotropic Flat Index (AFX) was further employed to explore the influence of vegetation type and mixed pixel distribution characteristics on the BRDF shape under different stages of the growing seasons and different FVC; that was then combined with an NDVI spatial distribution map to assess the feasibility of using the reflectance of other characteristic directions besides NBAR for FVC correction. The results revealed the following: (1) Generally, at the SOSs and EOSs, the differences in PCCs before vs. after the NBAR correction mainly ranged from 0 to 0.1. This implies that the accuracy of FVC derived from MODIS NBAR is lower than that derived from MODIS Surface Reflectance. Conversely, during the SGOSs, the differences in PCCs before vs. after the NBAR correction ranged between –0.2 and 0, suggesting the accuracy of FVC derived from MODIS NBAR surpasses that derived from MODIS Surface Reflectance. (2) As vegetation phenology shifts, the ensuing differences in NDVI patterning and AFX can offer auxiliary information for enhanced vegetation classification and interpretation of mixed pixel distribution characteristics, which, when combined with NDVI at characteristic directional reflectance, could enable the accurate retrieval of FVC. Our results provide data support for the BRDF correction timescale effect of various stages of the growing seasons, highlighting the potential importance of considering how they differentially influence the temporal effect of NBAR corrections prior to monitoring vegetation when using the MODIS NBAR product.
Why it matches plant phenotyping methodsMODIS反射率から作物のFVCを推定するリモートセンシング手法について、NBAR補正の時期効果を比較・検証しており、植物形質推定が研究の中心である。
abstractThe PROBA-V bioGEOphysical product Version 3 (GEOV3) Fractional Vegetation Cover (FVC) served as verification data, and the Pearson correlation coefficient (PCC) was used to compare and analyze the retrieval accuracy of FVC derived from the MODIS NBAR product and MODIS Surface Reflectance product.
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 · publicwhile the GEOV3 FVC data can be downloaded from:
https://land.copernicus.eu/global/products/fcoverOpen asset ↗pdf-page:17 lines:1-59Code / dataset availability confirmedEurope PMC · checked 14 Sept 2026
Temperature is a critical environmental factor affecting nearly all plant processes, including growth, development, and yield. Yet, despite decades of research, we lack the ability to predict plant performance at different temperatures, limiting the development of climate-resilient crops. Further, there is a pressing need to bridge the gap between the prediction of physiological and molecular traits to improve our understanding and manipulation of plant temperature responses. Here, we developed the first enzyme-constrained model of Arabidopsis thaliana ’s metabolism, facilitating predictions of growth-related phenotypes at different temperatures. We showed that the model can be employed for in silico identification of genes that affect plant growth at suboptimal growth temperature. Using mutant lines, we validated the genes predicted to affect plant growth, demonstrating the potential of metabolic modeling in accurately predicting plant thermal responses. The temperature-dependent enzyme-constrained metabolic model provides a template that can be used for developing sophisticated strategies to engineer climate-resilient crops.
Why it matches plant phenotyping methods温度依存性の酵素制約代謝モデルを開発し、成長関連表現型の予測と変異体による検証を行っており、植物表現型推定手法が研究の中心です。
abstractHere, we developed the first enzyme-constrained model of Arabidopsis thaliana ’s metabolism, facilitating predictions of growth-related phenotypes at different temperatures.
Reproduction assets foundThe paper's computational analysis code (simulations and statistics), the machine-learning tool for protein thermostability optima, and the refined AraCore metabolic model are all explicitly deposited in public GitHub repositories by the authors. No standalone public phenotype dataset URL is given; compiled RGR and CO2Code · public30
Declaration of interests
683
The authors declare no competing interests.
684
Code availability
685
Custom computer code that was developed for simulations and statistical analyses in this
686
study are publicly available at https://github.com/pwendering/AraTModel. The code
687
developed for machine learning of protein thermostability optima was deposited in a
688
separate repository, which is publicly available at https://github.com/pwendering/topt-689
predict. The refined AraCore model can be retrieved from
690
https://github.com/pwendering/ArabidopsisCoreModel. All remaining data areOpen asset ↗pwendering/AraTModelpdf-raw-page:30 lines:1-31Code · public686
study are publicly available at https://github.com/pwendering/AraTModel. The code
687
developed for machine learning of protein thermostability optima was deposited in a
688
separate repository, which is publicly available at https://github.com/pwendering/topt-689
predict. The refined AraCore model can be retrieved from
690
https://github.com/pwendering/ArabidopsisCoreModel. All remaining data are provided
691
with this manuscript and supplementary material.
692
693
.
CC-BY-NC 4.0 International license
available under a
(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made
The copyright holder foOpen asset ↗pwendering/ArabidopsisCoreModelpdf-raw-page:30 lines:1-31Code / dataset availability confirmedOpenAlex · Europe PMC · checked 7 Sept 2026
Cauliflower cultivation is subject to high-quality control criteria during sales, which underlines the importance of accurate harvest timing. Using time series data for plant phenotyping can provide insights into the dynamic development of cauliflower and allow more accurate predictions of when the crop is ready for harvest than single-time observations. However, data acquisition on a daily or weekly basis is resource-intensive, making selection of acquisition days highly important. We investigate which data acquisition days and development stages positively affect the model accuracy to get insights into prediction-relevant observation days and aid future data acquisition planning. We analyze harvest-readiness using the cauliflower image time series of the GrowliFlower dataset. We use an adjusted ResNet18 classification model, including positional encoding of the data acquisition dates to add implicit information about development. The explainable machine learning approach GroupSHAP analyzes time points' contributions. Time points with the lowest mean absolute contribution are excluded from the time series to determine their effect on model accuracy. Using image time series rather than single time points, we achieve an increase in accuracy of 4%. GroupSHAP allows the selection of time points that positively affect the model accuracy. By using seven selected time points instead of all 11 ones, the accuracy improves by an additional 4%, resulting in an overall accuracy of 89.3%. The selection of time points may therefore lead to a reduction in data collection in the future.
Why it matches plant phenotyping methodsカリフラワー画像時系列を用いた収穫適期という植物状態の推定手法を開発・評価し、データ取得時点の選択とモデル精度を検証しているため、フェノタイピング手法が中心である。
abstractUsing time series data for plant phenotyping can provide insights into the dynamic development of cauliflower and allow more accurate predictions of when the crop is ready for harvest than single-time observations.
Reproduction assets foundThe paper analyzes the GrowliFlower cauliflower UAV image time series dataset and provides a public data availability link to the dataset metadata on phenoroam.phenorob.de. No author analysis code or trained model checkpoint is explicitly deposited.Dataset · publicPublicly available datasets were analyzed in this study. This data can be found at: https://phenoroam.phenorob.de/geonetwork/srv/eng/catalog.search#/metadata/cb328232-31f5-4b84-a929-8e1ee551d66a .Open asset ↗phenoroam.phenorob.de · cb328232-31f5-4b84-a929-8e1ee551d66alines:386-397Code / dataset availability confirmedEurope PMC · checked 14 Sept 2026
The aim of the present research was to evaluate the efficiency of different vegetation indices (VI) obtained from satellites with varying spatial resolutions in discriminating the phenological stages of soybean crops. The experiment was carried out in a soybean cultivation area irrigated by central pivot, in Balsas, MA, Brazil, where weekly assessments of phenology and leaf area index were carried out. Throughout the crop cycle, spectral data from the study area were collected from sensors, onboard the Sentinel-2 and Amazônia-1 satellites. The images obtained were processed to obtain the VI based on NIR (NDVI, NDWI and SAVI) and RGB (VARI, IV GREEN and GLI), for the different phenological stages of the crop. The efficiency in identifying phenological stages by VI was determined through discriminant analysis and the Algorithm Neural Network-ANN, where the best classifications presented an Apparent Error Rate (APER) equal to zero. The APER for the discriminant analysis varied between 53.4% and 70.4% while, for the ANN, it was between 47.4% and 73.9%, making it not possible to identify which of the two analysis techniques is more appropriate. The study results demonstrated that the difference in sensors spatial resolution is not a determining factor in the correct identification of soybean phenological stages. Although no VI, obtained from the Amazônia-1 and Sentinel-2 sensor systems, was 100% effective in identifying all phenological stages, specific indices can be used to identify some key phenological stages of soybean crops, such as: flowering (R1 and R2); pod development (R4); grain development (R5.1); and plant physiological maturity (R8). Therefore, VI obtained from orbital sensors are effective in identifying soybean phenological stages quickly and cheaply.
Why it matches plant phenotyping methods衛星スペクトル指数と解析手法によりダイズの生育ステージを推定し、空間解像度や分類性能を評価しており、植物フェノタイピング手法の検証・適用が研究の中心です。
abstractThe aim of the present research was to evaluate the efficiency of different vegetation indices (VI) obtained from satellites with varying spatial resolutions in discriminating the phenological stages of soybean crops.
Reproduction assets foundThe authors state all relevant data (soybean phenology/vegetation index measurements from Sentinel-2 and Amazonia-1) are publicly available in their GitHub repository.Dataset · publicat the difference in spatial resolution of the two sensors evaluated, 10 meters per pixel of Sentinel-2 and 65 meters per pixel of Amazônia-1, is not a determining factor in the correct identification of soybean phenological stages.
Data Availability
All relevant data is available in the GitHub repository at the following link: https://github.com/FSilva-826/DADOS---AMAZONIA1-CENTINEL2 .
Funding Statement
This study was funded by the College of Food and Agriculture Sciences, King Saud University, RSPD2024R678 (to Mohamed A. El-Tayeb). This study was also funded by a scholarship from CAPES, Coordination for the Improvement of Higher Education Personnel, 88887.677482/2022-00 (to Airton Andrade Open asset ↗FSilva-826/DADOS---AMAZONIA1-CENTINEL2lines:240-262Code / dataset availability confirmedEurope PMC · checked 14 Sept 2026
Urochloa grasses are widely used forages in the Neotropics and are gaining importance in other regions due to their role in meeting the increasing global demand for sustainable agricultural practices. High-throughput phenotyping (HTP) is important for accelerating Urochloa breeding programs focused on improving forage and seed yield. While RGB imaging has been used for HTP of vegetative traits, the assessment of phenological stages and seed yield using image analysis remains unexplored in this genus. This work presents a dataset of 2,400 high-resolution RGB images of 200 Urochloa hybrid genotypes, captured over seven months and covering both vegetative and reproductive stages. Images were manually labelled as vegetative or reproductive, and a subset of 255 reproductive stage images were annotated to identify 22,340 individual racemes. This dataset enables the development of machine learning and deep learning models for automated phenological stage classification and raceme identification, facilitating HTP and accelerated breeding of Urochloa spp. hybrids with high seed yield potential.
Why it matches plant phenotyping methods植物のフェノロジー段階と穂状花序を画像から識別するための高解像度データセットであり、植物表現型取得・抽出を中心とする研究。
abstractThis work presents a dataset of 2,400 high-resolution RGB images of 200 Urochloa hybrid genotypes, captured over seven months and covering both vegetative and reproductive stages.
Reproduction assets foundThe paper is itself a data descriptor whose core asset is a public Harvard Dataverse dataset of 2,400 RGB images of Urochloa hybrids with phenological stage labels and COCO-format raceme polygon annotations, directly reproducing the paper's phenotyping data.Dataset · publict diffuser to ensure uniform lighting.
Data source location
Institution: Alliance Bioversity International & CIAT.
City: Palmira, Valle del Cauca.
Country: Colombia.
Geolocalization: 3°29′N, 76°21′W .
Data accessibility
Repository name: Harvard Dataverse
Data identification number: doi.org/10.7910/dvn/u0kl6y
Direct URL to data: https://doi.org/10.7910/dvn/u0kl6y
Instructions for accessing these data: The dataset [ 1 ] is licensed under the Creative Commons Attribution 4.0 International, which allows using, sharing, adapting, distribution and reproduction in any medium or format if attribution is given to the creator.
Related research article
None .
1
Value of the Data
•
The dataset offOpen asset ↗Harvard Dataverselines:1-58Code / dataset availability confirmedCrossref · checked 15 Sept 2026
Growth monitoring of crops is a crucial aspect of precision agriculture, essential for optimal yield prediction and resource allocation. Traditional crop growth monitoring methods are labor-intensive and prone to errors. This study introduces an automated segmentation pipeline utilizing multi-date aerial images and ortho-mosaics to monitor the growth of cauliflower crops (Brassica Oleracea var. Botrytis) using an object-based image analysis approach. The methodology employs YOLOv8, a Grounding Detection Transformer with Improved Denoising Anchor Boxes (DINO), and the Segment Anything Model (SAM) for automatic annotation and segmentation. The YOLOv8 model was trained using aerial image datasets, which then facilitated the training of the Grounded Segment Anything Model framework. This approach generated automatic annotations and segmentation masks, classifying crop rows for temporal monitoring and growth estimation. The study’s findings utilized a multi-modal monitoring approach to highlight the efficiency of this automated system in providing accurate crop growth analysis, promoting informed decision-making in crop management and sustainable agricultural practices. The results indicate consistent and comparable growth patterns between aerial images and ortho-mosaics, with significant periods of rapid expansion and minor fluctuations over time. The results also indicated a correlation between the time and method of observation which paves a future possibility of integration of such techniques aimed at increasing the accuracy in crop growth monitoring based on automatically derived temporal crop row segmentation masks.
Why it matches plant phenotyping methods航空画像・オルソモザイクから作物列を自動セグメンテーションし、時系列の生育・成長を推定する画像解析パイプラインが研究の中心であり、植物形質の取得手法として適格。
abstractThis study introduces an automated segmentation pipeline utilizing multi-date aerial images and ortho-mosaics to monitor the growth of cauliflower crops
Reproduction assets foundThe paper's Data Availability Statement points to the authors' public Mendeley Data repository (GobhiSet, DOI 10.17632/dcjjcwc5dh.4), which contains the raw, manually, and automatically annotated RGB aerial images and ortho-mosaics of cauliflower used for the YOLOv8x-seg and Grounded SAM training and growth analysis inDataset · publicon of the manuscript.
Funding: This research received no external funding.
Data Availability Statement: No new data was created. However, the data that were used to perform
this research can be found in the article published at https://doi.org/10.1016/j.dib.2024.110506 and
available in the repository DOI: 10.17632/dcjjcwc5dh.4 (https://data.mendeley.com/drafts/dcjjcwc5dh).Conflicts of Interest: The authors declare no conflicts of interest.
References
1. Di, L.; Ustundag, B. Crop Growth Modeling and Yield Forecasting. In Agro-Geoinformatics; Springer: Cham, Switzerland, 2021.
[CrossRef]
2. Mithen, S.; Jenkins, E.; Jamjoum, K.; Nuimat, S.; Nortcliff, S.; Finlayson, B. Experimental crop growingOpen asset ↗data.mendeley.com · 10.17632/dcjjcwc5dh.4pdf-raw-page:17 lines:1-52Code / 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 confirmedOpenAlex · Europe PMC · bioRxiv · checked 15 Sept 2026
Abstract The need for more sustainable agricultural systems is becoming increasingly apparent. The global demand for agricultural products — food, feed, fuel and fiber — will continue to increase as the global population continues to grow. This challenge is compounded by climate change. Not only does a changing climate make it difficult to maintain stable yields but current agricultural systems are a major source of greenhouse gas emissions and continue to drive the problem further. Therefore, future agricultural systems must not only increase production but also significantly decrease negative environmental impacts. One approach to addressing this is to begin breeding and cultivating new plant species that have fundamental sustainability advantages over our existing crops. The Lemnaceae, a.k.a duckweeds, are one such species that have potential to increase output and reduce the negative environmental impacts of agricultural production. Herein we describe the Automated Lab-scale PHenotyping Apparatus, ALPHA, for high-throughput phenotyping of Lemnaceae. ALPHA is being used for selective breeding of one species, Lemna gibba , toward the goal of creating a new crop for use in sustainable agricultural systems. ALPHA can be used on many small aquatic plant species to assess growth rates in different environmental conditions. A proof of principle use case is demonstrated where ALPHA is used to determine saltwater tolerance of 6 different varieties of L. gibba .
Why it matches plant phenotyping methods小型水生植物の成長率を高スループットに定量する自動フェノタイピング装置を開発・実証しており、表現型取得法が研究の中心です。
abstractHerein we describe the Automated Lab-scale PHenotyping Apparatus, ALPHA, for high-throughput phenotyping of Lemnaceae.
Reproduction assets foundThe authors state that all source code for the phenotyping system, the PlantCV image analysis pipeline, the R growth-curve analysis, 3D models, and the data generated for this study (including PlantCV_Output_Salinity.csv and Barcode_Sample_Map.csv) are publicly available in the ALPHA GitHub repository.Code · publicAll source code used in the phenotyping system, 3D models for printed parts and data generated
for this study are available in the ALPHA Github repository.Open asset ↗pdf-raw-page:2 lines:1-49Dataset · publicThis code requires data output from the quantification pipeline
“PlantCV_Output_Salinity.csv” and the barcode map “Barcode_Sample_Map.csv”. Both are also
available in the Github repository.Open asset ↗pdf-raw-page:7 lines:1-33Code / dataset availability confirmedCrossref · checked 7 Sept 2026
Vegetation vertical complexity influences biodiversity and ecosystem productivity. Rapid warming in the boreal region is altering patterns of vertical complexity. LiDAR sensors offer novel structural metrics for quantifying these changes, but their spatiotemporal limitations and their need for ecological context complicate their application and interpretation. Satellite variables can estimate LiDAR metrics, but retrievals of vegetation structure using optical reflectance can lack interpretability and accuracy. We compare vertical complexity from the airborne LiDAR Land Vegetation and Ice Sensor (LVIS) in boreal Canada and Alaska to plant functional type, optical, and phenological variables. We show that spring onset and green season length from satellite phenology algorithms are more strongly correlated with vegetation vertical complexity (R = 0.43–0.63) than optical reflectance (R = 0.03–0.43). Median annual temperature explained patterns of vegetation vertical complexity (R = 0.45), but only when paired with plant functional type data. Random forest models effectively learned patterns of vegetation vertical complexity using plant functional type and phenological variables, but the validation performance depended on the validation methodology (R2 = 0.50–0.80). In correlating satellite phenology, plant functional type, and vegetation vertical complexity, we propose new methods of retrieving vertical complexity with satellite data.
Why it matches plant phenotyping methods植生キャノピーの垂直複雑性という明示的な植物構造形質を、LiDAR・衛星フェノロジー・機械学習で推定し、検証する方法研究であり、測定手法が中心的です。
abstractLiDAR sensors offer novel structural metrics for quantifying these changes
Reproduction assets foundThe paper's phenotyping/structural analysis is built entirely on publicly archived datasets cited with DOIs/URLs in the text: NASA LVIS full-waveform LiDAR L1B/L2 (ABoVE and LVIS Facility versions) providing the vertical complexity measurements, NEON vegetation structure in situ plant trait data, the ABoVE Landsat landDataset · publicABoVE LVIS L1B Geolocated Return Energy Waveforms, Version 1 [Data Set]. Boulder, Colorado USA.
NASA National Snow and Ice Data Center Distributed Active Archive Center. 2018. Available online:
https://nsidc.org/data/ablvis1b/versions/1 (accessed on 18 April 2024). https://doi.org/10.5067/UMRAWS57QAFUOpen asset ↗NASA National Snow and Ice Data Center Distributed Active Archive Center · 10.5067/UMRAWS57QAFUpdf-page:29 lines:1-52Dataset · publicABoVE LVIS L2 Geolocated Surface Elevation Product, Version 1 [Data Set]. Boulder, Colorado USA.
NASA National Snow and Ice Data Center Distributed Active Archive Center. 2018. Available online:
https://nsidc.org/data/ablvis2/versions/1 (accessed on 18 April 2024). https://doi.org/10.5067/IA5WAX7K3YGYOpen asset ↗NASA National Snow and Ice Data Center Distributed Active Archive Center · 10.5067/IA5WAX7K3YGYpdf-page:29 lines:1-52Dataset · publicLVIS Facility L2 Geolocated Surface Elevation and Canopy Height Product, Version 1 [Data Set]. Boulder,
Colorado USA. NASA National Snow and Ice Data Center Distributed Active Archive Center. 2020. Available online:
https://nsidc.org/data/lvisf2/versions/1 (accessed on 18 April 2024). https://doi.org/10.5067/VP7J20HJQISDOpen asset ↗NASA National Snow and Ice Data Center Distributed Active Archive Center · 10.5067/VP7J20HJQISDpdf-page:29 lines:1-52Dataset · publicNEON (National Ecological Observatory Network). Vegetation Structure (DP1.10098.001), RELEASE-2024. Available online:
https://Data.Neonscience.Org/Data-Products/DP1.10098.001/RELEASE-2024 (accessed on 18 April 2024).
https://doi.org/10.48443/3bh3-Qz86.Open asset ↗NEON (National Ecological Observatory Network) · DP1.10098.001pdf-page:29 lines:1-52Dataset · publicHLS Operational Land
Imager Surface Reflectance and TOA Brightness Daily Global 30 m v2.0. 2021, Distributed by NASA EOSDIS Land Processes
DAAC. Available online: https://doi.org/10.5067/HLS/HLSL30.002 (accessed on 18 April 2024).Open asset ↗10.5067/HLS/HLSL30.002pdf-page:29 lines:1-52Code / dataset availability confirmedEurope PMC · checked 15 Sept 2026
The evaluation of plant and animal growth, separately for genetic and environmental effects, is necessary for genetic understanding and genetic improvement of environmental responses of plants and animals. We propose to extend an existing approach that combines nonlinear mixed-effects model (NLMEM) and the stochastic approximation of the Expectation-Maximization algorithm (SAEM) to analyze genetic and environmental effects on plant growth. These tools are widely used in many fields but very rarely in plant biology. During model formulation, a nonlinear function describes the shape of growth, and random effects describe genetic and environmental effects and their variability. Genetic relationships among the varieties were also integrated into the model using a genetic relationship matrix. The SAEM algorithm was chosen as an efficient alternative to MCMC methods, which are more commonly used in the domain. It was implemented to infer the expected growth patterns in the analyzed population and the expected curves for each variety through a maximum-likelihood and a maximum-a-posteriori approaches, respectively. The obtained estimates can be used to predict the growth curves for each variety. We illustrate the strengths of the proposed approach using simulated data and soybean plant growth data obtained from a soybean cultivation experiment conducted at the Arid Land Research Center, Tottori University. In this experiment, plant height was measured daily using drones, and the growth was monitored for approximately 200 soybean cultivars for which whole-genome sequence data were available. The NLMEM approach improved our understanding of the determinants of soybean growth and can be successfully used for the genomic prediction of growth pattern characteristics.
Why it matches plant phenotyping methods植物成長を対象に、遺伝・環境効果を分離し、品種別の成長曲線を推定するNLMEM/SAEM手法を中心的に提案・適用しているため、成長形質の計算的推定に該当する。
abstractWe propose to extend an existing approach that combines nonlinear mixed-effects model (NLMEM) and the stochastic approximation of the Expectation-Maximization algorithm (SAEM) to analyze genetic and environmental effects on plant growth.
Reproduction assets foundThe authors state that all materials, including the real soybean UAV-derived plant-height phenotype data, are publicly available in their GitHub repository, which also contains the analysis code for the NLMEM/SAEM approach.Dataset · publics proposed in this study could genetically model the growth patterns of plants and animals, and help to understand, control, and predict their inheritance. The method proposed in this study is scalable to larger data sets owing to its computational speed. The codes for the method and the data used in this study are available at https://github.com/madelattre/Scripts-soybean-paper and can be extended according to the conditions of the application in each research project.
Supporting information
S1 File
The section entitled “Algorithmic details” describes the SAEM algorithm key distributions and the algorithm steps for both parameter estimation (Algorithm A) and genetic effects prediction (AlgoOpen asset ↗madelattre/Scripts-soybean-paperlines:207-227Code / dataset availability confirmedEurope PMC · checked 13 Sept 2026
Feeding the increasing global population and reducing the carbon footprint of agricultural activities are two critical challenges of our century. Growing crops under protected horticulture and precise crop monitoring have emerged to address these challenges. Crop monitoring in commercial protected facilities remains mostly manual and labour intensive. Using computer vision to solve specific problems in image-based crop monitoring in these compact and complex growth environments is currently hindered by the scarcity of available data. We collected an RGBD dataset for vertically supported, hydroponically-grown capsicum plants in a commercial-scale glasshouse facility to fill this gap. Data were collected weekly using a single top-angled stereo camera mounted on a mobile platform running between the hydroponic gutters. The RGBD streams covered 80 % of the crop growing season in three different light conditions. The metadata include camera configurations and light condition information. Manually measured plant heights of ten selected plants per gutter are provided as ground truth. The images covered the whole plants and focused on the top third. This dataset will support research on plant height estimation, plant organ identification, object segmentation, organ measurements, 3D reconstruction, 3D data processing, and depth noise reduction. The usability of the dataset has been successfully demonstrated in a previously published study on plant height estimation using machine learning and 3D point cloud.
Why it matches plant phenotyping methods植物の草丈推定や器官計測を目的としたRGBD画像データセットを構築し、地上真値も提供しているため、植物フェノタイピング手法・データセットが中心です。
abstractWe collected an RGBD dataset for vertically supported, hydroponically-grown capsicum plants in a commercial-scale glasshouse facility to fill this gap.
Reproduction assets foundThis data article directly deposits its paper-specific phenotyping assets: the PC4C_CAPSI RGBD image dataset (Rosbag streams, JSON metadata, manual plant-height ground truth) on the Western Sydney University ResearchDirect repository, and the authors' RGBD processing code (image extraction, depth correction, 3D reconssDataset · publicData accessibility
Repository name: Image Data of Capsicum Plant Growth in Protected Horticulture: PC4C_CAPSI. [ 1 ]
Data identification number: 10.26183/1A0R-E318
Direct URL to data: https://rds.westernsydney.edu.au/Institutes/HIE/2024/Jayasuriya_N/Open asset ↗rds.westernsydney.edu.aulines:1-40Code / dataset availability confirmedEurope PMC · checked 7 Sept 2026
Precision farming has been developing with the intention of identifying within field variability to adjust management strategies and maximize end of season yield and profitability and minimize negative environmental impacts. The development of quick, easy, and low cost methods to quantify field level variation is essential to successful implementation of precision agriculture at scale. Temporal plant height and growth rates collected with unoccupied aerial vehicles mounted with red, green, blue sensors have the potential to predict end of season grain yield, which could facilitate mid-season management decisions. Image-based plant height data was collected weekly from commercial maize fields in three growing seasons to assess variation within fields and the relationship with grain yield variation. Plant height, growth rate, and grain yield had variable relationships depending on the time point and growth environment. Models developed using temporal traits predicted grain yield variation within a commercial field up to r = 0.7, though insufficient water affected the prediction accuracy in one field due to the limited representation of drought environments in the model development. In the future, with more data from stress environments, such as drought, this method has potential for high accuracy grain yield prediction across a range of environmental conditions. This study demonstrates the potential of using unoccupied aerial vehicles to derive vegetative growth patterns and model within field variations, and has application in making mid-season management decisions.
Why it matches plant phenotyping methodsUAV画像から植物高と成長率を抽出し、時系列形質による圃場内収量変動予測を評価しており、植物表現型の取得・解析手法が研究の中心です。
abstractTemporal plant height and growth rates collected with unoccupied aerial vehicles mounted with red, green, blue sensors have the potential to predict end of season grain yield
Reproduction assets foundThe paper explicitly states that all analysis scripts are on GitHub and all UAV-derived phenotypic data (plot heights, vegetative indices, orthomosaics, DEMs, plot boundaries, masks, manual heights, yield, weather) are deposited in DRUM with a DOI.Code · publicAll of the scripts and files used to generate and analyze data are available on GitHub at
https://github.com/HirschLabUMN/Production_Drone_Height.git.Open asset ↗HirschLabUMN/Production_Drone_Heightpdf-page:17 lines:1-56Code / dataset availability confirmedOpenAlex · Crossref · Europe PMC · checked 7 Sept 2026
BACKGROUND: Image-based crop growth modeling can substantially contribute to precision agriculture by revealing spatial crop development over time, which allows an early and location-specific estimation of relevant future plant traits, such as leaf area or biomass. A prerequisite for realistic and sharp crop image generation is the integration of multiple growth-influencing conditions in a model, such as an image of an initial growth stage, the associated growth time, and further information about the field treatment. While image-based models provide more flexibility for crop growth modeling than process-based models, there is still a significant research gap in the comprehensive integration of various growth-influencing conditions. Further exploration and investigation are needed to address this gap. METHODS: We present a two-stage framework consisting first of an image generation model and second of a growth estimation model, independently trained. The image generation model is a conditional Wasserstein generative adversarial network (CWGAN). In the generator of this model, conditional batch normalization (CBN) is used to integrate conditions of different types along with the input image. This allows the model to generate time-varying artificial images dependent on multiple influencing factors. These images are used by the second part of the framework for plant phenotyping by deriving plant-specific traits and comparing them with those of non-artificial (real) reference images. In addition, image quality is evaluated using multi-scale structural similarity (MS-SSIM), learned perceptual image patch similarity (LPIPS), and Fréchet inception distance (FID). During inference, the framework allows image generation for any combination of conditions used in training; we call this generation data-driven crop growth simulation. RESULTS: Experiments are performed on three datasets of different complexity. These datasets include the laboratory plant Arabidopsis thaliana (Arabidopsis) and crops grown under real field conditions, namely cauliflower (GrowliFlower) and crop mixtures consisting of faba bean and spring wheat (MixedCrop). In all cases, the framework allows realistic, sharp image generations with a slight loss of quality from short-term to long-term predictions. For MixedCrop grown under varying treatments (different cultivars, sowing densities), the results show that adding these treatment information increases the generation quality and phenotyping accuracy measured by the estimated biomass. Simulations of varying growth-influencing conditions performed with the trained framework provide valuable insights into how such factors relate to crop appearances, which is particularly useful in complex, less explored crop mixture systems. Further results show that adding process-based simulated biomass as a condition increases the accuracy of the derived phenotypic traits from the predicted images. This demonstrates the potential of our framework to serve as an interface between a data-driven and a process-based crop growth model. CONCLUSION: The realistic generation and simulation of future plant appearances is adequately feasible by multi-conditional CWGAN. The presented framework complements process-based models and overcomes their limitations, such as the reliance on assumptions and the low exact field-localization specificity, by realistic visualizations of the spatial crop development that directly lead to a high explainability of the model predictions.
Why it matches plant phenotyping methods植物画像を生成し、そこから植物個体別形質を推定する二段階の画像ベース表現型解析フレームワークを開発・評価しており、表現型取得・推定手法が研究の中心である。
abstractWe present a two-stage framework consisting first of an image generation model and second of a growth estimation model, independently trained.
Reproduction assets foundThe paper's authors explicitly state that source code and links to the phenotyping datasets (Arabidopsis, GrowliFlower, MixedCrop) are publicly available in their GitHub repository, which implements the multi-conditional CWGAN crop growth simulation and growth estimation framework.Code · publicSource code and links to the datasets are publicly available at https://github.com/luked12/crop-growth-cgan .Open asset ↗luked12/crop-growth-cganlines:216-253Code / dataset availability confirmedCrossref · Europe PMC · checked 14 Sept 2026
Growth chamberRGB / grayscaleRootObject detectionSegmentationGrowth / time-series analysisBiomass / plant weightGrowth / development / phenologyRoot system architecture
Abstract Flatbed scanners are commonly used for root analysis, but typical manual segmentation methods are time-consuming and prone to errors, especially in large-scale, multi-plant studies. Furthermore, the complex nature of root structures combined with noisy backgrounds in images complicates automated analysis. Addressing these challenges, this article introduces RhizoNet, a deep learning-based workflow to semantically segment plant root scans. Utilizing a sophisticated Residual U-Net architecture, RhizoNet enhances prediction accuracy and employs a convex hull operation for delineation of the primary root component. Its main objective is to accurately segment root biomass and monitor its growth over time. RhizoNet processes color scans of plants grown in a hydroponic system known as EcoFAB, subjected to specific nutritional treatments. The root detection model using RhizoNet demonstrates strong generalization in the validation tests of all experiments despite variable treatments. The main contributions are the standardization of root segmentation and phenotyping, systematic and accelerated analysis of thousands of images, significantly aiding in the precise assessment of root growth dynamics under varying plant conditions, and offering a path toward self-driving labs.
Why it matches plant phenotyping methods植物根の画像分割と根バイオマス・成長の表現型抽出ワークフローを開発・検証しており、フェノタイピング手法が中心である。
abstractthis article introduces RhizoNet, a deep learning-based workflow to semantically segment plant root scans.
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 · publicPython codes for root scans segmentation enabled by RhizoNet were created by the authors and are described in this paper. These codes will be available free of charge upon acceptance, and with open source at: https://github.com/lbl-camera/rhizonet .Open asset ↗lbl-camera/rhizonetlines:154-177Code / dataset availability confirmedCrossref · checked 7 Sept 2026
Abstract Enhancing rapid phenotyping for key plant traits, such as biomass and nitrogen content, is critical for effectively monitoring crop growth and maximizing yield. Studies have explored the relationship between vegetation indices (VIs) and plant traits using drone imagery. However, there is a gap in the literature regarding data availability, accessible datasets. Based on this context, we conducted a systematic review to retrieve relevant data worldwide on the state of the art in drone-based plant trait assessment. The final dataset consists of 41 peer-reviewed papers with 11,189 observations for 11 major crop species distributed across 13 countries. It focuses on the association of plant traits with VIs at different growth/phenological stages. This dataset provides foundational knowledge on the key VIs to focus for phenotyping key plant traits. In addition, future updates to this dataset may include new open datasets. Our goal is to continually update this dataset, encourage collaboration and data inclusion, and thereby facilitate a more rapid advance of phenotyping for critical plant traits to increase yield gains over time.
Why it matches plant phenotyping methodsドローン画像と植生指数に基づく作物形質評価研究を体系的に収集・統合したデータセットであり、植物フェノタイピングの再利用可能な資源が中心です。
titleA global dataset for assessing nitrogen-related plant traits using drone imagery in major field crop species
Reproduction assets foundThe paper's own dataset (Dataset.xlsx with UAV_dataset, sensor info, and quantitative analysis tabs) plus authors' analysis code (R scripts and Jupyter notebook for Figs. 2-4) are publicly deposited on figshare at https://doi.org/10.6084/m9.figshare.22938797.v4.Dataset · publicThe data are accessible on the figshare repository39, available at https://doi.org/10.6084/m9.figshare.22938797,
and includes the following files:
1. “Dataset.xlsx” includes the data. It contains three tabs: “UAV_dataset”, “Sensor and processing info”, and
“Quantitatively analysis”.Open asset ↗figsharepdf-page:3 lines:58-75Code / 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 confirmedEurope PMC · checked 7 Sept 2026
Tissue morphogenesis remains poorly understood. In plants, a central problem is how the 3D cellular architecture of a developing organ contributes to its final shape. We address this question through a comparative analysis of ovule morphogenesis, taking advantage of the diversity in ovule shape across angiosperms. Here, we provide a 3D digital atlas of Cardamine hirsuta ovule development at single cell resolution and compare it with an equivalent atlas of Arabidopsis thaliana. We introduce nerve-based topological analysis as a tool for unbiased detection of differences in cellular architectures and corroborate identified topological differences between two homologous tissues by comparative morphometrics and visual inspection. We find that differences in topology, cell volume variation and tissue growth patterns in the sheet-like integuments and the bulbous chalaza are associated with differences in ovule curvature. In contrast, the radialized conical ovule primordia and nucelli exhibit similar shapes, despite differences in internal cellular topology and tissue growth patterns. Our results support the notion that the structural organization of a tissue is associated with its susceptibility to shape changes during evolutionary shifts in 3D cellular architecture.
Why it matches plant phenotyping methods3Dデジタルアトラスと神経ベースのトポロジー解析、形態計測を用いて植物器官の細胞構造と形状を定量化しており、表現型取得・解析手法が研究の中心である。
abstractHere, we provide a 3D digital atlas of Cardamine hirsuta ovule development at single cell resolution and compare it with an equivalent atlas of Arabidopsis thaliana.
Reproduction assets foundThe paper's topological analysis and statistical evaluation code is publicly available on GitHub (NADO repository), with explicit availability language. The paper-specific 3D digital ovule dataset (S-BIAD957) is deposited in BioStudies, but no matching allowed URL exists for it, so it cannot be listed as an actionable,Code · publicThe source code and the Dockerfiles can be obtained from the Github repository at https://github.com/fabian-roll/NADO .Open asset ↗https://github.com/fabian-roll/NADO · NADOlines:109-124Code / dataset availability confirmedCrossref · checked 14 Sept 2026
Key message Analyzing fine branch length characteristics in beech trees using single-tree QSMs derived from laser scanning reveals insights into drought-induced changes in vitality, which include branch shedding and reduced shoot growth. Abstract Climate change causes increasing temperatures and precipitation anomalies, which result in deteriorations of tree health and declines in ecosystem services of forests. It is therefore crucial to monitor tree vitality to preserve forests and their functions. However, methods describing tree vitality in situ are lacking reproducibility or are too laborious. Thus, we tested a laser-scanning based approach, assuming that an objective measurement of a tree’s outer shape should reveal changes according to tree vitality. QSMs of similarly sized beech trees from stands with varying degrees of drought damage were used. Absolute and relative fine branch lengths, their ratio to lower order branches’ lengths and their progressions over relative height were targeted to identify fine branch dieback and reduced growth. The absolute fine branch length was significantly lower for less vital beech trees, especially within the upper crown, leading to a less top-heavy vertical distribution of fine branches and a reduced fine-to-base order branch length ratio. Hence, height-dependent characteristics of fine branch lengths differed between vitalities. We conclude that using fine branch length characteristics derived from QSMs can be helpful in vitality assessments of beech trees. Still, uncertainties with regard to the plotwise assessment and problems with QSM quality are present.
Why it matches plant phenotyping methods3DレーザースキャンとQSMから枝長形質を抽出し、樹木活力を客観評価する手法が研究の中心であるため。
abstractThus, we tested a laser-scanning based approach, assuming that an objective measurement of a tree’s outer shape should reveal changes according to tree vitality.
Reproduction assets foundThe paper's Data availability statement explicitly deposits the datasets generated and analyzed (QSM-derived fine branch length measurements of beech trees) in the GRO.data repository with a public DOI, making it a paper-specific, publicly actionable phenotype dataset.Dataset · publicand the
Federal Ministry for the Environment, Nature Conservation, Nuclear
Safety and Consumer Protection (BMUV) through the Fachagen-
tur Nachwachsende Rohstoffe e. V. (FNR) (Reference Number
2220WK10C1).
Data availability The datasets generated and analyzed during the cur-
rent study are available in the GRO.data repository, https://doi.org/10.25625/ZCPNBN
Declarations
Conflict of interest The authors have no relevant financial or non-fi-
nancial interests to disclose.
Open Access This article is licensed under a Creative Commons Attri-
bution 4.0 International License, which permits use, sharing, adapta-
tion, distribution and reproduction in any medium or format, as long
as youOpen asset ↗GRO.data · 10.25625/ZCPNBNpdf-raw-page:12 lines:1-80Code / dataset availability confirmedEurope PMC · checked 14 Sept 2026
This research introduces an extensive dataset of unprocessed aerial RGB images and orthomosaics of Brassica oleracea crops, captured via a DJI Phantom 4. The dataset, publicly accessible, comprises 244 raw RGB images, acquired over six distinct dates in October and November of 2020 as well as 6 orthomosaics from an experimental farm located in Portici, Italy. The images, uniformly distributed across crop spaces, have undergone both manual and automatic annotations, to facilitate the detection, segmentation, and growth modelling of crops. Manual annotations were performed using bounding boxes via the Visual Geometry Group Image Annotator (VIA) and exported in the Common Objects in Context (COCO) segmentation format. The automated annotations were generated using a framework of Grounding DINO + Segment Anything Model (SAM) facilitated by YOLOv8x-seg pretrained weights obtained after training manually annotated images dated 8 October, 21 October, and 29 October 2020. The automated annotations were archived in Pascal Visual Object Classes (PASCAL VOC) format. Seven classes, designated as Row 1 through Row 7, have been identified for crop labelling. Additional attributes such as individual crop ID and the repetitiveness of individual crop specimens are delineated in the Comma Separated Values (CSV) version of the manual annotation. This dataset not only furnishes annotation information but also assists in the refinement of various machine learning models, thereby contributing significantly to the field of smart agriculture. The transparency and reproducibility of the processes are ensured by making the utilized codes accessible. This research marks a significant stride in leveraging technology for vision-based crop growth monitoring.
Why it matches plant phenotyping methods作物の生育モニタリングを目的としたRGB画像・オルソモザイクの公開データセットで、手動／自動アノテーションと成長モデリングを中心的に扱っているため、植物フェノタイピング手法・データセットに該当する。
abstractThis research introduces an extensive dataset of unprocessed aerial RGB images and orthomosaics of Brassica oleracea crops
Reproduction assets foundThe paper's own GobhiSet dataset (raw RGB images, orthomosaics, manual/automatic annotations, binary masks) and Python analysis scripts are publicly deposited on Mendeley Data with an explicit direct URL and DOI.Dataset · public0137
Longitude: 14; 20; 47.7701
Data post-processing and storage location: Department of Engineering, University of Campania ‘Luigi Vanvitelli,’ Aversa, Italy
Coordinates: 40.96846317808221, 14.208207168044456
Data accessibility
Repository name: Mendeley Data
Data identification number: 10.17632/dcjjcwc5dh.3
Direct URL to data: https://data.mendeley.com/datasets/dcjjcwc5dh/3
1.
Value of the Data
•
This dataset is a collection of multi-date aerial imagery of the Brassica oleracea var. Botrytis crop [ 1 ]. The images were acquired between the first and seventh weeks after sowing the cauliflower, with the intention of observing its growth over this period. The images were annotated with two typOpen asset ↗Mendeley Data · 10.17632/dcjjcwc5dh.3lines:50-75Code / dataset availability confirmedOpenAlex · Europe PMC · checked 15 Sept 2026
Introduction: is a promising cellulosic feedstock crop for bioenergy due to its high biomass yields. However, early growth phases of sorghum are sensitive to cold stress, limiting its planting in temperate environments. Cold adaptability is crucial for cultivating bioenergy and grain sorghum at higher latitudes and elevations, or for extending the growing season. Identifying genes and alleles that enhance biomass accumulation under early cold stress can lead to improved sorghum varieties through breeding or genetic engineering. Methods: We conducted image-based phenotyping on 369 accessions from the sorghum Bioenergy Association Panel (BAP) in a controlled environment with early cold treatment. The BAP includes diverse accessions with dense genotyping and varied racial, geographical, and phenotypic backgrounds. Daily, non-destructive imaging allowed temporal analysis of growth-related traits and water use efficiency (WUE). A genome-wide association study (GWAS) was performed to identify genomic intervals and genes associated with cold stress response. Results: The GWAS identified transient quantitative trait loci (QTL) strongly associated with growth-related traits, enabling an exploration of the genetic basis of cold stress response at different developmental stages. This analysis of daily growth traits, rather than endpoint traits, revealed early transient QTL predictive of final phenotypes. The study identified both known and novel candidate genes associated with growth-related traits and temporal responses to cold stress. Discussion: The identified QTL and candidate genes contribute to understanding the genetic mechanisms underlying sorghum's response to cold stress. These findings can inform breeding and genetic engineering strategies to develop sorghum varieties with improved biomass yields and resilience to cold, facilitating earlier planting, extended growing seasons, and cultivation at higher latitudes and elevations.
Why it matches plant phenotyping methods日次の非破壊画像計測を用いて成長関連形質とWUEを時系列で抽出し、早期表現型を解析しており、画像ベースの植物表現型取得が研究の主要な方法として記述されている。
abstractWe conducted image-based phenotyping on 369 accessions from the sorghum Bioenergy Association Panel (BAP) in a controlled environment with early cold treatment.
Reproduction assets foundThe paper's image-derived phenotypic measurements and analysis tables (accession list with phenotypic data, germination data, heritability, trait rankings, SNP-trait correlations, candidate genes) are stated to be included in the article's Supplementary Materials, publicly available at the Frontiers supplementary URL. Supplement · 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.2024.1278802/full#supplementary-material
Supplementary File S1
Table of Bioenergy Association Panel accessions used in this study (adapted from Brenton et al., 2016 ) with image-derived phenotypic data.
Supplementary File S2
Heatmap of a kinship matrix showing correlation analysis among the 369 BAP accessions. The coloOpen asset ↗lines:229-258Code / dataset availability confirmedOpenAlex · Europe PMC · checked 7 Sept 2026
High-throughput phenotyping brings new opportunities for detailed genebank accessions characterization based on image-processing techniques and data analysis using machine learning algorithms. Our work proposes to improve the characterization processes of bean and peanut accessions in the CIAT genebank through the identification of phenomic descriptors comparable to classical descriptors including methodology integration into the genebank workflow. To cope with these goals morphometrics and colorimetry traits of 14 bean and 16 forage peanut accessions were determined and compared to the classical International Board for Plant Genetic Resources (IBPGR) descriptors. Descriptors discriminating most accessions were identified using a random forest algorithm. The most-valuable classification descriptors for peanuts were 100-seed weight and days to flowering, and for beans, days to flowering and primary seed color. The combination of phenomic and classical descriptors increased the accuracy of the classification of Phaseolus and Arachis accessions. Functional diversity indices are recommended to genebank curators to evaluate phenotypic variability to identify accessions with unique traits or identify accessions that represent the greatest phenotypic variation of the species (functional agrobiodiversity collections). The artificial intelligence algorithms are capable of characterizing accessions which reduces costs generated by additional phenotyping. Even though deep analysis of data requires new skills, associating genetic, morphological and ecogeographic diversity is giving us an opportunity to establish unique functional agrobiodiversity collections with new potential traits.
Why it matches plant phenotyping methods画像処理・形態計測・色彩計測と機械学習を用いて遺伝資源の表現型記述子を開発・比較し、遺伝資源管理ワークフローへ統合することが中心であるため。
abstractHigh-throughput phenotyping brings new opportunities for detailed genebank accessions characterization based on image-processing techniques and data analysis using machine learning algorithms.
Reproduction assets foundThe paper's Data Availability statement explicitly points to a public GitHub repository containing the phenomics and traditional descriptor data underlying the study, which directly reproduces the paper's plant-phenotyping measurements. Figures and tables in the article are not treated as separate assets.Dataset · publicData Availability: The data underlying the results presented in the study are available from https://github.com/agrocompuepidemlab/Digital-descriptors-genebank The data of the phenomics and traditional descriptors of the evaluated accessions are associated to this one.Open asset ↗agrocompuepidemlab/Digital-descriptors-genebanklines:143-155Code / dataset availability confirmedEurope PMC · checked 7 Sept 2026
Cell plate formation during cytokinesis entails multiple stages occurring concurrently and requiring orchestrated vesicle delivery, membrane remodelling, and timely deposition of polysaccharides, such as callose. Understanding such a dynamic process requires dissection in time and space; this has been a major hurdle in studying cytokinesis. Using lattice light sheet microscopy (LLSM), we studied cell plate development in four dimensions, through the behavior of yellow fluorescent protein (YFP)-tagged cytokinesis-specific GTPase RABA2a vesicles. We monitored the entire duration of cell plate development, from its first emergence, with the aid of YFP-RABA2a, in both the presence and absence of cytokinetic callose. By developing a robust cytokinetic vesicle volume analysis pipeline, we identified distinct behavioral patterns, allowing the identification of three easily trackable cell plate developmental phases. Notably, the phase transition between phase I and phase II is striking, indicating a switch from membrane accumulation to the recycling of excess membrane material. We interrogated the role of callose using pharmacological inhibition with LLSM and electron microscopy. Loss of callose inhibited the phase transitions, establishing the critical role and timing of the polysaccharide deposition in cell plate expansion and maturation. This study exemplifies the power of combining LLSM with quantitative analysis to decode and untangle such a complex process.
Why it matches plant phenotyping methodsLLSMによる4次元画像取得と、細胞板の小胞体積を定量化する解析パイプラインの開発が研究の中心であり、植物細胞の形態・発達状態を抽出する方法として substantive です。
abstractUsing lattice light sheet microscopy (LLSM), we studied cell plate development in four dimensions
Reproduction assets foundThe paper deposits representative 4D lattice light sheet microscopy datasets (YFP–RABA2a cell plate imaging) used for its quantitative analysis on Zenodo, a paper-specific public asset. No author analysis code repository with explicit availability language is stated; the other URLs are method guidelines, not paper dataDataset · publicRepresentative datasets used in the study are available on Zenodo at https://doi.org/10.5281/zenodo.10515765 .Open asset ↗Zenodo · 10.5281/zenodo.10515765lines:85-93Code / dataset availability confirmedOpenAlex · Crossref · Europe PMC · checked 7 Sept 2026
The analysis of plant phenotype parameters is closely related to breeding, so plant phenotype research has strong practical significance. This paper used deep learning to classify Arabidopsis thaliana from the macro (plant) to the micro level (organelle). First, the multi-output model identifies Arabidopsis accession lines and regression to predict Arabidopsis’s 22-day growth status. The experimental results showed that the model had excellent performance in identifying Arabidopsis lines, and the model’s classification accuracy was 99.92%. The model also had good performance in predicting plant growth status, and the regression prediction of the model root mean square error (RMSE) was 1.536. Next, a new dataset was obtained by increasing the time interval of Arabidopsis images, and the model’s performance was verified at different time intervals. Finally, the model was applied to classify Arabidopsis organelles to verify the model’s generalizability. Research suggested that deep learning will broaden plant phenotype detection methods. Furthermore, this method will facilitate the design and development of a high-throughput information collection platform for plant phenotypes.
Why it matches plant phenotyping methods深層学習による植物画像からの系統識別・生育状態推定を開発し、時間間隔データで検証しており、植物表現型取得・推定手法が研究の中心である。
abstractThis paper used deep learning to classify Arabidopsis thaliana from the macro (plant) to the micro level (organelle).
Reproduction assets foundThe paper's plant-phenotyping analysis is built on the Arabidopsis thaliana time-series image dataset from Namin et al., which the authors explicitly state is publicly available for download. No author analysis code or trained model is shared.Dataset · publicData is publicly available at: http://phenocam.anu.edu.au/cloud/a_data/_webroot/published-data/2017/2017-Namin-et-al-DeepPheno.zip (accessed on 16 April 2024).Open asset ↗phenocam.anu.edu.au · 2017-Namin-et-al-DeepPheno.ziplines:226-239Code / dataset availability confirmedEurope PMC · checked 7 Sept 2026
PoplarTissueSegmentationGrowth / time-series analysisGrowth / development / phenology
Plant regeneration is an important dimension of plant propagation and a key step in the production of transgenic plants. However, regeneration capacity varies widely among genotypes and species, the molecular basis of which is largely unknown. Association mapping methods such as genome-wide association studies (GWAS) have long demonstrated abilities to help uncover the genetic basis of trait variation in plants; however, the performance of these methods depends on the accuracy and scale of phenotyping. To enable a large-scale GWAS of in planta callus and shoot regeneration in the model tree Populus, we developed a phenomics workflow involving semantic segmentation to quantify regenerating plant tissues over time. We found that the resulting statistics were of highly non-normal distributions, and thus employed transformations or permutations to avoid violating assumptions of linear models used in GWAS. We report over 200 statistically supported quantitative trait loci (QTLs), with genes encompassing or near to top QTLs including regulators of cell adhesion, stress signaling, and hormone signaling pathways, as well as other diverse functions. Our results encourage models of hormonal signaling during plant regeneration to consider keystone roles of stress-related signaling (e.g. involving jasmonates and salicylic acid), in addition to the auxin and cytokinin pathways commonly considered. The putative regulatory genes and biological processes we identified provide new insights into the biological complexity of plant regeneration, and may serve as new reagents for improving regeneration and transformation of recalcitrant genotypes and species.
Why it matches plant phenotyping methods再生組織を時系列で定量するセマンティックセグメンテーションを中核としたフェノミクス・ワークフローを開発し、大規模GWASに適用しているため、植物表現型取得法が中心的です。
abstractTo enable a large-scale GWAS of in planta callus and shoot regeneration in the model tree Populus, we developed a phenomics workflow involving semantic segmentation to quantify regenerating plant tissues over time.
Reproduction assets foundThe authors publicly release their GWAS analysis code: the MTMC-SKAT R package and the inplantaGWAS repository containing phenotype data parsing, association mapping, and downstream analysis code used in this study. The SNP and image datasets are stated to be publicly available but only via a citation (Nagle et al. 202Code · publicThe MTMC-SKAT R package is available on GitHub ( https://github.com/naglemi/mtmcskat ), as is other R code used for this study, including phenotype data parsing, association mapping, and downstream interrogation of results ( https://github.com/naglemi/inplantaGWAS ).Open asset ↗naglemi/inplantaGWASlines:318-364Code · publicThe MTMC-SKAT R package is available on GitHub ( https://github.com/naglemi/mtmcskat ), as is other R code used for this study, including phenotype data parsing, association mapping, and downstream interrogation of results ( https://github.com/naglemi/inplantaGWAS ).Open asset ↗naglemi/mtmcskatlines:318-364Code / dataset availability confirmedEurope PMC · checked 14 Sept 2026
Predicting the plant cell response in complex environmental conditions is a challenge in plant biology. Here we developed a resource allocation model of cellular and molecular scale for the leaf photosynthetic cell of Arabidopsis thaliana, based on the Resource Balance Analysis (RBA) constraint-based modeling framework. The RBA model contains the metabolic network and the major macromolecular processes involved in the plant cell growth and survival and localized in cellular compartments. We simulated the model for varying environmental conditions of temperature, irradiance, partial pressure of CO 2 and O 2 , and compared RBA predictions to known resource distributions and quantitative phenotypic traits such as the relative growth rate, the C:N ratio, and finally to the empirical characteristics of CO 2 fixation given by the well-established Farquhar model. In comparison to other standard constraint-based modeling methods like Flux Balance Analysis, the RBA model makes accurate quantitative predictions without the need for empirical constraints. Altogether, we show that RBA significantly improves the autonomous prediction of plant cell phenotypes in complex environmental conditions, and provides mechanistic links between the genotype and the phenotype of the plant cell.
Why it matches plant phenotyping methodsRBAモデルを用いて植物細胞の生長率やC:N比などの表現型を定量予測する計算手法の開発が中心であり、単なる生物学的実験ではない。
abstractHere we developed a resource allocation model of cellular and molecular scale for the leaf photosynthetic cell of Arabidopsis thaliana
Reproduction assets foundThe authors publicly release the paper-specific RBA leaf model (XML) and the PlantCellRBA simulation/analysis software on Forgemia, with explicit availability statements in the Data availability and Supplementary material sections. No plant image/sensor/phenotype measurement datasets from this paper are deposited; the Code · publicinterest, such as the seed, in order to define and forecast quality
determinants under diverse environmental conditions. These insights will also be valuable in
fine-tuning plant breeding programs.
Data availability
The RBA leaf model (encoded in XML files) and the PlantCellRBA software for running simulations
are available at https://forgemia.inra.fr/anne.goelzer/rba-plant-cell-model.
Acknowledgements
We thank Wolfram Liebermeister, Ana Bulovic, Sophie Colombié and Jean-Denis Faure for critical
comments on the manuscript and the Métaprogramme Digitbio of INRAE for funding.
Author Contributions
AG and VF conceived the study. AG developed, implemented and simulated the different models
(RBA, Open asset ↗forgemia.inra.fr/anne.goelzer/rba-plant-cell-modelpdf-layout-page:28 lines:1-50Supplement · publicSupplementary Table 1) led to changes in growth rate
greater than 1% (Fig.Open asset ↗pdf-raw-page:20 lines:1-49Code / dataset availability confirmedEurope PMC · checked 7 Sept 2026
TissueMorphology / geometry measurementSegmentationGrowth / development / phenology
This methodological study describes the adaptation of a new method in digital wood anatomy, pixel-contrast densitometry, for angiosperm species. The new method was tested on eight species of shrubs and small trees in Southern Siberia, whose wood structure varies from ring-porous to diffuse-porous, with different spatial organizations of vessels. A two-step transformation of wood cross-section photographs by smoothing and Otsu's classification algorithm was proposed to separate images into cell wall areas and empty spaces within (lumen) and between cells. Good synchronicity between measurements within the ring allowed us to create profiles of wood porosity (proportion of empty spaces) describing the growth ring structure and capturing inter-annual differences between rings. For longer-lived species, 14-32-year series from at least ten specimens were measured. Their analysis revealed that maximum (for all wood types), mean, and minimum porosity (for diffuse-porous wood) in the ring have common external signals, mostly independent of ring width, i.e., they can be used as ecological indicators. Further research directions include a comparison of this method with other approaches in densitometry, clarification of sample processing, and the extraction of ecologically meaningful data from wood structures.
Why it matches plant phenotyping methods樹木断面画像から木材孔隙率・年輪構造を抽出する画像解析法を開発・検証しており、植物形質の取得方法が研究の中心です。
abstractThis methodological study describes the adaptation of a new method in digital wood anatomy, pixel-contrast densitometry, for angiosperm species.
Reproduction assets foundThe paper's authors publicly released the Python software implementing their PiC densitometry method on GitHub (OpenPiCDens), and the MDPI supplement contains paper-specific wood cross-section photographs, binary images, and porosity profiles. The underlying raw measurement data are only available on request.Code · publicThe source code of the software created for this study is available at
https://github.com/Timofey00/OpenPiCDens (accessed on 25 February 2024).Open asset ↗Timofey00/OpenPiCDenspdf-page:11 lines:1-58Code / dataset availability confirmedEurope PMC · checked 7 Sept 2026
Oilseed rape is an important oilseed crop planted worldwide. Maturity classification plays a crucial role in enhancing yield and expediting breeding research. Conventional methods of maturity classification are laborious and destructive in nature. In this study, a nondestructive classification model was established on the basis of hyperspectral imaging combined with machine learning algorithms. Initially, hyperspectral images were captured for 3 distinct ripeness stages of rapeseed, and raw spectral data were extracted from the hyperspectral images. The raw spectral data underwent preprocessing using 5 pretreatment methods, namely, Savitzky-Golay, first derivative, second derivative (D2nd), standard normal variate, and detrend, as well as various combinations of these methods. Subsequently, the feature wavelengths were extracted from the processed spectra using competitive adaptive reweighted sampling, successive projection algorithm (SPA), iterative spatial shrinkage of interval variables (IVISSA), and their combination algorithms, respectively. The classification models were constructed using the following algorithms: extreme learning machine, k -nearest neighbor, random forest, partial least-squares discriminant analysis, and support vector machine (SVM) algorithms, applied separately to the full wavelength and the feature wavelengths. A comparative analysis was conducted to evaluate the performance of diverse preprocessing methods, feature wavelength selection algorithms, and classification models, and the results showed that the model based on preprocessing-feature wavelength selection-machine learning could effectively predict the maturity of rapeseed. The D2nd-IVISSA-SPA-SVM model exhibited the highest modeling performance, attaining an accuracy rate of 97.86%. The findings suggest that rapeseed maturity can be rapidly and nondestructively ascertained through hyperspectral imaging.
Why it matches plant phenotyping methodsハイパースペクトル画像と機械学習により、ナタネの成熟状態を非破壊的に分類する取得・解析手法を開発し、前処理、波長選択、モデル性能を比較評価しているため、フェノタイピング手法が中心である。
abstracta nondestructive classification model was established on the basis of hyperspectral imaging combined with machine learning algorithms
Reproduction assets foundThe authors state that the primary script and dataset (spectral reflectance data and classification/feature-wavelength-extraction code) used in this rapeseed hyperspectral maturity classification study are publicly accessible via the provided link, which matches an allowed URL.Dataset · publicAll authors confirm that all raw experimental data are available upon request. The primary script and dataset used during the experimental procedure are accessible via the following link: http://plantphenomics.hzau.edu.cn/usercrop/Rice/download .Open asset ↗lines:421-450Code / dataset availability confirmedEurope PMC · checked 14 Sept 2026
Image analysis is widely used in plant biology to determine growth rates and other phenotypic characters, with segmentation into foreground and background being a primary challenge. Statistical clustering and learning approaches can reduce the need for user input into this process, though these are computationally demanding, can generalise poorly and are not intuitive to end users. As such, simple strategies that rely on the definition of a range of target colors are still frequently adopted. These are limited by the geometries in color space that are implicit to their definition; i.e. thresholds define cuboid volumes and selected colors with a radius define spheroid volumes. A more comprehensive specification of target color is a hull, in color space, enclosing the set of colors in the image foreground. We developed AlGrow, a software tool that allows users to easily define hulls by clicking on the source image or a three-dimensional projection of its colors. We implemented convex hulls and then alpha-hulls, i.e. a limit applied to hull edge length, to support concave surfaces and disjoint color volumes. AlGrow also provides automated annotation by detecting internal circular markers, such as pot margins, and applies relative indexes to support movement. Analysis of publicly available Arabidopsis image series and metadata demonstrated effective automated annotation and mean Dice coefficients of >0.95 following training on only the first and last images in each series. AlGrow provides both graphical and command line interfaces and is released free and open-source with compiled binaries for the major operating systems.
Why it matches plant phenotyping methods植物画像から面積・成長などの表現型を抽出する画像解析ソフトウェアを開発し、Arabidopsis画像系列で性能検証しているため、方法が中心的である。
abstractWe developed AlGrow, a software tool that allows users to easily define hulls by clicking on the source image or a three-dimensional projection of its colors.
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 · public163 obtained from https://www.plant-phenotyping.org/datasets-home, as these are also able to demonstrateOpen asset ↗pdf-page:4 lines:1-55Code / dataset availability confirmedEurope PMC · checked 7 Sept 2026
Published6 Mar 2024TAG. Theoretical and applied genetics. Theoretische und angewandte GenetikCited by 10 · OpenAlex ↗
Predictive breeding approaches, like phenomic or genomic selection, have the potential to increase the selection gain for potato breeding programs which are characterized by very large numbers of entries in early stages and the availability of very few tubers per entry in these stages. The objectives of this study were to (i) explore the capabilities of phenomic prediction based on drone-derived multispectral reflectance data in potato breeding by testing different prediction scenarios on a diverse panel of tetraploid potato material from all market segments and considering a broad range of traits, (ii) compare the performance of phenomic and genomic predictions, and (iii) assess the predictive power of mixed relationship matrices utilizing weighted SNP array and multispectral reflectance data. Predictive abilities of phenomic prediction scenarios varied greatly within a range of - 0.15 and 0.88 and were strongly dependent on the environment, predicted trait, and considered prediction scenario. We observed high predictive abilities with phenomic prediction for yield (0.45), maturity (0.88), foliage development (0.73), and emergence (0.73), while all other traits achieved higher predictive ability with genomic compared to phenomic prediction. When a mixed relationship matrix was used for prediction, higher predictive abilities were observed for 20 out of 22 traits, showcasing that phenomic and genomic data contained complementary information. We see the main application of phenomic selection in potato breeding programs to allow for the use of the principle of predictive breeding in the pot seedling or single hill stage where genotyping is not recommended due to high costs.
Why it matches plant phenotyping methodsドローン由来マルチスペクトルデータを用いたフェノミック予測をジャガイモ育種に適用し、複数の予測シナリオやゲノム予測との性能比較を行っており、植物形質推定法が中心である。
abstractexplore the capabilities of phenomic prediction based on drone-derived multispectral reflectance data in potato breeding
Reproduction assets foundThe paper's phenotypic and multispectral datasets are not publicly available (company secret, available upon request in encoded form), but the authors' R analysis scripts are explicitly stated to be publicly available on GitHub.Code · publicCode availability
R scripts for data analysis are available on GitHub: https://github.com/AlessioMR/ps_in_potato_breeding .Open asset ↗AlessioMR/ps_in_potato_breedinglines:179-254Code / dataset availability confirmedOpenAlex · Europe PMC · checked 7 Sept 2026
Introduction: ) serves as a vital staple crop that feeds over half the world's population. Optimizing rice breeding for increasing grain yield is critical for global food security. Heading-date-related or Flowering-time-related traits, is a key factor determining yield potential. However, traditional manual phenotyping methods for these traits are time-consuming and labor-intensive. Method: Here we show that aerial imagery from unmanned aerial vehicles (UAVs), when combined with deep learning-based panicle detection, enables high-throughput phenotyping of heading-date-related traits. We systematically evaluated various state-of-the-art object detectors on rice panicle counting and identified YOLOv8-X as the optimal detector. Results: Applying YOLOv8-X to UAV time-series images of 294 rice recombinant inbred lines (RILs) allowed accurate quantification of six heading-date-related traits. Utilizing these phenotypes, we identified quantitative trait loci (QTL), including verified loci and novel loci, associated with heading date. Discussion: Our optimized UAV phenotyping and computer vision pipeline may facilitate scalable molecular identification of heading-date-related genes and guide enhancements in rice yield and adaptation.
Why it matches plant phenotyping methodsUAV画像と深層学習によるイネ穂検出・計数を中核とし、出穂関連形質を高スループットに定量するフェノタイピング手法およびワークフローを評価・適用している。
abstractaerial imagery from unmanned aerial vehicles (UAVs), when combined with deep learning-based panicle detection, enables high-throughput phenotyping of heading-date-related traits
Reproduction assets foundThe article states that all relevant code for the UAV phenotyping and panicle detection pipeline is publicly available in the authors' GitHub repository r1cheu/phenocv. Other URLs (Ultralytics, COCO, WinQTLCart) are generic third-party tools, not paper-specific assets.Code · publicAll relevant code can be accessed at https://github.com/r1cheu/phenocv .Open asset ↗r1cheu/phenocvlines:317-328Code / dataset availability confirmedEurope PMC · checked 15 Sept 2026
Cuscuta spp., commonly known as dodders, are rootless and leafless stem parasitic plants. Upon germination, Cuscuta starts rotating immediately in a counterclockwise direction (circumnutation) to locate a host plant, creating a seamless vascular connection to steal water and nutrients from its host. In this study, our aim was to elucidate the dynamics of the coiling patterns of Cuscuta , which is an essential step for successful parasitism. Using time-lapse photography, we recorded the circumnutation and coiling movements of C. campestris at different inoculation times on non- living hosts. Subsequent image analyses were facilitated through an in-house Python-based image processing pipeline to detect coiling locations, angles, initiation and completion times, and duration of coiling stages in between. The study revealed that the coiling efficacy of C. campestris varied with the inoculation time of day, showing higher success and fastinitiation in morning than in evening. These observations suggest that Cuscuta , despite lacking leaves and a developed chloroplast, can discern photoperiod changes, significantly determining its parasitic efficiency. The automated image analysis results confirmed the reliability of our Python pipeline by aligning closely with manual annotations. This study provides significant insights into the parasitic strategies of C. campestris and demonstrates the potential of integrating computational image analysis in plant biology for exploring complex plant behaviors. Furthermore, this method provides an efficient tool for investigating plant movement dynamics, laying the foundation for future studies on mitigating the economic impacts of parasitic plants.
Why it matches plant phenotyping methodsCuscutaのコイリングや運動動態を画像から自動抽出するPythonパイプラインの開発・信頼性検証が研究の中心であり、植物状態・形態動態のフェノタイピング手法に該当する。
abstractSubsequent image analyses were facilitated through an in-house Python-based image processing pipeline to detect coiling locations, angles, initiation and completion times, and duration of coiling stages in between.
Reproduction assets foundThe paper's data availability statement points to two public, paper-specific assets: the authors' Python/Jupyter notebook phenotyping pipeline on GitHub and the time-lapse video datasets used in the study hosted as a YouTube playlist. Both are directly tied to this paper's Cuscuta coiling phenotyping measurements and分析Dataset · publicNT
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All authors declare that they have no conflicts of interest.
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DATA AVAILABILITY STATEMENT
405
The developed Python-based pipeline is available as a collection of Jupyter notebooks at
406
https://github.com/ejamezquita/cuscuta/ . The datasets used and/or analyzed during the current
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study are available here:
408
https://youtube.com/playlist?list=PLZkYcVyQr2u4tT0yoZAkrMqzQxRIDvxru&feature=shared
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ORCID
411
Max Bentelspacher https://orcid.org/0009-0004-7357-917X
412
Erik J. Amézquita https://orcid.org/0000-0002-9837-0397
413
Supral Adhikari https://orcid.org/0000-0002-9914-2986
414
Jaime Barros https://orcid.org/0000-0002-9545-312X
415
So-Yon Park https://orcid.org/0000-Open asset ↗PLZkYcVyQr2u4tT0yoZAkrMqzQxRIDvxrupdf-raw-page:14 lines:1-60Code / dataset availability confirmedEurope PMC · checked 14 Sept 2026
MaizeSunflowerField / plotLaboratory / benchtopStem / branchPhysiological trait estimationGrowth / development / phenologyWater status / transpiration
There is currently a need for inexpensive, continuous, non-destructive water potential measurements at high temporal resolution ( Helianthus annuus ) (petioles and stems) and a monocotyledon ( Zea mays ) species (stems) for 1 week during dehydration and re-watering treatments under laboratory conditions. We also demonstrated the ability of the device to record branch and trunk diameter variation of a woody dicotyledon ( Rhus typhina ) in the field. Under laboratory conditions, we compared our device (hereafter 'contact' dendrometer) with modified versions of another open-source dendrometer (the 'optical' dendrometer). Overall, contact and optical dendrometers were well aligned with one another, with Pearson correlation coefficients ranging from 0.77 to 0.97. Both dendrometer devices were well aligned with direct measurements of xylem water potential, with calibration curves exhibiting significant non-linearity, especially at water potentials near the point of incipient plasmolysis, with pseudo R 2 values (Efron) ranging from 0.89 to 0.99. Overall, both dendrometers were comparable and provided sufficient resolution to detect subtle differences in stem water potential (ca. 50 kPa) resulting from light-induced changes in transpiration, vapour pressure deficit and drying/wetting soils. All hardware designs, alternative configurations, software and build instructions for the contact dendrometers are provided.
Why it matches plant phenotyping methods安価なオープンソース樹幹径計を開発し、水ポテンシャルと茎径成長を高時間・空間分解能で測定する手法を比較検証しており、植物表現型取得が研究の中心である。
titleDevelopment and application of an inexpensive open-source dendrometer for detecting xylem water potential and radial stem growth at high spatial and temporal resolution.
Reproduction assets foundThe paper's Data Availability statement explicitly deposits all sensor software, 3D prints, photos, and schematics in a public GitHub repository, and the dendrometer measurement data are provided as a CSV in the supplementary information (no allowed URL for the SI itself). The GitHub repository is a paper-specific,公开,可Code · publicins, CO 80526, USA.
Data Availability
All data used in this study are available for download from the supplemental information as a CSV file (sup_data_all_dendro.csv). All software needed for operating sensors, 3D prints, photos, and schematics have been included in the SI materials, and can also be freely accessed via github ( https://github.com/sean-gl/dendrometer_water_potential_device ).
Sources of Funding
J.J.S. was supported by an NSF Postdoctoral Research Fellowship in Biology, Grant No. IOS-1907338.
Contributions by the Authors
All authors contributed meaningfully to the manuscript. S.M.G., J.J.S., B.A., S.K.P. and J.M. designed the experiment and collected the data. S.M.G. wrote theOpen asset ↗https://github.com/sean-gl/dendrometer_water_potential_devicelines:224-283Code / dataset availability confirmedEurope PMC · checked 15 Sept 2026
Why it matches plant phenotyping methodsトウモロコシ種子の発芽・活力をRGB画像で推定するデータセットであり、植物表現型の画像取得・解析基盤が中心と判断できる。
titleAn RGB image dataset for seed germination prediction and vigor detection - maize.
Reproduction assets foundThe authors publicly deposited the paper's maize seed germination RGB image dataset (19,800 annotated images, PASCAL VOC XML labels) on Kaggle and IEEE DataPort, with explicit URLs in the text. No analysis code was deposited.Dataset · public. germinating:7042; 3. germinated:1936; 4. primary root:5087; 5. secondary root:17343. For easier download, we uploaded the 120-folder dataset separately, which was generated each hour. It could be accessed on the Kaggle public dataset titled Seed Vigor Detection RGB Image. The dataset is available at the following two address: https://www.kaggle.com/datasets/chengchengchen/seed-vigor-detection-rgb-image
http://ieee-dataport.org/documents/rgb-image-dataset-seed-germination-prediction-and-seed-vigor
3.5.
Seed viability object detection experiments
In order to verify the validity of the dataset, we perform experiments on the seeds vitality object detection using the two-stage object detection Open asset ↗Kaggle · Seed Vigor Detection RGB Imagelines:59-108Dataset · publict:17343. For easier download, we uploaded the 120-folder dataset separately, which was generated each hour. It could be accessed on the Kaggle public dataset titled Seed Vigor Detection RGB Image. The dataset is available at the following two address: https://www.kaggle.com/datasets/chengchengchen/seed-vigor-detection-rgb-image
http://ieee-dataport.org/documents/rgb-image-dataset-seed-germination-prediction-and-seed-vigor
3.5.
Seed viability object detection experiments
In order to verify the validity of the dataset, we perform experiments on the seeds vitality object detection using the two-stage object detection model Faster RCNN ( Girshick, 2015 ), the one-stage model SSD ( Liu et al., 20Open asset ↗rgb-image-dataset-seed-germination-prediction-and-seed-vigorlines:59-108Code / dataset availability confirmedCrossref · checked 15 Sept 2026
Rice (Oryza sativa L.) is a staple cereal in the diet of more than half of the world’s population. Within the European Union, Spain is a leader in rice production due to its climate and tradition, accounting for 26% of total EU production in 2020. The Valencian rice area covers around 15,000 hectares and is strongly influenced by biotic and abiotic factors. An important biotic factor affecting rice production is weeds, which compete with rice for sunlight, water and nutrients. The dominant weed in Spain is Echinochloa spp., although wild rice is becoming increasingly important. Rice cultivation in Valencia takes place in the area of L’Albufera de Valencia, which is a natural park, i.e., a special protection area. In this natural area, the use of phytosanitary products is limited, so it is necessary to use the minimum amount possible. Therefore, the objective of this work is to evaluate the possibility of using remote sensing effectively to determine the effectiveness of the application of the herbicide cyhalofop-butyl by drone for the control of Echinochloa spp. in rice crops in Valencia. The results will be compared with those obtained by using sterilisation machines (electric backpack sprayers) to apply the herbicide. To evaluate the effectiveness of the application, the reflectance obtained by the satellite sensors in the red and near infrared (NIR) wavelengths, as well as the normalised difference vegetation index (NDVI), were used. The remote sensing results were analysed and complemented by the number of rice plants and weeds per area, plant dry weight, leaf area, BBCH phenological state, SPAD index values, chlorophyll content and relative growth rate. Remote sensing is validated as an effective tool for determining the efficacy of an herbicide in controlling weeds applied by both the drone and the electric backpack sprayer. The weeds slowed down their development after the treatment. Depending on the phenological state of the crop and the active ingredient of the herbicide, these results are applicable to other areas with different climatic and environmental conditions.
Why it matches plant phenotyping methodsドローン・衛星リモートセンシングとNDVI等を用いて除草剤効果を評価し、その手法を有効な評価ツールとして検証しているため、植物状態の取得・評価方法が中心である。
abstractTherefore, the objective of this work is to evaluate the possibility of using remote sensing effectively to determine the effectiveness of the application of the herbicide cyhalofop-butyl by drone for the control of Echinochloa spp. in rice crops in Valencia.
Reproduction assets foundThe article states 'Data are contained within the article' and provides no author code, model, or dataset deposit. The only paper-specific public asset is the MDPI supplementary file, which contains Figure S1 showing the control subplots affected by Echinochloa spp. (field imagery related to the phenotyping experiment,Supplement · publicdual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.
Supplementary Materials
The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/s24030804/s1 . Figure S1. Control subplots affected by Echinochloa spp. (Own elaboration).
Click here for additional data file.
Author ContributionsOpen asset ↗lines:90-101Code / 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 confirmedEurope PMC · checked 14 Sept 2026
Published24 Jan 2024The Plant journal : for cell and molecular biologyCited by 8 · OpenAlex ↗
The model moss species Physcomitrium patens has long been used for studying divergence of land plants spanning from bryophytes to angiosperms. In addition to its phylogenetic relationships, the limited number of differential tissues, and comparable morphology to the earliest embryophytes provide a system to represent basic plant architecture. Based on plant-fungal interactions today, it is hypothesized these kingdoms have a long-standing relationship, predating plant terrestrialization. Mortierellaceae have origins diverging from other land fungi paralleling bryophyte divergence, are related to arbuscular mycorrhizal fungi but are free-living, observed to interact with plants, and can be found in moss microbiomes globally. Due to their parallel origins, we assess here how two Mortierellaceae species, Linnemannia elongata and Benniella erionia, interact with P. patens in coculture. We also assess how Mollicute-related or Burkholderia-related endobacterial symbionts (MRE or BRE) of these fungi impact plant response. Coculture interactions are investigated through high-throughput phenomics, microscopy, RNA-sequencing, differential expression profiling, gene ontology enrichment, and comparisons among 99 other P. patens transcriptomic studies. Here we present new high-throughput approaches for measuring P. patens growth, identify novel expression of over 800 genes that are not expressed on traditional agar media, identify subtle interactions between P. patens and Mortierellaceae, and observe changes to plant-fungal interactions dependent on whether MRE or BRE are present. Our study provides insights into how plants and fungal partners may have interacted based on their communications observed today as well as identifying L. elongata and B. erionia as modern fungal endophytes with P. patens.
Why it matches plant phenotyping methodsP. patensの成長を測定する新しい高スループット手法とフェノミクス解析を提示しており、植物表現型取得が研究の実質的な方法的貢献である。
abstractCoculture interactions are investigated through high-throughput phenomics, microscopy, RNA-sequencing, differential expression profiling, gene ontology enrichment, and comparisons among 99 other P. patens transcriptomic studies.
Reproduction assets foundThe paper deposits authors' supplementary analysis code (DESeq2 differential expression and comparison scripts) on Zenodo and supplementary data on Dryad, both with explicit availability statements and public URLs. The paper also describes Raspberry Pi/ArduCam/PlantCV imaging hardware and software for phenotyping, but Dataset · publices. In particular, the University resides on Land DATA AVAILABILITY STATEMENT
ceded in the 1819 Treaty of Saginaw. We recognize, support, and
advocate for the sovereignty of Michigan’s 12 federally recognized The following Supplementary Data have been deposited at
Indian nations, for historic Indigenous communities in Michigan, https://datadryad.org/stash/share/2g3gZefPksJaPGlLpc8d7g
Ó 2024 The Authors.
The Plant Journal published by Society for Experimental Biology and John Wiley & Sons Ltd.,
The Plant Journal, (2024), doi: 10.1111/tpj.16605Open asset ↗datadryad · 2g3gZefPksJaPGlLpc8d7gpdf-layout-page:16 lines:58-78Code / dataset availability confirmedEurope PMC · checked 7 Sept 2026
Laboratory automation can boost precision and reproducibility of science workflows. However, current laboratory automation systems are difficult to modify for custom applications. Automating new experiment workflows therefore requires development of one-off research platforms, a process which requires significant time, resources, and experience. In this work, we investigate systems to lower the threshold to automation for plant biologists. Our approach establishes a direct connection with a generic motion platform to support experiment development and execution from a computational notebook environment. Specifically, we investigate the use of the open-source tool-changing motion platform Jubilee controlled using Jupyter notebooks. We present the Duckbot, a machine customized for automating laboratory research workflows with duckweed, a common multicellular plant. The Duckbot comprises (1) a set of end-effectors relevant for plant biology, (2) software modules which provide flexible control of these tools, and (3) computational notebooks which make use of these tools to automate duckweed experiments. We demonstrate the Duckbot's functionality by automating a particular laboratory research workflow, namely, duckweed growth assays. The Duckbot supports setting up sample plates with duckweed and growth media, gathering image data, and conducting relevant data analysis. We discuss the opportunities and limitations for developing custom laboratory automation with this platform and provide instructions on usage and customization.
Why it matches plant phenotyping methods植物研究向けの自動化プラットフォームを開発し、画像データ取得と解析を含むduckweed成長アッセイを自動化しており、表現型取得ワークフローが中心的です。
abstractWe present the Duckbot, a machine customized for automating laboratory research workflows with duckweed, a common multicellular plant.
Reproduction assets foundThe authors explicitly state that all design files, build instructions, example data (including the duckweed growth assay images) and analysis scripts are publicly available in their GitHub repositories, which directly reproduce this paper's duckweed phenotyping measurements and computational analysis.Code · publicData Availability: All design files, build instructions, example data and analysis scripts are available at GitHub: https://github.com/machineagency/duckbot and https://github.com/machineagency/science_jubilee/ .Open asset ↗machineagency/duckbotlines:150-163Code · publicData Availability: All design files, build instructions, example data and analysis scripts are available at GitHub: https://github.com/machineagency/duckbot and https://github.com/machineagency/science_jubilee/ .Open asset ↗machineagency/science_jubileelines:150-163Code / dataset availability confirmedCrossref · Europe PMC · checked 14 Sept 2026
Repeated measurements of crop height to observe plant growth dynamics in real field conditions represent a challenging task. Although there are ways to collect data using sensors on UAV systems, proper data processing and analysis are the key to reliable results. As there is need for specialized software solutions for agricultural research and breeding purposes, we present here a fast algorithm ALFA for the processing of UAV LiDAR derived point-clouds to extract the information on crop height at many individual cereal field-plots at multiple time points. Seven scanning flights were performed over 3 blocks of experimental barley field plots between April and June 2021. Resulting point-clouds were processed by the new algorithm ALFA. The software converts point-cloud data into a digital image and extracts the traits of interest–the median crop height at individual field plots. The entire analysis of 144 field plots of dimension 80 x 33 meters measured at 7 time points (approx. 100 million LiDAR points) takes about 3 minutes at a standard PC. The Root Mean Square Deviation of the software-computed crop height from the manual measurement is 5.7 cm. Logistic growth model is fitted to the measured data by means of nonlinear regression. Three different ways of crop-height data visualization are provided by the software to enable further analysis of the variability in growth parameters. We show that the presented software solution is a fast and reliable tool for automatic extraction of plant height from LiDAR images of individual field-plots. We offer this tool freely to the scientific community for non-commercial use.
Why it matches plant phenotyping methodsUAV LiDAR点群から圃場区画ごとの作物高を自動抽出するソフトウェアと処理アルゴリズムを開発・検証しており、植物形質取得が研究の中心である。
abstractwe present here a fast algorithm ALFA for the processing of UAV LiDAR derived point-clouds to extract the information on crop height at many individual cereal field-plots at multiple time points.
Laboratory / benchtopSeed / grainVisualization / data managementGrowth / development / phenology
Seed germination is a crucial phase of plant responses in early life to current and future environmental conditions. However, germination data are still scarce or disaggregated for many plant lineages and regions, including global biodiversity hotspots such as the Mediterranean Basin. We present MedGermDB, the first germination database for characteristic species of Mediterranean habitats, as defined by the EUNIS classification. We also present a systematic approach to build germination databases using automatic and semi‐automatic data extraction from the literature. MedGermDB contains germination data for 4680 laboratory tests performed with 236 angiosperm species from 43 families, extracted from 125 literature sources (2837 sources screened). Each test is associated to a seed lot (i.e., a seed collection of a plant species obtained from a specific location at a specific time) and its metadata, recording geographical information and experimental conditions (storage, dormancy‐breaking treatments, incubation temperature, and photoperiod). MedGermDB is available as a csv file, and through a web app: https://dianamariacruztejada.shinyapps.io/medgermdb/. MedGermDB can be used to explore eco‐evolutionary questions and provides a backbone data set for informing effective seed‐based conservation and ecological restoration activities targeting EUNIS habitats. Our methodological approach to data extraction can be extended to other study systems, contributing to global efforts to mobilize germination data.
Why it matches plant phenotyping methods植物の発芽状態に関する大規模データセットを構築し、文献からの自動・半自動データ抽出手法とWebアプリを提示しており、単なる生物学的実験のルーチン測定ではない。
abstractWe present MedGermDB, the first germination database for characteristic species of Mediterranean habitats
Reproduction assets foundThe paper's MedGermDB germination database (supplementary CSVs) and the code/workflow to join database files are publicly available in the authors' GitHub repository, with a Zenodo version of record and a Shiny app for visualization.Code · publicty and Research (MUR) as part of the PON 2014–
2020 “Research and Innovation” resources—Green/Innovation
Action—DM MUR 1061/2022, Number: DOT13GFICX-
2.
CONFLICT OF INTEREST STATEMENT
None.
DATA AVAILABILITY STATEMENT
All data are available as supplementary materials. The data and codes
to join the database files are stored at https://github.com/DianaCruzT
ejada/
MedGe
rmDB and visualized with the shiny app at https://diana
mariacruztejada.shinyapps.io/medgermdb/. A version of record of the
repository can be found at https://
doi.
org/
10.
5281/
zenodo.
10915154.
All people interested in contributing to the growth of this germination
database are encouraged to contact the correspOpen asset ↗MedGermDBpdf-raw-page:6 lines:1-151Code / dataset availability confirmedEurope PMC · checked 13 Sept 2026
Crop yield potential in breeding trials can be captured using unmanned aerial vehicle (UAV) based multispectral imagery. Several digital traits or phenotypes such as vegetation indices can represent canopy crop vigor and overall plant health, which can be used to evaluate differences in performance across varieties in crop breeding programs. This dataset contains agronomic data for named cultivars and breeding lines of spring-sown dry pea and chickpea, and over 275 multispectral images from advanced and preliminary breeding trials. The breeding trials were located at three locations in the "Palouse" region of Eastern Washington and Northern Idaho of the United States across 2017, 2018 and 2019 cropping seasons. The multispectral images were captured using a UAV integrated with a 5-band multispectral camera at multiple time points from early vegetative growth through pod development stages during each cropping season. This dataset details seed yield information from trials of dry peas and chickpea that were obtained from each location, as well as additional agronomic and phenological data recorded at one location (mostly Pullman, WA) for each cropping season. The dataset also includes 20-78 megabytes (MB) Tagged Image Format (TIF) uncalibrated stitched orthomosaic images generated from the photogrammetric software. The images can be processed using any convenient image processing algorithm to obtain vegetation indices and other useful information.
Why it matches plant phenotyping methodsUAVマルチスペクトル画像と抽出可能なデジタル形質を含む、育種利用可能な植物表現型データセットとして構築・公開されているため。
abstractThis dataset contains agronomic data for named cultivars and breeding lines of spring-sown dry pea and chickpea, and over 275 multispectral images from advanced and preliminary breeding trials.
Reproduction assets foundThis Data in Brief article describes its own pulse crop phenotyping dataset (agronomic trait tables and 275 UAV multispectral orthomosaic images), publicly deposited on Zenodo with an explicit DOI listed in the Specification Table under Data accessibility. This is a paper-specific, public, directly actionable dataset.Dataset · publicData accessibility
Repository name: Zenodo
Data identification number: https://doi.org/10.5281/zenodo.8280431 .Open asset ↗Zenodo · 10.5281/zenodo.8280431lines:1-49Code / dataset availability confirmedEurope PMC · checked 14 Sept 2026
Published26 Dec 2023Integrated environmental assessment and managementCited by 3 · OpenAlex ↗
Why it matches plant phenotyping methods作物の発育段階・生育時期という植物状態を対象に、調和化データベースを開発し独立データで評価しているため、規制用途であっても植物フェノタイピングのデータ基盤・検証研究として中心的です。
abstractwe describe the development of a harmonized, pan-European, CropLife Europe Crop Development Database (C2D2)
Reproduction assets foundThe paper's own harmonized crop development database (C2D2), containing the non-disclosive raw, intermediate, and final data needed to reproduce the reported results, is openly deposited on Zenodo with an Open Data Badge.Dataset · publicThis article has earned an Open Data Badge for
making publicly available the digitally shareable data
necessary to reproduce the reported results. The data are
available at https://zenodo.org/records/8393135. Learn
more about the Open Practices badges from the Center
for Open Science: https://osf.io/tvyxz/wiki.DATA AVAILABILITY STATEMENTSTATEMENT
The raw proprietary efficacy trial data supplied by the CLE
member companies for the project are not available to en-
sure compliance with data protection (to protect the identity
of growerOpen asset ↗zenodo · 8393135pdf-raw-page:14 lines:96-150Code / dataset availability confirmedbioRxiv · checked 15 Sept 2026
FlowerClassificationGrowth / time-series analysisGrowth / development / phenology
O_LIPhenology -- the timing of recurring life history events--is strongly linked to climate. Shifts in phenology have important implications for trophic interactions, ecosystem functioning and community ecology. However, data on plant phenology can be time consuming to collect and current records are biased across space and taxonomy. C_LIO_LIHere, we explore the performance of Convolutional Neural Networks (CNN) for classifying flowering phenology on a very large and taxonomically diverse dataset of citizen science images. We analyse >1.8 million iNaturalist records for plants listed in the National Botanical Gardens within South Africa, a country famed for its floristic diversity ([~]21,000 species) but poorly represented in phenological databases. C_LIO_LIWe were able to correctly classify images with >90% accuracy. Using metadata associated with each image, we then reconstructed the timing of peak flower production and length of the flowering season for the 6,986 species with >5 iNaturalist records. C_LIO_LIOur analysis illustrates how machine learning tools can leverage the vast wealth of citizen science biodiversity data to describe large-scale phenological dynamics. We suggest such approaches may be particularly valuable where data on plant phenology is currently lacking. C_LI
Why it matches plant phenotyping methods植物画像にCNNを適用して開花フェノロジーを分類し、開花時期と開花期間を推定する手法が研究の中心であるため、植物フェノタイピング手法研究に該当します。
abstractHere, we explore the performance of Convolutional Neural Networks (CNN) for classifying flowering phenology on a very large and taxonomically diverse dataset of citizen science images.
Reproduction assets foundThe authors publicly release all data and R code needed to recreate the analyses on GitHub (ML-Phenology-Code), and the phenotyping input data are iNaturalist research-grade observation images (1,807,310 images) downloaded via the iNaturalist GBIF DarwinCore Archive, both publicly accessible.Code · publicl images; R.D.S., N.B., and T.J.D. constructed and built
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the models. R.D.S. analysed the data; R.D.S. and T.J.D. interpreted results; R.D.S. and T.J.D.
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wrote the manuscript with significant input from N.B. and M.vdB.
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Data availability
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All data and R code needed to recreate analyses are available on GitHub at
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https://github.com/rossdstewart/ML-Phenology-Code and at doi: xx
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CC-BY-NC-ND 4.0 International license
perpetuity. It is made available under a
preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in
The copyright holder for this
this version posted December 23, 2023.
;
https://doi.Open asset ↗rossdstewart/ML-Phenology-Codepdf-raw-page:14 lines:1-47Code / dataset availability confirmedEurope PMC · checked 15 Sept 2026
Inefficient nitrogen (N) utilization in agricultural production has led to many negative impacts such as excessive use of N fertilizers, redundant plant growth, greenhouse gases, long-lasting toxicity in ecosystem, and even effect on human health, indicating the importance to optimize N applications in cropping systems. Here, we present a multiseasonal study that focused on measuring phenotypic changes in wheat plants when they were responding to different N treatments under field conditions. Powered by drone-based aerial phenotyping and the AirMeasurer platform, we first quantified 6 N response-related traits as targets using plot-based morphological, spectral, and textural signals collected from 54 winter wheat varieties. Then, we developed dynamic phenotypic analysis using curve fitting to establish profile curves of the traits during the season, which enabled us to compute static phenotypes at key growth stages and dynamic phenotypes (i.e., phenotypic changes) during N response. After that, we combine 12 yield production and N-utilization indices manually measured to produce N efficiency comprehensive scores (NECS), based on which we classified the varieties into 4 N responsiveness (i.e., N-dependent yield increase) groups. The NECS ranking facilitated us to establish a tailored machine learning model for N responsiveness-related varietal classification just using N-response phenotypes with high accuracies. Finally, we employed the Wheat55K SNP Array to map single-nucleotide polymorphisms using N response-related static and dynamic phenotypes, helping us explore genetic components underlying N responsiveness in wheat. In summary, we believe that our work demonstrates valuable advances in N response-related plant research, which could have major implications for improving N sustainability in wheat breeding and production.
Why it matches plant phenotyping methodsドローン画像とAirMeasurerを用いた作物形態・スペクトル・テクスチャ形質の取得、および曲線フィッティングによる動的表現型抽出が研究の中心であり、実質的な植物フェノタイピング手法の応用・解析である。
abstractPowered by drone-based aerial phenotyping and the AirMeasurer platform, we first quantified 6 N response-related traits as targets using plot-based morphological, spectral, and textural signals collected from 54 winter wheat varieties.
Reproduction assets foundThe authors explicitly deposit their phenotyping analysis code, testing aerial images, trait analysis outputs, and Jupyter notebooks in a public GitHub repository, and separately release the AirMeasurer phenotyping platform used for the drone-based trait analysis. Both are paper-specific, public, and actionable via theCode · publicmade available in this paper. The source code, testing data, and other datasets supporting the results presented in this article are available at https://Github.com/The-Zhou-Lab/Nitrogen-response-traits/releases . Other data and user guides are openly available upon request. The latest AirMeasurer platform can be downloaded via https://github.com/The-Zhou-Lab/UAV/releases ).
Supplementary Materials
Supplementary 1
Figs. S1 to S6
Tables S1 to S14
Notes S1 to S3
Supplementary 2
Data S1 to S9
References
1. Seppelt R, Klotz S, Peiter E, Volk M.
Agriculture and food security under a changing climate: An underestimated challenge. iScience. 2022;25(12):105551.
2. Li S, Tian Y, Wu K, Ye Y, Yu J, ZhaOpen asset ↗The-Zhou-Lab/UAVlines:143-192Code / dataset availability confirmedEurope PMC · OpenAlex · checked 15 Sept 2026
PoplarLaboratory / benchtopRootMorphology / geometry measurement2D/3D reconstructionGrowth / time-series analysisGrowth / development / phenologyRoot system architecture
Root system architecture (RSA) is an important measure of how plants navigate and interact with the soil environment. However, current methods in studying RSA must make tradeoffs between precision of data and proximity to natural conditions, with root growth in germination papers providing accessibility and high data resolution. Functional-structural plant models (FSPMs) can overcome this tradeoff, though parameterization and evaluation of FSPMs are traditionally based in manual measurements and visual comparison. Here, we applied a germination paper system to study the adventitious RSA and root phenology of Populus trichocarpa stem cuttings using time-series image-based phenotyping augmented by FSPM. We found a significant correlation between timing of root initiation and thermal time at cutting collection ( P value = 0.0061, R 2 = 0.875), but little correlation with RSA. We also present a use of RhizoVision [1] for automatically extracting FSPM parameters from time series images and evaluating FSPM simulations. A high accuracy of the parameterization was achieved in predicting 2D growth with a sensitivity rate of 83.5%. This accuracy was lost when predicting 3D growth with sensitivity rates of 38.5% to 48.7%, while overall accuracy varied with phenotyping methods. Despite this loss in accuracy, the new method is amenable to high throughput FSPM parameterization and bridges the gap between advances in time-series phenotyping and FSPMs.
Why it matches plant phenotyping methods時系列画像フェノタイピングとFSPMを統合し、根系形態パラメータを自動抽出・評価する方法が中心である。
abstractwe applied a germination paper system to study the adventitious RSA and root phenology of Populus trichocarpa stem cuttings using time-series image-based phenotyping augmented by FSPM.
Reproduction assets foundThe paper's Data Availability statement deposits all data and R scripts (the paper's phenotyping measurements and analysis) on Zenodo, and the adapted CropRootBox.jl model code on GitHub. Only the Zenodo URL matches an allowed URL, so the Zenodo asset is reported; the GitHub repository is noted but its URL is not in anDataset · publicview and editing: S.P., D.B., K.Y., S.D., and S.-H.K.
Competing interests: The authors declare that there is no conflict of interest regarding the publication of this article.
Data Availability
The model is housed in Github at github.com/uwkimlab/CropRootBox.jl_propagation.jl . All data and and R scripts are housed in Zenodo at https://doi.org/10.5281/zenodo.8083525 .
Supplementary Materials
Supplementary 1
Figs. S1 to S7
Tables S1 to S2
Click here for additional data file.
References
1.
Seethepalli
A , Dhakal
K , Griffiths
M , Guo
H , Freschet
GT , York
LM
.
RhizoVision explorer: Open-source software for root image analysis and measurement standardization
. AoB PLANTS . 2021 ; 13 ( 6 ): pOpen asset ↗Zenodo · 10.5281/zenodo.8083525lines:196-345Code / dataset availability confirmedCrossref · bioRxiv · checked 14 Sept 2026
Abstract Biodiversity monitoring is constrained by cost- and labour-intensive field sampling methods. Increasing evidence suggests that remotely sensed spectral diversity (SD) is linked to plant diversity, holding promise for monitoring applications. However, studies testing such a relationship reported conflicting findings, especially in challenging ecosystems such as grasslands, due to their high temporal dynamism and variety. It follows that a thorough investigation of the key factors, such as the metrics applied (i.e., continuous, categorical) and phenology (e.g., flowering), influencing such a relationship is necessary. Thus, this study aims to assess the applicability of SD for plant diversity monitoring at the local scale by testing six different SD metrics while considering the effect of the presence of flowering on the relationship and resampling the original data to assess how spatial resolution affects the results. Taxonomic diversity was calculated based on data collected in 159 plots with 1.5 m ×1.5 m experimental mesic grassland communities. Spectral information was collected using a UAV-borne sensor measuring reflectance across six bands in the visible and near-infrared range at ∼2 cm spatial resolution. Our results show that, in the presence of flowering, the relationship is significant and positive only when SD is calculated using categorical metrics. Despite the observed significance, the variance explained by the models had very low values, with no evident differences when resampling spectral data to coarser pixel sizes. Such findings suggest that new insights into the possible confounding effects on the SD∼plant diversity in grassland communities are needed to use SD for monitoring purposes.
Why it matches plant phenotyping methodsUAV搭載センサーによるスペクトル情報から植物多様性を推定し、複数のスペクトル多様性指標と空間解像度を比較検証しているため、植物フェノタイピング手法の応用・評価が中心です。
abstractThus, this study aims to assess the applicability of SD for plant diversity monitoring at the local scale by testing six different SD metrics
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 · publicData and scripts are provided at https://github.com/MichelaPerrone/SVH_Benesov.git
under CC-BY license.Open asset ↗MichelaPerrone/SVH_Benesovpdf-page:7 lines:1-43Code / dataset availability confirmedCrossref · checked 14 Sept 2026
Laboratory / benchtopCell / cellular structurePhysiological trait estimationVisualization / data managementGrowth / development / phenology
Abstract Background Plant defense activators offer advantages over pesticides by avoiding the emergence of drug-resistant pathogens. However, only a limited number of compounds have been reported. Reactive oxygen species (ROS) act as not only antimicrobial agents but also signaling molecules that trigger immune responses. They also affect various cellular processes, highlighting the potential ROS modulators as plant defense activators. Establishing a high-throughput screening system for ROS modulators holds great promise for identifying lead chemical compounds with novel modes of action (MoAs). Results We established a novel in silico screening system for plant defense activators using deep learning-based predictions of ROS accumulation combined with the chemical properties of the compounds as explanatory variables. Our screening strategy comprised four phases: (1) development of a ROS inference system based on a deep neural network that combines ROS production data in plant cells and multidimensional chemical features of chemical compounds; (2) in silico extensive-scale screening of seven million commercially available compounds using the ROS inference model; (3) secondary screening by visualization of the chemical space of compounds using the generative topographic mapping; and (4) confirmation and validation of the identified compounds as potential ROS modulators within plant cells. We further characterized the effects of selected chemical compounds on plant cells using molecular biology methods, including pathogenic signal-triggered enzymatic ROS induction and programmed cell death as immune responses. Our results indicate that deep learning-based screening systems can rapidly and effectively identify potential immune signal-inducible ROS modulators with distinct chemical characteristics compared with the actual ROS measurement system in plant cells. Conclusions We developed a model system capable of inferring a diverse range of ROS activity control agents that activate immune responses through the assimilation of chemical features of candidate pesticide compounds. By employing this system in the prescreening phase of actual ROS measurement in plant cells, we anticipate enhanced efficiency and reduced pesticide discovery costs. The in-silico screening methods for identifying plant ROS modulators hold the potential to facilitate the development of diverse plant defense activators with novel MoAs.
Why it matches plant phenotyping methods植物細胞のROS蓄積という生理状態を推定する深層学習モデルと、実測による検証を組み合わせたスクリーニング手法の開発が中心である。
abstractWe established a novel in silico screening system for plant defense activators using deep learning-based predictions of ROS accumulation combined with the chemical properties of the compounds as explanatory variables.
Reproduction assets found保存済みの本文根拠を更新済みルールで再検証し、公開資産1件を確認しました。Code · publicThe learning algorithm codes used during the current study are available on the GitHub address ( https://github.com/ma1206ko/in_silico_screening ).Open asset ↗ma1206ko/in_silico_screeninglines:168-249Code / dataset availability confirmedEurope PMC · checked 15 Sept 2026
ArabidopsisTissueClassificationCalibration / preprocessingGrowth / time-series analysisGrowth / development / phenology
Transcriptomic data can be used to predict environmentally impacted phenotypic traits. This type of prediction is particularly useful for monitoring difficult-to-measure phenotypic traits and has become increasingly popular for monitoring high-value agricultural crops and in precision medicine. Despite this increase in popularity, little research has been done on how many samples are required for these models to be accurate, and which normalization should be used. Here we create a massive RNA-seq dataset from publicly available Arabidopsis thaliana data with corresponding measurements for age and tissue type. We use this dataset to determine how many samples are required for accurate model prediction and which normalization method is required. We find that Median Ratios Normalization significantly increases performance when predicting age. We also find that in the case of our dataset, only a few hundred samples are required to predict tissue types, and only a few thousand samples are necessary to accurately predict age. Researchers should consider these results when choosing the number of samples in a transcriptomic experiment and during data-processing. Author Summary Large datasets have become ubiquitous in both research and industry, with thousands and sometimes millions of samples being collected for a single project. In biology a prominent new technology is RNA-seq, which can be used to measure the expression level of thousands of genes for a single sample. These measurements are used for a variety of downstream applications, including predicting phenotypic traits (i.e. height, disease, etc.). A number of experiments have attempted to use RNA-seq data to make phenotype predictions with varying success. This is partially due to the small sample size of their experiments. RNA-seq datasets are currently relatively small--only a dozen to a few hundred samples--due to the cost per sample. This is expected to change as the cost of sequencing decreases. In this paper we create a massive conglomerate RNA-seq dataset from publicly available Arabidopsis thaliana RNA-seq data. We use this dataset to determine how many samples are required to accurately predict plant age and tissue type using machine learning models. We also explore the best way to normalize large datasets. Our results show the potential of massive RNA-seq datasets, and can be used to inform experimental design for phenotype prediction.
Why it matches plant phenotyping methodsRNA-seqデータから植物の年齢・組織型を予測する機械学習について、必要サンプル数と正規化法を大規模Arabidopsisデータで評価しており、表現型推定手法の検証が中心である。
abstractWe use this dataset to determine how many samples are required for accurate model prediction and which normalization method is required.
Reproduction assets foundThe paper's normalized gene expression matrices, curated phenotype annotation datasets, and intermediary files are publicly deposited on Zenodo, and all analysis code is publicly available on GitLab. These directly reproduce the paper's plant-phenotyping measurements (Arabidopsis age/tissue annotations) and modeling/MLDataset · public(NoNo). TMM normalization [24,29] and MRN normalization [25] were performed using
the Python “conorm” package 1.2.0 [30]. TPM and NoNo normalization values were an
output of Kallisto [27]. How these normalizations impacted sample count is visualized as
S2 Figure. We have made these GEMs publicly available on Zenodo at the link
https://zenodo.org/records/10183151
Sample Phenotype Annotations Pre-Processing
Sample phenotype annotations were retrieved from the NCBI BioProject database
[16,17] using BioSampleParser which was slightly modified to check for successful data
retrieval [31]. Phenotype annotations were retrieved for 48696 NCBI BioSamples,
representing data from 2643 BioProjects.Open asset ↗Zenodopdf-raw-page:9 lines:1-55Dataset · publicData Availability Statement
All normalized gene expression datasets, phenotype datasets, and intermediary files
created for this research are publically available on Zenodo at link
https://zenodo.org/doi/10.5281/zenodo.10183150
All code written in support of this publication is publicly available on GitLab at link
https://gitlab.com/ficklinlab-public/modeling-with-transcriptomics
Funding
This work was supported by the Washington Tree Fruit Research Commission
(WTFRC) project #AP-22-101 and USDA ARS internal appropriation funds.
References
1. BostanciOpen asset ↗Zenodo · 10.5281/zenodo.10183150pdf-raw-page:43 lines:1-51Code · publicData Availability Statement
All normalized gene expression datasets, phenotype datasets, and intermediary files
created for this research are publically available on Zenodo at link
https://zenodo.org/doi/10.5281/zenodo.10183150
All code written in support of this publication is publicly available on GitLab at link
https://gitlab.com/ficklinlab-public/modeling-with-transcriptomics
Funding
This work was supported by the Washington Tree Fruit Research Commission
(WTFRC) project #AP-22-101 and USDA ARS internal appropriation funds.
References
1. Bostanci E, Kocak E, Unal M, Guzel MS, Acici K, Asuroglu T. Machine Learning
Analysis of RNA-seq Data for Diagnostic and Prognostic Prediction of Colon Open asset ↗GitLabpdf-raw-page:43 lines:1-51Code / dataset availability confirmedOpenAlex · arXiv · checked 15 Sept 2026
Image-based crop growth modeling can substantially contribute to precision agriculture by revealing spatial crop development over time, which allows an early and location-specific estimation of relevant future plant traits, such as leaf area or biomass. A prerequisite for realistic and sharp crop image generation is the integration of multiple growth-influencing conditions in a model, such as an image of an initial growth stage, the associated growth time, and further information about the field treatment. We present a two-stage framework consisting first of an image prediction model and second of a growth estimation model, which both are independently trained. The image prediction model is a conditional Wasserstein generative adversarial network (CWGAN). In the generator of this model, conditional batch normalization (CBN) is used to integrate different conditions along with the input image. This allows the model to generate time-varying artificial images dependent on multiple influencing factors of different kinds. These images are used by the second part of the framework for plant phenotyping by deriving plant-specific traits and comparing them with those of non-artificial (real) reference images. For various crop datasets, the framework allows realistic, sharp image predictions with a slight loss of quality from short-term to long-term predictions. Simulations of varying growth-influencing conditions performed with the trained framework provide valuable insights into how such factors relate to crop appearances, which is particularly useful in complex, less explored crop mixture systems. Further results show that adding process-based simulated biomass as a condition increases the accuracy of the derived phenotypic traits from the predicted images. This demonstrates the potential of our framework to serve as an interface between an image- and process-based crop growth model.
Why it matches plant phenotyping methods画像生成と成長推定を統合したフレームワークを開発し、生成画像から植物個体の形質を抽出・比較することが中心であるため、植物フェノタイピング手法として含める。
abstractWe present a two-stage framework consisting first of an image prediction model and second of a growth estimation model, which both are independently trained.
Reproduction assets foundThe paper explicitly states that its source code (the multi-conditional CWGAN crop growth simulation framework) is publicly available on GitHub. The SIMPLACE documentation URL is a generic external resource, not a paper-specific asset.Code · publicwth estimation model.
A transferability experiment demonstrates that our framework has the potential to be transferred to crop mixtures in another field with different environmental conditions.
2 Materials and Methods
This section introduces the data basis (Sec. 2.1 ) and the framework 1 1
1
Source code is publicly available at https://github.com/luked12/crop-growth-cgan , where a 2-step approach is followed.
First, an image is predicted (Sec. 2.2 ), and second, the growth is estimated using plant phenotyping (Sec. 2.3 ).
While existing state-of-the-art models are used for growth estimation, which is fine-tuned on our data, the methodological focus of this work is on the first part, image prOpen asset ↗luked12/crop-growth-cganlines:128-235Code / 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 14 Sept 2026
WheatRGB / grayscaleSeed / grainClassificationGrowth / development / phenology
Grain filling is essential for wheat yield formation, but is very susceptible to environmental stresses, such as high temperatures, especially in the context of global climate change. Grain RGB images include rich color, shape, and texture information, which can explicitly reveal the dynamics of grain filling. However, it is still challenging to further quantitatively predict the days after anthesis (DAA) from grain RGB images to monitor grain development. Results The WheatGrain dataset revealed dynamic changes in color, shape, and texture traits during grain development. To predict the DAA from RGB images of wheat grains, we tested the performance of traditional machine learning, deep learning, and few-shot learning on this dataset. The results showed that Random Forest (RF) had the best accuracy of the traditional machine learning algorithms, but it was far less accurate than all deep learning algorithms. The precision and recall of the deep learning classification model using Vision Transformer (ViT) were the highest, 99.03% and 99.00%, respectively. In addition, few-shot learning could realize fine-grained image recognition for wheat grains, and it had a higher accuracy and recall rate in the case of 5-shot, which were 96.86% and 96.67%, respectively. Materials and methods In this work, we proposed a complete wheat grain dataset, WheatGrain, which covers thousands of wheat grain images from 6 DAA to 39 DAA, which can characterize the complete dynamics of grain development. At the same time, we built different algorithms to predict the DAA, including traditional machine learning, deep learning, and few-shot learning, in this dataset, and evaluated the performance of all models. Conclusions To obtain wheat grain filling dynamics promptly, this study proposed an RGB dataset for the whole growth period of grain development. In addition, detailed comparisons were conducted between traditional machine learning, deep learning, and few-shot learning, which provided the possibility of recognizing the DAA of the grain timely. These results revealed that the ViT could improve the performance of deep learning in predicting the DAA, while few-shot learning could reduce the need for a number of datasets. This work provides a new approach to monitoring wheat grain filling dynamics, and it is beneficial for disaster prevention and improvement of wheat production.
Why it matches plant phenotyping methodsコムギ粒のRGB画像から登熟段階（日数）を推定する画像解析手法を開発・比較し、データセットとモデル性能を評価しており、表現型取得・推定が研究の中心である。
abstractTo predict the DAA from RGB images of wheat grains, we tested the performance of traditional machine learning, deep learning, and few-shot learning on this dataset.
Reproduction assets found保存済みの本文根拠を更新済みルールで再検証し、公開資産2件を確認しました。Code · publicThese traits were extracted via Python and OpenCV (a Python library), and the codes are available online at https://github.com/shem123456/wheat-grain-traits (accessed on 21 September 2023).Open asset ↗shem123456/wheat-grain-traitslines:61-116Code · publicFinally, the Siamese network with contrastive loss was built using PyTorch, and the configuration of its training was consistent with that of the deep learning model described above. The codes are available online at https://github.com/shem123456/grain-filling-classification (accessed on 21 September 2023).Open asset ↗shem123456/grain-filling-classificationlines:117-128Code / dataset availability confirmedCrossref · checked 7 Sept 2026
The monitoring of crop phenology informs decisions in environmental and agricultural management at both global and farm scales. Current methodologies for crop monitoring using remote sensing data track crop growth stages over time based on single, scalar vegetative indices (e.g., NDVI). Crop growth and senescence are indistinguishable when using scalar indices without additional information (e.g., planting date). By using a pair of normalized difference (ND) metrics derived from hyperspectral data—one primarily sensitive to chlorophyll concentration and the other primarily sensitive to water content—it is possible to track crop characteristics based on the spectral changes only. In a two-dimensional plot of the metrics (ND-space), bare soil, full canopy, and senesced vegetation data all plot in separate, distinct locations regardless of the year. The path traced in the ND-space over the growing season repeats from year to year, with variations that can be related to weather patterns. Senescence follows a return path that is distinct from the growth path.
Why it matches plant phenotyping methodsハイパースペクトル由来の2種類の正規化差分指標を組み合わせ、作物の成長・老化過程や生育段階を時系列で推定する手法が研究の中心であるため。
abstractBy using a pair of normalized difference (ND) metrics derived from hyperspectral data—one primarily sensitive to chlorophyll concentration and the other primarily sensitive to water content—it is possible to track crop characteristics based on the spectral changes only.
Reproduction assets foundThe paper's analysis is based on the publicly available GHISA EO-1 Hyperion hyperspectral dataset from USGS, explicitly named in the Data Availability Statement. No author analysis code or trained models are deposited.Dataset · publicData Availability Statement: The data are publicly available at https://www.usgs.gov/media/files/
ghisa-usa-eo-1-hyperion-dataset (accessed on 14 November 2023).Open asset ↗ghisa-usa-eo-1-hyperion-datasetpdf-page:13 lines:1-56Code / dataset availability confirmedEurope PMC · OpenAlex · checked 14 Sept 2026
The strong societal demand to reduce pesticide use and adaptation to climate change challenges the capacities of phenotyping new varieties in the vineyard. High-throughput phenotyping is a way to obtain meaningful and reliable information on hundreds of genotypes in a limited period. We evaluated traits related to growth in 209 genotypes from an interspecific grapevine biparental cross, between IJ119, a local genitor, and Divona, both in summer and in winter, using several methods: fresh pruning wood weight, exposed leaf area calculated from digital images, leaf chlorophyll concentration, and LiDAR-derived apparent volumes. Using high-density genetic information obtained by the genotyping by sequencing technology (GBS), we detected 6 regions of the grapevine genome [quantitative trait loci (QTL)] associated with the variations of the traits in the progeny. The detection of statistically significant QTLs, as well as correlations ( R 2 ) with traditional methods above 0.46, shows that LiDAR technology is effective in characterizing the growth features of the grapevine. Heritabilities calculated with LiDAR-derived total canopy and pruning wood volumes were high, above 0.66, and stable between growing seasons. These variables provided genetic models explaining up to 47% of the phenotypic variance, which were better than models obtained with the exposed leaf area estimated from images and the destructive pruning weight measurements. Our results highlight the relevance of LiDAR-derived traits for characterizing genetically induced differences in grapevine growth and open new perspectives for high-throughput phenotyping of grapevines in the vineyard.
Why it matches plant phenotyping methodsLiDARによるブドウ樹冠・剪定木体積の取得を、従来法との相関、遺伝率、QTL解析で評価しており、植物形質の高スループット計測法が研究の中心です。
abstractThe detection of statistically significant QTLs, as well as correlations ( R 2 ) with traditional methods above 0.46, shows that LiDAR technology is effective in characterizing the growth features of the grapevine.
Reproduction assets foundThe paper's Data availability statement provides a public repository deposit (DOI 10.57745/PETTGY) for the study data and an authors' public ImageJ script for estimating foliage coverage used in the RGB-image phenotyping analysis.Dataset · publictyping but also his expertise and helped with the manuscript review. D.M. supervised the program and helped with manuscript writing. É.D. supervised the whole study and wrote the first draft of the manuscript.
Competing interests: The authors declare that they have no competing interests.
Data availability
Data are available at https://doi.org/10.57745/PETTGY . ImageJ script for estimating foliage coverage: https://forgemia.inra.fr/eric.duchene/image-analysis-scripts/-/blob/main/FoliageCoverage_PC_EN.txt
Supplementary Materials
Supplementary 1
Fig. S1
Tables S1 to S5
Click here for additional data file.
References
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Carvalho
LC , Goncalves
EF ,
da
Silva
JM , Costa
JM
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Potential phOpen asset ↗10.57745/PETTGY · 10.57745/PETTGYlines:825-1015Code / dataset availability confirmedCrossref · checked 14 Sept 2026
A sound understanding of plant growth is critical to maintaining future crop productivity under ongoing climate change. Remotely sensed time series of crop functional traits from optical satellite imagery are an invaluable tool for deriving appropriate management practices that facilitate risk mitigation and increase the resilience of agroecosystems. However, the availability of imagery is limited by atmospheric disturbances that cause large temporal gaps and noise in the trait time series. Therefore, time series reconstruction methods are required for accurate crop growth modelling. Physiological priors, such as the fact that plant growth is mainly controlled by a few environmental covariates, among which air temperature plays a prominent role, represent a promising approach to improve the representation of crop growth. Here, a novel approach is proposed that combines Sentinel-2 Green Leaf Area Index (GLAI) observations with three dose response curve approaches describing the a priori physiological relationship between growth and temperature in winter wheat. A probabilistic ensemble Kalman filtering data assimilation scheme allows the combination of high temporal resolution air temperature data and satellite imagery, which also allows quantification of uncertainties. The proposed approach requires a smaller number of satellite observations compared to conventional remote sensing time series algorithms, making it suitable for agricultural areas with high cloud cover, and is considerably less complex than a mechanistic crop growth model. Validation was carried out using in-situ data collected on winter wheat plots in Switzerland in two consecutive years. The validation results suggest that the proposed assimilation of Sentinel-2 GLAI and temperature-response-based growth rates allows the reconstruction of physiologically meaningful GLAI time series. In particular, the systematic underestimation of high in-situ GLAI values (> 5 m^2 m^-2) often prevalent in purely remote sensing driven GLAI time series reconstruction was reduced. Thus, the proposed approach is advantageous compared to state-of-the-art remote sensing approach based on wide-spread logistic functions by means of physiological plausibility, fitting requirements and representation of high in-situ GLAI values. This has great potential to increase the reliability of remotely sensed crop productivity assessment.
Why it matches plant phenotyping methods衛星光学データと生理モデルを統合して作物GLAI時系列を再構築する手法を提案し、圃場データで検証しており、植物形質推定が研究の中心である。
abstractHere, a novel approach is proposed that combines Sentinel-2 Green Leaf Area Index (GLAI) observations with three dose response curve approaches describing the a priori physiological relationship between growth and temperature in winter wheat.
Reproduction assets foundThe authors explicitly state that code and data to reproduce the entire workflow (DRC fitting, Sentinel-2 GLAI assimilation, and validation) are publicly available on GitHub under GNU GPL v3.0. This is a paper-specific, public, actionable asset. Other URLs in the text are cited references or generic libraries (e.g., NLCode · publicCode and Data Availability
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Code to reproduce the entire workflow including calibration and validation data is
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available at https://github.com/EOA-team/sentinel2_crop_trait_timeseries
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under GNU General Public License v3.0.
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Credit Authorship Contribution Statement
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Lukas Valentin Graf: Conceptualization, Methodology, Formal analysis, Vali-
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dation, Visualization, Software, Writing - original draft. Flavian Tschurr: Formal
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Analysis, Methodology, Software, Methodology, Writing - original draftOpen asset ↗EOA-team/sentinel2_crop_trait_timeseriespdf-raw-page:55 lines:1-41Code / dataset availability confirmedCrossref · Europe PMC · checked 7 Sept 2026
Introduction The challenges associated with data availability, class imbalance, and the need for data augmentation are well-recognized in the field of plant disease detection. The collection of large-scale datasets for plant diseases is particularly demanding due to seasonal and geographical constraints, leading to significant cost and time investments. Traditional data augmentation techniques, such as cropping, resizing, and rotation, have been largely supplanted by more advanced methods. In particular, the utilization of Generative Adversarial Networks (GANs) for the creation of realistic synthetic images has become a focal point of contemporary research, addressing issues related to data scarcity and class imbalance in the training of deep learning models. Recently, the emergence of diffusion models has captivated the scientific community, offering superior and realistic output compared to GANs. Despite these advancements, the application of diffusion models in the domain of plant science remains an unexplored frontier, presenting an opportunity for groundbreaking contributions. Methods In this study, we delve into the principles of diffusion technology, contrasting its methodology and performance with state-of-the-art GAN solutions, specifically examining the guided inference model of GANs, named InstaGAN, and a diffusion-based model, RePaint. Both models utilize segmentation masks to guide the generation process, albeit with distinct principles. For a fair comparison, a subset of the PlantVillage dataset is used, containing two disease classes of tomato leaves and three disease classes of grape leaf diseases, as results on these classes have been published in other publications. Results Quantitatively, RePaint demonstrated superior performance over InstaGAN, with average Fréchet Inception Distance (FID) score of 138.28 and Kernel Inception Distance (KID) score of 0.089 ± (0.002), compared to InstaGAN’s average FID and KID scores of 206.02 and 0.159 ± (0.004) respectively. Additionally, RePaint’s FID scores for grape leaf diseases were 69.05, outperforming other published methods such as DCGAN (309.376), LeafGAN (178.256), and InstaGAN (114.28). For tomato leaf diseases, RePaint achieved an FID score of 161.35, surpassing other methods like WGAN (226.08), SAGAN (229.7233), and InstaGAN (236.61). Discussion This study offers valuable insights into the potential of diffusion models for data augmentation in plant disease detection, paving the way for future research in this promising field.
Why it matches plant phenotyping methods植物病害画像を対象に拡散モデルとGANを比較し、病害画像データ拡張の性能をFID・KIDで検証する研究であり、植物病害状態の画像ベース評価を支える方法が中心である。
titleHarnessing the power of diffusion models for plant disease image augmentation
Reproduction assets foundThe paper's experiments use a subset of the public PlantVillage image dataset, which the authors explicitly link in the data availability statement. No author analysis code, trained models, or generated-image deposits are mentioned.Dataset · publicPublicly available datasets were analyzed in this study. This data can be found here: https://www.kaggle.com/datasets/mohitsingh1804/plantvillage .Open asset ↗Kaggle · mohitsingh1804/plantvillagelines:841-873Code / dataset availability confirmedOpenAlex · Europe PMC · Crossref · checked 15 Sept 2026
Cosegmentation and coattention are extensions of traditional segmentation methods aimed at detecting a common object (or objects) in a group of images. Current cosegmentation and coattention methods are ineffective for objects, such as plants, that change their morphological state while being captured in different modalities and views. The Object State Change using Coattention-Cosegmentation (OSC-CO2) is an end-to-end unsupervised deep-learning framework that enhances traditional segmentation techniques, processing, analyzing, selecting, and combining suitable segmentation results that may contain most of our target object’s pixels, and then displaying a final segmented image. The framework leverages coattention-based convolutional neural networks (CNNs) and cosegmentation-based dense Conditional Random Fields (CRFs) to address segmentation accuracy in high-dimensional plant imagery with evolving plant objects. The efficacy of OSC-CO2 is demonstrated using plant growth sequences imaged with infrared, visible, and fluorescence cameras in multiple views using a remote sensing, high-throughput phenotyping platform, and is evaluated using Jaccard index and precision measures. We also introduce CosegPP+, a dataset that is structured and can provide quantitative information on the efficacy of our framework. Results show that OSC-CO2 out performed state-of-the art segmentation and cosegmentation methods by improving segementation accuracy by 3% to 45%.
Why it matches plant phenotyping methods植物画像から成長状態を抽出する画像セグメンテーション手法を開発し、ハイスループット表現型解析プラットフォームで評価しているため、方法が研究の中心である。
abstractThe Object State Change using Coattention-Cosegmentation (OSC-CO2) is an end-to-end unsupervised deep-learning framework
Reproduction assets foundThe paper's authors publicly released both their analysis code (OSC-CO2 framework on GitHub) and the paper-specific plant phenotyping image dataset (CosegPP+, a VSTEM plant imagery dataset from the UNL LemnaTec platform) with explicit availability statements and URLs.Code · publicthe object’s (plant’s) shape, orientation, and size at a specific point in time. OSC-CO 2 is designed to process datasets that contain a variety of features, such as perspective (V), species (S), temporality (T), environmental conditions (E) and modality (M) (VSTEM) (
Figure 1
). The code for OSC-CO 2 is publicly available at: https://github.com/rubiquinones/OSC-CO2 .
Figure 1
A preview of a VSTEM Dataset. This work will use the CosegPP dataset ( Quiñones et al., 2021 ) and modify it as CosegPP+ and categorize it as a VSTEM dataset for our problem definition. The first row shows the growth sequence of a Buckwheat plant from 3 rd July 2019 to 27 th July 2019. The second row shows the threeOpen asset ↗rubiquinones/OSC-CO2lines:37-49Dataset · publicasets through segmentation using Otsu’s method ( Otsu, 1979 ) and cosegmentation using Subdiscover ( Meng et al., 2016 ). These two methods were chosen since ( Quiñones et al., 2021 ) defined these as the top methods for being able to segment some of the challenging features of computer vision. CosegPP+ is publicly available at https://doi.org/10.5281/zenodo.6863013 .
We replaced the original images with the outputs generated by Otsu’s method and Subdiscover. Meaning that each time point
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will have at most
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is the number of algorithms (i.e., Otsu’s method and Subdiscover) used. Some groups do not contain Subdiscover binary masks due to the method’s limitation in notOpen asset ↗10.5281/zenodo.6863013lines:350-411Code / dataset availability confirmedEurope PMC · checked 7 Sept 2026
Genotype-by-environment (G×E) interactions can significantly affect crop performance and stability. Investigating G×E requires extensive data sets with diverse cultivars tested over multiple locations and years. The Genomes-to-Fields (G2F) Initiative has tested maize hybrids in more than 130 year-locations in North America since 2014. Here, we curate and expand this data set by generating environmental covariates (using a crop model) for each of the trials. The resulting data set includes DNA genotypes and environmental data linked to more than 70,000 phenotypic records of grain yield and flowering traits for more than 4000 hybrids. We show how this valuable data set can serve as a benchmark in agricultural modeling and prediction, paving the way for countless G×E investigations in maize. We use multivariate analyses to characterize the data set's genetic and environmental structure, study the association of key environmental factors with traits, and provide benchmarks using genomic prediction models.
Why it matches plant phenotyping methodsトウモロコシの収量・開花形質に関する大規模な再利用可能データセットを構築し、農業モデリングのベンチマークとして提示しており、表現型データセットが研究の中心である。
abstractThe resulting data set includes DNA genotypes and environmental data linked to more than 70,000 phenotypic records of grain yield and flowering traits for more than 4000 hybrids.
Reproduction assets foundThe paper's curated maize G×E dataset (phenotypes, SNP genotypes, environmental covariates) is publicly deposited on Figshare, and the authors' analysis scripts are publicly available on GitHub (MAIZE-HUB). Both are paper-specific, public, and actionable.Dataset · publicThe aggregated curated data set (including the SNP genotypes, phenotypes, and ECs) is available in the Figshare repository [ https://doi.org/10.6084/m9.figshare.22776806 ] 58 .Open asset ↗Figshare · 10.6084/m9.figshare.22776806lines:207-229Code · publicThe scripts used to implement all the analyses described in this study are provided in the GitHub repository [ https://github.com/QuantGen/MAIZE-HUB ].Open asset ↗GitHub · QuantGen/MAIZE-HUBlines:207-229Code / dataset availability confirmedEurope PMC · checked 7 Sept 2026
Breeding for resilience to climate change requires considering adaptive traits such as plant architecture, stomatal conductance and growth, beyond the current selection for yield. Robotized indoor phenotyping allows measuring such traits at high throughput for speed breeding, but is often considered as non-relevant for field conditions. Here, we show that maize adaptive traits can be inferred in different fields, based on genotypic values obtained indoor and on environmental conditions in each considered field. The modelling of environmental effects allows translation from indoor to fields, but also from one field to another field. Furthermore, genotypic values of considered traits match between indoor and field conditions. Genomic prediction results in adequate ranking of genotypes for the tested traits, although with lesser precision for elite varieties presenting reduced phenotypic variability. Hence, it distinguishes genotypes with high or low values for adaptive traits, conferring either spender or conservative strategies for water use under future climates.
Why it matches plant phenotyping methodsロボット化した屋内フェノタイピングによる適応形質の高スループット測定と、圃場条件への推定・整合性評価が研究の中心であり、単なる生物学的実験のルーチン測定ではない。
abstractRobotized indoor phenotyping allows measuring such traits at high throughput for speed breeding
Reproduction assets foundThe paper's Data availability statement deposits its phenotypic/genotypic datasets (diversity panel, genetic progress panel, recent hybrids panel) on Recherche Data Gouv with three public DOIs. These are paper-specific public phenotype datasets directly reproducing the study's measurements. The PhenoArch platform page,Dataset · publicThe datasets for phenotypic and genotypic values for the diversity panel are available at https://doi.org/10.15454/IASSTN .Open asset ↗10.15454/IASSTNlines:186-234Dataset · publicThe datasets for phenotypic and genotypic values for the genetic progress panel are available at https://doi.org/10.15454/KLD0GH .Open asset ↗10.15454/KLD0GHlines:186-234Dataset · publicThe dataset for the ‘recent hybrid’ panel is available at https://doi.org/10.57745/NZY1KL .Open asset ↗10.57745/NZY1KLlines:186-234Code / dataset availability confirmedEurope PMC · OpenAlex · Crossref · checked 7 Sept 2026
MaizeLaboratory / benchtopStereoRootMorphology / geometry measurement2D/3D reconstructionGrowth / time-series analysisGrowth / development / phenologyRoot system architecture
Background: The root of a plant is a fundamental organ for the multisensory perception of the environment. Investigating root growth dynamics as a mean of their interaction with the environment is of key importance for improving knowledge in plant behaviour, plant biology and agriculture. To date, it is difficult to study roots movements from a dynamic perspective given that available technologies for root imaging focus mostly on static characterizations, lacking of temporal and three-dimensional (3D) spatial information. This paper describes a new system based on timelapse for the 3D reconstruction and analysis of roots growing in hydroponics. Results The system is based on infrared stereo-cameras acquiring time-lapse images of the roots for 3D reconstruction. The acquisition protocol guarantees the root growth in complete dark while the upper part of the plant grows in normal light conditions. The system extracts the 3D trajectory of the root tip and a set of descriptive features in both the temporal and frequency domains. The system has been used on Zea mays L. (B73) during the first week of growth and shows good inter-reliability between operators with an Intra Class Correlation Coefficient (ICC) > 0.9 for all features extracted. It also showed measurement accuracy with a median difference of < 1 mm between computed and manually measured root length. Conclusions The system and the protocol presented in this study enable accurate 3D analysis of primary root growth in hydroponics. It can serve as a valuable tool for analyzing real-time root responses to environmental stimuli thus improving knowledge on the processes contributing to roots physiological and phenotypic plasticity.
Why it matches plant phenotyping methods根の3D動態を画像から再構成・解析するシステムを開発し、特徴量の信頼性と測定精度を検証しており、植物表現型取得法が中心である。
abstractThis paper describes a new system based on timelapse for the 3D reconstruction and analysis of roots growing in hydroponics.
Reproduction assets foundThe preprint explicitly states that the 3D root tip trajectory data underlying its phenotyping analysis are publicly deposited on Zenodo (record 8422242), making this a paper-specific, publicly accessible phenotype dataset. No author analysis code with a public URL is stated (SPROUTS is proprietary third-party softwareDataset · publicData describing 3D trajectories used in this paper are available here: https://zenodo.org/record/8422242Open asset ↗Zenodo · 8422242lines:119-156Code / dataset availability confirmedEurope PMC · OpenAlex · checked 7 Sept 2026
Abiotic stresses such as heat and frost limit plant growth and productivity. Image-based field phenotyping methods allow quantifying not only plant growth but also plant senescence. Winter crops show senescence caused by cold spells, visible as declines in leaf area. We accurately quantified such declines by monitoring changes in canopy cover based on time-resolved high-resolution imagery in the field. Thirty-six winter wheat genotypes were measured in multiple years. A concept termed "frost damage index" (FDI) was developed that, in analogy to growing degree days, summarizes frost events in a cumulative way. The measured sensitivity of genotypes to the FDI correlated with visual scorings commonly used in breeding to assess winter hardiness. The FDI concept could be adapted to other factors such as drought or heat stress. While commonly not considered in plant growth modeling, integrating such degradation processes may be key to improving the prediction of plant performance for future climate scenarios.
Why it matches plant phenotyping methods圃場の時系列高解像度画像からキャノピー被覆率の変化を定量化し、霜害指数（FDI）を開発・検証しており、画像ベース表現型取得が研究の中心である。
abstractImage-based field phenotyping methods allow quantifying not only plant growth but also plant senescence.
Reproduction assets foundThe authors state that all analysis code for the Frost Damage Index is publicly available on GitLab with example data; the full phenotype dataset is only available upon request.Code · publicAll code is available at https://gitlab.ethz.ch/ftschurr/fdi_example with example data. All data are available upon reasonable request.Open asset ↗gitlab.ethz.ch/ftschurr/fdi_examplelines:81-106Code / dataset availability confirmedEurope PMC · checked 14 Sept 2026
On-farm food loss (i.e., grade-out vegetables) is a difficult challenge in sustainable agricultural systems. The simplest method to reduce the number of grade-out vegetables is to monitor and predict the size of all individuals in the vegetable field and determine the optimal harvest date with the smallest grade-out number and highest profit, which is not cost-effective by conventional methods. Here, we developed a full pipeline to accurately estimate and predict every broccoli head size ( n > 3,000) automatically and nondestructively using drone remote sensing and image analysis. The individual sizes were fed to the temperature-based growth model and predicted the optimal harvesting date. Two years of field experiments revealed that our pipeline successfully estimated and predicted the head size of all broccolis with high accuracy. We also found that a deviation of only 1 to 2 days from the optimal date can considerably increase grade-out and reduce farmer's profits. This is an unequivocal demonstration of the utility of these approaches to economic crop optimization and minimization of food losses.
Why it matches plant phenotyping methodsドローンリモートセンシングと画像解析により、個々のブロッコリー頭部サイズを自動・非破壊推定するパイプラインを開発・検証しており、植物形質取得が中心的です。
abstractwe developed a full pipeline to accurately estimate and predict every broccoli head size ( n > 3,000) automatically and nondestructively using drone remote sensing and image analysis.
Reproduction assets foundThe authors' full phenotyping/analysis pipeline source code is publicly available on GitHub (UAVbroccoli). Original drone image data (224 GB for 2020, 72 GB for 2021) exist but are only available upon request via Google Drive. Generic tools (YOLOv5, BiSeNet, labelme, EasyIDP, scikit-image) are third-party libraries, soCode · publicurvey powered by ML/DL for sustainable agricultural development, there are some limitations to its use. First, our system is neither fully automated nor app-based; therefore, farmers without computer science backgrounds cannot use this system directly in their own fields. However, because the source code is open to the public ( https://github.com/UTokyo-FieldPhenomics-Lab/UAVbroccoli ), local agricultural institutes and agricultural companies are able to modify and use the system according to their target. This study is definitely not a one-stop solution, but is a pioneer in real agriculture applications. Second, unlike traditional manual methods with limited throughput, the proposed method Open asset ↗UTokyo-FieldPhenomics-Lab/UAVbroccolilines:291-292Code / dataset availability confirmedEurope PMC · checked 7 Sept 2026
Duckweeds, a family of floating aquatic plants, are ideal model plants for laboratory experiments because they are small, easy to cultivate, and reproduce quickly. Duckweed cultivation, for the purposes of scientific research, requires that lineages are maintained as continuous populations of asexually propagating fronds, so research teams need to develop optimized cultivation conditions and coordinate maintenance tasks for duckweed stocks. Additionally, computational image analysis is proving to be a powerful duckweed research tool, but researchers lack software tools to assist with data collection and storage in a way that can feed into scripted data analysis. We set out to support these processes using a laboratory management software called Aquarium, an open-source application developed to manage laboratory inventory and plan experiments. We developed a suite of duckweed cultivation and experimentation operation types in Aquarium, which we then integrated with novel data analysis scripts. We then demonstrated the efficacy of our system with a series of image-based growth assays, and explored how our framework could be used to develop optimized cultivation protocols. We discuss the unexpected advantages and the limitations of this approach, suggesting areas for future software tool development. In its current state, our approach helps to bridge the gap between laboratory implementation and data analytical software for duckweed biologists and builds a foundation for future development of end-to-end computational tools in plant science.
Why it matches plant phenotyping methodsアヒルウキクサの画像ベース成長アッセイを含む、培養・実験管理ソフトウェアとデータ解析ワークフローの開発が中心であり、植物表現型取得を支援する方法論的貢献である。
abstractcomputational image analysis is proving to be a powerful duckweed research tool, but researchers lack software tools to assist with data collection and storage in a way that can feed into scripted data analysis.
Reproduction assets foundThe paper's duckweed growth-assay experimental data and Python analysis scripts are publicly available in the authors' GitHub repository, explicitly stated in the availability statement and results sections. The Aquarium platform itself is a generic pre-existing tool, not a paper-specific asset.Code · publicAll code used in this study is available on Github.Open asset ↗lines:119-135Dataset · publicThe datasets generated and/or analyzed during the current study are available in the Github repository, https://github.com/mtscott321/duckweed_data_analysis .Open asset ↗mtscott321/duckweed_data_analysislines:119-135Code / dataset availability confirmedEurope PMC · checked 15 Sept 2026
For nearly two decades, genomic selection has supported efforts to increase genetic gains in plant and animal improvement programs. However, novel phenomic strategies helping to predict complex traits in maize have proven beneficial when integrated into across– and within-environment genomic prediction models. One phenomic data modality is near infrared spectroscopy (NIRS), which records reflectance values of biological samples (e.g., maize kernels) based on chemical composition. Predictions of seven maize agronomic traits and three kernel composition traits across two years (2011-2012) and two management conditions (water stressed and well-watered) were conducted using combinations of NIRS and genomic data within four different cross-validation prediction scenarios. In aggregate, models incorporating NIRS data alongside genomic data improved predictive ability over models using only genomic data in 5 of 28 trait/cross-validation scenarios for across-environment prediction and 15 of 28 trait/environment scenarios for within-environment prediction, while the model with NIRS data alone had the highest prediction ability in only 1 of 28 scenarios for within-environment prediction. Potential causes of the surprisingly lower phenomic than genomic prediction power in this study are discussed, including sample size, sample homogenization, and low G×E. A genome-wide association study (GWAS) implicated known (i.e., MADS69 , ZCN8, sh1, wx1, du1 ) and unknown candidate genes linked to plant height and flowering-related agronomic traits as well as compositional traits such as kernel protein and starch content. This study demonstrated that including NIRS with genomic markers is a viable method to predict multiple complex traits with improved predictive ability and elucidate underlying biological causes. Key message Genomic and NIRS data from a maize diversity panel were used for prediction of agronomic and kernel composition traits while uncovering candidate genes for kernel protein and starch content.
Why it matches plant phenotyping methodsNIRSを用いた植物試料の表現型推定と、ゲノム予測との比較検証が研究の中心であり、複数のトウモロコシ農業形質・種子組成形質を対象としているため。
abstractOne phenomic data modality is near infrared spectroscopy (NIRS), which records reflectance values of biological samples (e.g., maize kernels) based on chemical composition.
Reproduction assets foundThe paper's Data Availability section and Methods explicitly state that the annotated R analysis script, plus the data files (CSVs.zip, SNP60000.hmp.zip) needed to reproduce the prediction results, are publicly available in the authors' GitHub repository ajdesalvio/Maize-NIRS-GBS.Code · public52
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Data Availability
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An annotated script of the R code used in this research can be accessed via GitHub
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(https://github.com/ajdesalvio/Maize-NIRS-GBS.git). Supplementary Data 1
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(Supplementary_Data_1.xlsx) contains prediction results, GWAS results, and variable importance
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scores for NIRS bands. Files necessary to run the R script and reproduce the prediction results are
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available in the CSVs.zip folder and the SNP60000.hmp.zip folder. Supplementary figures are
1044Open asset ↗ajdesalvio/Maize-NIRS-GBSpdf-raw-page:52 lines:1-26Code / dataset availability confirmedEurope PMC · Crossref · checked 15 Sept 2026
In order for a country's economy to grow, agricultural development is essential. Plant diseases, however, severely hamper crop growth rate and quality. In the absence of domain experts and with low contrast information, accurate identification of these diseases is very challenging and time-consuming. This leads to an agricultural management system in need of a method for automatically detecting disease at an early stage. As a consequence of dimensionality reduction, CNN-based models use pooling layers, which results in the loss of vital information, including the precise location of the most prominent features. In response to these challenges, we propose a fine-tuned technique, GreenViT , for detecting plant infections and diseases based on Vision Transformers (ViTs). Similar to word embedding, we divide the input image into smaller blocks or patches and feed these to the ViT sequentially. Our approach leverages the strengths of ViTs in order to overcome the problems associated with CNN-based models. Experiments on widely used benchmark datasets were conducted to evaluate the proposed GreenViT performance. Based on the obtained experimental outcomes, the proposed technique outperforms state-of-the-art (SOTA) CNN models for detecting plant diseases.
Why it matches plant phenotyping methods植物画像から病害を検出するVision Transformer手法を提案し、ベンチマークデータセットで性能評価しているため、植物病害状態の表現型取得手法が中心です。
abstractwe propose a fine-tuned technique, GreenViT , for detecting plant infections and diseases based on Vision Transformers (ViTs).
Reproduction assets foundThe paper used two public plant leaf image datasets (PlantVillage and DRLI) for its GreenViT disease-detection experiments, with explicit availability links in the Data Availability Statement. No author analysis code or trained model checkpoint is released.Dataset · publictration, J.W.L.; Funding acquisition, N.S.A. and J.W.L. All authors have read and agreed to the published version of the manuscript.
Institutional Review Board Statement
Not applicable.
Informed Consent Statement
Not applicable.
Data Availability Statement
Publicly available datasets were analyzed in this study. Link to the PV: https://github.com/spMohanty/PlantVillage-Dataset and DRLI: https://data.mendeley.com/datasets/hb74ynkjcn/1 (accessed on 5 July 2023).
Conflicts of Interest
The authors declare no conflict of interest.
References
1.
World Bank
World Bank Survey
2021 Available online: https://data.worldbank.org/indicator/SL.AGR.EMPL.ZS (accessed on 5 June 2023)
2.
World Food Clock
2014Open asset ↗PlantVillage-Datasetlines:80-107Dataset · publicauthors have read and agreed to the published version of the manuscript.
Institutional Review Board Statement
Not applicable.
Informed Consent Statement
Not applicable.
Data Availability Statement
Publicly available datasets were analyzed in this study. Link to the PV: https://github.com/spMohanty/PlantVillage-Dataset and DRLI: https://data.mendeley.com/datasets/hb74ynkjcn/1 (accessed on 5 July 2023).
Conflicts of Interest
The authors declare no conflict of interest.
References
1.
World Bank
World Bank Survey
2021 Available online: https://data.worldbank.org/indicator/SL.AGR.EMPL.ZS (accessed on 5 June 2023)
2.
World Food Clock
2014 Available online: http://worldfoodclock.com/ (accessed on 5Open asset ↗data.mendeley.com/datasets/hb74ynkjcn · hb74ynkjcn/1lines:80-107Code / dataset availability confirmedEurope PMC · checked 15 Sept 2026
Background: Rapid and accurate detection of tassels is of great significance for maize breeding, seed production and the acquisition of key growth stage. To liberate manpower and improve the efficiency of production management, many automatic detection methods with acceptable accuracy have been proposed. However, images acquisition parameters of these methods were quite different, so they cannot provide an operable standard for practical applications. In this study, based on multi-temporal unmanned aerial vehicle (UAV) RGB images with maize flowering stage, we created UAV Maize Tassel Detection (UAVMTD) dataset, and used Faster R-CNN to answer what are the key factors affecting detection accuracy from two aspects of efficient use of samples and data acquisition standards. Based on the detection results, we estimated tasseling date of different plots and analyzed varieties’ differences. Results: The results show that model performance would not be greatly affected before the amount of training data changed by orders of magnitude, but it can be improved effectively by adjusting sub-images’ sizes, and the final model was selected with AP@0.5IOU was 0.916; images obtained at 12 pm were more suitable for tassels detection, AP@0.5IOU, recall and precision were 3%, 2% and 6% higher than that at 8 am; optimal spatial resolution was around 1cm for tassels detection by considering the recognition effect and data acquisition efficiency. Conclusions: This study analyzed key factors affecting maize tassels detection and provided a reasonable reference for future applications, which is helpful to screen out varieties from large-scale breeding materials.
Why it matches plant phenotyping methodsUAV画像によるトウモロコシ雄穂検出のデータセット構築、検出精度の検証、撮影条件の最適化を中心とする植物フェノタイピング研究である。
abstractwe created UAV Maize Tassel Detection (UAVMTD) dataset, and used Faster R-CNN to answer what are the key factors affecting detection accuracy
Reproduction assets foundThe paper's UAV maize tassel detection dataset (UAVMTD: 142 UAV RGB images, 28,182 labeled tassels) is explicitly stated to be publicly available on the authors' GitHub repository, which is an allowed URL. No separate analysis code or trained model checkpoints are explicitly deposited.Dataset · public1 Consent for publication
2 Not applicable.
3 Availability of data and materials
4 The dataset analyzed are available at: https://github.com/Xulizzz/UAVMTD
5 Competing interests
6 The authors declare that they have no known competing financial interests or personal
7 relationships that could have appeared to influence the work reported in this paper.
8 Funding
9 This work was supported by the National Key Research and Development Program of
10 China and Shandong Province, China（20Open asset ↗Xulizzz/UAVMTDpdf-layout-page:33 lines:1-49Code / dataset availability confirmedEurope PMC · bioRxiv · checked 15 Sept 2026
MicroscopyCell / cellular structureFlowerTissue2D/3D reconstructionGrowth / development / phenology
Floral spurs are invaginations borne by perianth organs (petals and/or sepals) that have evolved repeatedly in various angiosperm clades. They typically store nectar and can limit the access of pollinators to this reward, resulting in pollination specialization that can lead to speciation in both pollinator and plant lineages. Despite the ecological and evolutionary importance of nectar spurs, the cellular mechanisms involved during spur development have only been described in detail in a handful of species, primarily with respect to epidermal cells. These studies show that the mechanisms involved are taxon-specific. Using confocal microscopy and automated 3D image analysis, we studied spur morphogenesis in Staphisagria picta (Ranunculaceae) and showed that the process is marked by an early phase of dominant cell proliferation, followed by a phase of anisotropic (directional) cell expansion. The comparison with Aquilegia , another taxon of Ranunculaceae with spurred petals, revealed that the convergence in form between the spurs of both taxa is obtained by partially similar developmental processes. The analytical pipeline designed here is an efficient method to visualize in 3D each cell of a developing organ, paving the way for future comparative studies of organ morphogenesis in multicellular eukaryotes. Highlight A new method of 3D analysis of plant tissues at the cellular level revealed that spur morphogenesis in Staphisagria picta is marked by an early phase of dominant cell proliferation, followed by a phase of anisotropic cell expansion. Floral spur development is analysed for the first time quantitatively, taking into account all tissues composing the organ, namely epidermis and parenchyma.
Why it matches plant phenotyping methods共焦点顕微鏡と自動3D画像解析による発生器官の細胞形態・増殖・異方的伸長の定量化手法が研究の中心であり、植物器官の表現型取得・解析に該当する。
abstractUsing confocal microscopy and automated 3D image analysis, we studied spur morphogenesis in Staphisagria picta (Ranunculaceae)
Reproduction assets foundThe paper's 3D segmentation/visualization pipeline (PlantSeg + MorphoLibJ + homemade Python scripts) is the paper-specific computational analysis, and the authors explicitly state the automation and visualization code is publicly available on GitHub. No separate public phenotype dataset or image deposit is stated; dataCode · public”.
235 Cell outliers, i.e. the 5% largest and smallest cells in terms of volume, were filtered out. To
236 visualize the interior of the petals, we relied on the opacity of the dots or on virtual sections.
237 The code that allowed the automation of the segmentations and the visualization of the data is
238 available on github [https://github.com/paulinedlpch/morphogenesis].Open asset ↗paulinedlpch/morphogenesispdf-layout-page:6 lines:1-57Code / dataset availability confirmedOpenAlex · Europe PMC · Crossref · checked 7 Sept 2026
Studying the complex web of interactions in biological communities requires large multifactorial experiments with sufficient statistical power. Automation tools reduce the time and labor associated with setup, data collection, and analysis in experiments that untangle these webs. We developed tools for high-throughput experimentation (HTE) in duckweeds, small aquatic plants that are amenable to autonomous experimental preparation and image-based phenotyping. We showcase the abilities of our HTE system in a study with 6,000 experimental units grown across 2,000 treatments. These automated tools facilitated the collection and analysis of time-resolved growth data, which revealed finer dynamics of plant-microbe interactions across environmental gradients. Altogether, our HTE system can run experiments with up to 11,520 experimental units and can be adapted for other small organisms.
Why it matches plant phenotyping methodsアヒルウキクサを対象に、自動化された大規模実験系と画像ベースの時系列成長データ取得・解析を開発・提示しており、植物フェノタイピング基盤が研究の中心である。
abstractWe developed tools for high-throughput experimentation (HTE) in duckweeds, small aquatic plants that are amenable to autonomous experimental preparation and image-based phenotyping.
Reproduction assets foundThe paper's Data Availability statement explicitly deposits the authors' data (Dryad) and code (Zenodo) for this duckweed high-throughput imaging/phenotyping study.Dataset · publicData Availability: We have made our data and code available at the following repositories: Dryad: https://doi.org/10.5061/dryad.t4b8gtj6tOpen asset ↗Dryad · 10.5061/dryad.t4b8gtj6tlines:170-179Code / dataset availability confirmedOpenAlex · Europe PMC · Crossref · bioRxiv · checked 14 Sept 2026
Abstract Nondestructive plant phenotyping is fundamental for unraveling molecular processes underlying plant development and response to the environment. While the emergence of high-through phenotyping facilities can further our understanding of plant development and stress responses, their high costs significantly hinder scientific progress. To democratize high-throughput plant phenotyping, we developed sets of low-cost image- and weight-based devices to monitor plant growth and evapotranspiration. We paired these devices with a suite of computational pipelines for integrated and straightforward data analysis. We validated the suitability of our system for large screens by evaluating a cowpea diversity panel for responses to drought stress. The observed natural variation was subsequently used for Genome-Wide Association Study, where we identified nine genetic loci that putatively contribute to cowpea drought resilience during early vegetative development. We validated the homologs of the identified candidate genes in Arabidopsis using available mutant lines. These results demonstrate the varied applicability of this low-cost phenotyping system. In the future, we foresee these setups facilitating identification of genetic components of growth, plant architecture, and stress tolerance across a wide variety of species.
Why it matches plant phenotyping methods低コストの画像・重量ベース装置と計算パイプラインを開発し、植物成長・蒸発散を測定するフェノタイピングシステムとして検証しており、手法が研究の中心である。
abstractwe developed sets of low-cost image- and weight-based devices to monitor plant growth and evapotranspiration.
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 · public142 how to build and program the device can be found at https://github.com/ok84-star/AAWSMO. DetailsOpen asset ↗ok84-star/AAWSMOpdf-page:6 lines:1-42Code / dataset availability confirmedEurope PMC · checked 7 Sept 2026
Background Investigations into the growth and self-organization of plant roots is subject to fundamental and applied research in various areas such as botany, agriculture, and soil science. The growth activity of the plant tissue can be investigated by isotope labeling experiments with heavy water and subsequent detection of the deuterium in non-exchangeable positions incorporated into the plant biomass. Commonly used analytical methods to detect deuterium in plants are based on mass-spectrometry or neutron-scattering and they either suffer from elaborated sample preparation, destruction of the sample during analysis, or low spatial resolution. Confocal Raman micro-spectroscopy (CRM) can be considered a promising method to overcome the aforementioned challenges. The substitution of hydrogen with deuterium results in the measurable shift of the CH-related Raman bands. By employing correlative approaches with a high-resolution technique, such as helium ion microscopy (HIM), additional structural information can be added to CRM isotope maps and spatial resolution can be further increased. For that, it is necessary to develop a comprehensive workflow from sample preparation to data processing. Results A workflow to prepare and analyze roots of hydroponically grown and deuterium labeled Zea mays by correlative HIM-CRM micro-analysis was developed. The accuracy and linearity of deuterium detection by CRM were tested and confirmed with samples of deuterated glucose. A set of root samples taken from deuterated Zea mays in a time-series experiment was used to test the entire workflow. The deuterium content in the roots measured by CRM was close to the values obtained by isotope-ratio mass spectrometry. As expected, root tips being the most actively growing root zone had incorporated the highest amount of deuterium which increased with increasing time of labeling. Furthermore, correlative HIM-CRM analysis allowed for obtaining the spatial distribution pattern of deuterium and lignin in root cross-sections. Here, more active root zones with higher deuterium incorporation showed less lignification. Conclusions We demonstrated that CRM in combination with deuterium labeling can be an alternative and reliable tool for the analysis of plant growth. This approach together with the developed workflow has the potential to be extended to complex systems such as plant roots grown in soil.
Why it matches plant phenotyping methods植物根の成長状態を測定する相関HIM-CRMワークフローを開発し、重水素検出の精度・直線性と質量分析との一致を検証しているため、植物フェノタイピング手法が中心である。
abstractA workflow to prepare and analyze roots of hydroponically grown and deuterium labeled Zea mays by correlative HIM-CRM micro-analysis was developed.
Reproduction assets foundThe paper's data availability statement deposits the datasets generated and analyzed (CRM/HIM phenotyping measurements of deuterium-labeled maize roots) in the UFZ Data Investigation Portal, a public repository with an explicit URL. No author analysis code with a public URL is stated.Dataset · publicThe datasets generated and/or analyzed during the current study are available in the UFZ Data Investigation Portal ( https://www.ufz.de/record/dmp/archive/13952 ) repository.Open asset ↗UFZ Data Investigation Portal · archive/13952lines:198-288Code / dataset availability confirmedOpenAlex · 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 confirmedEurope PMC · Crossref · checked 7 Sept 2026
Plant phenotyping experiments are conducted under a variety of experimental parameters and settings for diverse purposes. The data they produce is heterogeneous, complicated, often poorly documented and, as a result, difficult to reuse. Meeting societal needs (nutrition, crop adaptation and stability) requires more efficient methods toward data integration and reuse. In this work, we examine what "making data FAIR" entails, and investigate the benefits and the struggles not only of reusing FAIR data, but also making data FAIR using genotype by environment and QTL by environment interactions for developmental traits in potato as a case study. We assume the role of a scientist discovering a phenotypic dataset on a FAIR data point, verifying the existence of related datasets with environmental data, acquiring both and integrating them. We report and discuss the challenges and the potential for reusability and reproducibility of FAIRifying existing datasets, using metadata standards such as MIAPPE, that were encountered in this process.
Why it matches plant phenotyping methods植物フェノタイピングデータセットのFAIR化、統合、再利用性・再現性を扱う研究であり、フェノタイピングデータ基盤とデータ標準化が中心です。
titleThe benefits and struggles of FAIR data: the case of reusing plant phenotyping data
Reproduction assets foundThe paper's FAIRified potato CxE phenotyping datasets (original and processed) and its analysis code (Jupyter notebooks, FDP/triple-store scripts) are publicly deposited on Zenodo and GitHub, with explicit availability statements.Dataset · publicAll associated data, original and processed, is available on Github 15 . The data is located under the paths “ all_containers/ common_files/data-original ” and “ all_containers/common_files/data-generated ”. These two folders ( data-original and data-generated ) are also available on Zenodo 29 .Open asset ↗Zenodolines:191-241Code · publicAll associated code is available on our Github repository and deposited on Zenodo 15 , including Jupyter notebooks to transform data, scripts to run the FDP and the triple store.Open asset ↗Zenodolines:191-241Code / dataset availability confirmedOpenAlex · Europe PMC · Crossref · checked 7 Sept 2026
Nematode migration, feeding site formation, withdrawal of plant assimilates, and activation of plant defence responses have a significant impact on plant growth and development. Plants display intraspecific variation in tolerance limits for root-feeding nematodes. Although disease tolerance has been recognized as a distinct trait in biotic interactions of mainly crops, we lack mechanistic insights. Progress is hampered by difficulties in quantification and laborious screening methods. We turned to the model plant Arabidopsis thaliana, since it offers extensive resources to study the molecular and cellular mechanisms underlying nematode-plant interactions. Through imaging of tolerance-related parameters, the green canopy area was identified as an accessible and robust measure for assessing damage due to cyst nematode infection. Subsequently, a high-throughput phenotyping platform simultaneously measuring the green canopy area growth of 960 A. thaliana plants was developed. This platform can accurately measure cyst nematode and root-knot nematode tolerance limits in A. thaliana through classical modelling approaches. Furthermore, real-time monitoring provided data for a novel view of tolerance, identifying a compensatory growth response. These findings show that our phenotyping platform will enable a new mechanistic understanding of tolerance to below-ground biotic stress.
Why it matches plant phenotyping methods根圏線虫感染による植物の耐性を定量化するため、画像による緑色キャノピー面積の測定と、960個体を同時測定する高スループット表現型解析プラットフォームを開発しており、表現型取得法が研究の中心である。
abstractThrough imaging of tolerance-related parameters, the green canopy area was identified as an accessible and robust measure for assessing damage due to cyst nematode infection.
Reproduction assets foundThe paper's authors publicly deposited the full plant image dataset (green canopy phenotyping pictures) on figshare and the analysis code/model (SYLM and R growth analysis scripts) on a WUR GitLab repository, both explicitly linked in the Data availability statement.Dataset · publicAlso, the full picture dataset has been made available at doi: https://doi.org/10.6084/m9.figshare.23518923.v1 .Open asset ↗figshare · 10.6084/m9.figshare.23518923.v1lines:263-263Code · publicUsing these equations, the tolerance limit T SYLM and the minimum yield m were estimated (model and code available via gitlab: https://git.wur.nl/published_papers/willig_2023_camera-setup ).Open asset ↗git.wur.nl · published_papers/willig_2023_camera-setuplines:53-66Code / dataset availability confirmedCrossref · checked 14 Sept 2026
Abstract Crop yield is determined by potential harvest organ size, source organ photosynthesis and carbohydrate partitioning. Filling the harvest organ efficiently remains a challenge. Here, we developed a kinetic model of rice grain filling, which scales from the primary biochemical and biophysical processes of photosynthesis to whole-plant carbon and nitrogen dynamics. The model reproduces the rice yield formation process under different environmental and genetic perturbations. In silico screening identified a range of post-anthesis targets—both established and novel—that can be manipulated to enhance rice yield. Remarkably, we pinpointed the stability of grain-filling rate from flowering to harvest as a critical factor for maximizing grain yield. This finding was further validated in two independent super-high-yielding rice cultivars, each yielding approximately 21 t ha−1 of rough rice at 14% moisture content. Furthermore, we revealed that stabilizing the grain-filling rate could lead to a potential yield increase of 30–40% in an elite rice cultivar. Notably, the instantaneous grain-filling rates around 15- and 38-day post-flowering significantly influence grain yield; and we introduced an innovative in situ approach using ear respiratory rates for precise quantification of these rates. We finally derived an equation to predict the maximum dried brown rice yield (Y, t ha−1) of a cultivar based on its potential gross photosynthetic accumulation from flowering to harvest (Apc, t CO2 ha−1): Y = 0.74 × Apc + 1.9. Overall, this work establishes a framework for quantitatively dissecting crop physiology and designing high-yielding ideotypes.
Why it matches plant phenotyping methods全植物の炭素・窒素動態と収量形成を推定する速度論モデルを開発し、耳の呼吸速度による粒充填速度の定量化手法も導入しているため、表現型取得・推定が中心的です。
abstractHere, we developed a kinetic model of rice grain filling, which scales from the primary biochemical and biophysical processes of photosynthesis to whole-plant carbon and nitrogen dynamics.
Reproduction assets foundThe paper's authors publicly released the WACNI model source code (the computational framework used for all simulations and analysis) on GitHub, with explicit availability language in the MODEL AND DATA AVAILABILITY section. Supplementary Data 2 contains literature-extracted experimental data but no separate public URLCode · publicip
help improve model parameterization.
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MODEL AND DATA AVAILABILITY
us
an
Experimental data extracted from literature, used in model-data comparison, are tabulated in
Supplementary Data 2.
M
The source code used for this study, along with the operational commands and user guide, is
freely available for non-commercial use at https://github.com/rootchang/WACNI-rice.git.
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Ac
28Open asset ↗rootchang/WACNI-ricepdf-layout-page:28 lines:1-44Code / dataset availability 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 confirmedOpenAlex · Crossref · checked 15 Sept 2026
The study of plant phenotypes based on 3D models has become an important research direction for automatic plant phenotype acquisition. Building a labeled three-dimensional dataset of the whole growth period can help the development of 3D crop plant models in point cloud segmentation. Therefore, the demand for 3D whole plant growth period model datasets with organ-level markers is growing rapidly. In this study, five different soybean varieties were selected, and three-dimensional reconstruction was carried out for the whole growth period (13 stages) of soybean using multiple-view stereo technology (MVS). Leaves, main stems, and stems of the obtained three-dimensional model were manually labeled. Finally, two-point cloud semantic segmentation models, RandLA-Net and BAAF-Net, were used for training. In this paper, 102 soybean stereoscopic plant models were obtained. A dataset with original point clouds was constructed and the subsequent analysis confirmed that the number of plant point clouds was consistent with corresponding real plant development. At the same time, a 3D dataset named Soybean-MVS with labels for the whole soybean growth period was constructed. The test result of mAccs at 88.52% and 87.45% verified the availability of this dataset. In order to further promote the study of point cloud segmentation and phenotype acquisition of soybean plants, this paper proposed an annotated three-dimensional model dataset for the whole growth period of soybean for 3D plant organ segmentation. The release of the dataset can provide an important basis for proposing an updated, highly accurate, and efficient 3D crop model segmentation algorithm. In the future, this dataset will provide important and usable basic data support for the development of three-dimensional point cloud segmentation and phenotype automatic acquisition technology of soybeans.
Why it matches plant phenotyping methods大豆全生育期の3D再構成、器官ラベル付き点群データセットの構築と検証が中心で、植物表現型自動取得を支援する再利用可能な基盤である。
abstractThe study of plant phenotypes based on 3D models has become an important research direction for automatic plant phenotype acquisition.
Reproduction assets foundThe paper's own 3D soybean phenotyping assets are publicly available: the original reconstructed 3D models and the annotated Soybean-MVS point cloud dataset are on Kaggle, and the authors' analysis code (BAAF-Net and RandLA-Net segmentation implementations used to train/test the dataset) is on GitHub with explicit dataDataset · publicNatural Science Foundation of Heilongjiang Province of
China (LH2021C021).
Institutional Review Board Statement: Not applicable.
Data Availability Statement: Original models are available in a publicly accessible repository: The
original contributions presented in the study are publicly available. These data can be found here:
https://www.kaggle.com/datasets/soberguo/soybean-original-model (accessed on 1 January 2023).
The soybean-MVS dataset is available in a publicly accessible repository: Publicly available datasets
were analyzed in this study. These data can be found here: https://www.kaggle.com/datasets/soberguo/soybeanmvs (accessed on 1 January 2023).
Conflicts of Interest: The authorsOpen asset ↗soberguo/soybean-original-modelpdf-raw-page:15 lines:1-47Dataset · publicesented in the study are publicly available. These data can be found here:
https://www.kaggle.com/datasets/soberguo/soybean-original-model (accessed on 1 January 2023).
The soybean-MVS dataset is available in a publicly accessible repository: Publicly available datasets
were analyzed in this study. These data can be found here: https://www.kaggle.com/datasets/soberguo/soybeanmvs (accessed on 1 January 2023).
Conflicts of Interest: The authors declare no conflict of interest.
Appendix A
Table A1. Image collection quantity of soybean plants of different varieties in different stages.
V1 V2 V3 V4 V5 R1 R2 R3 R4 R5 R6 R7 R8
2018 2019 2018 2019 2018 2019 2018 2019 2018 2019 2018 2019 2018 2019 20Open asset ↗soberguo/soybeanmvspdf-raw-page:15 lines:1-47Code / dataset availability confirmedEurope PMC · OpenAlex · checked 14 Sept 2026
As one of the most widely grown crops in the world, rice is not only a staple food but also a source of calorie intake for more than half of the world's population, occupying an important position in China's agricultural production. Thus, determining the inner potential connections between the genetic mechanisms and phenotypes of rice using dynamic analyses with high-throughput, nondestructive, and accurate methods based on high-throughput crop phenotyping facilities associated with rice genetics and breeding research is of vital importance. In this work, we developed a strategy for acquiring and analyzing 58 image-based traits (i-traits) during the whole growth period of rice. Up to 84.8% of the phenotypic variance of the rice yield could be explained by these i-traits. A total of 285 putative quantitative trait loci (QTLs) were detected for the i-traits, and principal components analysis was applied on the basis of the i-traits in the temporal and organ dimensions, in combination with a genome-wide association study that also isolated QTLs. Moreover, the differences among the different population structures and breeding regions of rice with regard to its phenotypic traits demonstrated good environmental adaptability, and the crop growth and development model also showed high inosculation in terms of the breeding-region latitude. In summary, the strategy developed here for the acquisition and analysis of image-based rice phenomes can provide a new approach and a different thinking direction for the extraction and analysis of crop phenotypes across the whole growth period and can thus be useful for future genetic improvements in rice.
Why it matches plant phenotyping methodsイネの全生育期間にわたる画像ベース形質の取得・解析戦略を開発しており、フェノタイピング手法が研究の中心である。
abstractIn this work, we developed a strategy for acquiring and analyzing 58 image-based traits (i-traits) during the whole growth period of rice.
Reproduction assets foundThe paper's Data Availability statement points to a public website hosting the related data and code (i-trait phenotype dataset and analysis code) for this rice image-based phenotyping study. The URL matches an allowed entry. Confidence is moderate because the statement uses future tense ('will be made available') and,Dataset · publicted in the experimental design and data analysis. W.Y. and W.H. supervised the project and designed the research. Competing interests: The authors declare that they have no competing interests.
Data Availability
The related data and code supporting the conclusion for this article will be made available on the following website: http://plantphenomics.hzau.edu.cn/usercrop/Rice/download .
Supplementary Materials
Supplementary 1
Movie S1. Dynamic graph of the processing method.
Note S1. Trait analysis technical documentation.
Table S1. Information on Oryza sativa .
Table S2. Statistical summary of the 6 developed models for estimating the panicle dry weight.
Table S3. Summary of the 5-fold crossOpen asset ↗plantphenomics.hzau.edu.cnlines:256-286Code / dataset availability confirmedEurope PMC · checked 7 Sept 2026
Plant architecture plays a major role in flowering and therefore in crop yield. Attempts to visualize and analyse strawberry plant architecture have been few to date. Here, we developed open-source software combining two- and three-dimensional representations of plant development over time along with statistical methods to explore the variability in spatio-temporal development of plant architecture in cultivated strawberry. We applied this software to six seasonal strawberry varieties whose plants were exhaustively described monthly at the node scale. Results showed that the architectural pattern of the strawberry plant is characterized by a decrease of the module complexity between the zeroth-order module (primary crown) and higher-order modules (lateral branch crowns and extension crowns). Furthermore, for each variety, we could identify traits with a central role in determining yield, such as date of appearance and number of branches. By modeling the spatial organization of axillary meristem fate on the zeroth-order module using a hidden hybrid Markov/semi-Markov mathematical model, we further identified three zones with different probabilities of production of branch crowns, dormant buds, or stolons. This open-source software will be of value to the scientific community and breeders in studying the influence of environmental and genetic cues on strawberry architecture and yield.
Why it matches plant phenotyping methodsイチゴ植物体の時空間的な構造形質を取得・解析するオープンソースソフトウェアを開発しており、表現型取得・解析手法が研究の中心である。
abstractwe developed open-source software combining two- and three-dimensional representations of plant development over time along with statistical methods to explore the variability in spatio-temporal development of plant architecture in cultivated strawberry.
Reproduction assets foundThe paper's strawberry architectural phenotype data (MTG-encoded plant descriptions) are publicly deposited in the authors' GitHub repository, and the OpenAlea.Strawberry analysis/visualization software is open-source on GitHub with a Docker image for deployment. The data.inrae.fr deposits contain only a demonstration,Dataset · publicAll data are available at Github: https://github.com/openalea/strawberry/tree/master/share/dataOpen asset ↗https://github.com/openalea/strawberry/tree/master/share/datalines:406-468Code · publicFirst, OpenAlea.Strawberry is an open-source Python package ( https://github.com/openalea/strawberry ), available in the OpenAlea platformOpen asset ↗https://github.com/openalea/strawberrylines:337-344Code / dataset availability confirmedEurope PMC · checked 7 Sept 2026
It is valuable to develop a generic model that can accurately estimate the leaf area index (LAI) of wheat from unmanned aerial vehicle-based multispectral data for diverse soil backgrounds without any ground calibration. To achieve this objective, 2 strategies were investigated to improve our existing random forest regression (RFR) model, which was trained with simulations from a radiative transfer model (PROSAIL). The 2 strategies consisted of (a) broadening the reflectance domain of soil background to generate training data and (b) finding an appropriate set of indicators (band reflectance and/or vegetation indices) as inputs of the RFR model. The RFR models were tested in diverse soils representing varying soil types in Australia. Simulation analysis indicated that adopting both strategies resulted in a generic model that can provide accurate estimation for wheat LAI and is resistant to changes in soil background. From validation on 2 years of field trials, this model achieved high prediction accuracy for LAI over the entire crop cycle (LAI up to 7 m 2 m -2 ) (root mean square error (RMSE): 0.23 to 0.89 m 2 m -2 ), including for sparse canopy (LAI less than 0.3 m 2 m -2 ) grown on different soil types (RMSE: 0.02 to 0.25 m 2 m -2 ). The model reliably captured the seasonal pattern of LAI dynamics for different treatments in terms of genotypes, plant densities, and water-nitrogen managements (correlation coefficient: 0.82 to 0.98). With appropriate adaptations, this framework can be adjusted to any type of sensors to estimate various traits for various species (including but not limited to LAI of wheat) in associated disciplines, e.g., crop breeding, precision agriculture, etc.
Why it matches plant phenotyping methodsUAVマルチスペクトルデータから小麦LAIを推定する汎用モデルを開発し、異なる土壌・圃場試験で検証しており、植物形質取得手法が中心である。
abstractdevelop a generic model that can accurately estimate the leaf area index (LAI) of wheat from unmanned aerial vehicle-based multispectral data
Reproduction assets foundThe paper's Data Availability statement points to public source code and data at UQ eSpace (DOI 10.48610/ac9642c), covering the RFR model code and supporting data. Additionally, the BASE soil reflectance dataset used to generate test soil backgrounds is publicly available on Zenodo (record 6265730).Code · publicOther data and source code supporting this work are available at UQ eSpace, and a unique DOI (https://doi.org/10.48610/ac9642c) is provided for public access.Open asset ↗UQ eSpace · 10.48610/ac9642clines:298-372Dataset · publicThe BASE soil reflectance data are available online ( https://zenodo.org/record/6265730 ).Open asset ↗Zenodo · 6265730lines:176-179Code / dataset availability confirmedEurope PMC · checked 14 Sept 2026
In order to mitigate the effects of a changing climate, agriculture requires more effective evaluation, selection, and production of crop cultivars in order to accelerate genotype-to-phenotype connections and the selection of beneficial traits. Critically, plant growth and development are highly dependent on sunlight, with light energy providing plants with the energy required to photosynthesize as well as a means to directly intersect with the environment in order to develop. In plant analyses, machine learning and deep learning techniques have a proven ability to learn plant growth patterns, including detection of disease, plant stress, and growth using a variety of image data. To date, however, studies have not assessed machine learning and deep learning algorithms for their ability to differentiate a large cohort of genotypes grown under several growth conditions using time-series data automatically acquired across multiple scales (daily and developmentally). Here, we extensively evaluate a wide range of machine learning and deep learning algorithms for their ability to differentiate 17 well-characterized photoreceptor deficient genotypes differing in their light detection capabilities grown under several different light conditions. Using algorithm performance measurements of precision, recall, F1-Score, and accuracy, we find that Suport Vector Machine (SVM) maintains the greatest classification accuracy, while a combined ConvLSTM2D deep learning model produces the best genotype classification results across the different growth conditions. Our successful integration of time-series growth data across multiple scales, genotypes and growth conditions sets a new foundational baseline from which more complex plant science traits can be assessed for genotype-to-phenotype connections.
Why it matches plant phenotyping methods植物の時系列成長データを用いた遺伝子型分類について、複数の機械学習・深層学習手法を比較評価しており、表現型データの自動取得・解析ワークフローが研究の中心です。
abstractOur successful integration of time-series growth data across multiple scales, genotypes and growth conditions sets a new foundational baseline from which more complex plant science traits can be assessed for genotype-to-phenotype connections.
Reproduction assets foundThe paper's authors explicitly state that all code implementing their ML/DL genotype-classification models is publicly available on GitHub at the authors' repository URL. PlantCV is a generic third-party library, not a paper-specific asset, and no separate phenotype dataset deposit is described beyond this repository.Code · publice. In our study, if a plant of another genotype misclassified as “wt” genotype. True negatives are data points that are truly negative despite being classified as negative. False negatives are data items with negative labels but positive values.
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Code availability
All codes implementing the models are available through GitHub (https://github.com/NazmusSakeef/Plant-Genomics-Using-Machine-Learning/tree/main/dataset).
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Results and discussionOpen asset ↗NazmusSakeef/Plant-Genomics-Using-Machine-Learninglines:107-116Code / dataset availability confirmedEurope PMC · checked 14 Sept 2026
Field / plotStem / branchGrowth / time-series analysisGrowth / development / phenology
Traditional phenological models use chilling and thermal forcing (temperature sum or degree-days) to predict budbreak. Because of the heightening impact of climate and other related biotic or abiotic stressors, a model with greater biological support is needed to better predict budbreak. Here, we present an original mechanistic model based on the physiological processes taking place before and during budbreak of conifers. As a general principle, we assume that phenology is driven by the carbon status of the plant, which is closely related to environmental variables and the annual cycle of dormancy-activity. The carbon balance of a branch was modelled from autumn to winter with cold acclimation and dormancy and from winter to spring when deacclimation and growth resumption occur. After being calibrated in a field experiment, the model was validated across a large area (> 34 000 km 2 ), covering multiple conifers stands in Québec (Canada) and across heated plots for the SPRUCE experiment in Minnesota (USA). The model accurately predicted the observed dates of budbreak in both Québec (±3.98 d) and Minnesota (±7.98 d). The site-independent calibration provides interesting insights on the physiological mechanisms underlying the dynamics of dormancy break and the resumption of vegetative growth in spring.
Why it matches plant phenotyping methods針葉樹の芽吹き時期という植物状態を予測する機 mechanistic phenology modelを開発し、野外実験と複数地域・加温プロットで較正および検証しており、方法が研究の中心である。
abstractHere, we present an original mechanistic model based on the physiological processes taking place before and during budbreak of conifers.
Reproduction assets foundThe paper's Data availability section points to the public SPRUCE experiment dataset (used for model validation of black spruce budbreak under warming treatments) hosted on the mnspruce.ornl.gov site, which is an allowed URL. The Canadian sugar/phenology dataset (doi: 10.5683/SP3/MVRZMQ) is also public but its URL is不在Dataset · publico Cartenı̀ https://orcid.org/0000-0001-8985-1132
Annie Deslauriers https://orcid.org/0000-0002-4994-5772
Stefano Mazzoleni https://orcid.org/0000-0002-1132-2625
Data availability
Sugar and phenological datasets for the Canadian experiments
are available at doi: 10.5683/SP3/MVRZMQ. Data for the
SPRUCE experiment can be found at: https://mnspruce.ornl.gov/datasets/public.References
Adams HD, Germino MJ, Breshears DD, Barron-Gafford GA, Guardiola-
Claramonte M, Zou CB, Huxman TE. 2013. Nonstructural leaf
carbohydrate dynamics of Pinus edulis during drought-induced tree mortality
reveal role for carbon metabolism in mortality mechanism. New Phytologist
197: 1142–1151.
Ainsworth EA, Bush DR. 2011Open asset ↗mnspruce.ornl.govpdf-raw-page:11 lines:1-85Code / dataset availability confirmedEurope PMC · OpenAlex · Crossref · checked 7 Sept 2026
High-throughput plant phenotyping-the use of imaging and remote sensing to record plant growth dynamics-is becoming more widely used. The first step in this process is typically plant segmentation, which requires a well-labeled training dataset to enable accurate segmentation of overlapping plants. However, preparing such training data is both time and labor intensive. To solve this problem, we propose a plant image processing pipeline using a self-supervised sequential convolutional neural network method for in-field phenotyping systems. This first step uses plant pixels from greenhouse images to segment nonoverlapping in-field plants in an early growth stage and then applies the segmentation results from those early-stage images as training data for the separation of plants at later growth stages. The proposed pipeline is efficient and self-supervising in the sense that no human-labeled data are needed. We then combine this approach with functional principal components analysis to reveal the relationship between the growth dynamics of plants and genotypes. We show that the proposed pipeline can accurately separate the pixels of foreground plants and estimate their heights when foreground and background plants overlap and can thus be used to efficiently assess the impact of treatments and genotypes on plant growth in a field environment by computer vision techniques. This approach should be useful for answering important scientific questions in the area of high-throughput phenotyping.
Why it matches plant phenotyping methods植物画像から重複個体を自動分割し、草丈を推定する自己教師ありCNNパイプラインを開発しており、フェノタイピング手法が研究の中心である。
abstractwe propose a plant image processing pipeline using a self-supervised sequential convolutional neural network method for in-field phenotyping systems.
Reproduction assets foundThe paper's Data Availability statement explicitly deposits the authors' R pipeline code and sample field image data (6 training photos, 52 test photos) on GitHub at the allowed URL.Code · publicThe R code of the proposed pipeline, sample image data (including 6 field photos for training and 52 example field photos taken by one of our cameras for testing), and description are available on Github at https://github.com/xingcheg/Plant-Traits-Extraction-2023 .Open asset ↗xingcheg/Plant-Traits-Extraction-2023lines:84-106Code / dataset availability confirmedEurope PMC · Crossref · checked 14 Sept 2026
Background The current development of sensor technologies towards ever more cost-effective and powerful systems is steadily increasing the application of low-cost sensors in different horticultural sectors. In plant in vitro culture, as a fundamental technique for plant breeding and plant propagation, the majority of evaluation methods to describe the performance of these cultures are based on destructive approaches, limiting data to unique endpoint measurements. Therefore, a non-destructive phenotyping system capable of automated, continuous and objective quantification of in vitro plant traits is desirable. Results An automated low-cost multi-sensor system acquiring phenotypic data of plant in vitro cultures was developed and evaluated. Unique hardware and software components were selected to construct a xyz-scanning system with an adequate accuracy for consistent data acquisition. Relevant plant growth predictors, such as projected area of explants and average canopy height were determined employing multi-sensory imaging and various developmental processes could be monitored and documented. The validation of the RGB image segmentation pipeline using a random forest classifier revealed very strong correlation with manual pixel annotation. Depth imaging by a laser distance sensor of plant in vitro cultures enabled the description of the dynamic behavior of the average canopy height, the maximum plant height, but also the culture media height and volume. Projected plant area in depth data by RANSAC (random sample consensus) segmentation approach well matched the projected plant area by RGB image processing pipeline. In addition, a successful proof of concept for in situ spectral fluorescence monitoring was achieved and challenges of thermal imaging were documented. Potential use cases for the digital quantification of key performance parameters in research and commercial application are discussed. Conclusion The technical realization of "Phenomenon" allows phenotyping of plant in vitro cultures under highly challenging conditions and enables multi-sensory monitoring through closed vessels, ensuring the aseptic status of the cultures. Automated sensor application in plant tissue culture promises great potential for a non-destructive growth analysis enhancing commercial propagation as well as enabling research with novel digital parameters recorded over time.
Why it matches plant phenotyping methods植物組織培養の形質を自動・非破壊・連続測定するマルチセンサーフェノタイピングシステムを開発し、画像分割や深度計測を検証しており、方法が研究の中心である。
abstractAn automated low-cost multi-sensor system acquiring phenotypic data of plant in vitro cultures was developed and evaluated.
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 dataset supporting the conclusions of this article (Hard- and Software of “Phenomenon” phenotyping system) are available in an open-access Github repository, https://github.com/halube/Phenomenon .Open asset ↗halube/Phenomenonlines:224-282Code / dataset availability confirmedOpenAlex · Europe PMC · Crossref · bioRxiv · checked 15 Sept 2026
Abstract Nematode migration, feeding site formation, withdrawal of plant assimilates, and activation of plant defence responses have a significant impact on plant growth and development. Plants display intraspecific variation in tolerance limits for root-feeding nematodes. Although disease tolerance has been recognised as a distinct trait in biotic interactions of mainly crops, we lack mechanistic insights. Progress is hampered by difficulties in quantification and laborious screening methods. We turned to the model plant Arabidopsis thaliana , since it offers extensive resources to study the molecular and cellular mechanisms underlying nematode-plant interactions. Through imaging of tolerance-related parameters the green canopy area was identified as an accessible and robust measure for assessing damage due to cyst nematode infection. Subsequently, a high-throughput phenotyping platform simultaneously measuring the green canopy area growth of 960 A. thaliana plants was developed. This platform can accurately measure cyst- and root-knot nematode tolerance limits in A. thaliana through classical modelling of tolerance limits. Furthermore, real-time monitoring provided data for a novel view of tolerance, identifying a compensatory growth response. These findings show that our phenotyping platform will enable further studies into a mechanistic understanding of tolerance to below-ground biotic stress. Highlight The mechanisms of tolerance to root-parasitic nematodes remain unknown. We developed a high-throughput phenotyping system that enables unravelling the underlying mechanisms of tolerance to nematodes.
Why it matches plant phenotyping methods線虫耐性を評価するためのキャノピー画像計測と高スループット表現型解析プラットフォームの開発が研究の中心である。
abstractThrough imaging of tolerance-related parameters the green canopy area was identified as an accessible and robust measure for assessing damage due to cyst nematode infection.
Reproduction assets foundThe authors state that custom R scripts and functions used to analyse the high-throughput green canopy area growth data are publicly available via their GitLab repository at git.wur.nl/published_papers/willig_2023_camera-setup, and the data availability statement points to the same repository. The protocols.io link is Code · publiclimits (T) and relative minimum yield (m) were estimated for all
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measurements.
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Plant growth analysis using the high-throughput phenotyping platform
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To analyse the growth data of the plants obtained from the high-throughput platform, custom scripts and
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functions were written in “R” (available via gitlab:
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https://git.wur.nl/published_papers/willig_2023_camera-setup). For analysis we used the median daily
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leaf area (cm2), which was calculated by taking the median leaf area of the daily measurements (15 per
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day). The data was log2-transformed before analysis for normalization. The rate of growth was
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𝑅𝑥,𝑡 = log2(𝐴𝑥,𝑡−1 − 𝐴𝑥,𝑡Open asset ↗git.wur.nl/published_papers/willig_2023_camera-setuppdf-raw-page:11 lines:1-60Code / dataset availability confirmedEurope PMC · checked 7 Sept 2026
Crop phenology is crucial information for crop yield estimation and agricultural management. Traditionally, phenology has been observed from the ground; however Earth observation, weather and soil data have been used to capture the physiological growth of crops. In this work, we propose a new approach for the within-season phenology estimation for cotton at the field level. For this, we exploit a variety of Earth observation vegetation indices (derived from Sentinel-2) and numerical simulations of atmospheric and soil parameters. Our method is unsupervised to address the ever-present problem of sparse and scarce ground truth data that makes most supervised alternatives impractical in real-world scenarios. We applied fuzzy c-means clustering to identify the principal phenological stages of cotton and then used the cluster membership weights to further predict the transitional phases between adjacent stages. In order to evaluate our models, we collected 1,285 crop growth ground observations in Orchomenos, Greece. We introduced a new collection protocol, assigning up to two phenology labels that represent the primary and secondary growth stage in the field and thus indicate when stages are transitioning. Our model was tested against a baseline model that allowed to isolate the random agreement and evaluate its true competence. The results showed that our model considerably outperforms the baseline one, which is promising considering the unsupervised nature of the approach. The limitations and the relevant future work are thoroughly discussed. The ground observations are formatted in an ready-to-use dataset and will be available at https://github.com/Agri-Hub/cotton-phenology-dataset upon publication.
Why it matches plant phenotyping methods綿花の生育段階・遷移を衛星観測指標と環境データから推定する手法を開発し、地上観測で評価している。さらに再利用可能なデータセットも提供するため、植物フェノタイピング手法が中心である。
abstractwe propose a new approach for the within-season phenology estimation for cotton at the field level.
Reproduction assets foundThe paper's ground-observation phenology dataset (1,285 field observations with photos, labels, field geometries) is publicly released on GitHub, and the data are additionally deposited on Zenodo. No author analysis code is explicitly shared.Dataset · publicData relevant to this paper are available from Zenodo at DOI: 10.5281/zenodo.7646864 ( https://doi.org/10.5281/zenodo.7646864 ).Open asset ↗zenodo · 10.5281/zenodo.7646864lines:153-165Code / dataset availability confirmedEurope PMC · checked 14 Sept 2026
A monitoring of apple fruit, shoot and trunk growth was performed on 15 trees, equally split according to three treatments, which determined heavily contrasting carbon assimilate availability: unmanipulated trees (FRU), thinned trees (THI) and defruited trees (DEF). Several variables describe the vegetative growth on FRU and DEF trees (shoot length, base diameter, number of fruits on shoot, and height, diameter, pruning intensity and number of fruits of the branch carrying the shoot; trunk circumference), as well as the fruit growth on FRU and THI trees (3 fruit diameters). Additional measurements from ancillary shoots (apical diameter, number of leaves, leaf dry weight, stem dry weight, fresh mass, volume) and fruits (3 diameters, dry weight) from trees undergoing the same treatments, provide a more complete (destructive) characterization of organs growth, thanks to several measurements performed across the growing season. Organs are provided with categorical variables indicating the treatment, tree, canopy height, orientation (for both shoots and fruit), as well as branch and shoot identifiers, so that hierarchical modeling of the dataset can be performed. The dataset is completed with dates and day of the year of the measurements and the accumulated growing degree days from full bloom. Data can be used to calculate apple tree absolute and relative growth rates, maximum potential growth rates, as well as shoot growth responses to thinning and pruning. The dataset can also be used to calibrate allometric relationships, estimate structural apple tree growth parameters and their variability.
Why it matches plant phenotyping methodsリンゴ器官の成長形質を階層的・反復的に収録した再利用可能なデータセットであり、成長率やアロメトリーの推定・モデル較正に用いるデータ資源として方法論的価値がある。
titleA hierarchical dataset of vegetative and reproductive growth in apple tree organs under conventional and non-limited carbon resources.
Reproduction assets foundThe paper is a Data in Brief article describing its own apple tree growth phenotype dataset (shoot, fruit, trunk measurements) deposited publicly on Mendeley Data with DOI 10.17632/852r5dnzd5.1 and a direct URL. This is a paper-specific, public, actionable phenotype dataset.Dataset · publiccommercial orchard
City: Caldaro, Bolzano/Bozen province, Trentino Alto Adige region
Country: Italy
Latitude and longitude collected samples/data: 46° 21’ N, 11° 16’ E, Altitude 240 m
Period: May-November 2014
Data accessibility
Repository name: Mendeley Data
Data identification number: 10.17632/852r5dnzd5.1
Direct URL to data: https://data.mendeley.com/datasets/852r5dnzd5/1
Related research article
F. Reyes, T. DeJong, P. Franceschi, M. Tagliavini, D. Gianelle, Maximum growth potential and periods of resource limitation in apple tree, Frontiers in Plant Science 7 (2016). doi: 10.3389/fpls.2016.00233
Value of the Data
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The dataset allows analysis of the impact of fruit load, on vegetative aOpen asset ↗Mendeley Data · 10.17632/852r5dnzd5.1lines:1-61Code / dataset availability confirmedEurope PMC · OpenAlex · Crossref · checked 14 Sept 2026
Wheat ( Triticum aestivum L.) is one of the most important crops as it provides 20% of calories and proteins to the human population. To overcome the increasing demand in wheat grain production, there is a need for a higher grain yield, and this can be achieved in particular through an increase in the grain weight. Moreover, grain shape is an important trait regarding the milling performance. Both the final grain weight and shape would benefit from a comprehensive knowledge of the morphological and anatomical determinism of wheat grain growth. Synchrotron-based phase-contrast X-ray microtomography (X-ray µCT) was used to study the 3D anatomy of the growing wheat grain during the first developmental stages. Coupled with 3D reconstruction, this method revealed changes in the grain shape and new cellular features. The study focused on a particular tissue, the pericarp, which has been hypothesized to be involved in the control of grain development. We showed considerable spatio-temporal diversity in cell shape and orientations, and in tissue porosity associated with stomata detection. These results highlight the growth-related features rarely studied in cereal grains, which may contribute significantly to the final grain weight and shape.
Why it matches plant phenotyping methodsシンクロトロンX線マイクロCTと3D再構成を中核に、発達中コムギ粒の3D形状・細胞形態・組織空隙を抽出しており、植物器官の形態表現型取得が中心である。
abstractSynchrotron-based phase-contrast X-ray microtomography (X-ray µCT) was used to study the 3D anatomy of the growing wheat grain during the first developmental stages.
Reproduction assets found保存済みの本文根拠を更新済みルールで再検証し、公開資産1件を確認しました。Code · publicThe development was integrated into the Imago software, which is
freely available at https://github.com/SciCompJ/Imago (accessed on 21 February 2023).Open asset ↗SciCompJ/Imagopdf-page:23 lines:1-59Code / dataset availability confirmedEurope PMC · checked 7 Sept 2026
LiDAR / point cloudFruitMorphology / geometry measurementGrowth / time-series analysisGrowth / development / phenologyFruit / seed / panicle traits
How fruit size and shape are determined is of research interest in agriculture and developmental biology. Fruit typically exhibits three-dimensional structures with genotype-dependent geometric features. Although minor developmental variations have been recognized, little research has fully visualized and measured these variations throughout fruit growth. In this study, a high-resolution 3D scanner was used to investigate the fruit development of 51 persimmon ( Diospyros kaki ) cultivars with various complex shapes. We obtained 2,380 3D fruit models that fully represented fruit appearance, and enabled precise and automated measurements of unique geometric features throughout fruit development. The 3D fruit model analysis identified key stages that determined the shape attributes at maturity. Typically, genetic diversity in vertical groove development was found, and such grooves can be filled by tissue expansion in the carpal fusion zone during fruit development. Furthermore, transcriptome analysis of fruit tissues from groove/non-groove tissues revealed gene co-expression networks that were highly associated with groove depth variation. The presence of YABBY homologs was most closely associated with groove depth and indicated the possibility that this pathway is a key molecular contributor to vertical groove depth variation. These results demonstrate the validity of fruit 3D growth analysis, which is a powerful tool for identifying the developmental mechanisms of fruit shape variation and the molecular basis of this diversity.
Why it matches plant phenotyping methods高解像度3Dスキャナと自動3Dモデル解析により、果実形状を発達期間 boyunca定量化する方法が研究の中心であり、果実形状の表現型解析手法として妥当です。
abstracta high-resolution 3D scanner was used to investigate the fruit development of 51 persimmon ( Diospyros kaki ) cultivars with various complex shapes
Reproduction assets foundThe paper's 3D fruit models (phenotyping inputs/outputs) are publicly deposited on figshare, and the authors' shape-measurement analysis code is on GitHub. Both are paper-specific, public, and actionable. The trimesh and visNetwork URLs are generic third-party libraries, not paper-specific assets.Code · public109 and measure shape features, and the code is available at https://github.com/pomology-ku/persimmon-fruit-3D-Open asset ↗github · pomology-ku/persimmon-fruit-3D-pdf-page:6 lines:1-59Code / 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
ArabidopsisLeaf2D/3D reconstructionGrowth / time-series analysisGrowth / development / phenology
Plant organ morphogenesis spans several orders of magnitude in time and space. Because of limitations in live-imaging, analysing whole organ growth from initiation to mature stages typically rely on static data sampled from different timepoints and individuals. We introduce a new model-based strategy for dating organs and for reconstructing morphogenetic trajectories over unlimited time windows based on static data. Using this approach, we show that Arabidopsis thaliana leaves are initiated at regular 1-day intervals. Despite contrasted adult morphologies, leaves of different ranks exhibited shared growth dynamics, with linear gradations of growth parameters according to leaf rank. At the sub-organ scale, successive serrations from same or different leaves also followed shared growth dynamics, suggesting that global and local leaf growth patterns are decoupled. Analysing mutants leaves with altered morphology highlighted the decorrelation between adult shapes and morphogenetic trajectories, thus stressing the benefits of our approach in identifying determinants and critical timepoints during organ morphogenesis.
Why it matches plant phenotyping methods静的データから器官の発生時系列と成長軌跡を再構成するモデルベース手法が研究の中心であり、葉の形態形成・成長という植物表現型を推定している。
abstractWe introduce a new model-based strategy for dating organs and for reconstructing morphogenetic trajectories over unlimited time windows based on static data.
Reproduction assets foundThe paper's data availability statement explicitly provides three paper-specific public assets: a new version of the MorphoLeaf phenotyping application, COPASI and R scripts for temporal calibration parameter estimation, and the leaf datasets used in the analysis, each with a public URL.Dataset · publicLeaf datasets are available at https://doi.org/10.15454/BMELNY .Open asset ↗10.15454/BMELNYlines:210-440Code · publicCOPASI and R scripts used to estimate temporal calibration parameters are available at https://doi.org/10.15454/DPFU1T .Open asset ↗10.15454/DPFU1Tlines:210-440Code / 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 7 Sept 2026
There is limited information regarding the morphometric relationships of panicle traits in oat (Avena sativa) and their contribution to phenology and growth, physiology, and pathology traits important for yield. To model panicle growth and development and identify genomic regions associated with corresponding traits, 10 diverse spring oat mapping populations (n = 2,993) were evaluated in the field and 9 genotyped via genotyping-by-sequencing. Representative panicles from all progeny individuals, parents, and check lines were scanned, and images were analyzed using manual and automated techniques, resulting in over 60 unique panicle, rachis, and spikelet variables. Spatial modeling and days to heading were used to account for environmental and phenological variances, respectively. Panicle variables were intercorrelated, providing reproducible archetypal and growth models. Notably, adult plant resistance for oat crown rust was most prominent for taller, stiff stalked plants having a more open panicle structure. Within and among family variance for panicle traits reflected the moderate-to-high heritability and mutual genome-wide associations (hotspots) with numerous high-effect loci. Candidate genes and potential breeding applications are discussed. This work adds to the growing genetic resources for oat and provides a unique perspective on the genetic basis of panicle architecture in cereal crops.
Why it matches plant phenotyping methodsオート麦の穂をスキャンし、手動・自動画像解析で60以上の形態形質を抽出して、再現可能な成長・形態モデルを構築している。遺伝解析を含むが、画像ベースの穂形態計測と解析ワークフローが主要な貢献である。
abstractRepresentative panicles from all progeny individuals, parents, and check lines were scanned, and images were analyzed using manual and automated techniques, resulting in over 60 unique panicle, rachis, and spikelet variables.
Reproduction assets foundThe paper's Data Availability statement deposits the paper-specific phenotype dataset (Supplementary Dataset 1), panicle image dataset (Supplementary Dataset 2), and R analysis code on Zenodo under DOI 10.5281/zenodo.7011302, which is an allowed URL. This directly reproduces the paper's panicle phenotyping measurementsCode · publicSupplementary Dataset 1 (phenotypes), Supplementary Dataset 2 (panicle images), R code, and corresponding metadata are available online at https://zenodo.org/ , registered under: https://doi.org/10.5281/zenodo.7011302 .Open asset ↗zenodo.org · 10.5281/zenodo.7011302lines:747-785Code / dataset availability confirmedbioRxiv · Europe PMC · Crossref · checked 14 Sept 2026
Field / plotRootWhole plant / canopy / plot / fieldMorphology / geometry measurementGrowth / time-series analysisGrowth / development / phenologyRoot system architecture
1Roots have a central role in plant resource capture and are the interface between the plant and the soil that affect multiple ecosystem processes. Field pennycress (Thlaspi arvense L.) is a diploid annual cover crop species that has potential utility for reducing soil erosion and nutrient losses; and has rich seeds (30-35% oil) amenable to biofuel production and as a protein animal feed. The objective of this research was to (1) precisely characterize root system architecture and development, (2) understand adaptive responses of pennycress roots to nitrate nutrition, (3) and determine genotypic variance available in root development and nitrate plasticity. Using a root imaging and analysis pipeline, 4D pennycress root system architecture was characterized under four nitrate regimes (from zero to high nitrate concentration) across four time points (days 5, 9, 13, and 17 after sowing). Significant nitrate condition response and genotype interactions were identified for many root traits with greatest impact on lateral root traits. In trace nitrate conditions a greater lateral root count, length, interbranch density, and a steeper lateral root angle was observed compared to high nitrate conditions. Genotype-by-nitrate condition interaction was observed for root width, width:depth ratio, mean lateral root length, and lateral root density. These results illustrate root trait variance available in pennycress accessions that could be useful targets for breeding of improved nitrate responsive cover crops for greater productivity, resilience, and ecosystem service.
Why it matches plant phenotyping methods根系画像・解析パイプラインを用いた4D根系構造の取得と多数の根形質の抽出が研究の中心であり、植物フェノタイピング手法の実質的な応用に該当する。
abstractUsing a root imaging and analysis pipeline, 4D pennycress root system architecture was characterized under four nitrate regimes
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 · public151 codes including the packages needed are available https://doi.org/10.5281/zenodo.7536940. A total of 44Open asset ↗zenodo · 10.5281/zenodo.7536940pdf-page:5 lines:1-52Code / dataset availability confirmedEurope PMC · checked 15 Sept 2026
RiceSeed / grainCountingSegmentationGrowth / development / phenology
The germination rate of rice grain is recognized as one of the most significant indicators of seed quality assessment. Currently, grain germination rate is generally determined manually by experienced researchers, which is time-consuming and labor-intensive. In this paper, a new method is proposed for counting the number of grains and germinated grains. In the coarse segmentation process, the k-means clustering algorithm is applied to obtain rough grain-connected regions. We further refine the segmentation results obtained by the k-means algorithm using a one-dimensional Gaussian filter and a fifth-degree polynomial. Next, the optimal single grain area is determined based on the area distribution curve. Accordingly, the number of grains contained in the connected region is equal to the area of the connected region divided by the optimal single grain area. Finally, a novel algorithm is proposed for counting germinated grains. This algorithm is based on the idea that the length of the intersection between the germ and the grain is less than the circumference of the germ. The experimental results show that the mean absolute error of the proposed method for germination rate is 2.7%. And the performance of the proposed method is robust to changes in grain number, grain varieties, scale, illumination, and rotation.
Why it matches plant phenotyping methodsイネ種子の発芽率という植物形質を画像処理で自動抽出・定量する手法を開発し、誤差と頑健性を評価しており、表現型取得法が中心である。
abstractIn this paper, a new method is proposed for counting the number of grains and germinated grains.
Reproduction assets foundThe paper's Data Availability statement explicitly deposits the authors' analysis code and the 90 rice grain images used for germination-rate phenotyping in a public GitHub repository, matching an allowed URL. The web application URL is a live service, not a deposited asset, and is excluded.Code · publicges of Luyou 911 and the germination detection results.
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Acknowledgments
We are deeply grateful to the editor and reviewers for their assistance with reviews and guidance of the paper.
Data Availability
The code and 90 images used in this study are available from our GitHub repository: https://github.com/DoctorXiong123456/CodeOfPaper .
Funding Statement
This study was supported by the National Natural Science Foundation of China (Grants No. 61373004). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
References
1. Wu W, Zhou L, Chen J, Qiu Z, He Y. GainTKW: a measurement sysOpen asset ↗DoctorXiong123456/CodeOfPaperlines:233-285Dataset · publicmages of II you 534 and the germination detection results.
(DOCX)
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S3 Table
30 images of Luyou 911 and the germination detection results.
(DOCX)
Click here for additional data file.
Data Availability Statement
The code and 90 images used in this study are available from our GitHub repository: https://github.com/DoctorXiong123456/CodeOfPaper .Open asset ↗DoctorXiong123456/CodeOfPaperlines:286-308Code / dataset availability confirmedEurope PMC · Crossref · checked 8 Sept 2026
SoybeanField / plotRootSeed / grainCountingGrowth / development / phenologyRoot system architecture
The symbiotic relationship between soybean [ Glycine max L. (Merr.)] roots and bacteria ( Bradyrhizobium japonicum ) lead to the development of nodules, important legume root structures where atmospheric nitrogen (N 2 ) is fixed into bio-available ammonia (NH 3 ) for plant growth and development. With the recent development of the Soybean Nodule Acquisition Pipeline (SNAP), nodules can more easily be quantified and evaluated for genetic diversity and growth patterns across unique soybean root system architectures. We explored six diverse soybean genotypes across three field year combinations in three early vegetative stages of development and report the unique relationships between soybean nodules in the taproot and non-taproot growth zones of diverse root system architectures of these genotypes. We found unique growth patterns in the nodules of taproots showing genotypic differences in how nodules grew in count, size, and total nodule area per genotype compared to non-taproot nodules. We propose that nodulation should be defined as a function of both nodule count and individual nodule area resulting in a total nodule area per root or growth regions of the root. We also report on the relationships between the nodules and total nitrogen in the seed at maturity, finding a strong correlation between the taproot nodules and final seed nitrogen at maturity. The applications of these findings could lead to an enhanced understanding of the plant- Bradyrhizobium relationship and exploring these relationships could lead to leveraging greater nitrogen use efficiency and nodulation carbon to nitrogen production efficiency across the soybean germplasm.
Why it matches plant phenotyping methodsSNAPという機械学習ベースの表現型取得・解析パイプラインを用いて、根粒数・サイズ・面積を定量化することが研究の中心的手法として明示されている。
titleUsing machine learning enabled phenotyping to characterize nodulation in three early vegetative stages in soybean
Reproduction assets foundThe paper states that all data and code will be shared through the authors' Singh group GitHub. This is the only paper-specific availability statement; it points to a lab-level GitHub organization rather than a specific repository, and uses future tense ('will be shared'), so the exact deposit for this paper's nod phenCode · publicAll data and code will be shared through the Singh group GitHub https://github.com/SoylabSingh .Open asset ↗SoylabSinghlines:1171-1263Code / dataset availability confirmedOpenAlex · Crossref · checked 8 Sept 2026
A phenotype is the composite of an observable expression of a genome for traits in a given environment. The trajectories of phenotypes computed from an image sequence and timing of important events in a plant’s life cycle can be viewed as temporal phenotypes and indicative of the plant’s growth pattern and vigor. In this paper, we introduce a novel method called FlowerPhenoNet, which uses deep neural networks for detecting flowers from multiview image sequences for high-throughput temporal plant phenotyping analysis. Following flower detection, a set of novel flower-based phenotypes are computed, e.g., the day of emergence of the first flower in a plant’s life cycle, the total number of flowers present in the plant at a given time, the highest number of flowers bloomed in the plant, growth trajectory of a flower, and the blooming trajectory of a plant. To develop a new algorithm and facilitate performance evaluation based on experimental analysis, a benchmark dataset is indispensable. Thus, we introduce a benchmark dataset called FlowerPheno, which comprises image sequences of three flowering plant species, e.g., sunflower, coleus, and canna, captured by a visible light camera in a high-throughput plant phenotyping platform from multiple view angles. The experimental analyses on the FlowerPheno dataset demonstrate the efficacy of the FlowerPhenoNet.
Why it matches plant phenotyping methods花の検出と時系列表現型の抽出を行う深層学習手法を開発し、ベンチマークデータセットと評価も提示しており、植物フェノタイピング手法が中心である。
abstractwe introduce a novel method called FlowerPhenoNet, which uses deep neural networks for detecting flowers from multiview image sequences for high-throughput temporal plant phenotyping analysis.
Reproduction assets foundThe paper publicly releases the FlowerPheno benchmark dataset (17,022 multiview RGB image sequences of sunflower, canna, and coleus with ground-truth flower bounding boxes) and the FlowerPhenoNet source code, both with explicit availability statements and URLs.Dataset · publicThe dataset can be freely downloaded
from https://plantvision.unl.edu/dataset, accessed on 15 February 2021.Open asset ↗plantvision.unl.edupdf-page:4 lines:1-41Code · publicThe source code is available at
https://github.com/localchocotaco/FlowerPhenoNet, accessed on 27 November 2022.Open asset ↗github.com/localchocotaco/FlowerPhenoNetpdf-page:18 lines:1-58Code / 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 confirmedOpenAlex · Europe PMC · Crossref · checked 8 Sept 2026
BACKGROUND: High-throughput phenotyping is crucial for the genetic and molecular understanding of adaptive root system development. In recent years, imaging automata have been developed to acquire the root system architecture of many genotypes grown in Petri dishes to explore the Genetic x Environment (GxE) interaction. There is now an increasing interest in understanding the dynamics of the adaptive responses, such as the organ apparition or the growth rate. However, due to the increasing complexity of root architectures in development, the accurate description of the topology, geometry, and dynamics of a growing root system remains a challenge. RESULTS: We designed a high-throughput phenotyping method, combining an imaging device and an automatic analysis pipeline based on registration and topological tracking, capable of accurately describing the topology and geometry of observed root systems in 2D + t. The method was tested on a challenging Arabidopsis seedling dataset, including numerous root occlusions and crossovers. Static phenes are estimated with high accuracy ([Formula: see text] and [Formula: see text] for primary and second-order roots length, respectively). These performances are similar to state-of-the-art results obtained on root systems of equal or lower complexity. In addition, our pipeline estimates dynamic phenes accurately between two successive observations ([Formula: see text] for lateral root growth). CONCLUSIONS: We designed a novel method of root tracking that accurately and automatically measures both static and dynamic parameters of the root system architecture from a novel high-throughput root phenotyping platform. It has been used to characterise developing patterns of root systems grown under various environmental conditions. It provides a solid basis to explore the GxE interaction controlling the dynamics of root system architecture adaptive responses. In future work, our approach will be adapted to a wider range of imaging configurations and species.
Why it matches plant phenotyping methods根系の静的・動的形質を画像から自動抽出する高スループット手法と解析パイプラインを開発・検証しており、表現型取得法が研究の中心である。
abstractWe designed a high-throughput phenotyping method, combining an imaging device and an automatic analysis pipeline based on registration and topological tracking
Reproduction assets foundThe paper's root reconstruction/phenotyping pipeline (RootSystemTracker) is released as open-source code on GitHub with an ImageJ plugin documentation page; an example time-lapse movie of the reconstruction is also available on YouTube. No public dataset of the 1000 time-lapse images or RSML outputs is stated in thesupCode · publicThe architecture reconstruction pipeline is supplied as an ImageJ plugin with online documentation (Plugin page: https://imagej.net/plugins/rootsystemtracker [ 9 ]) and as open-source code on GitHub ( https://github.com/Rocsg/RootSystemTracker ).Open asset ↗Rocsg/RootSystemTrackerlines:208-277Code / dataset availability confirmedEurope PMC · checked 14 Sept 2026
This article describes a dataset of high-resolution visible-spectrum images of safflower ( Carthamus tinctorius L.) plants obtained from a LemnaTec Scanalyser automated phenomics platform along with the associated image analysis output and manually acquired biomass data. This series contains 1832 images of 200 diverse safflower genotypes, acquired at the Plant Phenomics Victoria, Horsham, Victoria, Australia. Two Prosilica GT RGB (red-green-blue) cameras were used to generate 6576 × 4384 pixel portable network graphic (PNG) images. Safflower genotypes were either subjected to a salt treatment (250 mM NaCl) or grown as a control (0 mM NaCl) and imaged daily from 15 to 36 days after sowing. Each snapshot consists of four images collected at a point in time; one of which is taken from above (top-view) and the remainder from the side at either 0°, 120° or 240°. The dataset also includes analysis output quantifying traits and describing phenotypes, as well as manually collected biomass and leaf ion content data. The usage of the dataset is already demonstrated in Thoday-Kennedy et al. (2021) [1]. This dataset describes the early growth differences of diverse safflower genotypes and identified genotypes tolerant or susceptible to salinity stress. This dataset provides detailed image analysis parameters for phenotyping a large population of safflower that can be used for the training of image-based trait identification pipelines for a wide range of crop species.
Why it matches plant phenotyping methods高スループット画像データセットと画像解析出力、形質定量パラメータを提供しており、植物フェノタイピング手法・再利用可能なデータ資源が中心である。
abstractThis article describes a dataset of high-resolution visible-spectrum images of safflower ( Carthamus tinctorius L.) plants obtained from a LemnaTec Scanalyser automated phenomics platform along with the associated image analysis output and manually acquired biomass data.
Reproduction assets foundThe paper is itself a data descriptor for a public safflower salt-response phenotyping dataset (1832 RGB images, image analysis output, and manual biomass/ion data) deposited on Harvard Dataverse, with an explicit direct URL.Dataset · publicains Innovation Park, Agriculture Victoria, Department of Jobs, Precincts and Regions.
City/Town/Region: Horsham, Victoria, Australia
Latitude and longitude (and GPS coordinates, if possible) for collected samples/data: 36° 43’ 13.67” S, 142° 10’ 25.63” E
Data accessibility
Repository name: Harvard Dataverse
Direct URL to data: https://dataverse.harvard.edu/dataverse/H2018006
Related research article
E. Thoday-Kennedy, S. Joshi, H.D. Daetwyler, M. Hayden, D. Hudson, G. Spangenberg, S. Kant, Digital phenotyping to delineate salinity response in safflower genotypes, Frontiers in Plant Science (2021) 12 , 1196
https://doi.org/10.3389/fpls.2021.662498
Value of the Data
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This dataset is a collecOpen asset ↗Harvard Dataverse · H2018006lines:50-109Code / dataset availability confirmedOpenAlex · Europe PMC · Crossref · checked 8 Sept 2026
Plant height (PH) is an essential trait in maize (Zea mays) that is tightly associated with planting density, biomass, lodging resistance, and grain yield in the field. Dissecting the dynamics of maize plant architecture will be beneficial for ideotype-based maize breeding and prediction, as the genetic basis controlling PH in maize remains largely unknown. In this study, we developed an automated high-throughput phenotyping platform (HTP) to systematically and noninvasively quantify 77 image-based traits (i-traits) and 20 field traits (f-traits) for 228 maize inbred lines across all developmental stages. Time-resolved i-traits with novel digital phenotypes and complex correlations with agronomic traits were characterized to reveal the dynamics of maize growth. An i-trait-based genome-wide association study identified 4945 trait-associated SNPs, 2603 genetic loci, and 1974 corresponding candidate genes. We found that rapid growth of maize plants occurs mainly at two developmental stages, stage 2 (S2) to S3 and S5 to S6, accounting for the final PH indicators. By integrating the PH-association network with the transcriptome profiles of specific internodes, we revealed 13 hub genes that may play vital roles during rapid growth. The candidate genes and novel i-traits identified at multiple growth stages may be used as potential indicators for final PH in maize. One candidate gene, ZmVATE, was functionally validated and shown to regulate PH-related traits in maize using genetic mutation. Furthermore, machine learning was used to build predictive models for final PH based on i-traits, and their performance was assessed across developmental stages. Moderate, strong, and very strong correlations between predictions and experimental datasets were achieved from the early S4 (tenth-leaf) stage. Colletively, our study provides a valuable tool for dissecting the spatiotemporal formation of specific internodes and the genetic architecture of PH, as well as resources and predictive models that are useful for molecular design breeding and predicting maize varieties with ideal plant architectures.
Why it matches plant phenotyping methods自動化された高スループット画像表現型プラットフォームを開発し、77種類の画像形質を定量化・検証し、機械学習による草丈予測も評価しており、表現型取得・抽出法が研究の中心である。
abstractwe developed an automated high-throughput phenotyping platform (HTP) to systematically and noninvasively quantify 77 image-based traits (i-traits) and 20 field traits (f-traits) for 228 maize inbred lines across all developmental stages.
Reproduction assets foundThe paper explicitly states that all images and phenotypic data are available on figshare and that the HTP/RGB image and GWAS analysis pipeline code is available on the authors' GitHub repository (maizeHTP). Both are paper-specific, public, and actionable.Dataset · publicAll the images and phenotypic data are available at https://figshare.com/account/home#/projects/141743 .Open asset ↗figshare · projects/141743lines:168-200Code · publicThe code for HTP from LemnaTec and the code for the RGB image and GWAS analysis pipelines can be downloaded at https://github.com/GUOWEIJUN/maizeHTP .Open asset ↗GitHub · GUOWEIJUN/maizeHTPlines:168-200Code / dataset availability confirmedOpenAlex · Europe PMC · checked 14 Sept 2026
Unmanned aerial vehicle (UAV)-based imagery has become widely used in collecting agronomic traits, enabling a much greater volume of data to be generated in a time-series manner. As one of the cutting-edge imagery analysis tools, machine learning-based object detection provides automated techniques to analyze these imagery data. In our previous study, UAVs have been used to collect aerial photography for field trials of 233 diverse inbred lines, grown under different nitrogen treatments. Images were collected during different plant developmental stages throughout the growing season. This dataset of images has here been used in developing machine learning techniques to obtain automated tassel counts at the plot level through the season. To improve detection accuracy, we have developed an image segmentation method to remove non-tassel pixels and then feed these filtered images into machine learning algorithms. As a result, our method showed a significant improvement in the accuracy of maize tassel detection. This method can be used in future research to produce time-series counts of tassels at the plot level, and will allow for accurate estimates of flowering-related traits, such as the earliest detected flowering date and the duration of each plot's flowering period. This phenotypic data and the trait-associated genes provide new opportunities for crop improvement and to facilitate future plant breeding.
Why it matches plant phenotyping methodsUAV画像からトウモロコシの雄穂数と開花関連形質を自動推定する画像分割・機械学習手法の開発が中心であり、植物表現型測定法に該当する。
abstractThis dataset of images has here been used in developing machine learning techniques to obtain automated tassel counts at the plot level through the season.
Reproduction assets foundThe paper's UAV imagery, plot-level images, and metadata are publicly deposited in CyVerse under DOI 10.25739/4t1v-ab64, per the Data Availability Statement. No author analysis code or trained model checkpoints are reported as publicly available.Dataset · publicThe original UAV images, the clipped plot-level images, and the associated metadata used in this study
have been deposited in CyVerse (10.25739/4t1v-ab64).Open asset ↗CyVerse · 10.25739/4t1v-ab64pdf-raw-page:5 lines:1-39Code / 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 confirmedOpenAlex · Europe PMC · Crossref · checked 14 Sept 2026
High temperatures inhibit plant growth. A proposed strategy for improving plant productivity under elevated temperatures is the use of plant growth-promoting rhizobacteria (PGPR). While the effects of PGPR on plant shoots have been extensively explored, roots—particularly their spatial and temporal dynamics—have been hard to study, due to their below-ground nature. Here, we characterized the time- and tissue-specific morphological changes in bacterized plants using a novel non-invasive high-resolution plant phenotyping and imaging platform—GrowScreen-Agar II. The platform uses custom-made agar plates, which allow air exchange to occur with the agar medium and enable the shoot to grow outside the compartment. The platform provides light protection to the roots, the exposure of it to the shoots, and the non-invasive phenotyping of both organs. Arabidopsis thaliana, co-cultivated with Paraburkholderia phytofirmans PsJN at elevated and ambient temperatures, showed increased lengths, growth rates, and numbers of roots. However, the magnitude and direction of the growth promotion varied depending on root type, timing, and temperature. The root length and distribution per depth and according to time was also influenced by bacterization and the temperature. The shoot biomass increased at the later stages under ambient temperature in the bacterized plants. The study offers insights into the timing of the tissue-specific, PsJN-induced morphological changes and should facilitate future molecular and biochemical studies on plant–microbe–environment interactions.
Why it matches plant phenotyping methodsGrowScreen-Agar IIという非侵襲的な高解像度フェノタイピング・イメージングプラットフォームを用い、根とシュートの形態を時空間的に測定することが研究の中心的手法です。
abstractwe characterized the time- and tissue-specific morphological changes in bacterized plants using a novel non-invasive high-resolution plant phenotyping and imaging platform—GrowScreen-Agar II.
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/plants11212927/s1 , Figure S1: WinRhizo analyzed root lengths and root and shoot biomass; Figure S2: Root sampling and bacterial colonization confirmation; Figure S3: Sample root images generated by the GrowScreen-Agar II; Figure S4: Agar plates for GrowScreen-Agar II; Figure S5: Magazines for GrowScreen-Agar II; Figure S6: Imaging station of GrowScreen-Agar II; Table S1: Mean values and standard error of different root type morphological traits; Table S2: Mean values and standard error of different root system traits describing distribution and spread.Open asset ↗lines:106-120Code / dataset availability confirmedOpenAlex · Europe PMC · checked 8 Sept 2026
Abstract Context Mixing cultivars is an agroecological practice of crop diversification, increasingly used for cereals. The yield of such cereal mixtures is higher on average than the mean yield of their components in pure stands, but with a large variance. The drivers of this variance are plant- plant interactions leading to different plant phenotypes in pure and mixed stands, i.e phenotypic plasticity. Objectives The objectives were (i) to quantify the magnitude of phenotypic plasticity for yield in pure versus mixed stands, (ii) to identify the yield components that contribute the most to yield plasticity, and (iii) to link such plasticities to differences in functional traits, i.e. plant height and flowering earliness. Methods A new experimental design based on a precision sowing allowed phenotyping each cultivar in mixture, at the level of individual plants, for above-ground traits throughout growth. Eight commercial cultivars of Triticum aestivum L. were grown in pure and mixed stands in field plots repeated for two years (2019-2020, 2020-2021) with contrasted climatic conditions and with nitrogen fertilization, fungicide and weed removal management strategies. Two quaternary mixtures were assembled with cultivars contrasted either for height or earliness. Results Compared to the average of cultivars in pure stands, the height mixture strongly underyielded over both years (-29%) while the earliness mixture overyielded the second year (+11%) and underyielded the first year (-8%). The second year, the magnitude of cultivar’s grain weight plasticity, measured as the difference between pure and mixed stands, was significantly and positively associated with their relative yield differences in pure stands (R 2 =0.51). When grain weight plasticity, measured as the log ratio of pure over mixed stands, was partitioned as the sum of plasticities in each yield component, its strongest contributor was the plasticity in spike number per plant (∼56% of the sum), driven by even stronger but opposed underlying plasticities in both tiller emission and regression. For both years, the plasticity in tiller emission was significantly, positively associated with the height differentials between cultivars in mixture (R 2 =0.43 in 2019-2020 and 0.17 in 2020-2021). Conclusions Plasticity in the early recognition of potential resource competitors is a major component of cultivar strategies in mixtures, as shown here for tillering dynamics. Our results also highlighted a link between plasticity in tiller emission and height differential in mixture. Both height and tillering dynamics displayed plasticities typical of the shade avoidance syndrome. Implications Both the new experimental design and decomposition of plasticities developed in this study open avenues to better study plant-plant interactions in agronomically-realistic conditions. This study also contributed a unique, plant-level data set allowing the calibration of process-based plant models to explore the space of all possible mixtures.
Why it matches plant phenotyping methods精密播種による個体レベルの生育形質フェノタイピング実験デザインと、形質可塑性の分解手法が明示的な技術的貢献であり、単なる収量測定にとどまらない。
abstractA new experimental design based on a precision sowing allowed phenotyping each cultivar in mixture, at the level of individual plants, for above-ground traits throughout growth.
Reproduction assets foundThe paper's phenotyping data, supplementary material, and analysis code are openly deposited on Recherche Data Gouv at https://doi.org/10.57745/LZS8SU, as stated in the Reproducibility section. This is a paper-specific, public, actionable asset directly reproducing the wheat pure/mixed-stand phenotyping measurements (eDataset · publicSupplementary information, data
and code that support the findings of this study are openly available at the INRAE
space of the Recherche Data Gouv repository https://doi.org/10.57745/LZS8SU.Open asset ↗Recherche Data Gouv · 10.57745/LZS8SUpdf-page:15 lines:1-40Code / dataset availability confirmedEurope PMC · OpenAlex · checked 15 Sept 2026
CassavaRootMorphology / geometry measurementGrowth / time-series analysisGrowth / development / phenologyRoot system architecture
Storage roots of cassava plants crops are one of the main providers of starch in many South American, African, and Asian countries. Finding varieties with high yields is crucial for growing and breeding. This requires a better understanding of the dynamics of storage root formation, which is usually done by repeated manual evaluation of root types, diameters, and their distribution in excavated roots. We introduce a newly developed method that is capable to analyze the distribution of root diameters automatically, even if root systems display strong variations in root widths and clustering in high numbers. An application study was conducted with cassava roots imaged in a video acquisition box. The root diameter distribution was quantified automatically using an iterative ridge detection approach, which can cope with a wide span of root diameters and clustering. The approach was validated with virtual root models of known geometries and then tested with a time-series of excavated root systems. Based on the retrieved diameter classes, we show plausibly that the dynamics of root type formation can be monitored qualitatively and quantitatively. We conclude that this new method reliably determines important phenotypic traits from storage root crop images. The method is fast and robustly analyses complex root systems and thereby applicable in high-throughput phenotyping and future breeding.
Why it matches plant phenotyping methods根系画像から根径分布などの表現型形質を自動抽出する新手法を開発し、仮想モデルで検証しており、表現型取得・解析が研究の中心である。
abstractWe introduce a newly developed method that is capable to analyze the distribution of root diameters automatically
Reproduction assets foundThe paper's root diameter analysis software is publicly available on the authors' GitLab (grow-screen-field). The phenotype/image data are deposited on Zenodo (doi: 10.5281/zenodo.5883368), but no Zenodo URL is in the allowed list, so only the code asset is reported. Paraview and Detectron2 are generic third-party toolCode · publicthe Helmholtz. We thank Alexander Putz for his technical support and N. Punyasu for allowing us to use her parametrization of the OpenSimRoot cassava model.
Data Availability
The data presented in this study are openly available in Zenodo.org (doi: 10.5281/zenodo.5883368 ) [ 39 ]. The software has been published in Gitlab under https://gitlab-public.fz-juelich.de/grow-screen-field . The parameters of the OSR-models are available from the authors upon request.
Authors’ Contributions
J.W. did the software implementation and compiled all algorithms and methods into a software with graphical user interface. He also helped developing the methodology. T.W. provided the data for the real root case Open asset ↗gitlab-public.fz-juelich.de/grow-screen-fieldlines:110-132Code / dataset availability confirmedEurope PMC · checked 8 Sept 2026
Dormancy is a physiological state that confers winter hardiness to and orchestrates phenological phase progression in temperate perennial plants. Weather fluctuations caused by climate change increasingly disturb dormancy onset and release in plants including tree crops, causing aberrant growth, flowering and fruiting. Research in this field suffers from the lack of affordable non-invasive methods for online dormancy monitoring. We propose an automatic framework for low-cost, long-term, scalable dormancy studies in deciduous plants. It is based on continuous sensing of the photosynthetic activity of shoots via pulse-amplitude-modulated chlorophyll fluorescence sensors connected remotely to a data processing system. The resulting high-resolution time series of JIP-test parameters indicative of the responsiveness of the photosynthetic apparatus to environmental stimuli were subjected to frequency-domain analysis. The proposed approach overcomes the variance coming from diurnal changes of insolation and provides hints on the depth of dormancy. Our approach was validated over three seasons in an apple ( Malus × domestica Borkh.) orchard by collating the non-invasive estimations with the results of traditional methods (growing of the cuttings obtained from the trees at different phases of dormancy) and the output of chilling requirement models. We discuss the advantages of the proposed monitoring framework such as prompt detection of frost damage along with its potential limitations.
Why it matches plant phenotyping methods植物の休眠状態をクロロフィル蛍光センサーと周波数解析で非侵襲的に推定する監視手法を開発し、複数季節・従来法との比較で検証しており、表現型取得が研究の中心です。
abstractWe propose an automatic framework for low-cost, long-term, scalable dormancy studies in deciduous plants.
Reproduction assets foundThe authors explicitly state that the analysis code, accompanied by a subset of the data, is publicly available on GitHub (Lodinn/PAM-timeseries). The MDPI supplementary materials (S1) also contain paper-specific CF transient plots, correlation matrices, regression fits, and JIP-test parameter tables. Raw data and fullCode · publicCode used in the analysis, accompanied with a subset of the data, is available on GitHub ( https://github.com/Lodinn/PAM-timeseries , accessed on 20 September 2022).Open asset ↗Lodinn/PAM-timeserieslines:128-144Supplement · publicThe following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/plants11212811/s1 . A description of the PAM fluorimeter used in the work, including: Figure S1. The scheme of the experimental orchard plot.Open asset ↗lines:128-144Code / dataset availability confirmedEurope PMC · checked 8 Sept 2026
Capturing cell-to-cell signals in a three-dimensional (3D) environment is key to studying cellular functions. A major challenge in the current culturing methods is the lack of accurately capturing multicellular 3D environments. In this study, we established a framework for 3D bioprinting plant cells to study cell viability, cell division, and cell identity. We established long-term cell viability for bioprinted Arabidopsis and soybean cells. To analyze the generated large image datasets, we developed a high-throughput image analysis pipeline. Furthermore, we showed the cell cycle reentry of bioprinted cells for which the timing coincides with the induction of core cell cycle genes and regeneration-related genes, ultimately leading to microcallus formation. Last, the identity of bioprinted Arabidopsis root cells expressing endodermal markers was maintained for longer periods. The framework established here paves the way for a general use of 3D bioprinting for studying cellular reprogramming and cell cycle reentry toward tissue regeneration.
Why it matches plant phenotyping methods植物細胞の3Dバイオプリンティング枠組みと、大規模画像から生存性・細胞分裂・細胞同一性を解析する高スループット画像解析パイプラインを開発しており、表現型取得・解析が研究の中心的な技術的貢献である。
abstractIn this study, we established a framework for 3D bioprinting plant cells to study cell viability, cell division, and cell identity.
Reproduction assets foundThe paper's authors publicly released their high-throughput confocal z-stack cell quantification pipeline (Python scripts wrapped in an R Shiny GUI), used to analyze the paper's bioprinted plant cell imaging datasets, on GitHub with a Zenodo deposit (10.5281/zenodo.7012765). The Zenodo record 5537065 in allowed_urls isCode · publicScripts for our high-throughput and automatic image analysis are available at https://github.com/LisaVdB/Confocal-z-stack-cell-detection and 10.5281/zenodo.7012765 .Open asset ↗LisaVdB/Confocal-z-stack-cell-detection · 10.5281/zenodo.7012765lines:247-261Code / dataset availability confirmedEurope PMC · Crossref · checked 8 Sept 2026
The emergence of deep neural networks has allowed the development of fully automated and efficient diagnostic systems for plant disease and pest phenotyping. Although previous approaches have proven to be promising, they are limited, especially in real-life scenarios, to properly diagnose and characterize the problem. In this work, we propose a framework which besides recognizing and localizing various plant abnormalities also informs the user about the severity of the diseases infecting the plant. By taking a single image as input, our algorithm is able to generate detailed descriptive phrases (user-defined) that display the location, severity stage, and visual attributes of all the abnormalities that are present in the image. Our framework is composed of three main components. One of them is a detector that accurately and efficiently recognizes and localizes the abnormalities in plants by extracting region-based anomaly features using a deep neural network-based feature extractor. The second one is an encoder-decoder network that performs pixel-level analysis to generate abnormality-specific severity levels. Lastly is an integration unit which aggregates the information of these units and assigns unique IDs to all the detected anomaly instances, thus generating descriptive sentences describing the location, severity, and class of anomalies infecting plants. We discuss two possible ways of utilizing the abovementioned units in a single framework. We evaluate and analyze the efficacy of both approaches on newly constructed diverse paprika disease and pest recognition datasets, comprising six anomaly categories along with 11 different severity levels. Our algorithm achieves mean average precision of 91.7% for the abnormality detection task and a mean panoptic quality score of 70.78% for severity level prediction. Our algorithm provides a practical and cost-efficient solution to farmers that facilitates proper handling of crops.
Why it matches plant phenotyping methods画像から植物病害・害虫の位置と重症度を推定する深層学習システムの開発・評価が研究の中心であり、植物の病害状態を直接定量化するため、植物フェノタイピング手法として含める。
abstractwe propose a framework which besides recognizing and localizing various plant abnormalities also informs the user about the severity of the diseases infecting the plant.
Reproduction assets foundThe paper's data availability statement points to the authors' public GitHub repository (DIANA) hosting the raw paprika disease/pest dataset supporting the phenotyping analysis.Dataset · publicThe raw data supporting the conclusions of this article will be made available by the authors at following link: https://github.com/Mr-TalhaIlyas/DIANA .Open asset ↗Mr-TalhaIlyas/DIANAlines:1128-1209Code / dataset availability confirmedEurope PMC · checked 14 Sept 2026
Dormancy is a physiological state that confers winter hardiness to and orchestrates phenological phase progression in temperate perennial plants. Weather fluctuations caused by climate change increasingly disturb dormancy onset and release in many plant species including tree crops leading to aberrant growth, flowering, and fruiting. Currently, research in this field is impeded by the lack of affordable non-invasive methods for on-line monitoring of dormancy. We report on an automatic framework for low-cost, long-term, and scalable dormancy studies in deciduous plants. The proposed method is based on continuous near-field sensing of the photosynthetic activity of shoots via pulse-amplitude modulated chlorophyll fluorescence sensors connected remotely to a data processing system. The resulting high-resolution time series of JIP-test parameters indicative of the responsiveness of the photosynthetic apparatus to environmental stimuli are subjected to frequency-domain analysis. The proposed approach allows to overcome the variance coming from diurnal changes of insolation and to derive estimations on the depth of dormancy. Our approach was validated over three seasons in an experimental apple (Malus × domestica Borkh.) orchard by collating the non-invasive estimations with the results of traditional methods (growing of the cuttings obtained from the tress at different phases of dormancy) and the output of commonly used chilling requirement models. We discuss the advantages of the proposed monitoring framework such as prompt detection of freeze damages along with its potential limitations.
Why it matches plant phenotyping methods植物の休眠深度を非侵襲的に推定する蛍光センサーと時系列解析のフレームワークを開発し、従来法およびモデルと照合検証しており、表現型取得法が研究の中心である。
abstractWe report on an automatic framework for low-cost, long-term, and scalable dormancy studies in deciduous plants.
Reproduction assets foundThe authors state that the analysis code, accompanied by a subset of the data, is publicly available on GitHub. This is a paper-specific computational analysis asset for the chlorophyll fluorescence/JIP-test time-frequency analysis. The full raw data and derived parameters are only available on request, so they are notCode · publicic projects in priority areas of scientific and technological
development (grant number 075-15-2020-774).
Data Availability Statement: The raw data and derived parameters are available from the corre-
sponding author on reasonable request.
Code used in the analysis, accompanied with a subset of the data, is available on GitHub
(https://github.com/Lodinn/PAM-timeseries).
Acknowledgments: The indoors chlorophyll fluorescence measurements were carried out at the
Phototrophic Organisms Phenotyping user facilities of Lomonosov Moscow State University. TheOpen asset ↗Lodinn/PAM-timeseriespdf-layout-page:15 lines:1-58Code / dataset availability confirmedarXiv · OpenAlex · checked 14 Sept 2026
We propose a novel hybrid cable-based robot with manipulator and camera for high-accuracy, medium-throughput plant monitoring in a vertical hydroponic farm and, as an example application, demonstrate non-destructive plant mass estimation. Plant monitoring with high temporal and spatial resolution is important to both farmers and researchers to detect anomalies and develop predictive models for plant growth. The availability of high-quality, off-the-shelf structure-from-motion (SfM) and photogrammetry packages has enabled a vibrant community of roboticists to apply computer vision for non-destructive plant monitoring. While existing approaches tend to focus on either high-throughput (e.g. satellite, unmanned aerial vehicle (UAV), vehicle-mounted, conveyor-belt imagery) or high-accuracy/robustness to occlusions (e.g. turn-table scanner or robot arm), we propose a middle-ground that achieves high accuracy with a medium-throughput, highly automated robot. Our design pairs the workspace scalability of a cable-driven parallel robot (CDPR) with the dexterity of a 4 degree-of-freedom (DoF) robot arm to autonomously image many plants from a variety of viewpoints. We describe our robot design and demonstrate it experimentally by collecting daily photographs of 54 plants from 64 viewpoints each. We show that our approach can produce scientifically useful measurements, operate fully autonomously after initial calibration, and produce better reconstructions and plant property estimates than those of over-canopy methods (e.g. UAV). As example applications, we show that our system can successfully estimate plant mass with a Mean Absolute Error (MAE) of 0.586g and, when used to perform hypothesis testing on the relationship between mass and age, produces p-values comparable to ground-truth data (p=0.0020 and p=0.0016, respectively).
Why it matches plant phenotyping methods植物の多視点画像取得、SfM再構成、質量推定を中核とするロボット型フェノタイピング手法の開発・実証であり、単なる生物学的測定ではない。
abstractWe describe our robot design and demonstrate it experimentally by collecting daily photographs of 54 plants from 64 viewpoints each.
In the absence of pollination, female reproductive organs senesce, leading to an irrevocable loss in the reproductive potential of the flower, which directly affects seed set. In self-pollinating crops like wheat (Triticum aestivum), the post-anthesis viability of unpollinated carpels has been overlooked, despite its importance for hybrid seed production systems. To advance our knowledge of carpel development in the absence of pollination, we created a high-throughput phenotyping approach to quantify stigma and ovary morphology. We demonstrate the suitability of the approach, which uses light-microscopy imaging and machine learning, for the analysis of floral organ traits in field-grown plants using fresh and fixed samples. We show that the unpollinated carpel undergoes a well-defined initial growth phase, followed by a peak phase in which stigma area reaches its maximum and the radial expansion of the ovary slows, and a final deterioration phase. These developmental dynamics were consistent across years and could be used to classify male-sterile cultivars. This phenotyping approach provides a new tool for examining carpel development, which we hope will advance research into female fertility of wheat.
Why it matches plant phenotyping methodsコムギの柱頭・子房形態を定量化する高スループット画像・機械学習手法の開発と適用が研究の中心であり、植物表現型取得法として明確に該当する。
abstractwe created a high-throughput phenotyping approach to quantify stigma and ovary morphology
Reproduction assets foundThe paper's authors publicly deposited both the analysis code (CNN training/implementation scripts and R scripts) on GitHub and the carpel image/training/validation datasets on Earlham OpenData, directly reproducing this paper's wheat carpel phenotyping measurements and analysis.Code · publicTraining codes used for the development of the CNNs, adapted stigma and ovary CNNs, and R scripts used for data curation and visualisation can be found at https://github.com/Uauy-Lab/ML-carpel_traitsOpen asset ↗Uauy-Lab/ML-carpel_traitslines:122-188Dataset · publicDatasets for the training and validation of the models and raw images used for the different experimental analyses are freely available at https://opendata.earlham.ac.uk/wheat/under_license/toronto/Millan-Blanquez_etal_2022_machine-learning-carpel-traits/Open asset ↗lines:122-188Code / dataset availability confirmedEurope PMC · checked 8 Sept 2026
RGB / grayscaleLeafSegmentationGrowth / time-series analysisGrowth / development / phenology
Predicting plant growth is a fundamental challenge that can be employed to analyze plants and further make decisions to have healthy plants with high yields. Deep learning has recently been showing its potential to address this challenge in recent years, however, there are still two issues. First, image-based plant growth prediction is currently taken either from time series or image generation viewpoints, resulting in a flexible learning framework and clear predictions, respectively. Second, deep learning-based algorithms are notorious to require a large-scale dataset to obtain a competing performance but collecting enough data is time-consuming and expensive. To address the issues, we consider the plant growth prediction from both viewpoints with two new time-series data augmentation algorithms. To be more specific, we raise a new framework with a length-changeable time-series processing unit to generate images flexibly. A generative adversarial loss is utilized to optimize our model to obtain high-quality images. Furthermore, we first recognize three key points to perform time-series data augmentation and then put forward T-Mixup and T-Copy-Paste. T-Mixup fuses images from a different time pixel-wise while T-Copy-Paste makes new time-series images with a different background by reusing individual leaves extracted from the existing dataset. We perform our method in a public dataset and achieve superior results, such as the generated RGB images and instance masks securing an average PSNR of 27.53 and 27.62, respectively, compared to the previously best 26.55 and 26.92.
Why it matches plant phenotyping methods植物画像から成長状態を予測・生成する深層学習フレームワークと、時系列データ拡張手法を開発しており、画像およびインスタンスマスクによる表現型取得・解析が研究の中心である。
abstractTo address the issues, we consider the plant growth prediction from both viewpoints with two new time-series data augmentation algorithms.
Reproduction assets foundThe paper's plant growth prediction experiments use the public KOMATSUNA plant phenotyping dataset (Uchiyama et al., 2017), which the authors explicitly state is publicly available via the linked IEEE document. No author analysis code, trained models, or paper-specific supplementary data assets are described with a de-Dataset · publicPublicly available datasets were analyzed in this study. This data can be found here: https://ieeexplore.ieee.org/document/8265449 .Open asset ↗8265449lines:805-840Code / dataset availability confirmedEurope PMC · checked 8 Sept 2026
Background Phenomic prediction has been defined as an alternative to genomic prediction by using spectra instead of molecular markers. A reflectance spectrum provides information on the biochemical composition within a tissue, itself being under genetic determinism. Thus, a relationship matrix built from spectra could potentially capture genetic signal. This new methodology has been mainly applied in several annual crop species but little is known so far about its interest in perennial species. Besides, phenomic prediction has only been tested for a restricted set of traits, mainly related to yield or phenology. This study aims at applying phenomic prediction for the first time in grapevine, using spectra collected on two tissues and over two consecutive years, on two populations and for 15 traits, related to berry composition, phenology, morphological and vigour. A major novelty of this study was to collect spectra and phenotypes several years apart from each other. First, we characterized the genetic signal in spectra and under which condition it could be maximized, then phenomic predictive ability was compared to genomic predictive ability. Results For the first time, we showed that the similarity between spectra and genomic relationship matrices was stable across tissues or years, but variable across populations, with co-inertia around 0.3 and 0.6 for diversity panel and half-diallel populations, respectively. Applying a mixed model on spectra data increased phenomic predictive ability, while using spectra collected on wood or leaves from one year or another had less impact. Differences between populations were also observed for predictive ability of phenomic prediction, with an average of 0.27 for the diversity panel and 0.35 for the half-diallel. For both populations, a significant positive correlation was found across traits between predictive ability of genomic and phenomic predictions. Conclusion NIRS is a new low-cost alternative to genotyping for predicting complex traits in perennial species such as grapevine. Having spectra and phenotypes from different years allowed us to exclude genotype-by-environment interactions and confirms that phenomic prediction can rely only on genetics.
Why it matches plant phenotyping methodsブドウのスペクトルを用いたフェノミック予測法を開発・評価し、ゲノム予測との比較や予測能力の検証を行っており、植物形質推定手法が研究の中心である。
abstractThis study aims at applying phenomic prediction for the first time in grapevine, using spectra collected on two tissues and over two consecutive years, on two populations and for 15 traits, related to berry composition, phenology, morphological and vigour.
Reproduction assets foundThe paper explicitly deposits its grapevine phenotypic/genotypic data and its NIRS spectra, R analysis scripts, and result tables in the INRAE data portal under two DOIs, both listed in allowed_urls. These are paper-specific, publicly actionable assets directly reproducing the phenotyping measurements and computationalDataset · publicGenotypic values and genotypic data for half-diallel and diversity panel populations are available at https://doi.org/10.15454/PNQQUQOpen asset ↗10.15454/PNQQUQlines:204-268Dataset · publicSpectra, R scripts and result tables have been deposited in the INRAE data portal: https://doi.org/10.15454/BICRFXOpen asset ↗INRAE data portal · 10.15454/BICRFXlines:204-268Code / dataset availability confirmedCrossref · checked 14 Sept 2026
Monitoring within-field crop variability at fine spatial and temporal resolution can assist farmers in making reliable decisions during their agricultural management; however, it traditionally involves a labor-intensive and time-consuming pointwise manual process. To the best of our knowledge, few studies conducted a comparison of Sentinel-2 with UAV data for crop monitoring in the context of precision agriculture. Therefore, prospects of crop monitoring for characterizing biophysical plant parameters and leaf nitrogen of wheat and barley crops were evaluated from a more practical viewpoint closer to agricultural routines. Multispectral UAV and Sentinel-2 imagery was collected over three dates in the season and compared with reference data collected at 20 sample points for plant leaf nitrogen (N), maximum plant height, mean plant height, leaf area index (LAI), and fresh biomass. Higher correlations of UAV data to the agronomic parameters were found on average than with Sentinel-2 data with a percentage increase of 6.3% for wheat and 22.2% for barley. In this regard, VIs calculated from spectral bands in the visible part performed worse for Sentinel-2 than for the UAV data. In addition, large-scale patterns, formed by the influence of an old riverbed on plant growth, were recognizable even in the Sentinel-2 imagery despite its much lower spatial resolution. Interestingly, also smaller features, such as the tramlines from controlled traffic farming (CTF), had an influence on the Sentinel-2 data and showed a systematic pattern that affected even semivariogram calculation. In conclusion, Sentinel-2 imagery is able to capture the same large-scale pattern as can be derived from the higher detailed UAV imagery; however, it is at the same time influenced by management-driven features such as tramlines, which cannot be accurately georeferenced. In consequence, agronomic parameters were better correlated with UAV than with Sentinel-2 data. Crop growers as well as data providers from remote sensing services may take advantage of this knowledge and we recommend the use of UAV data as it gives additional information about management-driven features. For future perspective, we would advise fusing UAV with Sentinel-2 imagery taken early in the season as it can integrate the effect of agricultural management in the subsequent absence of high spatial resolution data to help improve crop monitoring for the farmer and to reduce costs.
Why it matches plant phenotyping methodsUAVおよびSentinel-2マルチスペクトル画像から草丈、LAI、バイオマス、葉窒素などの植物形質を推定し、参照データとの相関比較・技術評価を行っているため、植物フェノタイピング手法の検証・応用が中心です。
abstractprospects of crop monitoring for characterizing biophysical plant parameters and leaf nitrogen of wheat and barley crops were evaluated
Reproduction assets found保存済みの本文根拠を更新済みルールで再検証し、公開資産1件を確認しました。Supplement · publicSupplementary Materials: The following supporting information can be downloaded at:
https://www.mdpi.com/article/10.3390/rs14174426/s1, Table S1: Summary statistics of the plant trait
variables measured at 20 sample points in field A; Table S2: Summary statistics of the plant trait
variables measured at 20 sample points in field G; Table S3: Absolute correlation results of plant
maximum height and mean height in field AOpen asset ↗pdf-page:20 lines:1-58Code / dataset availability confirmedEurope PMC · checked 8 Sept 2026
ArabidopsisRootMorphology / geometry measurementGrowth / time-series analysisGrowth / development / phenologyRoot system architecture
The plant kingdom contains a stunning array of complex morphologies easily observed above-ground, but more challenging to visualize below-ground. Understanding the magnitude of diversity in root distribution within the soil, termed root system architecture (RSA), is fundamental in determining how this trait contributes to species adaptation in local environments. Roots are the interface between the soil environment and the shoot system and therefore play a key role in anchorage, resource uptake, and stress resilience. Previously, we presented the GLO-Roots (Growth and Luminescence Observatory for Roots) system to study the RSA of soil-grown Arabidopsis thaliana plants from germination to maturity (Rellán-Álvarez et al., 2015). In this study, we present the automation of GLO-Roots using robotics and the development of image analysis pipelines in order to examine the temporal dynamic regulation of RSA and the broader natural variation of RSA in Arabidopsis , over time. These datasets describe the developmental dynamics of two independent panels of accessions and reveal highly complex and polygenic RSA traits that show significant correlation with climate variables of the accessions' respective origins.
Why it matches plant phenotyping methodsロボティクスによる根系画像取得の自動化と画像解析パイプライン開発が中心で、根系構造・成長動態という植物形質を抽出するフェノタイピング基盤を提示している。
abstractwe present the automation of GLO-Roots using robotics and the development of image analysis pipelines
Reproduction assets foundThe paper deposits its root phenotyping imaging data, image analysis pipelines/scripts, RShiny exploration apps, and rhizotron build files on Zenodo, plus robotics software on GitHub — all paper-specific, public, and actionable.Dataset · publicThe raw data is available through Zenodo at https://doi.org/10.5281/zenodo.5709009 .Open asset ↗Zenodo · 10.5281/zenodo.5709009lines:160-163Code · publicImage analysis pipelines and scripts are available through Zenodo, DOI: https://doi.org/10.5281/zenodo.5708430 .Open asset ↗Zenodo · 10.5281/zenodo.5708430lines:224-389Code · publicGeneral code for software operating robotics available: GitHub: https://github.com/rhizolab/rhizo-server .Open asset ↗GitHub · rhizolab/rhizo-serverlines:224-389Code / dataset availability confirmedCrossref · checked 14 Sept 2026
LeafClassificationGrowth / development / phenology
Essential Biodiversity Variables (EBVs) make it possible to evaluate and monitor the state of biodiversity over time at different spatial scales. Its development is led by the Group on Earth Observations Biodiversity Observation Network (GEO BON) to harmonize, consolidate and standardize biodiversity data from varied biodiversity sources. This document presents a mechanism to obtain baseline data to feed the Species Traits Variable Phenology or other biodiversity indicators by extracting species characters and structure names from morphological descriptions of specimens and classifying such descriptions using machine learning (ML). A workflow that performs Named Entity Recognition (NER) and Classification of morphological descriptions using ML algorithms was evaluated with excellent results. It was implemented using Python, Pytorch, Scikit-Learn, Pomegranate, Python-crfsuite, and other libraries applied to 106,804 herbarium records from the National Biodiversity Institute of Costa Rica (INBio). The text classification results were almost excellent (F1 score between 96% and 99%) using three traditional ML methods: Multinomial Naive Bayes (NB), Linear Support Vector Classification (SVC), and Logistic Regression (LR). Furthermore, results extracting names of species morphological structures (e.g., leaves, trichomes, flowers, petals, sepals) and character names (e.g., length, width, pigmentation patterns, and smell) using NER algorithms were competitive (F1 score between 95% and 98%) using Hidden Markov Models (HMM), Conditional Random Fields (CRFs), and Bidirectional Long Short Term Memory Networks with CRF (BI-LSTM-CRF).
Why it matches plant phenotyping methods標本ラベルから植物の形態形質・構造名を機械学習で抽出・分類するワークフローを開発し、大規模データで性能評価しており、植物表現型の取得・抽出手法が中心である。
abstractThis document presents a mechanism to obtain baseline data to feed the Species Traits Variable Phenology or other biodiversity indicators by extracting species characters and structure names from morphological descriptions of specimens and classifying such descriptions using machine learning (ML).
Reproduction assets foundThe paper uses INBio herbarium specimen data (106,804 records) publicly available via GBIF, and provides an authors' replication code package on GitHub for data cleaning and analysis. Both are paper-specific, public, and actionable.Dataset · publiceveraging tagged descriptions from other languages.
For more complex texts, more robust algorithms, such as Recurrent Neural Networks -
LSTM and Transformers, can be applied.
Data and Code
Data from the National Biodiversity Institute of Costa Rica is used in this paper. The full
dataset and documentation can be downloaded from https://www.gbif.org/dataset/3717f916-d983-4a81-bb13-5f91200871a6. Code for data cleaning and analysis is provided
as part of the replication package. It is available at https://github.com/colibri-itcr.References
• Akella LM, Norton CN, Miller H (2012) NetiNeti: discovery of scientific names from text
using machine learning methods. BMC Bioinformatics 13 (1). https://Open asset ↗gbif.org · 3717f916-d983-4a81-bb13-5f91200871a6pdf-raw-page:19 lines:1-35Code · publicied.
Data and Code
Data from the National Biodiversity Institute of Costa Rica is used in this paper. The full
dataset and documentation can be downloaded from https://www.gbif.org/dataset/3717f916-d983-4a81-bb13-5f91200871a6. Code for data cleaning and analysis is provided
as part of the replication package. It is available at https://github.com/colibri-itcr.References
• Akella LM, Norton CN, Miller H (2012) NetiNeti: discovery of scientific names from text
using machine learning methods. BMC Bioinformatics 13 (1). https://doi.org/10.1186/1471-2105-13-211
• Balhoff JP, Dahdul WM, Dececchi T, Lapp H, Mabee PM, Vision TJ (2014) Annotation of
phenotypic diversity: decoupling data curation and Open asset ↗github.com/colibri-itcr.Referencespdf-raw-page:19 lines:1-35Code / dataset availability confirmedEurope PMC · checked 8 Sept 2026
Cell division and the resulting changes to the cell organization affect the shape and functionality of all tissues. Thus, understanding the determinants of the tissue-wide changes imposed by cell division is a key question in developmental biology. Here, we use a network representation of live cell imaging data from shoot apical meristems (SAMs) in Arabidopsis thaliana to predict cell division events and their consequences at the tissue level. We show that a support vector machine classifier based on the SAM network properties is predictive of cell division events, with test accuracy of 76%, which matches that based on cell size alone. Furthermore, we demonstrate that the combination of topological and biological properties, including cell size, perimeter, distance and shared cell wall between cells, can further boost the prediction accuracy of resulting changes in topology triggered by cell division. Using our classifiers, we demonstrate the importance of microtubule-mediated cell-to-cell growth coordination in influencing tissue-level topology. Together, the results from our network-based analysis demonstrate a feedback mechanism between tissue topology and cell division in A. thaliana SAMs.
Why it matches plant phenotyping methodsライブ細胞画像からSAMの細胞分裂イベントと組織トポロジー変化を推定するネットワーク表現・SVM分類法が研究の中心であり、植物の形態・発達状態を定量化している。
abstractwe use a network representation of live cell imaging data from shoot apical meristems (SAMs) in Arabidopsis thaliana to predict cell division events and their consequences at the tissue level.
Reproduction assets foundThe paper's Data availability statement explicitly deposits the entire code and data to reproduce the SAM cell division prediction analysis (phenotyping measurements, features, and classifiers) in a public GitHub repository.Code · publicData availability
The entire code and data to reproduce the findings are available at https://github.com/matz2532/SAM_division_predictionOpen asset ↗matz2532/SAM_division_predictionlines:102-128Code / dataset availability confirmedEurope PMC · checked 15 Sept 2026
Submergence during germination impedes aerobic metabolisms and limits the growth of most higher plants. However, some wetland plants including rice can germinate under submerged conditions. It has long been hypothesized that the first elongating shoot tissue, the coleoptile, acts as a snorkel to acquire atmospheric oxygen (O 2 ) to initiate the first leaf elongation and seminal root emergence. Here, we obtained direct evidence for this hypothesis by visualizing the spatiotemporal O 2 dynamics during submerged germination in rice using a planar O 2 optode system. In parallel with the O 2 imaging, we tracked the anatomical development of shoot and root tissues in real-time using an automated flatbed scanner. Three hours after the coleoptile tip reached the water surface, O 2 levels around the embryo transiently increased. At this time, the activity of alcohol dehydrogenase (ADH), an enzyme critical for anaerobic metabolism, was significantly reduced, and the coleorhiza covering the seminal roots in the embryo was broken. Approximately 10 h after the transient burst in O 2 , seminal roots emerged. A transient O 2 burst around the embryo was shown to be essential for seminal root emergence during submerged rice germination. The parallel application of a planar O 2 optode system and automated scanning system can be a powerful tool for examining how environmental conditions affect germination in rice and other plants.
Why it matches plant phenotyping methods水中イネ発芽時の酸素動態と器官発達を、平面O2オプトードおよび自動スキャナーで時空間的に可視化・追跡する手法が研究の中心であり、植物の生理状態・形態発達を測定する実質的なフェノタイピング手法である。
abstractvisualizing the spatiotemporal O 2 dynamics during submerged germination in rice using a planar O 2 optode system.
Reproduction assets foundThe paper's phenotyping measurements (time-lapse growth images and planar optode O2 imaging of submerged rice germination) are publicly available as Supplementary Videos 1–3 hosted at the Frontiers supplementary material URL. No author analysis code or trained models are deposited; ImageJ and UWSC are generic third‑dayDataset · 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.2022.946776/full#supplementary-material
Supplementary Video 1
Time-lapse images showing the germination process of submerged rice with normoxic or anoxic atmospheres.
Click here for additional data file.
Supplementary Video 2
Time-lapse images of the growth process following a shift from an anoxic to a normoxic atmosphOpen asset ↗lines:107-237Code / dataset availability confirmedEurope PMC · Crossref · checked 8 Sept 2026
Low-altitude aerial imaging, an approach that can collect large-scale plant imagery, has grown in popularity recently. Amongst many phenotyping approaches, unmanned aerial vehicles (UAVs) possess unique advantages as a consequence of their mobility, flexibility and affordability. Nevertheless, how to extract biologically relevant information effectively has remained challenging. Here, we present AirMeasurer, an open-source and expandable platform that combines automated image analysis, machine learning and original algorithms to perform trait analysis using 2D/3D aerial imagery acquired by low-cost UAVs in rice (Oryza sativa) trials. We applied the platform to study hundreds of rice landraces and recombinant inbred lines at two sites, from 2019 to 2021. A range of static and dynamic traits were quantified, including crop height, canopy coverage, vegetative indices and their growth rates. After verifying the reliability of AirMeasurer-derived traits, we identified genetic variants associated with selected growth-related traits using genome-wide association study and quantitative trait loci mapping. We found that the AirMeasurer-derived traits had led to reliable loci, some matched with published work, and others helped us to explore new candidate genes. Hence, we believe that our work demonstrates valuable advances in aerial phenotyping and automated 2D/3D trait analysis, providing high-quality phenotypic information to empower genetic mapping for crop improvement.
Why it matches plant phenotyping methodsUAV画像からイネの形態・生育形質を自動抽出するオープンソース基盤の開発と信頼性検証が中心であり、遺伝解析への応用も含むため採録。
abstractwe present AirMeasurer, an open-source and expandable platform that combines automated image analysis, machine learning and original algorithms to perform trait analysis using 2D/3D aerial imagery acquired by low-cost UAVs in rice (Oryza sativa) trials.
Reproduction assets foundThe paper's Data Availability statement explicitly provides the AirMeasurer source code, GUI software, and testing 2D/3D aerial images (orthomosaics and point clouds) at a public GitHub release URL, directly supporting this paper's phenotyping analysis.Code · publicThe source code, testing data, and other datasets supporting the results presented here are available at https://github.com/The‐Zhou‐Lab/UAV/releases/tag/V2.0.2 .Open asset ↗The‐Zhou‐Lab/UAV · V2.0.2lines:589-611Code / dataset availability confirmedCrossref · checked 8 Sept 2026
LeafClassificationStress / disease detectionGrowth / development / phenology
This paper aims to assist novice gardeners in identifying plant diseases to circumvent misdiagnosing their plants and to increase general horticultural knowledge for better plant growth. In this paper, we develop a mobile plant care support system (“AgroAId”), which incorporates computer vision technology to classify a plant’s [species–disease] combination from an input plant leaf image, recognizing 39 [species-and-disease] classes. Our method comprises a comparative analysis to maximize our multi-label classification model’s performance and determine the effects of varying the convolutional neural network (CNN) architectures, transfer learning approach, and hyperparameter optimizations. We tested four lightweight, mobile-optimized CNNs—MobileNet, MobileNetV2, NasNetMobile, and EfficientNetB0—and tested four transfer learning scenarios (percentage of frozen-vs.-retrained base layers): (1) freezing all convolutional layers; (2) freezing 80% of layers; (3) freezing 50% only; and (4) retraining all layers. A total of 32 model variations are built and assessed using standard metrics (accuracy, F1-score, confusion matrices). The most lightweight, high-accuracy model is concluded to be an EfficientNetB0 model using a fully retrained base network with optimized hyperparameters, achieving 99% accuracy and demonstrating the efficacy of the proposed approach; it is integrated into our plant care support system in a TensorFlow Lite format alongside the front-end mobile application and centralized cloud database. Finally, our system also uses the collective user classification data to generate spatiotemporal analytics about regional and seasonal disease trends, making these analytics accessible to all system users to increase awareness of global agricultural trends.
Why it matches plant phenotyping methods葉画像から植物の病害状態を分類する深層学習手法とモバイルシステムの開発・比較評価が研究の中心であり、植物病害フェノタイピングに該当する。
abstractwe develop a mobile plant care support system (“AgroAId”), which incorporates computer vision technology to classify a plant’s [species–disease] combination from an input plant leaf image
Reproduction assets foundThe paper's Data Availability Statement explicitly identifies the public augmented PlantVillage-derived image dataset (Mendeley Data, DOI 10.17632/tywbtsjrjv.1) used to train and evaluate the paper's plant disease classification models. No author analysis code, trained model checkpoints, or app repository is disclosed.Dataset · publicData Availability Statement: A publicly available augmented dataset was used in this study. This
data can be found openly on Mendeley Data at https://doi.org/10.17632/tywbtsjrjv.1 [accessed on 14
July 2022].Open asset ↗Mendeley Data · 10.17632/tywbtsjrjv.1pdf-page:43 lines:1-58Code / 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
Soybean has a recognized narrow genetic base that often makes it difficult to visualize available genetic and phenotypic variability and identify superior genotypes during the selection process. However, the phenotypic expression of soybean plants is highly affected by photoperiod and the cultivation of a given variety is performed in the latitude range that presents ideal conditions for its development based on its relative maturity group (RMG) for the optimization of the phenotypic expression of its genotype. Based on the above, this study aimed to evaluate the efficiency of artificial neural networks (ANNs) as a tool for the correct discrimination and classification of tropical soybean genotypes according to their relative maturity group during the population selection process with the aim of optimizing the phenotypic performance of these selected genotypes. For this purpose, three biparental populations were synthesized, one with a wide genetic variability for the RMG character obtained from the hybridization between genitors of maturity groups RMG 5 (Sub-tropical 23° LS) × RMG 9.4 (Tropical 0° LS) and two populations with a narrow variability obtained between genitors RMG 7.3 (Tropical 20° LS) × RMG 9.4 and RMG 5.3 × RMG 6.7, respectively. Criteria for comparing the developed ANN architecture with Fisher's linear and Anderson's quadratic parametric discriminant methodologies were applied to the data for the discrimination and classification of the genotypes. ANN showed an apparent error rate of less than 8.16% as well as a low influence of environmental factors, correctly classifying the genotypes in the populations even in cases of reduced genetic variability such as in the RMG 5 × RMG 6 population. In contrast, the discriminant functions were inefficient in correctly classifying the genotypes in the populations with genealogical similarity (RMG 5 × RMG 6) and wide genetic variability, with an error rate of more than 50%. Based on the results of this study, ANN can be used for the discrimination of genotypes in the initial generations of selection in breeding programs for the development of high performance cultivars for wide and reduced photoperiod amplitudes, even with fewer selection environments, more efficiently, and with fewer time and resources applied. As a result of similarity between the parents, ANN can correctly classify genotypes from populations with a narrow genetic base, in addition to pure lines and genotypes with a high degree of inbreeding.
Why it matches plant phenotyping methodsANNを用いたダイズ遺伝型の相対成熟期グループ分類を開発・評価し、他の判別法と性能比較しているため、植物表現型の抽出・分類手法が中心である。
abstractthis study aimed to evaluate the efficiency of artificial neural networks (ANNs) as a tool for the correct discrimination and classification of tropical soybean genotypes according to their relative maturity group
Reproduction assets foundThe paper's supplementary material is a public dataset of the soybean progeny phenotypic measurements (NDF, NDM, CYCLE, AIV, APM, Ac, VA, PG across three agricultural years) used directly in the ANN and discriminant analyses. No author analysis code or trained model is deposited; the Data Availability Statement only onDataset · publicedgments
We thank the State University “Júlio de Mesquita Filho”—Unesp/FCAV Jaboticabal Campus and the Graduate Agronomy Program (Genetics and Plant Breeding) and the Coordination for the Improvement of Higher Education Personnel (CAPES).
Supplementary Material
The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fpls.2022.814046/full#supplementary-material
Supplementary Table
Dataset collected from soybean progenies belonging to different relative maturity groups, during three agricultural years of evaluation.
Click here for additional data file.
References
Alliprandini L. F., Abatti C., Bertagnolli P. F., Cavassim J. E., Gabe H. LOpen asset ↗lines:442-477Code / dataset availability confirmedEurope PMC · checked 8 Sept 2026
Background and aims Seed germination is strongly influenced by environmental temperatures. With global temperatures predicted to rise, the timing of germination for thousands of plant species could change, leading to potential decreases in fitness and ecosystem-wide impacts. The thermogradient plate (TGP) is a powerful but underutilized research tool that tests germination under a broad range of constant and alternating temperatures, giving researchers the ability to predict germination characteristics using current and future climates. Previously, limitations surrounding experimental design and data analysis methods have discouraged its use in seed biology research. Methods Here, we have developed a freely available R script that uses TGP data to analyse seed germination responses to temperature. We illustrate this analysis framework using three example species: Wollemia nobilis, Callitris baileyi and Alectryon subdentatus. The script generates >40 germination indices including germination rates and final germination across each cell of the TGP. These indices are then used to populate generalized additive models and predict germination under current and future monthly maximum and minimum temperatures anywhere on the globe. Key results In our study species, modelled data were highly correlated with observed data, allowing confident predictions of monthly germination patterns for current and future climates. Wollemia nobilis germinated across a broad range of temperatures and was relatively unaffected by predicted future temperatures. In contrast, C. baileyi and A. subdentatus showed strong seasonal temperature responses, and the timing for peak germination was predicted to shift seasonally under future temperatures. Conclusions Our experimental workflow is a leap forward in the analysis of TGP experiments, increasing its many potential benefits, thereby improving research predictions and providing substantial information to inform management and conservation of plant species globally.
Why it matches plant phenotyping methods熱勾配プレート実験の種子発芽表現型を解析・予測するRスクリプトとワークフローが中心で、観測値との検証も行っているため。
abstractwe have developed a freely available R script that uses TGP data to analyse seed germination responses to temperature.
Reproduction assets foundThe paper's TGP germination data and R analysis workflow are openly deposited on Zenodo (record 5457222), with explicit availability statements in the Methods and Data Availability sections.Dataset · publicThe data that support the findings of this study are openly available in Zenodo at https://doi.org/10.5281/zenodo.5457222 .Open asset ↗Zenodo · 10.5281/zenodo.5457222lines:223-251Code / dataset availability confirmedEurope PMC · checked 14 Sept 2026
GrapevineField / plotDisease symptoms / severityGrowth / development / phenology
Phytosanitary bulletins released at weekly interval by eight Italian regional plant protection services in the growing seasons 2012-2017 were used to derive an harmonized dataset of grapevine downy mildew infection risk and phenological observations. The downy mildew infection risk ( n = 8816) was classified using a 5-point Likert response item ranging from 'very low' (1) to 'very high' (5) by six independent evaluators with domain expertise in agronomy, phytopathology and agrometeorology. Common criteria have been used in the risk assessment, considering (i) the presence of disease symptoms in field surveys, (ii) the host phenological susceptibility, (iii) the weather forecasts in the next week from the bulletin release date, (iv) the advice to apply a fungicide treatment and (v) the outputs of epidemiological models. The phenological observations are provided as BBCH codes ( n = 1689), which have been either transcribed from the phytosanitary bulletins or derived from the narrative description of the visual observation. Phenological data refer to the main early and late grapevine varieties in the eight regions (NUTS-2 administrative unit). Each record is associated with the NUTS-2 and NUTS-3 (31 provinces) administrative unit of reference, to the growing season (2012-2017), and refers to the individual risk assessment by the six evaluators. The dataset is hosted by the Centre for Agriculture and Environment of the Italian Council for Agricultural Research and Economics. These data could be helpful to researchers who develop either grapevine phenological models or process-based epidemiological predictive algorithms in order to refine their calibration and evaluation, as well as being a valuable resource for stakeholders in charge of evaluating the effective implementation of Integrated Pest Management in the decision-making process of public plant protection services in Italy. The dataset is freely available here.
Why it matches plant phenotyping methodsブドウのフェノロジー（BBCHコード）と病害リスクを体系化した再利用可能な調和データセットであり、植物状態の取得・整理自体が中心的な貢献である。
abstractused to derive an harmonized dataset of grapevine downy mildew infection risk and phenological observations
Reproduction assets foundThe paper is a Data in Brief describing a public Mendeley Data repository containing the paper's own grapevine downy mildew risk assessments and BBCH phenological observations, directly deposited by the authors.Dataset · publicCity: Bologna
Country: Italy
Data accessibility
Repository name: Mendeley Data
Data identification number: 10.17632/3jsh4y2bw4.1
Direct URL to data: https://data.mendeley.com/datasets/3jsh4y2bw4/1
Related research article
S. Bregaglio, F. Savian, E., Raparelli, D., Morelli, R., Epifani, F., Pietrangeli, C., Nigro, R., Bugiani, S., Pini, P., Culatti, D., Tognetti, F., Spanna, M., Gerardi, I., Delillo, S., Bajocco, D., Fanchini, G., Fila, F., Ginaldi, L. M., Manici, A public decision support system for the assessment of plOpen asset ↗Mendeley Data · 10.17632/3jsh4y2bw4.1lines:46-101Code / dataset availability confirmedEurope PMC · checked 15 Sept 2026
MicroscopyTissueMorphology / geometry measurementGrowth / development / phenology
In this study, we present a detailed protocol for live imaging and quantitative analysis of floral meristem development in Aquilegia coerulea , a member of the buttercup family (Ranunculaceae). Using confocal microscopy and the image analysis software MorphoGraphX, we were able to examine the cellular growth dynamics during floral organ primordia initiation, and the transition from floral meristem proliferation to termination. This protocol provides a powerful tool to study the development of the meristem and floral organ primordia, and should be easily adaptable to many plant lineages, including other emerging model systems. It will allow researchers to explore questions outside the scope of common model systems.
Why it matches plant phenotyping methods植物の花序メリステムを対象に、共焦点ライブイメージングと画像解析による細胞成長動態・器官原基形成の定量プロトコルを提示しており、表現型取得法が中心である。
abstractwe present a detailed protocol for live imaging and quantitative analysis of floral meristem development in Aquilegia coerulea
Reproduction assets foundThe protocol shares two original .czi confocal image files from the authors' own Aquilegia floral meristem study via a public Google Drive link, used to reproduce the paper's MorphoGraphX phenotyping analysis. Generic software links (Fiji/ImageJ, MorphoGraphX) are excluded as non-paper-specific.Dataset · publice stored, extracted, and processed. Here, we focus on the steps and parameters that are specific to processing confocal images of Aquilegia floral meristems, and steps to reproduce figures in Min et al. (2022). We will use two original .czi files from our study as an example, which can be downloaded from this google drive link:
https://drive.google.com/drive/folders/1WjaCieLGrnTW7d51143b8HOn-dYmsMU-?usp=sharing
Images of individual time points will be processed separately first, then loaded together for lineage tracing (details in the following section Parent Labeling & Lineage Tracing).
Software installation and equipment setup
Download the newest version of MGX from
https://morphographx.orOpen asset ↗lines:202-231Code / dataset availability confirmedEurope PMC · checked 14 Sept 2026
Duckweeds, a group of floating aquatic plants, are ideal model plants for laboratory experiments because they are small, easy to cultivate, and reproduce quickly. Duckweed laboratory biology, however, requires that lineages are maintained as continuous populations of asexually propagating fronds, so research teams need to calibrate cultivation conditions and coordinate maintenance tasks for duckweed stocks. Computational image data analysis is proving a powerful duckweed research tool, but researchers lack software tools to assist with data collection and storage in a way that can feed into scripted data analysis. We set out to support these processes — cultivation, and subsequent integration with data analysis tools — using a laboratory management software called Aquarium, an open-source application developed to manage laboratory inventory and plan experiments. We developed a suite of duckweed cultivation and experimentation operation types in Aquarium, which we then integrated with novel data analysis scripts. We then demonstrated the efficacy of our system with a series of image-based growth assays, and explored how our framework could be used to calibrate cultivation protocols. We discuss the unexpected advantages and the limitations of this approach, suggesting areas for future software tool development. In its current state, our approach helps to bridge the gap between laboratory implementation and data analytical software for duckweed biologists and builds a foundation for future development of end-to-end computational tools in plant science.
Why it matches plant phenotyping methodsアヒルウキクサの画像ベース成長アッセイと解析スクリプトを統合するオープンソースの実験・データ管理ワークフローを開発・実証しており、植物表現型取得と解析基盤が中心である。
abstractComputational image data analysis is proving a powerful duckweed research tool, but researchers lack software tools to assist with data collection and storage in a way that can feed into scripted data analysis.
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 · publicExperimental
data and analysis scripts can be downloaded from the Github directory
(https://github.com/mtscott321/duckweed_data_analysis).Open asset ↗mtscott321/duckweed_data_analysispdf-page:4 lines:1-47Code / dataset availability confirmedEurope PMC · checked 8 Sept 2026
RiceRaman / spectroscopyClassificationGrowth / development / phenology
Rice cultivation in cold regions of China is mainly distributed in Heilongjiang Province, where the growing season of rice is susceptible to low temperature and cold damage. Choosing and planting rice varieties with suitable GD according to the accumulated temperate zone is an important measure to prevent low temperature and cold damage. However, the traditional identification method of rice GD requires lots of field investigations, which are time consuming and susceptible to environmental interference. Therefore, an efficient, accurate, and intelligent identification method is urgently needed. In response to this problem, we took seven rice varieties suitable for three accumulated temperature zones in Heilongjiang Province as the research objects, and we carried out research on the identification of japonica rice GD based on Raman spectroscopy and capsule neural networks (CapsNets). The data preprocessing stage used a variety of methods (signal.filtfilt, difference, segmentation, and superposition) to process Raman spectral data to complete the fusion of local features and global features and data dimension transformation. A CapsNets containing three neuron layers (one convolutional layer and two capsule layers) and a dynamic routing protocol was constructed and implemented in Python. After training 160 epochs on the CapsNets, the model achieved 89% and 93% accuracy on the training and test datasets, respectively. The results showed that Raman spectroscopy combined with CapsNets can provide an efficient and accurate intelligent identification method for the classification and identification of rice GD in Heilongjiang Province.
Why it matches plant phenotyping methodsラマン分光とCapsNetを組み合わせ、イネの生育期間という植物形質を推定・分類する手法を開発し、学習・テスト精度で評価しているため、表現型取得・抽出法が中心です。
abstractwe carried out research on the identification of japonica rice GD based on Raman spectroscopy and capsule neural networks (CapsNets).
Reproduction assets foundThe paper's Data Availability Statement explicitly states that the study's data (Raman spectral data of 245 japonica rice grain samples) and code (spectral preprocessing and CapsNets classification) are openly available in the authors' public GitHub repository, which is listed in the allowed URLs.Code · publicData Availability Statement: The data and code presented in this study are openly available at:
https://github.com/zxxsnh/Rice_GD_classification_CapsNets (accessed on 14 June 2022).Open asset ↗zxxsnh/Rice_GD_classification_CapsNetspdf-page:12 lines:1-60Code / dataset availability confirmedCrossref · checked 14 Sept 2026
Abstract Effective management of invasive species requires rapid detection and dynamic monitoring. Remote sensing offers an efficient alternative to field surveys for invasive plants; however, distinguishing individual plant species can be challenging especially over geographic scales. Satellite imagery is the most practical source of data for developing predictive models over landscapes, but spatial resolution and spectral information can be limiting. We used two types of satellite imagery to detect the invasive plant, leafy spurge ( Euphorbia virgata ), across a heterogeneous landscape in Minnesota, USA. We developed convolutional neural networks (CNNs) with imagery from Worldview‐2 and Planetscope satellites. Worldview‐2 imagery has high spatial and spectral resolution, but images are not routinely taken in space or time. By contrast, Planetscope imagery has lower spatial and spectral resolution, but images are taken daily across Earth. The former had 96.1% accuracy in detecting leafy spurge, whereas the latter had 89.9% accuracy. Second, we modified the CNN for Planetscope with a long short‐term memory (LSTM) layer that leverages information on phenology from a time series of images. The detection accuracy of the Planetscope LSTM model was 96.3%, on par with the high resolution, Worldview‐2 model. Across models, most false‐positive errors occurred near true populations, indicating that these errors are not consequential for management. We identified that early and mid‐season phenological periods in the Planetscope time series were key to predicting leafy spurge. Additionally, green, red‐edge and near‐infrared spectral bands were important for differentiating leafy spurge from other vegetation. These findings suggest that deep learning models can accurately identify individual species over complex landscapes even with satellite imagery of modest spatial and spectral resolution if a temporal series of images is incorporated. Our results will help inform future management efforts using remote sensing to identify invasive plants, especially across large‐scale, remote and data‐sparse areas.
Why it matches plant phenotyping methods衛星画像とCNN/LSTMを用いて侵入植物個体群を直接検出する方法を開発・評価しており、植物状態の取得・抽出が研究の中心である。
abstractWe developed convolutional neural networks (CNNs) with imagery from Worldview‐2 and Planetscope satellites.
Reproduction assets foundThe article explicitly states that all analysis code for the leafy spurge deep learning models (WV-CNN, PS-CNN, PS-LSTM) is publicly available in the authors' GitHub repository. The 1-m land cover map used as ground truth is a cited prior dataset (Host et al., 2016), not a paper-specific asset, and no trained model or Code · publice trained each model
for 100 epochs, where each epoch comprised 200 samples
per batch with a batch size of eight. We applied random
image augmentations, including 0/90/180/270-degree rota-
tions, which are shown to assist in model generalization
(Cabezas et al., 2020; Shorten & Khoshgoftaar, 2019). All
code is available online (https://github.com/lake-thomas/spurge-remote-sensing).Model performance metrics
We assessed model performance for each class based on
the number of true positives (TP), false positives (FP),
true negatives (TN) and false negatives (FN). We calcu-
lated overall accuracy as the proportion of correctly iden-
tified pixels (TP + TN/TP + TN + FP + FN) to identify
the probaOpen asset ↗https://github.com/lake-thomas/spurge-remote-sensingpdf-raw-page:6 lines:1-93Code / dataset availability confirmedOpenAlex · Europe PMC · checked 8 Sept 2026
Recognizing rice seedling growth stages to timely do field operations, such as temperature control, fertilizer, irrigation, cultivation, and disease control, is of great significance of crop management, provision of standard and well-nourished seedlings for mechanical transplanting, and increase of yield. Conventionally, rice seedling growth stage is performed manually by means of visual inspection, which is not only labor-intensive and time-consuming, but also subjective and inefficient on a large-scale field. The application of machine learning algorithms on UAV images offers a high-throughput and non-invasive alternative to manual observations and its applications in agriculture and high-throughput phenotyping are increasing. This paper presented automatic approaches to detect rice seedling of three critical stages, BBCH11, BBCH12, and BBCH13. Both traditional machine learning algorithms and deep learning algorithms were investigated the discriminative ability of the three growth stages. UAV images were captured vertically downward at 3-m height from the field. A dataset consisted of images of three growth stages of rice seedlings for three cultivars, five nursing seedling densities, and different sowing dates. In the traditional machine learning algorithm, histograms of oriented gradients (HOGs) were selected as texture features and combined with the support vector machine (SVM) classifier to recognize and classify three growth stages. The best HOG-SVM model obtained the performance with 84.9, 85.9, 84.9, and 85.4% in accuracy, average precision, average recall, and F1 score, respectively. In the deep learning algorithm, the Efficientnet family and other state-of-art CNN models (VGG16, Resnet50, and Densenet121) were adopted and investigated the performance of three growth stage classifications. EfficientnetB4 achieved the best performance among other CNN models, with 99.47, 99.53, 99.39, and 99.46% in accuracy, average precision, average recall, and F1 score, respectively. Thus, the proposed method could be effective and efficient tool to detect rice seedling growth stages.
Why it matches plant phenotyping methodsUAV画像からイネ幼苗の生育段階という植物状態を自動推定する機械学習手法を開発・比較し、性能評価しているため、表現型取得が研究の中心である。
abstractThe application of machine learning algorithms on UAV images offers a high-throughput and non-invasive alternative to manual observations
Reproduction assets foundThe paper's data availability statement explicitly deposits the UAV rice seedling image datasets, trained models, and analysis code in public locations: a Google Drive folder (datasets and models) and a GitHub repository (code), both matching allowed URLs.Dataset · publicThe datasets, models and code used in this paper are available at the following locations: Datasets and models: https://drive.google.com/drive/folders/1AY-ro3HID9noOpen asset ↗lines:786-795Code · publicCode: https://github.com/imagevision-lab/rice_seedling_growth_stages_detectionOpen asset ↗imagevision-lab/rice_seedling_growth_stages_detectionlines:786-795Code / dataset availability confirmedEurope PMC · checked 8 Sept 2026
Common beanWhole plant / canopy / plot / fieldPhysiological trait estimationGrowth / development / phenology
Background Predicting the phenotype from the genotype is one of the major contemporary challenges in biology. This challenge is greater in plants because their development occurs mostly post-embryonically under diurnal and seasonal environmental fluctuations. Most current crop simulation models are physiology-based models capable of capturing environmental fluctuations but cannot adequately capture genotypic effects because they were not constructed within a genetics framework. Results We describe the construction of a mixed-effects dynamic model to predict time-to-flowering in the common bean (Phaseolus vulgaris L.). This prediction model applies the developmental approach used by traditional crop simulation models, uses direct observational data, and captures the Genotype, Environment, and Genotype-by-Environment effects to predict progress towards time-to-flowering in real time. Comparisons to a traditional crop simulation model and to a previously developed static model shows the advantages of the new dynamic model. Conclusions The dynamic model can be applied to other species and to different plant processes. These types of models can, in modular form, gradually replace plant processes in existing crop models as has been implemented in BeanGro, a crop simulation model within the DSSAT Cropping Systems Model. Gene-based dynamic models can accelerate precision breeding of diverse crop species, particularly with the prospects of climate change. Finally, a gene-based simulation model can assist policy decision makers in matters pertaining to prediction of food supplies.
Why it matches plant phenotyping methods遺伝子型・環境データから開花時期という植物形質を予測する動的モデルを構築・比較しており、形質推定手法が研究の中心である。
abstractWe describe the construction of a mixed-effects dynamic model to predict time-to-flowering in the common bean (Phaseolus vulgaris L.).
Reproduction assets foundThe authors publicly deposited the paper's MET phenotypic/meteorological observational data, synthetic data, and the R/FORTRAN analysis code (dynamic mixed-effects flowering model) on figshare (DOI 10.6084/m9.figshare.19692628), and separately deposited the RI family genotype data at a figshare link given in Methods.Dataset · publicComputer codes are available in the Supplementary Materials file, and observational and synthetic data in Additional file 1 , which have been uploaded to the figshare database repository ( https://doi.org/10.6084/m9.figshare.19692628 ).Open asset ↗figshare · 10.6084/m9.figshare.19692628lines:167-260Code / dataset availability confirmedEurope PMC · checked 8 Sept 2026
A fundamental question in biology concerns how molecular and cellular processes become integrated during morphogenesis. In plants, characterization of 3D digital representations of organs at single-cell resolution represents a promising approach to addressing this problem. A major challenge is to provide organ-centric spatial context to cells of an organ. We developed several general rules for the annotation of cell position and embodied them in 3DCoordX, a user-interactive computer toolbox implemented in the open-source software MorphoGraphX. 3DCoordX enables rapid spatial annotation of cells even in highly curved biological shapes. Using 3DCoordX, we analyzed cellular growth patterns in organs of several species. For example, the data indicated the presence of a basal cell proliferation zone in the ovule primordium of Arabidopsis (Arabidopsis thaliana). Proof-of-concept analyses suggested a preferential increase in cell length associated with neck elongation in the archegonium of Marchantia (Marchantia polymorpha) and variations in cell volume linked to central morphogenetic features of a trap of the carnivorous plant Utricularia (Utricularia gibba). Our work demonstrates the broad applicability of the developed strategies as they provide organ-centric spatial context to cellular features in plant organs of diverse shape complexity.
Why it matches plant phenotyping methods植物器官の3Dデジタル表現から細胞位置を注釈し、細胞成長や形態特徴を解析する専用ツールを開発しており、表現型取得・抽出手法が研究の中心です。
abstractWe developed several general rules for the annotation of cell position and embodied them in 3DCoordX, a user-interactive computer toolbox implemented in the open-source software MorphoGraphX.
Reproduction assets foundThe paper deposits its phenotyping datasets (raw cell boundaries, PlantSeg predictions, segmented cells, annotated 3D cell meshes, and csv attribute files) in the BioStudies repository under accession S-BSST734, making the paper-specific 3D plant organ data publicly available.Dataset · publicThe datasets of this study have been deposited with the BioStudies data repository ( https://www.ebi.ac.uk/biostudies ) under the accession S-BSST734. Example dataset contains raw cell boundaries, cell boundaries, predictions from PlantSeg, nuclei images, segmented cells as well as the annotated 3D cell meshes, and the associated attribute files in csv format. The 3D meshes used in different manuscript figures are also available for download from the repository.Open asset ↗BioStudies · S-BSST734lines:161-178Code / 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 · checked 15 Sept 2026
O_LIAnnual rings from 30 year old vines in a California rootstock trial were measured to determine the effects of 15 different rootstocks on Chardonnay and Cabernet Sauvignon scions. Viticultural traits measuring vegetative growth, yield, berry quality, and nutrient uptake were collected at the beginning and end of the lifetime of the vineyard. C_LIO_LIX-ray Computed Tomography (CT) was used to measure ring widths in 103 vines. Ring width was modeled as a function of ring number using a negative exponential model. Early and late wood ring widths, cambium width, and scion trunk radius were correlated with 27 traits. C_LIO_LIModeling of annual ring width shows that scions alter the width of the first rings but that rootstocks alter the decay thereafter, consistently shortening ring width throughout the lifetime of the vine. The ratio of yield to vegetative growth, juice pH, photosynthetic assimilation and transpiration rates, and stomatal conductance are correlated with scion trunk radius. C_LIO_LIRootstocks modulate secondary growth over years, altering hydraulic conductance, physiology, and agronomic traits. Rootstocks act in similar but distinct ways from climate to modulate ring width, which borrowing techniques from dendrochronology, can be used to monitor both genetic and environmental effects in woody perennial crop species. C_LI
Why it matches plant phenotyping methodsX線CTによる年輪幅・形成層幅・幹半径の測定が研究の主要な表現型取得手段であり、樹体の二次成長を遺伝的・環境的影響のモニタリングに用いる方法として扱われている。
abstractX-ray Computed Tomography (CT) was used to measure ring widths in 103 vines.
Reproduction assets foundThe paper deposits its X-ray CT cross-section images with landmarks (the phenotyping inputs for ring-width measurement) on Dryad, and all data plus analysis code in a public GitHub repository/Jupyter notebook. Both are paper-specific, publicly available, and actionable.Dataset · publicBMG, IK, MRM, ELM, AWS, ALD, SS, and DHC analyzed data. ZM and DHC
510 coordinated research, data analysis, and manuscript writing. DHC wrote a first draft of the
511 manuscript which all authors read, commented on, and edited.
512
513 Data Availability
514
515 X-ray CT cross-sections with landmarks are deposited on Dryad:
516 http://dx.doi.org/10.5061/dryad.gqnk98sqf. All data and code to reproduce results are posted on
517 the Github repository https://github.com/DanChitwood/grapevine_rings.
518
519 Supporting Information Table S1: Numbers of measured samples for each trait, for each
520 scion, for each year.
521
522 Table 1: Rootstock parentage
523
Rootstock Parentage
775 Paulsen V. berlaOpen asset ↗Dryad · 10.5061/dryad.gqnk98sqfpdf-layout-page:13 lines:1-51Code · publict writing. DHC wrote a first draft of the
511 manuscript which all authors read, commented on, and edited.
512
513 Data Availability
514
515 X-ray CT cross-sections with landmarks are deposited on Dryad:
516 http://dx.doi.org/10.5061/dryad.gqnk98sqf. All data and code to reproduce results are posted on
517 the Github repository https://github.com/DanChitwood/grapevine_rings.
518
519 Supporting Information Table S1: Numbers of measured samples for each trait, for each
520 scion, for each year.
521
522 Table 1: Rootstock parentage
523
Rootstock Parentage
775 Paulsen V. berlandieri Rességuier 2 × V. rupestris du Lot
1103 Paulsen V. berlandieri Rességuier 2 × V. rupestris du Lot
3309 Couderc V. Open asset ↗GitHub · DanChitwood/grapevine_ringspdf-layout-page:13 lines:1-51Code / dataset availability confirmedCrossref · Europe PMC · OpenAlex · checked 8 Sept 2026
Plant breeders, scientists, and commercial producers commonly use growth rate as an integrated signal of crop productivity and stress. Plant growth monitoring is often done destructively via growth rate estimation by harvesting plants at different growth stages and simply weighing each individual plant. Within plant breeding and research applications, and more recently in commercial applications, non-destructive growth monitoring is done using computer vision to segment plants in images from the background, either in 2D or 3D, and relating these image-based features to destructive biomass measurements. Recent advancements in machine learning have improved image-based localization and detection of plants, but such techniques are not well suited to make biomass predictions when there is significant self-occlusion or occlusion from neighboring plants, such as those encountered under leafy green production in controlled environment agriculture. To enable prediction of plant biomass under occluded growing conditions, we develop an end-to-end deep learning approach that directly predicts lettuce plant biomass from color and depth image data as provided by a low cost and commercially available sensor. We test the performance of the proposed deep neural network for lettuce production, observing a mean prediction error of 7.3% on a comprehensive test dataset of 864 individuals and substantially outperforming previous work on plant biomass estimation. The modeling approach is robust to the busy and occluded scenes often found in commercial leafy green production and requires only measured mass values for training. We then demonstrate that this level of prediction accuracy allows for rapid, non-destructive detection of changes in biomass accumulation due to experimentally induced stress induction in as little as 2 days. Using this method growers may observe and react to changes in plant-environment interactions in near real time. Moreover, we expect that such a sensitive technique for non-destructive biomass estimation will enable novel research and breeding of improved productivity and yield in response to stress.
Why it matches plant phenotyping methodsRGB-D画像と深層学習を用いて、遮蔽下のレタス個体バイオマスを非破壊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。
abstractwe develop an end-to-end deep learning approach that directly predicts lettuce plant biomass from color and depth image data as provided by a low cost and commercially available sensor.
Reproduction assets foundThe article provides an authors' public GitHub repository containing the analysis code for the RGB-D deep learning biomass estimation pipeline. The raw image/biomass dataset is only available on request (no public deposit).Code · publicCode available at https://github.com/NicoBux/Plant-Biomass-Monitoring .Open asset ↗NicoBux/Plant-Biomass-Monitoringlines:466-524Code / dataset availability confirmedOpenAlex · Europe PMC · bioRxiv · checked 8 Sept 2026
Abstract In the absence of pollination, female reproductive organs senesce leading to an irrevocable loss in the reproductive potential of the flower and directly affecting seed set. In self-pollinating crops like wheat ( Triticum aestivum ), the post-anthesis viability of the unpollinated carpel has been overlooked, despite its importance for hybrid seed production systems. To advance our knowledge of carpel development in the absence of pollination, we created a relatively high-throughput phenotyping approach to quantify stigma and ovary morphology. We demonstrate the suitability of the approach, which is based on light microscopy imaging and machine learning, for the detailed study of floral organ traits in field grown plants using both fresh and fixed samples. We show that the unpollinated carpel undergoes a well-defined initial growth phase, followed by a peak phase (in which stigma area reaches its maximum and the radial expansion of the ovary slows), and a final deterioration phase. These developmental dynamics were largely consistent across years and could be used to classify male sterile cultivars, however the absolute duration of each phase varied across years. This phenotyping approach provides a new tool for examining carpel morphology and development which we hope will help advance research into this field and increase our mechanistic understanding of female fertility in wheat.
Why it matches plant phenotyping methodsコムギの柱頭・子房形態を定量化する高スループット表現型解析法を、光学顕微鏡画像と機械学習で開発・適用しており、表現型取得手法が研究の中心である。
abstractwe created a relatively high-throughput phenotyping approach to quantify stigma and ovary morphology.
Reproduction assets foundThe paper explicitly states that implementation scripts, data, and the trained stigma/ovary CNNs are publicly available at the authors' GitHub repository, which is an allowed URL.Code · publicImplementation scripts and data are available at
https://github.com/marina-millan/ML-carpel_traits.Open asset ↗marina-millan/ML-carpel_traits · ML-carpel_traitspdf-page:4 lines:1-61Code / dataset availability confirmedEurope PMC · checked 15 Sept 2026
Seed vigor is an important index to evaluate seed quality in plant species. How to evaluate seed vigor quickly and accurately has always been a serious problem in the seed research field. As a new physical testing method, multispectral technology has many advantages such as high sensitivity and accuracy, nondestructive and rapid application having advantageous prospects in seed quality evaluation. In this study, the morphological and spectral information of 19 wavelengths (365, 405, 430, 450, 470, 490, 515, 540, 570, 590, 630, 645, 660, 690, 780, 850, 880, 940, 970 nm) of alfalfa seeds with different level of maturity and different harvest periods (years), representing different vigor levels and age of seed, were collected by using multispectral imaging. Five multivariate analysis methods including principal component analysis (PCA), linear discriminant analysis (LDA), support vector machine (SVM), random forest (RF) and normalized canonical discriminant analysis (nCDA) were used to distinguish and predict their vigor. The results showed that LDA model had the best effect, with an average accuracy of 92.9% for seed samples of different maturity and 97.8% for seed samples of different harvest years, and the average sensitivity, specificity and precision of LDA model could reach more than 90%. The average accuracy of nCDA in identifying dead seeds with no vigor reached 93.3%. In identifying the seeds with high vigor and predicting the germination percentage of alfalfa seeds, it could reach 95.7%. In summary, the use of Multispectral Imaging and multivariate analysis in this experiment can accurately evaluate and predict the seed vigor, seed viability and seed germination percentages of alfalfa, providing important technical methods and ideas for rapid non-destructive testing of seed quality.
Why it matches plant phenotyping methodsマルチスペクトル画像と多変量解析により、アルファルファ種子の活力・生存性・発芽率を非破壊推定する手法が研究の中心であるため。
abstractAs a new physical testing method, multispectral technology has many advantages such as high sensitivity and accuracy, nondestructive and rapid application having advantageous prospects in seed quality evaluation.
Reproduction assets foundThe authors provide a public Google Drive supplement containing the paper's own multispectral imaging data: mean reflectance at 19 wavelengths for all seeds (Table S1), morphological feature data for all seeds (Table S2), and multispectral images of the alfalfa seed samples (Figures S1–S6). These directly reproduce theDataset · publicThe following are available online at https://drive.google.com/file/d/13CXchEm81qnbIZCXLqdvupDPib7BS8FM/view?usp=sharing , Table S1: Mean reflectance of 19 wavelengths in all seeds, Table S2: Data of morphological feature in all seeds. Figure S1: Multispectral image of seeds harvested in 2004. Figure S2: Multispectral image of seeds harvested in 2008. Figure S3: Multispectral image of seeds harvested in 2019. Figure S4: Multispectral image of seeOpen asset ↗lines:79-239Code / dataset availability confirmedEurope PMC · checked 15 Sept 2026
Capturing cell-to-cell and cell-to-environment signals in a defined 3 dimensional (3D) microenvironment is key to study cellular functions, including cellular reprogramming towards tissue regeneration. A major challenge in current culturing methods is that these methods cannot accurately capture this multicellular 3D microenvironment. In this study, we established the framework of 3D bioprinting with plant cells to study cell viability, cell division, and cell identity. We established long-term cell viability for bioprinted Arabidopsis root cells and soybean meristematic cells. To analyze the large image datasets generated during these long-term viability studies, we developed an open source high-throughput image analysis pipeline. Furthermore, we showed the cell cycle re-entry of the isolated Arabidopsis and soybean cells leading to the formation of microcalli. Finally, we showed that the identity of isolated cells of Arabidopsis roots expressing endodermal markers maintained longer periods of time. The framework established in this study paves the way for a general use of 3D bioprinting for studying cellular reprogramming and cell cycle re-entry towards tissue regeneration.
Why it matches plant phenotyping methods植物細胞の3Dバイオプリンティング系と、長期画像データから細胞生存性・分裂・同一性を抽出するオープンソース解析パイプラインを開発しており、植物状態の取得・解析手法が中心である。
abstractIn this study, we established the framework of 3D bioprinting with plant cells to study cell viability, cell division, and cell identity.
Reproduction assets foundThe paper's authors publicly released their in-house confocal z-stack cell-counting image analysis pipeline (Python scripts wrapped in an R Shiny GUI) on GitHub, which directly reproduces the paper's computational analysis of bioprinted plant cell images. Generic dependencies (pyimageJ, OpenCV, ComDet, R shiny) and theCode · publicpipeline
contained in Python and further developed into an R Shiny application (44) can be easily accessed, along
with the usage instructions, from the Github repository at https://github.com/LisaVdB/Confocal-z-stack-
cell-detection.
Data availability
Scripts for our high-throughput and automatic image analysis are available at
https://github.com/LisaVdB/Confocal-z-stack-cell-detection.Open asset ↗LisaVdB/Confocal-z-stack-cell-detectionpdf-layout-page:10 lines:1-49Code / dataset availability confirmedEurope PMC · checked 15 Sept 2026
TomatoGreenhouseLeafPhysiological trait estimationGrowth / development / phenology
While the high year-round production of tomatoes has been facilitated by solar greenhouse cultivation, these yields readily fluctuate in response to changing environmental conditions. Mathematic modeling has been applied to forecast phenotypes of tomatoes using environmental measurements (e.g., temperature) as indirect parameters. In this study, metabolome data, as direct parameters reflecting plant internal status, were used to construct a predictive model of the anthesis rate of greenhouse tomatoes. Metabolome data were obtained from tomato leaves and used as variables for linear regression with the least absolute shrinkage and selection operator (LASSO) for prediction. The constructed model accurately predicted the anthesis rate, with an R 2 value of 0.85. Twenty-nine of the 161 metabolites were selected as candidate markers. The selected metabolites were further validated for their association with anthesis rates using the different metabolome datasets. To assess the importance of the selected metabolites in cultivation, the relationships between the metabolites and cultivation conditions were analyzed via correspondence analysis. Trigonelline, whose content did not exhibit a diurnal rhythm, displayed major contributions to the cultivation, and is thus a potential metabolic marker for predicting the anthesis rate. This study demonstrates that machine learning can be applied to metabolome data to identify metabolites indicative of agricultural traits.
Why it matches plant phenotyping methodsトマトの開花率という植物形質をメタボロームデータから予測するLASSOモデルを構築し、別データセットで検証しており、形質推定手法が中心である。
abstractmetabolome data, as direct parameters reflecting plant internal status, were used to construct a predictive model of the anthesis rate of greenhouse tomatoes.
Reproduction assets foundThe paper's tomato leaf metabolome dataset (DM0041) used for the LASSO anthesis-rate modeling is publicly deposited in DROP Met at PRIMe, with an explicit Data Availability Statement and URL. No author analysis code or trained model checkpoint is stated as publicly available; the supplementary material link is generic.Dataset · publicss spectrometer (LC-QqQ-MS) (UPLC coupled with Xevo TQ-S, Waters, Milford, MA, United States) ( Sawada et al., 2009 ; Sawada et al., 2019 ). The analytical conditions are described in detail in Supplementary Tables S1–S3 . The metabolome data were deposited in the DROP Met in PRIMe (the Platform for RIKEN Metabolomics) (DM0041, http://prime.psc.riken.jp/archives/data/DropMet/059/ ).
2.3.3 Measurement of Relative Metabolite Contents
For the Tsukuba data (TK01), the peak areas of 501 target metabolites (including two internal standards) were processed as follows. Values below the detection limit were set to zero. The peak area of each metabolite in a leaf sample was divided by the mean peak arOpen asset ↗DROP Met · DM0041lines:87-98Code / dataset availability confirmedEurope PMC · checked 14 Sept 2026
ArabidopsisLaboratory / benchtopRootMorphology / geometry measurementTrackingGrowth / development / phenologyRoot system architecture
Directional root growth control is crucial for plant fitness. The degree of root growth deviation depends on several factors, whereby exogenous growth conditions have a profound impact. The perception of mechanical impedance by wild-type roots results in the modulation of root growth traits, and it is known that gravitropic stimulus influences distinct root movement patterns in concert with mechanoadaptation. Mutants with reduced shootward auxin transport are described as being numb towards mechanostimulus and gravistimulus, whereby different growth conditions on agar-supplemented medium have a profound effect on how much directional root growth and root movement patterns differ between wild types and mutants. To reduce the impact of unilateral mechanostimulus on roots grown along agar-supplemented medium, we compared the root movement of Col-0 and auxin resistant 1-7 in a root penetration assay to test how both lines adjust the growth patterns of evenly mechanostimulated roots. We combined the assay with the D-root system to reduce light-induced growth deviation. Moreover, the impact of sucrose supplementation in the growth medium was investigated because exogenous sugar enhances root growth deviation in the vertical direction. Overall, we observed a more regular growth pattern for Col-0 but evaluated a higher level of skewing of aux1-7 compared to the wild type than known from published data. Finally, the tracking of the growth rate of the gravistimulated roots revealed that Col-0 has a throttling elongation rate during the bending process, but aux1-7 does not.
Why it matches plant phenotyping methodsD-rootシステムと根貫通アッセイを組み合わせ、根の成長パターン・伸長速度を追跡して評価する測定ワークフローが研究の中心であるため。
abstractWe combined the assay with the D-root system to reduce light-induced growth deviation.
Reproduction assets found保存済みの本文根拠を更新済みルールで再検証し、公開資産1件を確認しました。Supplement · publicThe following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/plants11050650/s1 , Table S1: raw data.Open asset ↗lines:48-103Code / dataset availability 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 · 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 · Crossref · checked 15 Sept 2026
Capturing complete internal anatomies of plant organs and tissues within their relevant morphological context remains a key challenge in plant science. While plant growth and development are inherently multiscale, conventional light, fluorescence, and electron microscopy platforms are typically limited to imaging of plant microstructure from small flat samples that lack a direct spatial context to, and represent only a small portion of, the relevant plant macrostructures. We demonstrate technical advances with a lab-based X-ray microscope (XRM) that bridge the imaging gap by providing multiscale high-resolution three-dimensional (3D) volumes of intact plant samples from the cell to the whole plant level. Serial imaging of a single sample is shown to provide sub-micron 3D volumes co-registered with lower magnification scans for explicit contextual reference. High-quality 3D volume data from our enhanced methods facilitate sophisticated and effective computational segmentation. Advances in sample preparation make multimodal correlative imaging workflows possible, where a single resin-embedded plant sample is scanned via XRM to generate a 3D cell-level map, and then used to identify and zoom in on sub-cellular regions of interest for high-resolution scanning electron microscopy. In total, we present the methodologies for use of XRM in the multiscale and multimodal analysis of 3D plant features using numerous economically and scientifically important plant systems.
Why it matches plant phenotyping methods植物の細胞から個体レベルの3D形態を取得するX線顕微鏡法と試料調製・計算セグメンテーションを中心に開発・提示しており、植物表現型取得手法が明確に主題である。
abstractWe demonstrate technical advances with a lab-based X-ray microscope (XRM) that bridge the imaging gap by providing multiscale high-resolution three-dimensional (3D) volumes of intact plant samples from the cell to the whole plant level.
Reproduction assets foundThe authors deposited fly-through animations of 2D image stacks and 3D volume rendering animations of the XRM scans shown in the paper's figures on figshare, directly reproducing this paper's plant phenotyping imaging data. No author analysis code or trained model checkpoints were explicitly deposited.Dataset · publicCanada) was used for data integration, visualization, and animation of the scan data, and to export image data as 2D 16-bit Tag Image File Format (TIFF) stacks. Fly-through animations of 2D image stacks for scans shown in all Figures, as well as 3D volume rendering animations of selected scans, are available for download from ( https://figshare.com/s/944efc8832e47fd4f203 ).
Image analysis and segmentation
Data from XRM scans were segmented using Amira software and with the assistance of a Wacom tablet for manual segmentation, in addition to ORS Dragonfly Deep Learning Module 2021.1.0.977 which is free for noncommercial use. Segmentation for Figure 1D combined automated and manual methods in Open asset ↗figsharelines:87-114Code / dataset availability confirmedEurope PMC · checked 8 Sept 2026
In many cases, the unprecedented availability of data provided by high-throughput sequencing has shifted the bottleneck from a data availability issue to a data interpretation issue, thus delaying the promised breakthroughs in genetics and precision medicine, for what concerns Human genetics, and phenotype prediction to improve plant adaptation to climate change and resistance to bioagressors, for what concerns plant sciences. In this paper, we propose a novel Genome Interpretation paradigm, which aims at directly modeling the genotype-to-phenotype relationship, and we focus on A. thaliana since it is the best studied model organism in plant genetics. Our model, called Galiana, is the first end-to-end Neural Network (NN) approach following the genomes in/phenotypes out paradigm and it is trained to predict 288 real-valued Arabidopsis thaliana phenotypes from Whole Genome sequencing data. We show that 75 of these phenotypes are predicted with a Pearson correlation ≥0.4, and are mostly related to flowering traits. We show that our end-to-end NN approach achieves better performances and larger phenotype coverage than models predicting single phenotypes from the GWAS-derived known associated genes. Galiana is also fully interpretable, thanks to the Saliency Maps gradient-based approaches. We followed this interpretation approach to identify 36 novel genes that are likely to be associated with flowering traits, finding evidence for 6 of them in the existing literature.
Why it matches plant phenotyping methods植物の遺伝子型から288形質を予測するエンドツーエンドのニューラルネットワーク手法を開発・評価しており、表現型推定手法が研究の中心である。
abstractOur model, called Galiana, is the first end-to-end Neural Network (NN) approach following the genomes in/phenotypes out paradigm and it is trained to predict 288 real-valued Arabidopsis thaliana phenotypes from Whole Genome sequencing data.
Reproduction assets foundThe paper's authors explicitly state that the Galiana model code is freely available from their public Bitbucket repository, which is a paper-specific computational analysis asset. The phenotype data come from third-party databases (1001 Genomes, AraPheno) and are not paper-specific deposits; supplementary tables are aCode · publicWe implemented the model using pytorch ( 26 ). The code is freely available from our git repository https://bitbucket.org/eddiewrc/galiana/src/master/ .Open asset ↗bitbucket.org/eddiewrc/galiana · eddiewrc/galianalines:44-55Code / dataset availability confirmedCrossref · checked 8 Sept 2026
Near-surface cameras, such as those in the PhenoCam network, are a common source of ground truth data in modelling and remote sensing studies. Despite having locations across numerous agricultural sites, few studies have used near-surface cameras to track the unique phenology of croplands. Due to management activities, crops do not have a natural vegetation cycle which many phenological extraction methods are based on. For example, a field may experience abrupt changes due to harvesting and tillage throughout the year. A single camera can also record several different plants due to crop rotations, fallow fields, and cover crops. Current methods to estimate phenology metrics from image time series compress all image information into a relative greenness metric, which discards a large amount of contextual information. This can include the type of crop present, whether snow or water is present on the field, the crop phenology, or whether a field lacking green plants consists of bare soil, fully senesced plants, or plant residue. Here, we developed a modelling workflow to create a daily time series of crop type and phenology, while also accounting for other factors such as obstructed images and snow covered fields. We used a mainstream deep learning image classification model, VGG16. Deep learning classification models do not have a temporal component, so to account for temporal correlation among images, our workflow incorporates a hidden Markov model in the post-processing. The initial image classification model had out of sample F1 scores of 0.83–0.85, which improved to 0.86–0.91 after all post-processing steps. The resulting time series show the progression of crops from emergence to harvest, and can serve as a daily, local-scale dataset of field states and phenological stages for agricultural research.
Why it matches plant phenotyping methodsPhenoCam画像から作物種と日次フェノロジーを抽出する深層学習・隠れマルコフモデルのワークフローを開発し、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中心である。
abstractHere, we developed a modelling workflow to create a daily time series of crop type and phenology, while also accounting for other factors such as obstructed images and snow covered fields.
Reproduction assets foundThe authors deposited all analysis code, data, and final model predictions in a public Zenodo repository (DOI 10.5281/zenodo.5618316), and the paper's phenotyping inputs are PhenoCam images from the public PhenoCam data portal (phenocam.sr.unh.edu). Both are paper-specific, public, and actionable.Code · publicData Availability Statement: All code and data to reproduce this analysis, as well as the final model
predictions, are available in a Zenodo repository [44].Open asset ↗Zenodopdf-page:15 lines:1-59Code / dataset availability confirmedEurope PMC · checked 15 Sept 2026
Field / plotFruitGrowth / time-series analysisGrowth / development / phenologyYield / yield components
Cacao production systems in Colombia are of high importance due to their direct impact in the social and economic development of smallholder farmers. Although Colombian cacao has the potential to be in the high value markets for fine flavour, the lack of expert support as well as the use of traditional, and often times sub-optimal technologies makes cacao production negligible. Traditionally, cacao harvest takes place at exactly the same time regardless of the geographic and climatic region where it is grown, the problem with this strategy is that cacao beans are often unripe or over matured and a combination of both will negatively affect the quality of the final cacao product. Since cacao fruit development can be considered as the result of a number of physiological and morphological processes that can be described by mathematical relationships even under uncontrolled environments. Environmental parameters that have more association with pod maturation speed should be taken into account to decide the appropriate time to harvest. In this context, crop models are useful tools to simulate and predict crop development over time and under multiple environmental conditions. Since harvesting at the right time can yield high quality cacao, we parameterised a crop model to predict the best time for harvest cacao fruits in Colombia. The cacao model uses weather variables such as temperature and solar radiation to simulate the growth rate of cocoa fruits from flowering to maturity. The model uses thermal time as an indicator of optimal maturity. This model can be used as a practical tool that supports cacao farmers in the production of high quality cacao which is usually paid at a higher price. When comparing simulated and observed data, our results showed an RRMSE of 7.2% for the yield prediction, while the simulated harvest date varied between +/-2 to 20 days depending on the temperature variations of the year between regions. This crop model contributed to understanding and predicting the phenology of cacao fruits for two key cultivars ICS95 y CCN51.
Why it matches plant phenotyping methodsカカオ果実の成熟・生育（フェノロジー）を気象変数と熱時間から推定する作物モデルを構築・パラメータ化し、観測値と比較検証しており、植物状態の推定手法が研究の中心である。
abstractwe parameterised a crop model to predict the best time for harvest cacao fruits in Colombia
Reproduction assets foundThe authors' cacao crop model (kocolatl), the modified SIMPLE model code used for the paper's phenology/yield simulations, is stated to be available in a public repository with an authors' GitHub URL. The Fedecacao field phenotype data are not independently deposited; the handle.net URL is a cited reference, not a dataCode · publicSoftware will be provided upon user request. The data used in this study can be found as follows: kocolatl is available in the research data repository. https://github.com/anyelacamargo/kocolatl.git accessed on 15 December 2021, UK.
Conflicts of Interest
The authors declare no conflict of interest.
Footnotes
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References
1. Zuidema P.A., Leffelaar P.A., Gerritsma W., Mommer L., Anten N.P. A physiologicaOpen asset ↗github.com/anyelacamargo/kocolatl.git · kocolatllines:278-301Code / dataset availability confirmedbioRxiv · Europe PMC · Crossref · checked 15 Sept 2026
Near surface cameras, such as those in the PhenoCam network, are a common source of ground truth data in modelling and remote sensing studies. Despite having locations across numerous agricultural sites, few studies have used near surface cameras to track the unique phenology of croplands. Due to management activities, crops do not have a natural vegetation cycle which many phenological extraction methods are based on. For example, a field may experience abrupt changes due to harvesting and tillage throughout the year. A single camera can also record several different plants due to crop rotations, fallow fields, and cover crops. Current methods to estimate phenology metrics from image time series compress all image information into a relative greenness metric, which discards a large amount of contextual information. This can include the type of crop present, whether snow or water is present on the field, the crop phenology, or whether a field lacking green plants consists of bare soil, fully senesced plants, or plant residue. Here we developed a modelling workflow to create a daily time series of crop type and phenology, while also accounting for other factors such as obstructed images and snow covered fields. We used a mainstream deep learning image classification model, VGG16. Deep learning classification models do not have a temporal component, so to account for temporal correlation among images our workflow incorporates a hidden markov model in the post-processing. The initial image classification model had out of sample F1 scores of 0.83-0.85, which improved to 0.86-0.91 after all post-processing steps. The resulting time series show the progression of crops from emergence to harvest, and can serve as a daily, local scale dataset of field states and phenological stages for agricultural research.
Why it matches plant phenotyping methods近接カメラ画像から作物種とフェノロジーを日次推定する深層学習・HMMワークフローを開発しており、植物の状態・生育段階の抽出が研究の中心である。
abstractHere we developed a modelling workflow to create a daily time series of crop type and phenology, while also accounting for other factors such as obstructed images and snow covered fields.
Reproduction assets foundThe authors state that all analysis code, data, and final model predictions are publicly available in two Zenodo repositories (DOIs 10.5281/zenodo.5618316 and 10.5281/zenodo.5579797), directly reproducing this paper's PhenoCam crop phenology classification and HMM post-processing analysis.Code · public.4 [36], NumPy v.1.20.2 [37], and Pomegranate v0.14.5 [38] in
the Python programming language v3.7 [39]. In the R language v4.1 [40] we used the
zoo v1.8.0 [41], tidyverse v1.3.1 [42], and ggplot2 v3.3.5 [43] packages. All code for the
analysis, as well as the final model predictions, are available in a Zenodo repository ([44],
https://doi.org/10.5281/zenodo.5618316).3. Results
The overall F1 score, a summary statistic which incorporates recall and precision,
was 0.90-0.92 for the training data across the three categories of Dominant Cover, Crop
Type, and Crop Status (Figure 2). The overall F1 score for validation data, which was not
used in the model fitting, was 0.83-0.85 for the three cOpen asset ↗Zenodo · 10.5281/zenodo.5618316pdf-raw-page:7 lines:1-64Code · publicll authors have read and agreed to the published version of the
manuscript.
Funding: D.B. was supported by the United States Department of Agriculture, CRIS:3050-11210-009-
00D
Data Availability Statement: All code and data to reproduce this analysis, as well as the final model
predictions, are available in a Zenodo repository (https://doi.org/10.5281/zenodo.5579797).USC 105 and is also made available for use under a CC0 license.
(which was not certified by peer review) is the author/funder. This article is a US Government work. It is not subject to copyright under 17
The copyright holder for this preprint
this version posted January 5, 2022.
;
https://doi.org/10.1101/2021.10.20.465168
doiOpen asset ↗Zenodo · 10.5281/zenodo.5579797pdf-raw-page:16 lines:1-60Code / dataset availability confirmedOpenAlex · Crossref · checked 14 Sept 2026
Abstract Greenhouse‐based high‐throughput phenotyping (HTP) presents a useful approach for studying novel plant growth‐promoting bacteria (PGPB). Despite the potential of this approach to leverage genetic variability for breeding new maize ( Zea Mays L.) cultivars exhibiting highly stable symbiosis with PGPB, greenhouse‐based HTP platforms are not yet widely used because they are highly expensive; hence, it is challenging to perform HTP studies under a limited budget. In this study, we built a low‐cost greenhouse‐based HTP platform to collect growth‐related image‐derived phenotypes. We assessed 360 inbred maize lines with or without PGPB inoculation under nitrogen‐limited conditions. Plant height, canopy coverage, and canopy volume obtained from photogrammetry were evaluated five times during early maize development. A plant biomass index was constructed as a function of plant height and canopy coverage. Inoculation with PGPB promoted plant growth in early developmental stages. Phenotypic correlations between the image‐derived phenotypes and manual measurements were at least 0.47 in the later stages of plant development. The genomic heritability estimates of the image‐derived phenotypes ranged from 0.23 to 0.54. Moderate‐to‐strong genomic correlations between the plant biomass index and shoot dry mass (0.24–0.47) and between HTP‐based plant height and manually measured plant height (0.55–0.68) across the developmental stages showed the utility of our HTP platform. Collectively, our results demonstrate the usefulness of the low‐cost HTP platform for large‐scale genetic and management studies to capture plant growth.
Why it matches plant phenotyping methods低コストの温室HTPプラットフォームを構築し、画像から植物高・群落被覆・群落体積・バイオマス指標を抽出して手動測定と検証しており、表現型取得法が研究の中心である。
abstractPlant height, canopy coverage, and canopy volume obtained from photogrammetry were evaluated five times during early maize development.
Reproduction assets foundThe paper's Data Availability section states that the image data underlying this greenhouse maize phenotyping study are publicly deposited on Mendeley Data (https://doi.org/10.17632/frsfpgnsyz.1), which is an allowed URL. The genotype data deposit (10.17632/5gvznd2b3n.3) is also mentioned but is genomic/omics data and,Dataset · publicestimate genomic heritability
using the following formula:
ℎ2
𝑔 =
σ2
𝑔
σ2
𝑔+
σ2
𝑒
𝑛𝑟
The estimates of genomic correlation were obtained from
the estimated variance-covariance matrix in the bivariate
Bayesian GBLUP model.
2.9 Data availability
The genotype and image data are available at https://doi.org/10.17632/5gvznd2b3n.3 and https://doi.org/10.17632/frsfpgnsyz.1, respectively.
3 RESULTS
3.1 Image processing and data extraction
A total of 756 plots (plants) in each replication across time
were evaluated during plant development. Each collection of
images took approximately 10 min. The ground resolution of
the orthomosaics was approximately 2.30 mm pix–1, and the
GCP error was approximatOpen asset ↗pdf-raw-page:6 lines:1-131Code / dataset availability confirmedOpenAlex · checked 14 Sept 2026
Abstract Temperature is a main driver of plant growth and development. New phenotyping tools enable quantifying the temperature response of hundreds of genotypes. Yet, for field-derived data, temperature response modelling bears flaws and pitfalls concerning the interpretation of derived parameters. In this study, climate data from five growing seasons with differing temperature distributions served as starting point for a growth simulation of wheat stem elongation, based on a four-parametric temperature response function (Wang–Engel) including all cardinal temperatures. In a novel approach, we re-extracted dose–responses from the simulation by combining high-resolution (hours) temperature courses with low-resolution (days) height data. The collection of such data is common in field phenotyping platforms. To take advantage of the lack of supra-optimal temperatures during the stem elongation, simpler (linear and asymptotic) models to predict temperature response parameters were investigated. The asymptotic model extracted the base temperature of growth and the maximum absolute growth rate with high precision, whereas simpler, linear models failed to do so. Additionally, the asymptotic model provided a proxy estimate for the optimum temperature. However, when including seasonally changing cardinal temperatures, the prediction accuracy of the asymptotic model was strongly reduced. In a field study with three winter wheat varieties, significant differences were found for all three asymptotic dose–response curve parameters. We conclude that the asymptotic model based on high-resolution temperature courses is suitable to extract meaningful parameters from field-based data.
Why it matches plant phenotyping methods高解像度温度データと反復草丈データから小麦の成長・温度応答形質を抽出するモデルを開発・評価しており、表現型抽出手法が研究の中心である。
titlePhenomics data processing: extracting dose–response curve parameters from high-resolution temperature courses and repeated field-based wheat height measurements
Reproduction assets foundThe paper's Data Availability statement explicitly deposits the source code supporting its phenotyping analysis (dose–response extraction from wheat height and temperature data) in a public ETH GitLab repository, archived in the ETH Research Collection with a DOI. Both URLs are allowed and the repository/identifier verCode · publicting—original draft. H.-P.P.: Conceptualization,
methodology, writing—review & editing. A.H.: Conceptualization,
supervision, project administration, funding acquisition, writing—
review & editing.
DATA AVAILABILITY
Data and source code that support the findings of this study are openly
available in the ETH gitlab repository at https://gitlab.ethz.ch/crop_phenotyping/htfp_data_processing and archived in the ETH research
collection (http://doi.org/10.5905/ethz-1007-385).LITERATURE CITED
Araus JL, Kefauver SC, Zaman-Allah M, Olsen MS, Cairns JE. 2018.
Translating high-throughput phenotyping into genetic gain. Trends
in Plant Science 23:451–466. doi:10.1016/j.tplants.2018.02.001.
Bonhomme R. 20Open asset ↗gitlab.ethz.ch/crop_phenotyping/htfp_data_processingpdf-raw-page:13 lines:1-88Code · publication,
supervision, project administration, funding acquisition, writing—
review & editing.
DATA AVAILABILITY
Data and source code that support the findings of this study are openly
available in the ETH gitlab repository at https://gitlab.ethz.ch/crop_phenotyping/htfp_data_processing and archived in the ETH research
collection (http://doi.org/10.5905/ethz-1007-385).LITERATURE CITED
Araus JL, Kefauver SC, Zaman-Allah M, Olsen MS, Cairns JE. 2018.
Translating high-throughput phenotyping into genetic gain. Trends
in Plant Science 23:451–466. doi:10.1016/j.tplants.2018.02.001.
Bonhomme R. 2000. Bases and limits to using ‘degree.day’
units. European Journal of Agronomy 13:1–10. doi:10.1016/
SOpen asset ↗10.5905/ethz-1007-385pdf-raw-page:13 lines:1-88Code / dataset availability confirmedEurope PMC · checked 8 Sept 2026
The scarcity of water for agricultural use is a serious problem that has increased due to intense droughts, poor management, and deficiencies in the distribution and application of the resource. The monitoring of crops through satellite image processing and the application of machine learning algorithms are technological strategies with which developed countries tend to implement better public policies regarding the efficient use of water. The purpose of this research was to determine the main indicators and characteristics that allow us to discriminate the phenological stages of maize crops ( Zea mays L.) in Sentinel 2 satellite images through supervised classification models. The training data were obtained by monitoring cultivated plots during an agricultural cycle. Indicators and characteristics were extracted from 41 Sentinel 2 images acquired during the monitoring dates. With these images, indicators of texture, vegetation, and colour were calculated to train three supervised classifiers: linear discriminant (LD), support vector machine (SVM), and k-nearest neighbours (kNN) models. It was found that 45 of the 86 characteristics extracted contributed to maximizing the accuracy by stage of development and the overall accuracy of the trained classification models. The characteristics of the Moran's I local indicator of spatial association (LISA) improved the accuracy of the classifiers when applied to the L*a*b* colour model and to the near-infrared (NIR) band. The local binary pattern (LBP) increased the accuracy of the classification when applied to the red, green, blue (RGB) and NIR bands. The colour ratios, leaf area index (LAI), RGB colour model, L*a*b* colour space, LISA, and LBP extracted the most important intrinsic characteristics of maize crops with regard to classifying the phenological stages of the maize cultivation. The quadratic SVM model was the best classifier of maize crop phenology, with an overall accuracy of 82.3%.
Why it matches plant phenotyping methods衛星画像からトウモロコシの生育段階（植物状態）を抽出・分類する画像解析および機械学習手法が研究の中心であり、特徴量比較と分類精度評価も実施している。
abstractThe purpose of this research was to determine the main indicators and characteristics that allow us to discriminate the phenological stages of maize crops ( Zea mays L.) in Sentinel 2 satellite images through supervised classification models.
Reproduction assets foundThe paper's supplement (Table S1) publicly lists the 41 Sentinel-2 satellite images analyzed for maize phenology classification, hosted on MDPI. No author analysis code or trained models are stated as publicly available; MATLAB scripts are described but no deposit/URL is given.Dataset · publicThe following are available online at https://www.mdpi.com/article/10.3390/s22010094/s1 , Table S1: The sentinel 2 satellite images analyzed in this research work.Open asset ↗MDPI · 10.3390/s22010094/s1lines:453-471Code / dataset availability confirmedCrossref · checked 8 Sept 2026
Optimised farm crop productivity requires careful management in response to the spatial and temporal variability of yield. Accordingly, combination of crop simulation models and remote sensing data provides a pathway for providing the spatially variable information needed on current crop status and the expected yield. An ensemble Kalman filter (EnKF) data assimilation framework was developed to assimilate plant and soil observations into a prediction model to improve crop development and yield forecasting. Specifically, this study explored the performance of assimilating state observations into the APSIM-Wheat model using a dataset collected during the 2018/19 wheat season at a farm near Cora Lynn in Victoria, Australia. The assimilated state variables include (1) ground-based measurements of Leaf Area Index (LAI), soil moisture throughout the profile, biomass, and soil nitrate-nitrogen; and (2) remotely sensed observations of LAI and surface soil moisture. In a baseline scenario, an unconstrained (open-loop) simulation greatly underestimated the wheat grain with a relative difference (RD) of −38.3%, while the assimilation constrained simulations using ground-based LAI, ground-based biomass, and remotely sensed LAI were all found to improve the RD, reducing it to −32.7%, −9.4%, and −7.6%, respectively. Further improvements in yield estimation were found when: (1) wheat states were assimilated in phenological stages 4 and 5 (end of juvenile to flowering), (2) plot-specific remotely sensed LAI was used instead of the field average, and (3) wheat phenology was constrained by ground observations. Even when using parameters that were not accurately calibrated or measured, the assimilation of LAI and biomass still provided improved yield estimation over that from an open-loop simulation.
Why it matches plant phenotyping methods植物のLAI・バイオマス等の状態観測をリモートセンシングとデータ同化で作物モデルへ統合し、収量推定性能を評価する計算・計測ワークフローが研究の中心であるため、植物表現型計測手法として収載する。
abstractthe assimilation of LAI and biomass still provided improved yield estimation over that from an open-loop simulation.
Reproduction assets foundThe paper's field validation dataset (wheat/soil state observations from the 2018/19 Cora Lynn experiment) is openly available on the authors' PRISM (Monash) site, and the authors' APSIM-EnKF data assimilation source code is explicitly stated to be publicly available on GitHub. Weather data sources (BoM, Weather UndergDataset · publicThe field validation data
presented in this study are openly available in the P-band Radiometer Inferred Soil Moisture (PRISIM)
website at https://www.prism.monash.edu/index.htmlOpen asset ↗pdf-page:19 lines:1-59Code · publicThe APSIM-EnKF data assimilation framework used in this study was the version
developed and described by Zhang [28] (source code available on https://github.com/Open asset ↗pdf-page:3 lines:1-53Code / dataset availability confirmedEurope PMC · OpenAlex · Crossref · checked 9 Sept 2026
Growing evaluation in the early stages of crop development can be critical to eventual yield. Point clouds have been used for this purpose in tasks such as detection, characterization, phenotyping, and prediction on different crops with terrestrial mapping platforms based on laser scanning. 3D model generation requires the use of specialized measurement equipment, which limits access to this technology because of their complex and high cost, both hardware elements and data processing software. An unmanned 3D reconstruction mapping system of orchards or small crops has been developed to support the determination of morphological indices, allowing the individual calculation of the height and radius of the canopy of the trees to monitor plant growth. This paper presents the details on each development stage of a low-cost mapping system which integrates an Unmanned Ground Vehicle UGV and a 2D LiDAR to generate 3D point clouds. The sensing system for the data collection was developed from the design in mechanical, electronic, control, and software layers. The validation test was carried out on a citrus crop section by a comparison of distance and canopy height values obtained from our generated point cloud concerning the reference values obtained with a photogrammetry method. A 3D crop map was generated to provide a graphical view of the density of tree canopies in different sections which led to the determination of individual plant characteristics using a Python-assisted tool. Field evaluation results showed plant individual tree height and crown diameter with a root mean square error of around 30.8 and 45.7 cm between point cloud data and reference values.
Why it matches plant phenotyping methods低コストUGV・LiDARによる3D植物計測システムを開発し、樹冠形態指標を抽出・検証しており、植物フェノタイピング手法が研究の中心である。
abstractAn unmanned 3D reconstruction mapping system of orchards or small crops has been developed to support the determination of morphological indices, allowing the individual calculation of the height and radius of the canopy of the trees to monitor plant growth.
Reproduction assets foundThe paper's Data Availability Statement provides an authors' public GitHub repository containing their code implementation for the UGV-LiDAR citrus crop mapping/phenotyping system. No separate phenotype dataset or point cloud deposit is stated.Code · publicOur code implementation is available online at https://github.com/HaroldMurcia/miniRover_LiDAR_citrush_crop.git , accessed on 25 November 2021.Open asset ↗HaroldMurcia/miniRover_LiDAR_citrush_croplines:356-358