Abstract. In the face of escalating global food demand and increasing climate variability, precise and granular crop yield monitoring is indispensable for maintaining regional agricultural stability. However, current deep learning approaches for yield estimation are severely constrained by their heavy reliance on massive in situ labeled data, which limits their application in data-scarce regions. Furthermore, these models often overlook the essential temporal evolution logic of yield formation and lack a systematic discussion regarding the contribution patterns of different feature dimensions, resulting in a black-box nature of the underlying model mechanisms. To address these challenges, this study proposes a field-label-free training framework for maize yield estimation that couples mechanistic model with deep learning. The framework's core strength lies in a physiologically complete simulation database, using the WOFOST model to exhaustively cover 30 years of climate variability and habitat combinations across Northeast China (1.24 × 106 km2). A Gated Recurrent Unit (GRU) network was then introduced for end-to-end modeling, accurately capturing the energy accumulation trajectory from vegetative to reproductive growth. Validation against 458 independent ground points (2022–2024) demonstrated robust generalization with an R2 of 0.69, an RMSE of 1.21 t ha−1, and an RRMSE of 13.73 %, despite using no ground data for training. Our analysis revealed that integrating photosynthetic intensity (LAImean), duration (LAD) and peak features (LAImax) across growth stages is critical for accuracy, while omitting early-stage features significantly impairs the model's ability to capture cumulative growth effects. Furthermore, the model successfully captured the spatiotemporal yield anomalies caused by the 2023 typhoon and flooding events. Ultimately, this study generated a 10 m resolution maize yield dataset (2019–2024) for Northeast China. The dataset exhibits consistent interannual stability, with the RRMSE ranging from 7.98 % to 12.92 % and the R2 remaining above 0.44 at the city level. By deeply coupling mechanistic simulation with data mining, this dataset provides detailed support for optimizing agricultural production and guiding farming practices. The Northeast China Maize Yield 10 m dataset is openly available at https://doi.org/10.5281/zenodo.19547014 (Hu et al., 2026).
Why it matches plant phenotyping methodsトウモロコシ収量という植物・作物群落の形質を推定する計算フレームワークを開発し、独立地点で性能検証したうえで再利用可能な10 m解像度データセットを生成しており、単なる農業実験の routine measurement ではない。
abstractthis study proposes a field-label-free training framework for maize yield estimation that couples mechanistic model with deep learning.
Reproduction assets foundThe paper's core output, the NortheastChinaMaizeYield10m maize yield dataset (2019–2024) with accompanying uncertainty layers, is openly deposited on Zenodo with an explicit availability statement and DOI. No author analysis code or trained model checkpoints are stated as publicly available.Dataset · publicThe Northeast China Maize Yield 10 m dataset is openly available at https://doi.org/10.5281/zenodo.19547014 (Hu et al., 2026).Open asset ↗Zenodo · 10.5281/zenodo.19547014lines:158-191Code / dataset availability confirmedEurope PMC · Crossref · checked 15 Sept 2026
Background and aims Phytolith analysis is widely applied in palaeoecological and archaeological research, but its interpretive strength depends on the availability of robust modern reference collections. This study expands the modern Australian phytolith reference collection by analysing 42 native plant species representing 24 families and 37 genera with emphasis on silicification patterns across major growth forms, including forbs, shrubs, trees, and C3 grasses. Methods Phytoliths were extracted from available plant parts, including leaves, stems, flowers, seeds, seed pods, cones, and roots, depending on sample availability. Morphotypes were identified following ICPN 2.0, with grass silica short cell phytoliths (GSSCPs) further classified by shape and size to examine subfamily-level patterns. Phytolith morphotype percentage data were analysed using Hellinger transformation, PerMANOVA, PCA, LDA, and hierarchical clustering to assess compositional differences among plant growth forms and grass subfamilies. Key results Phytolith production varied strongly among growth forms and plant parts. Grasses were abundant producers, whereas most forbs, shrubs, and trees were trace producers or non-producers. Leaves were the most consistent source of phytoliths, while seeds and seed pods were predominantly non-producers. Grass silica short-cell phytolith (GSSCP) morphotypes showed clear subfamily-level differentiation. Pooideae produced Rondel morphotypes. Danthonoideae produced Rondel as well as wide Bilobate types. Panicoideae and Oryzoideae exhibited a pronounced Bilobate signature, commonly associated with Polylobate and Cross forms. Non-grass taxa (woody, shrubs, and forbs) were dominated by Spheroids, Tracheary elements, Epidermal, and Polygonal sheets and other non-diagnostic forms. Phytolith assemblages differed significantly among plant families, with Poaceae uniquely producing GSSCPs, while non-grass families showed greater overlap in assemblage composition. Conclusion By expanding taxonomic and anatomical coverage, this study strengthens the capabilities of phytoliths in the reconstruction of grasslands and in general paleo vegetation in Australia, especially where other proxies such as pollen are limited.
Why it matches plant phenotyping methods植物部位の珪酸体を抽出・形態分類し、成長形態やイネ科亜科を識別する現代参照コレクションを構築しており、植物形質の取得・判別手法が研究の中心である。
abstractThis study expands the modern Australian phytolith reference collection by analysing 42 native plant species representing 24 families and 37 genera with emphasis on silicification patterns across major growth forms
Reproduction assets foundThe authors explicitly state that the R scripts used for data analysis and figure generation are publicly available on their GitHub repository, which directly reproduces this paper's phytolith statistical analyses (PCA, LDA, PerMANOVA, clustering, plots). Supplementary data files contain the paper's measurements but noCode · publicntification of all plant specimens collected for this
study.
A
14 FUNDING
M
15 Funding for this study was provided by ARC Discovery grant DP210100508 and a Ph.D.
D
16 fellowship (UQGSS) to MH.
TE
17 DATA AVAILABILITY
18 The R scripts used for data analysis and figure generation are publicly available on GitHub
EP
19 repository: https://github.com/Manoshi-sporo/Australian-Phytolith-Reference-Collection.
20 CONFLICTS OF INTEREST
CC
21 The authors declare no competing financial or commercial interests.
A
22 AUTHOR CONTRIBUTIONS
23 MH: writing original draft, conceptualization, software, investigation. AC: Supervision, Writing -
24 Review and editing, FM: Supervision, Writing-Review and editing.Open asset ↗Manoshi-sporo/Australian-Phytolith-Reference-Collectionpdf-layout-page:34 lines:1-87Code / dataset availability confirmedEurope PMC · checked 5 Sept 2026
Published18 Aug 2026TAG. Theoretical and applied genetics. Theoretische und angewandte GenetikCited by 0 · OpenAlex ↗
Key message Our scalable two-step method estimates genotypic performance and genetic parameters from ranking data, producing reliable results comparable to quantitative analyses, enabling the integration of ranking data into breeding pipelines. Plant breeding research has chiefly relied on on-station experiments to evaluate varietal performance. Nevertheless, these trials often fail to represent on-farm growing conditions and farmers' preferences, potentially leading to poorly defined breeding targets. Recent work has demonstrated the potential of using on-farm verification trials combined with ranking data to support farmers in evaluating varieties while providing information that is representative of farmers' needs. Despite this potential, scalable methods for quantifying genetic differences and assessing the strength of the genetic signal in such trials remain limited. Here, we present a two-step procedure for analyzing trials based on ranking data, allowing the estimation of genetic parameters. The approach follows a common strategy in quantitative genetics, in which parameters are estimated from tables of genotypic means and their variances. In our framework, these estimates are obtained from Thurstonian and/or Plackett-Luce models, which treat rankings as observations of an underlying continuous trait associated with genotypic performance. Using simulated data, we showed that genotypic mean estimates derived from ranking analyses are linearly related to those obtained from quantitative trait analyses and that their variances adequately capture estimation uncertainty. We further demonstrated that incorporating these estimates and their variances into a second-step mixed-effects model yields accurate estimates of variance components. Analyses of groundnut, maize, and sweetpotato datasets confirmed the applicability of the approach and showed that ranking data can provide reliable estimates of genetic parameters. We argue that this framework can be scaled to obtain genotypic performance estimates from multi-trial on-farm data.
Why it matches plant phenotyping methods作物品種の遺伝型性能をランキングデータから推定する統計的方法そのものが研究の中心であり、育種に再利用可能な植物性能の推定手法を開発・検証している。
abstractHere, we present a two-step procedure for analyzing trials based on ranking data, allowing the estimation of genetic parameters.
Reproduction assets foundThe paper's Data availability statement provides public access to the observed groundnut and sweetpotato ranking/trial datasets (Zenodo 17112492), the authors' R functions and simulation workflow (GitHub hdorado/tricot-ranking-analysis, archived Zenodo 17942919), and supplementary material with methods and figures (ZenDataset · publicThe observed data for groundnut and sweetpotato used in this study are publicly available and can be accessed at: Global multi-crop agricultural trial data supported by citizen science, Zenodo [ https://doi.org/10.5281/zenodo.17112492 ]Open asset ↗Zenodo · 10.5281/zenodo.17112492lines:205-225Code · publicThe R functions and simulation workflow used in this study are publicly available at: - Source code available from: [ https://github.com/hdorado/tricot-ranking-analysis ]Open asset ↗GitHub · hdorado/tricot-ranking-analysislines:205-225Code · public- Archived software available from: [ https://doi.org/10.5281/zenodo.17942919 ] - License: [MIT License]Open asset ↗Zenodo · 10.5281/zenodo.17942919lines:205-225Code / dataset availability confirmedEurope PMC · checked 5 Sept 2026
Stomatal traits are key microscopic phenotypes for evaluating plant physiology, stress responses, and crop breeding potential. However, in vivo high-magnification microscopy often suffers from a shallow depth of field, causing noticeable defocus blur across different spatial locations and making it difficult to capture clear and complete stomatal structures in a single image. Multi-focus image fusion offers a practical solution, yet existing methods typically rely on supervised training, paired data, or hand-crafted rules, limiting their use in real agricultural microscopy scenarios. In this study, we propose an unsupervised multi-focus fusion framework for reconstructing fully focused stomatal microscopic images. The method integrates two-dimensional feature extraction with three-dimensional cross-focal-plane modeling to capture both spatial details and complementary information across focal planes. A max-response-guided spatial gating module is introduced to enhance focused regions while suppressing defocused responses. Additionally, dual sharpness priors based on perceptual features and wavelet high-frequency information enable pixel-wise pseudo-supervised learning without requiring all-in-focus ground-truth images. The model also predicts a probabilistic focal-plane volume for interpretable all-in-focus reconstruction. Experiments on a maize multi-focus image dataset demonstrate that the proposed method achieves superior or competitive performance across multiple fusion metrics, with entropy (EN), edge information preservation ( Q AB∕F ), Chen-Blum contrast metric ( Q CB ), and visual information fidelity for fusion (VIFF) reaching 7.43, 0.21, 0.41, and 1.01, respectively. Ablation studies confirm the effectiveness of the 3D modeling, spatial gating, and dual-prior sharpness supervision. More importantly, when the fused images serve as input to a YOLO-based stomatal instance segmentation model, the proposed method yields the best segmentation accuracy, with mAP50 and mAP50-95 reaching 0.9937 and 0.9121, respectively. Phenotypic measurements derived from the segmentation masks show high consistency with manual annotations, with the highest coefficient of determination R 2 = 0.97 achieved for stomatal count. These results indicate that the framework can act as an effective front-end module for automated microscopic stomatal phenotyping in agriculture.
Why it matches plant phenotyping methods植物の気孔表現型を対象に、マルチフォーカス画像融合、セグメンテーション、形質測定までを中核的に開発・検証しているため。
abstractwe propose an unsupervised multi-focus fusion framework for reconstructing fully focused stomatal microscopic images.
Reproduction assets foundThe authors state their data and code are publicly available on GitHub, covering the multi-focus stomatal microscopy dataset and the UMF-stomata fusion/phenotyping code.Code · publicOur data and code are available at: https://github.com/Longer-S/UMF-Stomata.Open asset ↗Longer-S/UMF-Stomatahtml-lines:640-655Dataset · publicOur data and code are available at: https://github.com/Longer-S/UMF-Stomata.Open asset ↗Longer-S/UMF-Stomatahtml-lines:683-756Code / dataset availability confirmedCrossref · checked 15 Sept 2026
Abstract Plants employ non-photochemical quenching (NPQ) to protect their photosynthetic apparatus from photodamage. The response latency of NPQ following changes in light intensity is thought to significantly decrease photosynthetic efficiency. The amount of NPQ is commonly quantified from chlorophyll-fluorescence techniques using the Stern–Volmer equation, which requires fully closed reaction centres (RCs) of photosystem II, yielding NPQ in the absence of photochemical quenching ( $${\rm{NPQ}}^{\rm{Closed}}$$ NPQ Closed ). However, in nature, NPQ and photochemical quenching are normally present simultaneously. Therefore, to obtain a full understanding of this process, NPQ should also be explored when the RCs are open. Here we developed two methodologies to obtain NPQ in the presence of photochemistry ( $${\rm{NPQ}}^{\rm{Open}}$$ NPQ Open ) using both fluorescence lifetime and fluorescence yield measurements. A detailed comparison in Arabidopsis thaliana plants reveals that the value of $${\mathrm{NPQ}}^{\mathrm{Open}}$$ NPQ Open is ~35% lower than that of $${\rm{NPQ}}^{\rm{Closed}}$$ NPQ Closed . This difference is consistently observed across all measurements and is seen both upon closing ( $${\rm{NPQ}}^{\rm{Open}}\to {\rm{NPQ}}^{\rm{Closed}}$$ NPQ Open → NPQ Closed ) and upon reopening ( $${\mathrm{NPQ}}^{\mathrm{Closed}}\to {\mathrm{NPQ}}^{\mathrm{Open}}$$ NPQ Closed → NPQ Open ) of the RCs. We show that this difference can be explained by the presence of RC-induced ‘instantaneous’ switching of the NPQ quenching rate. This means that, in plants, NPQ is much more economical than is widely believed, it is large when its presence is needed, and it decreases instantaneously when the need disappears.
Why it matches plant phenotyping methods植物の光合成状態（NPQ）を測定するための蛍光寿命・蛍光収率に基づく2つの方法を開発し、比較検証しているため、方法開発が中心である。
abstractHere we developed two methodologies to obtain NPQ in the presence of photochemistry ( $${\rm{NPQ}}^{\rm{Open}}$$ NPQ Open ) using both fluorescence lifetime and fluorescence yield measurements.
Reproduction assets foundThe paper's custom ultrafast fluorescence analysis code (ICA-based PSII/PSI deconvolution and NPQ calculations) is explicitly deposited by the authors on GitHub, alongside the original data contributions.Code · publicr(s) for their contribution to the peer review of this work. Peer reviewer reports are available.
Funding
This work was supported by ‘Nanoscale regulators of photosynthesis’ NWO research project (project number: OCENW.GROOT.2019.86).
Data availability
The original contributions presented in the study are available via GitHub at https://github.com/L-Ramakers/Heimdall .
Code availability
The custom analysis code used in the study is available via GitHub at https://github.com/L-Ramakers/Heimdall .
Competing interests
The authors declare no competing interests.
Footnotes
Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional afOpen asset ↗L-Ramakers/Heimdalllines:88-125Code / dataset availability confirmedCrossref · checked 11 Sept 2026
Abstract Purpose Long-term monitoring of crop biophysical and biochemical traits remains challenging in high-latitude regions due to short growing seasons, frequent cloud cover, and highly variable weather. In this context, unmanned aerial vehicles (UAVs) offer flexible, high-resolution observations, but their added value relative to low-cost proximal sensors and their effectiveness for radiative transfer model (RTM) inversion across diverse crop canopies remain insufficiently quantified. This study evaluated the potential of a two-band proximal spectral reflectance sensor (SRS) and a five-band multispectral UAV sensor for retrieving leaf area index (LAI), leaf chlorophyll content (LCC), and canopy chlorophyll content (CCC) using PROSAIL inversion across major crops in Northern Europe over two growing seasons (2023–2024). Methods and Results Two inversion approaches – look-up table (LUT) and artificial neural network (ANN) were applied to PROSAIL simulations. UAV–PROSAIL–ANN outperformed LUT-based inversion and SRS observations, achieving the highest accuracy for LAI (R 2 = 0.81–0.95; RMSE = 0.27–0.77 m 2 /m 2 ), followed by CCC (R 2 = 0.58–0.94; RMSE 2 ), while LCC remained less accurately estimated (R 2 = 0.26–0.78; RMSE 2 ). Across sensors and methods, retrieval accuracy decreased in the order of LAI, CCC, and LCC, reflecting the stronger spectral control of canopy structure compared to biochemical traits. Conclusions The UAV–PROSAIL–ANN framework effectively captured spatial and temporal variability in crop traits, producing canopy-scale maps consistent with field observations. These results demonstrate the robustness and scalability of hybrid PROSAIL–ANN inversion for high-latitude crop monitoring, while highlighting current limitations in biochemical trait retrieval using multispectral data.
Why it matches plant phenotyping methodsUAV・近接分光センサーとPROSAIL反転、ANNを用いてLAIや葉・群落クロロフィルを推定し、精度比較と圃場観測との整合性評価を行うことが研究の中心である。
abstractThis study evaluated the potential of a two-band proximal spectral reflectance sensor (SRS) and a five-band multispectral UAV sensor for retrieving leaf area index (LAI), leaf chlorophyll content (LCC), and canopy chlorophyll content (CCC) using PROSAIL inversion across major crops in Northern Europe over two growing seasons (2023–2024).
Reproduction assets foundThe paper's Data availability statement explicitly deposits the authors' UAV image processing code (irradiance normalization, vignetting, exposure compensation, radiometric calibration) in a public GitHub repository under GPL v3.0; other data are available only upon request.Code · publicData availability Code to perform irradiance normalization, vignetting, exposure compensation, and radio-
metric calibration is available at https://github.com/fieldSITES/scripts/tree/main/UAV under GNU General
Public License v3.0. Other data will be made available upon request.Open asset ↗UAVpdf-page:34 lines:1-40Code / dataset availability confirmedEurope PMC · checked 5 Sept 2026
TomatoLiDAR / point cloudLeafMorphology / geometry measurementSegmentationLeaf traits
Leaf parameters are crucial indicators reflecting the growing status of plants. Monitoring and analysis of leaf parameters significantly contributes to the improvement of crop yield and food quality. This study focused on three tomato plant varieties commonly grown in the Netherlands and proposed a fully automatic pipeline for leaf phenotyping. Three-dimensional (3D) point clouds of target plants were acquired with a specially designed imaging unit naming Maxi-Marvin. A semantic segmentation of plant organs was performed with PointNet++ model. To mitigate point cloud resolution decrement, the down-sampling operation in the baseline model was replaced with a distributed segmentation strategy. Leaf instances were further identified with Density-Based Spatial Clustering of Applications with Noise (DBSCAN), followed by a morphological phenotypic trait quantification based on 3D geometrical analysis. Target phenotypic traits including leaf length, leaf width, and leaf area. The evaluation results indicated that the distributed segmentation strategy achieved the best F 1 scores of 0.98 with block size set to 30,000. The Mean Average Errors (MAE) of leaf length, leaf width, and leaf area estimation were 2.09 cm, 1.78 cm and 8.98 cm 2 respectively. The estimation accuracies for leaf length, leaf width, and leaf area were 91.98%, 92.66%, and 89.67%, respectively.
Why it matches plant phenotyping methodsトマト葉の3D画像取得、器官セグメンテーション、葉インスタンス識別、形態形質推定を統合した自動フェノタイピング手法を開発・評価しており、方法が研究の中心です。
abstractproposed a fully automatic pipeline for leaf phenotyping
Reproduction assets foundThe paper's tomato point cloud dataset (with semantic and leaf instance annotations used for the phenotyping pipeline) is publicly available on Kaggle via a footnote. NPEC website is a facility page, and Open3D is a generic library, so neither qualifies.Dataset · public2. ^ The dataset used in this study is available at: https://www.kaggle.com/datasets/xinbolai/vtc-tomatoOpen asset ↗Kaggle · xinbolai/vtc-tomatolines:545-624Code / dataset availability confirmedEurope PMC · checked 14 Sept 2026
Reliable traits are needed for identification of tea ( Camellia sinensis ) cultivars, yet the stability of leaf morphology and color across leaf positions remains unclear. This study evaluated inter-cultivar variation and positional stability in leaf morphological, RGB color, and SPAD traits in six predominant cultivars. One-year-old shoots were sampled in a completely randomized design, and five fully expanded leaves below the apical bud were analyzed. SPAD values were measured with a chlorophyll meter, and scanned images were used to extract contour and RGB traits. Data were analyzed using ANOVA, correlation analysis, PCA, and discriminant analysis. Leaf morphology differed among cultivars and leaf positions, with significant cultivar-by-position interactions; however, the width-to-length ratio differed among cultivars but remained stable across positions in these cultivars. SPAD values increased with leaf position and were strongly associated with RGB components, being negatively correlated with R and G and positively correlated with B. Morphological traits explained 52.988% of total variance in PCA and yielded 64.6% overall classification accuracy, with LaoHan showing the highest accuracy (83.3%). Misclassification was concentrated among genetically similar cultivars. These findings suggest that stable leaf shape proportions and SPAD-RGB relationships provide useful descriptors, whereas genetic relatedness limits morphology-based cultivar identification under the present conditions.
Why it matches plant phenotyping methods茶品種識別のため、葉の形態・RGB・SPAD特性の取得と安定性、分類性能を中心に評価しており、画像由来形質抽出を含む実質的な表現型解析である。
abstractscanned images were used to extract contour and RGB traits
Reproduction assets found保存済みの本文根拠を更新済みルールで再検証し、公開資産1件を確認しました。Dataset · publicThe following supporting information can be downloaded at https://www.mdpi.com/article/10.3390/biology15151283/s1 , Table S1: Original data of leaf morphological traits, RGB values, and SPAD values from six tea cultivars in this study.Open asset ↗lines:368-409Code / dataset availability confirmedEurope PMC · checked 5 Sept 2026
Idesia polycarpa Maxim. is a premier woody oil species in Guizhou Province, China, whose fruit yield and oil quality largely depend on effective pollination and fertilization. However, limited research on pollen viability and germination has hindered industrial progress. To address this gap, a comprehensive evaluation framework for elite I. polycarpa germplasm was developed, integrating micromorphological analysis, optimized staining protocols, and in vitro germination assay. Scanning electron microscopy (SEM) revealed that I. polycarpa pollen, while genetically conserved at the genus level-characterized by prolate shapes, tricolporate apertures, and reticulate exine ornamentation-exhibits notable micromorphological variation among genotypes. Of the nine staining protocols tested (2,3,5-triphenyl tetrazolium chloride [TTC], carbol fuchsin, acetocarmine, methylene blue, Alexander, peroxidase, 2,5-diphenylmonotetrazolium bromide [MTT], I2-KI, and red ink), TTC and red ink were the most effective, offering clear chromatic distinction between viable and non-viable pollen. Through orthogonal experimental designs, genotype-specific optimal media for in vitro germination were identified: 0.40 g/L H3BO3, 0.01 g/L KNO3, 0.02 g/L Ca(NO3)2·4H2O, and 0.20 g/L KH2PO4 for STZ-6; and 0.20 g/L H3BO3, 0.02 g/L KNO3, 0.02 g/L Ca(NO3)2·4H2O, and 0.10 g/L KH2PO4 for STZ-9. Regression analysis confirmed a highly significant positive correlation (P < 0.01) between in vitro germination rates and the staining results from both TTC and red ink across various concentrations. Notably, 5% TTC and 30% red ink exhibited the highest coefficients of determination. A hierarchical evaluation strategy is thus proposed: the 5% TTC method is recommended for precise laboratory quantification due to its stability, while the 30% red ink method, due to its ease of use, is suited for rapid field-based screening. This study provides valuable insights into the morphological characteristics of I. polycarpa pollen and establishes a standardized evaluation framework, supporting germplasm innovation and optimizing pollination management.
Why it matches plant phenotyping methods花粉の生存性・発芽という植物の生殖形質を対象に、染色法とin vitro発芽法を最適化・検証し、標準化した評価フレームワークを開発しているため、方法論が中心である。
abstracta comprehensive evaluation framework for elite I. polycarpa germplasm was developed, integrating micromorphological analysis, optimized staining protocols, and in vitro germination assay.
Reproduction assets foundThe article's Data Availability statement points to a public Biostudies deposit containing the study's data (pollen morphology measurements, staining viability counts, and in vitro germination results). No author analysis code or trained models are mentioned.Dataset · publicData Availability: The data that support the findings of this study are openly available in Biostudies at https://doi.org/10.6019/S-BSST3125 .Open asset ↗Biostudies · S-BSST3125lines:176-186Code / dataset availability confirmedCrossref · Europe PMC · checked 15 Sept 2026
Turfgrass phenotyping relies heavily on visual quality (VQ) ratings and RGB indices like DGCI, but these are limited by observer subjectivity, coarse ordinal scales, or ratio formulations that do not reflect perceptual color differences. Hyperspectral and machine-learning tools overcome some limitations but remain costly and difficult to generalize, motivating the need for scalable and interpretable RGB color metrics. We introduce ΔEg, a perceptually anchored CIELAB ΔE distance from an ideal green that provides a continuous and interpretable measure of canopy color evaluated alongside a panel of RGB-derived metrics. A 3 × 3 nitrogen × irrigation greenhouse experiment using hybrid bermudagrass (TifTuf, Cynodon dactylon × C. transvaalensis) quantified canopy responses with RGB imaging, spectral reflectance, CCM-300 fluorescence, and chlorophyll assays. ΔEg correlated strongly with chlorophyll (r = 0.72), similar to DGCI (r = 0.73), and both exceeded CCM-300 (r = 0.29). HSVi showed the strongest association with VQ (r = 0.84) and was most sensitive to irrigation (ηp2 = 0.63). CIELUV v* explained the greatest model variation (R2m = 0.94) and responded most to fertilizer (ηp2 = 0.84). The yellow fraction was significant across all main and interaction effects and captured canopy decline (r = −0.82 with VQ). An illustrative decision-support scenario using ΔEg indicated that moderate fertilizer combined with mild deficit irrigation optimized turf color and input efficiency. Conclusions apply to controlled conditions, with field-scale validation identified as future work. These results demonstrate that interpretable RGB color metrics, anchored by ΔEg, offer a scalable alternative to VQ scoring and spectral systems.
Why it matches plant phenotyping methodsRGB画像から芝草キャノピー色を定量化するΔEgなどの指標を導入・比較し、クロロフィルや品質評価との技術的関連性を検証しており、植物表現型取得法が中心である。
abstractWe introduce ΔEg, a perceptually anchored CIELAB ΔE distance from an ideal green that provides a continuous and interpretable measure of canopy color evaluated alongside a panel of RGB-derived metrics.
Reproduction assets foundThe paper's Data Availability Statement deposits the phenotype data and the authors' Python image-processing/metric-computation scripts and R statistical analysis scripts in the USDA National Agricultural Library Ag Data Commons, a public repository. The full RGB imagery archive, however, is only available upon requestCode · public2025;23:673–687. doi: 10.1002/lom3.10705.
Associated Data
Data Availability Statement
Data and Python scripts used for image processing and %G, %Gr, %Y, ΔEg, DGCI, HSVi, BA SD , CIELUV v* metric computation, and R scripts used for statistical analysis are be available in the USDA National Agricultural Library Ag Data Commons ( https://agdatacommons.nal.usda.gov/ ), Data for—Proxima Green: RGB Color Metrics for Turfgrass Phenotyping in Controlled Conditions, accessed on 27 July 2026. The full RGB imagery archive will be made available upon reasonable request.Open asset ↗USDA National Agricultural Library Ag Data Commonslines:691-695Code / 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
Premise Accurate species identification is crucial for ecological restoration and can be especially challenging for understudied non-model species. Quercus garryana is the only native oak species in the Pacific Northwest and is an important component of the endangered oak savanna ecosystem. Quercus robur is an imported ornamental species from Europe and has been found to be mistakenly planted as Q. garryana in habitat restoration projects. Methods We measured leaf morphological traits sampled from herbarium collections in their native ranges using the digital morphometric tools MorphoLeaf and Tomato Analyzer. We then used Lasso logistic analysis to generate a predictive model and tested it on leaves from Portland, Oregon. To streamline this species detection process, we developed Garryanalyzer, an ImageJ plug-in that automatically measures leaf traits and outputs species predictions. Results Garryanalyzer demonstrated 95% accuracy in predicting the species identity of herbarium specimens of oaks. Garryanalyzer correctly identified all Q. robur individuals sampled in Portland but showed lower accuracy for Q. garryana . Discussion Many existing morphometric software are not open source, which makes them unable to be customized to specific study systems. Garryanalyzer is built upon the widely used open-source ImageJ platform. This study also demonstrates a viable workflow for developing similar tools for other ecologically important non-model plant species.
Why it matches plant phenotyping methods葉の形態形質を自動測定し、種予測まで行うImageJプラグインとワークフローの開発・評価が中心であり、植物フェノタイピング手法として適格です。
abstractTo streamline this species detection process, we developed Garryanalyzer, an ImageJ plug-in that automatically measures leaf traits and outputs species predictions.
Reproduction assets foundThe paper's authors publicly released the Garryanalyzer ImageJ plug-in source code on GitHub, all original and modified leaf images used in the morphometric analyses on Zenodo, and the full leaf morphometric measurement dataset plus R Lasso analysis code in a second Zenodo repository. All are paper-specific, public,可直接Code · publicThe source code and installation instructions for Garryanalyzer can be accessed on GitHub at https://github.com/zxie8561/Garryanalyzer.Open asset ↗https://github.com/zxie8561/Garryanalyzer · zxie8561/Garryanalyzerhtml-lines:210-274Dataset · publicAll images used in the morphometric analyses, both original and modified, are available on Zenodo (https://doi.org/10.5281/zenodo.17462266).Open asset ↗https://doi.org/10.5281/zenodo.17462266 · 10.5281/zenodo.17462266html-lines:210-274Dataset · publicThe full dataset of leaf morphometric measurements of both GBIF and Portland samples, R code for Lasso analysis, and other miscellaneous files are available on a separate Zenodo repository (https://doi.org/10.5281/zenodo.17546152).Open asset ↗https://doi.org/10.5281/zenodo.17546152 · 10.5281/zenodo.17546152html-lines:210-274Code / 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 confirmedOpenAlex · Europe PMC · checked 14 Sept 2026
Yellow rust (YR) is a major threat to both bread and durum wheat production, often causing substantial yield losses. Conventional visual scoring of YR severity, while widely adopted, is labor-intensive, time-consuming, and prone to human error. In this study, we evaluated the predictability (PA), defined as the correlation between predicted and observed values, using genomic and phenomic data for YR severity under multiple prediction scenarios in two biparental wheat populations (bread and durum). YR scoring was conducted on two dates, with YR severity visually assessed while unmanned aerial vehicle (UAV)-based high-throughput phenotyping (HTP) data were collected using a multispectral camera. HTP data were processed to extract spectral wavelengths and vegetation indices (VIs), and all lines were also genotyped using SNP arrays. We tested a diverse set of models, including parametric, machine learning, and deep learning approaches. PA increased markedly when HTP-derived data were used compared with genomic markers alone. For example, support vector regression (SVR) improved from 0.35 (markers only) to 0.87 (wavelengths only). However, integrating genomic and phenomic data did not yield further improvements, as models often plateaued when using HTP-derived features alone. Cross-crop prediction demonstrated promising generalization across bread and durum wheat, achieving PA values up to 0.83. For this last task, best linear unbiased prediction (BLUP) and multilayer perception (MLP) consistently provided robust performance across scenarios. These findings highlight the strong potential of UAV-based HTP for rapid, scalable, and accurate prediction of YR severity in wheat. While genomics retains broad utility for breeding, the practical integration of phenomics and AI-driven prediction pipelines will ultimately depend on breeding program strategies, resources, and objectives.
Why it matches plant phenotyping methodsUAV multispectral HTPによる小麦黄さび病重症度の推定と、複数の予測モデルの比較・検証が研究の中心であり、植物病害状態を直接推定する実質的なフェノタイピング手法研究である。
abstractHTP data were processed to extract spectral wavelengths and vegetation indices (VIs)
Reproduction assets foundThe article's Data Availability statement deposits the datasets generated and analyzed in this study (yellow rust phenotyping with UAV spectral data and genomic markers in bread and durum wheat) in the CIMMYT repository under DOI 10.71682/10549375, which is an allowed URL. No author analysis code or trained model is avDataset · publicand scalable strategy for YR assessment in wheat breeding.
Funding
The authors gratefully acknowledge financial support from the Government of Mexico through
the “MasAgro – Cultivos para México” initiative.
Data Availability
The datasets generated and/or analyzed during the current study are available in the CIMMYT
repository: https://doi.org/10.71682/10549375.Acknowledgements
We are deeply grateful to Julio Huerta-Espino for his guidance and support throughout all stages
of this manuscript. We also thank Hedilberto Velásquez Miranda for his valuable assistance with
rust visual score phenotyping, and Neftalí Cruz Pérez for his dedicated support in trial sowing and
field management.
Conflict Open asset ↗10.71682/10549375pdf-raw-page:30 lines:1-37Code / 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 confirmedEurope PMC · checked 5 Sept 2026
The genetic identity of coffee cultivars is fundamental to the specialty coffee sector, where premium prices are paid under the assumption that the purchased planting material corresponds to the declared variety. However, many producing countries lack the certification infrastructure necessary to guarantee this identity in their informal seed systems, exposing producers to undetected varietal non-conformity. In this study, we examine a case from a specialty coffee ( Coffea arabica L.) farm in southern Ecuador where seeds labeled as Sidra (USD 100/kg) and Gesha (USD 500/kg) were purchased without genetic or phytosanitary certification. Using a combination of SSR-based DNA fingerprinting and quantitative morphological characterization, including plant architecture, leaf functional traits, and fruit characteristics, we documented varietal identity and assessed the discriminant capacity of morphological traits across the four resulting morphotypes. Using eleven microsatellite markers for SSR fingerprinting, we found that two of the four morphotypes did not match their declared commercial identity. One plant sold as Sidra was identified as compatible with Batian, a composite variety of Kenyan origin that is genetically unrelated to Ethiopian landraces. The plants acquired as Gesha corresponded to a pure Ethiopian landrace that is genetically similar to, but not identical to, the Panamanian Geisha reference accession T.02722. Only two morphotypes were confirmed as Sidra. Furthermore, the placement of Sidra within the Core Ethiopia genetic group is consistent with prior population-level analyses and with its likely status as a selected Ethiopian landrace rather than a variety of hybrid origin. Morphological linear discriminant analysis achieved 82.4% overall classification accuracy under leave-one-out cross-validation (LOOCV), with internode length dominating the first discriminant function (LD1 = 66.6%). These results demonstrate that varietal nonconformity in the specialty coffee seed sector can extend to the inadvertent introduction of genetically unrelated material and underscore the urgent need for accessible seed certification.
Why it matches plant phenotyping methodsコーヒー品種識別のための形態形質測定と判別分析が研究の中心であり、形態形質の識別性能をLOOCVで検証しているため、植物フェノタイピング手法の適用・検証に該当する。
abstractquantitative morphological characterization, including plant architecture, leaf functional traits, and fruit characteristics
Reproduction assets foundThe paper's morphological/functional trait dataset (used for the phenotyping and LDA analysis) is explicitly stated to be publicly available on Figshare (10.6084/m9.figshare.32841344). No author analysis code repository is stated; other URLs in the text are generic libraries or cited prior work.Dataset · publicThe morphological and functional trait dataset generated and analyzed in this study is publicly available in the Figshare repository at 10.6084/m9.figshare.32841344 .Open asset ↗Figshare · 10.6084/m9.figshare.32841344lines:526-568Code / dataset availability confirmedEurope PMC · checked 5 Sept 2026
Phenotyping stomatal traits and their developmental plasticity is time-consuming but holds potential to improve water use efficiency and photosynthesis for designing stress-tolerant crops under climate change. Here, we develop a robust, high-throughput pipeline for phenotyping 14 stomatal traits in winter wheat related to size, variation, maximum conductance, and spatial patterning. We (1) analyze over 25,000 images from 60 wheat cultivars grown in growth chamber, greenhouse, and field conditions; (2) investigate the impact of light, temperature, and reduced water and nitrogen supply on stomatal traits and their developmental plasticity across adaxial and abaxial surfaces; and (3) evaluate genetic diversity and breeding progress of stomatal traits. Stomatal traits were highly broad-sense heritable, were largely plastic in response to environmental conditions, and showed genotype-specific responses. Stomatal traits of third leaves under controlled environments with stable light and temperature conditions reliably captured the genetic variance of flag leaves under field conditions. Our data suggests that the upper leaf surface contributed more to transpiration and cooling through consistently higher stomatal density, area, and maximum conductance, while the lower surface facilitated CO₂ diffusion via systematic proper patterning and spacing. Breeding maintains the genetic diversity of stomatal traits, and our pipeline facilitates breeders to target them to enhance water use efficiency in high-yielding modern cultivars.
Why it matches plant phenotyping methods高スループットで14種類の気孔形質を抽出するパイプラインを開発しており、植物フェノタイピング手法が研究の中心である。
abstractwe develop a robust, high-throughput pipeline for phenotyping 14 stomatal traits in winter wheat related to size, variation, maximum conductance, and spatial patterning.
Reproduction assets foundThe paper's Data and code availability section states that all data are publicly available in a Zenodo repository and that the stomatal identification and trait quantification code is in the authors' public GitLab repository. Both URLs appear verbatim in the supplied blocks and match allowed_urls. The Zenodo DOI in theCode · publicThe code for all the programs in this paper, including the stomatal identification and trait quantification, can be found in our GitLab repository, https://scm.cms.hu-berlin.de/intensive-plant-food-systems-public/2026-mabrouk-stomatal-phenotyping .Open asset ↗intensive-plant-food-systems-public/2026-mabrouk-stomatal-phenotypinglines:197-215Code / dataset availability confirmedOpenAlex · Europe PMC · Crossref · checked 5 Sept 2026
Sweet potato virus disease (SPVD) is one of the most destructive diseases affecting sweet potato production worldwide, causing severe yield losses and posing a significant threat to food security. Vision-based intelligent diagnosis has emerged as a promising solution for large-scale SPVD monitoring due to its low cost and scalability. However, existing publicly available datasets for SPVD are extremely limited and typically focus on a single task, such as disease classification or lesion segmentation, under constrained imaging conditions. This lack of comprehensive, task-oriented datasets significantly restricts the development, evaluation, and fair comparison of advanced computer vision methods for SPVD analysis. In this study, we present SPVD-Field, a task-oriented multi-task visual dataset suite composed of two independently collected sub-datasets optimized for different computer vision tasks. Rather than constructing a single homogeneous dataset, SPVD-Field is deliberately organized into two complementary task-oriented sub-datasets: SPVD-DET, designed for disease detection with bounding-box annotations, and SPVD-SEG, designed for fine-grained lesion segmentation with pixel-level masks. The two sub-datasets were independently collected using different acquisition protocols optimized for their respective tasks, while sharing a unified semantic definition of SPVD symptoms, crop growth stages, and field environments. SPVD-Field captures substantial real-world variability in imaging scale, viewpoint, illumination, background complexity, and symptom manifestation, reflecting the inherent challenges of fieldbased disease diagnosis. We provide detailed documentation of data acquisition, annotation strategies, and quality control procedures, along with baseline benchmark results for both detection and segmentation tasks to demonstrate the usability and difficulty of the dataset. By offering a structured dataset suite rather than a single-task collection, SPVD-Field aims to support diverse research directions, including detection, segmentation, multi-task learning, and disease severity analysis, and to facilitate reproducible and comparable research in SPVD-related plant phenotyping.
Why it matches plant phenotyping methodsサツマイモの病徴を対象とする画像データセットで、検出・病斑セグメンテーション、データ取得・アノテーション・品質管理、ベンチマークを中心的に提供しており、植物病害状態の画像フェノタイピング手法・データ基盤に該当する。
abstractIn this study, we present SPVD-Field, a task-oriented multi-task visual dataset suite composed of two independently collected sub-datasets optimized for different computer vision tasks.
Reproduction assets foundThe paper's core asset is the SPVD-Field dataset (SPVD-DET detection images with bounding-box annotations and SPVD-SEG segmentation images with pixel-level masks), explicitly deposited in a public repository via the data availability statement with a DOI link.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://dx.doi.org/10.21227/hq1q-jp43 .Open asset ↗10.21227/hq1q-jp43lines:664-703Code / dataset availability confirmedEurope PMC · checked 14 Sept 2026
Sweet potato is a nutritionally valuable crop that contributes to food security, owing to its storage roots rich in starch, sugars, and antioxidants, while requiring minimal cultivation inputs. Estimating its yield based on visible above-ground traits remains challenging due to weak and inconsistent correlations between shoot biomass and storage root development. Therefore, direct assessment of underground biomass is essential. In this study, we demonstrate the field application of ground penetrating radar (GPR) for non-destructive detection and yield estimation of sweet potato. GPR is a geophysical technique that typically transmits ultra high frequency radio waves into the soil and records reflections from subsurface objects. Electromagnetic wave simulations within the soil-root system revealed GPR signals that strongly correlate with root length, forming the basis for yield quantification. We developed an image-processing pipeline comprising static correction, gain adjustment, noise filtering, and hyperbola segmentation via the Hough transform to enable semi-automated storage root detection from GPR data. By integrating detection and quantification approaches, a linear regression model predicting sweet potato yield from GPR signals achieved moderate accuracy ( R 2 = 0.567, normalized RMSE 0.190). We established a non-destructive and low-labor approach for monitoring root systems, providing a foundation for rapid, scalable, and field-ready yield estimation in sweet potato and other root and tuber crops.
Why it matches plant phenotyping methodsGPRによる地下貯蔵根の検出・定量化と収量推定を中心に、信号処理および画像処理パイプラインを開発・評価しているため、植物フェノタイピング手法として収載する。
abstractWe developed an image-processing pipeline comprising static correction, gain adjustment, noise filtering, and hyperbola segmentation via the Hough transform to enable semi-automated storage root detection from GPR data.
Reproduction assets found保存済みの本文根拠を更新済みルールで再検証し、公開資産1件を確認しました。Code · publicThe datasets analyzed during the current study consist of GPR line scans of the field at ALRC and list of sweet potato storage root weights. These data are available together with the analysis scripts on GitHub under open access. All data and scripts are the property of NARO and are distributed under the Creative Commons Attribution-NonCommercial 4.0 International License (CC BY-NC 4.0). The repository can be accessed at: https://github.com/mtei1/GPRScript.Open asset ↗mtei1/GPRScripthtml-lines:240-264Code / dataset availability confirmedCrossref · checked 15 Sept 2026
Accurate estimation of evapotranspiration (ET) is critical for irrigation management in water-scarce regions such as the Middle East and North Africa (MENA). This study compares sensible heat flux (H), latent heat flux (LE), and ET derived from eddy covariance (EC) and a boundary-layer scintillometer (BLS) operated with an aperture reducer, deployed simultaneously over an irrigated late-season potato field (1.8 ha) in the Beqaa Valley, Lebanon. Satellite NDVI observations indicate that the BLS–EC overlap period (13 October–27 November 2021) sampled the crop from peak canopy (NDVI ≈ 0.85–0.90) through the onset of senescence (NDVI ≈ 0.79). The BLS (Scintec BLS900) operated along a 140 m path. The EC system showed incomplete daytime energy-balance closure, with a regression slope of ≈0.69 and a seasonal Bowen-ratio-preserving correction factor of CF = 1.24 (a ~19% closure deficit) was used. Across the matched period, daily H from the BLS was strongly correlated with EC (r ≈ 0.82) but systematically lower, with a regression slope of ≈0.63 that persisted across timescales; this scale-invariant amplitude compression reflects the path-averaged, similarity-based nature of the scintillometer retrieval rather than the EC closure deficit, which instead governs the mean bias. BLS-derived daily ET showed a systematic positive bias relative to uncorrected EC (mean bias error, MBE = +0.30 mm d−1; +16% cumulative). Applying the Bowen-ratio-preserving correction (CF = 1.24) to EC reduced this to MBE = −0.14 mm d−1 (−6%), and the residual-to-LE correction yielded MBE = −0.15 mm d−1 (−6.4%); the latter comparison is only partly independent, as both methods share the same Rn and G. The Bowen-ratio-preserving method is therefore recommended for this dataset. Overall, the BLS captured the temporal variability of crop water use well, but residual-based ET estimates require careful treatment of the energy-balance-closure gap and are sensitive to the high BLS gap fraction (61.6% of 15 min records over the overlap, exceeding 90% at night). Once EC is closure-corrected to serve as the reference, the BLS offers a cost-effective alternative for field-scale ET monitoring in the MENA region, subject to the conditional agreement documented here.
Why it matches plant phenotyping methodsジャガイモ圃場の作物蒸発散量（ET）という生理・水利用状態を対象に、ECとBLSを比較検証し、補正法や測定誤差も評価している。センサー測定法の技術的妥当性が中心であり、単なる routine measurement ではない。
abstractThis study compares sensible heat flux (H), latent heat flux (LE), and ET derived from eddy covariance (EC) and a boundary-layer scintillometer (BLS) operated with an aperture reducer
Reproduction assets foundThe paper's flux/ET datasets are only available on request from the corresponding author, so they do not qualify as public assets. However, the Supplementary Information file (available at the MDPI supplementary URL) explicitly contains experiment sensor documentation and field/canopy images (Figures S1–S4: study site,Supplement · publicmeasurements along the beam. Because these results derive from a single crop, season, and phenological window, their generalization awaits multi-site, multi-season replication spanning the full-canopy cycle—the priority for subsequent campaigns.
Supplementary Materials
The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/s26144398/s1 , Figure S1: Study site and potato canopy—Beqaa Valley, Lebanon; Figure S2: Eddy covariance system—full tower view (peak canopy); Figure S3: EC sensor suite close-up and soil sensor installation; Figure S4: BLS900 scintillometer—transmitter, receiver, and meteorological station.
Author Contributions
Conceptualization, HOpen asset ↗lines:251-268Code / dataset availability confirmedEurope PMC · Crossref · checked 5 Sept 2026
Abstract Plant diseases affecting leaves and fruits cause substantial yield and economic losses worldwide, particularly in horticultural crops cultivated under diverse agro-climatic conditions. Early and accurate disease diagnosis is essential for effective crop management; however, manual inspection is time-consuming, subjective, and often infeasible at large scale. In this work, we present the Tomato–Chilli–Papaya (TCP) Fruit and Leaf Disease Dataset, a comprehensive multi-crop image dataset designed to support deep learning-based plant disease recognition. The dataset comprises labeled RGB images of healthy and diseased leaves and fruits from three economically important crops—tomato, chilli, and papaya—captured under real-field and semi-controlled environments, reflecting significant variability in illumination, background complexity, and disease severity. To demonstrate the applicability of the dataset, several commonly used convolutional neural network (CNN) architectures, including VGG, ResNet, DenseNet, MobileNet, and EfficientNet models, were trained and evaluated on the TCP dataset using transfer learning. Experimental results show that deep CNN models can effectively learn discriminative visual features corresponding to disease-specific patterns such as leaf spots, lesions, discoloration, curling, and fruit surface abnormalities. Lightweight models such as MobileNet achieve competitive performance with reduced computational cost, while deeper architectures provide improved accuracy at the expense of higher complexity. The results highlight the importance of dataset diversity for robust model generalization across multiple crops and plant organs. The TCP dataset provides a challenging benchmark for single-crop and multi-crop disease classification and supports the development of advanced deep learning, attention-based, and explainable AI models for precision agriculture. By enabling reproducible research and realistic performance evaluation, this dataset contributes toward scalable and practical AI-driven plant disease diagnosis systems aimed at reducing yield losses and supporting sustainable agriculture.
Why it matches plant phenotyping methods植物の葉・果実の病徴を画像から評価する大規模データセットとベンチマークを中心に扱っており、植物病害状態の画像ベース表現型解析に該当する。
abstractwe present the Tomato–Chilli–Papaya (TCP) Fruit and Leaf Disease Dataset, a comprehensive multi-crop image dataset designed to support deep learning-based plant disease recognition.
Reproduction assets foundThe paper introduces the TCP (Tomato-Chilli-Papaya) fruit and leaf disease image dataset and reports CNN experiments on it. The dataset is publicly deposited on Mendeley Data, and the authors state that analysis code is available on GitHub. Both are paper-specific, public, and actionable.Dataset · publicData is available on Mendeley:1Open asset ↗pdf-page:27 lines:1-51Code · publicCode availability: Code is available on GitHub 2Open asset ↗GitHubpdf-page:27 lines:1-51Code / dataset availability confirmedEurope PMC · OpenAlex · checked 5 Sept 2026
The co-evolutionary arms race between crops and their parasites requires continuous identification of new resistance mechanisms. Broomrape (Orobanche cumana), a root parasitic plant, poses a severe threat to sunflower (Helianthus annuus) production, yet the genetic architecture underlying host resistance remains poorly understood. To address this, we established a high-throughput phenotyping platform to quantify root infestation across a diverse sunflower association mapping (SAM) population. Combining this phenotypic resource with a dual genome-wide association study (GWAS) strategy based on both single nucleotide polymorphisms (SNPs) and k-mers, we highlight the genetic basis of broomrape resistance at unprecedented resolution. Our analyses revealed quantitative trait loci (QTLs) and identified novel candidate genes, including putative leucine-rich repeat receptor kinases potentially involved in parasite recognition and defense activation. Importantly, the k-mer approach circumvented reference genome bias and uncovered key genomic introgressions from wild Helianthus relatives that contribute substantially to resistance. These findings demonstrate the utility of integrating high-resolution phenotyping with advanced association mapping to dissect complex host-parasite interactions. Moreover, they emphasize the enduring value of wild germplasm as a reservoir of adaptive variation, providing crop breeders with crucial tools to counter the rapid evolutionary dynamics of parasitic plants.
Why it matches plant phenotyping methods根部の寄生程度を定量する高スループット表現型解析プラットフォームの確立が明示され、遺伝解析の基盤として方法が実質的に扱われている。
abstractwe established a high-throughput phenotyping platform to quantify root infestation across a diverse sunflower association mapping (SAM) population.
Reproduction assets foundThe paper's Data availability statement explicitly deposits the paper-specific raw phenotyping images on Zenodo, the k-mer genotype data on the sunflower genome database, and the authors' analysis code on the Hübner lab GitHub repository, all with public URLs.Dataset · publicAll phenotypes raw images for Gadot and Yavor are available through the Zenodo repository ( https://doi.org/10.5281/zenodo.18961268 ).Open asset ↗Zenodo · 10.5281/zenodo.18961268lines:238-238Code · publicCode is accessible through the Hübner lab github: https://github.com/hubner-lab/Sunflower-Broomrape-paper .Open asset ↗Hübner lab github · hubner-lab/Sunflower-Broomrape-paperlines:238-238Code / dataset availability confirmedCrossref · checked 14 Sept 2026
Wild-relative introgression broadens wheat diversity, as exemplified by the Multiple Synthetic Derivatives (MSD) population, a unique hexaploid wheat resource capturing extensive genetic diversity from Aegilops tauschii. However, root system architecture (RSA), a key determinant of resource acquisition and stress adaptation, remains poorly characterized in this population. Here, we established a practical two-dimensional root phenotyping framework that enables continuous imaging to track RSA traits and their responses to heat stress. Using this framework we evaluated MSD417 as a representative genotype against its recurrent parent, Norin 61 (N61). Under control conditions, MSD417 displayed greater total root length, root system width, and convex hull area than N61 (p < 0.001), indicating enhanced early root vigor. MSD417 also exhibited larger second pair seminal root angle (p < 0.001) and length (p < 0.01) across both conditions, suggesting enhanced horizontal root exploration while maintaining similar rooting depth to N61 (p = 0.981). Heat stress reduced overall root growth and narrowed genotypic differences, limiting RSA expression. Microscopic observations revealed a lower coleorhiza height-to-width ratio in MSD417. These findings demonstrate the effectiveness of the two-dimensional platform for early-stage RSA phenotyping and highlight Aegilops tauschii-derived germplasm as a source of favorable root traits in wheat breeding.
Why it matches plant phenotyping methods二次元画像による根系構造フェノタイピング基盤を構築し、連続撮像で根形質を追跡する方法が研究の中心であるため含める。
abstractHere, we established a practical two-dimensional root phenotyping framework that enables continuous imaging to track RSA traits and their responses to heat stress.
Reproduction assets foundThe paper's data availability statement deposits the paper-specific phenotyping inputs publicly on Zenodo: root images of wheat N61 and MSD417 (the two genotypes measured for RSA traits) and microscopic coleorhiza images. These are public, paper-specific image datasets directly underlying the study's measurements. No作者Dataset · publical development in arid regions.
ORCID
Sultan Md Monwarul Islam http://orcid.org/0009-0002-7219-2104
Izzat Sidahmed Ali Tahir http://orcid.org/0000-0002-1711-6961
Kinya Akashi http://orcid.org/0000-0002-9991-5766
Data availability statement
The root images of wheat N61 and MSD417 are deposited in the Zenodo data repository under https://doi.org/10.5281/zenodo.18080159 and https://doi.org/10.5281/zenodo.18079748, respectively. The microscopic images of
coleorhiza are deposited under https://doi.org/10.5281/zenodo.18091131. The other original contributions presented
in the study are included in the article and/or supplementary material.
References
Alahmad, S., El Hassouni, K., Bassi, F. M., DiOpen asset ↗Zenodo · 10.5281/zenodo.18080159pdf-raw-page:14 lines:1-49Dataset · publicMd Monwarul Islam http://orcid.org/0009-0002-7219-2104
Izzat Sidahmed Ali Tahir http://orcid.org/0000-0002-1711-6961
Kinya Akashi http://orcid.org/0000-0002-9991-5766
Data availability statement
The root images of wheat N61 and MSD417 are deposited in the Zenodo data repository under https://doi.org/10.5281/zenodo.18080159 and https://doi.org/10.5281/zenodo.18079748, respectively. The microscopic images of
coleorhiza are deposited under https://doi.org/10.5281/zenodo.18091131. The other original contributions presented
in the study are included in the article and/or supplementary material.
References
Alahmad, S., El Hassouni, K., Bassi, F. M., Dinglasan, E., Youssef, C., Quarry, G., Aksoy,Open asset ↗Zenodo · 10.5281/zenodo.18079748pdf-raw-page:14 lines:1-49Dataset · public-6961
Kinya Akashi http://orcid.org/0000-0002-9991-5766
Data availability statement
The root images of wheat N61 and MSD417 are deposited in the Zenodo data repository under https://doi.org/10.5281/zenodo.18080159 and https://doi.org/10.5281/zenodo.18079748, respectively. The microscopic images of
coleorhiza are deposited under https://doi.org/10.5281/zenodo.18091131. The other original contributions presented
in the study are included in the article and/or supplementary material.
References
Alahmad, S., El Hassouni, K., Bassi, F. M., Dinglasan, E., Youssef, C., Quarry, G., Aksoy, A., Mazzucotelli, E., Juhász, A.,
Able, J. A., Christopher, J., Voss-Fels, K. P., & Hickey, L. T. (2019). A majOpen asset ↗Zenodo · 10.5281/zenodo.18091131pdf-raw-page:14 lines:1-49Code / dataset availability confirmedEurope PMC · checked 5 Sept 2026
Low temperature stress severely restricts the cultivation and distribution of pear ( Pyrus L.) germplasms, frequently resulting in frost injury and yield reduction. To accurately evaluate the cold resistance of pear germplasm resources, this study investigates the physiological and biochemical responses of one-year-old branches to different degrees of low-temperature stress, as well as differences in the tissue structure of these pear germplasms after low-temperature stress. In this study, 122 pear germplasms were classified into high (HR), medium (MR), and low (LR) cold-tolerance categories based on their semi-lethal temperature (LT 50 ). Further analysis of pear germplasms with different levels of cold resistance revealed that, with decreasing temperature, HR germplasms exhibited smaller increases in relative electrolyte conductivity (REC) and malondialdehyde (MDA) content and higher accumulation of proline (Pro), soluble proteins (SP), soluble sugars (SS), and peroxidase activity compared with LR germplasms. In addition, the peak values of these indicators generally occurred at lower temperatures in HR germplasms. A correlation analysis and principal component analysis indicated that physiological indices, including REC, bound water/free water ratio, SS, and MDA, as well as branch anatomical traits related to xylem and cortex proportions, were closely associated with variation in LT 50 . An integrated assessment using membership function analysis produced rankings consistent with LT 50 -based clustering, supporting the reliability of the multivariate evaluation framework. Overall, this study establishes an integrated, indicator-based approach for evaluating cold resistance in pear germplasm by integrating physiological, biochemical, and anatomical characteristics. These results provide a theoretical basis and methodological reference for screening cold resistance germplasms.
Why it matches plant phenotyping methods生理・生化学・解剖学的形質を統合し、LT50と多変量評価によってナシ遺伝資源の耐寒性を分類・スクリーニングする評価フレームワークが研究の中心である。
abstractTo accurately evaluate the cold resistance of pear germplasm resources, this study investigates the physiological and biochemical responses of one-year-old branches to different degrees of low-temperature stress, as well as differences in the tissue structure of these pear germplasms after low-temperature stress.
Reproduction assets foundThe article's Data Availability statement links a public Zenodo deposit containing the paper's raw phenotyping data (LT50, physiological/biochemical and anatomical measurements for pear germplasms). Supplemental files also contain germplasm characteristics and LT50 comparisons, but the Zenodo raw-data deposit is the明确,Dataset · publicThe data is available at Zenodo: liu186253. (2025). liu186253/Data: raw data (Version V11). Zenodo. https://doi.org/10.5281/zenodo.17524773 .Open asset ↗Zenodo · 10.5281/zenodo.17524773lines:636-710Code / dataset availability confirmedEurope PMC · 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 confirmedarXiv · OpenAlex · checked 11 Sept 2026
Tomographic microscopy enables three-dimensional internal imaging but often requires expensive optical or X-ray instrumentation. Here we present an ultra-low-cost continuous-wave diffusive tomography (CWDT) system for biological samples. The system uses a smartphone microscope, a white LED coupled into an optical fiber, 3D-printed micropositioners, and a physics-based forward model optimized with machine learning. We demonstrate full-color volumetric reconstructions from a tartrazine-cleared poplar section, a scattering phantom, fungal mycelium near an Arabidopsis root, and thick poplar branch imaging with an inserted side-emitting fiber. The current results are qualitative and exploratory, but they show that scanned fiber illumination and inexpensive hardware can produce useful three-dimensional reconstruction outputs for low-cost microscopy experiments.
Why it matches plant phenotyping methods低コスト三次元断層イメージング法そのものを開発し、ポプラ組織・枝やシロイヌナズナ根近傍を対象に植物の内部構造を可視化しているため、植物形態の取得法として中心的です。
abstractHere we present an ultra-low-cost continuous-wave diffusive tomography (CWDT) system for biological samples.
Reproduction assets foundThe paper's raw imaging inputs, configurations, and reconstruction outputs for Figures 2–5 are publicly deposited on Kaggle. The analysis code repository is only 'prepared for release' (no confirmed public deposit yet), so it is listed as request-only. Hardware CAD mirrors are public but are instrument designs, not theDataset · publicFigure-level raw inputs, model configurations, selected outputs, and manifests are available through the Kaggle dataset https://www.kaggle.com/datasets/alingold/continuous-wave-diffusive-tomography .Open asset ↗continuous-wave-diffusive-tomographylines:108-129Code / dataset availability confirmedOpenAlex · Crossref · checked 15 Sept 2026
LeafScans-Orchard is a curated, multi-year RGB image dataset of orchard plant leaves designed to support research in computer vision, machine learning, and plant phenotyping. The dataset comprises 9708 high-quality leaf scans acquired during collection campaigns conducted between 2015 and 2025, covering seven orchard crop species: apple, pear, sweet cherry, sour cherry, plum, peach, and apricot. In total, the dataset includes 67 cultivar labels. All samples were acquired using flatbed scanning under controlled conditions on a uniform background, ensuring high visual consistency and minimal background variability. The original scans were captured at 1200 dpi and subsequently converted into a public release format at 300 dpi, stored as lossless TIFF images to preserve morphological and textural details. Each image corresponds to a single leaf and is organized in a hierarchical directory structure by species, cultivar, and acquisition year, accompanied by image-level metadata and aggregated species–cultivar–year counts. LeafScans-Orchard is suitable for plant species classification, cultivar recognition, leaf morphology analysis, texture analysis, and general visual feature extraction. In addition to the main release, a representative subset of 300 original 1200 dpi scans is provided to support high-resolution analyses. The dataset is particularly suited for fine-grained classification, morphology-driven analysis, and methodological studies under controlled imaging conditions.
Why it matches plant phenotyping methods果樹葉のRGBスキャン画像を収録した公開データセットで、植物フェノタイピングおよび葉形態解析を目的とする。標準化された画像取得と再利用可能なデータ構成が中心であり、フェノタイピング用データセットとして適格。
abstractLeafScans-Orchard is a curated, multi-year RGB image dataset of orchard plant leaves designed to support research in computer vision, machine learning, and plant phenotyping.
Reproduction assets foundThe paper's core asset is the LeafScans-Orchard dataset itself (9708 RGB leaf scans, 300 dpi TIFF release plus 1200 dpi subset, image-level metadata and summary counts), openly deposited on Zenodo with an explicit DOI and CC BY 4.0 license. This is a paper-specific, public, actionable phenotyping image dataset. No codeDataset · publicthe published version of the manuscript.
Funding: This research received no external funding.
Institutional Review Board Statement: Not applicable.
Informed Consent Statement: Not applicable.
Data Availability Statement: The dataset described in this article is openly available in Zenodo
as LeafScans-Orchard Dataset (v1.0.0) at https://doi.org/10.5281/zenodo.20187966 (accessed on
10 May 2026). The repository includes the 300 dpi image release, the 1200 dpi high-resolution subset,
image-level metadata, aggregated species–cultivar–year counts, and supporting documentation. The
complete archive of original 1200 dpi scans is retained locally by the authors but is not included in
the current pubOpen asset ↗Zenodo · 10.5281/zenodo.20187966pdf-raw-page:12 lines:1-46Code / dataset availability confirmedEurope PMC · checked 5 Sept 2026
The robust Dutch rose, also known as the Rosa hybrida is distinguished by its vibrant colors, superior product quality, and extended vase life. These rose varieties, originating from Netherlands, have proven highly successful in Indian agricultural conditions and the international export industry. The dataset consists of a total of 1,995 high resolution petal image collected during this research, encompassing petal color categories, such as red, yellow, white, pink, purple, orange, bi-color, and multi-color, as well as health statuses including fresh, dry, and diseased petals. The primary purpose of this dataset is to support machine learning activities in agriculture and specifically for tasks such as automatic petal health evaluation and rose variety categorization. Although the rose flower is scientifically rich and has a wide range of industrial uses, it has not been given much attention in machine learning, especially when compared to other plant-based datasets. This study adds to the accuracy of quality assessment through the use of modern computer vision and machine learning methods, thus helping the agriculture sector, rose-based edible product making, and flavor development industries.
Why it matches plant phenotyping methodsバラ花弁画像データセットの構築と、花弁の健康状態・色分類による植物状態評価が研究の中心であり、画像ベースの表現型計測データセットに該当する。
abstractThe dataset consists of a total of 1,995 high resolution petal image collected during this research, encompassing petal color categories, such as red, yellow, white, pink, purple, orange, bi-color, and multi-color, as well as health statuses including fresh, dry, and diseased petals.
Reproduction assets foundThe paper's own rose petal image dataset is publicly deposited on Mendeley Data, and the authors' validation/metadata scripts are publicly available on GitHub. Both are paper-specific, public, and actionable.Dataset · publicThe RoseVisuals dataset is publicly available on Mendeley Data at Direct URL to data: https://data.mendeley.com/datasets/f44jwtbfjg/5. Data Identification Number: 10.17632/f44jwtbfjg.5. Repository Name: RoseVisuals.Open asset ↗Mendeley Data · 10.17632/f44jwtbfjg.5html-lines:246-284Code · publicThe RoseVisuals codebase, comprising all validation scripts, is publicly available on GitHub Repository at https://github.com/Arya-S14/RoseVisuals-Validation-Doc.Open asset ↗GitHubhtml-lines:246-284Code / dataset availability confirmedOpenAlex · arXiv · checked 5 Sept 2026
The Vines-DB dataset contains 1,218 original high-resolution RGB images of seven ornamental vine species collected under field conditions at the Utah Agricultural Experiment Station's Greenville Research Farm in Logan, Utah, USA. The dataset was generated from 168 individual vine plants that were transplanted in 2022 and photographed repeatedly across multiple months during the 2023 and 2024 growing seasons (July-October). Images were captured with an iPhone 16 Pro equipped with a 48 MP camera between 10:00 AM and 12:00 PM under daylight. Vines were grown on 1.2m x 2.4m trellises and photographed from a distance of 1m against black or white Styrofoam backdrops to improve contrast and reduce background noise. The dataset includes Akebia quinata, Campsis radicans, Hydrangea anomala petiolaris, Lonicera x heckrottii, Campsis x tagliabuana 'Madame Galen', Parthenocissus quinquefolia, and Wisteria floribunda. All original images were manually annotated in Roboflow by trained annotators to produce polygon-based instance segmentation masks for eight classes, including seven species and background. After preprocessing and data augmentation, the working dataset was expanded to 2,307 images for model development and evaluation. The augmented dataset was divided into 2,019 training images, 192 validation images, and 96 test images using stratified sampling to maintain balanced representation. Vines-DB supports the development and evaluation of deep learning models for multi-class instance segmentation in precision horticulture and urban ecology. The dataset enables applications such as automated canopy cover estimation, species identification, and scalable field phenotyping. In addition, repeated monthly imaging of the plants captures temporal variation in canopy development and plant appearance, increasing the dataset's utility for segmentation benchmarking under realistic field conditions.
Why it matches plant phenotyping methods植物のRGB画像とポリゴン注釈から成るデータセットを構築し、セグメンテーション評価およびキャノピー被覆推定などの植物フェノタイピングを支援することが中心であるため。
abstractVines-DB supports the development and evaluation of deep learning models for multi-class instance segmentation in precision horticulture and urban ecology.
Reproduction assets foundThe paper's core asset is the Vines-DB RGB image dataset with instance segmentation annotations, publicly deposited on OSF with an explicit DOI and URL matching an allowed URL.Dataset · publicData accessibility Repository name: Vines-DB
Data identification number: 10.17605/OSF.IO/YJHCK
Direct URL to data: https://osf.io/yjhck/overviewOpen asset ↗OSF · 10.17605/OSF.IO/YJHCKpdf-page:2 lines:1-49Code / dataset availability confirmedEurope PMC · Crossref · checked 5 Sept 2026
An understanding of spike shape will be of great benefit for improving wheat yields. Traditional manual measurements of spike traits are slow and prone to human error, preventing large-scale phenotyping. Employing imaging techniques will allow researchers to measure multiple morphometric parameters simultaneously. While 2D imaging provides a rapid screening method, 3D imaging offer a more comprehensive understanding of spike shape, revealing complex external structures. This study addresses the challenge of developing a high-resolution 3D surface-scanning pipeline to accurately quantify wheat spike morphology across diverse genotypes. Using a 3D surface-scanner, sharp point clouds of individual spikes were reconstructed and automatically aligned and analysed to extract key morphological features including spike length, volume, and cross-sectional area profile. New shape descriptors based on cross-sectional area profiles, local extremes, statistical curve fitting, segmentation of spikes into zones of aborted spikelets, base and apical segments as well as the extraction of spike/spikelets branching and endpoints of components were introduced to capture detailed structural variation between genotypes. Correlations between the 3D-derived traits and traditional metrics such as spike weight, spikelet number and seed weight confirmed the biological relevance of the extracted parameters. The method distinguished morphological differences among twelve wheat genotypes, revealing distinct shape types such as long, short, compact, and awned spikes. By combining precise 3D imaging with computational analysis, this approach provides a non-destructive framework for spike phenotyping. These findings demonstrate that 3D surface-scanning can deliver accurate and reproducible measurements of wheat spike architecture, offering new opportunities for linking morphology with genetics and yield potential in modern breeding programs.
Why it matches plant phenotyping methods小麦穂の形態を3D画像取得と計算解析で定量化するパイプラインを開発し、形状記述子の抽出と遺伝子型間での検証を行う、植物フェノタイピング手法の中心的研究である。
abstractThis study addresses the challenge of developing a high-resolution 3D surface-scanning pipeline to accurately quantify wheat spike morphology across diverse genotypes.
Reproduction assets foundThe paper's Data Availability and Code Availability sections point to the authors' public GitHub repository containing sample 3D spike data and the analysis code for the wheat spike morphology pipeline.Code · publicCode Availability
The codes are available at the following link: https://github.com/LatifaGreche/3D-WheatSpikeMorphologyExtractionOpen asset ↗LatifaGreche/3D-WheatSpikeMorphologyExtractionlines:316-410Code / dataset availability confirmedOpenAlex · Europe PMC · checked 15 Sept 2026
The number of petals in an inflorescence is an important phenotypic indicator for quality evaluation and cultivar identification of cut chrysanthemums ( Chrysanthemum morifolium Ramat.). Current manual measurement methods are time-consuming, error-prone, and poorly suited to the complex geometry of chrysanthemum flowers, which limits their utility for large-scale phenotyping and breeding programs. Although image-based phenotyping has advanced rapidly, automated and reliable methods for petal counting in densely packed or partially obscured inflorescences remain underdeveloped. Here, we developed a deep learning-based framework for automatic extraction of petal number in cut chrysanthemums. Images from multiple varieties were collected to construct a representative dataset, and petal density maps were generated through manual annotation with Gaussian kernel function. We employed a Congested Scene Recognition Network (CSRNet) enhanced with a Squeeze-and-Excitation (SE) channel attention mechanism (SE-CSRNet) for petal density estimation. Spearman correlation analysis revealed strong agreement between visible and actual petal counts (Spearman’s r=0.953, p<0.0001). Compared with the original CSRNet, SE-CSRNet reduced mean absolute error (MAE) and root mean squared error (RMSE) by 5.2% and 7.4%, respectively. Further optimization using regression fitting revealed that random forest achieved the best performance (MAE = 4.24, RMSE = 5.06, R 2 = 0.967), indicating reliable stability and satisfactory generalization under the conditions evaluated in this work. Application of the optimized model to two cut chrysanthemum varieties confirmed its practicality by successfully detecting reductions in petal number under high-temperature stress. Our results demonstrate that integrating dataset construction, deep learning–based density estimation, and machine learning optimization enables efficient and accurate prediction of petal number in cut chrysanthemums.
Why it matches plant phenotyping methods花弁数という植物形質を画像から自動抽出する深層学習手法を開発し、データセット構築、性能比較、検証、実用適用まで行っており、表現型取得手法が研究の中心である。
abstractHere, we developed a deep learning-based framework for automatic extraction of petal number in cut chrysanthemums.
Reproduction assets foundThe article states that some data (the chrysanthemum petal-counting dataset and related materials) will be available at the authors' public GitHub repository (qwsdfgz/petalscount), with other data available from the corresponding author upon reasonable request. The repository URL is explicitly provided by the authors,但Dataset · publicnctional components of bud-leaves and flowers in edible chrysanthemum (Chrysanthemum morifolium Ramat)
Horticulturae 11 5 2025 448
10.3390/horticulturae11050448
Appendix A
Supplementary data
The following is the Supplementary data to this article.
Multimedia component 1
Data availability
Some data will be available at this URL: https://github.com/qwsdfgz/petalscount . Other data are openly available from the corresponding author upon reasonable request.
Appendix A
Supplementary data to this article can be found online at https://doi.org/10.1016/j.plaphe.2026.100238 .Open asset ↗qwsdfgz/petalscountlines:602-636Code / dataset availability confirmedEurope PMC · checked 5 Sept 2026
Apple leaf disease segmentation is critical for yield and quality preservation in what is globally one of the most economically significant fruit crops. Despite recent advances in deep learning, real-world orchard environments present three primary challenges: (1) low contrast between lesions and background textures, which hinders accurate localization; (2) leaf overlap and occlusion, leading to incomplete feature representation and increased false negatives; and (3) the inherent limitations of unimodal RGB imagery in capturing subtle pathological features, which constrains generalization and accuracy. To address these issues, we proposed Language-Infused Visual Mamba (LViM), a dual-path U-Net architecture that integrates Mamba and Transformer modules for semantic-visual feature fusion. LViM achieves robust segmentation in complex environments through three core innovations: (1) A U-shaped Multimodal Transformer (MTT) branch integrated with AMBERT, which leverages inter-modal semantic relationships to enhance textual feature extraction and provide high-level semantic cues, thereby improving lesion-background discriminability; (2) a U-shaped Visual State Space (VMamba) branch that employs 2D Selective Scanning (SS2D) and Visual State Space (VSS) blocks to capture global context and fine-grained details, mitigating the impact of occlusion; and (3) Cross-Attention Gate Fusion (CAGF) and Linguistic Cross-Nested (LCN) modules that facilitate efficient cross-modal alignment and hierarchical feature modeling to better identify subtle lesions. Experimental results demonstrate that LViM consistently outperforms the VM-UNet baseline, yielding improvements of 4.05% in Precision, 4.25% in Dice coefficient, 4.49% in mIoU, and 4.23% in Recall.
Why it matches plant phenotyping methodsリンゴ葉の病斑を画像から分割する手法を開発し、複雑な環境での性能を評価しており、植物病害状態の取得・推定が研究の中心である。
abstractApple leaf disease segmentation is critical for yield and quality preservation
Reproduction assets foundThe paper's curated multimodal apple leaf disease dataset (image-text pairs with pixel-level annotations for four disease types) is explicitly stated as publicly released in the authors' LViM GitHub repository. Code/models are only promised 'upon acceptance,' so the dataset asset qualifies as public, while the code is.Dataset · publicThe curated multimodal apple leaf disease dataset constructed in this study has been publicly released at https://github.com/csuft1906ll/LViMOpen asset ↗csuft1906ll/LViMlines:273-283Code / dataset availability confirmedEurope PMC · checked 5 Sept 2026
Litchi is an important economic fruit in southern China, and its precision management relies on the rapid and accurate estimation of the Soil and Plant Analyzer Development (SPAD) values in leaves. Addressing the limitations of existing SPAD detection methods, such as limited rapid coverage, inadequate modeling of dynamic environmental interference, and shallow fusion of multi-source data, this study constructed an Internet of Things (IoT) system to collect real-time environmental data from a litchi orchard, combined with unmanned aerial vehicle (UAV) multispectral imagery to obtain canopy vegetation index and texture features. A Long Short-Term Memory (LSTM) network model integrated with a feature level attention mechanism (MLSTM) was proposed to fuse IoT time-series data, vegetation index, and high dimensional texture features for dynamic SPAD value prediction. The results indicate that multi-source feature fusion significantly improves SPAD estimation accuracy. The MLSTM model achieved optimal performance under the all-features situation, with a coefficient of determination (R²) of 0.897 and a root mean square error (RMSE) of 2.638, outperforming other comparative models. The attention mechanism effectively enhanced the model's focus on key features, improving feature utilization efficiency and model interpretability. The multi-source data fusion method and MLSTM model proposed in this study enable high precision, dynamic estimation of SPAD values in litchi leaves, providing reliable data support for precision fertilization, stress diagnosis, and yield prediction in litchi orchards, as well as theoretical support for promoting the practical application of this technology in smart agriculture.
Why it matches plant phenotyping methodsIoT・UAVマルチスペクトル画像から葉のSPAD値を推定するデータ融合システムとMLSTMモデルを開発・評価しており、植物形質取得手法が研究の中心です。
abstractthis study constructed an Internet of Things (IoT) system to collect real-time environmental data from a litchi orchard, combined with unmanned aerial vehicle (UAV) multispectral imagery to obtain canopy vegetation index and texture features.
Reproduction assets foundThe paper's data availability statement points to a public Zenodo repository containing the study's multi-source SPAD/IoT/multispectral dataset.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://zenodo.org/records/18308090 .Open asset ↗zenodo · 18308090lines:427-441Code / dataset availability confirmedEurope PMC · checked 14 Sept 2026
Advancing wheat breeding requires reliable digital traits that capture genotype × environment interactions and improve yield prediction across diverse growing conditions. Although vegetation indices such as the normalized difference vegetation index (NDVI) are widely used, their performance relative to yield variability and environmental stress remains underexplored in multi-environment trials. This study utilized unmanned aerial vehicle multispectral imagery to derive NDVI and assess its relationship with grain yield in 34 spring and winter wheat variety trials. These trials included data across seven Washington State locations in different precipitation zones, five years (2019 to 2023), and some irrigated trials. Environments were grouped into high-, moderate-, and low-stress clusters based primarily on precipitation and temperature. Variability was quantified using the coefficient of variation, and correlations between grain yield and NDVI were evaluated within and between varieties across environments based on market classes (hard and soft spring and winter wheat). Across all environments and varieties, NDVI strongly correlated with grain yield ( r = 0.79-0.82, p r = 0.72 in hard spring, r = 0.53 in soft spring). These conditions also improved discrimination between varieties. Although heritability patterns were not clearly differentiated by stress clusters, environments with higher genetic control of yield also tended to show stronger NDVI heritability. Overall, NDVI reliably captured wheat grain yield, which is governed by the genotype × environment driven variability, with its predictive value strongest in stress-prone conditions. These findings underline NDVI's usability as a practical digital trait for improving variety testing and guiding breeding decisions in challenging environments.
Why it matches plant phenotyping methodsUAVマルチスペクトル画像からNDVIを抽出し、複数環境・品種で収量との関係、予測性、遺伝率を評価しており、デジタル植物形質の測定・検証が中心である。
abstractThis study utilized unmanned aerial vehicle multispectral imagery to derive NDVI and assess its relationship with grain yield in 34 spring and winter wheat variety trials.
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 · publicTrial data, including grain yield, variety, and market class information, were obtained from the Washington State University Extension Cereal Variety Selection and Testing Program ( https://smallgrains.wsu.edu/variety/ ).Open asset ↗lines:38-48Code / 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 confirmedCrossref · OpenAlex · checked 14 Sept 2026
Abstract Warmer temperatures, permafrost thaw, and increased wildfire activity are driving rapid ecological change across the Arctic, significantly altering plant productivity and aboveground biomass (AGB). These rapid changes highlight the urgent need to improve monitoring of vegetation dynamics in the Earth’s northern ecosystems, where high spatiotemporal heterogeneity occurs at scales finer than those captured by traditional satellite observations. The growing use of unoccupied aerial systems (UASs) presents an opportunity to overcome this limitation. Yet, the diversity of UAS platforms, sensors, and data collection and processing workflows presents challenges for developing standardized, generalizable approaches. To address this challenge, we compiled 672 AGB plots co-located with 183 UAS-based structure-from-motion (SfM) or light detection and ranging (LiDAR) surveys collected across the Arctic. Here, we: (1) evaluated the generalizability of UAS-derived canopy structure derived from high-resolution SfM and LiDAR for estimating AGB, (2) assessed scaling errors and their sources in two recent satellite-based AGB products derived from Landsat and moderate resolution imaging spectroradiometer, and (3) demonstrated the use of high-resolution AGB maps to quantify biomass variation across tundra plant functional types (PFTs) and to monitor post-fire recovery. Our results show that both SfM and LiDAR accurately captured AGB and its variability across tundra PFTs using a random forest model (overall root mean squared error: 0.332 kg m –2 ), with mapping performance varying slightly by region and data source. Using UAS-derived AGB maps as a benchmark, we identified systematic biases in satellite-derived AGB products, largely attributable to the magnitude of AGB and structural heterogeneity within coarse-resolution pixels. Applying our model to repeat UAS surveys following a tundra fire on Seward Peninsula, we observed rapid AGB recovery in non-shrub patches, with biomass recovering to pre-fire levels within two years. In contrast, shrub patches recovered more slowly, with AGB gains continuing over 2–4 years through both in-patch growth and lateral expansion (via dispersal) into remaining burned areas. Overall, these findings support the generalizability of UAS-based SfM and LiDAR data for estimating tundra AGB and highlight the need for broader collection and synthesis of such data to improve ecological monitoring and model benchmarking in the Arctic.
Why it matches plant phenotyping methodsUASのSfMおよびLiDARから植物群落の地上部 biomass (AGB) を推定する手法の一般化性能を評価し、衛星推定値のベンチマークにも用いており、植物形質取得が研究の中心である。
abstractevaluated the generalizability of UAS-derived canopy structure derived from high-resolution SfM and LiDAR for estimating AGB
Reproduction assets found保存済みの本文根拠を更新済みルールで再検証し、公開資産1件を確認しました。Code · publicThe codes and training data is available on GitHub: https://github.com/Daryl-Open asset ↗pdf-page:20 lines:1-30Code / dataset availability confirmedEurope PMC · checked 15 Sept 2026
MaizeTomatoLeafPhysiological trait estimationCalibration / preprocessingWater status / transpiration
Within the soil-plant-atmosphere continuum, water movement is driven by the water potential gradients between these three domains. To have a comprehensive understanding of such water relations, an examination of how plants respond to variations in soil water availability is required. The methodologies employed for measuring water potential in leaf (Ψ leaf ) and soil (Ψ soil ) have undergone a significant evolution; transitioning from qualitative assessments to the use of high-precision digital sensors over the past few decades. The present protocol aims to provide a comprehensive, step-by-step guide from the germination phase of maize and tomato plants to the installation of two sensors that continuously monitor water potential in the leaf (PSY1 psychrometer) and in the soil (TEROS 21 matric potential sensor). Additionally, we present the code for processing the raw data files in RStudio.
Why it matches plant phenotyping methods葉の水ポテンシャルを連続測定するセンサー設置、データ処理コード、手順を中心とした植物生理形質の測定プロトコルであり、方法論的貢献が明確。
abstractThe present protocol aims to provide a comprehensive, step-by-step guide from the germination phase of maize and tomato plants to the installation of two sensors that continuously monitor water potential in the leaf (PSY1 psychrometer) and in the soil (TEROS 21 matric potential sensor).
Reproduction assets foundThe paper deposits its authors' R analysis notebook with an example water-potential dataset, the CR800 datalogger program, and an installation video on Zenodo, all publicly accessible.Code · publicthat were missing, zero, or otherwise aberrant. It was also programmed to identify and remove inverted day-night cycle patterns, as well as values that were statistically insignificant.
Figure 9 shows applications of data cleaning on the example dataset. For more details, please check codes that have been deposited on Zenodo (
https://doi.org/10.5281/zenodo.20080750 ,
D’Agostino, 2026 ).
Figure 9.
Example of data cleaning using the algorithm.
Green is kept data and red is discarded data.
Conclusion
In summary, the present protocol is not confined to the descriptive monitoring of Ψ
soil
and Ψ
leafOpen asset ↗Zenodo · 10.5281/zenodo.20080750lines:452-504Code · public(1) the address of each Teros 21; (2) the data transporting port (“C1” or “C3”); (3) the creation of dataset files to store the recorded soil matric potential and temperature, as well as the voltage of the battery for power supply; (4) the time interval for the data recording.
An example of the program was deposited on Zenodo (
https://doi.org/10.5281/zenodo.17158115 ), with the document name of “Program-CR800”). Before starting, install the software of “Device Configuration Utility” and “PC400” from Campbell Scientific (
https://www.campbellsci.com/devconfig ;
https://www.campbellsci.com/pc400 ). “CRBasic Editor” is integrated inside PC400. For more details about the programming, please reOpen asset ↗Zenodo · 10.5281/zenodo.17158115lines:321-378Dataset · publiculic limitation, soil-root disconnection, and recovery. Consequently, this linkage of the protocol to mechanistic analyses of water transport in the SPAC is more direct.
Ethics and consent
Ethical approval and consent were not required.
Data availability
The datasets and codes to analyze the data have been deposited on Zenodo (
https://doi.org/10.5281/zenodo.20080750 ,
D’Agostino (2026) ).
Data are available under the terms of the Creative Commons Zero v1.0 Universal.
An additional explicative video for the psychrometer installation on leaves is available on Zenodo (
https://doi.org/10.5281/zenodo.17510720 ,
Degand
et al. (2025) ).
The author(s) declare that this video is released under theOpen asset ↗Zenodo · 10.5281/zenodo.20080750lines:505-651Code / dataset availability confirmedEurope PMC · OpenAlex · checked 13 Sept 2026
Among carnivorous plants, the Venus flytrap (Dionaea muscipula) is known for its rapid (<1 s) trap closure. Although buckling instability, hydrostatic pressure, and hydroelastic coupling have all been proposed to be involved, the nature of this process and the relationship between trap size and curvature remain elusive. Here, we monitored the closure of Venus flytraps and performed micro-CT scanning and 3D reconstruction, revealing that increasing angular velocity was correlated with higher values of a non-dimensional shape index. Based on these experimental data, we constructed a geometric model of the trap that takes leaf orientation into account. We found that leaf curvature is dependent on leaf size, a relationship we denote as a size-curvature constraint. We further propose a curvature design derived from differential deformations of a two-layer model of the leaf, which could be a powerful tool to control the curvatures of soft and bending surface structures in the field of biomimetics.
Why it matches plant phenotyping methodsマイクロCTと3D再構成で葉の閉鎖運動・曲率を定量化し、幾何モデルでサイズ–曲率関係を推定することが研究の中心であり、植物形態・運動状態のフェノタイピング手法に該当する。
abstractHere, we monitored the closure of Venus flytraps and performed micro-CT scanning and 3D reconstruction, revealing that increasing angular velocity was correlated with higher values of a non-dimensional shape index.
Reproduction assets foundThe paper's Data Availability statement points to an authors' GitHub page hosting all data files and related rendering files for the Venus flytrap closure measurements and 3D reconstructions, matching an allowed URL.Dataset · publicAll data files and related rendering files are available from the github ( https://satorutsugawa.github.io/flytrap_geometric_model_datashare/) .Open asset ↗githublines:105-144Code / dataset availability confirmedEurope PMC · Crossref · checked 5 Sept 2026
Abstract Chlorophyll estimation is fundamental in plant physiology, crop management, and ecological studies; however, destructive and non-destructive methods are often interpreted interchangeably despite differing measurement principles. The present study compared four chlorophyll estimation approaches—two non-destructive (SPAD meter and GreenSeeker) and two destructive (80% acetone and DMSO extraction)—across eight crop species under uniform field conditions. Significant interspecific variation was observed for all methods. Correlation and regression analyses revealed generally weak relationships among methods, particularly between leaf-level (SPAD, solvent extraction) and canopy-level (GreenSeeker) measurements, reflecting scale-dependent behavior and methodological differences. Moderate associations were observed between SPAD and acetone-extracted chlorophyll for certain traits, whereas GreenSeeker showed poor agreement with solvent-based estimates. Differences between DMSO and acetone extraction further highlighted solvent-specific extraction efficiency. The results demonstrate that chlorophyll estimation methods are not directly interchangeable and should be selected based on study objectives, biological scale, and leaf anatomical characteristics. Species-specific calibration and integration of canopy structural parameters are required to improve cross-method interpretability.
Why it matches plant phenotyping methods複数の葉・キャノピーのクロロフィル推定法を作物種間で比較し、相関、回帰、スケール依存性、互換性を評価しており、植物表現型測定法の技術的検証が中心である。
abstractThe present study compared four chlorophyll estimation approaches—two non-destructive (SPAD meter and GreenSeeker) and two destructive (80% acetone and DMSO extraction)—across eight crop species under uniform field conditions.
Reproduction assets foundThe preprint declares that the datasets generated in this chlorophyll-method comparison study (SPAD, GreenSeeker, acetone and DMSO measurements across eight crop species) are publicly deposited in Figshare under DOI 10.6084/m9.figshare.31817989. This is a paper-specific, publicly actionable phenotype dataset. No authorDataset · publicThe datasets generated during the current study are available in the Figshare repository, https://doi.org/10.6084/m9.figshare.31817989Open asset ↗Figshare · 10.6084/m9.figshare.31817989lines:163-185Code / dataset availability confirmedCrossref · checked 15 Sept 2026
Accurately estimating leaf area index (LAI) is vital for evaluating crop growth and predicting yields. Conventional approaches, however, often struggle due to the limited representativeness of available data and the complex structure of plant canopies, which reduce their reliability across diverse canopy architectures and observation conditions. To overcome these challenges, this work introduces an LAI retrieval framework that combines a three-dimensional radiative transfer model (3D RTM) with deep learning techniques. Representative 3D maize canopy scenarios were generated using the LESS model, producing synthetic LiDAR point clouds constrained by realistic structural parameters. A deep learning model based on PointNet++ was trained, and transfer learning (TL) was employed to facilitate knowledge transfer from simulated to actual measured data. The TL-enhanced model demonstrated significant improvement, with R2 rising from 0.537 to 0.842 and RMSE dropping from 0.541 to 0.288 m2·m−2. Moreover, retrieval performance was notably affected by scanning mode, angle, and stem diameter, achieving optimal results under TLS acquisition, moderate scanning angles, and intermediate stem widths. These findings suggest that integrating 3D RTM-generated synthetic point clouds with transfer learning is an effective strategy for enhancing the robustness and generalization of LiDAR-based LAI retrieval.
Why it matches plant phenotyping methodsLiDAR点群からトウモロコシのLAIを推定する手法を、3D放射伝達モデル、PointNet++、転移学習で開発・検証しており、植物形態形質の取得・推定が研究の中心です。
abstractthis work introduces an LAI retrieval framework that combines a three-dimensional radiative transfer model (3D RTM) with deep learning techniques.
Reproduction assets foundThe paper's field-measured LiDAR point cloud and LAI data (Yingke Oasis and Huazhaizi sites) come from a publicly accessible TPDC dataset with an explicit URL in the Data Availability Statement. No author analysis code, trained models, or synthetic dataset deposit is stated.Dataset · public2024WX06.
Data Availability Statement: The dataset used in this study was obtained from the National Tibetan
Plateau Data Center (TPDC, https://www.tpdc.ac.cn/ (accessed on 6 September 2025)), a publicly
accessible scientific data platform providing multi-source geoscientific datasets. The specific dataset
can be accessed via: https://www.tpdc.ac.cn/zh-hans/data/4d60d570-0aa9-417b-8a9d-c32b73b564
(accessed on 6 September 2025). The TPDC database integrates long-term observational and remote
sensing data with standardized quality control, ensuring the reliability and consistency of the datasets
for scientific research.
Acknowledgments: The authors would like to acknowledge the National TibetaOpen asset ↗4d60d570-0aa9-417b-8a9d-c32b73b564pdf-raw-page:19 lines:1-51Code / 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 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 · checked 14 Sept 2026
Given the increasing frequency, severity, and socioecological impacts of wildfires, there is an urgent need for robust frameworks to better characterize fire behavior and flammability patterns across ecosystems to support early warning, mitigation, and management strategies. However, flammability remains difficult to quantify and scale, as it involves multiple interacting components that are typically measured at the bench scale. This study aimed to establish empirical links between spectral information, plant traits, and flammability metrics, and to scale these relationships to satellite imagery to translate these metrics into a spatial context. We combined laboratory spectroscopy, plant trait measurements including leaf mass per area, carbon, and cellulose, and combustion experiments using a simple and reproducible burning device. In total, 84 samples were collected and analysed, allowing us to characterise how spectral signatures relate to vegetation traits and fire behaviour. Spectral indices were developed to estimate plant traits, which were subsequently used as predictors in flammability models. These models were then transferred to Environmental Mapping and Analysis Program (EnMAP) hyperspectral imagery to derive spatial estimates across eucalypt forests and grasslands of the Australian Capital Territory (ACT). Spectral information distinguished fuel types and captured variability of the plant traits, while these traits showed associations with combustion behaviour. Based on these links, the best-performing model predicted the rate of temperature increase, a combustibility metric, in eucalypt forests (R2 = 0.70; Root Mean Square Error = 32.48 °C/s). In contrast, grassland models showed limited predictive performance, likely due to weaker relationships between plant traits and flammability metrics. Overall, this study demonstrates a practical and scalable approach for deriving flammability maps from hyperspectral and in situ data, highlighting the potential of plant-trait-based remote sensing. The resulting maps should not be interpreted as standalone fire risk products, but rather as a characterization of the structural and biochemical drivers of flammability. The main constraint of this work is the limited sample size. Future research should expand spatial and temporal coverage to better capture vegetation variability and enable the inclusion of independent validation datasets. Exploring alternative combustion protocols and testing more advanced spectral modelling approaches for trait estimation would provide additional insights.
Why it matches plant phenotyping methods植物形質を分光情報から推定し、ハイパースペクトル画像へ展開して可燃性関連の植物状態を評価する手法が研究の中心であり、モデル性能も検証しているため。
abstractSpectral indices were developed to estimate plant traits, which were subsequently used as predictors in flammability models.
Reproduction assets foundThe paper's supplementary materials (hosted publicly by MDPI) contain the paper-specific plant phenotype measurements: sampled species lists, fractional cover, and measured vegetation traits across dates and plots, plus combustion replicate variability and trait–flammability relationship data. The raw underlying data,谱Supplement · publicbroader environmental coverage, improved plant trait retrieval meth-
ods, and independent validation. Future work should also explore non-linear modelling
frameworks to better capture the complexity of vegetation flammability across ecosystems.
Supplementary Materials: The following supporting information can be downloaded at:
https://www.mdpi.com/article/10.3390/rs18101546/s1, Supplementary Table S1 provides the list of
sampled plant species and their percentage cover across sites, paddocks, plots, and fuel types; Table
S2 presents the fractional cover of each species and litter component; Figure S1 shows the study-site
vegetation map; Figures S2–S6 show the measured vegetation traits acrosOpen asset ↗pdf-raw-page:22 lines:1-49Code / dataset availability confirmedOpenAlex · Crossref · checked 14 Sept 2026
Abstract has not been obtained from indexed metadata or an accessible article page.
Why it matches plant phenotyping methodsNDVIに基づくハイスループット植物フェノタイピングを主題としており、センサーによる植物形質取得が中心と明示されている。
titleAccelerating cassava genetic improvement through NDVI-based high-throughput phenotyping
Reproduction assets found保存済みの本文根拠を更新済みルールで再検証し、公開資産1件を確認しました。Code · publicThe data and scripts (R and SAS) that support the conclusions of this article can be freely and openly accessed at Zenodo: https://zenodo.org/records/18778974 [ 57 ].Open asset ↗Zenodolines:177-214Code / dataset availability confirmedEurope PMC · checked 5 Sept 2026
Abstract Purpose The primary objective of this study was to evaluate the potential of high spatial resolution Jilin-1 satellite imagery for plot-level prediction of sugarcane yield, millable stalk population, and Brix, and to assess whether integrating spectral, texture, soil, and fertilization information could improve prediction performance for precision sugarcane management. Methods Jilin-1 satellite imagery acquired at four growth stages, from seedling to maturity, was used to derive vegetation indices (VIs) and texture indices (TIs), including the normalized difference texture index (NDTI), enhanced vegetation texture index (EVTI), and double-difference ratio texture index (DDRTI). Soil chemical properties (SCPs) and fertilization information (FI) were further incorporated with the remotely sensed variables. Machine learning models were developed for plot-level prediction of sugarcane traits across plant cane and first ratoon cane, and texture window size was optimized to improve TI extraction and model performance. Results For yield, the combination of VIs and TIs outperformed VIs alone at the tillering stage (R 2 CV = 0.65, RMSECV = 15.06 t/ha, RPDCV = 1.68). Adding SCPs and FI further improved yield prediction across plant cane and first ratoon cane (R 2 CV = 0.70, RMSECV = 13.84 t/ha, RPDCV = 1.83). Millable stalk population was best predicted at the maturation stage by VIs and Tis, achieving the best performance (R 2 CV = 0.63, RMSECV = 6602 stalks/ha, RPDCV = 1.66). The best Brix model integrated VIs, TIs, SCPs, and FI at the maturation stage (R 2 CV = 0.44, RMSECV = 0.53 °Bx, RPDCV = 1.33). SHAP analysis identified VIs as the dominant features for sugarcane traits prediction. And, DDRTI contributed more than NDTI and EVTI in yield and Brix prediction. Conclusion It is concluded that integrating spectral, texture, soil, and fertilization information from high spatial resolution Jilin-1 imagery is a promising approach for improving plot-level prediction of key sugarcane traits.
Why it matches plant phenotyping methods衛星画像からサトウキビの収量、可販茎数、Brixを plot レベルで推定し、テクスチャ特徴抽出の最適化と機械学習性能評価を行っており、表現型取得・推定手法が中心である。
abstractThe primary objective of this study was to evaluate the potential of high spatial resolution Jilin-1 satellite imagery for plot-level prediction of sugarcane yield, millable stalk population, and Brix
Reproduction assets foundThe paper's data availability statement explicitly links a public GitHub repository containing part of the authors' model-training code and test data for the sugarcane trait prediction analysis. No public phenotype dataset or imagery deposit is stated; additional data are only available on request.Code · publicPart of the code and test data for model training are available at https://github.com/guangtaoxu08-dev/SPT_JL .Open asset ↗guangtaoxu08-dev/SPT_JLlines:228-248Code / dataset availability confirmedOpenAlex · Europe PMC · checked 5 Sept 2026
Introduction Herbicide dose-response assays are routinely implemented to compare herbicide resistance among weed biotypes, which requires plant biomass to estimate the dose that reduces growth by 50% relative to untreated plants (GR 50 ). The Phenospex TraitFinder is a high-throughput, non-destructive, digital phenotyping system that collects data from 7 spectral parameters and 13 morphological parameters, including Digital Biomass (DB), which offers the opportunity for researchers to eliminate the time and labor associated with manual biomass collection. However, DB is the product of 3D Leaf Area and Plant Height (PH) Mean, making it a measurement of plant volume and an indirect indicator of biomass. While DB is highly correlated with true biomass, digitally collected plant volume data has not been implemented for dose-response assays or assessed for accuracy relative to true biomass data. Additionally, inaccurate PH measurements could impact the accuracy of DB measurements. Methods This study sought to assess the accuracy and utility of DB and the 19 remaining parameters in dose-response assays by comparing dose-response curves and GR 50 estimates generated from digital data and fresh biomass (FB) data. Accuracy of PH measurements were also assessed by comparing digital and manual measurements with the paired t-test. Pre-emergence dose-response assays using fomesafen and atrazine were implemented with common lambsquarters ( Chenopodium album L.). At 21 days after treatment, manual measurements of FB and PH were collected following digital data collection. Results Consistently strong correlations ( r = 0.97, P < 0.05) were observed between digitally collected data and their equivalent manual measurements. Comparisons of the dose-response curves indicated that only 3D Leaf Area, DB, Convex Hull Area, Projected Leaf Area, and Voxel Volume Total generated highly similar curves and GR 50 estimates relative to FB data, indicating that any one or all of these parameters could be utilized instead of FB. Small differences (approximately 1.06 to 1.77 mm) between manual and digital PH measurements were identified with the paired t-test, but since DB consistently produced similar dose-response curves and GR 50 estimates relative to FB, these differences did not impact the accuracy of DB measurements. Discussion Without requiring manual biomass collection, turnaround time for dose-response and other phenotyping assays decreases and allows faster sharing of research. Furthermore, herbicide-resistant plants can be preserved for phenotyping at later growth stages, tissue collection, and to produce progeny for future experiments.
Why it matches plant phenotyping methodsデジタル表現型システムで植物体積・草丈などを取得し、手作業の生体重測定との精度比較および除草剤用量反応曲線への有用性を検証しており、表現型取得法が中心です。
abstractThe Phenospex TraitFinder is a high-throughput, non-destructive, digital phenotyping system that collects data from 7 spectral parameters and 13 morphological parameters
Reproduction assets foundThe paper's digital phenotyping dose-response datasets are publicly deposited: the data availability statement names Ag Data Commons DOI 10.15482/USDA.ADC/29815082 and a figshare link, both paper-specific. No author analysis code repository is explicitly stated.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: 10.15482/USDA.ADC/29815082 or https://figshare.com/s/64d1bbac59a95c4721f1 .Open asset ↗figshare · 10.15482/USDA.ADC/29815082lines:548-573Code / 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 14 Sept 2026
Wood apple (Feronia limonia L.) is an underutilized perennial fruit tree with substantial ecological, nutritional, and economic potential, yet its phenotypic diversity and trait organization remain poorly characterized. Here, we applied a mixed-scale multivariate framework to resolve phenotypic structure in 62 wood apple genotypes using 31 ordinal and categorical vegetative, leaf, floral, fruit, and seed descriptors. Trait interrelationships were examined through the complementary use of Spearman’s rank correlation and Cramér’s V association analyses, capturing both directional rank-based dependencies and scale-independent categorical linkages. Hierarchical clustering based on Gower distance separated the genotypes into three distinct phenotypic clusters, with inter-cluster dissimilarities (0.92–1.18) consistently exceeding intra-cluster variation (0.42–0.55), indicating well-supported phenotypic stratification based on cluster validation. Multiple Correspondence Analysis (MCA) explained 23.30% of total inertia across the first two dimensions, with tree growth habit, branch angle, tree shape, and fruit color emerging as the principal drivers of phenotypic differentiation. Vegetative and leaf traits formed a tightly integrated module, whereas fruit-related traits displayed weaker monotonic but persistent categorical associations, reflecting partial phenotypic independence. The strong concordance among association analyses, clustering, and MCA indicates structured patterns of coordinated and partially independent trait associations in wood apple. Overall, this study demonstrates the effectiveness of mixed-scale multivariate approaches for resolving complex trait architecture in underutilized perennial fruit crops and provides a quantitative phenotypic framework to support germplasm conservation, parent selection, and ideotype-oriented improvement of wood apple.
Why it matches plant phenotyping methods混合尺度の多変量解析を用いて植物遺伝資源の表現型構造を定量化する手法が研究の中心であり、単なる生物学的実験の routine 測定ではない。
abstractHere, we applied a mixed-scale multivariate framework to resolve phenotypic structure in 62 wood apple genotypes using 31 ordinal and categorical vegetative, leaf, floral, fruit, and seed descriptors.
Reproduction assets found保存済みの本文根拠を更新済みルールで再検証し、公開資産1件を確認しました。Supplement · publicAll data generated or analyzed during this study are available in the article and the accompanying Supplementary Table S1.Open asset ↗lines:137-161Code / dataset availability confirmedEurope PMC · checked 5 Sept 2026
LeafStem / branchPhysiological trait estimationLeaf traitsWater status / transpiration
Leaf and hydraulic traits are key determinants of growth rates, and hence potentially exhibit significant associations with wood density (WD) and its intraspecific variation (ITV). However, the extent to which functional traits could improve WD prediction accuracy, and how ITV in WD correlates with functional traits remain incompletely understood. We investigated WD and its ITV across 10,218 plant species, mapped the global distribution of WD, and analyzed the association of ITV in WD with niche breadth and functional traits. Plant species with an acquisitive resource-use strategy, characterized by higher specific leaf area (SLA), leaf nitrogen concentration (LN), and leaf maximum stomatal conductance (g max ), exhibited lower WD. Associations of WD with hydraulic traits indicated species with greater hydraulic safety exhibited higher WD. Moreover, the integration of leaf traits (i.e., SLA and LN) and hydraulic traits with environmental factors substantially enhanced WD prediction accuracy in a random forest model, raising the explained variance from 55% to 95%. Furthermore, resource-acquisitive species demonstrated higher ITV for WD. ITV was positively related to relative niche breadth concerning both climatic factors and soil properties. Overall, functional traits significantly improve WD prediction accuracy, and plant species with an acquisitive resource-use strategy exhibit lower WD but greater intraspecific variation.
Why it matches plant phenotyping methods木材密度という植物形質の予測モデルを構築し、機能形質・環境因子の統合による予測精度を検証しており、形質推定手法が中心的です。
abstractthe integration of leaf traits (i.e., SLA and LN) and hydraulic traits with environmental factors substantially enhanced WD prediction accuracy in a random forest model, raising the explained variance from 55% to 95%.
Reproduction assets foundThe paper's Data Availability Statement points to a public Zenodo deposit containing the authors' global wood density distribution data, which directly reproduces this paper's measurements. The TRY Plant Trait Database is a generic third-party database, not a paper-specific asset, and no author analysis code is stated.Dataset · publicData for the global distribution of wood density is available on Zenodo Repository https://sandbox.zenodo.org/records/425279.Open asset ↗Zenodo · 425279html-lines:405-429Code / dataset availability confirmedEurope PMC · checked 14 Sept 2026
Abstract Purpose Accurate estimation of crop transpiration is essential for optimizing irrigation management and improving water-use efficiency in precision agriculture. However, direct measurement of transpiration is often invasive, costly, and difficult to maintain at large scales. This study proposes a data-driven framework to estimate maize ( Zea mays L.) sap flow driven by transpiration using widely available climatic and soil moisture data combined with machine learning techniques. Methods Field experiments were conducted during the 2023 and 2024 growing seasons in central Italy under irrigated silage maize. Meteorological variables, soil water content, and crop growth indicators were used as inputs, while sap flow measurements served as reference outputs. Several machine learning models were evaluated, including Linear Regression, Support Vector Regression (SVR), Decision Tree Regressor, and Multi-Layer Perceptron Regressor (MLPR), using both Point Estimation and Temporal Estimation strategies. Temporal approaches incorporated short-term historical information through feature concatenation and previous-average windows. Results Results demonstrate that non-linear models, particularly MLPR and SVR, consistently outperform linear and tree-based approaches. The inclusion of short temporal windows (45 minutes to 2 hours) significantly improves predictive accuracy, enhancing reconstruction of the diurnal transpiration pattern. Feature concatenation proved more effective than averaging strategies in capturing soil–plant–atmosphere interactions. Model performance remained robust across two contrasting growing seasons, confirming good generalization capability under interannual variability and data discontinuities. Conclusion The proposed framework provides a reliable and minimally invasive solution for real-time estimation of maize transpiration, supporting precision irrigation management. These findings highlight the potential of machine learning models as practical decision-support tools for sustainable agricultural water management.
Why it matches plant phenotyping methodsトウモロコシの蒸散・樹液流という生理形質を、気象・土壌水分データと機械学習で推定する手法を開発・比較し、複数年で性能検証しているため、植物フェノタイピング手法が中心である。
abstractThis study proposes a data-driven framework to estimate maize ( Zea mays L.) sap flow driven by transpiration using widely available climatic and soil moisture data combined with machine learning techniques.
Reproduction assets foundThe paper's Data Availability statement says part of the datasets generated and analyzed (maize sap flow, climate, and soil moisture measurements) are publicly available on the authors' GitHub, while the analysis source code is only promised upon acceptance.Dataset · publicon; Datacuration; Formal
686
analysis; Funding acquisition; Investigation; Methodology; Project administration; Supervision;
687
Validation; Visualization; Writing – original draft; Writing – review and editing.
688
D t v il ility Part of the datasets generated and analyzed during the current study are
689
publicly available at https://github.com/isarlab-department-690
engineering/Agritech3.1.5FIWARE. The source code used for data processing and analysis will
691
be released upon acceptance of the paper in the GitHub repository https://github.com/isarlab-692
department-engineering/DD_Maize_Sap_Flow.
693
Funding This work was carried out within the framework of the project Agritech National ROpen asset ↗isarlab-department-690pdf-raw-page:31 lines:1-67Code / dataset availability confirmedEurope PMC · checked 5 Sept 2026
Abstract The global issue of water scarcity and climate change requires highly efficient and intelligent irrigation systems that are capable of optimizing water consumption with high crop productivity. The paper aims to provide a holistic machine learning framework for crop water stress prediction and efficient irrigation scheduling using multi-parametric agronomic data. The paper analyzes 55,450 soybean data with 13 physiological and biochemical parameters to implement and compare six regression models for predicting the water stress index. After eliminating tautology by removing the direct water content parameter from the prediction model, LightGBM and XGBoost ensemble tree models achieved near-perfect accuracy for predicting crop water stress using regular plant parameters alone, with R² = 1.0 and RMSE = 1.57×10⁻⁸ to 5.04×10⁻⁵. The Random Forest classifier, which was implemented without any direct stress indicators, achieved perfect discrimination between low, moderate, and high stress classes with precision/recall equal to 1.0, and 5-fold cross-validation and noise tests confirmed its robustness. SHAP analysis of the results showed protein percentage (PPE) and seed yield per unit area (SYUA) to be key drivers of water stress, providing valuable insights for precision agriculture. The model for determining irrigation requirements based on crop evapotranspiration and stress level achieved R² = 1.0 with zero error, making it possible to translate trait values directly into irrigation requirements. The framework presented in this paper brings together machine learning and agronomic knowledge to provide real-time data-driven solutions for irrigation systems, which have 30–50% water savings potential while maintaining healthy crops. It lays the ground for the development of AI-assisted irrigation systems that are applicable to different crops and climatic conditions, particularly in water-scarce countries such as Iraq.
Why it matches plant phenotyping methods作物の水ストレス状態を生理・農学データから機械学習で推定し、複数モデルの比較、交差検証、ノイズ試験、解釈分析まで行う計算的フェノタイピング手法が中心である。灌漑最適化への応用を含むが、単なる日常的測定ではない。
abstractThe paper aims to provide a holistic machine learning framework for crop water stress prediction and efficient irrigation scheduling using multi-parametric agronomic data.
Reproduction assets foundThe paper's soybean phenotyping dataset (55,450 records, 13 physiological/biochemical traits) is publicly available on Kaggle; the Data Availability statement points to it, though it ambiguously labels it as the code implementation location. No separate verified code repository is provided.Dataset · publicThe dataset used in this study (Advanced Soybean Agricultural Dataset) is available from the corresponding author upon reasonable request. The code implementation for all analyses is available at: https://www.kaggle.com/datasets/wisam1985/advanced-soybean-agricultural-dataset-2025 .Open asset ↗kaggle · wisam1985/advanced-soybean-agricultural-dataset-2025lines:372-406Code / dataset availability confirmedEurope PMC · checked 5 Sept 2026
Plant development and adaptation are highly dependent on cell morphology and growth. High turgor pressure in plants causes stress on the cell wall, followed by cell extension. In tip-growing cells, the localization of vesicles and cytoskeleton components has been well studied. However, there has been a lack of attention to the spatial profile of mechanical properties, specifically the cell wall elasticity. In this study, we introduce a new surface morphology-based method to measure the elasticity of the cell wall in tip-growing cells. Previous work is based on measurements from the wall meridional outline, a technique that cannot track the elastic deformation of the cell wall experimentally. Instead, we developed a way to infer the bulk modulus distribution from the cell surface by triangulating experimental marker points coming from fluorescent labeling. To justify the use of our protocol in tip-growing cells from the moss Physcomitrium patens, we replicated the experimental noise and moss morphology in simulated cells. In practice, we found that a larger triangulation improved robustness against noise, which agreed with our theoretical study. With multiple cell sampling, we determined that 10 cells were sufficient to recover the elasticity distribution with noise, but only when the elastic stretches were high enough. We then created a dimensionless map of inference error to verify a spatial change of P. patens bulk modulus within two folds. This technique will open the field to more comprehensive measurements of cell wall elasticity, providing a key step in understanding tip cell growth and morphogenesis.
Why it matches plant phenotyping methods植物細胞表面形態から細胞壁弾性分布を推定する新規測定法を開発・検証しており、植物形質の取得手法が研究の中心である。
abstractwe introduce a new surface morphology-based method to measure the elasticity of the cell wall in tip-growing cells.
Reproduction assets foundThe paper's Data Availability statement explicitly deposits all data and code (including code demonstrations for the surface morphology-based elasticity inference method) in a public GitHub repository, which is listed in allowed_urls.Code · publicData Availability: All relevant data and code, including code demonstrations, are available on the GitHub repository found here: https://github.com/rholee-xu/surface-model .Open asset ↗rholee-xu/surface-modellines:127-138Code / dataset availability confirmedEurope PMC · checked 5 Sept 2026
This dataset consists of a collection of high-resolution RGB images of grapevine leaves, designed to support research in plant pathology, precision viticulture, and computer vision. The images were collected in situ from experimental and commercial vineyards in the north of Portugal, covering different vineyard conditions and management practices. The dataset includes healthy leaves from three grapevine Portuguese cultivars Loureiro, Viosinho and Malvasia Fina, photographed under natural lighting conditions without artificial adjustments. It is organized into five categories: healthy leaves and leaves showing symptoms of downy mildew ( Plasmopara viticola ), powdery mildew ( Erysiphe necator ), Esca complex and Erineum Mite ( Colomerus vitis ). Images are provided in JPEG format with a resolution of 3000 × 3000 pixels and 1024 × 1024 pixels and arranged in folders by health status and disease type. This dataset can be used for machine learning and deep learning applications in disease detection/classification, cultivar identification, and can support other precision agriculture applications, as well as being used for agricultural robotics and educational purposes. An evaluation on three deep learning architectures demonstrated the suitability of the dataset into separating the five classes.
Why it matches plant phenotyping methodsブドウ葉の病徴を画像化した再利用可能なデータセットで、植物の健康状態・病害状態の画像ベース推定を支えることが中心です。深層学習による5クラス分類評価も記載されています。
abstractThis dataset consists of a collection of high-resolution RGB images of grapevine leaves, designed to support research in plant pathology, precision viticulture, and computer vision.
Reproduction assets foundThe paper is a Data in Brief article describing a public Zenodo repository of RGB grapevine leaf images (healthy plus downy mildew, powdery mildew, Esca complex, erineum mite) collected for plant disease/phenotyping research, with explicit data accessibility details. No author analysis code or trained model checkpointsDataset · publicData accessibility
Repository name: Zenodo
Data identification number: https://doi.org/10.5281/zenodo.17343473
Direct URL to data: https://zenodo.org/records/17343473Open asset ↗Zenodo · 10.5281/zenodo.17343473html-lines:93-144Code / dataset availability confirmedEurope PMC · bioRxiv · Crossref · checked 5 Sept 2026
Premise: Herbarium specimens are increasingly used to extract morphological traits for ecological and evolutionary studies, yet the effects of tissue desiccation on trait measurements remain poorly understood. Here, we tested whether higher tissue water content leads to greater measurement changes after herborization (H1) and whether fresh trait values can be reliably predicted from herbarium measurements (H2). Methods: We evaluated the reliability of herbarium-based measurements by comparing fresh and dried traits of leaves, flowers, fleshy fruits, and seeds across 262 individuals representing 133 Neotropical Myrtaceae species. Phylogenetic least square models and machine-learning regressions were used to test H1 and H2. Results: Leaves and flowers generally shrank after herborization, fruits size metrics tended to increase, and seeds were largely unaffected. Water content was significantly associated with the magnitude of herborization effects in flowers and some leaf and seed traits. Fresh trait values were accurately predicted from herbarium measurements. Prediction errors were lowest for leaf traits, followed by fruits, flowers, and seeds. Discussion: These results partially support H1 and support H2, indicating that herbarium specimens can be reliably used for trait analyses when organ-specific responses are considered, providing a practical framework to account for potential desiccation bias in functional trait research.
Why it matches plant phenotyping methodsハーバリウム標本による植物形態形質測定の信頼性評価と、生鮮形質の予測手法が研究の中心であり、植物フェノタイピング手法の検証に該当する。
abstractWe evaluated the reliability of herbarium-based measurements by comparing fresh and dried traits of leaves, flowers, fleshy fruits, and seeds across 262 individuals representing 133 Neotropical Myrtaceae species.
Reproduction assets foundThe authors explicitly state that the code used for the PGLS and machine-learning analyses is publicly available in a GitHub repository; raw phenotype data is promised only upon acceptance, so the code asset qualifies while the dataset is not yet actionable.Code · publicSupporting Information and the code used to perform the analyses are available at
https://github.com/ykilsztajn/fresh_dry_myrtaceae. All raw data will be made available in the
same repository upon acceptance for publication.Open asset ↗ykilsztajn/fresh_dry_myrtaceaepdf-page:9 lines:1-48Code / dataset availability confirmedEurope PMC · checked 5 Sept 2026
Field / plotMultimodalLeafSegmentationDisease symptoms / severity
Camellia oleifera is an economically vital woody oil crop. Its productivity and oil quality are severely compromised by various diseases. Implementing pixel-level lesion segmentation within complex field environments is crucial for advancing precision plant protection. Despite recent progress, existing segmentation methods struggle with three primary challenges: semantic ambiguity arising from evolving pathological stages, blurred boundaries due to overlapping lesions, and the high omission rate of micro-lesions. To address these issues, this paper presents TB-DLossNet (Text-Conditioned Boundary-Aware Network with Dynamic Loss Reweighting), a novel segmentation framework based on semantic-visual multi-modal fusion. Leveraging VMamba as the visual backbone, the proposed model innovatively integrates BERT-encoded structured text as an auxiliary modality to resolve visual ambiguities through cross-modal semantic guidance. Furthermore, a boundary enhancement branch is incorporated alongside a multi-scale deep supervision strategy to mitigate boundary displacement and ensure the topological continuity of lesion structures. To tackle the detection of small-scale targets, we designed a dynamic weight loss function conditioned on lesion area, significantly bolstering the model's sensitivity to minute pathological features. Additionally, to alleviate the scarcity of high-quality data, we curated a comprehensive multi-modal dataset encompassing seven typical diseases of Camellia oleifera . Experimental results demonstrate that TB-DLossNet achieves a Mean Intersection over Union (mIoU) of 87.02%, outperforming the state-of-the-art unimodal VMamba and multimodal Lvit by 4.9% and 2.59%, respectively. Qualitative evaluations confirm that our model exhibits lower false-negative rates and superior boundary-fitting precision in heterogeneous field scenarios. Finally, generalization tests on an apple disease dataset further validate the robustness and transferability of the proposed framework.
Why it matches plant phenotyping methods植物病害の病斑を画素レベルで抽出する新規セグメンテーション手法を開発し、データセット整備と性能比較・汎化検証も行っているため、病害状態の画像ベース表現型計測が中心である。
abstractImplementing pixel-level lesion segmentation within complex field environments is crucial for advancing precision plant protection.
Reproduction assets foundThe authors state their code and experimental dataset (the multimodal Camellia oleifera disease segmentation dataset) are publicly available on GitHub, matching an allowed URL.Code · publicOur code and experimental dataset are available at https://github.com/zzzsq239/TB-1.Open asset ↗zzzsq239/TB-1html-lines:820-841Code / 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 · bioRxiv · Crossref · checked 5 Sept 2026
A central problem in soft and biological physics is how molecular-scale activity and remodelling coarse-grain into emergent mechanical laws at larger scales. In growing cell walls (polymeric composite materials that surround 90% of living organisms’ cells) irreversible deformation is not controlled by elastic stress alone. Instead, growth depends on the interplay between energy storage, dissipation, and the local timing of viscoelastic relaxation. Although dynamic atomic force microscopy (AFM) resolves storage and loss moduli ( E′, E″) of living walls at nanometre resolution, these observables have remained phenomenological and disconnected from constitutive field variables. Here we introduce a physics-based inversion framework that converts AFM measurements of epidermal cells of living Arabidopsis plants into spatially resolved fields of stiffness k , viscosity η , and relaxation time τ . By analysing the spatial gradients of E′ and E″, we uncover organized mechanical heterogeneities governed by cellular confinement and stress focusing. We demonstrate that the local relaxation time is encoded directly in the coupling between storage and dissipation, yielding the pointwise relation τ = (1/ ω ) ∂ E ’/∂ E ’’, where ω is the indentation frequency. This relation enables model-independent extraction of mechanical timescales and establishes a general route from nanoscale non-equilibrium rheology to continuum descriptions of growth in living and active soft materials. Significance How molecular-scale activity gives rise to tissue-scale form is a central challenge in biological physics. Although growth is fundamentally a non-equilibrium mechanical process, experimental measurements at the nanoscale have not been directly connected to the constitutive parameters that govern morphogenesis. We introduce a framework that converts dynamic atomic force microscopy maps of storage and loss moduli into spatially resolved fields of stiffness, viscosity, and relaxation time in living cell walls. By revealing that mechanical relaxation is encoded in the local coupling between elastic storage and viscous dissipation, our work provides a route from nanoscale rheology to growth-relevant mechanical timing. This establishes a quantitative bridge between molecular remodeling and continuum mechanics, enabling direct experimental constraints on multiscale theories of morphogenesis.
Why it matches plant phenotyping methods生きたArabidopsis細胞のAFM測定を物理ベースで反転し、剛性・粘性・緩和時間という植物細胞壁の機械的形質を空間的に抽出する新規フレームワークが研究の中心である。
abstractHere we introduce a physics-based inversion framework that converts AFM measurements of epidermal cells of living Arabidopsis plants into spatially resolved fields of stiffness k , viscosity η , and relaxation time τ .
Reproduction assets foundThe paper's custom AFM viscoelastic analysis code is explicitly deposited and publicly available on GitHub (ForceMetric). The underlying AFM phenotype/measurement data are only available upon request, not publicly.Code · publicAFM data were analysed in Python 3.5 using previously described routines [34] (code
available at https://github.com/jcbs/ForceMetric ).Open asset ↗jcbs/ForceMetricpdf-page:14 lines:1-56Code / dataset availability confirmedEurope PMC · checked 14 Sept 2026
Recognizing lineages is a central challenge in plant systematics, making it essential to explore multiple analytical tools. In this context, this study investigates how frond shape can assist in discriminating against lineages within the Scaly clade of Microgramma (Polypodiaceae), and tests whether the integration of multiple lines of evidence enables a more consistent recognition of lineages than exclusively macromorphological approaches. We analyzed 271 specimens representing eight species, using Elliptical Fourier Analysis (EFA) to quantify frond shape, followed by multivariate statistical tests (PCA, MANOVA, LDA). Evolutionary relationships between spectral and morphometric data were assessed through phylogenetic generalized least squares (PGLS) regressions and phylogenetic partial least squares (Phylo-PLS) analyses. Dimorphic species exhibited higher discrimination capacity (average accuracy of 80–83%). Fertile and combined fronds yielded the highest accuracy values. Morphologically similar species, such as M. reptans and M. tobagensis, showed significant overlap, whereas M. percussa achieved the best performance (average accuracy of 80%). Morphometric-spectral integration showed a strong correlation (R² = 0.72; P = 0.003), and both the combined datasets (spectra and outline) and the individual datasets of spectral and shape features revealed a high phylogenetic signal (λ = 1–0.84), indicating partial coevolution between frond shape, chemical composition, and the evolutionary history of the group. Outline morphometry combined with infrared spectroscopy within a phylogenetic framework improves lineage discrimination, although overlap zones persist, reflecting complex evolutionary processes. Our study highlights the potential of integrative systematics to elucidate species boundaries in groups with high morphological disparity, as well as the need for broad sampling and multi-evidence approaches in future systematic reviews.
Why it matches plant phenotyping methodsフロンド形状をElliptical Fourier Analysisで定量化し、赤外分光との統合を用いて系統識別性能を評価しており、植物器官形質の取得・解析手法が研究の中心です。
abstractusing Elliptical Fourier Analysis (EFA) to quantify frond shape, followed by multivariate statistical tests (PCA, MANOVA, LDA).
Reproduction assets foundThe authors state that raw data, processed data, and R analysis code for the frond outline morphometrics are publicly available on GitHub (Microgramma-Outline), and the FT-NIR spectral data repository (Microgramma-FTNIR) is referenced in the methods. Both are paper-specific, public, and actionable.Code · publicSciELO Preprints - Este documento é um preprint e sua situação atual está disponível em: https://doi.org/10.1590/SciELOPreprints.15500
573 The raw data, processed data, and R analysis code are publicly available on GitHub:
574 https://github.com/labevofern/Microgramma-Outline.git.
575
576 REFERENCES
577 Ackerly D.D. (2004) Adaptation, Niche Conservatism, and Convergence: Comparative
578 Studies of Leaf Evolution in the California Chaparral. The American Naturalist, 163, 654–
579 671.
580 Adams D.C., Collyer M.L. (2018) Multivariate Phylogenetic Comparative Methods:
581 Evaluations, Comparisons, and RecoOpen asset ↗labevofern/Microgramma-Outline · Microgramma-Outlinepdf-layout-page:25 lines:1-48Dataset · publicbiting the highest
157 perpendicular distance from the line connecting the first and last bands in the R² × ranking
158 plot (Fig. S2). Following the methods described in Mendonça et al. (2026), spectral data were
159 acquired using a PerkinElmer Frontier™ near-infrared Fourier transform spectrometer (FT-
160 NIR) available at (https://github.com/labevofern/Microgramma-FTNIR).
161 Phylogenetic comparative analyses
162 To provide a phylogenetic framework for comparative morphometric and spectral analyses,
163 we used the pruned version of the Microgramma chloroplast phylogenetic inference from
164 Mendonça et al. (2026). This tree was based on the Bayesian phylogenetic tree published by
165 AOpen asset ↗labevofern/Microgramma-FTNIR · Microgramma-FTNIRpdf-layout-page:8 lines:1-55Code / 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 · 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 confirmedEurope PMC · checked 5 Sept 2026
Accurate classification of maize yield potential is essential for food security and effective agricultural planning, particularly in regions characterized by environmental variability and socio-economic constraints. This study explores the binary classification of maize kernel weight into low ( n = 160). A Hybrid Cascade - Deep Belief Network (HCA-DBN) is proposed, utilizing the feature extraction capabilities of Deep Belief Networks (DBN) coupled with Hill Climbing Algorithm (HCA) as a lightweight hyperparameter tuning strategy. The model's performance was benchmarked against standard classifiers including Logistic Regression, Random Forest, XGBoost, Decision Tree, Multi-Layer Perceptron (MLP), and Support Vector Classifier (SVC). The proposed HCA-DBN achieved a peak classification accuracy of 94%, demonstrating its potential to outperform conventional baselines even under small sample conditions. Rigorous validation, including bootstrapping and stratified 10-fold cross-validation, confirmed the statistical stability of the results. While these findings serve as a proof-of-concept given the dataset constraints, this study contributes a methodological benchmark for field-based maize yield classification and provides a scalable framework for future validation on larger, multi-season datasets.
Why it matches plant phenotyping methodsトウモロコシの収量ポテンシャル（kernel weight）を分類する計算手法を提案し、複数モデルとのベンチマークおよび交差検証で技術的に評価しているため、植物形質推定法が中心である。
abstractA Hybrid Cascade - Deep Belief Network (HCA-DBN) is proposed, utilizing the feature extraction capabilities of Deep Belief Networks (DBN) coupled with Hill Climbing Algorithm (HCA) as a lightweight hyperparameter tuning strategy.
Reproduction assets foundThe paper's maize field phenotyping dataset (plant/ear traits, canopy temperature, chlorophyll from 160 tagged plants at VIT Sevur farm) is explicitly stated as publicly available via a Data in Brief DOI deposit, and the same dataset is cited in the references as a Mendeley Data deposit authored by the paper's authors.Dataset · publicPublicly available datasets were analysed in this study. This data can be found here: https://doi.org/10.1016/j.dib.2024.110367.Open asset ↗html-lines:851-875Dataset · publicRadhakrishnan S., Sandhya P., Venkatramana B., Pradeep Kumar T.
Analyzing various maize varieties grown organically: VIT Vellore’s phenotypic, yield, and canopy data. (2024) 1. Available online at: https://data.mendeley.com/datasets/6py9v57sf2/1Open asset ↗6py9v57sf2/1html-lines:900-924Code / dataset availability confirmedCrossref · checked 15 Sept 2026
Although the chasmophytic vegetation of Sicily has been examined previously, it remains insufficiently explored due to the formidable challenges associated with accessing vertical cliff habitats. This study employed drone-based surveys combined with Braun-Blanquet methodology to investigate cliff vegetation in the Peloritani and Madonie Mountains. High-resolution aerial imagery enabled species identification and cover estimation on inaccessible rock faces. Twenty-three new relevés were combined with 33 literature records for multivariate analysis. Cluster analysis and DCA revealed floristic differentiation between Peloritani and Madonie phytocoenoses, contrasting with communities from Apennines that we used as an outgroup. We describe Athamanto siculae-Saxifragetum australis for the calcareous cliffs of Rocca Salvatesta (Peloritani), characterized by Athamanta sicula , Hypochaeris laevigata , and Saxifraga callosa subsp. australis . Additionally, we propose to change the name Asperuletum gussonei to Cynanchicetum gussonei for the high-elevation vegetation of the Madonie dominated by Cynanchica gussonei . Drone methodology proved effective for documenting cliff vegetation, offering a safe and replicable approach for advancing phytosociological knowledge in extreme habitats. This research contributes to the syntaxonomic revision of Mediterranean chasmophytic vegetation within the alliance Saxifragion australis .
Why it matches plant phenotyping methodsドローン画像を用いてアクセス困難な崖面の植物種同定と被覆率推定を行う手法が、植生調査・分類の中心的手段として明示されているため。
abstractHigh-resolution aerial imagery enabled species identification and cover estimation on inaccessible rock faces.
Reproduction assets foundThe paper's drone-based phytosociological relevé dataset (the plant cover/trait measurements underlying the classification and DCA analysis) is published as Supplementary table S1 under an open license. Supplementary figure S1 is only an ordination diagram, and no author analysis code or raw drone imagery is stated to Dataset · publiclable under the Open
Database License (http://opendatacommons.org/licenses/odbl/1.0). The Open Database License (ODbL) is a license
agreement intended to allow users to freely share, modify, and
use this Dataset while maintaining this same freedom for oth-
ers, provided that the original source and author(s) are credited.
Link: https://doi.org/10.3897/ved.182223.suppl1
Supplementary material 2
Supplementary figure S1
Authors: Gianmarco Tavilla, Pietro Minissale, Salvatore Cambria
Data type: docx
Explanation note: The supplementary file includes the DCA or-
dination diagram of species scores.
Copyright notice: This dataset is made available under the Open
Database License (http://opendatacommOpen asset ↗10.3897/ved.182223.suppl1pdf-raw-page:13 lines:1-46Code / dataset availability confirmedEurope PMC · checked 5 Sept 2026
Volume is an important shape descriptor in postharvest quality evaluation and breeding programs of sweetpotatoes and is also valuable for other agricultural engineering applications. Traditional volume measurement methods based on water displacement are, however, laborious, destructive, and unsuitable for high-throughput online scenarios. To address this gap, this dataset was developed to support the advancement of non-destructive, automated online volume estimation using a LiDAR (light detection and ranging)-based three-dimensional (3-D) machine vision system. A total of 200 sweetpotato storage roots of the cultivar "Beauregard" were collected for constructing a 3-D multi-view imagery dataset. Each sample was imaged online using a short-range LiDAR camera (Intel RealSense™ L515) while traveling on a custom-built roller conveyor system that enables simultaneous translation and rotation for full-surface coverage. The curated dataset comprises raw color images (1280 × 720 pixels, .png format) and corresponding raw and segmented point clouds (1280 × 720 pixels, .laz format) for individual samples, alongside the reference volume measurements obtained using the standard water displacement method. In addition, to illustrate the modeling pipeline for volume prediction, the dataset provides the extracted geometric features derived from the segmented two-dimensional (2-D) masks and point clouds, and volume prediction results obtained through regression modeling. As the first publicly available LiDAR-based dataset for sweetpotato volume estimation, this dataset provides a valuable resource for developing and validating image processing pipelines, optimizing machine learning models, and advancing 3-D vision technologies for non-destructive, rapid measurement of the volume of irregularly shaped agricultural products.
Why it matches plant phenotyping methodsサツマイモ貯蔵根の体積という植物器官形質をLiDAR 3D画像から推定する公開データセットであり、取得系・参照測定・特徴抽出・予測結果を含むため、フェノタイピング手法とデータセットが中心です。
abstractthis dataset was developed to support the advancement of non-destructive, automated online volume estimation using a LiDAR (light detection and ranging)-based three-dimensional (3-D) machine vision system.
Reproduction assets foundThe paper's own LiDAR sweetpotato dataset (images, point clouds, ground-truth volumes, feature data, and Python modeling scripts) is publicly deposited on Zenodo with an explicit DOI. The librealsense GitHub link is a generic camera SDK, not a paper-specific asset.Dataset · publicDirect URL to data: https://doi.org/10.5281/zenodo.18378019Open asset ↗Zenodo · 10.5281/zenodo.18378019html-lines:90-113Code · publicThe complete Python modeling script and the associated feature datasets have been included in the public dataset repository [13] to facilitate reproducibility and provide a benchmark for future algorithm development.Open asset ↗html-lines:168-182Code / dataset availability confirmedEurope PMC · checked 15 Sept 2026
This article describes a multi-sensor dataset collected during the TIRAMISU (Thermal InfraRed Anisotropy Measurements in India and Southern eUrope) campaign at the Nawagam research site in Gujarat, India, during the 2023 monsoon season. The objective was to acquire continuous ground-based optical and thermal measurements over a homogeneous rice canopy across different crop growth stages. The dataset integrates several complementary components. Thermal data were acquired with an Optris longwave infrared camera (8-14 µm) at high temporal resolution, capturing canopy temperature dynamics throughout the diurnal cycle. Optical data were obtained with a Micasense RedEdge-M multispectral sensor, providing imagery in Blue, Green, Red, RedEdge, and Near-Infrared bands with radiometric corrections. An Apogee radiometer supplied reference radiometric temperature. Meteorological measurements included air temperature, humidity, wind speed and direction, and net radiation. Ancillary field measurements comprised Leaf Area Index (LAI), plant height, emissivity sampling, hyperspectral observations, and crop stage information. The datasets are provided with metadata and processing workflows, including calibration procedures for optical reflectance and thermal radiance. Together, these components form a comprehensive record of canopy-atmosphere interactions over a homogeneous rice field. The datasets can support research on optical and thermal directional anisotropy, canopy radiative transfer, emissivity characterization, and crop biophysical parameter estimation. In addition, they are relevant for applications in vegetation monitoring, agricultural water stress assessment, and surface energy balance studies. By combining optical, thermal, and meteorological observations, the resource is suited for multidisciplinary investigations in remote sensing, agronomy, and environmental sciences.
Why it matches plant phenotyping methods光学・熱画像、校正手順、処理ワークフロー、LAIや草丈などの植物形質を含む再利用可能な作物キャノピーデータセットが研究の中心であり、植物表現型取得基盤として適格。
abstractThe dataset integrates several complementary components.
Reproduction assets foundThe paper is a Data in Brief describing the TIRAMISU rice-canopy dataset (thermal/multispectral images, meteorological, ancillary LAI/height, hyperspectral, emissivity) publicly deposited at doi.org/10.6096/1028, including processing scripts (Thermal_CSV_to_Image.py, MicaSense notebook) for reproducibility.Dataset · publicRepository name: Optical, Thermal Infrared, and Meteorological Dataset from the Thermal InfraRed Anisotropy Measurements in India and Southern eUrope (TIRAMISU) Rice Canopy Experiment
Data identification number: doi.org/10.6096/1028
Direct URL to data: https://doi.org/10.6096/1028
Instructions for access: Publicly accessible repository; representative subsets provided with metadata and processing scripts.
Related research article
Pinnepalli, C., Roujean, J.-L., Irvine, M., et al. [ 1 ]. Measuring and modelling directional effects in the frame of TIRAMISU. ISPRS Annals, X–3–2024 , 325–330. https://doi.orgOpen asset ↗doi.org · 10.6096/1028lines:49-77Code / dataset availability confirmedEurope PMC · checked 5 Sept 2026
We used domestication as an in vivo replicated experiment to investigate how divergent selection has shaped the evolution of multivariate phenotypic spaces. We measured 11-57 qualitative and quantitative traits in 13 species, either unique or shared between species, and established a framework for cross-species comparisons. Our results revealed significant convergence that translated into a cross-species domestication syndrome. Most species exhibited a reduction of the multivariate phenotypic space during domestication. We brought evidence that Near-Infrared spectra measured on leaves reflect phenotypic evolution unrelated to domestication, enabling its use as a control for sampling effects across species. Building on this, we developed a multivariate phenotypic divergence index (mPDI) to rank species by the extent of phenotypic divergence under domestication. We found a high disjunction of wild and domestic phenotypic spaces in all species. Neither the mPDI nor the relative size of wild vs domestic multivariate phenotypic spaces was influenced by the domestication timing or mating system. Lastly, we observed a progressive decoupling of trait correlations with increasing time since domestication. In addition to introducing a new index that can be applied for cross-species comparisons, our study uncovers recurring patterns shared among species, pointing to general principles underlying plant domestication.
Why it matches plant phenotyping methods多変量形質空間を比較する枠組みと新しいmPDI指標を開発しており、植物形質の統合・比較手法が明示的な貢献であるため。
abstractestablished a framework for cross-species comparisons
Reproduction assets foundThe paper's phenotypic data, NIR spectra, and trait ontology are deposited at doi 10.57745/QWEKVK, and the authors' R analysis scripts are publicly available on INRAE Forge. Both are paper-specific, public, and actionable.Dataset · publicPhenotypic data and NIR spectra are available on https://doi.org/10.57745/QWEKVK .Open asset ↗10.57745/QWEKVK · 10.57745/QWEKVKlines:283-349Code · publicR scripts are available on the INRAE Forge at https://forge.inrae.fr/gqe‐gevad/domisol_phenotypic_spaces .Open asset ↗forge.inrae.fr/gqe‐gevad/domisol_phenotypic_spaceslines:283-349Code / dataset availability confirmedOpenAlex · Europe PMC · checked 5 Sept 2026
To address the inefficiency and high cost of manual counting of tobacco leaves, this study proposes a UAV-based method for automatic leaf counting in field-grown tobacco using 3D point clouds and an improved PointNext network. Although UAV imagery has been applied to crop phenotyping, most existing UAV-based leaf-counting methods still rely on 2D images or hand-crafted features and rarely exploit 3D point clouds with dedicated leaf-level segmentation, which limits accuracy and robustness under leaf overlap, variable viewing angles, and complex field backgrounds. In this work, oblique UAV photogrammetry is used to reconstruct individual plants into 3D point clouds, and a segmentation network, SRW-PointNext, is developed by integrating an SCSA attention mechanism and a Residual-SegHead to enhance feature extraction and segmentation performance, while a re-weighted loss alleviates class imbalance. Leaf point clouds are then clustered using MeanShift to obtain leaf counts. Experiments on field-grown tobacco demonstrate that the proposed method achieves a point-cloud segmentation precision of 92.09%, a MIoU of 76.13%. Compared with the original PointNext baseline, SRW-PointNext increased MIoU and overall precision by 3.34% and 2.42% respectively. The final accuracy rate of leaf counting was 92.61%, effectively achieving accurate and stable leaf counting under actual field conditions, and providing technical support for digital management, yield estimation and seedling breeding in tobacco production.
Why it matches plant phenotyping methodsUAV三次元画像と改良セグメンテーション手法により圃場タバコの葉数を推定する方法を開発・検証しており、表現型取得が研究の中心である。
abstractthis study proposes a UAV-based method for automatic leaf counting in field-grown tobacco using 3D point clouds and an improved PointNext
Reproduction assets foundThe paper reports a UAV-based tobacco leaf counting method with an annotated 1000-plant point cloud dataset and SRW-PointNext code, both explicitly declared publicly available at author-provided Zenodo and GitHub URLs matching the allowed list.Dataset · publicData supporting the reported results can be found at: https://zenodo.org/records/15130271 .Open asset ↗zenodo · 15130271lines:531-564Code · publicThe code used in this study is available at: https://github.com/Nan20377/SRW-Pointnext.git .Open asset ↗github · Nan20377/SRW-Pointnextlines:531-564Code / 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 confirmedbioRxiv · Europe PMC · checked 5 Sept 2026
WheatGrowth chamberRootMorphology / geometry measurementRoot system architectureStress response / tolerance
The Multiple Synthetic Derivatives (MSD) population is a unique hexaploid wheat resource that captures extensive genetic diversity from Aegilops tauschii and exhibits wide variation in agronomic traits. However, root system architecture (RSA), a key determinant of resource acquisition and stress adaptation, remains poorly characterized in this population. Here, we established a practical phenotyping framework for RSA analysis and evaluated MSD417 as a representative genotype. A two-dimensional cultivation platform enabling continuous imaging of seedling root growth under controlled conditions was established to quantify RSA traits and their responses to high temperatures. MSD417 was compared with its recurrent parent, Norin 61 (N61). Under controlled conditions, MSD417 displayed greater total root length, root system width, and convex hull area than N61, indicating enhanced early root vigor. This genotype also exhibited a wider seminal root angle, suggesting improved horizontal soil exploration while maintaining root depth. High-temperature treatment reduced overall root growth and minimized genotypic differences, indicating that temperature stress constrains RSA expression. Microscopic observations further revealed a lower height-to-width ratio of coleorhiza tissue of MSD417, suggesting restricted downward expansion. Collectively, this study establishes a practical framework for RSA phenotyping and demonstrates the potential of Aegilops tauschii-derived germplasm to enhance wheat root-related adaptive traits.
Why it matches plant phenotyping methods根系構造を連続画像化して定量する2次元表現型解析プラットフォームを構築し、RSA形質の測定に実質的に適用しているため、方法が中心的である。
abstractHere, we established a practical phenotyping framework for RSA analysis and evaluated MSD417 as a representative genotype.
Reproduction assets foundThe paper deposits its paper-specific root images (N61 and MSD417) and coleorhiza microscopic images in Zenodo with explicit DOIs. The R analysis scripts are only in Supplementary Document S1 with no public URL, so they do not qualify as a public code asset.Dataset · publicThe microscopic images of coleorhiza are deposited under https://doi.org/10.5281/zenodo.18091131.Open asset ↗Zenodo · 10.5281/zenodo.18091131pdf-page:14 lines:1-71Code / dataset availability confirmedEurope PMC · checked 14 Sept 2026
Phenotypic characters have long been central to species diagnosis and remain indispensable even in the age of genomics. However, phenotypic datasets are often complex-spanning dozens of traits of varying types and units, with correlated variables and unbalanced sampling-posing challenges for robust, reproducible analysis. Existing software solutions are fragmented, usually requiring labor-intensive workflows across multiple tools and manual steps, which undermines reproducibility and hinders comparisons across studies. To address these methodological and practical challenges, I introduce Orangutan , an R package designed to provide a reproducible, easy-to-implement framework for comparing groups using mensural and meristic data. Orangutan integrates statistical analysis and visualization for species diagnosis and population comparisons within a single workflow. The package streamlines the identification of diagnostic, nonoverlapping traits between species, while enabling rigorous assessment of both individual and multivariate trait differences in overlapping traits. Core features include optional allometric correction to remove size effects, optional outlier removal, automated selection of appropriate univariate tests with post hoc comparisons, and integrated multivariate analyses. All outputs, including tables and publication-ready figures, are generated with minimal coding, ensuring accessibility and standardization. Empirical validation with real-world datasets-including animal and plant species-demonstrates that Orangutan robustly identifies diagnostic traits, reveals both subtle and clear group differences, and achieves high classification accuracy with phenotypic data alone. By automating and unifying key analytical steps, Orangutan promotes reproducibility, transparency, and efficiency in phenotypic research. This package could empower researchers in taxonomy, ecology, and evolutionary biology to adopt quantitative good practices for species diagnoses, facilitating comparative studies and advancing methodological standards in morphological data analysis. Orangutan is freely available as open-source software with comprehensive documentation to facilitate broad adoption.
Why it matches plant phenotyping methods植物を含む表現型データの解析・可視化を統合するRパッケージを開発し、実データで検証しているため、植物表現型解析ソフトウェアとして方法論が中心である。
abstractI introduce Orangutan , an R package designed to provide a reproducible, easy-to-implement framework for comparing groups using mensural and meristic data.
Reproduction assets found保存済みの本文根拠を更新済みルールで再検証し、公開資産3件を確認しました。Code · publicThe data to reproduce this work and software are freely and publicly available at https://github.com/metalofis/Orangutan‐R , https://cran.r‐project.org/web/packages/Orangutan/index.html and https://zenodo.org/records/18488056 .Open asset ↗GitHub · metalofis/Orangutan‐Rlines:328-395Code · publicThe data to reproduce this work and software are freely and publicly available at https://github.com/metalofis/Orangutan‐R , https://cran.r‐project.org/web/packages/Orangutan/index.html and https://zenodo.org/records/18488056 .Open asset ↗Zenodo · 18488056lines:396-502Dataset · publicThe anole datasets can be downloaded from https://github.com/metalofis/Orangutan‐R/tree/main/example_datasets .Open asset ↗GitHub · metalofis/Orangutan‐Rlines:88-96Code / dataset availability confirmedCrossref · Europe PMC · checked 15 Sept 2026
Abstract Agricultural research increasingly relies on data-driven approaches for crop yield prediction that complement more established crop growth models, including machine learning techniques. However, these approaches rely on large training datasets. Here, we present the Crop Yields, Climate, Soils, and Satellites (CYCleSS) dataset, a large-scale crop yield dataset derived from precision yield data for 934 fields across England on which a variety of crops are grown. In addition, the data also contains satellite-derived remote sensing data, weather data, and data on soil type, all aligned at a grid resolution of 10 km. Weather data is available at a daily temporal resolution, satellite data at 5-day resolution, while crop yield data is available at yearly resolution. This effort has been made possible through careful anonymisation of the yield data while preserving the alignment with remote sensing, weather, and soil data. This data will be useful both to train machine learning models of yield prediction as well as to parameterize mechanistic crop growth models. Furthermore, the anonymisation procedure itself will be of interest to the research community, as it represents a solution to a common problem on the interface of agricultural research and farming practice.
Why it matches plant phenotyping methods圃場単位の作物収量という植物形質を、衛星・気象・土壌情報と整合した再利用可能な大規模データセットとして構築しており、収量予測モデルの訓練・評価用データ基盤が中心です。
abstractHere, we present the Crop Yields, Climate, Soils, and Satellites (CYCleSS) dataset, a large-scale crop yield dataset derived from precision yield data for 934 fields across England
Reproduction assets foundThe paper's authors provide public R code for merging/aligning climate, soil, and Sentinel-1 data and anonymising yield data in a GitHub repository. The CYCLeSS dataset itself is on figshare, but that URL is not in the allowed list, so only the code asset is reported.Code · publicnts of this repository. Researchers who are further interested in the underlying data should contact the authors affiliated with UKCEH.
Code availability
R code used to merge and align available UK climate, soil, and Sentinel-1 synthetic aperture radar data to the same 1 km 2 grid is provided in the following GitHub repository: https://github.com/alan-turing-institute/CYCLeSS-dataset-code . Dummy data and code needed to replicate the final process of merging climate, soil, and satellite data with UKCEH precision yield data and anonymisation of field locations is contained within the ‘CLYCESS_anonymisation.zip’ folder shared as part of this repository. R version 4.2.3 was used for the creatioOpen asset ↗https://github.com/alan-turing-institute/CYCLeSS-dataset-codelines:200-271Code / dataset availability confirmedEurope PMC · Crossref · checked 15 Sept 2026
Abstract An understanding of spike shape will be of great benefit for improving wheat yields. Traditional manual measurements of spike traits are slow and prone to human error, preventing large-scale phenotyping. Employing imaging techniques will allow researchers to measure multiple morphometric parameters simultaneously. While 2D imaging provides a rapid screening method, 3D imaging offer a more comprehensive understanding of spike shape, revealing complex external structures. This study addresses the challenge of developing a high-resolution 3D surface-scanning pipeline to accurately quantify wheat spike morphology across diverse genotypes. Using a 3D surface-scanner, sharp point clouds of individual spikes were reconstructed and automatically aligned and analysed to extract key morphological features including spike length, volume, and thickness profile. New shape descriptors based on thickness profiles, local extremes, statistical curve fitting, segmentation of spikes into zones of aborted spikelets, base and apical segments as well as the extraction of spike/spikelets branching and endpoints of components were introduced to capture detailed structural variation between genotypes. Correlations between the 3D-derived traits and traditional metrics such as spike weight, spikelet number and seed weight confirmed the biological relevance of the extracted parameters. The method distinguished morphological differences among twelve wheat genotypes, revealing distinct shape types such as long, short, compact, and awned spikes. By combining precise 3D imaging with computational analysis, this approach provides a non-destructive framework for spike phenotyping. These findings demonstrate that 3D surface-scanning can deliver accurate and reproducible measurements of wheat spike architecture, offering new opportunities for linking morphology with genetics and yield potential in modern breeding programs.
Why it matches plant phenotyping methods小麦穂の形態形質を3D画像から抽出するパイプラインを開発し、形質の相関・遺伝子型間比較で検証しており、表現型取得法が研究の中心である。
abstractThis study addresses the challenge of developing a high-resolution 3D surface-scanning pipeline to accurately quantify wheat spike morphology across diverse genotypes.
Reproduction assets foundThe preprint explicitly shares sample 3D spike scan data and the trait-extraction analysis code in the authors' public GitHub repository, with separate Data and code availability statements.Dataset · public1003/1)
587
Consent for publication
588
Not applicable.
589
Ethics approval and consent to participate
590
Not applicable.
591
Conflicts of Interest
592
The authors declare that there are no conflicts of interest regarding the publication of this paper.
593
Data Availability
594
Sample data are shared in the following link:
595
https://github.com/LatifaGreche/3D-WheatSpikeMorphologyExtraction/tree/main/Data
596
Code Availability
597
The codes are available at the following link:
598
https://github.com/LatifaGreche/3D-WheatSpikeMorphologyExtraction
599
References
600
1. Sanchez-Bragado R, Molero G, Araus JL, and Slafer GA. Awned versus awnless wheat spikes:
601
does it matter? Trends in plantOpen asset ↗LatifaGreche/3D-WheatSpikeMorphologyExtractionpdf-raw-page:26 lines:1-57Code · public1003/1)
587
Consent for publication
588
Not applicable.
589
Ethics approval and consent to participate
590
Not applicable.
591
Conflicts of Interest
592
The authors declare that there are no conflicts of interest regarding the publication of this paper.
593
Data Availability
594
Sample data are shared in the following link:
595
https://github.com/LatifaGreche/3D-WheatSpikeMorphologyExtraction/tree/main/Data
596
Code Availability
597
The codes are available at the following link:
598
https://github.com/LatifaGreche/3D-WheatSpikeMorphologyExtraction
599
References
600
1. Sanchez-Bragado R, Molero G, Araus JL, and Slafer GA. Awned versus awnless wheat spikes:
601
does it matter? Trends in plantOpen asset ↗LatifaGreche/3D-WheatSpikeMorphologyExtractionpdf-raw-page:26 lines:1-57Code / dataset availability confirmedEurope PMC · checked 15 Sept 2026
Introduction Sorghum ( Sorghum bicolor (L.) Moench) is a vital cereal crop for food, feed, and biofuel production. Accurate estimation of grain biochemical composition, crude protein (CP), lysine from grain (LysG) and protein (LysP), starch (SC), amylose from grain (AMLG) and starch (AMLS), and crude fat (CF), is crucial for improving breeding and management strategies. Our aim is not pre-harvest forecasting but reducing laboratory cost by identifying a minimal set of post-harvest measurements required to estimate other grain composition traits accurately. Methods We used machine learning (ML) models to predict grain quality traits in commercial sorghum hybrids under different management practices, including precision nitrogen application, cover cropping, and no-till methods. Multi-year field trials (2023-2024) in Saint Charles, Missouri, integrated agronomic, physiological, UAV-based, and environmental data for model training and validation. Results Phenotypic analysis showed that grain composition traits varied significantly by year and management practices. Among ML models, LASSO and ElasticNet achieved the highest predictive accuracy for crude protein (R² = 0.90) and amylose content (AMLS, R² = 0.99; AMLG, R² = 0.92). Bayesian Ridge was most effective for lysine from protein (R² = 0.64), while Partial Least Squares (PLS) excelled in starch content prediction (R² = 0.80). The correlation between grain composition (LysP, CF) and photosystem II efficiency (PhiPS2) indicated that enhanced photosynthesis and yield promote their accumulation. However, Partial Dependence Plots (PDPs) revealed strong non-linear effects, where slight variations in leaf temperature (Tleaf) and stomatal conductance (gsw) were associated with significant shifts in amylose content. Discussion This study highlights the role of genotype × management interactions in sorghum breeding and demonstrates the value of integrating ML-driven models to enhance grain quality and precision agriculture strategies.
Why it matches plant phenotyping methods穀粒の生化学的形質を少数の測定値から推定する機械学習モデルの開発・検証が研究の中心であり、単なる農業実験の routine 測定ではない。
abstractreducing laboratory cost by identifying a minimal set of post-harvest measurements required to estimate other grain composition traits accurately
Reproduction assets foundThe article's data availability statement points to a Figshare deposit containing the study's datasets (agronomic, physiological, UAV-based, and grain composition data used for ML modeling). No author analysis code or trained model checkpoints are explicitly deposited.Dataset · publicith weather data acquisition.
Edited by: Filipe Matias , University of Wisconsin-Madison, United States
Reviewed by: Xiaolong Yang , Nantong University, China
David Mojaravscki , State University of Campinas, Brazil
Data availability statement
The datasets presented in this study can be found in online repositories, on Figshare https://figshare.com/s/2765f89c7ea840e5c6be?file=59367320 . The names of the repository/repositories and accessionnumber(s) can be found in the article/ Supplementary Material .
Author contributions
BG: Data curation, Formal analysis, Investigation, Methodology, Software, Visualization, Writing – original draft, Writing – review & editing. MC: Conceptualization, Data Open asset ↗Figsharelines:471-515Code / dataset availability confirmedCrossref · checked 13 Sept 2026
Field / plotLiDAR / point cloudRGB / grayscaleLeafStem / branchWhole plant / canopy / plot / fieldClassificationSegmentation
Abstract Annotated datasets are essential for training and evaluating machine learning models in forest ecology. This dataset provides high-resolution, annotated LiDAR point clouds of 674 individual trees from 12 forest plots in the Shivalik Range of northern Haryana, India, representing 24 species. Data were acquired using Terrestrial Laser Scanning (TLS) and Airborne Laser Scanning (ALS), include field-measured attributes such as species identity and Diameter at Breast Height (DBH), and terrestrial and aerial RGB imagery. TLS point clouds were georeferenced and co-registered with centimetre-level accuracy, enabling precise integration with ALS data. The dataset includes segmented individual trees and wood–leaf classifications, suitable for applications such as tree morphology analysis, biomass estimation, and species classification. To support benchmarking, outputs from established classification algorithms (LeWoS, TLSeparation, CANUPO, and Random Forest) are included. As one of the first open-access LiDAR datasets from Indian tropical forests, it provides critical reference data for developing and validating forest structure models. It can also aid biomass mapping efforts in support of large-scale missions such as NASA-ISRO’s NISAR and ESA’s BIOMASS.
Why it matches plant phenotyping methods個体樹木のLiDAR点群・RGB画像と樹木セグメンテーションを含む公開データセットで、樹形解析や森林構造モデルの開発・検証、分類アルゴリズムのベンチマークを目的としており、植物形質取得が中心です。
abstractThis dataset provides high-resolution, annotated LiDAR point clouds of 674 individual trees from 12 forest plots in the Shivalik Range of northern Haryana, India, representing 24 species.
Reproduction assets foundThe paper's authors explicitly state that all code used for data processing, wood-leaf classification, feature extraction, and tree volume estimation is openly available on GitHub at https://github.com/moonis-ali/Dataset, which is an allowed URL. The paper's core LiDAR dataset is deposited on Zenodo (10.5281/zenodo.153Code · publicAll code used for data processing, wood-leaf classification, feature extraction, and tree volume estimation is openly available on GitHub at https://github.com/moonis-ali/Dataset .Open asset ↗https://github.com/moonis-ali/Datasetlines:479-553Code / dataset availability confirmedCrossref · checked 14 Sept 2026
This data descriptor presents a dataset comprising crop and soil parameters measured in winter wheat fields near the town of Knezha, Bulgaria. The data were collected as part of a project evaluating the potential of vegetation indices derived from Sentinel-2 satellite imagery to predict biophysical and biochemical crop parameters. The core dataset consists of measurements obtained from 20 m × 20 m field plots and includes a broad range of parameters: leaf area index, fraction of absorbed photosynthetically active radiation, vegetation cover fraction, chlorophyll content, above-ground biomass, plant nitrogen content, biological yield, surface soil moisture, spectral reflectance, plant density, crop height, visual assessments of disease or pest damage, and data on weed occurrence. The dataset is complemented by unmanned aerial vehicle imagery, crop calendars, and field management information. The main soil types in the study area were characterized through soil profiles, while meteorological data were obtained from an automated weather station. The data were collected during the 2016–2017 and 2017–2018 agricultural seasons. The dataset is freely available for download and serves as a valuable resource for researchers in remote sensing—particularly for validating satellite-derived products—as well as for specialists involved in winter wheat monitoring, modeling, and agronomic studies.
Why it matches plant phenotyping methods冬小麦の複数の植物形質を含む再利用可能なデータセットを提示し、UAV画像や衛星由来指標の検証を主目的としているため、植物フェノタイピング用データセットとして採用。
abstractThis data descriptor presents a dataset comprising crop and soil parameters measured in winter wheat fields near the town of Knezha, Bulgaria.
Reproduction assets found保存済みの本文根拠を更新済みルールで再検証し、公開資産1件を確認しました。Dataset · publicDataset: In situ and UAV dataset with crop and soil parameters obtained from winter
wheat fields. https://doi.org/10.5281/zenodo.17475742.Open asset ↗zenodo · 10.5281/zenodo.17475742pdf-page:1 lines:1-56Code / dataset availability confirmedEurope PMC · checked 5 Sept 2026
Soybean ( Glycine max L. ) performs an important position as a main resource of protein in Indonesia. Its quality and productivity can be assessed based on the characteristics of its seed. Accordingly, the identification process through the observation of soybean seed traits is a crucial step in plant breeding and quality assurance. Manual approaches rely on manual observation, which is subjective, prone to human error and time-consuming. With the improvement of artificial intelligence, automated seed identification has appeared as a potential solution. However, progress is constrained by the lack of open and standardized image datasets, especially for locally bred varieties in developing countries. To address this gap, we propose an open image dataset of Indonesian soybean seeds from three widely cultivated and plant-bred varieties: Anjasmoro, Grobogan, and DEGA-1. The dataset consists of high-resolution seed images captured with an Epson L360 flatbed scanner, with the optical resolution fixed at 800 dots per inch, yielding images of 6800 × 9359 pixels. All raw images are saved in JPG format. No manually segmentation masks are released in this version, instead of using Deeplab V3+ with MobileNet as backbone to enable the automated seed image segmentation. The curated dataset is intended to support a broad range of applications, including computer vision tasks such as image classification and segmentation, as well as research in plant breeding, seed quality assessment, and agricultural informatics. By providing a standardized and publicly accessible resource, this dataset contributes to the advancement of interdisciplinary studies at the intersection of agriculture and artificial intelligence.
Why it matches plant phenotyping methods大豆種子画像を標準化して公開するデータセット研究であり、種子形質の自動画像解析・セグメンテーションを支援する方法論的資源が中心です。
titleAn open image dataset of Indonesian soybean seed varieties (Anjasmoro, Grobogan, DEGA-1) for agricultural research and machine learning applications.
Reproduction assets foundThe paper is a data descriptor for a public Mendeley Data repository containing the authors' own soybean seed image dataset (raw scans and segmented seed images) used for seed phenotyping, with an explicit direct URL and DOI.Dataset · publicData accessibility
Repository name: Mendeley Data
Data identification number: DOI: 10.17632/c733bjz4m3.3
Direct URL to data: https://data.mendeley.com/datasets/c733bjz4m3/3Open asset ↗Mendeley Data · 10.17632/c733bjz4m3.3html-lines:115-142Code / 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 confirmedOpenAlex · Europe PMC · checked 5 Sept 2026
Field / plotAnnotation / quality controlVisualization / data management
BACKGROUND: Accurate acquisition of phenotypic data is critical for cataloguing and utilising genetic variation in cultivated crops, landraces, and their wild relatives. The collection of phenotypic data using handwritten notes often introduces errors which can and should be avoided. Electronic data collection is crucial for ensuring error prevention and data standardisation and thus ensuring high-quality, reliable data. IMPLEMENTATION: This paper describes the development of GridScore NEXT, a new plant phenotyping application that significantly advances the state of the art for collecting field trial data in plant genetics, pre-breeding and crop improvement research. Building on its predecessor, GridScore, the development of GridScore NEXT was driven by real life, in the field interactions with expert user groups across a number of crops. This iterative design methodology allowed the development and testing of new features. Collaborators from the 'Biodiversity for Opportunities, Livelihoods and Development' (BOLD) project, focusing on crops including rice, grasspea, and alfalfa, along with barley, potato, vegetable and blueberry teams, provided invaluable insights through training sessions and interviews and in the field use of the application. RESULTS: Key improvements to GridScore NEXT include enhanced data collection tools, supporting individual plant phenotyping within plots and enabling new data types such as GPS coordinates and image traits. GridScore NEXT provides customisable user defined validation rules to help prevent errors and incorporates barcode scanning for accurate, efficient data capture. The application offers an increased toolbox of data visualizations over its predecessor including heatmaps and statistical box plots, which aid in identifying potential data issues and understanding trial performance in the field. GridScore NEXT is cross-platform and can operate without an internet connection, making it ideal for field use in remote areas. Its adoption has led to standardisation of methods, significant error reduction, and the timely sharing of data, enabling quicker decision-making in pre-breeding and characterisation experiments. GridScore NEXT is available under an open-source (Apache 2.0) licence and freely available to all with no restrictions. It offers self-hosting options for enhanced data security and privacy. GridScore NEXT shows broad applicability across a diverse range of not only plant phenotyping experiments, but any experiment that requires the collection of accurate data.
Why it matches plant phenotyping methods植物表現型データ収集アプリケーションの開発と検証が論文の中心であり、個体表現型や画像形質を含む圃場データ取得を支援するため、対象範囲に含める。
abstractThis paper describes the development of GridScore NEXT, a new plant phenotyping application that significantly advances the state of the art for collecting field trial data in plant genetics, pre-breeding and crop improvement research.
Reproduction assets foundThe paper describes GridScore NEXT and its use in BOLD/CPC phenotyping. Authors' public code (GitHub, Zenodo) and public phenotype datasets (BOLD alfalfa, grasspea, rice; CPC characterisation data) are available; blueberry and UKVGB data are request-only.Dataset · publicDatasets used in this study were part of the BOLD project (alfalfa, grasspea and rice) which are available from https://germinate.hutton.ac.uk/cwr/alfalfa/, https://germinate.hutton.ac.uk/cwr/grasspea and https://germinate.hutton.ac.uk/cwr/rice/.Open asset ↗html-lines:528-593Dataset · publicDatasets used in this study were part of the BOLD project (alfalfa, grasspea and rice) which are available from https://germinate.hutton.ac.uk/cwr/alfalfa/, https://germinate.hutton.ac.uk/cwr/grasspea and https://germinate.hutton.ac.uk/cwr/rice/.Open asset ↗html-lines:528-593Dataset · publicDatasets used in this study were part of the BOLD project (alfalfa, grasspea and rice) which are available from https://germinate.hutton.ac.uk/cwr/alfalfa/, https://germinate.hutton.ac.uk/cwr/grasspea and https://germinate.hutton.ac.uk/cwr/rice/.Open asset ↗html-lines:528-593Dataset · publicThe CPC datasets used are characterisation datasets which are available from https://germinate.hutton.ac.uk/cpc.Open asset ↗html-lines:528-593Code / 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
Maize-soybean intercropping is a sustainable intensive agroecosystem, though the productivity is constrained by interspecific competition for water and light resources. To enhance the water use efficiency in this intercropping system and understand canopy structure dynamics under the water-limited conditions of arid northwest China, this study proposes a novel optimization strategy that synchronizes deficit irrigation scheduling with crop-specific water requirements during critical phenological phases. Four irrigation regimes were implemented: W1 (full irrigation for both maize and soybean crops), W2 (maize-full and soybean-deficit), W3 (maize-deficit and soybean-full), and W4 (dual deficit). Through UAV-based high-resolution 3D canopy reconstruction (R = 0.98 for plant height validation), 14 spatial-geometric descriptors were quantified. The W2 strategy demonstrated superior competitive coordination, enhancing aggressivity of maize (Ams) by 85.9 % through strategic canopy reconfiguration: 11.8 % reduction in maize maximum leaf layer width position (MLLWP), 28.3 % decrease in inter-specific canopy overlap area (COA), and 40.0 % compression of shading convex hull volume (SCHV). These optimized structural adaptations synergistically enhanced photosynthetically active radiation interception (+13.4 %) while achieving concurrent reductions in crop evapotranspiration (ET, -19.7 %) without yield penalty, thereby elevating irrigation water use efficiency (IWUE) by 14.4 % and water equivalent ratio (WER) by 15.9 %. This work provides mechanistic insights into canopy architecture-mediated resource competition mitigation and establishes a technological framework for sustainable intensification in water-limited environments.
Why it matches plant phenotyping methodsUAVによる3Dキャノピー再構成を用いた植物構造形質の取得と検証が、灌漑試験の主要な解析基盤として明示されているため、実質的なフェノタイピング手法の応用に該当する。
abstractThrough UAV-based high-resolution 3D canopy reconstruction (R = 0.98 for plant height validation), 14 spatial-geometric descriptors were quantified.
Reproduction assets foundThe paper's data availability statement explicitly deposits the authors' analysis source code on a public GitHub repository, which qualifies as a paper-specific public code asset. The study's phenotype data (UAV-derived 3D canopy point clouds, geometric trait measurements, yield/biomass data) are only available upon请求,Code · publicThe source code used in this study is available for noncommercial use and the code can be downloaded from https://github.com/Pepe-oss/3D-Reconstruction-analysis-of-maize-soybean-intercropping-competition-under-water-stress . The data of this study are available from the corresponding author upon request.Open asset ↗Pepe-oss/3D-Reconstruction-analysis-of-maize-soybean-intercropping-competition-under-water-stresslines:320-407Code / dataset availability confirmedEurope PMC · checked 5 Sept 2026
Abstract Phenotypic clustering is a cornerstone of population structure analysis in agrobiodiversity research, especially for neglected and underutilized species (NUS) where genomic data are scarce. However, there is currently no formal method to determine whether a given dataset contains sufficient biological signal to justify clustering, leading to potential overinterpretation of spurious patterns. To address this, we introduce a signal-first diagnostic framework. This framework mandates the assessment of phenotypic differentiation prior to any unsupervised classification, providing clear, data-driven thresholds to decide if clustering is statistically meaningful. We developed this framework through a large-scale, empirically-grounded simulation study. Using realistic trait architectures calibrated on fonio ( Digitaria exilis ), we evaluated 11 clustering algorithms across a continuous gradient of phenotypic differentiation (Pst = 0.05–0.85). Our results establish quantitative detectability thresholds: under the calibrated trait architecture, clustering fails to recover meaningful structure below Pst ≈ 0.30, a range typical for many NUS. Even the best-performing algorithm required Pst > 0.47 for moderate accuracy. We further demonstrate that internal validation metrics (e.g., Silhouette score) are unreliable under weak differentiation, often misleadingly suggesting robust clusters. The proposed framework shifts the analytical paradigm from algorithm selection to signal assessment. We provide practical guidelines and an openly available simulation template to help researchers implement this workflow, thereby supporting more reliable diversity assessments, core collection design, and germplasm management decisions in data-scarce systems.
Why it matches plant phenotyping methods植物の表現型データを対象に、クラスタリングの妥当性を事前評価する統計的診断フレームワークをシミュレーションで開発しており、再利用可能な表現型解析手法が中心である。
abstractTo address this, we introduce a signal-first diagnostic framework.
Reproduction assets foundThe preprint states that simulation scripts, clustering implementations, parameter sets, and representative synthetic datasets are publicly available on Zenodo, with a specific DOI (10.5281/zenodo.15877862) given in the data availability statement. This is a paper-specific, publicly actionable asset covering the studyâCode · publicThe datasets and code supporting the conclusions of this article are available in the Zenodo repository, DOI: 10.5281/zenodo.15877862.Open asset ↗Zenodo · 10.5281/zenodo.15877862lines:256-273Code / dataset availability confirmedEurope PMC · checked 5 Sept 2026
This study employed an HY-6010-S hyperspectral imaging system, covering a spectral range of 400-1000 nm, combined with an RGB industrial camera to acquire multimodal data. The dataset simulates phenotypic analysis scenarios of maize seeds under controlled laboratory conditions, with the ambient temperature maintained at 20-25°C. Comprehensive testing was conducted using 12 different maize varieties. Approximately 200 seed samples were collected per variety, resulting in a total sample size of about 2400, each subjected to hyperspectral and RGB image acquisition. Preprocessing steps included noise reduction, background removal, band selection, and modality alignment. To ensure the accuracy and reliability of the experimental data, HHIT software and Python were utilized for data processing. This dataset plays a significant role in seed variety classification, phenotypic analysis, precision agriculture, and machine learning applications.
Why it matches plant phenotyping methodsトウモロコシ種子のマルチモーダル画像を収集・前処理した再利用可能なデータセットであり、種子の表現型解析を主要目的としているため、フェノタイピング手法・データセット研究に該当する。
abstractThis study employed an HY-6010-S hyperspectral imaging system, covering a spectral range of 400-1000 nm, combined with an RGB industrial camera to acquire multimodal data.
Reproduction assets foundThe paper is a Data in Brief article depositing its own multimodal maize seed hyperspectral and RGB image dataset (2400 seeds, 12 varieties) on Mendeley Data, with a direct public URL and DOI given in the article.Dataset · publicRepository name: Mendeley Data
Data identification number: doi: 10.17632/4n4xbnx8sr.1
Direct URL to data: https://data.mendeley.com/datasets/4n4xbnx8sr/1Open asset ↗Mendeley Data · 10.17632/4n4xbnx8sr.1html-lines:1-110Code / 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
470
N.F., the National Science Foundation (grant numbers 2120153 and 2346101 to N.F.),
471
the USDA NIFA AFRI (grant number 2022-67021-36467 to N.F.), and by the Bellwether
472
Foundation.
473
474
Data Availability
475
Code and data associated with this manuscript are available on GitHub
476
(https://github.com/danforthcenter/teff-manuscript).477
478
.
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 · checked 14 Sept 2026
Abstract Plant breeders and weed scientists address weed management collaboratively by selecting for herbicide tolerance in breeding programs. Metribuzin, a Group 5 PSII‐inhibiting herbicide, is labeled for use in wheat ( Triticum aestivum L.). However, application to currently available lines results in frequent, variable, and unpredictable crop injury. Breeding for enhanced metribuzin tolerance would allow growers to utilize this herbicide effectively while minimizing the risk of crop injury. Incorporating an additional herbicide mode of action in winter wheat production would enhance rotational flexibility and weed resistance management. Selection for improved herbicide tolerance in crops has traditionally relied on visual estimation, yet assessments can be variable. The objective of this study was to improve the accuracy and efficiency of selecting for herbicide tolerance in a breeding program by utilizing a drone‐mounted multispectral sensor. Multispectral data were collected on paired rows of an diversity panel and advanced generation lines grown in paired plot yield trials. Vegetation indices calculated include normalized difference vegetation index (NDVI), normalized difference red edge (NDRE), transformed chlorophyll absorption reflectance index, normalized water index, and modified triangular vegetation index. Visual assessments of injury, plant height, and grain yield were also recorded. Correlations between reflectance indices and grain yield were stronger than those between visual injury assessments and grain yield. The top 10 lines overlapped 45%–53% when selected by highest yield and highest NDVI or NDRE, respectively, in treated plots. The relationship between yield and index differences in treated and nontreated plots showed that the difference in indices (multiple R 2 = 0.0802–0.5434) explained more yield variation than visual assessments (multiple R 2 = 0.0003–0.1915). These results suggest that multispectral analysis at the plot level is a more accurate and efficient indicator of herbicide injury in winter wheat than traditional visual assessments.
Why it matches plant phenotyping methodsドローン搭載マルチスペクトルセンサーと植生指数を用いて、冬コムギの除草剤傷害・耐性を従来の目視評価より高精度かつ効率的に推定する方法を実証しており、表現型取得法が研究の中心である。
abstractThe objective of this study was to improve the accuracy and efficiency of selecting for herbicide tolerance in a breeding program by utilizing a drone‐mounted multispectral sensor.
Reproduction assets foundThe article's Data Availability Statement explicitly deposits the datasets generated and analyzed (phenotype/trait and vegetation index data from the metribuzin tolerance phenotyping experiments) in the Washington State University Research Exchange repository with a public DOI. No author analysis code repository is URLDataset · public20-
67037-30671, 2022-67013-36426, and 2022-68013-36439.
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 I B I L I T Y S TAT E M E N T
The datasets generated and analyzed for this study are avail-
able in the Washington State University Research Exchange
repository (https://doi.org/10.7273/000007507).O RC I D
Melinda Zubrod https://orcid.org/0000-0001-7024-8421
AndrewW. Herr https://orcid.org/0000-0001-5111-2342
ArronH. Carter https://orcid.org/0000-0002-8019-6554
R E F E R E N C E S
Ahmadi, Z., Mehrabadi, M., Fazli, M., Khalesro, S., Abedi, R., &
Mokhtassi-Bidgoli, A. (2025). Enhancing tolerance of wheat culti-
vars to meOpen asset ↗Washington State University Research Exchange · 10.7273/000007507pdf-raw-page:12 lines:1-81Code / dataset availability confirmedEurope PMC · checked 5 Sept 2026
Timely crop stress detection is essential for safeguarding yields and promoting sustainable agriculture. Traditional vegetation indices (e.g., NDVI, EVI) are widely used but remain static, crop-agnostic, and often insensitive to early stress signals. This study proposed RL-VI, a reinforcement learning-based framework that dynamically formulates vegetation indices optimized for rice stress detection. Unlike existing methods, RL-VI integrates Sentinel-2 multispectral imagery with smartphone-captured RGB data, creating the first cross-platform environment where vegetation indices are learned rather than predefined. The reinforcement learning agent adaptively selects stress-sensitive spectral band combinations guided by classification rewards. Experiments on real-world rice fields in Tamil Nadu, India, and benchmark datasets (Indian Pines, wheat salt stress) show that RL-VI achieves an overall accuracy of 89.4% and F1-score of 0.88, outperforming static and machine-learned indices by up to 12%. Importantly, RL-VI enables early stress detection up to 10 14 days before visible symptoms, providing actionable lead time for intervention. The proposed framework is computationally lightweight and scalable to UAV or edge devices, offering a farmer-ready tool for precision agriculture, bridging field-level mobile sensing with satellite monitoring for low-cost, real-time crop health management. Statistical validation using ANOVA (F = 88.24, p < 0.001) and pairwise t-tests (p < 0.001) confirmed RL-VI's superiority, while SHAP analyses emphasized the physiological significance of red-edge and SWIR bands in stress discrimination.
Why it matches plant phenotyping methods植物ストレス状態を推定する動的植生指数と強化学習フレームワークを開発し、実圃場・ベンチマークデータで性能検証しているため、フェノタイピング手法が中心である。
abstractThis study proposed RL-VI, a reinforcement learning-based framework that dynamically formulates vegetation indices optimized for rice stress detection.
Reproduction assets foundThe paper publicly releases its authors' field-captured mobile RGB rice canopy dataset on Kaggle and its full RL-VI analysis code (RL formulation, preprocessing, VI computation, training, evaluation) on GitHub. Sentinel-2 imagery and benchmark datasets are third-party public sources, not paper-specific deposits.Dataset · publicThe Mobile RGB dataset, consisting of field-captured rice canopy images collected by the authors at Polur, Tamil Nadu, India, is publicly available on Kaggle under a CC BY-NC 4.0 license (DOI: [https://doi.org/10.34740/kaggle/dsv/14105754](https:/doi.org/10.34740/kaggle/dsv/14105754)).Open asset ↗Kaggle · 10.34740/kaggle/dsv/14105754html-lines:616-683Code · publicAll custom code developed for this work including the RL-VI (Reinforcement Learning–based Vegetation Index) formulation algorithm, image preprocessing scripts, vegetation index computation modules, model training pipelines, and evaluation routines is openly accessible in a public GitHub repository. The code is available without restriction for non-commercial research use and fully available at Github Repository (https://github.com/Poornisrm/Vegetation-Index.git).Open asset ↗GitHub · Poornisrm/Vegetation-Indexhtml-lines:684-711Code / dataset availability confirmedEurope PMC · checked 13 Sept 2026
Field / plotPhotogrammetry / SfM / MVSLiDAR / point cloudMultispectral / hyperspectralLeaf2D/3D reconstructionArchitecture / morphology / geometry
Conifer shoots possess highly complex geometrical structures at a very fine spatial resolution. Accurately characterizing the full architecture of a conifer shoot, which influences how radiation is scattered, has proven challenging. Previous radiative transfer models for coniferous stands have represented these structures in a relatively simplified or coarse manner. This paper presents a dataset that can be used for up-scaling of needle to shoot optical properties and studying the influence of detailed three-dimensional (3D) structure of shoot to light scattering within tree crown. The dataset includes 3D structural information as well optical properties of needles and twigs for 27 shoots of two conifer species present in both locations (3 shoots per species and position in the crown) - Scots pine ( Pinus sylvestris L.) and Norway spruce ( Picea abies L. Karst. ). The samples were collected on 22nd April 2024 in Rájec, the Czech Republic and 17th September 2024 in Järvselja, Estonia. Subsequently blue light 3D photogrammetry scanning technique was used to obtain their high-resolution 3D point cloud representations. Reflectance and transmittance measurements of needles were obtained using a spectroradiometer and an integrating sphere. For each of these samples, the dataset comprises a photo of the sampled shoot, obtained 3D surface reconstruction, and optical properties of conifer needles and twigs (hemispherical-conical reflectance and transmittance factors) in the spectral range of 400-2000 nm. A detailed 3D representation of needle shoots, when combined with radiative transfer modeling, may offer a means to study and compensate for inaccuracies in the measurement of needle optical properties and to enhance the assessment of shoot scattering characteristics.
Why it matches plant phenotyping methods針葉樹シュートの3D構造をフォトグラメトリで取得し、光学特性とともに再利用可能なデータセットとして提供しているため、植物形態・構造の計測手法が中心です。
abstractThis paper presents a dataset that can be used for up-scaling of needle to shoot optical properties and studying the influence of detailed three-dimensional (3D) structure of shoot to light scattering within tree crown.
Reproduction assets foundThe paper is a Data in Brief article describing a public Mendeley Data repository containing the paper's own phenotyping measurements: 3D surface geometry models (.obj) of Scots pine and Norway spruce shoots, sample photos (.jpg), and needle/twig optical property spectra (HCRF/HCTF, .csv, 400-2000 nm). The repository, Dataset · publicRepository name: Mendeley
Data identification number: 10.17632/h39f9t7fjg.1
Direct URL to data: https://data.mendeley.com/datasets/h39f9t7fjg/2Open asset ↗Mendeley · 10.17632/h39f9t7fjg.1lines:47-74Code / dataset availability confirmedEurope PMC · checked 5 Sept 2026
WheatRootMorphology / geometry measurementRoot system architecture
Root anatomical phenotyping has become a demonstrably essential part of investigating root physiology and in acquiring a holistic understanding of plant development. However, accessible high throughput methods for root anatomical analysis are still lacking. Here, we present the Rapid Anatomics Tool (RAT), a novel, low-cost platform for high throughput root anatomical imaging with a shallow learning curve for obtaining high quality images suitable for comparative analysis across a number of plant species. Its efficiency comes from combining blockface-like imaging and stain-free imaging using near-ultraviolet (nUV) autofluorescence utilising a combination of low-cost commercial equipment, readily available mechanical components, and custom designed and 3D printed tools. Using this platform, we investigated the anatomy of mature tissue along the axis of wheat crown roots, revealing a tendency of reduction in vascular complexity (expressed through a reduction in metaxylem number, area, and mean area per metaxylem file) from the basal to the distal region of the root. This study highlights the importance of thorough sampling strategies for investigating root anatomy in relation to organ function and introduces an accessible, relatively high-throughput platform to support such research.
Why it matches plant phenotyping methods根の解剖学的形質を高スループットに画像取得する低コスト基盤を開発しており、植物フェノタイピング手法が研究の中心です。
abstractHere, we present the Rapid Anatomics Tool (RAT), a novel, low-cost platform for high throughput root anatomical imaging
Reproduction assets foundThe paper's supplementary materials (hosted at the publisher DOI page) explicitly include the 3D design files (STL) for the RAT platform and the Python script used to control image acquisition, which are paper-specific phenotyping hardware/analysis assets. The phenotype datasets generated and analysed are only 'on the'Code · public3D design files (STL) are provided in the supplementary material. The Python script used to control image acquisition using the specific USB microscope used in this study is available in the supplementary materialsOpen asset ↗lines:229-267Code / dataset availability confirmedEurope PMC · checked 14 Sept 2026
Aim Phenotypic characters have long been central to species diagnosis and delimitation and remain indispensable even in the age of genomics. However, phenotypic datasets are often complex— spanning dozens of traits of varying types and units, with correlated variables and unbalanced sampling—posing challenges for robust, reproducible analysis. Existing software solutions are fragmented, usually requiring labor-intensive workflows across multiple tools and manual steps, which undermines reproducibility and hinders comparisons across studies. To address these methodological and practical challenges, I introduce Orangutan, an R package designed to provide a flexible, easy-to-implement framework for comparing groups using mensural and meristic data. Innovation Orangutan provides a flexible and efficient framework for analyzing mensural and meristic data, supporting a full suite of statistical and visualization tools optimized for species delimitation and population comparisons. The package streamlines the identification of diagnostic, non-overlapping traits between species, while enabling rigorous assessment of both individual and multivariate trait differences. Core features include optional allometric correction to remove size effects, automated selection of appropriate univariate tests with post hoc comparisons, and integrated multivariate analyses. All outputs, including summary statistics and annotated publication-ready figures, are generated with minimal coding, ensuring accessibility and standardization. Main Conclusions Empirical validation with real-world datasets—including animal and plant species— demonstrates that Orangutan robustly identifies diagnostic traits, reveals both subtle and clear group differences, and achieves high classification accuracy with phenotypic data alone. By automating and unifying key analytical steps, Orangutan promotes reproducibility, transparency, and efficiency in phenotypic research. This package empowers researchers in taxonomy, ecology, and evolutionary biology to adopt quantitative best practices for species delimitation, facilitating comparative studies and advancing methodological standards in morphological data analysis. Orangutan is freely available with comprehensive documentation to support widespread adoption.
Why it matches plant phenotyping methods植物を含む形態形質データの解析・可視化を標準化するRパッケージの開発論文であり、植物種データでの検証も行っているため、表現型解析手法が中心です。
titleOrangutan: an R package for analyzing and visualizing phenotypic data in the context of ecology and systematics
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 data to reproduce this work and software are freely and publicly available at
https://github.com/metalofis/Orangutan-R.Open asset ↗metalofis/Orangutan-Rpdf-page:15 lines:1-28Dataset · publicThe anole datasets can be downloaded from
https://github.com/metalofis/Orangutan-R/tree/main/example_datasets.Open asset ↗metalofis/Orangutan-R · example_datasetspdf-page:5 lines:1-51Code / dataset availability confirmedEurope PMC · checked 5 Sept 2026
Background The breeding process enables plants to inherit desirable traits, such as yield, flowering time, pest resistance, and cannabinoid and/or terpene content. As a result of these intensive genetic improvement practices, where genetically similar individuals or those from the same lineage are crossed, the expression of unfavorable recessive alleles may occur due to homozygosity. This can lead to less productive plants, increased susceptibility to diseases, and reduced quality. Despite the potential negative effects associated with inbreeding, self-pollination (a form of inbreeding) is a necessary cultivation technique used to obtain seeds that produce phenotypically female plants (feminized seeds) for commercialization and/or to fix desirable traits, albeit at the cost of reduced genetic variability. The Cannabis sativa L. seed market has grown significantly in recent decades, driven by the legalization and regulation of medicinal and recreational use. Self-pollinated feminized seeds are popular among growers and commercial seed banks because, in most cases, they guarantee that inflorescences will express the cannabinoid and terpene profile of the single parent plant. The objective of this work is to compare the morphological variation of seeds obtained from the reversal of female clones followed by self-pollination, and seeds obtained from crossing genetically distinct parental. To study seed shape and size, we employed 2D geometric morphometrics (GM) based on landmarks and semilandmarks, coupled with a supervised machine learning approach and multivariate statistical approach for analysis. Results No direct relationship was observed between size and seed type, although significant differences between varieties were detected. The shape of seeds from crosses between different parents (male and female) showed lower classification accuracy compared to feminized seeds. These results support the hypothesis that inbreeding reduces the variability, as feminized seeds from self-pollination were correctly identified at a high rate using a discriminant function. Conclusions Our research demonstrates that 2D geometric morphometrics can effectively distinguish and trace feminized and self-pollinated cannabis seeds. These seeds exhibit the least morphological variation, enabling accurate identification and providing a reliable foundation for practical applications. The Random Forest classifier's high performance confirms the effectiveness of using morphological traits for seed discrimination. These results open the door for advanced machine-learning techniques aimed to improve scalability and automation.
Why it matches plant phenotyping methods2D幾何形態計測と機械学習を用いて種子の形状・サイズを抽出し、種子タイプを識別する手法が研究の中心であるため。
abstractTo study seed shape and size, we employed 2D geometric morphometrics (GM) based on landmarks and semilandmarks, coupled with a supervised machine learning approach and multivariate statistical approach for analysis.
Reproduction assets foundThe authors publicly deposited the custom Python machine-learning code and the Procrustes coordinate dataset used for the paper's seed-shape classification in a GitHub repository, explicitly stated in the Data availability section.Code · publicTo ensure full reproducibility, the Procrustes coordinates and the custom Python code used for the machine learning classification are provided in a public GitHub repository: https://github.com/Francisco-ft/Self-pollinated-cannabis-seeds-lead-to-less-variation-in-shape.Open asset ↗Francisco-ft/Self-pollinated-cannabis-seeds-lead-to-less-variation-in-shapelines:115-151Code / dataset availability confirmedEurope PMC · checked 14 Sept 2026
Grapevine water relations are increasingly influenced by drought under climate change, with significant implications for yield, fruit composition and wine quality. Stable isotopes of hydrogen, oxygen, carbon and nitrogen (δ 2 H, δ 18 O, δ 13 C and δ 15 N) provide sensitive tracers of plant water sources and physiological responses to stress. Here, we combined dual water isotopes (δ 2 H, δ 18 O), carbon and nitrogen isotopes (δ 13 C, δ 15 N), and high-resolution micrometeorological/soil observations to diagnose drought dynamics in Vitis vinifera cv. Sauvignon blanc (Orlești, Romania; 2023-2024). Dual-isotope relationships delineated progressive evaporative enrichment along the soil-plant-atmosphere continuum, with slopes LMWL ≈ 6.41 > stem ≈ 5.0 > leaf ≈ 2.2, consistent with kinetic fractionation during transpiration (leaf) superimposed on source-water signals (stem). Weekly leaf δ 18 O covaried strongly with relative humidity (RH; r = -0.69) and evapotranspiration (ET; r = +0.56), confirming atmospheric control of short-term enrichment, while stem isotopes showed buffered responses to soil water. We integrated Δ 18 O (leaf-stem), RH, ET, and soil matric potential at 60 cm (Soil 60 ) into an Isotopic Drought Index (IDI), which captured the onset, intensity, and persistence of the July-August 2024 drought (IDI 0-100 > 90; RH 40 mm wk -1 , Soil 60 > 100 cb). Carbon and nitrogen isotopes provided complementary, integrative diagnostics: δ 13 C increased (less negative) with drought (r = -0.52 with RH; +0.49 with IDI), reflecting higher intrinsic water-use efficiency, whereas δ 15 N rose with soil dryness and IDI (leaf: r ≈ +0.48 with Soil 60 ; +0.42 with IDI), indicating constraints on N acquisition and enhanced internal remobilization. Together, multi-isotope and environmental data yield a mechanistic, field-validated framework linking atmospheric demand and edaphic limitation to vine physiological and biogeochemical responses and demonstrate the operational value of an isotope-informed drought index for precision viticulture.
Why it matches plant phenotyping methods複数同位体と環境データからブドウの水分状態・干ばつ応答を推定するIsotopic Drought Indexを構築し、圃場で検証した研究であり、植物の生理状態取得手法が中心である。
abstractWe integrated Δ 18 O (leaf-stem), RH, ET, and soil matric potential at 60 cm (Soil 60 ) into an Isotopic Drought Index (IDI), which captured the onset, intensity, and persistence of the July-August 2024 drought
Reproduction assets found保存済みの本文根拠を更新済みルールで再検証し、公開資産1件を確認しました。Supplement · publicTable S1: Isotopic data of leaf and stem of Vitis vinifera cv. Sauvignon Blanc blanc from Orlești-Vâlcea (Romania), during 2023-2024 vintage; Table S2: Meteorological and soil measurements (Romania), during the sampling campaign (Orlești – Vâlcea, Romania; 2023-2024 vintage)Open asset ↗lines:149-204Code / dataset availability confirmedEurope PMC · OpenAlex · checked 13 Sept 2026
PoplarField / plotRoot2D/3D reconstructionRoot system architecture
Root system architecture (RSA) is pivotal for comprehending the ecological adaptation strategies and resource acquisition mechanisms of urban flora, playing a vital role in soil stability, carbon sequestration, and ecosystem sustainability. However, the non-destructive detection and precise three-dimensional (3D) reconstruction of RSA within urban environments remain challenging. In this study, a non-destructive reconstruction method utilizing ground-penetrating radar (GPR) technology was developed to achieve 3D reconstruction and visualization of RSA, with the goal of advancing the intelligent construction and precise ecological management of urban forest parks. Field-based GPR surveys of a 9-year-old triploid poplar were conducted using a square grid and concentric circular scanning scheme. A 3D data volume (C-scan) was constructed from two-dimensional (2D) profiles, and the spatial distribution of RSA was reconstructed using instantaneous amplitude analysis. The method was validated by comparing the results with actual root structures in sandy loam environments. The research results of the 1600 MHz GPR under the square grid scanning scheme show that extracting the instantaneous amplitude isosurface of GPR can effectively reflect the spatial distribution of roots with diameters greater than 1 cm within a depth of 0.4 m subsurface. The accuracy of RSA reconstruction can reach 89 %. The results demonstrate the applicability of the proposed method for non-destructive environmental monitoring in urban forest parks, showing significant potential for the large-scale detection and reconstruction of subsurface root systems. This research provides a novel approach for RSA reconstruction with significant implications for urban ecosystem management, soil conservation, and climate resilience research. The method enhances our capability to monitor the growth and adaptation of urban roots, laying the groundwork for the large-scale, non-destructive analysis of RSA.
Why it matches plant phenotyping methodsGPRと瞬時振幅解析を用いて樹木根系構造を3D再構成する方法を開発し、実際の根構造との比較で検証しており、根系形態の取得が中心的な研究目的である。
abstracta non-destructive reconstruction method utilizing ground-penetrating radar (GPR) technology was developed to achieve 3D reconstruction and visualization of RSA
Reproduction assets foundThe paper's Data Availability statement explicitly releases the GPR root scanning data on Zenodo and the RSA reconstruction analysis code on GitHub, both with public URLs matching allowed entries.Code · publicCode is available at https://github.com/Niceguoqiu/RSA-Reconstruction-Code.git .Open asset ↗GitHub · Niceguoqiu/RSA-Reconstruction-Codelines:268-286Code / dataset availability confirmedCrossref · checked 6 Sept 2026
CoffeeMultispectral / hyperspectralLeafClassificationPhysiological trait estimationWater status / transpiration
Water potential is an important indicator used to study water relations in plants, as it reflects the level of hydration in their tissues. There are different numerical variables that describe plant properties and can be acquired from leaf reflectance. The objective of this study was to estimate water potential in coffee plants using spectral variables. For this, a range of wavelengths that provided analytical flexibility was used. After this, machine learning techniques were employed to build data-driven models. The dataset used presents spectral characteristics (wavelength) of coffee plants, collected through the CI-710 Mini-Leaf Spectrometer equipment and also the water potential of each coffee plant, measured by the Scholander Chamber equipment. The dataset was divided into two crop management groups: irrigated and rainfed. Four machine learning techniques were implemented: Multi-Layer Perceptron (MLP), Decision Tree, Random Forest and K-Nearest Neighbor (KNN). The implementation of machine learning techniques followed two distinct strategies: regression and classification. The results indicate that the decision tree-based model demonstrated superior performance under irrigated conditions for regression tasks. In contrast, the KNN technique achieved the best performance for classification. Under rainfed conditions, the MLP model outperformed the other techniques for regression, while the Random Forest method exhibited the highest accuracy in classification tasks. While no hardware prototype was developed, the machine learning-based methods presented here suggest a possible pathway toward future intelligent, user-friendly, and accessible sensing technologies for coffee plantations.
Why it matches plant phenotyping methodsコーヒー植物の葉スペクトルから水ポテンシャルという生理形質を機械学習で推定・分類する手法が研究の中心であり、植物フェノタイピング手法の開発・評価に該当する。
abstractThe objective of this study was to estimate water potential in coffee plants using spectral variables.
Reproduction assets foundThe paper's Data Availability Statement explicitly states that the study's datasets (coffee leaf spectral reflectance and water potential measurements) and the MATLAB analysis codes are publicly available at the authors' UFLA repository, which is an allowed URL. This is a paper-specific, public, actionable asset.Dataset · publicData Availability Statement: The datasets and MATLAB codes used in this study are available at
http://www.aia.ufla.br/home/filesdatasets/, accessed on 27 November 2025.Open asset ↗aia.ufla.brpdf-page:18 lines:1-54Code / dataset availability confirmedEurope PMC · checked 6 Sept 2026
Vegetation vertical structure refers to the 3D distribution of vegetation aboveground biomass. Vegetation vertical structure of tropical forests influences other ecological and environmental variables that are essential for the functioning of the ecosystems. Integrating over 5.9 million Globel Ecosystem Dynamics Investigation (GEDI) LiDAR (Light Detection and Ranging) footprints, multispectral, and synthetic aperture radar (SAR) imagery, we built five national maps at 25 m resolution of five forest structural metrics for Colombia, South America, for the year 2020. We mapped canopy height, the height of half the cumulative returned energy from GEDI (RH50), total canopy cover, foliage height diversity, and total plant area index. The resulting maps tended to have the highest errors in the Amazon and Andean regions. Total cover had the highest relative error. Interrelationship curves between forest structural metrics of GEDI footprints are maintained across mapped metrics, indicating that the predictive models preserve structural relationships observed in GEDI data. Due to the medium-high spatial resolution and national coverage of the forest structural maps presented in this work, these maps will be useful for evaluating and mapping other ecological variables and conservation priorities in Colombia.
Why it matches plant phenotyping methodsGEDI LiDAR・マルチスペクトル・SARを統合し、森林キャノピー高、被覆率、葉群高多様性、植物面積指数などの植物構造形質を全国規模で推定・検証することが中心であり、単なる生態学的応用ではない。
abstractIntegrating over 5.9 million Globel Ecosystem Dynamics Investigation (GEDI) LiDAR (Light Detection and Ranging) footprints, multispectral, and synthetic aperture radar (SAR) imagery, we built five national maps at 25 m resolution of five forest structural metrics for Colombia, South America, for the year 2020.
Reproduction assets foundThe paper's resulting forest vertical structure maps (CH, COVER, FHD, PAI, RH50 for Colombia, 2020) are publicly available on Zenodo and via Google Earth Engine assets, and the authors' analysis code is publicly available on GitHub. These are paper-specific, public, actionable assets.Code · publicCode availability
The code is publicly accessible on Github76: https://github.com/CamiloFaguaUNAL/Forest_Structure_Colombia.Open asset ↗GitHubhtml-lines:731-755Code / dataset availability confirmedEurope PMC · checked 15 Sept 2026
Field / plotLeafStem / branchMorphology / geometry measurementLeaf traitsStress response / toleranceWater status / transpiration
Background Plant functional traits provide key information about species' ecological strategies and their responses to environmental disturbances such as fire. This dataset documents 14 morpho-functional traits of leaves (specific leaf area, leaf water content and leaf dry matter content), stems (maximum height, bark thickness, diameter at 40 cm, wood density, stem water content and stem dry matter content), one regenerative trait (resprouting capacity), as well as fire-related traits (ignition time, flaming time and flammability) and growth form in 50 woody plant species (27 trees, 22 shrubs and one liana) inhabiting a pine-oak forest in the "Barranca del Cupatitzio" National Park (BCNP), located in Uruapan, Michoacán, Mexico. This dataset is formatted according to the Darwin Core Archive standard and is publicly available for use. New information This dataset is standardised under the Darwin Core framework. It includes 14 morpho-functional and fire-related traits. The data were obtained from 50 woody species with a diameter at breast height (DBH) > 2.5 cm (27 trees, 22 shrubs and one liana), in a pine-oak forest located in the western Trans-Mexican Volcanic Belt, in the Municipality of Uruapan, Michoacán, Mexico. Here, we report flammability-related traits for these species for the first time. The collection of biological material and the measurement of functional traits followed internationally recognised protocols, ensuring methodological consistency and facilitating integration with other global datasets. The dataset includes values for flammability, ignition time, flaming time, specific leaf area, wood density, stem water and dry matter content, bark thickness, leaf water and dry matter content, maximum height, stem diameter at 40 cm above the ground, plant growth form and resprouting capacity. This information is particularly valuable for studies in functional ecology, ecological restoration, the dynamics of woody plant communities and fire management in temperate, fire-prone ecosystems.
Why it matches plant phenotyping methods植物の形態・機能・火災関連形質を体系的に収集し、Darwin Coreで標準化した再利用可能なデータセットであり、形質測定とデータ提供が中心である。
abstractThis dataset documents 14 morpho-functional traits of leaves
Reproduction assets foundThe paper is a data paper whose own trait/flammability dataset is deposited publicly on GBIF via DOI 10.15468/46f8xe, explicitly linked as the data package for this study's measurements.Dataset · publiche Creative Commons Attribution 4.0 International License (CC-BY 4.0), which permits use, distribution and reproduction in any medium, provided the original authors are properly credited.
Data resources
Data package title
Functional traits related to fire in woody species from Barranca del Cupatitzio National Park
Resource link
https://doi.org/10.15468/46f8xe
Number of data sets
2
Data set 1.
Data set name
occurrence.txt
Data format
Darwin Core
Data set 1.
Column label
Column description
id
Unique identifier for each occurrence.
institutionID
The identifier for the institution having custody of the specimens.
institutionCode
Full name of the institution having custody of the specimeOpen asset ↗10.15468/46f8xelines:87-297Code / dataset availability confirmedCrossref · checked 14 Sept 2026
SoybeanLiDAR / point cloudRootMorphology / geometry measurement2D/3D reconstructionRoot system architecture
Abstract Accurate phenotyping of root traits is essential for understanding how plants respond to varying soil water treatment conditions, yet traditional phenotyping methods are often destructive and limited in capturing the full three‐dimensional (3D) complexity of root systems. Existing two‐dimensional imaging techniques and advanced 3D methods for performing root phenotyping, like magnetic resonance imaging or computed tomography, either compromise on resolution, are cost‐prohibitive, or lack scalability. To address these limitations, this study proposes fringe projection profilometry (FPP), a rapid, nondestructive 3D imaging method, for root phenotyping. Using FPP, two architectural root traits were extracted: the number of root tips and the volumetric occupancy of the root system. These traits, difficult to obtain through manual phenotyping or conventional imaging, were automatically derived from the FPP 3D point clouds and validated against expert‐assigned fibrosity scores serving as the biological reference. The study involved 36 soybean ( Glycine max (L.) Merr.) plants from six genotypes, pre‐classified as either stress‐treated or grown under rain‐fed conditions. Results showed strong alignment between FPP‐derived traits and expert evaluations. Stress‐ treated plants consistently exhibited more root tips and greater volumetric occupancy, confirming the biological relevance of these metrics. While this study does not attempt to classify drought tolerance directly, the structural variations observed under drought stress may serve as a foundation for identifying stress‐responsive phenotypes in future work. Overall, the findings demonstrate that FPP provides a fast, scalable, and accurate tool for 3D root phenotyping under variable water conditions.
Why it matches plant phenotyping methodsFPPによる根系の3次元形質取得・自動抽出を開発し、専門家評価と検証した研究であり、フェノタイピング手法が中心です。
abstractthis study proposes fringe projection profilometry (FPP), a rapid, nondestructive 3D imaging method, for root phenotyping.
Reproduction assets foundThe paper's Data Availability Statement provides a public Google Drive link to the datasets generated and/or analyzed in this soybean root FPP phenotyping study, which is an allowed URL. No author analysis code is explicitly deposited.Dataset · publicying and Overcoming Weaknesses via Breed-
ing, Genomics, Phenomics and Physiology).
C O N F L I C T O F I N T E R E S T S TAT E M E N T
The authors declare no conflicts of interest.
DATA AVA I L A B I L I T Y S TAT E M E N T
The datasets generated and/or analyzed dur-
ing the current research are available at Google
Drive link: https://drive.google.com/file/d/1BJ4yq8QEWY3E5qQIQmYcOXHhEYn1zTE-
/view?usp=sharing
O RC I D
JiaqiongLi https://orcid.org/0009-0006-2247-425X
ZengluLi https://orcid.org/0000-0003-4114-9509
BeiwenLi https://orcid.org/0000-0001-8130-7730
R E F E R E N C E S
Balasubramaniam, B., Li, J., Liu, L., & Li, B. (2023). 3D imaging with
fringe projection for food and agriculturalOpen asset ↗pdf-raw-page:17 lines:1-91Code / dataset availability confirmedEurope PMC · checked 15 Sept 2026
Varieties show their unique characteristics in morphology, growth, and fruits. Tomato maturity is related to multiple dimensional characteristics including color, texture, smell, etc. An effective classification method of tomato variety and maturity is crucial for evaluating its growth and yield. However, due to the complex growth environment, some problems such as leaf occlusion and fruit shaded by each other make it difficult to accurately and efficiently identify them. To solve these problems, this study innovatively proposes a simultaneous detection model on tomato variety and maturity based on improved YOLOv8n, with the combination of frequency-adaptive dilated convolution (FADC) feature extraction module and the high-level screening-feature path aggregation network (HSPAN) with the aim of local and global feature fusion by the channel attention module and feature selection fusion mechanism. In addition, we use the Powerful-IoU (PIoU) loss function to replace the original Complete IoU (CIoU) to enhance the accuracy of bounding boxes. We also introduce a dynamic detection head as the final output of the model, which can adaptively adjust the focus of feature extraction according to the color and size of tomato fruits, thereby improving the recognition accuracy. Experimental results show that our model with better global perception capability achieves the highest detection accuracy and lower computation complexity among the comparative models.
Why it matches plant phenotyping methodsトマト果実の成熟度という観察可能な植物状態を画像から推定する検出モデルを開発しており、特徴抽出・検出ヘッド・損失関数の改良と比較評価が研究の中心である。
abstractthis study innovatively proposes a simultaneous detection model on tomato variety and maturity based on improved YOLOv8n
Reproduction assets foundThe paper's tomato variety/maturity detection experiments are built on the public Laboro Tomato dataset, which is explicitly cited with a public GitHub URL. No author analysis code, trained models, or supplementary assets are reported as available.Dataset · publicThe constructed dataset in this study is based on the Laboro Tomato open-access dataset [ 24 ], which is an image dataset of tomatoes with different maturity collected in a greenhouse in winter (15 December 2019).Open asset ↗lines:33-42Code / dataset availability confirmedEurope PMC · checked 15 Sept 2026
MaizeTomatoLeafPhysiological trait estimationWater status / transpiration
Within the soil-plant-atmosphere continuum, water movement is driven by the water potential gradients between these three domains. To have a comprehensive understanding of such water relations, an examination of how plants respond to variations in soil water availability is required. The methodologies employed for measuring water potential in leaf (Ψ leaf ) and soil (Ψ soil ) have undergone a significant evolution; transitioning from qualitative assessments to the use of high-precision digital sensors over the past few decades. The present protocol aims to provide a comprehensive, step-by-step guide from the germination phase of maize and tomato plants to the installation of two sensors that continuously monitor water potential in the leaf (PSY1 psychrometer) and in the soil (TEROS 21 matric potential sensor). Additionally, we present the code for processing the raw data files in RStudio.
Why it matches plant phenotyping methods葉の水ポテンシャルという植物生理形質を連続測定するセンサー設置手順とデータ処理コードを中心に扱うプロトコルであり、植物フェノタイピング手法が研究の中心である。
abstractThe present protocol aims to provide a comprehensive, step-by-step guide from the germination phase of maize and tomato plants to the installation of two sensors that continuously monitor water potential in the leaf (PSY1 psychrometer) and in the soil (TEROS 21 matric potential sensor).
Reproduction assets foundThe paper deposits its example water-potential datasets (soil matric potential from Teros 21, leaf water potential from PSY1, transpiration from scales) and the authors' data extraction/cleaning/analysis code on Zenodo (10.5281/zenodo.17158115), under CC0/CC-BY. A supplementary installation video is separately on ZenodDataset · public52.
PubMed Abstract | Publisher Full Text
Cotrozzi L, Couture JJ, Cavender-Bares J, et al.: Using foliar spectral properties
References
Figure 9. Example of data cleaning using the algorithm. Green is kept data and red is discarded data.
Data availability
The datasets and codes to analyze the data have been deposited
on Zenodo (https://doi.org/10.5281/zenodo.17158115, D'Agostino
(2025)).
Data are available under the terms of the Creative Commons
Zero v1.0 Universal
An additional explicative video for the psychrometer instal-
lation on leaves is available on Zenodo (https://doi.org/10.5281/zenodo.17510720, Degand et al. (2025)).
The author(s) declare that this video is released under the
CreOpen asset ↗Zenodo · 10.5281/zenodo.17158115pdf-raw-page:11 lines:1-61Code · publicat were missing,
zero, or otherwise aberrant. It was also programmed to iden-
tify and remove inverted day-night cycle patterns, as well as
values that were statistically insignificant. Figure 9 shows appli-
cations of data cleaning on the example dataset. For more
details, please check codes that have been deposited on Zenodo
(https://doi.org/10.5281/zenodo.17158115, D'Agostino, 2025).
Ethics and consent
Ethical approval and consent were not required
Figure 8. Example of the charging effects on the data recordings.
Page 10 of 18
Open Research Europe 2025, 5:363 Last updated: 19 JUN 2026Open asset ↗Zenodo · 10.5281/zenodo.17158115pdf-raw-page:10 lines:1-58Supplement · publicavailability
The datasets and codes to analyze the data have been deposited
on Zenodo (https://doi.org/10.5281/zenodo.17158115, D'Agostino
(2025)).
Data are available under the terms of the Creative Commons
Zero v1.0 Universal
An additional explicative video for the psychrometer instal-
lation on leaves is available on Zenodo (https://doi.org/10.5281/zenodo.17510720, Degand et al. (2025)).
The author(s) declare that this video is released under the
Creative Commons CC0 1.0 Universal Public Domain Dedica-
tion. This means the video is free of all copyright restrictions
and may be copied, modified, distributed, and used without
permission, including for commercial purposes.
Data are availablOpen asset ↗Zenodo · 10.5281/zenodo.17510720pdf-raw-page:11 lines:1-61Code / dataset availability confirmedEurope PMC · checked 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
307
agricultural planners to optimize production systems in response to evolving climatic
308
conditions, thereby enhancing regional food security resilience.
309
Data Availability
310
The dataset generated during this study is openly available in the Science Data Bank at
311
https://doi.org/10.57760/sciencedb.28709 or https://cstr.cn/31253.11.sciencedb.28709.312
Code availability
313
No custom code was created for the production of this dataset.
314
References
315
1.Cai C, Ding T, Chen W. (2024). Potential yield of world maize under global warming based on ARIMA-TR model.
316
Journal of Agrometeorology, 2024, 26(1).
317
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 / 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 14 Sept 2026
Precision agriculture technologies based on satellite remote sensing remain largely inaccessible to smallholder farmers in developing countries due to technical complexity, cost barriers, and infrastructure demands. This study presents the design and implementation of an open-source, web-based platform for processing Sentinel-2 Level-2A imagery tailored to the specific needs of family farming systems. The platform integrates a FastAPI backend for geospatial data processing with a Next.js frontend providing simplified tools for spectral index computation (NDVI, EVI, SAVI, NDWI, NDBI), crop classification using supervised and unsupervised machine learning, and interactive 2D/3D visualization. A laboratory module implements thirteen digital image processing techniques—including Gaussian filtering, edge detection, morphological operations, and thresholding—for educational and comparative analysis. The browser-based system eliminates installation requirements and automates key workflows such as coordinate reprojection, JP2 band extraction, and statistical evaluation. Validation using ground-truth data from coffee and soybean fields in the Brazilian Cerrado achieved classification accuracies above 85% and correlation coefficients exceeding 0.90 for biomass estimation based on NDVI-derived metrics. The platform contributes to the democratization of remote sensing technologies and enhances accessibility of precision agriculture tools for smallholder farmers.
Why it matches plant phenotyping methods植物圃場の衛星画像を処理し、NDVI等からバイオマスを推定するオープンソース基盤の設計・実装・検証が中心であり、植物形質推定ワークフローとして収録対象。
titleAn Open-Source Web Platform for Sentinel-2 Multispectral Analysis in Smallholder Agriculture: Design, Implementation and Validation
Reproduction assets foundThe paper's Data Availability Statement explicitly deposits the authors' complete source code, documentation, and example datasets for the Sentinel-2 phenotyping/analysis platform on a public GitHub repository under MIT license. Sentinel-2 imagery is from the public Copernicus browser, but that is a generic data sourceCode · publicresearch received no external funding
Institutional Review Board Statement: Not applicable. This study did not involve humans or animals.
Informed Consent Statement: Not applicable. This study did not involve humans.
Data Availability Statement: Complete source code, documentation, and example datasets are publicly available
at https://github.com/rexionmars/icev-remote-sensing under MIT license. The platform can be deployed locally
or accessed via hosted instance for testing purposes. Sentinel-2 satellite imagery used in this study was obtained
from the Copernicus Open Access Hub (https://browser.dataspace.copernicus.eu/) and is freely available.
Acknowledgments: The authors thank the iCEV IOpen asset ↗https://github.com/rexionmars/icev-remote-sensing · icev-remote-sensingpdf-layout-page:11 lines:1-70Code / dataset availability confirmedEurope PMC · checked 14 Sept 2026
Field / plotFlowerLeafRootStem / branchClassificationDisease symptoms / severity
This dataset comprises 5452 images of durian plant parts-including leaves, flowers, branches, stems, and roots-affected by ten common disease classes. The images were captured from one family-owned durian orchard and four nearby orchards in Vinh Long Province, Vietnam. Each class contains approximately 405-427 raw images, photographed using an iPhone 14 under natural field conditions. These conditions simulate typical farmer photography practices, featuring varied angles, inconsistent lighting, and complex environmental backgrounds, resulting in significant visual noise. All raw JPEG images were manually reviewed and cropped on macOS systems using MacBook devices equipped with Apple M4 chips to focus on disease-affected regions, reduce file size, and minimize background noise. The processed, cropped images are provided in PNG format with variable dimensions. Images were resized to 224×224 pixels only during model training for machine learning experiments. Disease symptoms were verified in collaboration with plant pathologists to ensure accurate classification. This dataset is publicly available on Mendeley Data and is suitable for developing and evaluating machine learning models in plant disease classification. It is particularly valuable for testing model performance under real-world, noisy conditions and for supporting the creation of mobile or edge-based diagnostic tools in agriculture.
Why it matches plant phenotyping methods植物病徴を画像で直接捉えた公開データセットで、植物病害状態の分類モデル開発・評価を主目的とするため、表現型計測データセットとして中心的です。
abstractThis dataset comprises 5452 images of durian plant parts-including leaves, flowers, branches, stems, and roots-affected by ten common disease classes.
Reproduction assets foundThe paper is a Data in Brief describing a public durian disease image dataset (5452 field images, ten classes) deposited on Mendeley Data with an explicit DOI and direct URL, matching an allowed URL. This is a paper-specific, publicly available image dataset directly reproducing the paper's phenotyping measurements. NoDataset · publicRepository name: Mendeley Data
Data identification number: 10.17632/mhjwyb5p48
Direct URL to data: https://data.mendeley.com/datasets/mhjwyb5p48/1Open asset ↗Mendeley Data · 10.17632/mhjwyb5p48lines:47-125Code / dataset availability confirmedEurope PMC · checked 6 Sept 2026
We investigate the potential of phenomic prediction (PP) in remote-sensing-based phenotyping for genetic studies. Rather than relying on a single vegetation index, we utilize all available data collectively to predict the human-assigned visual score (VS). The conceptual motivation is that when a trained model is available, these predictions may provide a more accurate assessment of disease symptoms than the use of a specific vegetation index (VI). To evaluate the PP approach, we employ the predicted VS in a genome-wide association study (GWAS) and consider strength and position of the detected genetic signal. We use two different sets of predictor variables: i) the five basic wavelengths captured by a multispectral and a thermal camera (basic traits model, BT) or ii) all traits (AT), consisting of the five basic wavelengths plus ten vegetation indices. As statistical methods, we compare a) (linear) ordinary least squares regression (OLS), b) (linear) ridge regression (RR), c) (linear) least absolute shrinkage and selection operator (LASSO) d) an artificial neural network (ANN) and e) a gradient boosted regression tree method (GBRT). Our results indicate that the simple linear OLS regression on the five basic wavelengths (BT-OLS) performs on a level comparable to the best individual vegetation index G. The use of all traits in the OLS regression (AT-OLS) leads to overfitting, which was prevented by the regularization in AT-RR and AT-LASSO. The non-linear ANN approach seems to improve the results further, but the differences between the methods were not statistically significant. The strongest improvement for the purification of the genetic signal was observed when genomic estimated breeding values (GEBVs) for the different traits (VS, basic wavelengths, vegetation indices) instead of their adjusted phenotypes were used. Across all approaches, the combination of GEBVs with Ridge Regression or the non-linear ANN provided the best results.
Why it matches plant phenotyping methodsリモートセンシング画像・センサーデータからトウモロコシさび病の視覚的症状スコアを推定する複数の統計・機械学習手法を比較評価しており、表現型取得・抽出法が研究の中心である。
abstractWe investigate the potential of phenomic prediction (PP) in remote-sensing-based phenotyping for genetic studies.
Reproduction assets foundThe paper's phenotypic (visual scores, adjusted phenotypes) and remote-sensing (multispectral/thermal) data, plus genomic marker data, are publicly deposited in the CIMMYT Research Data repository. No authors' analysis code or trained model checkpoints are deposited; R packages cited are generic libraries.Dataset · publicWe use the data previously published by Loldaze et al. [ 21 , 22 ], which is available on the CIMMYT Research Data repository at https://hdl.handle.net/11529/10548898 .Open asset ↗CIMMYT Research Data repository · 11529/10548898lines:35-47Code / dataset availability confirmedEurope PMC · checked 14 Sept 2026
Field / plotLeafMorphology / geometry measurementLeaf traits
We present a comprehensive dataset of qualitative and quantitative leaf functional traits for 93 dominant woody species representing two distinct leafing phenologies and three growth forms from the central Western Ghats of India. Quantitative assessments were conducted for nine key traits: leaf area (LA), mean thickness (LTH), specific leaf area (SLA), leaf dry matter content (LDMC), leaf tissue density (LTD), leaf nitrogen concentration (Leaf N), carbon-to-nitrogen ratio (C/N), and phytolith yield, following standard protocols. For each species, 30 leaves were sampled from a minimum of five individuals, totalling 2790 leaf samples. Qualitative traits, including leaf shape, margin, surface, texture, apex, base, type, and latex presence, were recorded in the field and validated using field manuals. The majority of species sampled were evergreen (74 %), with deciduous species comprising the remainder. Given the growing importance of plant functional traits in ecological research, this dataset offers valuable species-level leaf trait information at the regional scale. The phytolith yield data, in particular, represent one of the few globally available datasets, providing essential baselines for palaeoecological research and enabling quantitative reconstruction of vegetation composition and environmental change over millennial timescales.
Why it matches plant phenotyping methods植物の葉形質を標準化プロトコルで体系的に収集した再利用可能なデータセット論文であり、データセット自体が中心的な成果である。
abstractWe present a comprehensive dataset of qualitative and quantitative leaf functional traits for 93 dominant woody species
Reproduction assets foundThe paper's own leaf functional trait dataset (2790 leaves, 93 woody species, central Western Ghats) is publicly deposited on Zenodo with an explicit DOI/URL given in the article.Dataset · publicduals per species. Quantitative leaf functional traits were analyzed following the standard protocol [ 1 , 2 ].
Data source location
Country: India
Sampling site: Gerusoppa Reserve Forest, Central Western Ghats (14°12′ N to 14°24′ N and 74°36′ E to 74°48′ E)
Data accessibility
Repository name: Zenodo
Data identification number: https://doi.org/10.5281/zenodo.16717435
Direct URL to data: https://doi.org/10.5281/zenodo.16717435
Related research article
None
1
Value of the Data
•
This dataset provides high-resolution leaf-level data ( n = 2790) on 17 functional traits for 93 dominant woody species of the central Western Ghats, supporting trait-based ecological research.
•
It enables assessmentOpen asset ↗Zenodo · 10.5281/zenodo.16717435lines:1-54Code / dataset availability confirmedEurope PMC · checked 14 Sept 2026
Field / plotLeafClassificationDisease symptoms / severity
The visual diagnosis based on foliar traits remains a cornerstone technique for the early identification of biotic stress, for instance, disease and pest infestations, in many economically valuable crops, including Aquilaria Malaccensis (agarwood). As a species of immense commercial and ecological significance, Aquilaria Malaccensis is particularly vulnerable to a range of pathogens and insect threats that can severely compromise resin production and tree viability. With the increasing integration of disruptive sustainable agricultural technologies, such as artificial intelligence (AI), especially in plant phenotyping and pathology, the development of robust and generalizable AI models hinges on the availability of large-scale and high-resolution image datasets. However, the current lack of such curated datasets for agarwood poses a substantial bottleneck to progress in automated identification systems. This deficiency limits the ability of scientists, technologists, and plant health experts to leverage machine learning and computer vision techniques for timely, accurate, and scalable solutions to different stresses in agarwood disease and pest management, including nematodes, viroids, viruses, pests, phytoplasmas, bacteria, fungi, and Protozoa. This paper presents a dataset of pests and diseases affecting agarwood trees, which impact farmers. It includes a total of 5472 leaf images classified into 14 categories. These categories consist of 8 types of agarwood diseases, 5 types of pests, and 1 category of healthy leaf images, encompassing both insect-damaged and healthy leaves. The images were captured using a PowerShot G7X Mark III camera. The images were captured from three different agarwood plantation sites of Batong, Benutan, and Bukit Silat in 2024, led by the Institute for Biodiversity and Environmental Research (IBER), Universiti Brunei Darussalam, by Botanical Research Centre (UBD BRC) scientists and biologists. This dataset is particularly valuable for training and validating deep learning (DL), computer vision, and machine learning algorithms aimed at identifying agarwood diseases and pests in agarwood leaves. Offering researchers and learners a robust data resource for analyzing and improving agarwood plant health through the development of advanced computational models. The designed models are vital and hold immense practical value for farmers, equipping them with the tools that timely detect and identify diseases in their agarwood trees, empowering them to make informed decisions and potentially intensify their profits.
Why it matches plant phenotyping methods葉画像から病害・害虫による植物状態を識別するための大規模データセットを構築しており、データ取得と再利用可能な解析基盤が研究の中心であるため。
abstractThis dataset is particularly valuable for training and validating deep learning (DL), computer vision, and machine learning algorithms aimed at identifying agarwood diseases and pests in agarwood leaves.
Reproduction assets foundThe paper is a data descriptor for a public agarwood leaf image dataset (5472 images, 14 classes) deposited on Zenodo and Mendeley, with explicit direct URLs and DOIs provided in the Data Accessibility section. This is the paper's own phenotyping image dataset, publicly available and actionable. No separate author codeDataset · publicc.iber.ubd.edu.bn ), Universiti Brunei Darussalam, Gadong, BE1410, Brunei Darussalam
Data accessibility
Repository name: Zendo and Mendeley
Repository Title: Agarwood Leaf Image Dataset for Pest and Disease Analysis in Real-World Environment
Data identification number: https://doi.org/10.5281/zenodo.14842099
Direct URL to data: https://zenodo.org/records/14842100
Direct URL to data: https://data.mendeley.com/datasets/8f8wtr9zwn/2
Related research article
Shafik, W., Tufail, A., De Silva, L.C. et al. A lightweight deep learning model for multi-plant biotic stress classification and detection for sustainable agriculture. Sci Rep 15, 12,195 (2025). https://doi.org/10.1038/s41598-025-90487-Open asset ↗Zenodo · 10.5281/zenodo.14842099lines:36-67Dataset · publicg, BE1410, Brunei Darussalam
Data accessibility
Repository name: Zendo and Mendeley
Repository Title: Agarwood Leaf Image Dataset for Pest and Disease Analysis in Real-World Environment
Data identification number: https://doi.org/10.5281/zenodo.14842099
Direct URL to data: https://zenodo.org/records/14842100
Direct URL to data: https://data.mendeley.com/datasets/8f8wtr9zwn/2
Related research article
Shafik, W., Tufail, A., De Silva, L.C. et al. A lightweight deep learning model for multi-plant biotic stress classification and detection for sustainable agriculture. Sci Rep 15, 12,195 (2025). https://doi.org/10.1038/s41598-025-90487-1 .
1.
Value of the Data
•
The dataset comprises 5472 high-quOpen asset ↗Mendeley · 8f8wtr9zwnlines:36-67Code / dataset availability confirmedCrossref · checked 6 Sept 2026
ABSTRACT As an essential species across European forests, Scots pine ( Pinus sylvestris L.) plays a vital ecological and economic role, yet its physiological variability underlying its adaptive potential remains underexplored. Understanding this intraspecific variability is crucial for uncovering the genetic basis of adaptation. Traditional genetic evaluations require large sample sizes and are time‐consuming, whereas hyperspectral sensing/imaging enables rapid, nondestructive assessment of physiological traits across many individuals, facilitating more efficient exploration of adaptive variation. We assessed needle functional traits (NFTs) linked to foliar structure, water content, and pigment composition in clonal seed orchards over two seasons, integrating hyperspectral measurements at needle and canopy levels with genotyping using a new 50 K single‐nucleotide polymorphism (SNP) array. Linear mixed models revealed substantial genetic variation, with the carotenoid‐to‐total‐chlorophyll ratio showing the highest heritability (0.29) among pigment traits, and structural/water‐related traits reaching heritability values up to 0.38. Significant genetic correlations were observed between stress‐related traits (pigment content, equivalent water thickness) and reflectance, suggesting that spectral traits could serve as proxies for indirect selection of adaptive traits or in breeding programs. Low genotype‐by‐environment interaction and stable clonal performance across years further underscore the reliability of these traits for identifying resilient genotypes. Overall, our findings highlight hyperspectral phenotyping and NFTs as promising tools for accelerating climate‐adaptive breeding in Scots pine.
Why it matches plant phenotyping methods針葉および林冠レベルのハイパースペクトル測定を用いて植物の機能形質を評価し、育種への再利用可能性を検討しており、フェノタイピング手法の適用が中心的です。
abstracthyperspectral sensing/imaging enables rapid, nondestructive assessment of physiological traits across many individuals
Reproduction assets foundThe paper's Data Availability Statement points to a public Figshare deposit (DOI 10.6084/m9.figshare.27134907.v2) containing the data supporting the study's hyperspectral phenotyping and genetic analyses. This URL is in the allowed list and the identifier occurs verbatim in the quote. No separate author analysis code, Dataset · publicThe data supporting the findings of this study are openly available in Figshare at https://doi.org/10.6084/m9.figshare.27134907.v2 .Open asset ↗Figshare · 10.6084/m9.figshare.27134907.v2lines:454-598Code / 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
Abstract. Carbonyl sulfide (COS) has been proposed as a proxy for gross primary production (GPP), as it is taken up by plants through a pathway comparable to that of CO2. COS diffuses into the leaf, where it undergoes an essentially one-way reaction in the mesophyll cells, irreversibly catalyzed by the enzyme carbonic anhydrase (CA), and is likely not respired by the leaf. In order to use COS as a proxy for GPP, the mechanisms of COS uptake and its coupling to photosynthesis need to be well understood. Characterizing the isotopic discrimination of COS during plant uptake could provide valuable information on the physiological COS uptake process and may help to constrain the COS budget. This study presents joint measurements of isotope discrimination during plant uptake for COS (CO34S) and CO2 (13CO2 and C18O16O). A C3 plant, sunflower (Helianthus annuus), and a C4 plant, papyrus (Cyperus papyrus), were enclosed in a flow-through plant chamber and exposed to varying light levels. The incoming and outgoing gas compositions were measured online, and discrete air samples were taken for isotope analysis. Simultaneously measuring fluxes and isotope discrimination of both COS and CO2 yielded a unique dataset that includes information on the plant's behavior and allowed for the estimation of stomatal- and mesophyll conductances. The average COS uptake fluxes were 73.3 ± 1.5 pmol m−2 s−1 for sunflower and 107.3 ± 1.5 pmol m−2 s−1 for papyrus (PAR > 0) and displayed virtually no trend with increasing PAR from 200 to 600 µmol m−2 s−1. The mean observed 34Δ for COS was 3.4 ± 1.0 ‰ for sunflower and 2.6 ± 1.0 ‰ for papyrus. 34Δ was stable across all light intensities, which could be explained by a sufficient stomatal opening and low variability in the ratio of mesophyll vs. ambient COS mole fraction, CmS/CaS. For both C3 and C4 plants, for CO2, a negative relationship was observed between the uptake flux and the isotopic discriminations 13Δ and 18Δ. The CO2 uptake and 13CO2 and C16O18O discriminations of sunflower have expected values for a C3 plant, while the low CO2 flux and high 13Δ and 18Δ values observed for papyrus were not in the typical C4 range, which was perhaps due to the relatively low light conditions during our experiments.
Why it matches plant phenotyping methods植物のCOS・CO2取り込み、同位体識別、気孔・葉肉コンダクタンスをフロースルー植物チャンバーで定量する生理的表現型測定が研究の中心であり、再利用可能な測定データセットと推定手法を提示している。
abstractThis study presents joint measurements of isotope discrimination during plant uptake for COS (CO34S) and CO2 (13CO2 and C18O16O).
Reproduction assets foundThe paper's isotope discrimination and gas-exchange dataset from the flow-through chamber experiments is publicly deposited on Zenodo by the authors.Dataset · publicynthetically available radiation at the top of the chamber, 34 Δ is the discrimination against CO 34 S and LRU is the leaf relative uptake ratio.
* n =1 , error states is the single measurement precision instead of the repeatability precision.
Download Print Version | Download XLSX
Data availability
The dataset is available at: https://doi.org/10.5281/zenodo.14677494 (Baartman et al., 2025).
Author contributions
Conceptualization: SLB, MCK, MEP, LW. Data curation: SLB. Formal analysis: SLB, NUL. Funding acquisition: MCK. Investigation: SLB, SMD, MW, LMJK, LM, AC, SH. Methodology: SLB, SMD, MW, LMJK, MEP. Resources: SMD, MW, LM, SH. Supervision: MEP, TR, MCK. Visualization: SLB, NUL. WritingOpen asset ↗Zenodo · 10.5281/zenodo.14677494lines:652-942Code / dataset availability confirmedEurope PMC · checked 6 Sept 2026
Summary We used domestication as an in vivo replicated experiment to investigate how divergent selection has shaped the evolution of multivariate phenotypic spaces. We measured 11 to 57 qualitative and quantitative traits in 13 species, either unique or shared between species, and established a framework for cross-species comparisons. Our results revealed significant convergence that translated into a cross-species domestication syndrome. Most species exhibited a reduction of the multivariate phenotypic space during domestication. We brought evidence that Near Infrared spectra measured on leaves reflect phenotypic evolution unrelated with domestication, enabling its use as a control for sampling effects across species. Building on this, we developed a multivariate Phenotypic Divergence Index (mPDI) to rank species by the extent of phenotypic divergence under domestication. We found a high disjunction of wild and domestic phenotypic spaces in all species. Neither the mPDI nor the relative size of wild versus domestic multivariate phenotypic spaces was influenced by the domestication timing or mating system. Lastly, we observed a progressive decoupling of trait correlations with increasing time since domestication. In addition to introducing a new index that can be applied for cross-species comparisons, our study uncovers recurring patterns shared among species, pointing to general principles underlying plant domestication.
Why it matches plant phenotyping methods多変量形質空間を比較する枠組みと新しいPhenotypic Divergence Index（mPDI）を開発しており、植物表現型の定量・比較手法が主要な貢献です。
abstractBuilding on this, we developed a multivariate Phenotypic Divergence Index (mPDI) to rank species by the extent of phenotypic divergence under domestication.
Reproduction assets foundThe paper's phenotypic data, NIR spectra, trait ontology, and R analysis scripts are explicitly deposited publicly: phenotype/NIRS data and MIAPPE trait ontology at doi 10.57745/QWEKVK, and R scripts on INRAE Forge. Both are paper-specific, public, and actionable.Dataset · publicPhenotypic data and NIR spectra are available on https://doi.org/10.57745/QWEKVK .Open asset ↗10.57745/QWEKVK · 10.57745/QWEKVKlines:283-349Code · publicR scripts are available on the INRAE Forge at https://forge.inrae.fr/gqe‐gevad/domisol_phenotypic_spaces .Open asset ↗forge.inrae.fr/gqe‐gevad/domisol_phenotypic_spaceslines:283-349Code / dataset availability confirmedEurope PMC · checked 15 Sept 2026
In the present scenario, medicinal plants play a crucial role in promoting a healthy lifestyle by protecting against numerous diseases. They also hold significant potential as a source of income, particularly for rural populations across the globe. Plants used for herbal medicine are known as medicinal plants, and each part of these plants may be utilized for medicinal purposes. Further, medicinal plants are beneficial in enhancing the human immune system. In this research, a new medicinal plant named as Central India Medicinal Plant Dataset (CIMPD) has been developed to support significant research in human health. The dataset contains 9130 leaf images (both healthy and unhealthy) from 23 medicinal plant species. These images were collected from various locations in central India. The entire work was carried out over a period of five months, which included plant selection, leaf collection, image capturing, and data organization into folders. This dataset provides comprehensive information, including the botanical name, common name, geographical origin, healthy and unhealthy leaf images, and medicinal uses of the plants. It serves as a valuable resource for research in machine learning, computer vision, and related domains. Additionally, it will enable the development and evaluation of methodologies for disease detection, plant identification, and other relevant applications.
Why it matches plant phenotyping methods健康・不健康な葉画像を含む再利用可能なデータセットを構築し、植物の病害状態を画像から判定する研究基盤として提供しているため、画像ベースの植物状態計測に該当する。植物同定も含むが、データセット構築自体が中心である。
abstractThe dataset contains 9130 leaf images (both healthy and unhealthy) from 23 medicinal plant species.
Reproduction assets foundThe paper is a data descriptor for the Central India Medicinal Plant Dataset (CIMPD), a public Kaggle dataset of 9130 healthy/unhealthy medicinal plant leaf images from 23 species, directly reproducing the paper's phenotyping (leaf image) measurements. The ResNet18 feature-visualization analysis code is not explicitly,Dataset · publichas 9130 images from 23 classes. Within the dataset, there’s an unequal distribution of samples among various classes.
Data source location
For this project, a large no of gardens of various places of central India has visited to collect the medicinal plant leaves.
Data accessibility
Repository name: Kaggle
Direct URL to data: https://www.kaggle.com/datasets/satyamtomar08/indian-medicinal-plant-dataset
1.
Value of the Data
•
The development of a medicinal plant dataset plays a crucial role in the exploration of advanced machine learning models for significant investigations such as plant identification, disease detection, crop management, and more [ [1] , [2] , [3] , [4] ].
•
This plant leafOpen asset ↗Kagglelines:1-54Code / 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 confirmedCrossref · Europe PMC · checked 6 Sept 2026
The Ash Gourd dataset is valuable since it was collected from the diverse regions within the district of Dhaka in Bangladesh. This dataset represents one of the first attempts to document, elicit, and categorize the health conditions of Ash Gourd (Benincasa hispida) plants in Bangladesh based on healthy samples, aphid plurality, downy mildew, leaf curl, and leaf miner-infested categories. Ash Gourd is one of the region's most important vegetables because of its nutritional and economic value; thus, it is essential to know diseases' manifestation in the improvement of agricultural productivity. The Ash Gourd dataset contains 2676 images, structured into the five categories of Healthy, Aphid, Downy Mildew, Leaf Curl, and Leaf Miner. All images in all categories are raw which can be used flexibly according to the needs of analysis and model training. Concretely, the Healthy class consists of 803 images, while the four other classes contain 1,873 images. This structured way of collecting data will, in turn, enable deeper analysis and help construct machine learning models for disease classification, hence providing worthy insights into Ash Gourd plant health.
Why it matches plant phenotyping methodsアッシュゴード葉の画像データセットを構築し、植物の健康状態・病徴カテゴリを分類するための再利用可能なデータ資源を提供しており、植物病害状態の画像ベース表現型解析が中心です。
abstractThe Ash Gourd dataset contains 2676 images, structured into the five categories of Healthy, Aphid, Downy Mildew, Leaf Curl, and Leaf Miner.
Reproduction assets foundThe paper's own ash gourd leaf image dataset (2676 images, five classes) is publicly deposited on Mendeley Data with an explicit direct URL and DOI, matching the paper's phenotyping measurements.Dataset · publicRepository name: Mendeley Data
Data identification number: 10.17632/zj4th6xvdp.2
Direct URL to data:https://data.mendeley.com/datasets/zj4th6xvdp/2Open asset ↗Mendeley Data · 10.17632/zj4th6xvdp.2html-lines:1-98Code / 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 · checked 14 Sept 2026
Field / plotLeafClassificationStress / disease detectionDisease symptoms / severity
This study highlights the growing significance of flowers, especially roses, in the global agricultural market, where they are cultivated for both personal enjoyment and commercial purposes. Among these, roses are considered one of the most popular and widely cultivated flowers. However, rose cultivators often encounter substantial challenges due to diseases that affect the plants, which can lead to significant economic losses in the agricultural sector. Timely and accurate detection of these diseases is crucial to mitigating their impact, potentially saving millions of dollars in crop losses. The dataset utilized in this research consists of 10,000 high-quality images collected from an initial set of 3113 images taken from several rose gardens located in Amin Model Town, Khagan, Ashulia, and Savar, Bangladesh. The data collection process spanned from October 30 to November 6, 2024. These images are categorized into four distinct classes: Healthy Leaf, Black Spot, Leaf Hole, and Dry Leaf, representing various stages of disease development in rose plants. The images were captured using a Vivo IQOO Z9x phone, ensuring high resolution and detailed imagery necessary for research analysis. This dataset serves as a valuable resource for researchers and developers working on creating efficient algorithms for the early and accurate identification of rose leaf diseases. By leveraging machine learning and image processing techniques, these algorithms could significantly enhance disease detection and prevention, helping to safeguard crops and reduce economic losses in the agricultural sector.
Why it matches plant phenotyping methodsバラ葉の病徴を画像データセットとして体系的に収集・分類し、植物病害状態の画像ベース推定を支援する研究であり、データセット構築が中心です。
titleRoseLeafSet: Real-world leaf image dataset for AI-based agricultural solutions.
Reproduction assets foundThe paper is a data descriptor for RoseLeafSet, a public rose leaf image dataset (3113 original images, augmented to 10,000) deposited on Mendeley Data with an explicit direct URL and DOI (10.17632/9g668bfhy5.3). This is a paper-specific, publicly available plant image dataset directly reproducing the paper's phenotypyDataset · publiclocation
City: Amin Model Town, Khagan, Ashulia, Savar, Dhaka Country: Bangladesh. Local location: Shumi Nursery, Shetu Nursery, Bismillah Nursery etc. Geographical Location: 23 ° 53′ 2″ N and 90 ° 19′ 28″ E.
Data accessibility
Repository name: Mendeley Data Data identification number: 10.17632/9g668bfhy5.3 Direct URL to data: https://data.mendeley.com/datasets/9g668bfhy5/3
Related research article
None
1.
Value of the Data
•
The dataset presented here, a collaborative effort of researchers and industry professionals, is suitable for training machine learning models for rose leaf disease classification and detection. This makes it a valuable resource for all of us, as we work together to devOpen asset ↗Mendeley Data · 10.17632/9g668bfhy5.3lines:1-48Code / dataset availability confirmedCrossref · checked 6 Sept 2026
Abstract has not been obtained from indexed metadata or an accessible article page.
Why it matches plant phenotyping methods植物葉の病害検出法そのものの改良を主題としており、植物の病徴・病害状態を画像から推定する方法開発に該当する。
titleResearch on Plant Leaf Disease Detection Method Based on Improved YOLOv11
Reproduction assets foundThe paper's own generated datasets are not publicly available (available only from the corresponding author upon request), but the authors explicitly cite a public Roboflow Universe plant-disease dataset used as their benchmark, which is a paper-specific, publicly actionable asset. No author analysis code or trained ADDataset · publicRoboflow Universe (2024). 1112. Train model dataset [EB/OL]. July 2024. Available at https://universe.roboflow.com/1112-1p9z6/train-model-izydu . Accessed 12 Mar 2025Open asset ↗Roboflow Universe · train-model-izydulines:100-149Code / dataset availability confirmedEurope PMC · bioRxiv · checked 6 Sept 2026
Plant development and adaptation are highly dependent on cell morphology and growth. High turgor pressure in plants causes stress on the cell wall, followed by cell extension. In tip-growing cells, the localization of vesicles and cytoskeleton components has been well studied. However, there has been a lack of attention to the spatial profile of mechanical properties, specifically the cell wall elasticity. In this study, we introduce a new surface morphology-based method to measure the elasticity of the cell wall in tip-growing cells. Previous work is based on measurements from the wall meridional outline, a technique that cannot track the elastic deformation of the cell wall experimentally. Instead, we developed a way to infer the bulk modulus distribution from the cell surface by triangulating experimental marker points coming from fluorescent labeling. To justify the use of our protocol in tip-growing cells from the moss Physcomitrium patens , we replicated the experimental noise and moss morphology in simulated cells. In practice, we found that a larger triangulation improved robustness against noise, which agreed with our theoretical study. With multiple cell sampling, we determined that 10 cells were sufficient to recover the elasticity distribution with noise, but only when the elastic stretches were high enough. We then created a dimensionless map of inference error to verify a spatial change of P. patens bulk modulus within two folds. This technique will open the field to more comprehensive measurements of cell wall elasticity, providing a key step in understanding tip cell growth and morphogenesis. Author summary Tip-growing cells can be characterized by their fast growth concentrated at the cell’s apex. Their growth and morphogenesis are tightly regulated processes involving cell wall addition and rearrangement while the cell wall is under stress originating from the cell’s internal turgor pressure. We start by studying the cell wall’s elastic properties, one aspect of the cell growth process. We use a method of marker point tracking across the surface of the tip-growing cell to measure the wall’s elasticity profile. In this work, we present a parameter sensitivity study of this method on synthetic cells and report our results on experimental moss tip-growing cells. Our results suggest that this inference method can reliably measure a cell wall elasticity gradient under combined geometric and mechanical conditions that create elastic strains within 5% at the tip.
Why it matches plant phenotyping methodsコケの先端成長細胞における細胞壁弾性分布を、蛍光マーカーと表面形態から推定する新規測定法を開発し、シミュレーションおよび実細胞で検証しているため、植物フェノタイピング手法が中心である。
abstractIn this study, we introduce a new surface morphology-based method to measure the elasticity of the cell wall in tip-growing cells.
Reproduction assets foundThe authors' Data Availability statement explicitly deposits all relevant data and code, including code demonstrations, in a public GitHub repository (rholee-xu/surface-model), which contains the analysis code for the cell wall elasticity inference method.Code · publicAll relevant data and code, including code demonstrations, are available on the GitHub repository found here: https://github.com/rholee-xu/surface-modelOpen asset ↗rholee-xu/surface-modellines:45-63Code / dataset availability confirmedEurope PMC · checked 14 Sept 2026
Sugarcane stem node detection is critical for monitoring sugarcane growth, enabling precision cutting, reducing spuriousness, and improving breeding for resistance to downfall. However, in complex field environments, sugarcane stem nodes often suffer from reduced detection accuracy due to background interference and shadowing effects. For this reason, this paper proposes an improved sugarcane stem node detection model based on YOLO11. This study incorporates the ASF-YOLO (Attentional Scale Sequence Fusion based You Only Look Once) mechanism to enhance the feature fusion layer of YOLO11. Additionally, a high-resolution detection layer, P2, is integrated into the fusion module to improve the model's ability to detect small objects-particularly sugarcane stem nodes-and to better handle multi-scale feature representations. Secondly, to better align with the P2 small-object detection layer, this paper adopts a shared convolutional detection head named LSDECD (Lightweight Shared Detail-Enhanced Convolutional Detection Head), which can better deal with small target detection while reducing the number of model parameters through parameter sharing and detail-enhanced convolution. Using soft-NMS (non-maximum suppression) to replace the original NMS and combining with Shape-IoU, a bounding box regression method that focuses on the shape and scale of the bounding box itself, makes the bounding box regression more accurate, and solves the problem of the impact of detection caused by occlusion and illumination. Finally, to address the increased complexity introduced by the addition of the P2 detection layer and the replacement of the detection head, channel pruning is applied to the model, effectively reducing its overall complexity and parameter count. The experimental results show that the model before pruning has 96.1% and 53.2% mean average precision mAP50 and mAP50:95, respectively, which are 11.9% and 11.1% higher than the original YOLO11n, and the model after pruning also has 10.8% and 9.3% higher than the original YOLO11n, respectively, and the number of parameters is reduced to 279,778, and model size is reduced to 1.3MB. The computational cost decreased from 11.6 GFlops to 6.6 GFlops.
Why it matches plant phenotyping methodsサトウキビ茎節という植物器官の検出を対象に、改良YOLOモデルの開発と性能評価を中心的に行っており、再利用可能な画像ベース表現型取得手法に該当する。
abstractThe experimental results show that the model before pruning has 96.1% and 53.2% mean average precision mAP50 and mAP50:95
Reproduction assets foundThe paper's Data Availability statement explicitly deposits the sugarcane stem node dataset and the study's source code on ScienceDB with public DOIs, both matching allowed URLs.Dataset · publicppress-copyright no pmc-prop-is-real-version no pmc-prop-is-scanned-article no pmc-prop-preprint no pmc-prop-in-epmc yes pmc-license-ref CC BY Data Availability All data and code underlying the findings of this study are fully available without restriction from the ScienceDB. The sugarcane stem node dataset is available at DOI: https://doi.org/10.57760/sciencedb.27078 The source code used in this study is available at DOI: https://doi.org/10.57760/sciencedb.27287 .
Data Availability
All data and code underlying the findings of this study are fully available without restriction from the ScienceDB. The sugarcane stem node dataset is available at DOI: https://doi.org/10.57760/sciencedb.27078 ThOpen asset ↗ScienceDB · 10.57760/sciencedb.27078lines:65-90Code · publicno pmc-prop-in-epmc yes pmc-license-ref CC BY Data Availability All data and code underlying the findings of this study are fully available without restriction from the ScienceDB. The sugarcane stem node dataset is available at DOI: https://doi.org/10.57760/sciencedb.27078 The source code used in this study is available at DOI: https://doi.org/10.57760/sciencedb.27287 .
Data Availability
All data and code underlying the findings of this study are fully available without restriction from the ScienceDB. The sugarcane stem node dataset is available at DOI: https://doi.org/10.57760/sciencedb.27078 The source code used in this study is available at DOI: https://doi.org/10.57760/sciencedb.27287 .Open asset ↗ScienceDB · 10.57760/sciencedb.27287lines:65-90Code / dataset availability confirmedarXiv · checked 15 Sept 2026
Street trees are vital to urban livability, providing ecological and social benefits. Establishing a detailed, accurate, and dynamically updated street tree inventory has become essential for optimizing these multifunctional assets within space-constrained urban environments. Given that traditional field surveys are time-consuming and labor-intensive, automated surveys utilizing Mobile Mapping Systems (MMS) offer a more efficient solution. However, existing MMS-acquired tree datasets are limited by small-scale scene, limited annotation, or single modality, restricting their utility for comprehensive analysis. To address these limitations, we introduce WHU-STree, a cross-city, richly annotated, and multi-modal urban street tree dataset. Collected across two distinct cities, WHU-STree integrates synchronized point clouds and high-resolution images, encompassing 21,007 annotated tree instances across 50 species and 2 morphological parameters. Leveraging the unique characteristics, WHU-STree concurrently supports over 10 tasks related to street tree inventory. We benchmark representative baselines for two key tasks--tree species classification and individual tree segmentation. Extensive experiments and in-depth analysis demonstrate the significant potential of multi-modal data fusion and underscore cross-domain applicability as a critical prerequisite for practical algorithm deployment. In particular, we identify key challenges and outline potential future works for fully exploiting WHU-STree, encompassing multi-modal fusion, multi-task collaboration, cross-domain generalization, spatial pattern learning, and Multi-modal Large Language Model for street tree asset management. The WHU-STree dataset is accessible at: https://github.com/WHU-USI3DV/WHU-STree.
Why it matches plant phenotyping methods樹木の個体セグメンテーションと形態パラメータを含むマルチモーダルデータセットを構築し、ベンチマークする研究であり、植物個体の状態・形態抽出手法が中心である。
abstractWHU-STree, a cross-city, richly annotated, and multi-modal urban street tree dataset.
Reproduction assets foundThe paper's core asset is the WHU-STree multi-modal street tree dataset (point clouds, panoramic images, 21,007 annotated tree instances, 50 species, height/DBH), which the authors state is publicly accessible via their GitHub organization WHU-USI3DV. The Zenodo DOIs in the reference list belong to cited prior datasetsDataset · publicticular, we
identify key challenges and outline potential future works for fully exploit-
ing WHU-STree, encompassing multi-modal fusion, multi-task collaboration,
cross-domain generalization, spatial pattern learning, and Multi-modal Large
Language Model for street tree asset management. The WHU-STree dataset
is accessible at: https://github.com/WHU-USI3DV /WHU-STree.
Keywords: Deep learning, Tree inventory, Individual tree segmentation,
Tree species classification, Multi-modal, Mobile mapping system
1. Introduction
Street trees, vital to urban ecosystems, provide ecological benefits (e.g.,
shade (Kumar et al., 2024), air purification (Grundstrém and Pleijel, 2014),
noise reductiOpen asset ↗WHU-STreepdf-raw-page:2 lines:1-35Code / dataset availability confirmedEurope PMC · checked 6 Sept 2026
Quantifying crop responses to increasing temperatures is critical for predicting the productivity and sustainability of agricultural systems under environmental change. Physiological trait data associated with maximum Rubisco carboxylation ( V cmax ) and maximum electron transport ( J max ) rates are especially important predictors of crop response to elevated temperatures. However, when generating V cmax and J max data, steady-state methods of gas exchange measurements are time-consuming; thus, non-steady-state methods have been developed to obtain these measurements faster, prospectively allowing for trait data collection of considerably more varieties of crops. Globally important and geographically widespread vineyards are of particular interest due to the high economic value and the susceptibility of these managed systems to climate warming, especially in Canada, where the annual rate of warming far exceeds global averages. In this study, we examined the efficacy of the high-throughput, non-steady-state dynamic assimilation technique (DAT) for obtaining V cmax and J max data from wine grapes. Specifically, we measured V cmax and J max (alongside leaf nitrogen [N] concentrations and leaf mass per unit area [LMA]) across seven of the world's most common wine grape ( Vitis vinifera L.) varieties, namely, Cabernet franc, Cabernet sauvignon, Merlot, Pinot noir, Riesling, Sauvignon blanc, and Viognier. Our results show that V cmax and J max estimates derived from the DAT were strongly correlated to those obtained through the steady-state method ( r 2 = 0.748 and 0.908, respectively), and J max did not differ significantly between the two methods. Additionally, leaf N explained 43%-46% and 56%-58% of the variation in V cmax and J max , respectively, across both methods. Our results suggest that the DAT represents a viable tool for rapidly estimating intraspecific variation in important physiological traits and allows for increased replication and the inclusion of additional varieties when evaluating the responses of wine grape and other crops to climate warming.
Why it matches plant phenotyping methodsワインブドウの生理形質を高速取得する動的同化技術（DAT）を定常法と比較検証しており、植物表現型の測定法が中心的である。
abstractwe examined the efficacy of the high-throughput, non-steady-state dynamic assimilation technique (DAT) for obtaining V cmax and J max data from wine grapes.
Reproduction assets foundThe paper's physiological trait data (Vcmax, Jmax, leaf N, LMA for seven wine grape varieties) are openly deposited in the University of Toronto Borealis Dataverse, per the Data Availability Statement. No author analysis code or trained models are reported.Dataset · publicThe data that support the findings of this study are openly available in the Borealis Repository—University of Toronto Dataverse at https://doi.org/10.5683/SP3/URPVFF .Open asset ↗Borealis Repository—University of Toronto Dataverse · 10.5683/SP3/URPVFFlines:277-347Code / dataset availability confirmedEurope PMC · checked 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
348
(Table SA1).
349
2.9 Verification of CAMP predictions
350
2.9.1 Model set-up and operation.
351
The CAMP model was coded into a Python script which is available at
352
https://github.com/HamishBrownPFR/CAMP/blob/master/CAMP.ipynb. A formal
353
description of the code and parameterisation scheme is given in the supplementary material.
354
The FLN developmental phenotypes measured for each genotype (Section 3.1) were used to
355
derive the Vrn expression parameters needed for CAMP. Each of the treatments was
356
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
356
simulated using CAMP with its corresponding daily temperature and Pp, so its predictions of
357
Vrn gene expression could be compared with those observed. The script running the CAMP
358
code and producing the graphs displayed in this paper can be viewed at
359
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
689
broader contexts. EW contributed substantially to the improvement of model concepts and the
690
manuscript and all authors provided final checking.
691
8. Data Availability
692
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
695
Allard V, Otto V, Bela K, Rousset M, Le Gouis J, Martre P. 2012. The quantitative
696
response of wheat vernalization to environmental variables indicates that vernalization is not
697
a response to cold temperature. Journal of Experimental Botany 63: 847–857.
698
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 6 Sept 2026
The vertical distribution of leaves plays a crucial role in the growth process of maize. Understanding the vertical spectral characteristics of maize leaves is crucial for monitoring their growth. However, accurate estimation of the vertical distribution of leaf area remains a significant challenge in practical investigations. To address this, we used a 3D RTM to simulate the layered canopy spectra of maize, revealing the impact of canopy structure on remote sensing penetration depth across different growth stages and planting densities. The results of this study revealed differences in detection depth across growth stages. During the early growth stage, the depth was concentrated in the bottom 1 to 3 leaves of the canopy, reaching 1 to 4 leaves at the ear stage and 1 to 7 leaves during the grain-filling stage. The planting density had a notable effect on the detection depth at the bottom of the canopy. Moreover, compared with the other spectral bands, the near-infrared spectral range exhibited greater sensitivity to density variations. In terms of LAI inversion, a FuseBell-Hybrid model was constructed. We analyzed VIs across different planting density and canopy structural scenarios and found that compared with lower layers, increased density reduced the relative change rate in the upper leaf layers. The sensitivity patterns differed between plant architectures: VIred exhibited density-dependent sensitivity, with distinct responses between plant types, and MTVI2 demonstrated optimal performance for mid-canopy monitoring. This study highlights the influence of the heterogeneous structural characteristics of maize canopies on remote sensing detection depth during different phenological stages, providing theoretical support for enhancing multilayer crop monitoring in precision agriculture.
Why it matches plant phenotyping methods分光計測と3D放射伝達モデルを用いてトウモロコシ冠層の検出深度およびLAI推定法を構築・評価しており、植物形質取得手法が研究の中心である。
abstractTo address this, we used a 3D RTM to simulate the layered canopy spectra of maize
Reproduction assets foundThe paper states its analysis code was uploaded to a public GitHub repository, which qualifies as an authors' public code asset for the LAI phenotyping analysis. No separate phenotype dataset or model checkpoint deposit is explicitly stated.Code · publicData availability
The code have been uploaded to Github: https://github.com/aaawitch/code .Open asset ↗https://github.com/aaawitch/codelines:290-311Code / dataset availability confirmedEurope PMC · checked 15 Sept 2026
Computer vision has emerged as a critical enabler of sustainable production in protected agriculture by offering efficient and non-invasive crop disease diagnosis. The development of accurate disease recognition models relies heavily on the availability of high-quality image datasets. This study introduces a tomato disease image dataset collected in 2024 from greenhouse facilities within a modern agricultural park in Sichuan Province, China. The dataset comprises 1026 high-resolution images, including 417 images of viral disease, 82 images of gray mold, and 527 images of bacterial wilt, totaling approximately 2.78 GB. Captured under real-world greenhouse conditions and from multiple angles and distances, the images effectively capture multi-scale phenotypic disease features. Manual annotation was conducted using the LabelImg tool under the guidance of plant pathology experts, with labeled regions covering leaves, fruits, and stems. Annotation files are stored in XML format, each corresponding to a specific image. This dataset is well-suited for research in disease classification, object detection, and phenotyping, and supports deep learning model training and cross-crop transfer learning applications.
Why it matches plant phenotyping methodsトマト病害の症状を画像で捉え、分類・検出モデル用に専門家アノテーションした再利用可能なデータセットであり、植物病害状態の表現型取得が中心である。
abstractThe development of accurate disease recognition models relies heavily on the availability of high-quality image datasets.
Reproduction assets foundThe paper is a Data in Brief article describing a public tomato disease image dataset (1026 annotated images) deposited on Mendeley Data with a direct URL and DOI, matching an allowed URL exactly.Dataset · publicwas conducted at the Modern Agricultural Science and Technology Innovation Demonstration Park of the Sichuan Academy of Agricultural Sciences (30.7797° N, 104.2082° E), located in Sichuan Province, China.
Data accessibility
Repository name: Mendeley Data
Data identification number: DOI: 10.17632/c2×8rynybg.1
Direct URL to data: https://data.mendeley.com/datasets/c2×8rynybg/1
Related research article
None.
1
Value of the Data
The dataset contains 1026 annotated images of tomato plants exhibiting three major disease types, collected in 2024 from greenhouse environments in Sichuan’s Modern Agricultural Demonstration Park. Plant pathology specialists manually labeled all samples. Its technical sOpen asset ↗Mendeley Data · 10.17632/c2×8rynybg.1lines:1-52Code / dataset availability confirmedEurope PMC · OpenAlex · checked 6 Sept 2026
Understanding the genetic basis of root system architecture (RSA) in crops requires innovative approaches that enable both high-throughput and precise phenotyping in field conditions. In this study, we evaluated multiple phenotyping and analytical frameworks for quantifying RSA in mature, field-grown maize in three field experiments. We used forward and reverse genetic approaches to evaluate >1700 maize root crowns, including a diversity panel, a biparental mapping population, and maize mutant and wild-type alleles at two known RSA genes, DEEPER ROOTING 1 (DRO1) and Rootless1 (Rt1). We show the utility of increasing the dimensionality of traditional two-dimensional (2D) techniques, referred to as the "2D multi-view" method, to improve the capture of whole root system information for mapping genetic variation influencing RSA. Comparison of univariate and multivariate genome-wide association study (GWAS) approaches revealed that multivariate traits were effective at dissecting complex RSA phenotypes and identifying pleiotropic quantitative trait loci (QTLs). Overall, three-dimensional (3D) root models generated from X-ray computed tomography and digital phenotyping captured a larger proportion of RSA trait variations compared to other methods of root phenotyping, as evidenced by both genome-wide and single-gene analyses. Among the individual root traits, root pulling force emerged as a highly heritable estimate of RSA that identified the largest number of shared QTLs with 3D phenotypes. Our study shows that integrating complementary phenotyping technologies helps to provide a more comprehensive understanding of the genetic architecture of RSA in field-grown maize.
Why it matches plant phenotyping methods根系構造を定量化する複数の表現型解析法を比較・評価し、2Dマルチビュー、X線CT、デジタル表現型などの技術性能を遺伝解析で検証しており、表現型取得法が研究の中心である。
abstractwe evaluated multiple phenotyping and analytical frameworks for quantifying RSA in mature, field-grown maize
Reproduction assets foundThe paper deposits raw phenotypic metadata (root crown/RSA measurements from the field experiments) on Dryad, and uses the authors' public 3D root crown analysis pipeline (RCAP) on GitHub for the XRT feature extraction. Both are paper-specific, public, and actionable. Generic R packages and cited prior work are not.Dataset · publicRaw phenotypic metadata are available on the Dryad Digital Repository ( https://doi.org/10.5061/dryad.z34tmpgq4 , http://datadryad.org/share/HeNYoxNMdN_GrHMyZHFN3rUTN1UiG8OFhU-B107E7mM ).Open asset ↗Dryad Digital Repository · 10.5061/dryad.z34tmpgq4lines:499-731Code · publicreferred to here as the root crown analysis pipeline (RCAP). Detailed descriptions of RCAP trait implementations and related resources are available at: https://github.com/Topp‐Roots‐Lab/3d‐root‐crown‐analysis‐pipeline/ .Open asset ↗GitHub · Topp‐Roots‐Lab/3d‐root‐crown‐analysis‐pipelinelines:162-175Code / dataset availability confirmedEurope PMC · checked 6 Sept 2026
The links between intraspecific trait variation and community assembly remain little studied, partially due to the lack of statistical methods to jointly model intraspecific trait variation and species abundances at the community level. Here, we extend the joint species distribution modeling (JSDM) framework into the joint species-trait distribution modeling (JSTDM) framework to explicitly link species abundances to phenotypic variation in traits for multiple species simultaneously. Using a case study of 65 tundra plant species abundances and 3 key functional traits measured across 325 sites, we show how the JSTDM approach (1) estimates the statistical associations among species abundances, species-level traits, and site-level traits, relative to environmental variation; (2) improves predictions on trait variation by using information on species abundances; and (3) generates hypotheses about trait-driven community assembly mechanisms. The JSTDM methodology presented in this study allows assessing the interplay between species abundances and traits at the community level, providing the much needed modeling tools to quantify the role of phenotypic trait variation in eco-evolutionary community assembly.
Why it matches plant phenotyping methods植物の機能形質変異を種 abundance と共同モデル化する新しい統計手法を中心に提示しており、形質変異の推定・予測が主要な方法論的成果である。
abstractwe extend the joint species distribution modeling (JSDM) framework into the joint species-trait distribution modeling (JSTDM) framework
Reproduction assets foundThe paper's data availability statement explicitly deposits the tundra plant trait/abundance data and analysis scripts on Zenodo (DOI 10.5281/zenodo.15280766), which is an allowed URL and matches the reference to 'Data and Scripts for Joint Species-Trait Distribution Modelling...'. This qualifies as a paper-specific,公开Code · publicData and code (Abrego, 2025 ) are available on Zenodo at https://doi.org/10.5281/zenodo.15280766 .Open asset ↗Zenodo · 10.5281/zenodo.15280766lines:86-144Code / 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,
1,
Downloaded
from
https://acsess.onlinelibrary.wiley.com/doi/10.1002/ppj2.70040,
Wiley
Online
Library
on
[20/08/2025].
See
the
Terms
and
Conditions
(https://onlinelibrary.wiley.com/terms-and-conditions)
on
WileyOpen asset ↗derekmichaelwright.github.io/AGILE_LDP_UAV · LDP_UAV_Vignettepdf-raw-page:3 lines:1-106Code / dataset availability confirmedbioRxiv · checked 15 Sept 2026
The accessibility of flying drones (Unoccupied Aerial Vehicles) presents scientists and managers with reproducible and cost-effective methods to monitor submerged aquatic vegetation. In particular, drone-borne topobathymetric LiDAR provides high-resolution (cm-scale), three-dimensional information about the geometry and structure of surveyed areas, allowing for quantification of vegetation volume in addition to bathymetry. For habitat-forming submerged and intertidal vegetation like seagrass, this information can advance research regarding the structure and patchiness of canopies in relation to biodiversity, blue carbon storage, and hydrodynamic processes. Here, we report how drone-borne LiDAR can be used to estimate the habitat volume of eelgrass (Zostera marina) within a sheltered bay in south-eastern Norway. After classifying LiDAR points using a Random Forest model, we created a Digital Terrain Model of the sea floor and a Digital Surface Model of the eelgrass canopy. From these models, we estimated eelgrass canopy volume to range between 862 and 1099 m3 across the small study area. From the volume, we estimated above-ground carbon storage in living eelgrass tissue to range between 96 and 122 kg. To our knowledge, this is the first study to utilise drone-borne LiDAR to quantify the volume and carbon-storage potential of a marine habitat-forming species like eelgrass, thereby demonstrating the potential of drone-borne LiDAR as an efficient tool to provide reproducible and high-resolution data for submerged aquatic habitats, including seagrass meadows.
Why it matches plant phenotyping methodsドローン搭載LiDARを用いて eelgrass のキャノピー体積という植物形態形質を推定する方法が研究の中心であり、分類、地形・表面モデル作成、再現可能な高解像度測定手法として記述されているため。
abstractHere, we report how drone-borne LiDAR can be used to estimate the habitat volume of eelgrass (Zostera marina) within a sheltered bay in south-eastern Norway.
Reproduction assets foundThe paper's R analysis code (point cloud cleaning, Random Forest classification, DTM/DSM/canopy height and biomass/carbon computations) is publicly available on the corresponding author's GitHub repository. The underlying LiDAR/field data are only available upon request, so no public data asset qualifies.Code · publicPre-print 15
Code for the present analysis is available at the corresponding author’s GitHub
585
(https://github.com/charles-patrick-lavin/NIVA-SeaBee-LiDAR), while the data
586
analysed are available upon request.
587
Acknowledgements
588
This work was funded by the Research Council of Norway and is a product of SeaBee
589
(Norwegian Infrastructure for drone- based research, mapping and monitoring in the
590
coastal zone, RCN project ID #296478). Additional funding was received frOpen asset ↗charles-patrick-lavin/NIVA-SeaBee-LiDARpdf-raw-page:15 lines:1-32Code / 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
[ 1 ]
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 · OpenAlex · Crossref · checked 14 Sept 2026
Spike morphology is crucial for wheat (Triticum aestivum L.) yield and environmental adaptation. We developed a high-throughput phenotyping platform to dissect spike morphology traits based on 54 traits in 1,359 wheat accessions. These 54 spike morphology traits exhibited clear geographical differences among 306 worldwide accessions and breeding selection trend across different time windows for 1,053 accessions released from 1900 to 2020 in China. Based on geographical distribution and breeding selection of haplotypes, we attribute the differences in spike morphology to variable haplotype combinations. Wheat breeding breaks the trade-off between spike length and width/thickness, resulting in increased spike volume. A large proportion of genomic regions has been identified across wheat varieties and utilized as a fixed group to facilitate the targeted improvement and selection of desirable traits during wheat breeding programs. Overall, we provide a resource for the molecular design of spike morphology to facilitate future wheat breeding.
Why it matches plant phenotyping methodsコムギ穂の形態形質を多数個体から取得するハイスループット表現型解析プラットフォームの開発と適用が研究の中心である。
abstractWe developed a high-throughput phenotyping platform to dissect spike morphology traits based on 54 traits in 1,359 wheat accessions.
Reproduction assets foundThe paper's high-resolution spike phenotyping platform software is explicitly released as public code by the authors on GitHub. The genotype datasets (GVM000272/GVM000720) are molecular omics deposits and do not qualify as phenotype/trait data; other listed tools are generic third-party libraries.Code · publicn/gvm) under accession number GVM00027239
or GVM000720.
• The genotype data for 1053 Chinese accessions (1900–2020) are pub
licly available at the Genome Variation Map (https://bigd.big.ac.cn/gvm)
under accession number GVM000720.
• The software for the high-resolution phenotyping platform is publicly avail
able with the link https://github.com/ShenKC-hub/wheat_platform1.0.
• Any additional information required to reanalyze the data reported in this
paper is available from the lead contact upon request.
ACKNOWLEDGMENTS
This work was supported by the National Natural Science Foundation
of China (32272122, 32401876, and 32225038)，the Strategic Priority
Research Program of Chinese Academy of Open asset ↗ShenKC-hub/wheat_platform1.0 · wheat_platform1.0pdf-raw-page:15 lines:1-81Code / dataset availability confirmedEurope PMC · bioRxiv · checked 15 Sept 2026
Chlorophyll breakdown is a central process during plant senescence or stress responses and leaf chlorophyll content is therefore a strong predictor of plant health. Chlorophyll quantification can be done in several ways, most of which are time-consuming or require specialized equipment. A simple alternative to these methods is the use of image-based chlorophyll estimation, which uses the color values in RGB images to calculate colorimetric visual indexes as a measure for the leaf chlorophyll content. Image-based chlorophyll measurement is non-destructive and, apart from a digital camera, requires no specialized equipment. Here, we developed the ImageJ plugin GreenLeafVI that facilitates high-throughput image analysis for measuring leaf chlorophyll content. Our plugin offers the option to white-balance images to decrease variation between images and has an optional background removal step. We show that this method can reliably quantify leaf chlorophyll content in a variety of plant species. In addition, we show that image-based chlorophyll quantification can replicate GWAS results based on traditional chlorophyll extraction methods, showing that this method is highly accurate.
Why it matches plant phenotyping methods葉のクロロフィル量を画像から推定するFIJIプラグインを開発し、複数植物種で信頼性とGWAS再現性を検証しており、植物フェノタイピング手法が中心である。
abstractHere, we developed the ImageJ plugin GreenLeafVI that facilitates high-throughput image analysis for measuring leaf chlorophyll content.
Reproduction assets foundThe paper's GreenLeafVI FIJI plugin (the authors' phenotyping analysis code) is publicly available on GitHub with explicit availability language. The underlying phenotype/trait datasets are only available upon request, so they do not qualify as public assets.Code · publicank BSc/MSc students Marion Larue, Karin Verkerk and Kim Roos for their help in phenotyping.
28
29
30 Data availability
31 The data that support the findings of this study are available from the corresponding author upon reasonable
32 request. The GreenLeafVI source code, documentation and further information is available at
33 https://github.com/jelmervanlieshout/GreenLeafVI.
9Open asset ↗jelmervanlieshout/GreenLeafVIpdf-layout-page:9 lines:1-45Code / dataset availability confirmedEurope PMC · bioRxiv · checked 13 Sept 2026
ArabidopsisRootMorphology / geometry measurementRoot system architecture
ABSTRACT Root system architecture (RSA) is central to plant adaptation and fitness, yet the design principles and regulatory mechanisms connecting RSA to environmental adaptation are not well understood. We developed Ariadne, a semi-automated software for quantifying cost-efficiency tradeoffs of RSA by mapping root networks onto a Pareto-optimality framework, which describes the balance between resource transport efficiency and construction cost. Applying Ariadne to Arabidopsis thaliana , we found that root architectures consistently assume Pareto-optimal forms across developmental stages, genotypes, and environmental conditions. Using the Discovery Engine, an engine that combines machine learning together with interpretability techniques, we found developmental stage, the hy5/chl1-5 genotype, and manganese availability as important determinants of the cost-efficiency tradeoff, with manganese exerting a unique influence not observed for other nutrients. These results reveal that RSA plasticity is genetically constrained to cost-efficiency optimal configurations and that developmental and environmental factors shift RSA on the pareto front, with manganese acting as a strong modulator of the transport efficiency and construction cost balance.
Why it matches plant phenotyping methodsRSAのコスト効率トレードオフを定量化する半自動ソフトウェアを開発し、植物形態形質の解析に適用しており、表現型取得・抽出手法が研究の中心である。
abstractWe developed Ariadne, a semi-automated software for quantifying cost-efficiency tradeoffs of RSA by mapping root networks onto a Pareto-optimality framework
Reproduction assets foundThe paper's authors developed the Ariadne software used for all RSA phenotyping and Pareto analysis in this study, and explicitly state it is publicly available on PyPI and provide a GitHub code availability URL. Both are paper-specific, public, actionable code assets. No public phenotype dataset deposit is stated; theCode · publicCode availability : https://github.com/Salk-Harnessing-Plants-Initiative/AriadneOpen asset ↗Salk-Harnessing-Plants-Initiative/Ariadnelines:235-276Code / 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 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 confirmedCrossref · checked 15 Sept 2026
This study presents a novel and efficient approach to accurately assess post-sowing rice plant density by leveraging unmanned aerial vehicles (UAVs) equipped with high-resolution RGB cameras. In contrast to labor-intensive and spatially limited traditional methods that rely on manual sampling and extrapolation, our proposed methodology uses UAVs to rapidly and comprehensively survey entire paddy fields at optimized altitudes (4, 6, 8, and 10 m). Aerial imagery was autonomously acquired 17 days post-sowing, following a pre-defined flight path. The robust rice plant density estimation process incorporates two key innovations: first, a dynamic system of 12 adaptive segmentation thresholding blocks that effectively detects rice seed presence across diverse and variable background conditions. Second, a tailored three-layer convolutional neural network (CNN) accurately classifies vegetative situations. To maximize the training efficiency and performance, we implemented both a pretrained model and a deep learning model, conducting a rigorous comparative analysis against the state-of-the-art YOLOv10. Notably, under favorable imaging conditions, our findings indicate that a 6-m flight altitude yields optimal results, achieving a high degree of accuracy with rice plant density estimates that closely align with those obtained through traditional ground-based methods. This investigation unequivocally highlights the significant advantages of UAV-based monitoring as an economically viable, spatially comprehensive, and demonstrably accurate tool for precise rice field management, ultimately contributing to enhanced crop yields, improved food security, and the promotion of sustainable agricultural practices.
Why it matches plant phenotyping methodsUAV RGB画像と適応的セグメンテーション、CNNを用いてイネ個体密度を推定する手法を開発・比較・検証しており、植物形質の取得が研究の中心です。
abstractThis study presents a novel and efficient approach to accurately assess post-sowing rice plant density by leveraging unmanned aerial vehicles (UAVs) equipped with high-resolution RGB cameras.
Reproduction assets foundThe paper's data availability statement points to a public Zenodo deposit containing the study's datasets (UAV RGB imagery/labels used for rice plant density estimation). No separate author analysis code repository is stated.Dataset · publicvaluate the accuracy of the proposed labels, subsequently enhancing the training model's speed, convergence, accuracy, and efficiency.
Statements
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 at: https://zenodo.org/records/10960906 .
Author contributions
TH: Writing – original draft. TN: Data curation, Resources, Validation, Writing – original draft. QN: Data curation, Writing – review & editing. HN: Funding acquisition, Investigation, Methodology, Writing – review & editing. PP: Methodology, Software, Supervision, Writing – review & editing.
Funding
TheOpen asset ↗zenodo · 10960906lines:500-523Code / dataset availability confirmedCrossref · Europe PMC · checked 13 Sept 2026
GreenhouseThermalLeafPhysiological trait estimationStress response / tolerancePlant / canopy temperature
ABSTRACT Extreme heat can push plants beyond their thermal safety margin ( TSM ) if maximum leaf temperature ( T leaf_max ) exceeds leaf critical temperature ( T crit ). The TSM is potentially useful for assessing heat vulnerability across species but needs further validation, so we exposed 50 tree/shrub species in controlled glasshouses to a 6‐day heatwave (peak air temperature = 41°C). Many species increased their mean T crit during the heatwave (42%), with Δ T crit ranging from +1°C to 4°C, but other species did not acclimate or were impaired by heat stress (58%). Species T leaf_max explained ~55% of the variation in species T crit and was a key correlate of the plasticity of T crit among species. Species with high Δ T crit also had higher Δ T leaf_max , with leaves being 7°‒12°C hotter during the heatwave than under baseline conditions. Both T leaf_max and TSMs were correlated with heatwave damage across diverse species from contrasting climate zones. Species differences in TSMs were stable across measurement temperatures, correctly identified the most vulnerable species, and were strongly associated with T leaf_max . Our results suggest that (1) T leaf_max alone is more informative than T crit for ranking species heat tolerance, and (2) species vulnerability to heatwaves is most reliably assessed by using TSMs that integrate T leaf_max with T crit across species.
Why it matches plant phenotyping methods葉温・熱安全余裕度（TSM）を用いた植物の熱脆弱性評価手法を、多種の植物で検証し、損傷予測性能や種間比較の妥当性を評価しているため、方法的役割が中心である。
abstractThe TSM is potentially useful for assessing heat vulnerability across species but needs further validation
Reproduction assets foundThe article's Data Availability Statement explicitly states the supporting data (phenotype measurements: Tcrit, Tleaf_max, TSM, damage indicators for 50 species) are openly available on Figshare at the authors' public DOI, which is an allowed URL.Dataset · publicData Availability Statement
The data that support the findings of this study are openly available in Figshare at https://doi.org/10.6084/m9.figshare.29345549.v1 .Open asset ↗Figshare · 10.6084/m9.figshare.29345549.v1lines:721-817Code / dataset availability confirmedEurope PMC · checked 6 Sept 2026
Abstract Tomato leaf disease poses a significant threat to global agricultural productivity, underscoring the need for accurate and automated segmentation techniques for early detection and intervention. In this study, we proposed DSGSU-Net, an enhanced U-Net-based architecture explicitly designed for the precise segmentation of tomato leaf diseases. The model incorporates depthwise separable convolutions for efficient feature extraction, dilated convolutions in deeper layers for multi-scale context aggregation, and Ghost Sampling in the decoder for improved upsampling. To further enhance segmentation performance, a hybrid loss function combining Dice Loss and Focal Loss is utilized to manage class imbalance and enhance the boundary delineation. Experiments conducted on the PlantVillage dataset (bacterial spot class) demonstrated that DSGSU-Net achieved an accuracy of 0.9572, an F1-score of 0.8276,precision of 0.7156,recall of 0.9885, IoU of 0.7102, and a Dice coefficient of 0.9822. The results show that DSGSU-Net outperforms conventional U-Net models in segmentation accuracy and computational efficiency, making it a strong contender for practical use in precision agriculture and disease surveillance.
Why it matches plant phenotyping methodsトマト葉の病徴を画像からセグメンテーションするモデルを開発・比較しており、植物病害状態の表現型抽出が研究の中心である。
abstractwe proposed DSGSU-Net, an enhanced U-Net-based architecture explicitly designed for the precise segmentation of tomato leaf diseases.
Reproduction assets foundThe paper's tomato leaf disease segmentation study uses the public PlantVillage tomato leaf dataset (bacterial spot class) from Kaggle, explicitly declared in the Data Availability section. No author analysis code, trained model checkpoints, or custom mask annotations are stated as publicly available.Dataset · publicThe datasets generated and analyzed during the current study are available in the Kaggle repository: https://www.kaggle.
com/datasets/charuchaudhry/plantvillage-tomato-leaf-datasetOpen asset ↗Kaggle · plantvillage-tomato-leaf-datasetpdf-page:20 lines:1-51Code / dataset availability confirmedOpenAlex · checked 14 Sept 2026
Natural biocomposites such as wood and plant cell walls exhibit remarkable mechanical properties largely attributed to their nanoscale chiral organization of fibrous components, such as cellulose. However, resolving the three-dimensional (3D) arrangement of these structures at the nanoscale remains a significant challenge, particularly in beam-sensitive materials. This study introduces a method for 3D reconstruction of orientation based on scanning electron diffraction (SED), enabling the quantitative mapping of chiral supramolecular organization with sub-100 nm spatial resolution. By acquiring low-dose SED data at multiple tilt angles and applying a symmetry-based reconstruction algorithm, we resolved the 3D orientation of cellulose fibrils in native oat husk and birch wood. Our results reveal a multilayered cell wall architecture with alternating helical handedness, providing precise measurements of 3D fibril orientation. This method reveals complex hierarchical structures at the nanoscale, enabling rapid data acquisition and analysis using widely available instrumentation. The ability to resolve such chiral organization opens new understanding of materials properties as well as opportunities for the design of bio-inspired materials with tunable mechanical and functional properties.
Why it matches plant phenotyping methods植物細胞壁中のセルロース fibril の3D配向を定量マッピングする画像計測・再構成法が研究の中心であり、植物構造形質の取得手法を開発している。
abstractThis study introduces a method for 3D reconstruction of orientation based on scanning electron diffraction (SED), enabling the quantitative mapping of chiral supramolecular organization with sub-100 nm spatial resolution.
Reproduction assets foundThe article's Data and Code Availability statement declares that the SED datasets (diffraction data from oat husk and birch wood) and the authors' custom Python analysis script are publicly available on Zenodo (DOI: 10.5281/zenodo.15647651). This is a paper-specific, public, actionable asset directly reproducing the 3DDataset · publicData and Code Availability
SED data and Python script for SED data analysis used in this study are available from Zenodo
(DOI: 10.5281/zenodo.15647651).Open asset ↗Zenodo · 10.5281/zenodo.15647651pdf-page:19 lines:1-36Code · publicSED data and Python script for SED data analysis used in this study are available from Zenodo
(DOI: 10.5281/zenodo.15647651).Open asset ↗Zenodo · 10.5281/zenodo.15647651pdf-page:19 lines:1-36Code / dataset availability confirmedOpenAlex · Europe PMC · checked 13 Sept 2026
Forest phenotypic responses are significantly influenced by extreme climate conditions, particularly canopy structure and photosynthetic traits. However, the underlying mechanisms driving these responses, especially in conifer species, remain poorly understood. This study employs advanced phenotyping technologies, combining three-dimensional (3D) canopy reconstruction with high-resolution physiological trait analysis, quantifying changes in key physiological traits that light interception, gas exchange parameters stomatal conductance, and chlorophyll content. Developing 3D reconstruction algorithms tailored to conifer canopies is essential for simulating forest ecosystem responses under varying canopy densities. We investigate the following questions: (1) How does thinning affect canopy light penetration and photosynthetic efficiency? Thinning significantly increased light penetration from 15 % (CK) to 22 %, enhancing photosynthetic efficiency, resulting in an 18 % increase in carbon absorption under drought conditions. (2) How does reduced-rainfall affect photosynthetically active radiation (PAR) and stomatal conductance? Reduced-rainfall caused a 12 % decrease in PAR, a 20 % reduction in stomatal conductance, and an 8 % decrease in chlorophyll content. (3) What are the synergistic effects of thinning and reduced-rainfall in carbon absorption? Thinning under reduced-rainfall increased carbon absorption by 25 %. This study reveals a significant correlation between chlorophyll content, leaf nitrogen content, and canopy structural dynamics under drought and elevated temperature conditions, offering new insights into the adaptive mechanisms plants employ to adjust their photosynthetic processes. In conclusion, the development of 3D reconstruction algorithms tailored for conifer canopies, in regulating photosynthetic traits, is crucial for improving forest adaptation, contributing to functional trait-based forest management and ecosystem modeling.
Why it matches plant phenotyping methods針葉樹林冠の3D再構成アルゴリズム開発と生理形質推定が明示されており、植物表現型取得法が研究の中心的要素である。
abstractThis study employs advanced phenotyping technologies, combining three-dimensional (3D) canopy reconstruction with high-resolution physiological trait analysis
Reproduction assets foundThe paper's Data Availability statement explicitly deposits the datasets generated during the study (phenotype/physiological measurements and 3D canopy reconstruction outputs) in a public GitHub repository with an authors' URL, making it a paper-specific, publicly actionable asset.Dataset · publicThe datasets generated during this study are available in the GitHub repository: https://github.com/wuchunyanhehe/Plant-Phenomics-Wu-2025 .Open asset ↗https://github.com/wuchunyanhehe/Plant-Phenomics-Wu-2025lines:270-306Code / dataset availability confirmedEurope PMC · checked 6 Sept 2026
The aboveground biomass (AGB) of crops is an essential metric for monitoring crop growth, making timely and accurate AGB forecasting critical for effective agricultural management. The introduction of Unmanned Aerial Vehicles (UAVs) and advanced sensor technologies has revolutionized traditional AGB prediction techniques. Currently, machine learning (ML) combined with UAV data are commonly utilized, along with the Vegetation Index Weighted Canopy Volume Model (CVM VI ) for AGB prediction. Nevertheless, there is limited investigation into how these methods perform across different agricultural conditions. This study aims to fill this gap by creating specific methodologies for estimating corn AGB under diverse fertilization and irrigation treatments. We utilized LiDAR, multispectral (MS), thermal infrared (TIR), along with measured AGB and Leaf Area Index (LAI) data from various growth stages to develop a stacking ensemble learning model. This model effectively integrates data from multiple sources, resulting in a strong prediction performance with R 2 of 0.86, Mean Absolute Error (MAE) of 1.54 t/ha, and Root Mean Square Error (RMSE) of 2.06 t/ha. Meanwhile, the analysis of the accuracy of CVM VI revealed its efficacy during the early-stage when corn is short, with its predictive capability diminishing as AGB increases. Consequently, we recommend the CVM VI for early-stage AGB prediction, which can streamline data collection and computational efforts. In contrast, the ML approach, which benefits from data fusion, is more appropriate for predicting AGB during the mid to late growth stages. This study enhances AGB prediction accuracy and speed, providing critical understanding of regional AGB dynamics and supporting better agricultural decision-making.
Why it matches plant phenotyping methodsUAVのLiDAR・マルチスペクトル・熱赤外データを統合し、トウモロコシの地上部バイオマスを推定するモデルを開発・比較・評価しており、植物形質取得手法が中心である。
abstractThis study aims to fill this gap by creating specific methodologies for estimating corn AGB under diverse fertilization and irrigation treatments.
Reproduction assets foundThe paper's Data Availability statement explicitly deposits authors' model training code and test data at a public GitHub repository, which qualifies as a paper-specific public code asset for the AGB prediction analysis.Code · publicCode and test data for model training are available at https://github.com/Joker1xuan/model_training .Open asset ↗Joker1xuan/model_traininglines:323-356Code / dataset availability confirmedEurope PMC · checked 6 Sept 2026
Genomic selection (GS) and phenotypic selection (PS) are widely used for accelerating plant breeding. However, the accuracy, robustness, and transferability of these two selection methods are underexplored, especially when addressing complex traits. In this study, we introduce a novel data fusion framework, GPS (genomic and phenotypic selection), designed to enhance predictive performance by integrating genomic and phenotypic data through three distinct fusion strategies: data fusion, feature fusion, and result fusion. The GPS framework was rigorously tested using an extensive suite of models, including statistical approaches (GBLUP and BayesB), machine learning models (Lasso, RF, SVM, XGBoost, and LightGBM), a deep learning method (DNNGP), and a recent phenotype-assisted prediction model (MAK). These models were applied to large datasets from four crop species, maize, soybean, rice, and wheat, demonstrating the versatility and robustness of the framework. Our results indicated that: (1) data fusion achieved the highest accuracy compared with the feature fusion and result fusion strategies. The top-performing data fusion model (Lasso_D) improved the selection accuracy by 53.4% compared to the best GS model (LightGBM) and by 18.7% compared to the best PS model (Lasso). (2) Lasso_D exhibited exceptional robustness, achieving high predictive accuracy even with a sample size as small as 200 and demonstrating resilience to single-nucleotide polymorphism (SNP) density variations, underscoring its adaptability to diverse data conditions. Moreover, the model's accuracy improved with the number of auxiliary traits and their correlation strength with target traits, further highlighting its adaptability to complex trait prediction. (3) Lasso_D demonstrated broad transferability, with substantial improvements in predictive accuracy when incorporating multi-environmental data. This enhancement resulted in only a 0.3% reduction in accuracy compared to predictions generated using data from the same environment, affirming the model's reliability in cross-environmental scenarios. This study provides groundbreaking insights, pushing the boundaries of predictive accuracy, robustness, and transferability in trait prediction. These findings represent a significant contribution to plant science, plant breeding, and the broader interdisciplinary fields of statistics and artificial intelligence.
Why it matches plant phenotyping methods植物の形質予測を目的とするGPSデータ融合フレームワークを開発し、複数作物・モデルで精度、頑健性、環境間移 transferability を評価しており、形質推定手法が研究の中心である。
abstractwe introduce a novel data fusion framework, GPS (genomic and phenotypic selection), designed to enhance predictive performance by integrating genomic and phenotypic data through three distinct fusion strategies: data fusion, feature fusion, and result fusion.
Reproduction assets foundThe paper's authors publicly released their GPS analysis scripts on GitHub, and the study used public genomic+phenotypic datasets (rice, maize, wheat, SoyNAM) with explicit URLs. DNNGP and MAK repositories are cited third-party tools, not paper-specific assets.Code · publicThe GPS scripts are available in the release package on GitHub ( https://github.com/Jinlab-AiPhenomics/BioGPS ). All public datasets used in this study are listed in the main text ( Table 1 ).Open asset ↗Jinlab-AiPhenomics/BioGPSlines:244-249Code / dataset availability confirmedEurope PMC · checked 14 Sept 2026
ArabidopsisSoybeanCell / cellular structureRootMorphology / geometry measurementRoot system architecture
Abstract Root system architecture (RSA), the three-dimensional arrangement of roots in soil, is a critical determinant of plant productivity, resource use efficiency, and resilience to environmental stress. Despite its agronomic importance, RSA remains a largely untapped breeding target due to historical technical barriers in root phenotyping. We present RADICYL (Root Architecture 3D Cylinder), a scalable, non-invasive, gel-based platform enabling high-throughput, high-resolution quantification of 15 RSA traits in intact root systems. Applying RADICYL to a genetically diverse panel of 371 soybean accessions, we combined 3D phenotyping with genome-wide association studies (GWAS), single-nucleus RNA sequencing (snRNA-seq), and gene co-expression network (GCN) analysis to identify RCE1 and NPR3 as central regulators of RSA, suggesting auxin and salicylic acid-mediated signaling impacts RSA in specific root tissues. Functional validation in Arabidopsis mutants revealed conserved effects on root width and lateral root development. Our findings position the endodermis and metaphloem as key regulatory cell types and demonstrate how multi-omic frameworks can accelerate the discovery of functional genes underlying complex traits. This study establishes a foundation for cell-type-targeted genome editing and climate-smart crop engineering, offering actionable genetic targets to optimize root systems for improved nutrient acquisition, drought resilience, and deep carbon sequestration. By bridging genotype, cellular context, and phenotype, this work redefines RSA as a tractable and transformative trait for the future of crop improvement.
Why it matches plant phenotyping methodsRADICYLという根系構造を定量化する高スループット3Dフェノタイピング基盤の開発・適用が研究の中心であり、15形質を測定している。
abstractWe present RADICYL (Root Architecture 3D Cylinder), a scalable, non-invasive, gel-based platform enabling high-throughput, high-resolution quantification of 15 RSA traits in intact root systems.
Reproduction assets foundThe paper's Data and code availability section names public repositories containing the authors' analysis code: a GitLab repo for WGCNA/single-cell network analysis, a GitHub repo for the RADICYL root image segmentation/phenotyping pipeline, and PyGNA2 on PyPI/GitLab. These are paper-specific, publicly actionable code/Code · publicn every 5°, resulting in 72 images per plant per timepoint for subsequent 3D root
1103 reconstruction. Phenotypic traits were quantified using the same automated pipeline described
1104 above for soybean.
1105
1106 Data and code availability
1107 The code to analyze the WGCNA network and single-cell data can be found here:
1108 https://gitlab.com/salk-tm/soybean-root-gwas/. RADYCL Segmentation pipeline for image
1109 analysis can be found here: https://github.com/Salk-Harnessing-Plants-Initiative/SSRAPC-Soy-
1110 Segmentation-Root-Architecture-Phenotyping-for-Cylinder.git. PyGNA2 is available on PyPI
1111 (https://pypi.org/project/pygna2/) and GitLab (https://gitlab.com/salk-tm/pygna2).
1112Open asset ↗salk-tm/soybean-root-gwaspdf-layout-page:30 lines:1-54Code / dataset availability confirmedEurope PMC · checked 15 Sept 2026
Fluctuations and propagation of cytosolic calcium levels at both the cellular and tissue levels show complex patterns, referred to as calcium signatures, that regulate growth, organ development, damage responses, and survival. The quantitative analysis of calcium signatures at the cellular level is essential for identifying unique patterns that coordinate biological processes. However, a versatile framework applicable to multiple tissue types, allowing researchers to compare, measure, and validate diverse responses and recognize conserved patterns across model organisms, is missing. Here, we present a post-processing tool, CalciumInsights, which leverages the R packages Shiny and Golem. This tool has a graphical user interface and does not require software programming experience to perform calcium signal analysis. The open-source software has a modular framework with standardized functionalities that can be tailored for various research approaches. CalciumInsights provides descriptive statistical analysis through various metrics extracted from dynamic calcium transients and oscillations, such as peak amplitude, area under the curve, frequency, among others. The tool was evaluated with fluorescence imaging data from three model organisms: Danio rerio , Arabidopsis thaliana , and Drosophila melanogaster , demonstrating its ability to analyze diverse biological responses and models. Finally, the open-source nature of CalciumInsights enables community-driven improvements and developments for enabling new applications. Author Summary This manuscript introduces CalciumInsights, an open-source tool for calcium signature analysis. Designed to be a versatile tool that works with various tissue types and biological systems, CalciumInsights has an easy-to-use graphical user interface. Our program simplifies metrics extraction while maintaining the quality of the analysis by integrating several algorithms. CalciumInsights stands out for its user-friendliness, ease of use, and robust data exploration features, such as tunable filters for improved accuracy. These features promote inclusivity and lower barriers to scientific research by making calcium signature analysis accessible to users of all programming skill levels.
Why it matches plant phenotyping methods植物の蛍光イメージングからカルシウム動態という生理状態を抽出・定量するオープンソース解析ツールが中心であり、植物を含む複数生物種のデータで評価されている。
abstractHere, we present a post-processing tool, CalciumInsights, which leverages the R packages Shiny and Golem.
Reproduction assets foundThe paper describes CalciumInsights, an open-source R/Shiny tool for calcium transient analysis. The authors explicitly state their code is publicly available on GitHub, which constitutes the paper's computational analysis asset. No plant-phenotyping datasets, images, or trained models are described; the tool is tissueCode · publicnt for publication
All authors have reviewed the manuscript and approved the final draft for publication.
Resource availability
Lead contact: Further information and requests for data may be directed to and will be
fulfilled by Mauricio Cabrera (mauricio.cabrera1@upr.edu)
Code: All codes used are publicly available in GitHub at https://github.com/AOG-Lab/CalciumInsights
References
1. Berridge MJ, Lipp P, Bootman MD. The versatility and universality of calcium signalling. Nat
Rev Mol Cell Biol [Internet]. 2000 Oct [cited 2024 Oct 21];1(1):11–21. Available from:
https://www.nature.com/articles/35036035
2. Sanderson MJ, Charles AC, Boitano S, Dirksen ER. Mechanisms and function of intercellularOpen asset ↗AOG-Lab/CalciumInsightspdf-raw-page:20 lines:1-37Code / dataset availability confirmedEurope PMC · checked 15 Sept 2026
1. The competitor, stress-tolerator, ruderal (CSR) theory, first proposed by John Philip Grime, is a useful framework for understanding plant ecological strategies and predicting responses to environmental changes and pressures. However, current tools for assigning CSR strategies are limited to an Excel sheet format and have yet to be integrated into modern computational platforms that enable reproducible research. 2. We present CSRcalculator (https://github.com/TeddyGaskin/CSRcalculator), an open-source R package and shiny application that calculates CSR scores and assigns strategies based on user-uploaded trait data. CSRcalculator supports CSR assignments according to the three most prominent models: The original soft approach, the global StrateFy, and a morpho-physiological model. 3. The R package outputs a table including CSR scores, assigned strategies and intermediate traits for the selected model. The shiny application produces this same table alongside an interactive ternary plot to visualise the strategy distribution and an optional summary table describing the overall CSR strategy distribution, using metrics such as, the modal strategy, axis means, standard deviations, and ranges. Group-level analyses calculate the same statistics across user-defined categories. 4. We provide a worked example using our tool to assign CSR strategies to plants for an example dataset.
Why it matches plant phenotyping methods植物の形態・生理形質データからCSRスコアと生態戦略を算出するRパッケージ／Shinyアプリの開発であり、再現可能な形質解析ツールが研究の中心です。
abstractWe present CSRcalculator (https://github.com/TeddyGaskin/CSRcalculator), an open-source R package and shiny application that calculates CSR scores and assigns strategies based on user-uploaded trait data.
Reproduction assets foundThe paper's computational analysis is implemented in the authors' publicly available CSRcalculator R package and Shiny application, both with explicit GitHub URLs, plus a hosted web app. The adapted Novakovskiy et al. (2021) trait dataset is bundled in the package.Code · publicthe accessibility and reproducibility of CSR analysis for a diverse range of ecological
141
contexts.
142
2 Methods
143
2.1 Tool structure and implementation
144
The CSRcalculator was developed in R and is available as both an R package and a
145
Shiny web application (https://portal.bethchatto.co.uk/csr.php; local version:
146
https://github.com/TeddyGaskin/CSRcalculator-Shiny-application). The standalone R
147
package is available at: https://github.com/TeddyGaskin/CSRcalculator and includes
148
three model-specific functions, strateFy(), morphoPhys(), and hodgson(), each of
149
which can be applied to a data frame containing species information and the
150
necessary trait data:
151
strateOpen asset ↗TeddyGaskin/CSRcalculatorpdf-raw-page:5 lines:1-87Code · publicthe accessibility and reproducibility of CSR analysis for a diverse range of ecological
141
contexts.
142
2 Methods
143
2.1 Tool structure and implementation
144
The CSRcalculator was developed in R and is available as both an R package and a
145
Shiny web application (https://portal.bethchatto.co.uk/csr.php; local version:
146
https://github.com/TeddyGaskin/CSRcalculator-Shiny-application). The standalone R
147
package is available at: https://github.com/TeddyGaskin/CSRcalculator and includes
148
three model-specific functions, strateFy(), morphoPhys(), and hodgson(), each of
149
which can be applied to a data frame containing species information and the
150
necessary trait data:
151
strateOpen asset ↗TeddyGaskin/CSRcalculator-Shiny-applicationpdf-raw-page:5 lines:1-87Code / dataset availability confirmedCrossref · checked 15 Sept 2026
Flow cytometry is widely applied to infer the ploidy and genome size (GS) of plant nuclei. The conventional approach of sample preparation, reliant on fresh plant material to release intact nuclei, often results in poor yields of nuclei in conditions when a plant material cannot be kept fresh due to logistical constraints. Previous attempts to use frozen plant material were mainly limited to ploidy analysis and relied on chopping methods, which restrict the material input and often result in poor nuclei yield, especially in frozen samples, due to incomplete disruption. Here, we present a modified protocol for GS estimation using frozen plant material that facilitates larger volumes of tissue to be processed while improving debris removal. Nuclei isolated from this protocol can also be used for DNA or RNA extraction. Genome size estimates from frozen material are similar to those from fresh material, with a reduction in error range, although not always significant (p > 0.05). In certain species, frozen samples can yield substantially more nuclei than fresh material. With the addition of specific debris compensation algorithms, coefficient of variation (CV%) can be maintained below 5%. This method has special value in estimating the GS of samples collected from remote locations and frozen for use in plant genome sequencing. Freezing preserves high-quality DNA and RNA, enabling the same sample to be used for both flow cytometry and genome sequencing.
Why it matches plant phenotyping methods凍結植物材料からフローサイトメトリーで植物ゲノムサイズを推定する改良プロトコルを開発・検証しており、植物形質取得法が研究の中心である。
abstractHere, we present a modified protocol for GS estimation using frozen plant material that facilitates larger volumes of tissue to be processed while improving debris removal.
Reproduction assets foundThe paper deposits its raw flow cytometry fluorescence dataset (genome size estimation of fresh vs frozen plant material) in FlowRepository and its supplementary materials (ANOVA table, histogram/peak-modeling figures, microscopy images, protocol) in a Zenodo database. Both are paper-specific, publicly accessible, andSupplement · publicg across diverse taxa and
storage durations, this method could significantly enhance field-based and conservation
genomics efforts.
Supplementary Materials: The following supplementary data can be accessed online from the
database titled “A flow cytometry protocol for measurement of plant genome size using frozen mate-
rial” at https://doi.org/10.5281/zenodo.14873353 (Accessed on 1 April 2025). Table S1: Results of
one-way ANOVA for all combinations of species, nuclei extraction method, and debris compensation
on genome size estimation. Figure S1. The process of nuclei isolation from frozen leaf material.
Figure S2. Conventional histogram analysis for the fluorescence data of fresh preparatOpen asset ↗zenodo · 10.5281/zenodo.14873353pdf-raw-page:12 lines:1-50Code / dataset availability 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 6 Sept 2026
Abstract Pecan ( Carya illinoinensis ) cultivation is expanding in Argentina, with Catamarca Province emerging as a significant production region. However, fungal diseases such as Alternaria black spot (ABS), caused by Alternaria spp., pose an increasing threat to crop yield and health. Considering that disease quantification is crucial for epidemiological studies and management, this study aimed to design and validate a standard area diagram (SAD) set to improve the visual estimation of ABS severity on pecan leaves. Using 255 diseased leaves, an eight-image SAD set with severity levels that linearly ranged from 2.2–88.9% was designed. Thirty-four raters participated in the validation process using the online platform TraineR2 in two phases: unaided and aided assessments. The use of the SAD set significantly improved accuracy metrics. Lin’s concordance correlation coefficient (CCC) increased from 0.93 to 0.97, while precision (r) rose from 0.92 to 0.97. Additionally, inter-rater reliability, measured using the intraclass correlation coefficient (ICC), improved from 0.86 to 0.93. This study demonstrates the effectiveness of the SAD set tool in enhancing the accuracy and consistency of ABS severity estimations, highlighting its potential as a practical resource for pecan producers and researchers.
Why it matches plant phenotyping methodsペカン葉の病斑重症度という植物状態を視覚推定する標準面積図（SAD）を開発・検証しており、表現型取得・評価手法が研究の中心です。
abstractthis study aimed to design and validate a standard area diagram (SAD) set to improve the visual estimation of ABS severity on pecan leaves.
Reproduction assets foundThe preprint explicitly states that the datasets generated and analyzed (the ABS severity leaf-image data and validation data) are publicly available in an INTA institutional repository. The TraineR2 and SADBank platforms are third-party tools, not paper-specific assets.Dataset · publicThe datasets generated during and/or analyzed during the current study are available at the following repository link: https://repositorio.inta.gob.ar/xmlui/handle/20.500.12123/22231Open asset ↗repositorio.inta.gob.ar · 20.500.12123/22231lines:97-109Code / dataset availability confirmedEurope PMC · checked 14 Sept 2026
Grapes are widely cultivated fruit crops, essential for fresh consumption, winemaking and dried product production. However, their yield and quality are significantly impacted by various fungal diseases. This paper provides a large dataset of 2,726 high-quality grape leaf disease images collected from grapes farm of Nashik, India in two years of span 2023 to 2025. The dataset is precisely annotated under the guidance and observation of agriculture domain expert and organized in a well-defined folder structure. The dataset captures the two major categories healthy leaves and unhealthy leaves, during cultivation period. A primary directory containing two main classes Heathy Leaf Images and Unhealthy Leaf images. Further unhealthy class is divided into three subfolders for disease class, namely Downy Mildew, Powdery Mildew and Bacterial Leaf Spot. These are the major fungal disease observed on grape crop causes substantially crop losses and ultimately impact on the yield production. Timely identification of these diseases can significantly reduce the risk of crop loss and help to improve quality of fruit with maximum yield production. This High-quality annotated image dataset can help to design standard advanced AI models for automated disease detection, classification, and prediction. The dataset was validated through a transfer learning approach using the ResNet-18 algorithm and demonstrated the remarkable classification accuracy of 96 % . These results validate the dataset's quality and its suitability for deep learning-based grape disease detection. Overall, this open-access resource provides a valuable foundation for computer vision, machine learning, and agricultural technology researchers aims to enhance disease management practices in grape production. thus, this is an effective source of data for future studies and real-world applications in sustainable grape production.
Why it matches plant phenotyping methodsブドウ葉の病徴・健全状態を画像で取得した注釈付きデータセットを提供し、分類モデルで検証しているため、植物病害表現型のデータセット開発・検証が中心です。
abstractThis paper provides a large dataset of 2,726 high-quality grape leaf disease images collected from grapes farm of Nashik, India in two years of span 2023 to 2025.
Reproduction assets foundThe paper is a data descriptor for the Niphad Grape Leaf Disease Dataset (NGLD), 2,726 annotated grape leaf images, publicly deposited on Mendeley Data with direct URL and DOI. No author analysis code is shared.Dataset · publicges were labelled sequentially for clear association within the dataset.
Data source location
Niphad Grapes farms, located at District Nashik 422209, MH-India
Longitude and Latitude: 20.0771° N, 74.1094° E
Data accessibility
Repository Name: Niphad Grape Leaf Disease Dataset (NGLD)
DOI: 10.17632/8nnd2ypcv3.5
Direct URL to Data: https://data.mendeley.com/datasets/8nnd2ypcv3/5
1.
Value of the Data
•
Comprehensive Dataset : The Dataset is comprehensive and consists of 2726 high-quality images, in four subfolder such as Downy Mildew, Powdery Mildew, Bacterial Leaf Spot and Healthy Grapes Leaf. Unlike existing public datasets that primarily focus on diseases such as Esca, Black Rot, and Leaf BligOpen asset ↗10.17632/8nnd2ypcv3.5lines:1-43Code / dataset availability confirmedCrossref · checked 14 Sept 2026
Chlorophyll fluorescenceLeafRootPhysiological trait estimationPhotosynthesis / fluorescenceWater status / transpiration
The invasive aquatic macrophyte Pontederia crassipes (water hyacinth) exhibits exceptional adaptability across a wide range of light environments, yet the mechanistic basis of its photosynthetic plasticity under both high- and low-light stress remains poorly resolved. This study integrated chlorophyll fluorescence and gas-exchange analyses to evaluate three photosynthetic models—rectangular hyperbola (RH), non-rectangular hyperbola (NRH), and the Ye mechanistic model—in capturing light-response dynamics in P. crassipes. The Ye model provided superior accuracy (R2 > 0.996) in simulating the net photosynthetic rate (Pn) and electron transport rate (J), outperforming empirical models that overestimated Pnmax by 36–46% and Jmax by 1.5–24.7% and failed to predict saturation light intensity. Mechanistic analysis revealed that P. crassipes maintains high photosynthetic efficiency in low light (LUEmax = 0.030 mol mol−1 at 200 µmol photons m−2 s−1) and robust photoprotection under strong light (NPQmax = 1.375, PSII efficiency decline), supported by a large photosynthetic pigment pool (9.46 × 1016 molecules m−2) and high eigen-absorption cross-section (1.91 × 10−21 m2). Unlike terrestrial plants, its floating leaves experience enhanced irradiance due to water-surface reflection and are decoupled from water limitation via submerged root uptake, enabling flexible stomatal and energy regulation. Distinct thresholds for carboxylation efficiency (CEmax = 0.085 mol m−2 s−1) and water-use efficiency (WUEi-max = 45.91 μmol mol−1 and WUEinst = 1.96 μmol mmol−1) highlighted its flexible energy management strategies. These results establish the Ye model as a reliable tool for characterizing aquatic photosynthesis and reveal how P. crassipes balances light harvesting and dissipation to thrive in fluctuating environments. These resulting insights have implications for both understanding invasiveness and managing eutrophic aquatic systems.
Why it matches plant phenotyping methods複数の光合成モデルを実測データで比較・検証し、植物の光合成生理形質を推定するモデルの精度と適用性を中心的に評価しているため。
abstractThis study integrated chlorophyll fluorescence and gas-exchange analyses to evaluate three photosynthetic models—rectangular hyperbola (RH), non-rectangular hyperbola (NRH), and the Ye mechanistic model—in capturing light-response dynamics in P. crassipes.
Reproduction assets found保存済みの本文根拠を更新済みルールで再検証し、公開資産1件を確認しました。Supplement · publicThe following supporting information can be downloaded at https://www.mdpi.com/article/10.3390/biology14060600/s1 : Table S1. Gas-exchange measurement data.Open asset ↗lines:329-346Code / dataset availability 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
Field / plotLeafClassificationStress / disease detectionDisease symptoms / severity
Essentially, visual identification of plant health is vital for research in agriculture and medicinal plants for important crops, both in terms of economics and pharmacology, such as betel leaves. The strong integration of AI-based methods in precision agriculture and herbal medicine quality control makes these systems effective only when trained on well-structured, diversified datasets.The plant betel leaf (Piper betle) is cultivated throughout the world for its medicinal, cultural, and economic importance, but improper classification and quality assessment of this plant occur because of environmental conditions and variations in handling. To solve this problem, we hereby present the Betel Leaf Dataset, which is systematically curated, consisting of 1,800 high-resolution images (1080 × 1080 pixels) exhibiting the three different conditions of betel leaves: Healthy (Fresh), Diseased, and Dried. The dataset was collected from Veer, Taluka-Purandar, Pune, India, under both natural and controlled conditions so that different appearances could be ensured. Categories include images that have been taken under varied light, backgrounds, and orientations, which comprehensively can cover all real variations in betel leaves. Hence, this systematically collected, standardized, and accessible dataset can enhance agricultural research in leaf classification studies and quality assessment techniques to facilitate better documentation and understanding of betel leaf characteristics. This dataset can be utilized in machine learning applications for plant disease detection, precision agriculture, and automated quality control systems.
Why it matches plant phenotyping methods植物の健康・病気・乾燥状態を画像化したデータセット自体が中心的な成果であり、植物状態の画像ベース表現型解析に該当する。
abstractwe hereby present the Betel Leaf Dataset, which is systematically curated, consisting of 1,800 high-resolution images (1080 × 1080 pixels) exhibiting the three different conditions of betel leaves: Healthy (Fresh), Diseased, and Dried.
Reproduction assets foundThe paper is a data descriptor for the authors' own betel leaf image dataset (1,800 images, healthy/diseased/dried, field and controlled environment), publicly deposited on Mendeley Data with an explicit direct URL and DOI.Dataset · publicand diseased as 509.
Data source location
At Veer, Taluka-Purandar, District-Pune, Maharashtra, India.
Latitude :18.1507784, Longitude :74.0872852
Data accessibility
Repository name: Betel Leaf Dataset: A Primary Dataset From Field And Controlled Environment
Data identification number: 10.17632/btdym2t6mt.1
Direct URL to data: https://data.mendeley.com/datasets/btdym2t6mt/1
Related research article
None
1.
Value of the Data
•
Betel leaves are highly grown and best known within South and Southeast Asia as piper betles due to their culinary, cultural, and medical uses. In India, the leaves are mostly grown due to warm humid conditions in states such as West Bengal, Assam, Odisha, Karnataka, TaOpen asset ↗10.17632/btdym2t6mt.1lines:1-45Code / 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 14 Sept 2026
The bacterium Xylella fastidiosa (Xf) is a plant pathogen first identified in Europe in 2013, specifically in olive groves in the Apulia region (south-eastern Italy). It is now spreading across the Mediterranean basin and poses a serious threat to the local economy by causing branch desiccation and the rapid death of olive trees, a condition known as olive quick decline syndrome (OQDS). Several studies have investigated the potential of remote sensing (RS) technology to monitor OQDS over time and space; however, accurate and reliable data on OQDS occurrence remain scarce. To enhance the distribution data of Xf-infected trees in the Apulia region, we investigated an infection hotspot of 25 km² area in the province of Brindisi, where records of infections were documented in 2019 and 2020. Three very high resolution, commercial WorldView-2 images were acquired and segmented, resulting in a dataset of 76637 olive trees. Through visual interpretation, 2340 trees were identified most likely as either infected or removed due to OQDS. This dataset provides a valuable resource for developing or validating RS techniques for early detection of OQDS. Furthermore, it could support studies aimed to evaluate spectral bands or indices most correlated with infection presence. Finally, the dataset can be integrated with other Xf-infection presence data to support species distribution model studies.
Why it matches plant phenotyping methods衛星画像のセグメンテーションと感染・枯死オリーブ樹のラベル化による、植物病害状態の検出・検証用データセットが研究の中心である。
abstractThree very high resolution, commercial WorldView-2 images were acquired and segmented, resulting in a dataset of 76637 olive trees.
Reproduction assets foundThe paper is a Data in Brief article describing a public Figshare dataset (OQDS-Insight) containing WorldView-2 satellite raster imagery (RGB and NDVI GeoTIFFs) and a shapefile of 76,637 olive tree points with OQDS infection labels — directly the paper's phenotyping measurements.Dataset · publicsouth-eastern Italy. The extent (EPSG:32633) is from 706164.541 N to 713395.999 N, and from 4508710.411 E to 4513574.414 E.
Data are stored at the Council for Agricultural Research and Economics, Research Centre for Agriculture and Environment, Italy.
Data accessibility
Repository name: OQDS-Insight
Data identification number: https://doi.org/10.6084/m9.figshare.28191245.v4
Direct URL to data: https://doi.org/10.6084/m9.figshare.28191245.v4
Related research article
None.
Open in a new tab
1. Value of the Data
•
The dataset provides a detailed record of OQDS olive groves within an infection hotspot in the province of Brindisi, Apulia region (south-eastern Italy) ( Fig. 1 ).
•
It can support rOpen asset ↗figshare · 10.6084/m9.figshare.28191245.v4lines:95-140Code / dataset availability confirmedEurope PMC · checked 14 Sept 2026
Image analysis can be useful for assessing crop health and predicting yield. Instead of expensive equipment, smartphones are considered an accessible and low-cost alternative. The objectives of this study were to evaluate whether fresh weight in green and red lettuce could be predicted by leaf color (intensity of green color measured by RGB) under different fertilizer treatments using RGB imaging from two widely used smartphone models (Samsung Galaxy and Apple iPhone). The two smartphones showed similar longitudinal patterns of RGB data (the intensity and dark green proportion), but the absolute difference in the RGB data was significantly different. Therefore, the averaged results were used for the analyses. Color intensity and dark green proportion were associated with the fresh lettuce weight (p = 0.005, 0.003, 0.014 and p < 0.001, respectively). This study suggests that farmers and practitioners can use these economic devices as a non-destructive method to diagnose and monitor the nutritional status and predict lettuce yield.
Why it matches plant phenotyping methodsスマートフォンRGB画像から葉色を抽出し、レタスの生体重・栄養状態を非破壊推定する手法が研究の中心であり、植物表現型取得への実質的な応用に該当する。
abstractsmartphones are considered an accessible and low-cost alternative
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 separated GIF files were uploaded to the image analysis program freely available at http://mkwak.org/imgarea .Open asset ↗lines:321-349Code / dataset availability confirmedEurope PMC · checked 14 Sept 2026
RootVisualization / data managementBiomass / plant weightRoot system architecture
Tropical ecosystems contain the world's largest biodiversity of vascular plants. Yet, our understanding of tropical functional diversity and its contribution to global diversity patterns is constrained by data availability. This discrepancy underscores an urgent need to bridge data gaps by incorporating comprehensive tropical root data into global datasets. Here, we provide a database of tropical root characteristics. This new database, TropiRoot 1.0, will be instrumental in evaluating an array of hypotheses pertaining to root functional ecology and plant biogeography, both within the tropics and relative to other global biomes. The data compilation was conducted by the TropiRoot Initiative, in partnership with the Fine-Root Ecology Database (FRED) and the Global Root Trait (GRooT) database, Colorado State University (CSU) and the Smithsonian Tropical Research Institute (STRI). Literature search and data extraction were conducted between 2020 and 2024. Literature was identified using Web of Science, Scopus, and complemented using the expert knowledge of members of TropiRoot. To provide broad environmental and geographical distributions, literature searches included root characteristics (traits) across global change drivers, natural gradients, and from different continents. We adopted FRED standardized data columns and streamlined the format to enhance accessibility for data extraction across various user groups. This optimized framework resulted in a smaller, yet comprehensive datasheet. To make the database compatible with other global root trait initiatives, column identification was standardized following the codes provided by FRED. These efforts culminated in data extracted from 104 new sources, resulting in more than 8000 rows of data (either species or community data). Most of the data in TropiRoot 1.0 include root characteristics such as root biomass, morphology, root dynamics, mass fraction, architecture, anatomy, physiology, and root chemistry. This initiative represents a 30% increase in the currently available data for tropical roots in FRED. TropiRoot 1.0 contains root characteristics from 25 different countries, where seven are located in Asia, six in South America, five in Central America and the Caribbean, four in Africa, two in North America, and 1 in Oceania. Due to the volume of data, when ancillary data were available, including soil data, these data were either extracted and included in the database or its availability was recorded in an additional column. Multiple contributors checked the entries for outliers during the collation process to ensure data quality. For text-based observations, we examined all cells to ensure that their content relates to their specific categories. For numerical observations, we ordered each numerical value from least to greatest and plotted the values, checking apparent outliers against the data in their respective sources and correcting or removing incorrect or impossible values. Some data (soil and aboveground) have different columns for the same variable presented in different units, including originally published units, but root characteristics data had units converted to match those reported in FRED. By filling a gap from global databases, TropiRoot 1.0 expands our knowledge of otherwise so far underrepresented regions and our ability to assess global trends. This advancement can be used to improve tropical forest representation in vegetation models. The data are freely available and should be cited when used.
Why it matches plant phenotyping methods熱帯植物の根形態・構造・生理などの表現型特性を標準化して収録した再利用可能なデータベースであり、データセット構築と品質管理が中心です。
abstractHere, we provide a database of tropical root characteristics.
Reproduction assets foundThe paper's core asset is the TropiRoot 1.0 root trait database itself, publicly deposited in ESS-DIVE (DOI 10.15485/2507279) and also provided as Supporting Information (Data S1). This is a paper-specific public phenotype/trait dataset directly reproducing the paper's measurements.Dataset · publich, et al. 2025. “
TropiRoot 1.0: Database of Tropical Root Characteristics across Environments.” Ecology
106(5): e70074. 10.1002/ecy.70074
Handling Editor: Simona Picardi
DATA AVAILABILITY STATEMENT
The dataset is available as Supporting Information to this Ecology data paper and is also accessible in the ESS‐DIVE repository at https://doi.org/10.15485/2507279.
Associated Data
Supplementary Materials
Data S1.
Data Availability Statement
The dataset is available as Supporting Information to this Ecology data paper and is also accessible in the ESS‐DIVE repository at https://doi.org/10.15485/2507279.Open asset ↗10.15485/2507279html-lines:63-80Code / 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
Cauliflower is among the more well-known vegetables there are. Consumed all around the globe due to it being rich in nutrients such as vitamins, antioxidants, and for being high in fibre. These are nutritional qualities that help with digestion, immune-system, and minimizing inflammation. It is a common issue among farmers to have to deal with various diseases in cauliflower leaves that are difficult to diagnose in their early stages. These diseases have a tendency to propagate in a really swift pace throughout entire fields worth of crops. This in-turn causes heavy losses in the harvest, and makes it much more tedious and resource-intensive to protect the crops. As a result, farmers get more likely to use high amounts of pesticides and harmful chemicals to streamline the process of getting a more reliable yield on their crops. This is not only costly, but it is also harmful both to the quality of crops and to the well-being of the environment. In this publication, we are introducing a dataset containing a considerable number of images of cauliflower leaves. This is intended to drive development on this topic at a faster pace than it is now, and to help enhance disease monitoring, diagnosis, and precautionary techniques. We collected our dataset images between November 2024 and January 2025. In this dataset, cauliflower leaves were categorized into three classes: Healthy, Insect Holes, and Black Rot, each reflecting a specific condition that impacts plant health at different stages. This dataset consists of 2,661 images. The pictures were captured at different locations in Bangladesh, under different weather conditions, dates, temperatures, and with different devices. To enhance the data quality, we used several steps to process the dataset, making sure it would reflect real-world conditions and be ready for training. The images were resized to a standard size of 3000 × 3000 pixels, brightness was adjusted to make the images more easily discernible, and we removed duplicates and poor-quality images. These actions helped ensure the dataset was in the best possible shape for effective model training. This dataset will be highly effective for agricultural research, precision agriculture, and effective management of diseases. It should help develop highly accurate machine learning models for early detection of Cauliflower leaf diseases. The dataset is employed to train deep learning models to support automated monitoring and smart decision-making in precision agriculture. This data set also has immense potential for real-time and practical use. It can be utilized to develop applications like mobile apps or automated systems where farmers can easily identify diseases at early stages and take immediate action, without the requirement of expert on-site knowledge. This data set can also be utilized with smart farming equipment like drones and sensors to track big fields in real time.
Why it matches plant phenotyping methodsカリフラワー葉の健康状態・病害状態を画像で分類するデータセット自体が研究の中心であり、植物病害表現型の取得・解析基盤に該当します。
abstractIn this publication, we are introducing a dataset containing a considerable number of images of cauliflower leaves.
Reproduction assets foundThe paper's core asset is its own cauliflower leaf disease image dataset (2,661 images, three classes), publicly deposited on Mendeley Data with DOI 10.17632/x995snz7p3.1 and a direct URL matching an allowed URL.Dataset · publiced from the following geographic locations:
1. Zailla, Singair, Manikganj
Latitude : 23°47′46.11"N Longitude : 90°13′15.73"E
2. Dattapara, Ashulia, Savar, Dhaka
Latitude : 23°52′26.3"N Longitude : 90°19′06.3"E
Data accessibility
Repository name: Mendeley Data
Data identification number: 10.17632/x995snz7p3.1
Direct URL to data: https://data.mendeley.com/datasets/x995snz7p3/1
The dataset is publicly available and can be accessed via the provided Mendeley Data repository link.
Related research article
None
1.
Value of the Data
•
This dataset holds high-resolution images of diseased cauliflower leaves infected with multiple diseases, which provide a wealth of material for the development and vaOpen asset ↗Mendeley Data · 10.17632/x995snz7p3.1lines:35-107Code / dataset availability confirmedEurope PMC · checked 6 Sept 2026
Chlorophyll levels are a key indicator of plant nitrogen status, which plays a critical role in optimizing agricultural yields. This study evaluated the performance of three low-cost multi-spectral sensors, AS7262, AS7263, and AS7265x, for non-destructive chlorophyll measurement. Measurements were taken from a diverse set of five leaf types, including smooth, uniform leaves (banana and mango), textured leaves (jasmine and sugarcane), and narrow leaves (rice). Partial least squares regression models were used to fit sensor spectra to chlorophyll levels, using nested cross-validation to ensure robust model evaluation. Sensor performance was assessed using R2 and mean absolute error (MAE) scores. The AS7265x demonstrated the best performance on smooth, uniform leaves with validation R2 scores of 0.96-0.95. Its performance decreased for the other leaves, with R2 scores of 0.75-0.85. The AS7262 and AS7263 sensors, while slightly less accurate, achieved reasonable R2 scores ranging from 0.93 to 0.86 for smooth leaves, and from 0.85 to 0.73 for the other leaves. All sensors, particularly the AS7265x, show potential for non-destructive chlorophyll measurement in agricultural applications. Their low cost and reasonable accuracy make them suitable for agricultural applications such as monitoring plant nitrogen levels.
Why it matches plant phenotyping methods低コストマルチスペクトルセンサーによる葉のクロロフィル測定法を評価・比較し、交差検証で性能を検証しているため、植物フェノタイピング手法が中心です。
abstractThis study evaluated the performance of three low-cost multi-spectral sensors, AS7262, AS7263, and AS7265x, for non-destructive chlorophyll measurement.
Reproduction assets foundThe authors publicly release raw sensor data, analysis scripts, firmware, and GUI in the GitHub repository KyleLopin/asm_chloro_test, plus supplementary information including extracted chlorophyll reference measurements (S2) at the MDPI supplement URL.Code · publicRaw data, scripts to generate the data and figures used in the manuscript, programs to run the sensors, and GUI used to collect the data are available at https://github.com/KyleLopin/asm_chloro_test (accessed on 25 March 2025).Open asset ↗KyleLopin/asm_chloro_test · KyleLopin/asm_chloro_testlines:187-200Code · publicThe microcontroller code to operate the sensor and a GUI for data collection are available at https://github.com/KyleLopin/asm_chloro_test/tree/master/source (accessed on 25 March 2025).Open asset ↗KyleLopin/asm_chloro_test · KyleLopin/asm_chloro_testlines:155-167Supplement · publicThe following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/s25072198/s1 . Supplementary Information S1: Device Electrical Characterization. Supplementary Information S2: Extracted Chlorophyll Reference Measurements.Open asset ↗lines:176-186Code / dataset availability confirmedEurope PMC · checked 14 Sept 2026
This article introduces OliveTreeCrownsDb, a comprehensive dataset of high-resolution images captured by a DJI Phantom 4 RTK drone. The dataset includes 46 images covering an entire olive farm, focusing on the detection and analysis of olive tree crowns and supporting segmentation tasks. Each image is accompanied by detailed metadata, such as focal distance, capture altitude, GPS coordinates, and other essential parameters for accurate tree mapping and localization. OliveTreeCrownsDb is publicly accessible, promoting research in precision agriculture, including tree crown detection, segmentation, geometric shape analysis, automation, yield estimation, and computer vision applications. It facilitates the development of innovative algorithms to optimize resource allocation and improve crop management. By enabling studies on tree crown analysis and farm monitoring, OliveTreeCrownsDb advances agricultural technologies and enhances management practices in olive cultivation.
Why it matches plant phenotyping methodsオリーブ樹冠の画像検出・セグメンテーションと幾何形状解析を可能にする公開データセットが研究の中心であり、植物の樹冠形態を抽出する再利用可能な基盤に該当する。
abstractThis article introduces OliveTreeCrownsDb, a comprehensive dataset of high-resolution images captured by a DJI Phantom 4 RTK drone.
Reproduction assets foundThe paper's own UAV olive tree crown dataset (images, annotations, point cloud, DEM) is publicly deposited on Mendeley Data with explicit direct URL and DOI.Dataset · public/ Town / Region: Meknas farm site
Country: Morocco
The GPS coordinates of the olive farm are 33°53′17"N 5°25′22"W, or in decimal format: 33.88802°N, -5.42281°W.
Data accessibility
Repository name: OliveTreeCrownsDb
Data identification number : doi: 10.17632/xym8rd2srf.2
Direct URL to data:
Instructions for accessing these data: https://data.mendeley.com/datasets/xym8rd2srf/2
Related research article
none
1.
Value of the Data
The OliveTreeCrownsDb dataset is a valuable resource for research in computer vision and precision agriculture. Here are the key aspects that highlight its importance:
•
Unique and Specialized Source: OliveTreeCrownsDb offers an exclusive high-resolution dataset specificOpen asset ↗10.17632/xym8rd2srf.2lines:1-54Code / dataset availability confirmedCrossref · checked 13 Sept 2026
Leaf rust and yellow rust are globally significant fungal diseases that severely impact wheat production, causing yield losses of up to 60% in highly susceptible cultivars. Early and accurate detection is crucial for integrating precision crop protection strategies to mitigate these losses. This study investigates the potential of 3D LiDAR technology for monitoring rust-induced physiological changes in wheat by analyzing variations in plant height, biomass, and light reflectance intensity. Results showed that grain yield decreased by 10–50% depending on cultivar susceptibility, with the durum wheat cultivar ‘Kiko Nick’ and bread wheat ‘Califa’ exhibiting the most severe reductions (~50–60%). While plant height and biomass remained relatively unaffected, LiDAR-derived intensity values strongly correlated with disease severity (R2 = 0.62–0.81, depending on the cultivar and infection stage). These findings demonstrate that LiDAR can serve as a non-destructive, high-throughput tool for early rust detection and biomass estimation, highlighting its potential for integration into precision agriculture workflows to enhance disease monitoring and improve wheat yield forecasting. To promote transparency and reproducibility, the dataset used in this study is openly available on Zenodo, and all processing code is accessible via GitHub, cited at the end of this manuscript.
Why it matches plant phenotyping methodsLiDARによる小麦の病害状態・バイオマス等の非破壊推定を中心に評価しており、植物フェノタイピング手法の実質的な適用・検証に該当する。
abstractThis study investigates the potential of 3D LiDAR technology for monitoring rust-induced physiological changes in wheat by analyzing variations in plant height, biomass, and light reflectance intensity.
Reproduction assets foundThe paper's LiDAR-derived wheat rust phenotyping dataset is openly available on Zenodo (DOI 10.5281/zenodo.14889285), and the authors' point-cloud processing and parameter-extraction code is publicly available on GitHub (eapolo/agrolidarwheatrust). Both are explicitly stated in the Data Availability Statement.Dataset · publicData Availability Statement: The dataset used in this study has been published on the Zenodo
platform under the DOI: https://doi.org/10.5281/zenodo.14889285Open asset ↗Zenodo · 10.5281/zenodo.14889285pdf-page:21 lines:1-60Code · publicalong with the code, which is
available in the GitHub repository at https://github.com/eapolo/agrolidarwheatrust, accessed on
10 March 2025.Open asset ↗github.com/eapolo/agrolidarwheatrustpdf-page:21 lines:1-60Code / dataset availability confirmedEurope PMC · checked 14 Sept 2026
The thylakoid membrane is the site of the light-dependent reactions of photosynthesis. It is a continuous membrane, folded into grana stacks and the interconnecting stroma lamellae. The CURVATURE THYLAKOID1 (CURT1) protein family is involved in the folding of the membrane into the grana stacks. The thylakoid membrane remodels its architecture in response to light conditions, but its 3D organisation and dynamics remain incompletely understood. To resolve these details, an imaging technique is needed that provides high-resolution 3D images in a high-throughput manner. Recently, we have used expansion microscopy, a technique that meets these criteria, to visualise the thylakoid membrane isolated from spinach. Here, we show that this protocol can also be used to visualise enveloped spinach chloroplasts. Additionally, we present an improved protocol for resolving the thylakoid structure of Arabidopsis thaliana. Using this protocol, we show the changes in thylakoid architecture in response to long-term far-red light acclimation and due to knocking out CURT1A. We show that far-red light acclimation results in higher grana stacks that are packed closer together. In addition, the distance between stroma lamellae, which are wrapped around the grana, decreases. In the curt1a mutant, grana have an increased diameter and height, and the distance between grana is increased. Interestingly, in this mutant, the stroma lamellae occasionally approach the grana stacks from the top. These observations show the potential of expansion microscopy to study the thylakoid membrane architecture.
Why it matches plant phenotyping methods植物のチラコイド膜構造を高解像度3D画像で取得する拡大顕微鏡法の改良・適用が中心であり、膜構造という植物形態形質を測定しているため。
abstractTo resolve these details, an imaging technique is needed that provides high-resolution 3D images in a high-throughput manner.
Reproduction assets foundThe article states that the data underlying the publication (expansion microscopy imaging/measurements of thylakoid architecture) are publicly available in the 4TU Research Data repository via the DOI 10.4121/75fa3c66-8505-4d6a-9bd9-16973e5ca885. This is a paper-specific, publicly accessible data deposit with an authorDataset · publicUte Armbruster for providing the
seeds of the Ler0 curt1a-1 mutant. This work was supported by the Dutch
Organisation for Scientific Research (NWO) via a Vidi grant no. VI.Vidi
192.042 (E.W.) and by Wageningen Graduates Schools through a PhD
grant (J.B.).
Data availability
The data underlying this publication can be accessed at https://doi.org/10.4121/75fa3c66-8505-4d6a-9bd9-16973e5ca885.References
[1] R.E. Blankenship, Molecular Mechanisms of Photosynthesis, John Wiley & Sons,
2021, https://doi.org/10.1002/9780470758472.
[2] H. Kirchhoff, Chloroplast ultrastructure in plants, New Phytol. 223 (2) (2019)
565–574, https://doi.org/10.1111/nph.15730.
[3] H. Kirchhoff, C. Hall, M. Wood, M. HerbstOpen asset ↗10.4121/75fa3c66-8505-4d6a-9bd9-16973e5ca885pdf-raw-page:9 lines:1-68Code / dataset availability confirmedCrossref · Europe PMC · checked 6 Sept 2026
MaizeMorphology / geometry measurementRoot system architecture
Abstract Root System Architecture (RSA) plays an essential role in influencing maize yield by enhancing anchorage and nutrient uptake. Analyzing maize RSA dynamics holds potential for ideotype‐based breeding and prediction, given the limited understanding of the genetic basis of RSA in maize. Here, we obtained 16 root morphology‐related traits (R‐traits), 7 weight‐related traits (W‐traits), and 108 slice‐related microphenotypic traits (S‐traits) from the meristem, elongation, and mature zones by cross‐sectioning primary, crown, and lateral roots from 316 maize lines. Significant differences were observed in some root traits between tropical/subtropical and temperate lines, such as primary and total root diameters, root lengths, and root area. Additionally, root anatomy data were integrated with genome‐wide association study (GWAS) to elucidate the genetic architecture of complex root traits. GWAS identified 809 genes associated with R‐traits, 261 genes linked to W‐traits, and 2577 key genes related to 108 slice‐related traits. We confirm the function of a candidate gene, fucosyltransferase5 ( FUT5 ), in regulating root development and heat tolerance in maize. The different FUT5 haplotypes found in tropical/subtropical and temperate lines are associated with primary root features and hold promising applications in molecular breeding. Furthermore, we performed machine learning prediction models of RSA using root slice traits, achieving high prediction accuracy. Collectively, our study offers a valuable tool for dissecting the genetic architecture of RSA, along with resources and predictive models beneficial for molecular design breeding and genetic enhancement.
Why it matches plant phenotyping methodsトウモロコシ根系形態を大規模に取得し、根スライス形質に基づく機械学習予測モデルと再利用可能な資源を構築しており、表現型取得・推定が研究の主要部分です。
Reproduction assets foundThe paper's root phenotyping images, phenotypic data, and RNA-seq data are deposited on figshare, and the authors' GWAS analysis pipeline code is publicly available on GitHub, both explicitly stated in the Data Availability Statement.Dataset · publicAll the images, phenotypic data, and RNAs‐seq data are available at https://doi.org/10.6084/m9.figshare.27605208.v1 .Open asset ↗figshare · 10.6084/m9.figshare.27605208.v1lines:197-303Code · publicThe original data and code for GWAS analysis pipelines can be downloaded at https://github.com/GUOWEIJUN/maizerootphenomics .Open asset ↗GitHub · GUOWEIJUN/maizerootphenomicslines:197-303Code / 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 · checked 6 Sept 2026
Summary Leaf water loss after stomatal closure is key to understanding the effects of prolonged drought on vegetation. It is therefore important to accurately quantify such water losses to improve physiology‐based models of drought‐induced plant mortality. We measured water loss of detached leaves continuously during dehydration in nine woody angiosperm species. We computed minimum leaf conductance ( g min ) at different water potential thresholds along a sequence of physiological function losses, spanning from turgor loss point to hydraulic failure. A mechanistic model evaluated the impact of different g min estimations on the time to hydraulic failure (THF). Residual conductance is not steady and decreases continuously at varying rates across species during the entire dehydration process, even after correcting for leaf shrinkage and vapor pressure deficit shifts. Different estimations of g min had a significant impact on the THF predicted by the model, especially for drought‐resistant species. We demonstrate that residual conductance is variable during dehydration, and thus, it is important to use physiological or water status boundaries for its estimation in order to determine distinct g min values of water loss. We describe an accurate, repeatable and open‐source methodology to estimate g min . Such methodology could enhance models of plant mortality under drought.
Why it matches plant phenotyping methods葉の脱水過程における最小葉コンダクタンスの定量法を開発・評価し、反復可能な方法論として提示しているため、植物生理フェノタイピング手法が中心です。
abstractWe describe an accurate, repeatable and open‐source methodology to estimate g min .
Reproduction assets foundThe paper's Data Availability Statement provides two paper-specific public assets: the authors' analysis/acquisition code (gminComputation in Python, g_Residual in R, and the 'cuticular' acquisition software) hosted on a public Gitlab repository, and the manuscript's underlying dehydration/gmin measurement data on the法Code · publicCodes developed for data acquisition (software ‘cuticular’ for Windows) and computation of raw residual conductance (project ‘gminComputation’ is developed as a console version in python, and ‘g_Residual’ is a script written in R language) are available in the following public Gitlab repository: https://gitub.u‐bordeaux.fr/phenoboisOpen asset ↗https://gitub.u‐bordeaux.fr/phenoboislines:509-550Code / dataset availability confirmedEurope PMC · checked 6 Sept 2026
Field / plotWhole plant / canopy / plot / fieldVisualization / data managementStress response / tolerance
An understanding of fire-response traits is essential for predicting how fire regimes structure plant communities and for informing fire management strategies for biodiversity conservation. Quantification of these traits is complex, encompassing several levels of data abstraction scaling up from field observations of individuals, to general categories of species responses. We developed the Fire Ecology Database to accommodate this complexity. Its conceptual framework is underpinned by a flexible data pipeline enabling links between fire-related trait data and event information at individual, population, and community levels. Key features include: (a) concise and documented trait and method vocabularies; (b) documented uncertainty in observations and aggregation; and (c) documented origin of data including field observations, laboratory experiments, and expert elicitation. We demonstrated application of our framework using data from new field surveys and existing data sets in New South Wales, Australia. The database includes 14 traits for 6,287 plant species derived from 8,936 field work records from 2007 to 2018, 7,054 field records from surveys after 2019, and 48,306 records from 301 existing sources.
Why it matches plant phenotyping methods火災応答形質を体系的に収集・標準化するデータベースとデータパイプライン自体が中心的な方法論的貢献であり、植物形質データの不確実性・測定法・由来も記録しているため、フェノタイピング用データ基盤として含める。
abstractWe developed the Fire Ecology Database to accommodate this complexity. Its conceptual framework is underpinned by a flexible data pipeline enabling links between fire-related trait data and event information at individual, population, and community levels.
Reproduction assets foundThe paper's core outputs (Fire Ecology Database v1.1 SQL dump, R data frames, CSV/XLSX exports on FigShare/OSF, and the Python import scripts/Jupyter notebooks) are stated to be publicly available, but no concrete repository URL or identifier for them appears in the supplied blocks, and none matches an allowed URL, so Code · publicCustomised scripts were written in Python to automate the importation of field data from the spreadsheets
into the database. These scripts are available for download (see Code availability section)Open asset ↗pdf-page:6 lines:1-78Dataset · publicStatic versions of the Fire Ecology Database, including version 1.1 used in this descriptor, are available via
FigShare or OSF in three different formatsOpen asset ↗FigSharepdf-page:9 lines:1-78Code / dataset availability confirmedEurope PMC · OpenAlex · Crossref · checked 6 Sept 2026
Plant height (PH) is a key agronomic trait influencing plant architecture. Suitable PH values for cotton are important for lodging resistance, high planting density, and mechanized harvesting, making it crucial to elucidate the mechanisms of the genetic regulation of PH. However, traditional field PH phenotyping largely relies on manual measurements, limiting its large-scale application. In this study, a high-throughput phenotyping platform based on UAV-mounted RGB and light detection and ranging (LiDAR) was developed to efficiently and accurately obtain time series PHs of 419 cotton accessions in the field. Different strategies were used to extract PH values from two sets of sensor data, and the extracted values were used to train using linear regression and machine learning methods to obtain PH predictions. These predictions were consistent with manual measurements of the PH for the LiDAR (R 2 = 0.934) and RGB (R 2 = 0.914) data. The predicted PH values were used for GWAS analysis, and 34 PH-related genes, two of which have been demonstrated to regulate PH in cotton, namely, GhPH1 and GhUBP15 , were identified. We further identified significant differences in the expression of a new gene named GhPH_UAV1 in the stems of the G. hirsutum cultivar ZM24 harvested on the 15th, 35th, and 70th days after sowing compared with those from a dwarf mutant ( pag1 ), which presented shortened stem and internode phenotypes. The overexpression of GhPH_UAV1 significantly promoted cotton stem development, whereas its knockout by CRISPR-Cas9 dramatically inhibited stem growth, suggesting that GhPH_UAV1 plays a positive regulatory role in cotton PH. This field-scale high-throughput phenotype monitoring platform significantly improves the ability to obtain high-quality phenotypic data from large populations, which helps overcome the imbalance between massive genotypic data and the shortage of field phenotypic data and facilitates the integration of genotype and phenotype research for crop improvement.
Why it matches plant phenotyping methodsUAV搭載RGB・LiDARによる綿花草丈の高スループット取得・推定プラットフォームの開発と精度検証が研究の中心であり、GWASや遺伝子機能解析は応用部分です。
abstracta high-throughput phenotyping platform based on UAV-mounted RGB and light detection and ranging (LiDAR) was developed to efficiently and accurately obtain time series PHs of 419 cotton accessions in the field
Reproduction assets foundThe paper's Data availability statement explicitly deposits the authors' source code, UAV-captured images, and analysis datasets in a public GitHub repository, which directly supports this paper's cotton plant-height phenotyping measurements and computational analysis.Code · publicThe source code, images captured by UAVs, data obtained from the analysis, and other datasets supporting the results presented here are available at https://github.com/Liqiangfan/419-cotton-plant-height-datasets .Open asset ↗Liqiangfan/419-cotton-plant-height-datasetslines:142-154Code / dataset availability confirmedEurope PMC · checked 6 Sept 2026
Published26 Feb 2025TAG. Theoretical and applied genetics. Theoretische und angewandte GenetikCited by 8 · OpenAlex ↗
Key message Phenomic selection using intact seeds is a promising tool to improve gain and complement genomic selection in corn breeding. Models that combine genomic and phenomic data maximize the predictive ability. Phenomic selection (PS) is a cost-effective method proposed for predicting complex traits and enhancing genetic gain in breeding programs. The statistical procedures are similar to those utilized in genomic selection (GS) models, but molecular markers data are replaced with phenomic data, such as near-infrared spectroscopy (NIRS). However, the use of NIRS applied to PS typically utilized destructive sampling or collected data after the establishment of selection experiments in the field. Here, we explored the application of PS using nondestructive, single-kernel NIRS in a sweet corn breeding program, focusing on predicting future, unobserved field-based traits of economic importance, including ear and vegetative traits. Three models were employed on a diversity panel: genomic and phenomic best linear unbiased prediction models, which used relationship matrices based on SNP and NIRS data, respectively, and a combined model. The genomic relationship matrices were evaluated with varying numbers of SNPs. Additionally, the PS model trained on the diversity panel was used to select doubled haploid (DH) lines for germination before planting, with predictions validated using observed data. The findings indicate that PS generated good predictive ability (e.g., 0.46 for plant height) and distinguished between high and low germination rates in untested DH lines. Although GS generally outperformed PS, the model combining both information yielded the highest predictive ability, with higher accuracies than GS when low marker densities were used. This study highlights NIRS's potential to achieve genetic gain where GS may not be feasible and to maintain/improve accuracy with SNP-based information while reducing genotyping costs.
Why it matches plant phenotyping methods単一種子NIRSを用いた非破壊フェノタイピングと予測モデルを開発・適用し、圃場形質および発芽を観測値で検証しているため、植物表現型取得・推定法が研究の中心です。
abstractHere, we explored the application of PS using nondestructive, single-kernel NIRS in a sweet corn breeding program, focusing on predicting future, unobserved field-based traits of economic importance, including ear and vegetative traits.
Reproduction assets foundThe paper explicitly states that all code and data used in the analyses are publicly available in the authors' GitHub repository (Resende-Lab/Graciano_skNIR_Phenomic_Seleciton), and additionally points to a second public repository (Resende-Lab/PLS_skNIR_Audrey) containing the kernel composition trait dataset derived/详Code · publicAll the codes and the data used in the analyses are available at https://github.com/Resende-Lab/Graciano_skNIR_Phenomic_Seleciton .Open asset ↗Resende-Lab/Graciano_skNIR_Phenomic_Selecitonlines:120-132Dataset · publicFor further information, the dataset is available at: https://github.com/Resende-Lab/PLS_skNIR_Audrey .Open asset ↗Resende-Lab/PLS_skNIR_Audreylines:78-85Code / dataset availability confirmedEurope PMC · checked 14 Sept 2026
In this work, a unique database of 6726 multispectral images of coffee leaves is presented. These images were captured in JPG format for the RGB photos and in TIF format for the five multispectral bands: blue, green, red, NIR and red edge, providing a detailed view of different wavelengths of the electromagnetic spectrum. Images in TIF format have a color depth of 16 bits per pixel, ensuring good quality. The blue band (Band 1) captures light in the blue region of the spectrum, approximately 450 to 500 nm. The green band (Band 2) records light in the green region, approximately between 500 and 620 nm. The red band (Band 3) captures light in the red region, between 620 and 750 nm. The red-edge band (Band 4) lies between the red band and the NIR, and is sensitive to the transition between green vegetation and non-vegetation, around 840 nm. Finally, the near infrared band (Band 5) captures light in the near infrared region, between 750 and 900 nm. For ease of identification, images are labeled as follows: if the image name ends in 0, it is an RGB image; if it ends in 1, it corresponds to the blue band; if it ends in 2, to the green band; if it ends in 3, to the red band; if it ends in 4, to the red-edge band; and if it ends in 5, to the near-infrared band. The images show coffee leaves with and without lesions caused by the Hemileia vastatrix fungus, known as coffee rust. These samples were collected from Colombian coffee farms and the images were captured under controlled lighting conditions to ensure quality and consistency. This database is an invaluable resource for precision agriculture research and early detection of crop diseases. With these 6726 images, researchers can use advanced image processing and machine learning techniques to identify differences between healthy leaves and those affected by rust. This can lead to the development of effective predictive models, enabling early detection and more efficient management of diseases in coffee plantations, optimizing production and reducing economic losses for farmers.
Why it matches plant phenotyping methodsコーヒー葉の病斑という植物の病害状態を対象としたマルチスペクトル画像データセットであり、再利用可能なフェノタイピング用データセットの提供が中心です。
abstractIn this work, a unique database of 6726 multispectral images of coffee leaves is presented.
Reproduction assets foundThe paper is a data descriptor whose own multispectral coffee leaf image dataset is publicly deposited on Kaggle with an explicit direct URL and DOI, matching an allowed URL.Dataset · publicth of 16 bits per pixel .
Data source location
Institution: Escuela Colombiana de Ingeniería Julio Garavito University
City/Town/Region: Bogotá D.C.
Country: Colombia Latitude: 4.5983° * Longitude: 74.0051°.
Data accessibility
Repository name: Coffe Rust
Data identification number: 10.34740/kaggle/ds/5644659
Direct URL to data: https://www.kaggle.com/ds/5644659
Instructions for accessing these data: Data available free of charge to anyone with access to the Internet and the web server address provided.
Related research article
[ 1 ] Jorge Luis Aroca Trujillo, Alexander Pérez-Ruiz. “Technologies Applied in the Field of Early Detection of Coffee Rust Fungus Diseases: A Review.” Nongye JOpen asset ↗Kaggle · 10.34740/kaggle/ds/5644659lines:1-53Code / 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 confirmedEurope PMC · checked 14 Sept 2026
This article presents a hyperspectral imaging (HSI) database of healthy leaves and leaves infected with Zymoseptoria tritici fungal pathogen responsible for leaf blotch (Lb) disease. Leaves of two durum wheat genotypes were studied under controlled conditions to track the evolution of Lb disease and capture significant spectral and spatial differences until the onset of symptoms. Hyperspectral image acquisitions were purchased with two cameras in visible-near infrared (VNIR) and short-wave infrared (SWIR) spectral ranges on eighteen dates between one day before inoculation and twenty days after inoculation. For each wavelength range studied, a total of 1175 images provided information on 3326 leaves measured throughout the experiment. These data are valuable since they can be used as a basis to monitor disease's development over time, to build leaf classification models according to their infection status per genotype per day, to develop prediction models related to symptoms' appearance, or to test imaging and spectral analysis methods.
Why it matches plant phenotyping methodsコムギ葉の病害状態をハイパースペクトル画像で取得したデータベースを構築し、感染状態分類・症状出現予測や画像解析手法の評価基盤として提供しており、表現型取得法が中心である。
abstractThis article presents a hyperspectral imaging (HSI) database of healthy leaves and leaves infected with Zymoseptoria tritici fungal pathogen responsible for leaf blotch (Lb) disease.
Reproduction assets foundThe paper is a Data in Brief article describing a public hyperspectral imaging dataset of healthy and Zymoseptoria tritici-infected durum wheat leaves, deposited on Data INRAE with DOI 10.57745/WVP0FJ. This is the paper's own plant-phenotyping measurement data (VNIR/SWIR hyperspectral images, pixel coordinates, and CSVDataset · publicand HySpex SWIR-384 (Norsk Elektro Optikk, Norway).
Data source location
Institution: Institut National de Recherche pour l'Agriculture, l'Alimentation et l'Environnement (INRAE)
City: Montpellier
Country: France
Data accessibility
Repository name: Data INRAE
Data identification number: doi: 10.57745/WVP0FJ
Direct URL to data: https://doi.org/10.57745/WVP0FJ
1
Value of the Data
•
This dataset depicts the visual appearance and spectral information related to the onset kinetics of Lb disease symptoms on wheat leaves using hyperspectral images acquired post-inoculation.
•
The images captured are valuable to monitor the evolution of the Lb disease on wheat leaves through the developmenOpen asset ↗Data INRAE · 10.57745/WVP0FJlines:1-60Code / 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 confirmedEurope PMC · checked 6 Sept 2026
The likelihood that a host will be susceptible to infection is influenced by the interaction of diverse biotic and abiotic factors. As a result, substantial experimental replication and scalability are required to identify the contributions of and interactions between the host, the environment, and biotic factors such as the microbiome. For example, pathogen infection success is known to vary by host genotype, bacterial strain identity and dose, and pathogen dose. Elucidating the interactions between these factors in vivo has been challenging because testing combinations of these variables quickly becomes experimentally intractable. Here, we describe a novel high throughput plant growth system (MYCroplanters) to test how multiple host, non-pathogenic bacteria, and pathogen variables predict host health. Using an Arabidopsis-Pseudomonas host-microbe model, we found that host genotype and bacterial strain order of arrival predict host susceptibility to infection, but pathogen and non-pathogenic bacterial dose can overwhelm these effects. Host susceptibility to infection is therefore driven by complex interactions between multiple factors that can both mask and compensate for each other. However, regardless of host or inoculation conditions, the ratio of pathogen to non-pathogen emerged as a consistent correlate of disease. Our results demonstrate that high-throughput tools like MYCroplanters can isolate interacting drivers of host susceptibility to disease. Increasing the scale at which we can screen drivers of disease, such as microbiome community structure, will facilitate both disease predictions and treatments for medicine and agricultural applications.
Why it matches plant phenotyping methodsMYCroplantersという植物の高スループット生育・感染評価システム自体を開発・記述し、宿主の感染感受性（病害状態）を測定する方法が研究の中心であるため。
abstractHere, we describe a novel high throughput plant growth system (MYCroplanters) to test how multiple host, non-pathogenic bacteria, and pathogen variables predict host health.
Reproduction assets foundThe paper's Data Availability statement explicitly deposits paper-specific assets: raw plant scan images and 3D printing files in Dryad, and processed data plus analysis/figure-generation code on GitHub. Both are public, actionable, and directly reproduce this paper's plant-phenotyping measurements and analysis.Dataset · publicThe datasets (raw images and 3D printing files) supporting the conclusions of this article are available in the Dryad repository ( https://doi.org/10.5061/dryad.w9ghx3fxd ).Open asset ↗Dryad · 10.5061/dryad.w9ghx3fxdlines:199-208Code · publicProcessed data, code for analysis and figure generation, and a copy of 3D printing files can be found on our GitHub ( https://github.com/mech3132/mycroplanter ).Open asset ↗GitHub · mech3132/mycroplanterlines:199-208Code / dataset availability confirmedEurope PMC · checked 6 Sept 2026
The cuticle is a polymeric membrane covering all plant aerial organs of primary origin. It regulates water loss and defends against environmental stressors and pathogens. Despite its significance, understanding of the micro-mechanical properties of the cuticle (cuticular membrane; CM) remains limited. In this study, non-invasive Brillouin light scattering (BLS) spectroscopy was applied to probe the micro-mechanics of native CM, dewaxed CM (DCM), and isolated cutin matrix (CU) of mature apple fruit. The BLS signal arises from the photon interaction with thermally induced pressure waves and allows for imaging with mechanical contrast. The derived loss tangent showed significant differences with wax extraction from the CM and further with carbohydrate extraction from the DCM, consistent with tensile test results. Spatial heterogeneity between anticlinal and periclinal regions was observed by BLS microscopy of CM and DCM, but not in CU. The key conclusions are: (1) BLS is sensitive to micro-mechanical variations, particularly the strain-stiffening effect of the cutin framework, offering insights into the CM's micro-mechanical behavior and underlying chemical structures; (2) CM and DCM exhibit spatial micro-mechanical heterogeneity between periclinal and anticlinal regions.
Why it matches plant phenotyping methodsリンゴ果実のクチクラの微力学特性を、BLS顕微鏡による非侵襲的イメージングで測定・比較しており、植物器官の物性形質取得が研究の中心である。
abstractnon-invasive Brillouin light scattering (BLS) spectroscopy was applied to probe the micro-mechanics of native CM, dewaxed CM (DCM), and isolated cutin matrix (CU) of mature apple fruit.
Reproduction assets foundThe paper deposits its underlying Brillouin light scattering measurement data (primary and supplementary figures) in a public LUIS repository (DOI 10.25835/xvsi5g6m). The Brillouin analysis python script is only available on request, so it is not a public code asset.Dataset · publicThe underlying data for all the primary and Supplementary Figs. has been deposited in a publicly accessible repository [ https://doi.org/10.25835/xvsi5g6m ] 67 . Raw data may be obtained from the authors upon reasonable request.Open asset ↗10.25835/xvsi5g6mlines:177-254Code / dataset availability confirmedEurope PMC · checked 14 Sept 2026
Predicting phenotypes from a combination of genetic and environmental factors is a grand challenge of modern biology. Slight improvements in this area have the potential to save lives, improve food and fuel security, permit better care of the planet, and create other positive outcomes. In 2022 and 2023, the first open-to-the-public Genomes to Fields initiative Genotype by Environment prediction competition was held using a large dataset including genomic variation, phenotype and weather measurements, and field management notes gathered by the project over 9 years. The competition attracted registrants from around the world with representation from academic, government, industry, and nonprofit institutions as well as unaffiliated. These participants came from diverse disciplines, including plant science, animal science, breeding, statistics, computational biology, and others. Some participants had no formal genetics or plant-related training, and some were just beginning their graduate education. The teams applied varied methods and strategies, providing a wealth of modeling knowledge based on a common dataset. The winner's strategy involved 2 models combining machine learning and traditional breeding tools: 1 model emphasized environment using features extracted by random forest, ridge regression, and least squares, and 1 focused on genetics. Other high-performing teams' methods included quantitative genetics, machine learning/deep learning, mechanistic models, and model ensembles. The dataset factors used, such as genetics, weather, and management data, were also diverse, demonstrating that no single model or strategy is far superior to all others within the context of this competition.
Why it matches plant phenotyping methods遺伝・環境情報からトウモロコシ収量という植物形質を予測するモデルを競争形式で比較・評価しており、計算的形質推定とベンチマークが中心である。
titleGlobal genotype by environment prediction competition reveals that diverse modeling strategies can deliver satisfactory maize yield estimates.
Reproduction assets foundThe paper is the G2F maize G×E prediction competition report. Its curated phenotype/genotype/weather/EC dataset is public (DOI 10.25739/tq5e-ak26), but that DOI is not among the allowed URLs, so it cannot be listed. However, the authors explicitly state that code from all participating teams is publicly available, and Code · publicour abilities to
solve critical, and technically challenging, problems.
Data availability
All data used in this manuscript are publicly available at https://
doi.org/10.25739/tq5e-ak26. Code from all teams is publicly avail
able as follows:
AgAdaptAR: https://github.com/EcoEvoInfo/maize-gxe-pre
diction-challenge-2023
AIMaize: https://github.com/ksegaba/Genomes2Field_Competition
All Models are Wrong: https://zenodo.org/record/7830071
arulrich: https://github.com/mwylerCH/GxEcompetition
CLAC: https://github.com/alenxav/Lectures/tree/master/MGC_2023
DataJanitors: https://github.com/qchen33/g2fcompetition2022
DeepCropVision: https://github.com/Ved-Piyush/DeepCrop
Vision_maizegxeprediction2022
EOpen asset ↗ksegaba/Genomes2Field_Competitionpdf-raw-page:13 lines:1-91Code · publicta availability
All data used in this manuscript are publicly available at https://
doi.org/10.25739/tq5e-ak26. Code from all teams is publicly avail
able as follows:
AgAdaptAR: https://github.com/EcoEvoInfo/maize-gxe-pre
diction-challenge-2023
AIMaize: https://github.com/ksegaba/Genomes2Field_Competition
All Models are Wrong: https://zenodo.org/record/7830071
arulrich: https://github.com/mwylerCH/GxEcompetition
CLAC: https://github.com/alenxav/Lectures/tree/master/MGC_2023
DataJanitors: https://github.com/qchen33/g2fcompetition2022
DeepCropVision: https://github.com/Ved-Piyush/DeepCrop
Vision_maizegxeprediction2022
EnBiSys: https://github.com/dperondi/maizegxeprediction2022
gartyboiOpen asset ↗pdf-raw-page:13 lines:1-91Code · publicript are publicly available at https://
doi.org/10.25739/tq5e-ak26. Code from all teams is publicly avail
able as follows:
AgAdaptAR: https://github.com/EcoEvoInfo/maize-gxe-pre
diction-challenge-2023
AIMaize: https://github.com/ksegaba/Genomes2Field_Competition
All Models are Wrong: https://zenodo.org/record/7830071
arulrich: https://github.com/mwylerCH/GxEcompetition
CLAC: https://github.com/alenxav/Lectures/tree/master/MGC_2023
DataJanitors: https://github.com/qchen33/g2fcompetition2022
DeepCropVision: https://github.com/Ved-Piyush/DeepCrop
Vision_maizegxeprediction2022
EnBiSys: https://github.com/dperondi/maizegxeprediction2022
gartybois: https://github.com/Thyra/g2f-maize-challenge-202Open asset ↗mwylerCH/GxEcompetitionpdf-raw-page:13 lines:1-91Code · public0.25739/tq5e-ak26. Code from all teams is publicly avail
able as follows:
AgAdaptAR: https://github.com/EcoEvoInfo/maize-gxe-pre
diction-challenge-2023
AIMaize: https://github.com/ksegaba/Genomes2Field_Competition
All Models are Wrong: https://zenodo.org/record/7830071
arulrich: https://github.com/mwylerCH/GxEcompetition
CLAC: https://github.com/alenxav/Lectures/tree/master/MGC_2023
DataJanitors: https://github.com/qchen33/g2fcompetition2022
DeepCropVision: https://github.com/Ved-Piyush/DeepCrop
Vision_maizegxeprediction2022
EnBiSys: https://github.com/dperondi/maizegxeprediction2022
gartybois: https://github.com/Thyra/g2f-maize-challenge-2022
Kernel of Truth: https://github.com/robertkhu/mOpen asset ↗alenxav/Lecturespdf-raw-page:13 lines:1-91Code / dataset availability confirmedEurope PMC · checked 14 Sept 2026
Common beanLeafMorphology / geometry measurementCalibration / preprocessingSegmentationLeaf traits
Leaf dimensioning is relevant for analyzing plant responses to several conditions such as soil fertility, availability of light, agricultural pesticide effect, and access to water in the soil or periods of drought. In this paper, we present a dataset composed of 6981 images of 612 common bean leaves ( Phaseolus vulgaris ). We captured the images of each leaf accompanied by a fiducial marker and annotated the known leaf dimensions (area, perimeter, length, and width). We provide annotations concerning image segmentation, known area uniformly distributed over the leaf region, real area of the marker region, marker pose, capture conditions, and camera calibration. This dataset can be useful for developing deep learning algorithms for leaf dimensioning and related problems. Therefore, there is a potential to contribute to computer vision and plant physiology researchers and specialists.
Why it matches plant phenotyping methods葉面積・周長・長さ・幅の画像ベース計測用データセットを提供し、セグメンテーション、マーカー姿勢、カメラ校正も含むため、植物表現型取得手法の基盤として中心的です。
abstractWe captured the images of each leaf accompanied by a fiducial marker and annotated the known leaf dimensions (area, perimeter, length, and width).
Reproduction assets foundThe paper is itself a data descriptor for the LSID-Beans bean leaf image dataset (6981 images, 612 leaves, with leaf dimension annotations, segmentation masks, area maps, and camera calibration). The dataset is publicly deposited on Mendeley Data (DOI 10.17632/f42hwwrpgn.2), and the authors' data-processing scripts areDataset · publicstakes and improved the data quality.
Data source location
The images were collected in the city of Ouro Branco, Minas Gerais, Latitude −20.535912, Longitude −43.711031, Brazil.
Data accessibility
Repository name: Leaf on Stem Image Dataset Beans (LSID-Beans)
Data identification number: 10.17632/f42hwwrpgn.2
Direct URL to data: https://data.mendeley.com/datasets/f42hwwrpgn/2
1
Value of the Data
•
The dataset images are useful for developing deep learning methods for non-destructive leaf dimension estimation. We provide each leaf's known area, perimeter, width, and length, which can be used to train supervised machine learning algorithms.
•
Methods developed using the dataset can help to moniOpen asset ↗10.17632/f42hwwrpgn.2lines:1-50Code · publicfor that split. Section Cross-validation protocol definition details our proposed cross-validation protocol.
4
Experimental Design, Materials and Methods
Fig. 3 shows the steps performed to build our dataset. We describe each step in the next sections. The source codes used to process the data are available in this repository: https://github.com/gcg-ufjf/LSID-Beans-Scripts . Fig. 3
Steps of the dataset construction.
Fig 3
4.1
Plant cultivation
We selected black bean seeds and carried out planting in April 2022. On average, 3 seeds were sown in each pit, made with the aid of a hoe, along 9 rows of 30 plants. The soil used had never been cultivated and had rejects of construction material on tOpen asset ↗githublines:66-146Code / dataset availability confirmedEurope PMC · checked 15 Sept 2026
Soil imaging in the field and laboratory has greatly advanced our understanding of plant root systems. Soil fungi function as important plant symbionts and decomposers of complex organic material in soil environments. For fungal hyphae, however, the application of soil imaging remains scarce, limiting our understanding of hyphal systems in soil. This scarce application is partly due to the challenging development of a soil imaging device for hyphae: technical requirements to resolve fine hyphae (2-5 μm in diameter) are high, while the device cost must be low to facilitate sufficient deployment that can capture the high spatial heterogeneity of hyphal dynamics in soil. This protocol describes the do-it-yourself assembly and application of a low-cost high-resolution imaging device for observing hyphae in soil. The assembly of the open-source imaging device relies on many 3D-printed parts, reducing material costs to ca. 930 USD. The application of the imaging device yields soil profile images with a resolution of up to 0.52 μm px-1 (49000 dpi) within an observable volume of 70 × 210 × 1.5 mm. By repeatedly imaging a soil profile using the presented techniques, changes in the amount, distribution, and morphology of hyphae in soil can be observed and quantified.
Why it matches plant phenotyping methods土壌中の菌糸の量・分布・形態を画像から観察・定量する低コスト高解像度イメージング装置の組立・応用プロトコルであり、植物関連状態の取得手法が中心である。
abstractThis protocol describes the do-it-yourself assembly and application of a low-cost high-resolution imaging device for observing hyphae in soil.
Reproduction assets foundThe paper deposits its imaging-device design files, operating software, and high-resolution soil profile image sets on Zenodo, all paper-specific and publicly available.Dataset · publicerials and methods
The protocol described in this peer-reviewed article is published on protocols.io, https://dx.doi.org/10.17504/protocols.io.bp2l6xo3zlqe/v1 , and is included for printing as S1 File with this article. Furthermore, three sets of high-resolution images yielded following the protocol are available from Zenodo at https://doi.org/10.5281/zenodo.10730414 . The high-resolution images were acquired between May and October 2023 in a Quercus serrata grove at the Kansai Research Center of the Forestry and Forest Products Research Institute (FFPRI) in Kyoto City, Japan (34°56’N, 135°46’E). The grove was located on a Cambisol [ 44 ] in flat terrain. Monthly mean air temperature rangedOpen asset ↗Zenodo · 10.5281/zenodo.10730414lines:31-38Dataset · publicl.
The protocol is also available on protocols.io.
(PDF)
S1 Dataset
Set of soil profile images acquired at focus depths of 0, 0.025, and 0.05 mm.
Original images in the JPG format taken at an imaging resolution of 0.65 μm px -1 (39200 dpi).
(ZIP)
Data Availability Statement
All design files used to assemble the imaging device ( https://doi.org/10.5281/zenodo.10689905 ), all software to operate the imaging device ( https://doi.org/10.5281/zenodo.10815832 ), and several sets of images yielded with the imaging device ( https://doi.org/10.5281/zenodo.10730414 ) are available from the data repository Zenodo. All remaining data are within the manuscript and its Supporting Information files.Open asset ↗Zenodo · 10.5281/zenodo.10689905lines:147-155Code / dataset availability confirmedEurope PMC · checked 6 Sept 2026
Background Apple breeding schemes can be improved by using genomic prediction models to forecast the performance of breeding material. The predictive ability of these models depends on factors like trait genetic architecture, training set size, relatedness of the selected material to the training set, and the validation method used. Alternative genotyping methods such as RADseq and complementary data from near-infrared spectroscopy could help improve the cost-effectiveness of genomic prediction. However, the impact of these factors and alternative approaches on predictive ability beyond experimental populations still need to be investigated. In this study, we evaluated 137 prediction scenarios varying the described factors and alternative approaches, offering recommendations for implementing genomic selection in apple breeding. Results Our results show that extending the training set with germplasm related to the predicted breeding material can improve average predictive ability across eleven studied traits by up to 0.08. The study emphasizes the usefulness of leave-one-family-out cross-validation, reflecting the application of genomic prediction to a new family, although it reduced average predictive ability across traits by up to 0.24 compared to 10-fold cross-validation. Similar average predictive abilities across traits indicate that imputed RADseq data could be a suitable genotyping alternative to SNP array datasets. The best-performing scenario using near-infrared spectroscopy data for phenomic prediction showed a 0.35 decrease in average predictive ability across traits compared to conventional genomic prediction, suggesting that the tested phenomic prediction approach is impractical. Conclusions Extending the training set using germplasm related with the target breeding material is crucial to improve the predictive ability of genomic prediction in apple. RADseq is a viable alternative to SNP array genotyping, while phenomic prediction is impractical. These findings offer valuable guidance for applying genomic selection in apple breeding, ultimately leading to the development of breeding material with improved quality.
Why it matches plant phenotyping methodsリンゴ育種におけるNIR分光データを用いたフェノミック予測を、ゲノム予測と多数のシナリオで比較評価しており、植物形質推定ワークフローの技術的検証が中心的です。
titleEvaluation of genomic and phenomic prediction for application in apple breeding.
Reproduction assets foundThe paper's own phenotypic, genomic, and near-infrared spectroscopy (NIRS) data acquired in this study are publicly deposited in the ETH Research Collection, directly reproducing the paper's phenotyping measurements and phenomic/genomic prediction analysis inputs. The NCBI SRA deposit contains only raw RADseq reads (m-Dataset · publicThe phenotypic, genomic, and near-infrared spectroscopy data acquired in this study are available in the ETH Research Collection at https://doi.org/10.3929/ethz-b-000699803 .Open asset ↗ETH Research Collection · 10.3929/ethz-b-000699803lines:180-211Code / dataset availability confirmedbioRxiv · checked 15 Sept 2026
Leaf osmotic potential at full turgor ({pi}0) has been used frequently to indicate turgor loss point of plant leaves. However, even a rapid measurement of{pi} 0 using osmometry is time-consuming, if numerous leaf samples need to be measured. Because of this, researchers tend to use a small sample size to determine{pi} 0 and relate it to indices of crop performance. Yet the statistical and agronomic significance of using a small sample size of{pi} 0 to indicate crop performance is not known. We address this question using field measurements and statistical resampling. Six mature leaf samples were collected at the peak bloom stage from each of the 54 cotton plots in Texas, USA in 2024. The{pi} 0 of the collected leaves were measured using an osmometer. Seed cotton yields from the field plots were measured near the end of cotton season. To test the effect of sample size on strength of the linear relation between{pi} 0 and cotton yield, 1-6 resamples of{pi} 0 were randomly drawn with replacement from the original 6 measurements per plot for the 54 plots. The resampled data of{pi} 0 were then used as independent variable to predict cotton yield. We found that, considering the labor and cost, sampling 3 or 6 leaves per plot may not make a significant difference for the linear regression between{pi} 0 and cotton yield.
Why it matches plant phenotyping methods葉の浸透ポテンシャル測定におけるサンプル数の妥当性と、収量との関係に対する影響を再サンプリングで評価しており、測定プロトコルの技術的検証が中心である。
titleDoes sample size of leaf osmotic potential affect its relationship with cotton yield?
Reproduction assets foundThe paper's field-measured leaf osmotic potential and seed cotton yield dataset, plus the authors' resampling/regression computer code, are explicitly deposited publicly on Zenodo (record 14635663), as stated in the Data availability section.Dataset · publicect 9574-
2, is appreciated. We thank Jose Teran and Joe Gonzalez,
Farm Manager and Farm Foreman, respectively, at Uvalde
Research Center, and collaborating farmer Rick Kruger for
time/efforts invested in crop management.
Data availability
The data and computer code for reproduc-
ing the results of this paper are available from
https://zenodo.org/records/14635663.Bibliography
1. Megan K. Bartlett, Ya Zhang, Christine Scoffoni, Shanwen Sun, Rico Ardy, Kunfang Cao,
and Lawren Sack. Rapid determination of comparative drought tolerance traits: using an
osmometer to predict turgor loss point. Methods in Ecology and Evolution, 3:880–888, 2012.
2. Y. N. S. Cheung, M. T. Tyree, and J. Dainty. WOpen asset ↗Zenodo · 14635663pdf-raw-page:3 lines:1-85Code / dataset availability confirmedOpenAlex · checked 15 Sept 2026
Field / plotNeRF / 3D Gaussian SplattingLiDAR / point cloudStem / branchMorphology / geometry measurement2D/3D reconstructionArchitecture / morphology / geometry
Forest mapping provides critical observational data needed to understand the dynamics of forest environments. Notably, tree diameter at breast height (DBH) is a metric used to estimate forest biomass and carbon dioxide (CO2) sequestration. Manual methods of forest mapping are labor intensive and time consuming, a bottleneck for large-scale mapping efforts. Automated mapping relies on acquiring dense forest reconstructions, typically in the form of point clouds. Terrestrial laser scanning (TLS) and mobile laser scanning (MLS) generate point clouds using expensive LiDAR sensing and have been used successfully to estimate tree diameter. Neural radiance fields (NeRFs) are an emergent technology enabling photorealistic, vision-based reconstruction by training a neural network on a sparse set of input views. In this paper, we present a comparison of MLS and NeRF forest reconstructions for the purpose of trunk diameter estimation in a mixed-evergreen Redwood forest. In addition, we propose an improved DBH-estimation method using convex-hull modeling. Using this approach, we achieved 1.68 cm RMSE (2.81%), which consistently outperformed standard cylinder modeling approaches.
Why it matches plant phenotyping methods森林内の樹木DBHという個体形態形質を、NeRF・MLS再構成と凸包モデルで推定し、手法比較と精度評価を行っているため、植物形質取得法が中心です。
abstractIn this paper, we present a comparison of MLS and NeRF forest reconstructions for the purpose of trunk diameter estimation in a mixed-evergreen Redwood forest.
Reproduction assets foundThe authors explicitly state that their forest datasets (SLAM and NeRF reconstructions, imagery) and TreeTool modeling code contributions are freely available on their public GitHub repository, which is listed in the allowed URLs.Code · publicAR-inertial SLAM with regards to DBH estimation accuracy.
• Improved DBH estimation accuracy via a trunk modeling approach using convex-hull
and density-based filtering methods.
• Open-source modeling code and forest datasets, including SLAM and NeRF recon-
structions of a mixed-evergreen Redwood forest, are freely available at
https://github.com/harelab-ucsc/RedwoodNeRF (accessed on 7 January 2025).
2. Theoretical Background
2.1. The SLAM Approach
The SLAM problem can be broken into two tasks: building a map of the environment
and simultaneously estimating the robot’s trajectory within that map. More specifically,Open asset ↗harelab-ucsc/RedwoodNeRFpdf-raw-page:2 lines:1-50Code / dataset availability confirmedEurope PMC · checked 6 Sept 2026
A wide range of portable chlorophyll meters are increasingly being used to measure leaf chlorophyll content as an indicator of plant performance, providing reference data for remote sensing studies. We tested the effect of leaf anatomy on the relationship between optical assessments of chlorophyll (Chl) against biochemically determined Chl content as a reference. Optical Chl assessments included measurements taken by four chlorophyll meters: three transmittance-based (SPAD-502, Dualex-4 Scientific, and MultispeQ 2.0), one fluorescence-based (CCM-300), and vegetation indices calculated from the 400-2500 nm leaf reflectance acquired using an ASD FieldSpec and a contact plant probe. Three leaf types with different anatomy were included: dorsiventral laminar leaves, grass leaves, and needles. On laminar leaves, all instruments performed well for chlorophyll content estimation (R 2 > 0.80, nRMSE 2 > 0.90, nRMSE 2 = 0.45, nRMSE = 11%) and failed for SPAD. For Norway spruce needles, the relation of CCM-300 values to chlorophyll content was also weak (R 2 = 0.45, nRMSE = 11%). To improve the accuracy of data used for remote sensing algorithm development, we recommend calibration of chlorophyll meter measurements with biochemical assessments, especially for species with anatomy other than laminar dicot leaves. The take-home message is that portable chlorophyll meters perform well for laminar leaves and grasses with wider leaves, however, their accuracy is limited for conifer needles and narrow grass leaves. Species-specific calibrations are necessary to account for anatomical variations, and adjustments in sampling protocols may be required to improve measurement reliability.
Why it matches plant phenotyping methods携帯型クロロフィルメーターによる葉クロロフィル量推定を、葉の解剖学的差異と生化学測定を基準に比較・検証し、校正とサンプリング改善を提案しているため、植物表現型取得法が中心です。
abstractWe tested the effect of leaf anatomy on the relationship between optical assessments of chlorophyll (Chl) against biochemically determined Chl content as a reference.
Reproduction assets foundThe paper's Data availability statement explicitly deposits the study's chlorophyll measurement and trait data in a public Zenodo repository, which is an allowed URL. No separate author analysis code URL is given (analyses were in Matlab/R), so the qualifying asset is the deposited dataset.Dataset · publicData are available in Zenodo repository found by https://zenodo.org/records/14615430.Open asset ↗Zenodo · 14615430pdf-page:14 lines:1-62Code / dataset availability confirmedEurope PMC · checked 14 Sept 2026
The evaluation of fruit genetic resources regarding a resistance to pathogens is an essential basis for subsequent selection in fruit breeding. Both genetic analysis and phenotyping of defined traits are important tools and provide decision data in the evaluation process. However, the phenotyping of plants is often carried out 'by hand' and remains the bottleneck in fruit breeding and fruit growing. The development of a digital and UAV (unmanned aerial vehicle)-based phenotyping method for the assessment of genotype-specific susceptibility or resistance against diseases in orchards would significantly increase the efficiency of plant breeding. In this framework, a workflow for drone-based monitoring of pathogens in orchards was developed using the European pear rust ( Gymnosporangium sabinae ) as model pathogen. Pear rust is widespread in orchards and causes conspicuous, clearly visible, yellow to orange-colored disease symptoms. In this paper, we provide a dataset with expert-annotated high-resolution RGB images with pear rust symptoms. For data collection, ten UAV-flight campaigns were realized between 2021 and 2023 under various weather conditions and with different flight parameters in the experimental orchard of the Julius Kühn-Institute for Breeding Research on Fruit Crops in Dresden-Pillnitz (Germany). 1394 images were captured of different pear genotypes, including varieties, wild species and progeny from breeding. The dataset contains manually labelled images with a size of 768 × 768 pixels of leaves infected with pear rust at different stages of development, labelled as class GYMNSA, as well as background images without symptoms. Each leaf with pear rust symptoms was annotated with the drawing method by two points (bounding boxes) using the Computer Vision Annotation Tool (CVAT, v1.1.0) [1] and presented in YOLO 1.1 file format (.txt files). A total of 584 annotated images and 162 background images, organized into a training and validation set, are included in the GYMNSA dataset. This GYMNSA dataset can be used as a resource for researchers and developers working on drone-based plant disease monitoring systems.
Why it matches plant phenotyping methodsナシさび病の植物症状をUAV画像から検出するための注釈付きデータセットを提供しており、植物病害状態の画像ベース表現型取得・解析ワークフローが中心的です。
abstractThe development of a digital and UAV (unmanned aerial vehicle)-based phenotyping method for the assessment of genotype-specific susceptibility or resistance against diseases in orchards would significantly increase the efficiency of plant breeding.
Reproduction assets foundThe paper's GYMNSA dataset — annotated UAV RGB images of pear rust symptoms with YOLO labels — is publicly deposited on Mendeley Data under DOI 10.17632/44kjgc4gkc.1, directly reproducing the paper's phenotyping measurements.Dataset · publicl orchard of the Julius Kühn-Institute (JKI - Federal Research Centre for Cultivated Plants) at the Institute for Breeding Research on Fruit Crops located in Dresden-Pillnitz (Germany) [51°00ʹ01"N 13°53ʹ12"E].
Data accessibility
Repository name: Mendeley Data
Data identification number: 10.17632/44kjgc4gkc.1
Direct URL to data: https://data.mendeley.com/datasets/44kjgc4gkc/1
1.
Value of the Data
•
These data were collected on an approximately 1.6 ha experimental field with over 1000 different pear genotypes (breeding material and genetic resources of pear varieties and species) and presents a wide spectrum of phenotypic characteristics of pear rust infections at different stages of developmeOpen asset ↗Mendeley Data · 10.17632/44kjgc4gkc.1lines:43-69Code / dataset availability confirmedEurope PMC · checked 6 Sept 2026
Rain cracking compromises quality and quantity of sweet cherries worldwide. Cracking susceptibility differs among genotypes. The objective was to (1) phenotype the progeny of a cross between a tolerant and a susceptible sweet cherry cultivar for cuticle mass per unit area, strain release on cuticle isolation, cuticular microcracking and calcium/dry mass ratio and (2) relate these characteristics to cracking susceptibilities evaluated in laboratory immersion assays and published multiyear field observations. Mass of the dewaxed cuticle per unit area and strain release upon cuticle isolation were significantly related to cracking susceptibility in lab or field. Cuticular microcracking in the stylar end region as indexed by infiltration with acridine orange was more severe in susceptible than in tolerant genotypes and significantly correlated with susceptibility to cracking in lab and field. The Ca/dry mass ratio was lower (-8%) for susceptible than for tolerant genotypes. Fruit that cracked early had less Ca than those that cracked later. Only the Ca/dry mass ratio of the stylar end region was significantly correlated with cracking susceptibility in the field. Based on stepwise regression analyses microcracking of the cuticle accounted for most of the cracking susceptibilities in field and lab (partial r2 = 0.331 to 0.338 for field vs. r2 = 0.326 to 0.453 for lab). The variability in cracking susceptibility accounted for increased to a r2 = 0.571 (lab) when adding mass of dewaxed cuticle, up to r2 = 0.421 (field) when adding the Ca/dry mass ratio in the stylar end region or up to r2 = 0.478 (field) when entering the strain release on isolation into the model. A protocol for phenotyping is suggested that allows larger progenies to be phenotyped for microcracking, DCM mass and strain release.
Why it matches plant phenotyping methodsサクランボ果実の微細亀裂、クチクラ質量、ひずみ解放などを用いた表現型評価を扱い、大規模後代を評価するためのフェノタイピングプロトコルを提案しているため、方法が中心的である。
abstractThe objective was to (1) phenotype the progeny of a cross between a tolerant and a susceptible sweet cherry cultivar for cuticle mass per unit area, strain release on cuticle isolation, cuticular microcracking and calcium/dry mass ratio and (2) relate these characteristics to cracking susceptibilities evaluated in laboratory immersion assays and published multiyear field observations.
Reproduction assets foundThe paper's supporting information S1 Dataset contains the raw phenotyping data (cracking susceptibility, cuticle mass, strain release, microcracking, Ca/dry mass ratios) underlying all figures, publicly available as an XLSX supplement on the PLOS ONE article page. No author analysis code or trained models are reportedDataset · publicS1 Dataset. The raw data of all figures and the data on mean fruit mass of the individual genotypes are available in the S1 Dataset.Open asset ↗lines:305-314Code / dataset availability confirmedOpenAlex · Crossref · checked 15 Sept 2026
Tree height and vertical forest structure are important attributes in forestry, but their traditional measurement or assessment in the field is expensive, time-consuming, and often inaccurate. One of the main advantages of using remote sensing data to estimate vertical forest structure is the ability to obtain accurate data for larger areas in a more time- and cost-efficient manner. Temporal changes are also important for estimating and analysing tree heights, and in many countries, national airborne laser scanning (ALS) surveys have been conducted either only once or at specific, longer intervals, whereas aerial surveys are more often arranged in cycles with shorter intervals. In this study, we reviewed all freely available national airborne remote sensing data describing three-dimensional forest structures in Slovenia and compared them with traditional field measurements in an area dominated by uneven-aged forests. The comparison of ALS and digital aerial photogrammetry (DAP) data revealed that freely available national ALS data provide better estimates of dominant forest heights, vertical structural diversity, and their changes compared to cyclic DAP data, but they are still useful due to their temporally dense data. Up-to-date data are very important for forest management and the study of forest resilience and resistance to disturbance. Based on field measurements (2013 and 2023) and all remote sensing data, dominant and maximum heights are statistically significantly higher in uneven-aged forests than in mature, even-aged forests. Canopy height diversity (CHD) information, derived from lidar ALS and DAP data, has also proven to be suitable for distinguishing between even-aged and uneven-aged forests. The CHDALS 2023 was 1.64, and the CHDCAS 2022 was 1.38 in uneven-aged stands, which were statistically significantly higher than in even-aged forest stands.
Why it matches plant phenotyping methodsALSと航空写真測量による樹高・森林垂直構造・樹冠高多様性の推定を現地測定と比較検証しており、植物（森林）の形態形質測定が研究の中心です。
abstractThe comparison of ALS and digital aerial photogrammetry (DAP) data revealed that freely available national ALS data provide better estimates of dominant forest heights, vertical structural diversity, and their changes compared to cyclic DAP data
Reproduction assets foundThe paper's own field measurements (2013, 2023) and derived analysis data are not publicly deposited; the Data Availability Statement says raw data are available only upon reasonable request from the corresponding author. The freely available national ALS/DAP source data are public via the Slovenian national remote-snsDataset · publicmote Sens. Environ. 2018, 208, 1–14. [CrossRef]
14. Goodbody, T.R.H.; Coops, N.C.; White, J.C. Digital Aerial Photogrammetry for Updating Area-Based Forest Inventories: A Review
of Opportunities, Challenges, and Future Directions. Curr. For. Rep. 2019, 5, 55–75. [CrossRef]
15. GURS. Daljinsko zaznavanje. 2024. Available online: https://www.e-prostor.gov.si/podrocja/drzavni-topografski-sistem/daljinsko-zaznavanje/ (accessed on 13 November 2024).
16. Haala, N. The landscape image matching algorithms. In Proceedings of the 54th Photogrammetric Week, Stuttgart, Germany,
9–13 September 2013; pp. 271–284.
17. Triglav Čekada, M.; Bric, V. Končan je projekt Laserskega skeniranja Slovenije. Geod. VOpen asset ↗GURSpdf-raw-page:14 lines:1-48Code / dataset availability confirmedCrossref · Europe PMC · checked 13 Sept 2026
Abstract Functional–structural plant (FSP) models are useful tools for understanding plant functioning and how plants react to their environment. Developing tree FSP models is data-intensive and measuring tree architecture using conventional measurement tools is a laborious process. Light detection and ranging (LiDAR) could be an alternative nondestructive method to obtain structural information about tree architecture. This research investigated how terrestrial LiDAR (TLS)-derived tree traits could be used in the design and parameterization of tree FSP models. A systematic literature search was performed to create an overview of tree parameters needed for FSP model development. The resulting structural parameters were compared to LiDAR literature to get an overview of the possibilities and limitations. Furthermore, a tropical tree and Scots pine FSP model were selected and parametrized with TLS-derived parameters. Quantitative structural models were used to derive the parameters and a total of 37 TLS-scanned tropical trees and 10 Scots pines were included in the analysis. Ninety papers on FSP tree models were screened and eight papers fulfilled all the selection criteria. From these papers, 50 structural parameters used for FSP model development were identified, from which 28 parameters were found to be derivable from LiDAR. The TLS-derived parameters were compared to measurements, and the accuracy was variable. It was found that branch angle could be used as model input, but internode length was unsuitable. Outputs of the FSP models with TLS-derived branch angle differed from the FSP model outcomes with default branch angle. Results showed that it is possible to use TLS for FSP model inputs, although with caution as this has implications for the model variable outputs. In the future, LiDAR could help improve efficiency in building new FSP models, increase the accuracy of existing models, add metrics for optimization, and open new possibilities to explore previously unobtainable plant traits.
Why it matches plant phenotyping methodsTLSによる樹木構造形質の取得・精度比較と、機能構造モデルへの適用が研究の中心であり、植物表現型計測法の実質的な検証・応用に該当する。
abstractLight detection and ranging (LiDAR) could be an alternative nondestructive method to obtain structural information about tree architecture.
Reproduction assets foundThe paper's Data availability statement points to a public 4TU.Centre for Research Data deposit (DOI 10.4121/2b7e832f-12e9-4d0e-92e2-5aef9bcf9142) containing the data underlying the study, i.e. the TLS-derived tree structural measurements (Guyana tropical trees and Loobos Scots pines) used for FSP model parameterizatonDataset · publicThe data underlying this article are available in 4TU.Centre for Research Data, at https://dx.doi.org/10.4121/2b7e832f-12e9-4d0e-92e2-5aef9bcf9142 .Open asset ↗4TU.Centre for Research Data · 10.4121/2b7e832f-12e9-4d0e-92e2-5aef9bcf9142lines:728-782Code / 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 confirmedEurope PMC · checked 14 Sept 2026
Epipremnum aureum, sometimes known as the Money Plant, is a popular houseplant known for its hearts-shaped leaves and durability. Commonly referred to as Golden Pothos or Devil's Ivy, it is also appreciated for its ornamental value and air cleaning ability. They say that these plants are attractive to many people owing to their tolerance to several conditions and easy care, therefore, it is no surprise that they are found in many households and workplaces. Money Plants are hardy, but like any other plant they can also be infected by various diseases, which may render them less attractive, or even unattractive. This work encompasses bacterial wilt, manganese poisoning aspects and together with a healthy leaves aspect presents all prevalent masses and offer a comprehensive image of diseases. A dataset of 224 × 224 pixel images is utilized to accomplish this work with the intention to further enhance support in Ornamental Horticulture practices and diagnose more accurately. This work not only contributes ideas and approaches in understanding the field of plants pathology but also stresses on the fact how image processing can be beneficial in looking after plants. The dataset serves as a solid foundation for deep learning approaches into Ornamental Agriculture and provides useful insights for researchers studying the cultivation of money plants.
Why it matches plant phenotyping methods植物病害の症状を画像から分類するデータセットが研究の中心であり、罹病状態という植物表現型を直接評価している。
abstractA dataset of 224 × 224 pixel images is utilized to accomplish this work with the intention to further enhance support in Ornamental Horticulture practices and diagnose more accurately.
Reproduction assets foundThe paper's money plant leaf disease image dataset (original and augmented) is publicly deposited on Mendeley Data, and the authors' data augmentation code is publicly available on GitHub; both are paper-specific, public, and directly actionable.Dataset · publicen in collaboration with an expert from the Ministry of Agriculture, Bangladesh
Data source location
Location: Bangladesh Agricultural Development Corporation.
The area: Kashimpur, Gazipur.
Country: Bangladesh
Data accessibility
Repository name: Mendeley Data
Data identification number: 10.17632/rzjww3vdxt.3
Direct URL to data: https://data.mendeley.com/datasets/rzjww3vdxt/3
Related research article
None
1.
Value of the Data
Epipremnum aureum, popularly called Devil's ivy, Golden images, or Money Plant, is a common houseplant that is appreciated for its durability as well as its heart shaped leaves [ 1 ]. Known not only for its pleasing aesthetic but also for its air purifying qualities, theOpen asset ↗Mendeley Data · 10.17632/rzjww3vdxt.3lines:1-46Code / dataset availability confirmedCrossref · Europe PMC · checked 13 Sept 2026
Agriculture greatly impacts Bangladesh's economy, and vegetable cultivation plays a significant role in Agriculture by providing nourishment, and food security as well as improving the economy. The necessity of food production is growing similarly to the population growth. The farmers of Bangladesh are working hard to meet this need for food production and to gain yields. However, every year the farmers face a significant amount of loss in production due to the attack of different diseases and viruses due to the lack to technological development. The reason behind most of these losses is the lack of knowledge about diseases and being unable to detect the diseases early. Therefore, the early detection of plant disease is significant in balancing the country's economy and preventing undesirable losses. To bring a solution to this problem our dataset provides a total of 4467 images of Beans and Cowpeas leaf images which include different disease classes and fresh leaves. The dataset comprises 2,273 images of Bean and 2,194 images of Cowpea plants where each plant provides 4 classes of different disease along with the healthy leaves. This dataset will assist researchers in identifying plant diseases and farmers as well as contribute to the economy of the country.
Why it matches plant phenotyping methods豆類葉の画像から病害状態を推定する画像データセットが研究の中心であり、植物病害表現型のデータ資源として収録対象です。
titleComprehensive smartphone image dataset for bean and cowpea plant leaf disease detection and freshness assessment from Bangladesh vegetable fields
Reproduction assets foundThe paper is a Data in Brief article describing a smartphone image dataset of bean and cowpea leaf disease/freshness. The authors' own dataset is publicly deposited on Mendeley Data with an explicit direct URL and DOI, making it a paper-specific, publicly actionable asset. The Kaggle bean disease dataset is cited priorDataset · publicData accessibility
Repository name: Mendeley Data
Data identification number: 10.17632/ykvcrjffzd.1
Direct URL to data: https://data.mendeley.com/datasets/ykvcrjffzd/1Open asset ↗Mendeley Data · 10.17632/ykvcrjffzd.1lines:1-51Code / dataset availability confirmedOpenAlex · Crossref · Europe PMC · checked 14 Sept 2026
Rice tiller angle is a key agronomic trait that regulates plant architecture and plays a critical role in determining rice yield. Given that tiller angle is regulated by multiple genes, it is important to identify quantitative trait loci (QTL) associated with tiller angle. Recently, with the advancement of imaging technology for plant phenotyping, it has become possible to quickly and accurately measure agronomic traits of breeding populations. In this study, we extracted tiller angle and various image-based parameters from Red-Green-Blue (RGB) images of a recombinant inbred line (RIL) population derived from a cross between Milyang23 (Indica) and Giho (Japonica). Correlations among the obtained data were analyzed, and through dynamic QTL mapping, five major QTLs (qTA1, qTA1-1, qTA2, qTA2-1, and qTA9) related to tiller angle were detected on chromosomes 1, 2, and 9. Among them, 26 candidate genes related to auxin signaling and plant growth, including the TAC1 (Tiller Angle Control 1) gene, were identified in qTA9 (RM257-STS09048). These results demonstrate the potential of image-based phenotyping to overcome the limitations of traditional manual measurements in crop structure research. Furthermore, the identification of key QTLs and candidate genes related to tiller angle provides valuable genetic insights for the development of high-yielding varieties through crop morphology control.
Why it matches plant phenotyping methodsRGB画像からイネの分げつ角度と画像パラメータを抽出する画像ベース表現型解析を、育種集団で実質的に適用・評価しており、表現型取得法が中心です。
abstractRecently, with the advancement of imaging technology for plant phenotyping, it has become possible to quickly and accurately measure agronomic traits of breeding populations.
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/plants13233288/s1 , Figure S1: Quantitative trait loci (QTL) analysis associated with tiller angle in rice using RIL population; Figure S2: QTL distribution for tiller angle across different development stages.Open asset ↗lines:89-100Code / dataset availability confirmedEurope PMC · checked 7 Sept 2026
Recruitment poses significant challenges for narrow endemic plant species inhabiting extreme environments like vertical cliffs. Investigating seed traits in these plants is crucial for understanding the adaptive properties of chasmophytes. Focusing on the Iberian endemic genus Petrocoptis A. Braun ex Endl., a strophiole-bearing Caryophyllaceae, this study explored the relationships between seed traits and climatic variables, aiming to shed light on the strophiole's biological role and assess its classificatory power. We analysed 2773 seeds (557 individuals) from 84 populations spanning the genus' entire distribution range. Employing cluster and machine learning algorithms, we delineated well-defined morphogroups based on seed traits and evaluated their recognizability. Linear mixed-effects models were utilized to investigate the relationship between climate predictors and strophiole area, seed area and the ratio between both. The combination of seed morphometric traits allows the division of the genus into three well-defined morphogroups. The subsequent validation of the algorithm allowed 87% of the seeds to be correctly classified. Part of the intra- and interpopulation variability found in strophiole raw and relative size could be explained by average annual rainfall and average annual maximum temperature. Strophiole size in Petrocoptis could have been potentially driven by adaptation to local climates through the investment of more resources in the production of bigger strophioles to increase the hydration ability of the seed in dry and warm climates. This reinforces the idea of the strophiole being involved in seed water uptake and germination regulation in Petrocoptis . Similar relationships have not been previously reported for strophioles or other analogous structures in Angiosperms.
Why it matches plant phenotyping methods種子形態計測とクラスタリング・機械学習による形態群分類を中心に扱い、分類アルゴリズムの検証も行っているため、植物形質の計測・抽出手法として収録対象。
abstractEmploying cluster and machine learning algorithms, we delineated well-defined morphogroups based on seed traits and evaluated their recognizability.
Reproduction assets foundThe authors openly deposited the study's seed morphometric phenotype data (2773 seeds, 557 individuals, 84 populations) in Zenodo, as stated in the Data Availability Statement. The MDPI supplementary materials contain population tables and model statistics but the Zenodo deposit is the paper-specific public dataset.Dataset · publicThe data presented in this study are contained within the article and Supplementary Materials and openly available in Zenodo at https://doi.org/10.5281/zenodo.13972509 .Open asset ↗Zenodo · 10.5281/zenodo.13972509lines:397-411Code / dataset availability confirmedEurope PMC · checked 7 Sept 2026
Early stress detection of crops requires a thorough understanding of the signals showing the very first symptoms of the alterations in the photosynthetic light reactions. Detection of the activation of the regulated heat dissipation mechanism is crucial to complement passively induced fluorescence to resolve ambuiguities in energy partitioning. Using leaf spectroscopy, we evaluated the capability of pigment spectral unmixing to calculate the fluorescence quantum efficiency (FQE) and simultaneously retrieve fast absorption changes in a drought and nitrogen deficiency experiment with tomato. In addition, active fluorescence measurements and pigment analyses of xanthophylls, carotenes and chlorophylls were conducted. We observed notable responses in noninvasive proximal sensing-retrieved FQE values under stress, but as expected, these alone were not enough to identify the constraints in photosynthetic efficiency. Reflectance-based detection of the 535-nm peak absorption change was able to complement FQE and indicate the activation of regulated heat dissipation for both stress treatments under growing light conditions. However, further complexity in the light harvesting energy regulation needs to be accounted for when considering additional light stress. Our results underscore the potential of complementary in vivo quantitative spectroscopy-based products in the early and nondestructive stress diagnosis of plants, marking the path for further applications.
Why it matches plant phenotyping methods葉分光法とスペクトルアンミキシングにより、植物のFQEや熱散逸に関連する吸収変化を非破壊・定量的に取得し、ストレス診断への有効性を評価しているため、植物生理フェノタイピング手法の応用・評価が中心です。
abstractUsing leaf spectroscopy, we evaluated the capability of pigment spectral unmixing to calculate the fluorescence quantum efficiency (FQE) and simultaneously retrieve fast absorption changes in a drought and nitrogen deficiency experiment with tomato.
Reproduction assets foundThe article's Data Availability Statement explicitly deposits the paper's raw and processed phenotyping/spectroscopy measurements open access on Zenodo (doi: 10.5281/zenodo.12800064). This is a paper-specific, public, actionable dataset. However, the Zenodo URL is not among the allowed_urls, so no asset URL is providedDataset · publicData Availability Statement
Raw and processed data are available open access through the Zenodo repository (doi: 10.5281/zenodo.12800064 ).Zenodo · 10.5281/zenodo.12800064lines:539-574Code / dataset availability confirmedEurope PMC · checked 15 Sept 2026
Hyperspectral imaging provides high-dimensional spatial-temporal-spectral information showing intrinsic matter characteristics 1-5 . Here we report an on-chip computational hyperspectral imaging framework with high spatial and temporal resolution. By integrating different broadband modulation materials on the image sensor chip, the target spectral information is non-uniformly and intrinsically coupled to each pixel with high light throughput. Using intelligent reconstruction algorithms, multi-channel images can be recovered from each frame, realizing real-time hyperspectral imaging. Following this framework, we fabricated a broadband visible-near-infrared (400-1,700 nm) hyperspectral image sensor using photolithography, with an average light throughput of 74.8% and 96 wavelength channels. The demonstrated resolution is 1,024 × 1,024 pixels at 124 fps. We demonstrated its wide applications, including chlorophyll and sugar quantification for intelligent agriculture, blood oxygen and water quality monitoring for human health, textile classification and apple bruise detection for industrial automation, and remote lunar detection for astronomy. The integrated hyperspectral image sensor weighs only tens of grams and can be assembled on various resource-limited platforms or equipped with off-the-shelf optical systems. The technique transforms the challenge of high-dimensional imaging from a high-cost manufacturing and cumbersome system to one that is solvable through on-chip compression and agile computation.
Why it matches plant phenotyping methods植物のクロロフィルおよび糖含量を定量可能なオンチップ・ハイパースペクトル画像センサーを開発しており、センサー技術と植物形質取得への応用が中心的である。
abstractHere we report an on-chip computational hyperspectral imaging framework with high spatial and temporal resolution.
Reproduction assets foundThe paper explicitly states that all data generated or analysed are available in a public GitHub repository (hyperspectral image/video dataset collected with the HyperspecI sensors) and that demo code is available in another public GitHub repository. Both are paper-specific, public, and actionable.Dataset · publiced the project.
Peer review
Peer review information
Nature thanks Yidong Huang, Yunfeng Nie and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.
Data availability
All data generated or analysed during this study are included in this published article and the public repository at GitHub ( https://github.com/bianlab/Hyperspectral-imaging-dataset ).
Code availability
The demo code of this work is available from the public repository at GitHub ( https://github.com/bianlab/HyperspecI ).
Competing interests
L.B., Z.W., Yuzhe Zhang and J. Zhang hold patents on technologies related to the devices developed in this work (China patent nos. ZL 2022 1 0764166.5, Open asset ↗bianlab/Hyperspectral-imaging-datasetlines:148-189Code · publiche peer review of this work.
Data availability
All data generated or analysed during this study are included in this published article and the public repository at GitHub ( https://github.com/bianlab/Hyperspectral-imaging-dataset ).
Code availability
The demo code of this work is available from the public repository at GitHub ( https://github.com/bianlab/HyperspecI ).
Competing interests
L.B., Z.W., Yuzhe Zhang and J. Zhang hold patents on technologies related to the devices developed in this work (China patent nos. ZL 2022 1 0764166.5, ZL 2022 1 0764143.4, ZL 2022 1 0764141.5, ZL 2019 1 0441784.4, ZL 2019 1 0482098.1 and ZL 2019 1 1234638.0) and submitted the related patent applications.Open asset ↗bianlab/HyperspecIlines:148-189Code / dataset availability confirmedEurope PMC · Crossref · checked 14 Sept 2026
Plant cover and biochemical composition are essential parameters for evaluating cover crop management. Destructive sampling or estimates with aerial imagery require substantial labor, time, expertise, or instrumentation cost. Using low-cost consumer and mobile phone cameras to estimate plant canopy coverage and biochemical composition could broaden the use of high-throughput technologies in research and crop management. Here, we estimated canopy development, tissue nitrogen, and biomass of medium red clover (Trifolium pratense L.), a perennial forage legume and common cover crop, using red-green-blue (RGB) indices collected with standard settings in non-standardized field conditions. Pixels were classified as plant or background using combinations of four RGB indices with both unsupervised machine learning and preset thresholds. The excess green minus red (ExGR) index with a preset threshold of zero was the best index and threshold combination. It correctly identified pixels as plant or background 86.25% of the time. This combination also provided accurate estimates of crop growth and quality: Canopy coverage correlated with red clover biomass (R² = 0.554, root mean square error [RMSE] = 219.29 kg ha⁻¹), and ExGR index values of vegetation pixels were highly correlated with clover nitrogen content (R² = 0.573, RMSE = 3.5 g kg⁻¹) and carbon:nitrogen ratio (R² = 0.574, RMSE = 1.29 g g⁻¹). Data collection were simple to implement and stable across imaging conditions. Pending testing across different sensors, sites, and crop species, this method contributes to a growing and open set of decision support tools for agricultural research and management.
Why it matches plant phenotyping methods低コストRGB画像と画素分類を用いて、植物被覆、バイオマス、窒素含量、C:N比を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す。
abstractUsing low-cost consumer and mobile phone cameras to estimate plant canopy coverage and biochemical composition could broaden the use of high-throughput technologies in research and crop management.
Reproduction assets foundThe paper's authors state that all referenced analysis scripts for the RGB vegetation index processing, thresholding, and canopy cover estimation are publicly available on GitHub. The phenotype/trait data (images, biomass, N, C:N measurements) are deposited at a U of M repository (hdl.handle.net/11299/263900), but thatCode · publicreferenced scripts are available at https://github.com/RTGS- of nitrogen dictated by biomass and nitrogen content, and theOpen asset ↗pdf-page:4 lines:1-49Code / dataset availability confirmedEurope PMC · checked 15 Sept 2026
Field / plotTissuePhysiological trait estimationGrowth / time-series analysisGrowth / development / phenology
Intra-annual variations of carbon stable isotope ratios (δ13C) in different tree compartments could represent valuable indicators of plant carbon source-sink dynamics, at weekly time scale. Despite this significance, the absence of a methodological framework for tracking δ13C values in tree rings persists due to the complexity of tree ring development. To fill this knowledge gap, we developed a method to monitor weekly variability of δ13C in the cambium-xylem continuum of black spruce species [Picea mariana (Mill.) BSP.] during the growing season. We collected and isolated the weekly incremental growth of the cambial region and the developing tree ring from five mature spruce trees over three consecutive growing seasons (2019-21) in Simoncouche and two growing seasons (2020-21) in Bernatchez, both located in the boreal forest of Quebec, Canada. Our method allowed for the creation of intra-annual δ13C series for both the growing cambium (δ13Ccam) and developing xylem cellulose (δ13Cxc) in these two sites. Strong positive correlations were observed between δ13Ccam and δ13Cxc series in almost all study years. These findings suggest that a constant supply of fresh assimilates to the cambium-xylem continuum may be the dominant process feeding secondary growth in the two study sites. On the other hand, rates of carbon isotopic fractionation appeared to be poorly affected by climate variability, at an inter-weekly time scale. Hence, increasing δ13Ccam and δ13Cxc trends highlighted here possibly indicate shifts in carbon allocation strategies, likely fostering frost resistance and reducing water uptake in the late growth season. Additionally, these trends may be related to the black spruce trees' responses to the seasonal decrease in photosynthetically active radiation. Our findings provide new insights into the seasonal carbon dynamics and growth constraints of black spruce in boreal forest ecosystems, offering a novel methodological approach for studying carbon allocation at fine temporal scales.
Why it matches plant phenotyping methods樹木の形成層・木部における週次δ13C変動を追跡する測定法を開発し、複数年・地点で適用して検証しているため、植物の生理状態を取得する方法が中心である。
abstractwe developed a method to monitor weekly variability of δ13C in the cambium-xylem continuum of black spruce species
Reproduction assets foundThe paper's weekly δ13C cambium/xylem measurements are stated to be publicly available via the authors' Quebec-Labrador tree-ring dashboard. A GitHub repository for figure data is mentioned but without a URL and only 'upon publication', so it is not actionable. NOAA GML and the Arizona repository URL are external/citedDataset · publicCanada, 490 de La Couronne, Québec, QC G1K 9A9, Canada.
Conflict of interest
None declared.
Funding
This work was funded by the National Sciences and Engineering Research Council of Canada (NSERC) to É.B. (RGPIN 2021-04216).
Data availability
The weekly carbon isotope measurements published in the study will be available here: https://quebeclabradortr.shinyapps.io/TRdashboard4/ . Additional data used to produce the figures will be available from a GitHub repository, upon publication of the article.
References
Alvarez C, Bégin C, Savard MM, Dinis L, Marion J, Smirnoff A, Bégin Y. (2018). Relevance of using whole-ring stable isotopes of black spruce trees in the perspective of climate reconstrOpen asset ↗TRdashboard4lines:362-389Code / dataset availability confirmedOpenAlex · checked 13 Sept 2026
Three raster-based (RB) and one point cloud-based (PCB) algorithms were tested to segment individual Aleppo pine trees and extract their tree height (H) and crown diameter (CD) using two types of point clouds generated from two different techniques: (1) Low-Density (≈1.5 points/m2) Airborne Laser Scanning (LD-ALS) and (2) photogrammetry based on high-resolution unmanned aerial vehicle (UAV) images. Through intensive experiments, it was concluded that the tested RB algorithms performed best in the case of UAV point clouds (F1-score > 80.57%, H Pearson’s r > 0.97, and CD Pearson´s r > 0.73), while the PCB algorithm yielded the best results when working with LD-ALS point clouds (F1-score = 89.51%, H Pearson´s r = 0.94, and CD Pearson´s r = 0.57). The best set of algorithm parameters was applied to all plots, i.e., it was not optimized for each plot, in order to develop an automatic pipeline for mapping large areas of Mediterranean forests. In this case, tree detection and height estimation showed good results for both UAV and LD-ALS (F1-score > 85% and >76%, and H Pearson´s r > 0.96 and >0.93, respectively). However, very poor results were found when estimating crown diameter (CD Pearson´s r around 0.20 for both approaches).
Why it matches plant phenotyping methods個体樹のセグメンテーション手法を比較・検証し、樹高と樹冠径という植物形質を点群から推定する自動パイプラインを評価しており、フェノタイピング手法が中心です。
titleBenchmarking of Individual Tree Segmentation Methods in Mediterranean Forest Based on Point Clouds from Unmanned Aerial Vehicle Imagery and Low-Density Airborne Laser Scanning
Reproduction assets foundThe paper's Data Availability Statement states that the data presented in the study (the UAV/LD-ALS point clouds, reference tree measurements, and segmentation outputs underlying the phenotyping analysis) are openly available on Zenodo under DOI 10.5281/zenodo.10518411. This is a paper-specific, publicly actionablephenDataset · publicData Availability Statement: The data presented in this study are openly available in zenodo at
10.5281/zenodo.10518411.Open asset ↗zenodo · 10.5281/zenodo.10518411pdf-page:25 lines:1-56Code / dataset availability confirmedEurope PMC · checked 7 Sept 2026
Identification of high carotenoid germplasm is crucial to assist breeders in provitamin-A biofortification of sorghum (Sorghum bicolor [L.] Moench). High-performance liquid chromatography is the gold standard for carotenoid quantification, however, it is not feasible for large scale phenotyping due to its high cost and low throughput. In this study, we tested the feasibility of using grain color as a high-throughput method of carotenoid biofortification breeding. We hypothesized that visual, color-based selection can be an effective strategy to identify high-carotenoid accessions. Yellow grain had significantly higher carotenoid content than red, brown, and white grain. The degree of yellowness could distinguish the presence or absence of carotenoids, but could not distinguish carotenoid concentrations within yellow-only accessions. The degree of luminosity of the grain, however, was able to better predict carotenoid concentrations within yellow-only accessions. Genome-wide association studies identified significant marker-trait associations for qualitative and quantitative grain color traits and carotenoid concentrations near carotenoid pathway genes-ZEP, PDS, CYP97A, NCED, CCD, and LycE-three of which were common between grain color and carotenoid traits. These findings suggest that using grain color as a method for screening germplasm may be an effective high-throughput selection tool for prebreeding and early-stage breeding in carotenoid biofortification.
Why it matches plant phenotyping methods穀粒色を用いたカロテノイド含量推定・高スループット選抜法の実現可能性を検証しており、植物形質取得法が研究の中心である。
abstractIn this study, we tested the feasibility of using grain color as a high-throughput method of carotenoid biofortification breeding.
Reproduction assets foundThe paper's grain-color/carotenoid phenotyping data are in public supplementary files (Supplementary Data S1–S3: GRIN color traits, visual scores, colorimeter measurements), and the authors' analysis code is publicly deposited on GitHub with an explicit availability statement.Code · publicAll other data files are available in the supplemental files and code is available at: https://github.com/rmcdower/sorghumbiofortification/tree/a8457f87068867eb687235c255a6222863102e1aOpen asset ↗rmcdower/sorghumbiofortification · a8457f87068867eb687235c255a6222863102e1alines:134-147Dataset · publicThree grains each per accession were scored independently by two individuals and classified as white, yellow, red, or brown (Supplementary Data S2).Open asset ↗lines:71-78Code / dataset availability confirmedCrossref · checked 13 Sept 2026
Unmanaged forest ecosystems play a critical role in addressing the ongoing climate and biodiversity crises. As there is no commercial interest in monitoring the health and development of such inaccessible habitats, low-cost assessment approaches are needed. We used a method combining RGB imagery acquired using an Unmanned Aerial Vehicle (UAV), Sentinel-2 data, and field surveys to determine the carbon stock of an unmanaged forest in the UNESCO World Heritage Site wilderness area Dürrenstein-Lassingtal in Austria. The entry-level consumer drone (DJI Mavic Mini) and freely available Sentinel-2 multispectral datasets were used for the evaluation. We merged the Sentinel-2 derived vegetation index NDVI with aerial photogrammetry data and used an orthomosaic and a Digital Surface Model (DSM) to map the extent of woodland in the study area. The Random Forest (RF) machine learning (ML) algorithm was used to classify land cover. Based on the acquired field data, the average carbon stock per hectare of forest was determined to be 371.423 ± 51.106 t of CO2 and applied to the ML-generated class Forest. An overall accuracy of 80.8% with a Cohen’s kappa value of 0.74 was achieved for the land cover classification, while the carbon stock of the living above-ground biomass (AGB) was estimated with an accuracy within 5.9% of field measurements. The proposed approach demonstrated that the combination of low-cost remote sensing data and field work can predict above-ground biomass with high accuracy. The results and the estimation error distribution highlight the importance of accurate field data.
Why it matches plant phenotyping methodsUAV・衛星リモートセンシングと機械学習により森林の地上部バイオマス（炭素蓄積量）を推定し、現地測定と精度検証しているため、植物群落レベルの形質推定手法が中心です。
abstractWe used a method combining RGB imagery acquired using an Unmanned Aerial Vehicle (UAV), Sentinel-2 data, and field surveys to determine the carbon stock of an unmanaged forest
Reproduction assets foundThe paper's Data Availability Statement points to an openly available Zenodo deposit containing the original study data (field carbon stock measurements, UAV-derived datasets, and Sentinel-2 based analysis inputs). No separate author analysis code or trained model repository is mentioned.Dataset · publicData Availability Statement: The original data presented in the study are openly available here:
https://doi.org/10.5281/zenodo.11657557, accessed on 5 June 2024.Open asset ↗zenodo · 10.5281/zenodo.11657557pdf-page:17 lines:1-58Code / 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 confirmedEurope PMC · checked 15 Sept 2026
Background Transcriptome-based prediction of complex phenotypes is a relatively new statistical method that links genetic variation to phenotypic variation. The selection of large-effect genes based on a priori biological knowledge is beneficial for predicting oligogenic traits; however, such a simple gene selection method is not applicable to polygenic traits because causal genes or large-effect loci are often unknown. Here, we used several gene-level features and tested whether it was possible to select a gene subset that resulted in better predictive ability than using all genes for predicting a polygenic trait. Results Using the phenotypic values of shoot and root traits and transcript abundances in leaves and roots of 57 rice accessions, we evaluated the predictive abilities of the transcriptome-based prediction models. Leaf transcripts predicted shoot phenotypes, such as plant height, more accurately than root transcripts, whereas root transcripts predicted root phenotypes, such as crown root length, more accurately than leaf transcripts. Furthermore, we used the following three features to train the prediction model: (1) tissue specificity of the transcripts, (2) ontology annotations, and (3) co-expression modules for selecting gene subsets. Although models trained by a gene subset often resulted in lower predictive abilities than the model trained by all genes, some gene subsets showed improved predictive ability. For example, using genes expressed in roots but not in leaves, the predictive ability for crown root diameter was improved by more than 10% (R 2 = 0.59 when using all genes; R 2 = 0.66, using 1,554 root-specifically expressed genes). Similarly, genes annotated as "gibberellic acid sensitivity" showed higher predictive ability than using all genes for root dry weight. Conclusions Our results highlight both the possibility and difficulty of selecting an appropriate gene subset to predict polygenic traits from transcript abundance, given the current biological knowledge and information. Further integration of multiple sources of information, as well as improvements in gene characterization, may enable the selection of an optimal gene set for the prediction of polygenic phenotypes.
Why it matches plant phenotyping methods遺伝子発現データから植物の複合形質を予測する統計的手法を開発・評価しており、形質予測モデルの性能比較が研究の中心である。
abstractTranscriptome-based prediction of complex phenotypes is a relatively new statistical method that links genetic variation to phenotypic variation.
Reproduction assets foundThe authors state that all analysis code for the transcriptome-based prediction study is publicly available on Figshare, which is a paper-specific, publicly actionable code asset. Phenotype data are only in a prior study's supplementary file and transcriptome data in GEO (GSE162313), which are cited prior deposits, notCode · publicw sequence data were deposited in the DNA Data Bank of Japan Sequence Read Archive in a previous study [27]. Transcriptome data are available from the Gene Expression Omnibus ( GSE162313 ) and phenotype data are available in the supplementary file of a previous study [28]. All codes for the data analysis are shown in Figshare ( https://doi.org/10.6084/m9.figshare.26067532.v1 ).
Declarations
Ethics approval and consent to participate
Not applicable.
Consent for publication
Not applicable.
Competing interests
The authors declare no competing interests.
Abbreviations
WRCOpen asset ↗Figshare · 10.6084/m9.figshare.26067532.v1lines:370-396Code / dataset availability confirmedEurope PMC · checked 7 Sept 2026
The spike shape and morphometric characteristics are among the key characteristics of cultivated cereals, being associated with their productivity. These traits are often used for the plant taxonomy and authenticity of hexaploid wheat species. Manual measurement of spike characteristics is tedious and not precise. Recently, the authors of this study developed a method for wheat spike morphometry utilizing 2D image analysis. Here, this method is applied to study variations in spike size and shape for 190 plants of seven hexaploid (2 n = 6 x = 42) species and one artificial amphidiploid of wheat. Five manually estimated spike traits and 26 traits obtained from digital image analysis were analyzed. Image-based traits describe the characteristics of the base, center and apex of the spike and common parameters (circularity, roundness, perimeter, etc.). Estimates of similar traits by manual measurement and image analysis were shown to be highly correlated, suggesting the practical importance of digital spike phenotyping. The utility of spike traits for classification into types (spelt, normal and compact) and species or amphidiploid is shown. It is also demonstrated that the estimates obtained made it possible to identify the spike characteristics differing significantly between species or between accessions within the same species. The present work suggests the usefulness of wheat spike shape analysis using an approach based on characteristics obtained by digital image analysis.
Why it matches plant phenotyping methods小麦穂の2D画像解析による形態計測法を実際に適用し、手動測定との相関検証とデジタル形質の有用性評価を行っており、植物フェノタイピング手法が中心である。
abstractRecently, the authors of this study developed a method for wheat spike morphometry utilizing 2D image analysis.
Reproduction assets foundThe paper's spike image dataset (the 2D images used for quadrangle-model phenotyping of 190 wheat plants) is publicly deposited on Zenodo, explicitly linked in the Data Availability Statement. The supplementary files contain statistical results (normality tests, ANOVA tables, confusion matrices, specimen descriptions)衍Dataset · publicThe spike image dataset is available at https://zenodo.org/records/13837454 , accessed on 27 September 2024.Open asset ↗Zenodo · 13837454lines:895-912Code / 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
Structural complexity refers to the three-dimensional arrangement and variability of both biotic and abiotic components of an ecosystem. Metrics that characterize structural complexity are often used to manage various aspects of ecosystem function, such as light transmittance, wildlife habitat, and biological diversity. Additionally, these metrics aid in evaluating resilience to disturbance events, including hurricanes, bark-beetle outbreaks, and wildfire. Recent advances in wildland fire modelling have facilitated the integration of forest structural complexity metrics into the QUIC-Fire model, enabling real-time prediction of fire spread and behaviour by simulating interactions between fire, weather, topography, and forest structure. While QUIC-Fire is designed to be highly adaptable, model performance depends on the availability and accuracy of local data inputs. Expanding the model's usability across different regions can be facilitated by the availability of more comprehensive and high-quality data. Thus, the primary goal behind the data products we developed was to establish a basis for collaborative research across various disciplines, particularly within the focal areas of the Southern Research Station, such as forestry, wildland fire, hydrology, soil science, and cultural resources at Bent Creek, Coweeta, Escambia, and Hitchiti Experimental Forests (EFs). Airborne laser scanning (ALS) was used to collect point-cloud data for each EF during the leaf-off season to minimize interference from foliage. Subsequent processing of the raw lidar data involved outlier detection and filtering, ground and non-ground classification, and the computation of a variety of metrics representing various aspects of topography and forest structure at both the pixel-level and the tree-level. Pixel-level topographic data products include: digital elevation model (DEM), slope, aspect, topographic position index (TPI), topographic roughness index (TRI), roughness, and flow direction. Forest structural-complexity metrics include canopy height, foliar height diversity (FHD), vertical distribution ratio (VDR), canopy rugosity, crown relief ratio (CRR), understory complexity index (UCI), vertical complexity index (VCI), canopy cover, mean vegetation height, and the standard deviation of vegetation height. Tree-level data products were computed from the point cloud using multiple algorithms to perform individual tree detection (ITD) and individual tree segmentation (ITS). The datasets have been harmonized and are openly accessible through the USDA Forest Service Research Data Archive.
Why it matches plant phenotyping methods航空レーザースキャンから樹冠高、植生高、樹冠構造、個体樹木を抽出した再利用可能なデータセットであり、植物の構造形質取得と処理が中心です。
abstractAirborne laser scanning (ALS) was used to collect point-cloud data for each EF during the leaf-off season
Reproduction assets foundThis Data in Brief article describes its own openly archived dataset: lidar-derived forest structural-complexity metrics (raster and vector products, including tree detection/segmentation outputs) for four experimental forests, deposited in the USFS Research Data Archive (RDS-2024-0019) with R processing code in theSupDataset · public−83.450054
Coweeta Experimental Forests: 31.007539, −87.078571
Escambia Experimental Forests: 33.057078, −83.679620
Hitchiti Experimental Forest: 35.484250, −82.633346
Data accessibility
Repository name: US Forest Service Research Data Archive
Data identification number:
https://doi.org/10.2737/RDS-2024-0019
Direct URL to data: https://www.fs.usda.gov/rds/archive/catalog/RDS-2024-0019
Raw ALS point-cloud data are located at https://app.box.com/s/4s3412g8mtky0hb6wb63a44epv08c22o
Related research article
none.
1.
Value of the Data
•Open asset ↗US Forest Service Research Data Archive · RDS-2024-0019lines:1-50Dataset · publicarch Station.
Bent Creek Experimental Forests: 35.050580, −83.450054
Coweeta Experimental Forests: 31.007539, −87.078571
Escambia Experimental Forests: 33.057078, −83.679620
Hitchiti Experimental Forest: 35.484250, −82.633346
Data accessibility
Repository name: US Forest Service Research Data Archive
Data identification number:
https://doi.org/10.2737/RDS-2024-0019
Direct URL to data: https://www.fs.usda.gov/rds/archive/catalog/RDS-2024-0019
Raw ALS point-cloud data are located at https://app.box.com/s/4s3412g8mtky0hb6wb63a44epv08c22o
Related research article
none.
1.
Value of the Data
•Open asset ↗US Forest Service Research Data Archive · RDS-2024-0019lines:1-50Dataset · publicean crown diameter. Additionally, the generalized additive model (GAM) that was developed from the inventory data was used to predict bole height at the tree-level.
Limitations
The size of the raw point-cloud data precluded storage on the USFS Research Data Archive. Therefore, this data is accessible for download from box.com ( https://app.box.com/s/4s3412g8mtky0hb6wb63a44epv08c22o ). Additionally, the volume of the point-cloud data may pose computational limitations.
Ethics Statement
The authors have read and follow the ethical requirements for publication in Data in Brief and confirm that the current work does not involve human subjects, animal experiments, or any data collected from sociaOpen asset ↗lines:420-434Code / dataset availability confirmedEurope PMC · checked 7 Sept 2026
Kelps are vital for marine ecosystems, yet the genetic diversity underlying their capacity to adapt to climate change remains unknown. In this study, we focused on the kelp Macrocystis pyrifera a species critical to coastal habitats. We developed a protocol to evaluate heat stress response in 204 Macrocystis pyrifera genotypes subjected to heat stress treatments ranging from 21 °C to 27 °C. Here we show that haploid gametophytes exhibiting a heat-stress tolerant (HST) phenotype also produced greater biomass as genetically similar diploid sporophytes in a warm-water ocean farm. HST was measured as chlorophyll autofluorescence per genotype, presented here as fluorescent intensity values. This correlation suggests a predictive relationship between the growth performance of the early microscopic gametophyte stage HST and the later macroscopic sporophyte stage, indicating the potential for selecting resilient kelp strains under warmer ocean temperatures. However, HST kelps showed reduced genetic variation, underscoring the importance of integrating heat tolerance genes into a broader genetic pool to maintain the adaptability of kelp populations in the face of climate change.
Why it matches plant phenotyping methods熱ストレス耐性という植物状態をクロロフィル自家蛍光で定量するプロトコルを開発しており、表現型取得法が研究の中心的要素として明示されている。
abstractWe developed a protocol to evaluate heat stress response in 204 Macrocystis pyrifera genotypes subjected to heat stress treatments ranging from 21 °C to 27 °C.
Reproduction assets foundThe authors publicly deposited both the analysis scripts and the numerical source data (including raw fluorescence intensity values underlying the heat-stress phenotyping) in a Zenodo repository, with explicit availability statements and URLs in the Data availability and Code availability sections.Code · publicAll scripts used in this study are available in a Zenodo repository at https://doi.org/10.5281/zenodo.13315681 .Open asset ↗Zenodo · 10.5281/zenodo.13315681lines:168-242Dataset · publicNumerical source data for the graph presented in Figs. 1 – 3 , and Fig. 5 can be found in the Zenodo repository here: https://doi.org/10.5281/zenodo.13315681 .Open asset ↗Zenodo · 10.5281/zenodo.13315681lines:148-167Code / dataset availability confirmedOpenAlex · Europe PMC · checked 15 Sept 2026
BACKGROUND: Phenotyping of plant traits presents a significant bottleneck in Quantitative Trait Loci (QTL) mapping and genome-wide association studies (GWAS). Computerized phenotyping using digital images promises rapid, robust, and reproducible measurements of dimension, shape, and color traits of plant organs, including grain, leaf, and floral traits. RESULTS: We introduce GRABSEEDS, which is specifically tailored to extract a comprehensive set of features from plant images based on state-of-the-art computer vision and deep learning methods. This command-line enabled tool, which is adept at managing varying light conditions, background disturbances, and overlapping objects, uses digital images to measure plant organ characteristics accurately and efficiently. GRABSEED has advanced features including label recognition and color correction in a batch setting. CONCLUSION: GRABSEEDS streamlines the plant phenotyping process and is effective in a variety of seed, floral and leaf trait studies for association with agronomic traits and stress conditions. Source code and documentations for GRABSEEDS are available at: https://github.com/tanghaibao/jcvi/wiki/GRABSEEDS .
Why it matches plant phenotyping methods植物器官画像から形状・寸法・色などの形質を抽出するソフトウェア手法の開発が中心であり、植物フェノタイピング手法として明確に該当する。
abstractWe introduce GRABSEEDS, which is specifically tailored to extract a comprehensive set of features from plant images based on state-of-the-art computer vision and deep learning methods.
Reproduction assets foundThe paper's authors publicly release the GRABSEEDS software (the computational phenotyping tool used for all measurements in this paper) along with the example images and datasets generated, at the GitHub wiki URL stated in the abstract, availability section, and data availability statement.Code · publicures including label recognition and color correction in a batch setting.
Conclusion
GRABSEEDS streamlines the plant phenotyping process and is effective in a variety of seed, floral and leaf trait studies for association with agronomic traits and stress conditions. Source code and documentations for GRABSEEDS are available at: https://github.com/tanghaibao/jcvi/wiki/GRABSEEDS .
Keywords: Image analysis, Phenotype, Seed traits, High throughput, QTL mapping
status released display-pdf yes is-olf no is-manuscript no is-preprint no is-journal-matter no is-scanned no is-retracted no
Received 2024 May 21; Accepted 2024 Sep 6; Collection date 2024.
IntroductionOpen asset ↗github.com/tanghaibao/jcvi · GRABSEEDSlines:1-28Code / dataset availability confirmedEurope PMC · checked 7 Sept 2026
Plant sensors are commonly used in agricultural production, landscaping, and other fields to monitor plant growth and environmental parameters. As an important basic parameter in plant monitoring, leaf inclination angle (LIA) not only influences light absorption and pesticide loss but also contributes to genetic analysis and other plant phenotypic data collection. The measurements of LIA provide a basis for crop research as well as agricultural management, such as water loss, pesticide absorption, and illumination radiation. On the one hand, existing efficient solutions, represented by light detection and ranging (LiDAR), can provide the average leaf angle distribution of a plot. On the other hand, the labor-intensive schemes represented by hand measurements can show high accuracy. However, the existing methods suffer from low automation and weak leaf-plant correlation, limiting the application of individual plant leaf phenotypes. To improve the efficiency of LIA measurement and provide the correlation between leaf and plant, we design an image-phenotype-based noninvasive and efficient optical sensor measurement system, which combines multi-processes implemented via computer vision technologies and RGB images collected by physical sensing devices. Specifically, we utilize object detection to associate leaves with plants and adopt 3-dimensional reconstruction techniques to recover the spatial information of leaves in computational space. Then, we propose a spatial continuity-based segmentation algorithm combined with a graphical operation to implement the extraction of leaf key points. Finally, we seek the connection between the computational space and the actual physical space and put forward a method of leaf transformation to realize the localization and recovery of the LIA in physical space. Overall, our solution is characterized by noninvasiveness, full-process automation, and strong leaf-plant correlation, which enables efficient measurements at low cost. In this study, we validate Auto-LIA for practicality and compare the accuracy with the best solution that is acquired with an expensive and invasive LiDAR device. Our solution demonstrates its competitiveness and usability at a much lower equipment cost, with an accuracy of only 2. 5° less than that of the widely used LiDAR. As an intelligent processing system for plant sensor signals, Auto-LIA provides fully automated measurement of LIA, improving the monitoring of plant physiological information for plant protection. We make our code and data publicly available at http://autolia.samlab.cn.
Why it matches plant phenotyping methods葉傾斜角という植物形質を自動取得する画像ベース手法を開発し、LiDARと精度比較で検証しており、フェノタイピング手法が中心である。
abstractwe design an image-phenotype-based noninvasive and efficient optical sensor measurement system
Reproduction assets foundThe authors explicitly state that their code and data (the Auto-LIA LIA measurement system, including RGB image datasets and processing pipeline) are publicly available at their project site, which is among the allowed URLs.Code · publicWe make our code and data publicly available at http://autolia.samlab.cn .Open asset ↗autolia.samlab.cnlines:334-498Code / dataset availability confirmedEurope PMC · checked 14 Sept 2026
The creation and use of a comprehensive cotton leaf disease dataset offer significant benefits in agricultural research, precision farming, and disease management. This dataset enables the development of accurate machine learning models for early disease detection, reducing manual inspections and facilitating timely interventions. It serves as a benchmark for testing algorithms and training deep learning models, aiding in automated monitoring and decision support tools in precision agriculture. This leads to targeted interventions, reduced chemical use, and improved crop management. Global collaboration is fostered, contributing to the development of disease-resistant cotton varieties and effective management strategies, ultimately reducing economic losses and promoting sustainable farming. Field surveys conducted from October 2023 to January 2024 ensured meticulous image capture under diverse conditions. The images are categorized into eight classes, representing specific disease manifestations, pests, or environmental stress in cotton plants. The dataset comprises 2137 original images and 7000 augmented images, enhancing deep learning model training. The Inception V3 model demonstrated high performance, with an overall accuracy of 96.03 %. This underscores the dataset's potential in advancing automated disease detection in cotton agriculture.
Why it matches plant phenotyping methods綿花葉の病徴を画像で分類するデータセットを構築し、深層学習モデルのベンチマークとして評価しており、植物病害表現型の取得・解析が中心である。
abstractThe creation and use of a comprehensive cotton leaf disease dataset offer significant benefits in agricultural research, precision farming, and disease management.
Reproduction assets foundThe paper is a Data in Brief article describing the authors' own cotton leaf disease image dataset (SAR-CLD-2024), publicly deposited on Mendeley Data with a direct URL and DOI provided in the article.Dataset · publicining and evaluating machine learning models aimed at accurately classifying and diagnosing cotton leaf diseases.
Data source location
The National Cotton Research Institute field in Gazipur, Dhaka, Bangladesh
Data accessibility
Repository name: Mendeley Data
Data identification number: 10.17632/b3jy2p6k8w.2
Direct URL to data: https://data.mendeley.com/datasets/b3jy2p6k8w/2
Related research article
None
1.
Value of the Data
•
The presence of diseases such as Cotton Leaf Curl Disease and leaf hopper in cotton plants poses significant challenges to farmers worldwide, leading to substantial yield losses, reduced crop quality, and economic hardships. Timely detection and effective management ofOpen asset ↗Mendeley Data · 10.17632/b3jy2p6k8w.2lines:1-44Code / dataset availability confirmedEurope PMC · checked 14 Sept 2026
Field / plotStomata / guard-cell complexClassificationObject detectionStomatal traits
Plants' leaf stomata are crucial for various scientific research, including identifying species, studying ecology, conserving ecosystems, improving agriculture, and advancing the field of deep learning. This dataset, containing 1083 images, encompasses 11 species from two distinct locations in Bangladesh: nine from the Sundarbans mangrove forest and two from the Ratargul Swamp Forest. It is a valuable tool for refining machine learning algorithms that specialize in detecting stomata and categorizing species accurately. Researchers can explore a deeper understanding of plant physiology, adaptation mechanisms, and environmental interactions by employing pattern recognition, deep learning, and feature extraction techniques. Additionally, this dataset could be a potential tool for enhancing research in macroscopic metamaterials, extending its impact beyond traditional biological studies into interdisciplinary fields of technology and material science.
Why it matches plant phenotyping methods気孔画像データセット自体が研究の中心で、画像から気孔を検出する再利用可能な表現型取得基盤を提供しているため。
abstractThis dataset, containing 1083 images, encompasses 11 species from two distinct locations in Bangladesh
Reproduction assets foundThis Data in Brief article deposits its own stomata image dataset (1083 images, 11 species) and stomatal trait measurements in Mendeley Data, with a direct public URL and DOI given in the Specifications Table.Dataset · publicSiedentopf Trinocular Compound Microscope (AmScope T340A) was used to image stomata with AmScope camera software.
Data source location
Country: BangladeshForest: Sundarbans Mangrove Forest, Ratargul Swamp Forest
Data accessibility
Repository name: Mendeley DataData identification number: 10.17632/4brcwhmvyk.4Direct URL to data: https://data.mendeley.com/datasets/4brcwhmvyk/4
Related research article
Dey, B., Ahmed, R., Ferdous, J., Haque, M.M.U., Khatun, R., Hasan, F.E., Uddin, S.N., 2023. Automated plant species identification from the stomata images using deep neural network: A study of selected mangrove and freshwater swamp forest tree species of Bangladesh. Ecol. Inform. 75, 102128.httpsOpen asset ↗Mendeley Data · 10.17632/4brcwhmvyk.4html-lines:1-37Code / dataset availability confirmedEurope PMC · checked 14 Sept 2026
Bioassay with an insect herbivore is a common approach to studying plant defense. While measuring insect growth rate as a negative indicator of plant defense levels is simple and straightforward, analysing more detailed feeding behavior parameters of insects, such as feeding rates, leaf area consumed per feeding event, intervals between feeding events, and spatio-temporal patterns of feeding sites on leaves, is more informative. However, such observations are generally time consuming and labor-intensive. Here, we provide a semi-automated system for quantifying feeding behavior parameters of insects feeding on plant leaves. Automated photo scanners record the time-course development of feeding marks on leaves. An image analysis pipeline processes the scanned images and extracts leaf area. By analysing changes in leaf area over time, it detects insect feeding events and calculates the leaf area consumed during each feeding event, providing quantitative parameters of the feeding behavior of insects. In addition, it visualizes spatio-temporal changes in feeding sites, providing a measure of the complex behavior of insects on leaves. Using this analysis pipeline, we demonstrate that Arabidopsis trichomes reduce insect feeding rate, but not feeding duration or intervals between feeding events. Our image acquisition system requires only a photo scanner and a laptop computer and does not require any specialized equipment. The analysis software is provided as an ImageJ macro and R package and is available at no cost. Taken together, our work provides a scalable method for quantitative assessment of the feeding behavior of insects on leaves, facilitating understanding of plant defense mechanisms.
Why it matches plant phenotyping methods葉の摂食痕をスキャン画像と解析パイプラインで定量し、摂食イベントごとの消費葉面積や時空間的な摂食部位を抽出する方法が研究の中心であるため、植物の損傷状態を測定するフェノタイピング手法として採用する。
abstractHere, we provide a semi-automated system for quantifying feeding behavior parameters of insects feeding on plant leaves.
Reproduction assets found保存済みの本文根拠を更新済みルールで再検証し、公開資産1件を確認しました。Code · publicThe software and documentation for the analysis pipeline is available online ( https://github.com/nsotta/feeding-mark-analysis ).Open asset ↗nsotta/feeding-mark-analysis · nsotta/feeding-mark-analysislines:91-152Code / dataset availability confirmedEurope PMC · checked 14 Sept 2026
Leaf rolling is a common adaptive response that plants have evolved to counteract the detrimental effects of various environmental stresses. Gaining insight into the mechanisms underlying leaf rolling alterations presents researchers with a unique opportunity to enhance stress tolerance in crops exhibiting leaf rolling, such as maize. In order to achieve a more profound understanding of leaf rolling, it is imperative to ascertain the occurrence and extent of this phenotype. While traditional manual leaf rolling detection is slow and laborious, research into high-throughput methods for detecting leaf rolling within our investigation scope remains limited. In this study, we present an approach for detecting leaf rolling in maize using the YOLOv8 model. Our method, LRD-YOLO, integrates two significant improvements: a Convolutional Block Attention Module to augment feature extraction capabilities, and a Deformable ConvNets v2 to enhance adaptability to changes in target shape and scale. Through experiments on a dataset encompassing severe occlusion, variations in leaf scale and shape, and complex background scenarios, our approach achieves an impressive mean average precision of 81.6%, surpassing current state-of-the-art methods. Furthermore, the LRD-YOLO model demands only 8.0 G floating point operations and the parameters of 3.48 M. We have proposed an innovative method for leaf rolling detection in maize, and experimental outcomes showcase the efficacy of LRD-YOLO in precisely detecting leaf rolling in complex scenarios while maintaining real-time inference speed.
Why it matches plant phenotyping methodsトウモロコシの葉巻きという植物形質を画像から検出する深層学習法を開発・評価しており、表現型取得手法が研究の中心である。
abstractIn this study, we present an approach for detecting leaf rolling in maize using the YOLOv8 model.
Reproduction assets foundThe authors explicitly state that source code for the LRD-YOLO leaf rolling detection method is publicly available on GitHub. The maize leaf rolling image dataset itself is only available from the corresponding author upon reasonable request, so it does not qualify as a public asset.Code · public00501 and no.32300239), Shenzhen Science and Technology Program (Grant No. RCBS20210609103819020), the Innovation Program of Chinese Academy of Agricultural Sciences, National Key R&D Program of China (Grant No. 2023ZD04076).
Data availability
Some of the data, source codes and more details about our project are in the GitHub ( https://github.com/WangYH1740/LRD-YOLO ). In addition, the original datasets are available from the corresponding author upon reasonable request.
Declarations
Conflict of interest
The authors declare no conflicts of interest.
References
Bänziger M Edmeades GO Beck D Bellon M
Breeding for drought and nitrogen stress tolerance in maize: from theory to practice 2000
MeOpen asset ↗WangYH1740/LRD-YOLOlines:662-712Code / dataset availability confirmedCrossref · checked 14 Sept 2026
Field / plotLeafMorphology / geometry measurementArchitecture / morphology / geometry
Abstract. Leaf inclination angle (LIA), the angle between leaf surface normal and zenith directions, is a vital parameter in radiative transfer, rainfall interception, evapotranspiration, photosynthesis, and hydrological processes. Due to the difficulty in obtaining large-scale field measurement data, LIA is typically assumed to follow the spherical leaf distribution or simply considered constant for different plant types. However, the appropriateness of these simplifications and the global LIA distribution are still unknown. This study compiled global LIA measurements and generated the first global 500 m mean LIA (MLA) product by gap-filling the LIA measurement data using a random forest regressor. Different generation strategies were employed for noncrops and crops. The MLA product was evaluated by validating the nadir leaf projection function (G(0)) derived from the MLA product with high-resolution reference data. The global MLA is 41.47°±9.55°, and the value increases with latitude. The MLAs for different vegetation types follow the order of cereal crops (54.65°) > broadleaf crops (52.35°) > deciduous needleleaf forest (50.05°) > shrubland (49.23°) > evergreen needleleaf forest (47.13°) ≈ grassland (47.12°) > deciduous broadleaf forest (41.23°) > evergreen broadleaf forest (34.40°). Cross-validation shows that the predicted MLA presents a medium consistency (r = 0.75, RMSE = 7.15°) with the validation samples for noncrops, whereas crops show relatively lower correspondence (r = 0.48 and 0.60 for broadleaf crops and cereal crops) because of limited LIA measurements and strong seasonality. The global G(0) distribution is opposite to that of the MLA and agrees moderately with the reference data (r = 0.62, RMSE = 0.15). This study shows that the common spherical and constant LIA assumptions may underestimate the intercept capability for most vegetation. The MLA and G(0) products derived in this study would enhance our knowledge about global LIA and should greatly facilitate remote sensing retrieval and land surface modeling studies. The global MLA and G(0) products can be accessed at: Li, S. and Fang, H. 2024, https://doi.org/10.5281/zenodo.10940673.
Why it matches plant phenotyping methods全球の葉傾斜角という明示的な植物形質について、測定データの補間によるプロダクト作成と独立データによる検証が中心であり、単なる生態・農業研究での routine 測定ではない。
abstractThis study compiled global LIA measurements and generated the first global 500 m mean LIA (MLA) product by gap-filling the LIA measurement data using a random forest regressor.
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 global MLA and G(0) products can be accessed at https://doi.org/10.5281/zenodo.12739662 (Li and Fang,
2025).Open asset ↗Zenodo · 10.5281/zenodo.12739662pdf-page:1 lines:1-51Code / dataset availability confirmedEurope PMC · checked 15 Sept 2026
Nicotiana tabacum is a kind of plant cultivated for its leaves used for manufacturing medicine and cigarettes. With the common name, the Tobacco plant is grown in many countries including China, Indonesia, Malawi and Tanzania just to mention a few. Literatures suggest a technical gap in the proper identification of grade labels for various parts of the plant. In addition, manual grading has resulted in various gaps and biases. To mitigate this, a data-driven grading solution is necessary. However, relevant datasets to train grade classifiers from various countries become of the essence. This article presents images concentrated on tobacco leaf plant position namely Leaf position which normally carries 23 grade labels. Due to high rainfall which swiped away the applied fertilizer on the tobacco plants in the farms, we failed to get images of one grade. Therefore, this research could capture and label 22 grade labels. Images of tobacco leaves based on the tobacco plant position were collected in Tanzania through participatory community research. Canon 5D mark III cameras with 100 mm micro lens were used to take pictures of tobacco leaves based on the tobacco plant position. Domain experts were used for image labelling and cleaning according to tobacco grade labels identified in Tanzania. The dataset carries 49,779 images, which can be used to develop machine learning models for tobacco leaf grade label identification. The collected dataset can be used to train models and enhance the performance of pre-trained models in any country of interest.
Why it matches plant phenotyping methodsタバコ葉の位置・等級を画像として収集し、専門家ラベル付きデータセットを構築しており、植物器官の状態・品質を画像から分類する再利用可能な方法資源が中心である。
abstractThis article presents images concentrated on tobacco leaf plant position namely Leaf position which normally carries 23 grade labels.
Reproduction assets foundThe paper is a data descriptor whose own tobacco leaf image dataset (49,779 images, 22 grade labels) is publicly deposited in the Harvard Dataverse with an explicit direct URL, making it a paper-specific, publicly actionable phenotyping image dataset.Dataset · publicr tobacco leaves image sample, the names of each grade label within leaf position in the dataset were identified.
Data source location
Tanzania Tobacco Board (TTB), Tobacco Research Institute of Tanzania (TORITA)
City/Town/Region: Tabora
Country: Tanzania
Data accessibility
Repository name: Harvard Dataverse
Direct URL to data: https://dataverse.harvard.edu/dataset.xhtml?persistentId=doi:10.7910/DVN/TTPLFT
1
Value of the Data
•Open asset ↗Harvard Dataverse · doi:10.7910/DVN/TTPLFTlines:1-49Code / dataset availability confirmedCrossref · Europe PMC · checked 7 Sept 2026
Background Plant height is a significant indicator of maize phenotypic morphology, and is closely related to crop growth, biomass, and lodging resistance. Obtaining the maize plant height accurately is of great significance for cultivating high-yielding maize varieties. Traditional measurement methods are labor-intensive and not conducive to data recording and storage. Therefore, it is very essential to implement the automated reading of maize plant height from measurement scales using object detection algorithms. Method This study proposed a lightweight detection model based on the improved YOLOv5. The MobileNetv3 network replaced the YOLOv5 backbone network, and the Normalization-based Attention Module attention mechanism module was introduced into the neck network. The CioU loss function was replaced with the EioU loss function. Finally, a combined algorithm was used to achieve the automatic reading of maize plant height from measurement scales. Results The improved model achieved an average precision of 98.6%, a computational complexity of 1.2 GFLOPs, and occupied 1.8 MB of memory. The detection frame rate on the computer was 54.1 fps. Through comparisons with models such as YOLOv5s, YOLOv7 and YOLOv8s, it was evident that the comprehensive performance of the improved model in this study was superior. Finally, a comparison between the algorithm’s 160 plant height data obtained from the test set and manual readings demonstrated that the relative error between the algorithm’s results and manual readings was within 0.2 cm, meeting the requirements of automatic reading of maize height measuring scale.
Why it matches plant phenotyping methodsトウモロコシの草丈という植物形質を画像・物体検出で自動推定する手法を開発し、既存モデルとの比較および手動測定による検証を行っており、フェノタイピング手法が中心である。
abstractTherefore, it is very essential to implement the automated reading of maize plant height from measurement scales using object detection algorithms.
Reproduction assets foundThe authors deposited the paper's maize plant height measuring scale image dataset on figshare, explicitly linked in the Data Availability statement. Supplemental detection code and model configuration exist but their URLs are not among the allowed URLs, so only the figshare dataset is reported.Dataset · publicThe data is available at figshare: Li, Joish (2023). Maize plant height measuring scale data set. figshare. Figure. https://doi.org/10.6084/m9.figshare.24547165.v1 .Open asset ↗figshare · 10.6084/m9.figshare.24547165.v1lines:445-552Code / dataset availability confirmedEurope PMC · checked 15 Sept 2026
Pollen is becoming an increasingly important subject for molecular researchers in genetic engineering, plant breeding, and environmental monitoring. To broaden the scope of these studies, it is essential to develop accessible methods for scientists who are not specialized in palynology. The article presents a simplified technical procedure for preparing pollen grains for scanning electron microscopy (SEM). The protocol is convenient for any molecular laboratory due to its small set of reagents, ease of execution, low cost, does not require special equipment, and takes only one hour to complete. The high penetrating ability of formaldehyde and the final delicate dehydration using hexamethyldisilazane (HMDS) instead of critical point drying allow for sufficient preservation of the architecture of the aperture, which is considered a gateway for the passage of biomolecules. The method was successfully applied to pollen grains of representatives of dicotyledons (beetroot, petunia, radish, tomato and tobacco) and monocotyledons (lily, onion, corn, rye and wheat). Species studied included insect-pollinated (entomophilous) and wind-pollinated (anemophilous) species. A comparative analysis of the sizes of fresh living pollen grains under a light microscope and those prepared for SEM showed some shrinkage. Quantitative analysis of the degree of pollen grain shrinkage showed that this process depends on the initial shape of dry pollen grains, and the number and structure of apertures. The results support the theoretical model of the folding/unfolding pathways of pollen grains.
Why it matches plant phenotyping methods植物花粉のSEM観察用試料調製法そのものを開発し、複数植物で適用・比較検証しているため、形態計測に関する中心的な方法論研究である。
abstractThe article presents a simplified technical procedure for preparing pollen grains for scanning electron microscopy (SEM).
Reproduction assets foundThe paper's quantitative pollen shrinkage measurements (Table S1) and light microscopy images (Figures S3–S4) are contained in the publicly downloadable MDPI Supplementary Materials, which directly reproduce this paper's phenotyping measurements. No author analysis code or trained models are mentioned.Supplement · publicoly Bogdanov—at the department of electron microscopy, Lomonosov Moscow State University.
Abbreviations
The following abbreviations are used in this manuscript:
SEM
Scanning Electron Microscopy
HMDS
Hexamethyldisilazane
SA
Short axis
LA
Long axis
Supplementary Materials
The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/plants13152140/s1 , Figure S1: The order of steps for pollen preparation according to the developed protocol; Figure S2: Scheme of measured pollen grain diameters; Figure S3: Light microscopy of pollen grains of insect-pollinated species; Figure S4: Light microscopy of pollen grains of wind-pollinated species; Table S1: Comparison Open asset ↗lines:98-127Code / dataset availability confirmedEurope PMC · checked 14 Sept 2026
Early prediction of crop production by remote sensing (RS) may help to plan the harvest and ensure food security. This study aims to improve the quantification of yield, grain protein concentration (GPC), and nitrogen (N) output in winter wheat with RS imagery. Ground-truth wheat traits were measured at flowering and harvest in a field experiment combining four N and two water levels in central Spain over 2 years. Hyperspectral and thermal airborne images coincident with Sentinel-1 and Sentinel-2 were acquired at flowering. A parametric linear model using all hyperspectral normalized difference spectral indices (NDSI) and two non-parametric models (artificial neural network and random forest) were used to assess their estimation ability combining NDSIs and other RS indicators. The feasibility of using freely available multispectral satellite was tested by applying the same methodology but using Sentinel-1 and Sentinel-2 bands. Yield estimation obtained the highest R² value, showing that the visible and short-wave infrared region (VSWIR) had similar accuracy to the hyperspectral and Sentinel-2 imagery (R² ≈ 0.84). The SWIR bands were important in the GPC estimation with both sensors, whereas N output was better estimated using red-edge-based NDSIs, obtaining satisfactory results with the hyperspectral sensor (R² = 0.74) and with the Sentinel-2 (R² = 0.62). When including the Sentinel-2 SWIR index, the NDSI (B11, B3) improved the estimation of N output (R² = 0.71). Ensemble models based on Sentinel were found to be as reliable as those based on hyperspectral imagery, and including SWIR information improved the quantification of N-related traits.
Why it matches plant phenotyping methods航空ハイパースペクトル画像とSentinel画像、複数の推定モデルを用いて小麦の収量・タンパク質濃度・窒素出力を定量化し、センサー間の性能を比較しているため、表現型取得・推定法が研究の中心である。
abstractThis study aims to improve the quantification of yield, grain protein concentration (GPC), and nitrogen (N) output in winter wheat with RS imagery.
Reproduction assets foundThe paper's Data availability statement points to a public Figshare deposit (DOI 10.6084/m9.figshare.21865410.v1) containing the data supporting the study's winter wheat trait estimations from airborne hyperspectral and Sentinel imagery. This is a paper-specific, publicly accessible dataset with an authors' URL. No作者分析Dataset · publicatory work,
and QuantaLab-IAS-CSIC staff members A. Hornero, A. Vera, D. Notario, and R. Romero for airborne and
laboratory assistance.
Funding Open Access funding provided thanks to the CRUE-CSIC agreement with Springer Nature.
Data availability The data that support the findings presented in this study are available online at https://doi.org/10.6084/m9.figshare.21865410.v1.Declarations
Conflict of interest The authors declare no conflict of interest.
Open Access This article is licensed under a Creative Commons Attribution 4.0 International License,
which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long
as you give appropriate credit to the oOpen asset ↗figshare · 10.6084/m9.figshare.21865410.v1pdf-raw-page:20 lines:1-46Code / 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 confirmedEurope PMC · Crossref · checked 15 Sept 2026
To study plant organs, it is necessary to investigate the three-dimensional (3D) structures of plants. In recent years, non-destructive measurements through computed tomography (CT) have been used to understand the 3D structures of plants. In this study, we use the Chrysanthemum seticuspe capitulum inflorescence as an example and focus on contact points between the receptacles and florets within the 3D capitulum inflorescence bud structure to investigate the 3D arrangement of the florets on the receptacle. To determine the 3D order of the contact points, we constructed slice images from the CT volume data and detected the receptacles and florets in the image. However, because each CT sample comprises hundreds of slice images to be processed and each C. seticuspe capitulum inflorescence comprises several florets, manually detecting the receptacles and florets is labor-intensive. Therefore, we propose an automatic contact point detection method based on CT slice images using image recognition techniques. The proposed method improves the accuracy of contact point detection using prior knowledge that contact points exist only around the receptacle. In addition, the integration of the detection results enables the estimation of the 3D position of the contact points. According to the experimental results, we confirmed that the proposed method can detect contacts on slice images with high accuracy and estimate their 3D positions through clustering. Additionally, the sample-independent experiments showed that the proposed method achieved the same detection accuracy as sample-dependent experiments.
Why it matches plant phenotyping methodsCT画像から花序内の小花と花托の接触点を自動検出し、3D位置を推定する手法の開発・精度評価が研究の中心であるため、植物フェノタイピング手法に該当する。
abstractTherefore, we propose an automatic contact point detection method based on CT slice images using image recognition techniques.
Reproduction assets foundThe authors publicly deposited the labeled CT slice-image dataset (contact point annotations and receptacle segmentation labels) on Figshare, and a 3D visualization video of the contact point estimation results is available on YouTube. Raw CT volumes are only available on request. No author analysis code repository is.Dataset · publicre task is to automate the clustering parameters, which are currently determined manually. We also plan to develop a mathematical model of the position of the contact point between the receptacle and florets based on the estimation results.
Data availability statement
The labeled data for this study can be found in the Figshare https://doi.org/10.6084/m9.figshare.25388434 . The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.
Author contributionsOpen asset ↗Figshare · 10.6084/m9.figshare.25388434lines:513-540Code / dataset availability confirmedCrossref · checked 15 Sept 2026
Field / plotWhole plant / canopy / plot / fieldPhysiological trait estimationGrowth / time-series analysisPhotosynthesis / fluorescenceWater status / transpiration
Abstract. Long-term time series of transpiration, evaporation, plant photosynthesis, and soil respiration are essential for addressing numerous research questions related to ecosystem functioning. However, quantifying these fluxes is challenging due to the lack of reliable and direct measurement techniques, which has left gaps in the understanding of their temporal cycles and spatial variability. To help address this open challenge, we generated a dataset of these four components by implementing five (conventional and novel) approaches to partition total ET and CO2 fluxes into plant and soil fluxes across 47 NEON sites. The final dataset (https://doi.org/10.5281/zenodo.12191876) spans a five-year period and covers various ecosystems, including forests, grasslands, and agricultural terrain. This is the first comprehensive dataset covering such a wide spatial and temporal distribution. Overall, we observed good agreement across most methods for ET components, increasing the reliability of these estimates. Partitioning of CO2 components was found to be less robust and more dependent on prior knowledge of water-use efficiency. This dataset has several potential future applications, such as addressing critical questions regarding the response of ecosystems to extreme weather events, which are expected to become more severe and frequent with climate change.
Why it matches plant phenotyping methods植物・土壌フラックスを分離推定する5手法を47地点で実装し、手法間の一致度を評価した長期データセットであり、植物の生理状態（蒸散・光合成）の取得・推定法が中心的です。
abstractwe generated a dataset of these four components by implementing five (conventional and novel) approaches to partition total ET and CO2 fluxes into plant and soil fluxes across 47 NEON sites.
Reproduction assets foundThe paper's five-year NEON flux-partitioning dataset and the authors' partitioning-method scripts are explicitly deposited on Zenodo with public DOIs.Dataset · publicows the availability
of flux components as a fraction of the total number of half-
hour periods in the record. Overall, all the methods cover
a similar temporal distribution of flux partitioning and are
potential candidates for ensemble averaging.
4 Description of the final dataset
The final dataset is available for download at
https://doi.org/10.5281/zenodo.12191876 (Zahn and Bou-
Zeid, 2024). It is organized into different folders for each
site, with each site containing a .csv file for each method.
This format is selected to be user-friendly and accessible in
various programming languages and software packages. For
FVS and CECw, in addition to their ensemble averages for
https://doi.org/Open asset ↗Zenodo · 10.5281/zenodo.12191876pdf-raw-page:9 lines:136-149Code · publicnthesis, transpiration and stomatal conduc-
tance: potential and limitations, Plant Cell Environ., 35, 657–
667, https://doi.org/10.1111/j.1365-3040.2011.02451.x, 2011.
Zahn, E.: einaraz/PartitioningMethods: Processing Eddy-
Covariance Data: Five Evapotranspiration Flux Parti-
tioning Methods (v1.0.1) [Software], Zenodo [code],
https://doi.org/10.5281/zenodo.11510363, 2024.
Zahn, E. and Bou-Zeid, E.: Partitioning of water and CO2 fluxes at
NEON sites into soil and plant components: a five-year dataset
for spatial and temporal analysis [dataset], Zenodo [data set],
https://doi.org/10.5281/zenodo.12191876, 2024.
Zahn, E., Chor, T. L., and Dias, N. L.: A Simple Methodology for
Quality ControlOpen asset ↗Zenodo · 10.5281/zenodo.11510363pdf-raw-page:22 lines:1-58Code / dataset availability confirmedEurope PMC · checked 14 Sept 2026
Chlorophyll fluorescence is a well-established method to estimate chlorophyll content in leaves. A popular fluorescence-based meter, the Opti-Sciences CCM-300 Chlorophyll Content Meter (CCM-300), utilizes the fluorescence ratio F735/F700 and equations derived from experiments using broadleaf species to provide a direct, rapid estimate of chlorophyll content used for many applications. We sought to quantify the performance of the CCM-300 relative to more intensive methods, both across plant functional types and years of use. We linked CCM-300 measurements of broadleaf, conifer, and graminoid samples in 2018 and 2019 to high-performance liquid chromatography (HPLC) and/or spectrophotometric (Spec) analysis of the same leaves. We observed a significant difference between the CCM-300 and HPLC/Spec, but not between HPLC and Spec. In comparison to HPLC, the CCM-300 performed better for broadleaves (r = 0.55, RMSE = 154.76) than conifers (r = 0.52, RMSE = 171.16) and graminoids (r = 0.32, RMSE = 127.12). We observed a slight deterioration in meter performance between years, potentially due to meter calibration. Our results show that the CCM-300 is reliable to demonstrate coarse variations in chlorophyll but may be limited for cross-plant functional type studies and comparisons across years.
Why it matches plant phenotyping methodsCCM-300による葉のクロロフィル含量測定法を、HPLCおよび分光測定と比較して信頼性・校正性能を検証しており、植物表現型取得法が研究の中心である。
abstractWe sought to quantify the performance of the CCM-300 relative to more intensive methods, both across plant functional types and years of use.
Reproduction assets found保存済みの本文根拠を更新済みルールで再検証し、公開資産1件を確認しました。Supplement · publicThe following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/s24154784/s1 , Figure S1: HPLC measurements from the UW-Madison dataset ( n = 26).Open asset ↗lines:262-279Code / dataset availability confirmedCrossref · checked 7 Sept 2026
Precision agriculture integrates multiple sensors and data types to support farmers with informed decision-making tools throughout crop cycles. This study evaluated Aboveground Biomass (AGB) estimates of Rye using attributes derived from PlanetScope (PS) optical, Sentinel-1 Synthetic Aperture Radar (SAR), and hybrid (optical plus SAR) datasets. Optical attributes encompassed surface reflectance from PS’s blue, green, red, and near-infrared (NIR) bands, alongside the Normalized Difference Vegetation Index (NDVI) and Enhanced Vegetation Index (EVI). Sentinel-1 SAR attributes included the C-band Synthetic Aperture Radar Ground Range Detected, VV and HH polarizations, and both Ratio and Polarization (Pol) indices. Ground reference AGB data for Rye (Secale cereal L.) were collected from 50 samples and four dates at a farm located in southern Brazil, aligning with image acquisition dates. Multiple linear regression models were trained and validated. AGB was estimated based on individual (optical PS or Sentinel-1 SAR) and combined datasets (optical plus SAR). This process was repeated 100 times, and variable importance was extracted. Results revealed improved Rye AGB estimates with integrated optical and SAR data. Optical vegetation indices displayed higher correlation coefficients (r) for AGB estimation (r = +0.67 for both EVI and NDVI) compared to SAR attributes like VV, Ratio, and polarization (r ranging from −0.52 to −0.58). However, the hybrid regression model enhanced AGB estimation (R2 = 0.62, p
Why it matches plant phenotyping methods光学・SARセンサーデータを統合してライムギの地上部バイオマスを推定し、回帰モデルを訓練・検証しているため、植物形質の取得・推定手法が研究の中心です。
abstractThis study evaluated Aboveground Biomass (AGB) estimates of Rye using attributes derived from PlanetScope (PS) optical, Sentinel-1 Synthetic Aperture Radar (SAR), and hybrid (optical plus SAR) datasets.
Reproduction assets foundThe paper's Sentinel-1 SAR processing workflow is publicly shared as a Google Earth Engine JavaScript script with an explicit availability statement and URL. The field AGB measurements and PlanetScope data are not public (available only on reasonable request from the corresponding author).Code · publicData Availability Statement: The Google Earth Engine script to process Sentinel-1 data is available at
[https://code.earthengine.google.com/219fc3c05b8ae8132ae2d758ccba3d1e?noload=true] (accessed
on 12 July 2024).Open asset ↗pdf-page:17 lines:1-59Code / 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 · OpenAlex · checked 7 Sept 2026
Plant phenotype detection plays a crucial role in understanding and studying plant biology, agriculture, and ecology. It involves the quantification and analysis of various physical traits and characteristics of plants, such as plant height, leaf shape, angle, number, and growth trajectory. By accurately detecting and measuring these phenotypic traits, researchers can gain insights into plant growth, development, stress tolerance, and the influence of environmental factors, which has important implications for crop breeding. Among these phenotypic characteristics, the number of leaves and growth trajectory of the plant are most accessible. Nonetheless, obtaining these phenotypes is labor intensive and financially demanding. With the rapid development of computer vision technology and artificial intelligence, using maize field images to fully analyze plant-related information can greatly eliminate repetitive labor and enhance the efficiency of plant breeding. However, it is still difficult to apply deep learning methods in field environments to determine the number and growth trajectory of leaves and stalks due to the complex backgrounds and serious occlusion problems of crops in field environments. To preliminarily explore the application of deep learning technology to the acquisition of the number of leaves and stalks and the tracking of growth trajectories in field agriculture, in this study, we developed a deep learning method called Point-Line Net, which is based on the Mask R-CNN framework, to automatically recognize maize field RGB images and determine the number and growth trajectory of leaves and stalks. The experimental results demonstrate that the object detection accuracy (mAP50) of our Point-Line Net can reach 81.5%. Moreover, to describe the position and growth of leaves and stalks, we introduced a new lightweight "keypoint" detection branch that achieved a magnitude of 33.5 using our custom distance verification index. Overall, these findings provide valuable insights for future field plant phenotype detection, particularly for datasets with dot and line annotations.
Why it matches plant phenotyping methodsトウモロコシの葉・茎の数と成長軌跡をRGB画像から抽出する深層学習手法を開発し、精度評価も行っており、植物表現型取得が研究の中心である。
abstractin this study, we developed a deep learning method called Point-Line Net, which is based on the Mask R-CNN framework, to automatically recognize maize field RGB images and determine the number and growth trajectory of leaves and stalks.
Reproduction assets foundThe authors explicitly deposit the code (and data) supporting this maize leaf/stalk trajectory phenotyping study in a public GitHub repository, matching an allowed URL.Code · publicThe computer code and data that support the findings of this study are deposited in a GitHub repository at https://github.com/VEGETALOADING/Point-Line-Net .Open asset ↗VEGETALOADING/Point-Line-Netlines:319-524Code / dataset availability confirmedOpenAlex · Europe PMC · checked 15 Sept 2026
Sesame (Sesamum indicum) is an important oilseed crop with rising demand owing to its nutritional and health benefits. There is an urgent need to develop and integrate new genomic-based breeding strategies to meet these future demands. While genomic resources have advanced genetic research in sesame, the implementation of high-throughput phenotyping and genetic analysis of longitudinal traits remains limited. Here, we combined high-throughput phenotyping and random regression models to investigate the dynamics of plant height, leaf area index, and five spectral vegetation indices throughout the sesame growing seasons in a diversity panel. Modeling the temporal phenotypic and additive genetic trajectories revealed distinct patterns corresponding to the sesame growth cycle. We also conducted longitudinal genomic prediction and association mapping of plant height using various models and cross-validation schemes. Moderate prediction accuracy was obtained when predicting new genotypes at each time point, and moderate to high values were obtained when forecasting future phenotypes. Association mapping revealed three genomic regions in linkage groups 6, 8, and 11, conferring trait variation over time and growth rate. Furthermore, we leveraged correlations between the temporal trait and seed-yield and applied multi-trait genomic prediction. We obtained an improvement over single-trait analysis, especially when phenotypes from earlier time points were used, highlighting the potential of using a high-throughput phenotyping platform as a selection tool. Our results shed light on the genetic control of longitudinal traits in sesame and underscore the potential of high-throughput phenotyping to detect a wide range of traits and genotypes that can inform sesame breeding efforts to enhance yield.
Why it matches plant phenotyping methods高スループット表現型プラットフォームによる時系列の植物形質取得が研究の中心的データ基盤であり、複数形質の縦断測定と予測への応用を評価している。
abstractwe combined high-throughput phenotyping and random regression models to investigate the dynamics of plant height, leaf area index, and five spectral vegetation indices throughout the sesame growing seasons
Reproduction assets foundThe paper's Data Availability Statement points to a public figshare deposit containing all phenotypic data (temporal HTP traits: plant height, LAI, spectral vegetation indices), genomic data, and GWAS results for this sesame study. No author analysis code repository is explicitly deposited.Dataset · publicfor longitudinal traits derived from single time points (green) and random regression (orange) analysis.
Figure S3 . Phenotypic (green) and genetic (orange) correlations between longitudinal traits at each time point and seed‐yield.
Data Availability Statement
All phenotypic data, genomic data, and GWAS results can be found at https://doi.org/10.6084/m9.figshare.24961491 .Open asset ↗figshare · 10.6084/m9.figshare.24961491lines:1055-1061Code / dataset availability confirmedOpenAlex · Europe PMC · checked 7 Sept 2026
To maximise the throughput of novel, high-throughput phenotyping platforms, many researchers have utilised smaller pot sizes to increase the number of biological replicates that can be grown in spatially limited controlled environments. This may confound plant development through a process known as “pot binding”, particularly in larger species including potato (Solanum tuberosum), and under water-restricted conditions. We aimed to investigate the water availability hypothesis of pot binding, which predicts that small pots have insufficient water holding capacities to prevent drought stress between irrigation periods, in potato. Two cultivars of potato were grown in small (5 L) and large (20 L) pots, were kept under polytunnel conditions, and were subjected to three irrigation frequencies: every other day, daily, and twice daily. Plants were phenotyped with two Phenospex PlantEye F500s and canopy and tuber fresh mass and dry matter were measured. Increasing irrigation frequency from every other day to daily was associated with a significant increase in fresh tuber yield, but only in large pots. This suggests a similar level of drought stress occurred between these treatments in the small pots, supporting the water availability hypothesis of pot binding. Further increasing irrigation frequency to twice daily was still not sufficient to increase yields in small pots but it caused an insignificant increase in yield in the larger pots, suggesting some pot binding may be occurring in large pots under daily irrigation. Canopy temperatures were significantly higher under each irrigation frequency in the small pots compared to large pots, which strongly supports the water availability hypothesis as higher canopy temperatures are a reliable indicator of drought stress in potato. Digital phenotyping was found to be less accurate for larger plants, probably due to a higher degree of self-shading. The research demonstrates the need to define the optimum pot size and irrigation protocols required to completely prevent pot binding and ensure drought treatments are not inadvertently applied to control plants.
Why it matches plant phenotyping methodsPlantEyeを用いたデジタルフェノタイピングの適用と精度評価が研究上の主要要素であり、植物のキャノピー温度や成長状態を測定し、植物サイズによる測定精度低下も検討している。
abstractPlants were phenotyped with two Phenospex PlantEye F500s and canopy and tuber fresh mass and dry matter were measured.
Reproduction assets foundThe paper's data availability statement points to a public Zenodo deposit containing the datasets generated and analysed in this potato pot-binding phenotyping study.Dataset · publicThe datasets generated and analysed for this study can be found in the Zendo repository at https://doi.org/10.5281/zenodo.10707587 .Open asset ↗Zenodo · 10.5281/zenodo.10707587lines:845-856Code / dataset availability confirmedEurope PMC · checked 14 Sept 2026
Field / plotRGB / grayscaleLeafClassificationDisease symptoms / severity
Papaya, renowned for its nutritional benefits, represents a highly profitable crop. However, it is susceptible to various diseases that can significantly impede fruit productivity and quality. Among these, leaf diseases pose a substantial threat, severely impacting the growth of papaya plants. Consequently, papaya farmers frequently encounter numerous challenges and financial setbacks. To facilitate the easy and efficient identification of papaya leaf diseases, a comprehensive dataset has been assembled. This dataset, comprising approximately 1400 images of diseased, infected, and healthy leaves, aims to enhance the understanding of how these ailments affect papaya plants. The images, meticulously collected from diverse regions and under varying weather conditions, offer detailed insights into the disease patterns specific to papaya leaves. Stringent measures have been taken to ensure the dataset's quality and enhance its utility. The images, captured from multiple angles and boasting high resolution are designed to aid in the development of a highly accurate model. Additionally, RGB mode has been employed to meticulously capture each detail, ensuring a flawless representation of the leaves. The dataset meticulously identifies and categorizes five primary types of leaf diseases: Leaf Curl (inclusive of its initial stage), Papaya Mosaic, Ring Spot, Mites (specifically, those affected by Red Spider Mites), and Mealybug. These diseases are recognized for their detrimental effects on both the leaves and the overall fruit production of the papaya plant. By leveraging this curated dataset, it is possible to train a model for the real-time detection of leaf diseases, significantly aiding in the timely identification of such conditions.
Why it matches plant phenotyping methodsパパイヤ葉の健全・病害状態を画像で取得したデータセットであり、植物病害表現型の再利用可能なデータ基盤として中心的です。
abstracta comprehensive dataset has been assembled
Reproduction assets foundThe paper is a Data in Brief article describing a smartphone image dataset of healthy and diseased papaya leaves (~1400 original, 6618 augmented images across six classes) collected in Bangladesh. The authors' own dataset is publicly deposited on Mendeley Data with an explicit direct URL and DOI, making it a paper-phenDataset · publicn Rangpur district (latitude: 25° 34′ 30.6942″, longitude: 89° 16′ 22.2672″), and
4. Chotali Purbo Para village papaya garden in Rangpur district (latitude: 25° 34′ 30.6942″, longitude: 89° 16′ 22.2672″).
Data accessibility
Repository name: Mendeley Data
Data identification number: DOI: 10.17632/44p8v6ywsm.1
Direct URL to data: https://data.mendeley.com/datasets/44p8v6ywsm/1
1
Value of the Data
•Open asset ↗Mendeley Data · 10.17632/44p8v6ywsm.1lines:1-48Code / 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
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 confirmedOpenAlex · checked 15 Sept 2026
Published23 May 2024International Journal of Applied Earth Observation and GeoinformationCited by 9 · OpenAlex ↗
In smallholder areas, the abandonment of orchards is a recent phenomenon with socioeconomic and environmental consequences. Biomass estimation and monitoring of these areas is essential to analyze their influence on the CO2 balance and to quantify carbon pools. In the current context of energy supply uncertainties and considering the demanding use of alternative energy sources, the quantification of fruit tree biomass in abandoned areas is a question of great interest. In this study, the above biomass of abandoned orange trees was estimated using tree parameters calculated from 3D points derived from images captured by a UAV and applying the Structure from Motion (SfM) technique. From these data, a canopy height model was calculated and used to apply a developed crown contour detection algorithm. Using this information and 3D points, the tree parameters crown area, crown diameter, crown length, maximum tree height, minimum tree height, mean and standard deviation of crown point heights were calculated for a set of 36 felled and weighted orange trees. Stepwise regression was used to estimate the above biomass values. All previously reported variables were included. The crown area parameter produced the most accurate model with R2, RMSE and RMSE % values of 0.85, 10.165 kg and 19.56 %, respectively. These results demonstrate the potential of UAV-SfM-derived 3D point clouds to estimate the above-ground biomass of abandoned fruit trees, relevant information for environmental analysis and biofuel energy production.
Why it matches plant phenotyping methodsUAV-SfMによる3D画像から樹冠形状・樹高などの植物形質を抽出し、樹冠検出アルゴリズムと回帰モデルで個体バイオマスを推定する方法が中心である。
abstractthe above biomass of abandoned orange trees was estimated using tree parameters calculated from 3D points derived from images captured by a UAV and applying the Structure from Motion (SfM) technique
Reproduction assets foundThe article's 'Research data' section states that the point cloud, crown delineation source code, reference data, and tree parameters are publicly available in a Mendeley Data repository (doi:10.17632/j3k2mctbnv.1). This is a paper-specific, public, actionable asset. Note: the Mendeley DOI itself is not in the allowed-Dataset · publicThe dataset containing point cloud, source code for crown delinea
tion, reference data, and parameters of abandoned orange groves, is
available for download from Mendeley data repository doi: 10
.17632/j3k2mctbnv.1.Open asset ↗Mendeley data repositorypdf-raw-page:9 lines:1-76Code / dataset availability confirmedCrossref · Europe PMC · checked 7 Sept 2026
Abstract Background The sweet potato whitefly ( Bemisia tabaci ) is a globally important insect pest that damages crops through direct feeding and by transmitting viruses. Current B. tabaci management revolves around the use of insecticides, which are economically and environmentally costly. Host plant resistance is a sustainable option to reduce the impact of whiteflies, but progress in deploying resistance in crops has been slow. A major obstacle is the high cost and low throughput of screening plants for B. tabaci resistance. Oviposition rate is a popular metric for host plant resistance to B. tabaci because it does not require tracking insect development through the entire life cycle, but accurate quantification is still limited by difficulties in observing B. tabaci eggs, which are microscopic and translucent. The goal of our study was to improve quantification of B. tabaci eggs on several important crop species: cassava, cowpea, melon, sweet potato and tomato. Results We tested a selective staining process originally developed for leafhopper eggs: submerging the leaves in McBryde’s stain (acetic acid, ethanol, 0.2% aqueous acid Fuchsin, water; 20:19:2:1) for three days, followed by clearing under heat and pressure for 15 min in clearing solution (LGW; lactic acid, glycerol, water; 17:20:23). With a less experienced individual counting the eggs, B. tabaci egg counts increased after staining across all five crops. With a more experienced counter, egg counts increased after staining on melons, tomatoes, and cowpeas. For all five crops, there was significantly greater agreement on egg counts across the two counting individuals after the staining process. The staining method worked particularly well on melon, where egg counts universally increased after staining for both counting individuals. Conclusions Selective staining aids visualization of B. tabaci eggs across multiple crop plants, particularly species where leaf morphological features obscure eggs, such as melons and tomatoes. This method is broadly applicable to research questions requiring accurate quantification of B. tabaci eggs, including phenotyping for B. tabaci resistance.
Why it matches plant phenotyping methods植物葉上のコナジラミ卵を染色して定量し、計数値と計数者間一致を改善する方法を評価しており、抵抗性フェノタイピングへの応用が明示された中心的な手法研究。
abstractThe goal of our study was to improve quantification of B. tabaci eggs on several important crop species: cassava, cowpea, melon, sweet potato and tomato.
Reproduction assets foundThe paper's Data availability statement explicitly deposits the study's egg-count datasets and the R Markdown analysis code in a Dryad repository, which is a public, paper-specific asset directly reproducing the phenotyping measurements and analysis.Dataset · publicThe datasets generated and analyzed during this study, and an R Markdown document containing the code used to perform these analyses are available in a Dryad repository (DOI: doi: https://doi.org/10.5061/dryad.vmcvdnd1m ).Open asset ↗Dryad · 10.5061/dryad.vmcvdnd1mlines:138-163Code / dataset availability confirmedEurope PMC · checked 15 Sept 2026
Bread wheat (Triticum aestivum L.) is a globally important food crop, which was domesticated about 8-10,000 years ago. Bread wheat is an allopolyploid, and it evolved from two hybridization events of three species. To widen the genetic base in breeding, bread wheat has been re-synthesized by crossing durum wheat (Triticum turgidum ssp. durum) and goat grass (Aegilops tauschii Coss), leading to so-called synthetic hexaploid wheat (SHW). We applied the quantitative genetics tools of "hybrid prediction"-originally developed for the prediction of wheat hybrids generated from different heterotic groups - to a situation of allopolyploidization. Our use-case predicts the phenotypes of SHW for three quantitatively inherited global wheat diseases, namely tan spot (TS), septoria nodorum blotch (SNB), and spot blotch (SB). Our results revealed prediction abilities comparable to studies in 'traditional' elite or hybrid wheat. Prediction abilities were highest using a marker model and performing random cross-validation, predicting the performance of untested SHW (0.483 for SB to 0.730 for TS). When testing parents not necessarily used in SHW, combination prediction abilities were slightly lower (0.378 for SB to 0.718 for TS), yet still promising. Despite the limited phenotypic data, our results provide a general example for predictive models targeting an allopolyploidization event and a method that can guide the use of genetic resources available in gene banks.
Why it matches plant phenotyping methods遺伝マーカーから作物病害表現型を予測するモデルを中心的に適用・評価しており、植物の病害状態を推定する計算的フェノタイピング手法に該当する。
abstractOur use-case predicts the phenotypes of SHW for three quantitatively inherited global wheat diseases
Reproduction assets foundThe article's data availability statement points to a public CIMMYT repository deposit containing the paper's own phenotypic (tan spot, septoria nodorum blotch, spot blotch) and genotypic datasets, matching the allowed handle URL. No author analysis code or trained model deposit is stated.Dataset · publicAgreement Research Fund (JA) through the Research Council of Norway for grants 301835 (Sustainable Management of Rust Diseases in Wheat) and 320090 (Phenotyping for Healthier and more Productive Wheat Crops).
DATA AVAILABILITY STATEMENT
The phenotypic (TS, SNB and SB) and genotypic data sets can be found in the following link: https://hdl.handle.net/11529/10548948 .
REFERENCES
Aberkane , H.
,
Payne , T.
,
Kishi , M.
,
Smale , M.
,
Amri , A.
, &
Jamora , N.
( 2020 ).
Transferring diversity of goat grass to farmers’ fields through the development of synthetic hexaploid wheat
. Food Security , 12 ( 5 ), 1017 – 1033 .
10.1007/s12571-020-01051-w
Acosta‐Pech , R.
,
Crossa , J.
,
De Los CamposOpen asset ↗lines:705-933Code / dataset availability confirmedEurope PMC · checked 13 Sept 2026
Field / plotPhotogrammetry / SfM / MVSLiDAR / point cloudStem / branch2D/3D reconstructionArchitecture / morphology / geometry
Conifer shoots exhibit intricate geometries at an exceptionally detailed spatial scale. Describing the complete structure of a conifer shoot, which contributes to a radiation scattering pattern, has been difficult, and the previous respective components of radiative transfer models for conifer stands were rather coarse. This paper presents a dataset aimed at models and applications requiring detailed 3D representations of needle shoots. The data collection was conducted in the Järvselja RAdiation transfer Model Intercomparison (RAMI) pine stand in Estonia. The dataset includes 3-dimensional surface information on 10 shoots of two conifer species present in the stand (5 shoots per species) - Scots pine ( Pinus sylvestris L.) and Norway spruce ( Picea abies L. Karst. ). The samples were collected on 26th July 2022, and subsequently blue light 3D photogrammetry scanning technique was used to obtain their high-resolution 3D point cloud representations. For each of these samples, the dataset comprises of a photo of the sampled shoot and its obtained 3-dimensional surface reconstruction. Scanned shoots may replace previous, artificially generated models and contribute to the more realistic representation of 3D forest representations and, consequently, more accurate estimates of related parameters and processes by radiative transfer models.
Why it matches plant phenotyping methods針葉樹シュートの3次元形状を高解像度スキャンで取得した再利用可能なデータセットであり、植物形態の計測・表現が中心。
abstractThis paper presents a dataset aimed at models and applications requiring detailed 3D representations of needle shoots.
Reproduction assets foundThe paper describes a public Mendeley Data repository containing the paper's own 3D surface geometry (.stl) models and photos (.jpg) of 10 scanned conifer shoots, directly reproducing the paper's phenotyping measurements.Dataset · publicRepository name: Mendeley
Data identification number: 10.17632/rs3f6trdvw.1
Direct URL to data: https://data.mendeley.com/datasets/rs3f6trdvw/1Open asset ↗Mendeley · 10.17632/rs3f6trdvw.1lines:1-53Code / dataset availability confirmedEurope PMC · checked 14 Sept 2026
Southern leaf blight (SLB) is a foliar disease caused by the fungus Cochliobolus heterostrophus infecting maize plants in humid, warm weather conditions. SLB causes production losses to corn producers in different regions of the world such as Latin America, Europe, India, and Africa. In this paper, we demonstrate a non-destructive method to quantify the signs of fungal infection in SLB-infected corn plants using a deep UV (DUV) fluorescence spectrometer, with a 248.6 nm excitation wavelength, to acquire the emission spectra of healthy and SLB-infected corn leaves. Fluorescence emission spectra of healthy and diseased leaves were used to train an Autoencoder (AE) anomaly detection algorithm-an unsupervised machine learning model-to quantify the phenotype associated with SLB-infected leaves. For all samples, the signature of corn leaves consisted of two prominent peaks around 450 nm and 325 nm. However, SLB-infected leaves showed a higher response at 325 nm compared to healthy leaves, which was correlated to the presence of C. heterostrophus based on disease severity ratings from Visual Scores (VS). Specifically, we observed a linear inverse relationship between the AE error and the VS (R2 = 0.94 and RMSE = 0.935). With improved hardware, this method may enable improved quantification of SLB infection versus visual scoring based on e.g., fungal spore concentration per unit area and spatial localization.
Why it matches plant phenotyping methodsトウモロコシ葉の病徴・感染程度という植物状態を、深紫外蛍光分光とオートエンコーダで非破壊的に定量する手法が研究の中心であり、視覚評価との相関による技術評価も行っている。
abstractwe demonstrate a non-destructive method to quantify the signs of fungal infection in SLB-infected corn plants using a deep UV (DUV) fluorescence spectrometer
Reproduction assets found保存済みの本文根拠を更新済みルールで再検証し、公開資産1件を確認しました。Dataset · publicData Availability: The data can be downloaded from here: https://datadryad.org/stash/share/NW062Bv9Cpe5VslBiiA52nweUJEQCHC2yzAqKlQYx6w .Open asset ↗Dryadlines:138-151Code / dataset availability 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 confirmedEurope PMC · checked 14 Sept 2026
Premise Poikilohydric plants respond to hydration by undergoing dry-wet-dry cycles. Carbon balance represents the net gain or loss of carbon from each cycle. Here we present the first standard protocol for measuring carbon balance, including a custom-modified chamber system for infrared gas analysis, 12-h continuous monitoring, resolution of plant-substrate relationships, and in-chamber specimen hydration. Methods and results We applied the carbon balance technique to capture responses to water stress in populations of the moss Syntrichia caninervis , comparing 19 associated physiological variables. Carbon balance was negative in desiccation-acclimated (field-collected) mosses, which exhibited large respiratory losses. Contrastingly, carbon balance was positive in hydration-acclimated (lab-cultivated) mosses, which began exhibiting net carbon uptake Conclusions Carbon balance is a functional trait indicative of physiological performance, hydration stress, and survival in poikilohydric plants, and the carbon balance method can be applied broadly across taxa to test hypotheses related to environmental stress and global change.
Why it matches plant phenotyping methodsコケ植物の炭素収支を測定する標準プロトコルとカスタムチャンバーを開発し、炭素収支を機能形質として評価しているため、植物フェノタイピング手法が中心である。
abstractHere we present the first standard protocol for measuring carbon balance, including a custom-modified chamber system for infrared gas analysis, 12-h continuous monitoring, resolution of plant-substrate relationships, and in-chamber specimen hydration.
Reproduction assets foundThe authors deposit all case-study data and analysis materials in a public GitHub repository, explicitly stated in the Data Availability Statement. The R Markdown/R analysis workflow (Appendix S3) and supporting files are also provided, making the paper's carbon-balance phenotyping data and computational analysis code,Code · publiclability Statement
A detailed carbon balance protocol, RMD file, custom chamber baseplate data files, and standard curve data are available in the Supporting Information for this manuscript. All other data used in the manuscript, including in the hydration‐acclimation case study, are available via the public GitHub repository ( https://github.com/KirstenKCoe/Coe-et-al.-2024-APPS ).Open asset ↗KirstenKCoe/Coe-et-al.-2024-APPSlines:474-476Code / dataset availability confirmedEurope PMC · OpenAlex · checked 15 Sept 2026
Leaves, crucial for plant physiology, exhibit various morphological traits that meet diverse functional needs. Traditional leaf morphology quantification, largely 2-dimensional (2D), has not fully captured the 3-dimensional (3D) aspects of leaf function. Despite improvements in 3D data acquisition, accurately depicting leaf morphologies, particularly at the edges, is difficult. This study proposes a method for 3D leaf edge reconstruction, combining 2D image segmentation with curve-based 3D reconstruction. Utilizing deep-learning-based instance segmentation for 2D edge detection, structure from motion for estimation of camera positions and orientations, leaf correspondence identification for matching leaves among images, and curve-based 3D reconstruction for estimating 3D curve fragments, the method assembles 3D curve fragments into a leaf edge model through B-spline curve fitting. The method's performances were evaluated on both virtual and actual leaves, and the results indicated that small leaves and high camera noise pose greater challenges to reconstruction. We developed guidelines for setting a reliability threshold for curve fragments, considering factors occlusion, leaf size, the number of images, and camera error; the number of images had a lesser impact on this threshold compared to others. The method was effective for lobed leaves and leaves with fewer than 4 holes. However, challenges still existed when dealing with morphologies exhibiting highly local variations, such as serrations. This nondestructive approach to 3D leaf edge reconstruction marks an advancement in the quantitative analysis of plant morphology. It is a promising way to capture whole-plant architecture by combining 2D and 3D phenotyping approaches adapted to the target anatomical structures.
Why it matches plant phenotyping methods植物の葉縁形態を3D再構築して定量化する手法を開発し、仮想葉と実葉で性能評価・検証しており、フェノタイピング手法が研究の中心である。
abstractThis study proposes a method for 3D leaf edge reconstruction, combining 2D image segmentation with curve-based 3D reconstruction.
Reproduction assets foundThe paper's Data Availability statement explicitly deposits the datasets and analysis code for this 3D leaf edge reconstruction study in a public GitHub repository (MorphometricsGroup/Murata-2024), and the virtual-leaf simulation inputs (Sketchfab 3D leaf models) are publicly available. Generic libraries (Detectron2, 3Dataset · publicto S4
Data Availability Statement
The datasets used and/or analyzed during the current study are available in the repositories on Zenodo (10.5281/zenodo.10836254, 10.5281/zenodo.10836258, 10.5281/zenodo.10836260, 10.5281/zenodo.10065546, 10.5281/zenodo.10828962, 10.5281/zenodo.10121073, and 10.5281/zenodo.10829007) and GitHub ( https://github.com/MorphometricsGroup/Murata-2024 ).Open asset ↗MorphometricsGroup/Murata-2024lines:311-320Code / dataset availability confirmedCrossref · Europe PMC · checked 7 Sept 2026
Abstract Background Despite major efforts over the last decades, the rising demands of the growing global population makes it of paramount importance to increase crop yields and reduce losses caused by plant pathogens. One way to tackle this is to screen novel resistant genotypes and immunity-inducing agents, which must be conducted in a high-throughput manner. Results Colour-analyzer is a free web-based tool that can be used to rapidly measure the formation of lesions on leaves. Pixel colour values are often used to distinguish infected from healthy tissues. Some programs employ colour models, such as RGB, HSV or L*a*b*. Colour-analyzer uses two colour models, utilizing both HSV ( Hue, Saturation, Value ) and L*a*b* values. We found that the a* b* values of the L*a*b* colour model provided the clearest distinction between infected and healthy tissue, while the H and S channels were best to distinguish the leaf area from the background. Conclusion By combining the a* and b* channels to determine the lesion area, while using the H and S channels to determine the leaf area, Colour-analyzer provides highly accurate information on the size of the lesion as well as the percentage of infected tissue in a high throughput manner and can accelerate the plant immunity research field.
Why it matches plant phenotyping methods植物病斑面積と感染組織割合を画像から定量するウェブツールの開発であり、植物病害状態の表現型取得が中心的な貢献である。
abstractColour-analyzer is a free web-based tool that can be used to rapidly measure the formation of lesions on leaves.
Reproduction assets foundThe paper's authors publicly released both the Colour-analyzer web tool/code on GitHub and the datasets generated in the study, hosted in a publications subfolder of the same repository. Both are paper-specific, public, and actionable.Dataset · publicThe datasets generated are available at https://github.com/VittorioAccomazzi/LeafSize/tree/main/publications/A_new_dual_colour_model-based_imaging_tool .Open asset ↗VittorioAccomazzi/LeafSizelines:129-216Code / dataset availability confirmedEurope PMC · checked 7 Sept 2026
The net CO2 assimilation (A) response to intercellular CO2 concentration (Ci) is a fundamental measurement in photosynthesis and plant physiology research. The conventional A/Ci protocols rely on steady-state measurements and take 15-40 min per measurement, limiting data resolution or biological replication. Additionally, there are several CO2 protocols employed across the literature, without clear consensus as to the optimal protocol or systematic biases in their estimations. We compared the non-steady-state Dynamic Assimilation Technique (DAT) protocol and the three most used CO2 protocols in steady-state measurements, and tested whether different CO2 protocols lead to systematic differences in estimations of the biochemical limitations to photosynthesis. The DAT protocol reduced the measurement time by almost half without compromising estimation accuracy or precision. The monotonic protocol was the fastest steady-state method. Estimations of biochemical limitations to photosynthesis were very consistent across all CO2 protocols, with slight differences in Rubisco carboxylation limitation. The A/Ci curves were not affected by the direction of the change of CO2 concentration but rather the time spent under triose phosphate utilization (TPU)-limited conditions. Our results suggest that the maximum rate of Rubisco carboxylation (Vcmax), linear electron flow for NADPH supply (J), and TPU measured using different protocols within the literature are comparable, or at least not systematically different based on the measurement protocol used.
Why it matches plant phenotyping methods植物の光合成生理形質を測定するCO2応答プロトコルを比較・検証し、測定時間、精度、再現性を評価しているため、方法検証が中心です。
abstractWe compared the non-steady-state Dynamic Assimilation Technique (DAT) protocol and the three most used CO2 protocols in steady-state measurements
Reproduction assets foundThe paper's primary A/Ci gas-exchange measurement data are openly deposited in Dryad. The msuRACiFit GitHub repository is cited prior work, not this paper's analysis code, and no author analysis code URL is given for this study.Dataset · publicAll primary data to support the findings of this study are openly available in Dryad at https://doi.org/10.5061/dryad.pk0p2ngst ( Tejera-Nieves and Walker, 2024 ).Open asset ↗Dryad · 10.5061/dryad.pk0p2ngstlines:120-161Code / dataset availability confirmedEurope PMC · checked 7 Sept 2026
Stay-green (SG) in wheat is a beneficial trait that increases yield and stress tolerance. However, conventional phenotyping techniques limited the understanding of its genetic basis. Spectral indices (SIs) as non-destructive tools to evaluate crop temporal senescence provide an alternative strategy. Here, we applied SIs to monitor the senescence dynamics of 565 diverse wheat accessions from anthesis to maturation stages over 2 field seasons. Four SIs (normalized difference vegetation index, green normalized difference vegetation index, normalized difference red edge index, and optimized soil-adjusted vegetation index) were normalized to develop relative stay-green scores (RSGS) as the SG indicators. An RSGS-based genome-wide association study identified 47 high-confidence quantitative trait loci (QTL) harboring 3,079 single-nucleotide polymorphisms associated with SG and 1,085 corresponding candidate genes. Among them, 15 QTL overlapped or were adjacent to known SG-related QTL/genes, while the remaining QTL were novel. Notably, a set of favorable haplotypes of SG-related candidate genes such as TraesCS2A03G1081100 , TracesCS6B03G0356400 , and TracesCS2B03G1299500 are increasing following the Green Revolution, further validating the feasibility of the pipeline. This study provided a valuable reference for further quantitative SG and genetic research in diverse wheat panels.
Why it matches plant phenotyping methodsUAV時系列スペクトル指標を用いてコムギのstay-green（老化動態）を定量化し、RSGS指標と解析パイプラインを開発・適用しており、表現型取得法が研究の中心である。
abstractSpectral indices (SIs) as non-destructive tools to evaluate crop temporal senescence provide an alternative strategy.
Reproduction assets foundThe paper's Data Availability statement explicitly deposits the study's genotype and phenotype data (the RSGS stay-green phenotypes and SNP genotypes for the 565-accession wheat panel) in a public GitHub repository under the authors' account, matching an allowed URL. No separate analysis code or raw UAV imagery depositDataset · publicThe genotype and phenotype data presented in this study are available at the website https://github.com/zengqd/PopulationGenetics/tree/main/Wheat/StayGreen .Open asset ↗zengqd/PopulationGenetics · Wheat/StayGreenlines:298-318Code / dataset availability confirmedCrossref · checked 15 Sept 2026
Plant diseases significantly reduce the yield and the production of crops across the globe.Crop productivity, plant development and human access to food have all been hampered by the prevalence of plant diseases throughout the history.In general, leaves exhibit symptoms if the plant is affected by diseases.Therefore, it is essential to identify the type of infestation to reduce the destructiveness of the disease.This scenario allows one to replicate the spread of infectious diseases and the inability of farmers to recognize and remember them.One possible approach to tackle this issue is to utilise Deep Learning (DL) techniques in conjunction with Machine Learning (ML) approaches within the domain of Computer Vision (CV).The current research has introduced the APLDD-ESOSDL approach, which utilises deep learning to optimise the search for symbiotic organisms in order to automate the detection of plant leaf diseases.The objective of the proposed APLDD-ESOSDL approach is to enhance agricultural yields and reduce crop losses by offering farmers a visual depiction of disease symptoms.The goal of the APLDD-ESOSDL approach is to accurately classify the presence of leaf diseases.The APLDD-ESOSDL technique utilises the inception ResNet-v2 model as a feature extractor and the Stacked Long Short-Term Memory (SLSTM) model for classification.In addition, the hyperparameters of the SLSTM algorithm are adjusted using the Enhanced Symbiotic Organism Search (ESOS) approach.A comprehensive experiment was carried out utilising the reference data set to verify the effectiveness of the APLDD-ESOSDL approach.The APLDD-ESOSDL algorithm outperformed more advanced systems, achieving a maximum accuracy of 99.22%, precision of 98.52%, sensitivity of 98.06%, and specificity of 99.54% in experimental experiments employing six distinct cutting-edge approaches.
Why it matches plant phenotyping methods葉画像から植物病害を自動分類する深層学習手法を提案し、データセットで性能検証しており、植物表現型取得・判定手法が中心である。
abstractThe current research has introduced the APLDD-ESOSDL approach, which utilises deep learning to optimise the search for symbiotic organisms in order to automate the detection of plant leaf diseases.
Reproduction assets foundThe paper's plant leaf disease detection experiments use a public Kaggle image dataset (PlantVillage, emmarex/plantdisease) with 3,503 corn leaf images across four classes, which is the direct input to the paper's phenotyping/classification measurements. No author analysis code, trained models, or supplementary assets,Dataset · publicThe
evaluation is based on the plant disease dataset obtained from
the Kaggle repository [23]. The dataset comprises 3,503
samples that have been categorised into four categoriesOpen asset ↗Kagglepdf-raw-page:6 lines:1-54Code / dataset availability confirmedEurope PMC · OpenAlex · checked 7 Sept 2026
Stem diameter is a critical phenotypic parameter for maize, integral to yield prediction and lodging resistance assessment. Traditionally, the quantification of this parameter through manual measurement has been the norm, notwithstanding its tedious and laborious nature. To address these challenges, this study introduces a non-invasive field-based system utilizing depth information from RGB-D cameras to measure maize stem diameter. This technology offers a practical solution for conducting rapid and non-destructive phenotyping. Firstly, RGB images, depth images, and 3D point clouds of maize stems were captured using an RGB-D camera, and precise alignment between the RGB and depth images was achieved. Subsequently, the contours of maize stems were delineated using 2D image processing techniques, followed by the extraction of the stem's skeletal structure employing a thinning-based skeletonization algorithm. Furthermore, within the areas of interest on the maize stems, horizontal lines were constructed using points on the skeletal structure, resulting in 2D pixel coordinates at the intersections of these horizontal lines with the maize stem contours. Subsequently, a back-projection transformation from 2D pixel coordinates to 3D world coordinates was achieved by combining the depth data with the camera's intrinsic parameters. The 3D world coordinates were then precisely mapped onto the 3D point cloud using rigid transformation techniques. Finally, the maize stem diameter was sensed and determined by calculating the Euclidean distance between pairs of 3D world coordinate points. The method demonstrated a Mean Absolute Percentage Error ( MAPE ) of 3.01%, a Mean Absolute Error ( MAE ) of 0.75 mm, a Root Mean Square Error ( RMSE ) of 1.07 mm, and a coefficient of determination ( R ²) of 0.96, ensuring accurate measurement of maize stem diameter. This research not only provides a new method of precise and efficient crop phenotypic analysis but also offers theoretical knowledge for the advancement of precision agriculture.
Why it matches plant phenotyping methodsRGB-Dカメラと画像・3D処理によりトウモロコシ茎径を非破壊測定する手法を開発し、誤差指標で精度検証しており、フェノタイピング手法が中心である。
abstractthis study introduces a non-invasive field-based system utilizing depth information from RGB-D cameras to measure maize stem diameter
Reproduction assets foundThe paper's data availability statement points to a public Figshare deposit (DOI 10.6084/m9.figshare.25450039) containing the study's datasets (RGB/depth imagery and stem diameter measurements used for the maize stem diameter phenotyping analysis). No author analysis code or trained models are explicitly deposited.Dataset · publicThe datasets presented in this study can be found in online repositories. The names of the repository/repositories and accession number(s) can be found below: http://dx.doi.org/10.6084/m9.figshare.25450039 .Open asset ↗figshare · 10.6084/m9.figshare.25450039lines:909-917Code / dataset availability confirmedCrossref · checked 7 Sept 2026
Accurate retrieval of canopy nutrient content has been made possible using visible-to-shortwave infrared (VSWIR) imaging spectroscopy. While this strategy has often been tested on closed green plant canopies, little is known about how nutrient content estimates perform when applied to pixels not dominated by photosynthetic vegetation (PV). In such cases, contributions of bare soil (BS) and non-photosynthetic vegetation (NPV), may significantly and nonlinearly reduce the spectral features relied upon for nutrient content retrieval. We attempted to define the loss of prediction accuracy under reduced PV fractional cover levels. To do so, we utilized VSWIR imaging spectroscopy data from the Global Airborne Observatory (GAO) and a large collection of lab-calibrated field samples of nitrogen (N) content collected across numerous crop species grown in several farming regions of the United States. Fractional cover values of PV, NPV, and BS were estimated from the GAO data using the Automated Monte Carlo Unmixing algorithm (AutoMCU). Errors in prediction from a partial least squares N model applied to the spectral data were examined in relation to the fractional cover of the unmixed components. We found that the most important factor in the accuracy of the partial least squares regression (PLSR) model is the fraction of photosynthetic vegetation (PV) cover, with pixels greater than 60% cover performing at the optimal level, where the coefficient of determination (R2) peaks to 0.66 for PV fractions of more than 60% and bare soil (BS) fractions of less than 20%. Our findings guide future spaceborne imaging spectroscopy missions as applied to agricultural cropland N monitoring.
Why it matches plant phenotyping methodsVSWIR画像分光とスペクトル混合分解・PLSRを用いて作物キャノピー窒素含量の推定精度を検証しており、植物形質取得法が研究の中心である。
abstractAccurate retrieval of canopy nutrient content has been made possible using visible-to-shortwave infrared (VSWIR) imaging spectroscopy.
Reproduction assets foundThe authors' PLSR nitrogen-retrieval Python code is publicly available on GitHub (NitrogenRetrieval repository) and archived on Zenodo (10.5281/zenodo.7967292). The AutoMCU code, airborne imaging spectroscopy data, and spectral reflectance data are only available by request from the corresponding author, so those are 'Code · publicAdditional
details regarding the algorithm employed for N retrieval and the corresponding Python
code can be found in the NitrogenRetrieval repository on our GitHub page, accessible at
the following URL: https://github.com/CMLandOcean/NitrogenRetrievalOpen asset ↗CMLandOcean/NitrogenRetrievalpdf-page:8 lines:1-56Code / dataset availability confirmedCrossref · checked 7 Sept 2026
Abstract. Trait-based approaches are of increasing concern in predicting vegetation changes and linking ecosystem structures to functions at large scales. However, a critical challenge for such approaches is acquiring spatially continuous plant functional trait maps. Here, six key plant functional traits were selected as they can reflect plant resource acquisition strategies and ecosystem functions, including specific leaf area (SLA), leaf dry matter content (LDMC), leaf N concentration (LNC), leaf P concentration (LPC), leaf area (LA) and wood density (WD). A total of 34 589 in situ trait measurements of 3447 seed plant species were collected from 1430 sampling sites in China and were used to generate spatial plant functional trait maps (∼1 km), together with environmental variables and vegetation indices based on two machine learning models (random forest and boosted regression trees). To obtain the optimal estimates, a weighted average algorithm was further applied to merge the predictions of the two models to derive the final spatial plant functional trait maps. The models showed good accuracy in estimating WD, LPC and SLA, with average R2 values ranging from 0.48 to 0.68. In contrast, both the models had weak performance in estimating LDMC, with average R2 values less than 0.30. Meanwhile, LA showed considerable differences between the two models in some regions. Climatic effects were more important than those of edaphic factors in predicting the spatial distributions of plant functional traits. Estimates of plant functional traits in northeastern China and the Qinghai–Tibetan Plateau had relatively high uncertainties due to sparse samplings, implying a need for more observations in these regions in the future. Our spatial trait maps could provide critical support for trait-based vegetation models and allow exploration of the relationships between vegetation characteristics and ecosystem functions at large scales. The six plant functional trait maps for China with 1 km spatial resolution are now available at https://doi.org/10.6084/m9.figshare.22351498 (An et al., 2023).
Why it matches plant phenotyping methods植物機能形質を機械学習で推定・検証し、空間形質マップとデータセットを作成することが研究の中心であり、単なる生態学的な形質測定ではない。
abstractused to generate spatial plant functional trait maps (∼1 km), together with environmental variables and vegetation indices based on two machine learning models (random forest and boosted regression trees).
Reproduction assets foundThe paper's in situ plant functional trait dataset (34,589 measurements) and the six 1-km trait maps are publicly deposited on figshare by the authors, as stated in the Data availability section.Dataset · publicThe original plant functional trait data collected in this study that were used for machine learning models (named by the data file used for machine learning models.csv) and the final maps of plant functional traits in GeoTIFF format (named by the plant functional trait category) are available at https://doi.org/10.6084/m9.figshare.22351498 (An et al., 2023).Open asset ↗figshare · 10.6084/m9.figshare.22351498lines:350-355Code / dataset availability confirmedEurope PMC · OpenAlex · checked 7 Sept 2026
MaizeGrowth chamberStereoRoot2D/3D reconstructionGrowth / time-series analysisRoot 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 temporal and three-dimensional (3D) spatial information. This paper describes a new system based on time-lapse 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 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 analysing 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動態を取得・解析する画像計測システムを開発し、特徴量の信頼性と精度を検証しており、植物表現型取得が中心である。
abstractThis paper describes a new system based on time-lapse for the 3D reconstruction and analysis of roots growing in hydroponics.
Reproduction assets foundThe paper's 3D root tip trajectory data (phenotyping measurements from maize root imaging) are publicly deposited on Zenodo. The analysis software and scripts are only available upon request, so they qualify as request_only.Dataset · publicData describing 3D trajectories used in this paper are available here: https://zenodo.org/record/8422242 . Software and scripts are available for research purposes upon request through the email address: mindtheplantlab@gmail.com.Open asset ↗Zenodo · 8422242lines:141-172Code / 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
Genomic prediction (GP) using haplotypes is considered advantageous compared to GP solely reliant on single nucleotide polymorphisms (SNPs), owing to haplotypes' enhanced ability to capture ancestral information and their higher linkage disequilibrium with quantitative trait loci (QTL). Many empirical studies supported the advantages of haplotype-based GP over SNP-based approaches. Nevertheless, the performance of haplotype-based GP can vary significantly depending on multiple factors, including the traits being studied, the genetic structure of the population under investigation, and the particular method employed for haplotype construction. In this study, we compared haplotype and SNP based prediction accuracies in four populations derived from European maize landraces. Populations comprised either doubled haploid lines (DH) derived directly from landraces, or gamete capture lines (GC) derived from crosses of the landraces with an inbred line. For two different landraces, both types of populations were generated, genotyped with 600k SNPs and phenotyped as lines per se for five traits. Our study explores three prediction scenarios: (i) within each of the four populations, (ii) across DH and GC populations from the same landrace, and (iii) across landraces using either DH or GC populations. Three haplotype construction methods were evaluated: 1. fixed-window blocks (FixedHB), 2. LD-based blocks (HaploView), and 3. IBD-based blocks (HaploBlocker). In within population predictions, FixedHB and HaploView methods performed as well as or slightly better than SNPs for all traits. HaploBlocker improved accuracy for certain traits but exhibited inferior performance for others. In prediction across populations, the parameter setting from HaploBlocker which controls the construction of shared haplotypes between populations played a crucial role for obtaining optimal results. When predicting across landraces, accuracies were low for both, SNP and haplotype approaches, but for specific traits substantial improvement was observed with HaploBlocker. This study provides recommendations for optimal haplotype construction and identifies relevant parameters for constructing haplotypes in the context of genomic prediction.
Why it matches plant phenotyping methodsハプロタイプ構築法を用いたゲノム予測の精度比較・評価が研究の中心であり、複数のトウモロコシ形質の予測という計算的な形質推定法を検証している。
abstractIn this study, we compared haplotype and SNP based prediction accuracies in four populations derived from European maize landraces.
Reproduction assets foundThe paper's data availability statement explicitly points to a public figshare dataset (Data_from_HoelkerMayer_et_al) containing the phenotypic/genotypic data analyzed, and a public GitHub repository (TUMplantbreeding/HaplotypeGP) with the authors' analysis code for haplotype-based genomic prediction.Dataset · publicPublicly available datasets were analyzed in this study. This data can be found here: https://figshare.com/articles/dataset/Data_from_HoelkerMayer_et_al/17014421 and https://github.com/TUMplantbreeding/HaplotypeGP .Open asset ↗figshare · Data_from_HoelkerMayer_et_al/17014421lines:428-438Code / dataset availability confirmedEurope PMC · checked 7 Sept 2026
Background Cotton accounts for 80% of the global natural fibre production. Its leaf hairiness affects insect resistance, fibre yield, and economic value. However, this phenotype is still qualitatively assessed by visually attributing a Genotype Hairiness Score (GHS) to a leaf/plant, or by using the HairNet deep-learning model which also outputs a GHS. Here, we introduce HairNet2, a quantitative deep-learning model which detects leaf hairs (trichomes) from images and outputs a segmentation mask and a Leaf Trichome Score (LTS). Results Trichomes of 1250 images were annotated (AnnCoT) and a combination of six Feature Extractor modules and five Segmentation modules were tested alongside a range of loss functions and data augmentation techniques. HairNet2 was further validated on the dataset used to build HairNet (CotLeaf-1), a similar dataset collected in two subsequent seasons (CotLeaf-2), and a dataset collected on two genetically diverse populations (CotLeaf-X). The main findings of this study are that (1) leaf number, environment and image position did not significantly affect results, (2) although GHS and LTS mostly correlated for individual GHS classes, results at the genotype level revealed a strong LTS heterogeneity within a given GHS class, (3) LTS correlated strongly with expert scoring of individual images. Conclusions HairNet2 is the first quantitative and scalable deep-learning model able to measure leaf hairiness. Results obtained with HairNet2 concur with the qualitative values used by breeders at both extremes of the scale (GHS 1-2, and 5-5+), but interestingly suggest a reordering of genotypes with intermediate values (GHS 3-4+). Finely ranking mild phenotypes is a difficult task for humans. In addition to providing assistance with this task, HairNet2 opens the door to selecting plants with specific leaf hairiness characteristics which may be associated with other beneficial traits to deliver better varieties.
Why it matches plant phenotyping methods綿花葉の毛状突起という植物形質を画像から定量抽出する深層学習モデルを開発し、複数データセットで検証しており、フェノタイピング手法が研究の中心である。
abstractwe introduce HairNet2, a quantitative deep-learning model which detects leaf hairs (trichomes) from images and outputs a segmentation mask and a Leaf Trichome Score (LTS).
Reproduction assets foundThe paper's Availability of data and materials section explicitly deposits the four paper-specific image/annotation datasets (AnnCoT, CotLeaf-1, CotLeaf-2, CotLeaf-X) with public CSIRO DOIs, all present in allowed_urls. No code or model checkpoint deposit is stated.Dataset · publicThe CotLeaf-1 image dataset is available at https://doi.org/10.25919/9vqw-7453 .Open asset ↗10.25919/9vqw-7453lines:256-289Dataset · publicThe CotLeaf-2 image dataset is available at https://doi.org/10.25919/v0qb-er50 .Open asset ↗10.25919/v0qb-er50lines:256-289Dataset · publicThe CotLeaf-X image dataset is available at https://doi.org/10.25919/eqhx-1x73 .Open asset ↗10.25919/eqhx-1x73lines:256-289Code / dataset availability confirmedOpenAlex · checked 13 Sept 2026
Forests are a vital source of food, fuel, and medicine and play a crucial role in climate change mitigation. Strategic and policy decisions on forest management and conservation require accurate and up-to-date information on available forest resources. Forest inventory data such as tree parameters, heights, and crown diameters must be collected and analysed to monitor forests effectively. Traditional manual techniques are slow and labour-intensive, requiring additional personnel, while existing non-contact methods are costly, computationally intensive, or less accurate. Kenya plans to increase its forest cover to 30% by 2032 and establish a national forest monitoring system. Building capacity in forest monitoring through innovative field data collection technologies is encouraged to match the pace of increase in forest cover. This study explored the applicability of low-cost, non-contact tree inventory based on stereoscopic photogrammetry in a recently reforested stand in Kieni Forest, Kenya. A custom-built stereo camera was used to capture images of 251 trees in the study area from which the tree heights and crown diameters were successfully extracted quickly and with high accuracy. The results imply that stereoscopic photogrammetry is an accurate and reliable method that can support the national forest monitoring system and REDD+ implementation.
Why it matches plant phenotyping methodsステレオ写真測量を用いて樹高と樹冠径を非接触で抽出し、精度と信頼性を評価しているため、植物形質取得手法が研究の中心です。
abstractThis study explored the applicability of low-cost, non-contact tree inventory based on stereoscopic photogrammetry
Reproduction assets foundThe paper's Data Availability Statement explicitly states that the supporting data and software code for the stereoscopic photogrammetry tree inventory (tree height, crown diameter, DBH extraction from stereo images of 251 trees in Kieni Forest) are publicly available on the authors' GitHub repository DeKUT-DSAIL/TreeVCode · publicData Availability Statement: The data that support the findings of this study, as well as the software
code, are publicly available on GitHub: https://github.com/DeKUT-DSAIL/TreeVision (accessed on
11 August 2023).Open asset ↗DeKUT-DSAIL/TreeVisionpdf-page:11 lines:1-61Code / dataset availability confirmedEurope PMC · checked 14 Sept 2026
Background In soybeans, faster canopy coverage (CC) is a highly desirable trait but a fully covered canopy is unfavorable to light interception at lower levels in the canopy with most of the incident radiation intercepted at the top of the canopy. Shoot architecture that influences CC is well studied in crops such as maize and wheat, and altering architectural traits has resulted in enhanced yield. However, in soybeans the study of shoot architecture has not been as extensive. Results This study revealed significant differences in CC among the selected soybean accessions. The rate of CC was found to decrease at the beginning of the reproductive stage (R1) followed by an increase during the R2-R3 stages. Most of the accessions in the study achieved maximum rate of CC between R2-R3 stages. We measured Light interception (LI), defined here as the ratio of Photosynthetically Active Radiation (PAR) transmitted through the canopy to the incoming PAR or the radiation above the canopy. LI was found to be significantly correlated with CC parameters, highlighting the relationship between canopy structure and light interception. The study also explored the impact of plant shape on LI and CO 2 assimilation. Plant shape was characterized into distinct quantifiable parameters and by modeling the impact of plant shape on LI and CO 2 assimilation, we found that plants with broad and flat shapes at the top maybe more photosynthetically efficient at low light levels, while conical shapes were likely more advantageous when light was abundant. Shoot architecture of plants in this study was described in terms of whole plant, branching and leaf-related traits. There was significant variation for the shoot architecture traits between different accessions, displaying high reliability. We found that that several shoot architecture traits such as plant height, and leaf and internode-related traits strongly influenced CC and LI. Conclusion In conclusion, this study provides insight into the relationship between soybean shoot architecture, canopy coverage, and light interception. It demonstrates that novel shoot architecture traits we have defined here are genetically variable, impact CC and LI and contribute to our understanding of soybean morphology. Correlations between different architecture traits, CC and LI suggest that it is possible to optimize soybean growth without compromising on light transmission within the soybean canopy. In addition, the study underscores the utility of integrating low-cost 2D phenotyping as a practical and cost-effective alternative to more time-intensive 3D or high-tech low-throughput methods. This approach offers a feasible means of studying basic shoot architecture traits at the field level, facilitating a broader and efficient assessment of plant morphology.
Why it matches plant phenotyping methods低コスト2D画像フェノタイピングを用いて、シュート構造やキャノピー被覆を定量化し、圃場での植物形態評価法としての有用性を扱っているため、フェノタイピング手法が実質的に中心である。
abstractIn addition, the study underscores the utility of integrating low-cost 2D phenotyping as a practical and cost-effective alternative to more time-intensive 3D or high-tech low-throughput methods.
Reproduction assets found保存済みの本文根拠を更新済みルールで再検証し、公開資産1件を確認しました。Supplement · publicSupplementary Material 4: Table S3: (download XLSX )
Reliability estimates, BLUPs calculated for the traits measured in this study and data associated with each trait measured in the study.Open asset ↗lines:429-492Code / dataset availability confirmedEurope PMC · checked 7 Sept 2026
Introduction Early detection of leaf diseases is necessary to control the spread of plant diseases, and one of the important steps is the segmentation of leaf and disease images. The uneven light and leaf overlap in complex situations make segmentation of leaves and diseases quite difficult. Moreover, the significant differences in ratios of leaf and disease pixels results in a challenge in identifying diseases. Methods To solve the above issues, the residual attention mechanism combined with atrous spatial pyramid pooling and weight compression loss of UNet is proposed, which is named RAAWC-UNet. Firstly, weights compression loss is a method that introduces a modulation factor in front of the cross-entropy loss, aiming at solving the problem of the imbalance between foreground and background pixels. Secondly, the residual network and the convolutional block attention module are combined to form Res_CBAM. It can accurately localize pixels at the edge of the disease and alleviate the vanishing of gradient and semantic information from downsampling. Finally, in the last layer of downsampling, the atrous spatial pyramid pooling is used instead of two convolutions to solve the problem of insufficient spatial context information. Results The experimental results show that the proposed RAAWC-UNet increases the intersection over union in leaf and disease segmentation by 1.91% and 5.61%, and the pixel accuracy of disease by 4.65% compared with UNet. Discussion The effectiveness of the proposed method was further verified by the better results in comparison with deep learning methods with similar network architectures.
Why it matches plant phenotyping methodsリンゴ葉および病斑の画像セグメンテーション手法を開発し、既存手法と比較検証しているため、植物病害状態の画像ベース表現型計測が中心である。
titleRAAWC-UNet: an apple leaf and disease segmentation method based on residual attention and atrous spatial pyramid pooling improved UNet with weight compression loss.
Reproduction assets foundThe paper's apple leaf disease image datasets (ALDD) are publicly available via a Google Drive link provided in the Data availability statement. No author analysis code or trained model checkpoints are explicitly deposited.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://drive.google.com/file/d/1qV3zZCNh8FhrMwQwZds9rRkm9SUQXV5P/view?usp=sharing .Open asset ↗lines:783-804Code / dataset availability confirmedEurope PMC · checked 7 Sept 2026
Introduction Plants undergo various natural changes that dramatically modify their genomes. One is polyploidization and the second is hybridization. Both are regarded as key factors in plant evolution and result in phenotypic differences in different plant organs. In Silene , we can find both examples in nature, and this genus has a seed shape diversity that has long been recognized as a valuable source of information for infrageneric classification. Methods Morphometric analysis is a statistical study of shape and size and their covariations with other variables. Traditionally, seed shape description was limited to an approximate comparison with geometric figures (rounded, globular, reniform, or heart-shaped). Seed shape quantification has been based on direct measurements, such as area, perimeter, length, and width, narrowing statistical analysis. We used seed images and processed them to obtain silhouettes. We performed geometric morphometric analyses, such as similarity to geometric models and elliptic Fourier analysis, to study the hybrid offspring of S. latifolia and S. dioica . Results We generated synthetic tetraploids of Silene latifolia and performed controlled crosses between diploid S. latifolia and Silene dioica to analyze seed morphology. After imaging capture and post-processing, statistical analysis revealed differences in seed size, but not in shape, between S. latifolia diploids and tetraploids, as well as some differences in shape among the parentals and hybrids. A detailed inspection using fluorescence microscopy allowed for the identification of shape differences in the cells of the seed coat. In the case of hybrids, differences were found in circularity and solidity. Overal seed shape is maternally regulated for both species, whereas cell shape cannot be associated with any of the sexes. Discussion Our results provide additional tools useful for the combination of morphology with genetics, ecology or taxonomy. Seed shape is a robust indicator that can be used as a complementary tool for the genetic and phylogenetic analyses of Silene hybrid populations.
Why it matches plant phenotyping methods種子画像を処理し、幾何学的形態計測と楕円フーリエ解析で種子形状・サイズを定量化する手法が研究の中心であり、植物器官の形態表現型を抽出している。
abstractWe used seed images and processed them to obtain silhouettes. We performed geometric morphometric analyses, such as similarity to geometric models and elliptic Fourier analysis, to study the hybrid offspring of S. latifolia and S. dioica .
Reproduction assets foundThe authors state that the raw seed images used for the morphometric phenotyping analyses are publicly deposited in Zenodo (DOI 10.5281/zenodo.8366177). This is a paper-specific, publicly accessible dataset of the seed/cell images underlying this study's measurements. No author analysis code with an explicit public URLDataset · publicRaw images used in this work are available in Zenodo DOI 10.5281/zenodo.8366177 .Open asset ↗Zenodo · 10.5281/zenodo.8366177lines:436-491Code / dataset availability confirmedEurope PMC · checked 7 Sept 2026
Abstract Being capable of accurately predicting morphological parameters of the plant weeks before achieving fruit maturation is of great importance in the production and selection of suitable ornamental pepper plants. The objective of this article is evaluating the feasibility and assessing the performance of CNN-based models using RGB images as input to forecast two morphological parameters: plant height and canopy diameter. To this end, four CNN-based models are proposed to predict these morphological parameters in four different scenarios: first, using as input a single image of the plant; second, using as input several images from different viewpoints of the plant acquired on the same date; third, using as input two images from two consecutive weeks; and fourth, using as input a set of images consisting of one image from each week up to the current date. The results show that it is possible to accurately predict both plant height and canopy diameter. The RMSE for a forecast performed 6 weeks in advance to the actual measurements was below 4.5 cm and 4.2 cm, respectively. When information from previous weeks is added to the model, better results can be achieved and as the prediction date gets closer to the assessment date the accuracy improves as well.
Why it matches plant phenotyping methodsRGB画像からCNNで植物体高と樹冠径を予測し、予測精度を評価する手法開発・検証が研究の中心であるため。
abstractThe objective of this article is evaluating the feasibility and assessing the performance of CNN-based models using RGB images as input to forecast two morphological parameters: plant height and canopy diameter.
Reproduction assets foundThe paper's curated dataset of morphological measurements (plant height, canopy diameter) and weekly RGB photographs of the 15 Capsicum accessions is explicitly deposited on Zenodo with a DOI matching an allowed URL. No author analysis code or trained model checkpoints are stated as publicly available.Dataset · publicThe resulting dataset, already curated, has been made publicly available at Zenodo (Alves Barroso et al., 2024 ).Open asset ↗Zenodolines:146-227Code / 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 confirmedEurope PMC · checked 14 Sept 2026
Field / plotLiDAR / point cloudMultispectral / hyperspectralLeafWhole plant / canopy / plot / fieldLeaf traitsPigment / colour / senescenceWater status / transpiration
Mediterranean forests represent critical areas that are increasingly affected by the frequency of droughts and fires, anthropic activities and land use changes. Optical remote sensing data give access to several essential biodiversity variables, such as species traits (related to vegetation biophysical and biochemical composition), which can help to better understand the structure and functioning of these forests. However, their reliability highly depends on the scale of observation and the spectral configuration of the sensor. Thus, the objective of the SENTHYMED/MEDOAK experiment is to provide datasets from leaf to canopy scale in synchronization with remote sensing acquisitions obtained from multi-platform sensors having different spectral characteristics and spatial resolutions. Seven monthly data collections were performed between April and October 2021 (with a complementary one in June 2023) over two forests in the north of Montpellier, France, comprised of two oak endemic species with different phenological dynamics (evergreen: Quercus ilex and deciduous: Quercus pubescens ) and a variability of canopy cover fractions (from dense to open canopy). These collections were coincident with satellite multispectral Sentinel-2 data and one with airborne hyperspectral AVIRIS-Next Generation data. In addition, satellite hyperspectral PRISMA and DESIS were also available for some dates. All these airborne and satellite data are provided from free online download websites. Eight datasets are presented in this paper from thirteen studied forest plots: (1) overstory and understory inventory, (2) 687 canopy plant area index from Li-COR plant canopy analyzers, (3) 1475 in situ spectral reflectances (oak canopy, trunk, grass, limestone, etc.) from ASD spectroradiometers, (4) 92 soil moistures and temperatures from IMKO and Campbell probes, (5) 747 leaf-clip optical data from SPAD and DUALEX sensors, (6) 2594 in-lab leaf directional-hemispherical reflectances and transmittances from ASD spectroradiometer coupled with an integrating sphere, (7) 747 in-lab measured leaf water and dry matter content, and additional leaf traits by inversion of the PROSPECT model and (8) UAV-borne LiDAR 3-D point clouds. These datasets can be useful for multi-scale and multi-temporal calibration/validation of high level satellite vegetation products such as species traits, for current and future imaging spectroscopic missions, and by fusing or comparing both multispectral and hyperspectral data. Other targeted applications can be forest 3-D modelling, biodiversity assessment, fire risk prevention and globally vegetation monitoring.
Why it matches plant phenotyping methods森林の葉からキャノピーまでの植物形質データとマルチプラットフォーム光学・LiDARデータを体系的に整備し、衛星植生形質プロダクトの較正・検証に用いるデータセット研究であり、形質取得と再利用可能な検証基盤が中心です。
titleMulti-scale datasets for monitoring Mediterranean oak forests from optical remote sensing during the SENTHYMED/MEDOAK experiment in the north of Montpellier (France).
Reproduction assets foundThis Data in Brief article deposits the paper's own SENTHYMED/MEDOAK plant-phenotyping measurements (forest inventory, canopy plant area index, forest/leaf optical properties, soil moisture, leaf-clip sensor data, leaf traits, UAV-borne LiDAR point clouds) in the public SEDOO repository with explicit DOIs and a direct,Dataset · publicat https://eoweb.dlr.de/egp/ (image rasters, .tif for GeoTIFF format)
•
Sentinel-2 data can be downloaded from the THEIA portal at https://www.theia-land.fr/en/product/sentinel-2-surface-reflectance (image rasters, .tif for GeoTIFF format)
Data accessibility
Repository name: SEDOO
Data identification number:
•
Forest inventory: https://doi.org/10.6096/8005
•
Canopy plant area index: https://doi.org/10.6096/8007
•
Forest optical properties : https://doi.org/10.6096/8006
•
Soil moisture : https://doi.org/10.6096/8001
•
Leaf-clip optical sensor data : https://doi.org/10.6096/8002
•
Leaf optical properties: https://doi.org/10.6096/8004
•
Leaf traits : https://doi.org/10.6096/8003
•
UOpen asset ↗SEDOO · 10.6096/8005lines:31-86Dataset · publicoTIFF format)
•
Sentinel-2 data can be downloaded from the THEIA portal at https://www.theia-land.fr/en/product/sentinel-2-surface-reflectance (image rasters, .tif for GeoTIFF format)
Data accessibility
Repository name: SEDOO
Data identification number:
•
Forest inventory: https://doi.org/10.6096/8005
•
Canopy plant area index: https://doi.org/10.6096/8007
•
Forest optical properties : https://doi.org/10.6096/8006
•
Soil moisture : https://doi.org/10.6096/8001
•
Leaf-clip optical sensor data : https://doi.org/10.6096/8002
•
Leaf optical properties: https://doi.org/10.6096/8004
•
Leaf traits : https://doi.org/10.6096/8003
•
UAV-borne LiDAR 3-D point clouds : https://doi.org/10.154Open asset ↗SEDOO · 10.6096/8007lines:31-86Dataset · publicTHEIA portal at https://www.theia-land.fr/en/product/sentinel-2-surface-reflectance (image rasters, .tif for GeoTIFF format)
Data accessibility
Repository name: SEDOO
Data identification number:
•
Forest inventory: https://doi.org/10.6096/8005
•
Canopy plant area index: https://doi.org/10.6096/8007
•
Forest optical properties : https://doi.org/10.6096/8006
•
Soil moisture : https://doi.org/10.6096/8001
•
Leaf-clip optical sensor data : https://doi.org/10.6096/8002
•
Leaf optical properties: https://doi.org/10.6096/8004
•
Leaf traits : https://doi.org/10.6096/8003
•
UAV-borne LiDAR 3-D point clouds : https://doi.org/10.15454/AGBW7G , https://doi.org/10.15454/DMYWPB
Direct URL to aOpen asset ↗SEDOO · 10.6096/8006lines:31-86Dataset · publicoduct/sentinel-2-surface-reflectance (image rasters, .tif for GeoTIFF format)
Data accessibility
Repository name: SEDOO
Data identification number:
•
Forest inventory: https://doi.org/10.6096/8005
•
Canopy plant area index: https://doi.org/10.6096/8007
•
Forest optical properties : https://doi.org/10.6096/8006
•
Soil moisture : https://doi.org/10.6096/8001
•
Leaf-clip optical sensor data : https://doi.org/10.6096/8002
•
Leaf optical properties: https://doi.org/10.6096/8004
•
Leaf traits : https://doi.org/10.6096/8003
•
UAV-borne LiDAR 3-D point clouds : https://doi.org/10.15454/AGBW7G , https://doi.org/10.15454/DMYWPB
Direct URL to all data: https://remotetree.sedoo.fr/catalogue/Open asset ↗SEDOO · 10.6096/8001lines:31-86Dataset · publiceoTIFF format)
Data accessibility
Repository name: SEDOO
Data identification number:
•
Forest inventory: https://doi.org/10.6096/8005
•
Canopy plant area index: https://doi.org/10.6096/8007
•
Forest optical properties : https://doi.org/10.6096/8006
•
Soil moisture : https://doi.org/10.6096/8001
•
Leaf-clip optical sensor data : https://doi.org/10.6096/8002
•
Leaf optical properties: https://doi.org/10.6096/8004
•
Leaf traits : https://doi.org/10.6096/8003
•
UAV-borne LiDAR 3-D point clouds : https://doi.org/10.15454/AGBW7G , https://doi.org/10.15454/DMYWPB
Direct URL to all data: https://remotetree.sedoo.fr/catalogue/ Instructions for accessing the datasets on the website: the daOpen asset ↗SEDOO · 10.6096/8002lines:31-86Dataset · public//doi.org/10.6096/8007
•
Forest optical properties : https://doi.org/10.6096/8006
•
Soil moisture : https://doi.org/10.6096/8001
•
Leaf-clip optical sensor data : https://doi.org/10.6096/8002
•
Leaf optical properties: https://doi.org/10.6096/8004
•
Leaf traits : https://doi.org/10.6096/8003
•
UAV-borne LiDAR 3-D point clouds : https://doi.org/10.15454/AGBW7G , https://doi.org/10.15454/DMYWPB
Direct URL to all data: https://remotetree.sedoo.fr/catalogue/ Instructions for accessing the datasets on the website: the datasets are visible under the search menu through projects and then by selecting FOREST/SENTHYMED
1
Value of the Data
•
These datasets were collected to provide calibratioOpen asset ↗SEDOO · 10.15454/AGBW7Glines:31-86Dataset · publictical properties : https://doi.org/10.6096/8006
•
Soil moisture : https://doi.org/10.6096/8001
•
Leaf-clip optical sensor data : https://doi.org/10.6096/8002
•
Leaf optical properties: https://doi.org/10.6096/8004
•
Leaf traits : https://doi.org/10.6096/8003
•
UAV-borne LiDAR 3-D point clouds : https://doi.org/10.15454/AGBW7G , https://doi.org/10.15454/DMYWPB
Direct URL to all data: https://remotetree.sedoo.fr/catalogue/ Instructions for accessing the datasets on the website: the datasets are visible under the search menu through projects and then by selecting FOREST/SENTHYMED
1
Value of the Data
•
These datasets were collected to provide calibration/validation data for methods aimiOpen asset ↗SEDOO · 10.15454/DMYWPBlines:31-86Dataset · publicoisture : https://doi.org/10.6096/8001
•
Leaf-clip optical sensor data : https://doi.org/10.6096/8002
•
Leaf optical properties: https://doi.org/10.6096/8004
•
Leaf traits : https://doi.org/10.6096/8003
•
UAV-borne LiDAR 3-D point clouds : https://doi.org/10.15454/AGBW7G , https://doi.org/10.15454/DMYWPB
Direct URL to all data: https://remotetree.sedoo.fr/catalogue/ Instructions for accessing the datasets on the website: the datasets are visible under the search menu through projects and then by selecting FOREST/SENTHYMED
1
Value of the Data
•
These datasets were collected to provide calibration/validation data for methods aiming at linking ground observations on Mediterranean forests withOpen asset ↗SEDOOlines:31-86Code / dataset availability confirmedEurope PMC · checked 14 Sept 2026
Premise The adaptive significance of amphistomy (stomata on both upper and lower leaf surfaces) is unresolved. A widespread association between amphistomy and open, sunny habitats suggests the adaptive benefit of amphistomy may be greatest in these contexts, but this hypothesis has not been tested experimentally. Understanding amphistomy informs its potential as a target for crop improvement and paleoenvironment reconstruction. Methods We developed a method to quantify "amphistomy advantage" ( AA $\text{AA}$ ) as the log-ratio of photosynthesis in an amphistomatous leaf to that of the same leaf but with gas exchange blocked through the upper surface (pseudohypostomy). Humidity modulated stomatal conductance and thus enabled comparing photosynthesis at the same total stomatal conductance. We estimated AA $\text{AA}$ and leaf traits in six coastal (open, sunny) and six montane (closed, shaded) populations of the indigenous Hawaiian species 'ilima (Sida fallax). Results Coastal 'ilima leaves benefit 4.04 times more from amphistomy than montane leaves. Evidence was equivocal with respect to two hypotheses: (1) that coastal leaves benefit more because they are thicker and have lower CO 2 conductance through the internal airspace and (2) that they benefit more because they have similar conductance on each surface, as opposed to most conductance being through the lower surface. Conclusions This is the first direct experimental evidence that amphistomy increases photosynthesis, consistent with the hypothesis that parallel pathways through upper and lower mesophyll increase CO 2 supply to chloroplasts. The prevalence of amphistomatous leaves in open, sunny habitats can partially be explained by the increased benefit of amphistomy in "sun" leaves, but the mechanistic basis remains uncertain.
Why it matches plant phenotyping methods葉の両面気孔性が光合成に与える効果を定量化する新しい実験手法を開発し、複数集団で適用しているため、植物の生理形質取得法が中心である。
abstractWe developed a method to quantify "amphistomy advantage" ( AA $\text{AA}$ ) as the log-ratio of photosynthesis in an amphistomatous leaf to that of the same leaf but with gas exchange blocked through the upper surface (pseudohypostomy).
Reproduction assets foundThe paper's raw phenotyping data (stomatal traits, leaf thickness, gas exchange) are publicly deposited on Dryad, and the authors' custom analysis scripts are on GitHub with a Zenodo archive; both are paper-specific and directly actionable.Dataset · public7341. This is publication #213 from the School of Life
Sciences, University of Hawaiʻi at Mānoa.
DATA AVAILABILITY STATEMENT
Custom scripts are available on a GitHub repository
(https://github.com/cdmuir/stomata-ilima) and archived
on Zenodo: https://doi.org/10.5281/zenodo.10369114 (Muir,
2023). Raw data are deposited on Dryad: https://doi.org/10.5061/dryad.rxwdbrvfw (Triplett et al., 2024).
ORCID
Thomas N. Buckley http://orcid.org/0000-0001-7610-7136
Christopher D. Muir http://orcid.org/0000-0003-2555-3878
REFERENCES
Anonymous. 2022. Yellow ʻilima (Sida fallax). https://www.inaturalist.org/taxa/54995-Sida-fallax. iNaturalist.
Ball, J. T., I. E. Woodrow, and J. A. Berry. 1987. A model prediOpen asset ↗Dryad · 10.5061/dryad.rxwdbrvfwpdf-raw-page:9 lines:1-93Code · publicfor advice on leaf sectioning. Startup
funds were provided by the University of Hawaiʻi, NSF
Award 1929167 to C.D.M., and T.N.B. received NSF Award
2307341. This is publication #213 from the School of Life
Sciences, University of Hawaiʻi at Mānoa.
DATA AVAILABILITY STATEMENT
Custom scripts are available on a GitHub repository
(https://github.com/cdmuir/stomata-ilima) and archived
on Zenodo: https://doi.org/10.5281/zenodo.10369114 (Muir,
2023). Raw data are deposited on Dryad: https://doi.org/10.5061/dryad.rxwdbrvfw (Triplett et al., 2024).
ORCID
Thomas N. Buckley http://orcid.org/0000-0001-7610-7136
Christopher D. Muir http://orcid.org/0000-0003-2555-3878
REFERENCES
Anonymous. 2022. YellowOpen asset ↗GitHub · cdmuir/stomata-ilimapdf-raw-page:9 lines:1-93Code / dataset availability confirmedEurope PMC · checked 7 Sept 2026
Identifying the spatiotemporal distributions and phenotypic characteristics of understory saplings is beneficial in exploring the internal mechanisms of plant regeneration and providing technical assistances for continues cover forest management. However, it is challenging to detect the understory saplings using 2-dimensional (2D) spectral information produced by conventional optical remotely sensed data. This study proposed an automatic method to detect the regenerated understory saplings based on the 3D structural information from aerial laser scanning (ALS) data. By delineating individual tree crown using the improved spectral clustering algorithm, we successfully removed the overstory canopy and associated trunk points. Then, individual understory saplings were segmented using an adaptive-mean-shift-based clustering algorithm. This method was tested in an experimental forest farm of North China. Our results showed that the detection rates of understory saplings ranged from 94.41% to 152.78%, and the matching rates increased from 62.59% to 95.65% as canopy closure went down. The ALS-based sapling heights well captured the variations of field measurements [ R 2 = 0.71, N = 3,241, root mean square error (RMSE) = 0.26 m, P R 2 = 0.78, N =443, RMSE = 0.23 m, P R 2 = 0.64, N = 443, RMSE = 0.24 m). This study provides a solution for the quantification of understory saplings, which can be used to improve forest ecosystem resilence through regulating the dynamics of forest gaps to better utilize light resources.
Why it matches plant phenotyping methods航空LiDARの3D構造情報を用いて林冠下の実生を自動検出・分割し、樹高を野外測定と検証する手法が研究の中心であるため。
abstractThis study proposed an automatic method to detect the regenerated understory saplings based on the 3D structural information from aerial laser scanning (ALS) data.
Reproduction assets foundThe paper's Data Availability statement points to two public GitHub repositories containing the authors' code (and stated relevant data) for the NSC overstory segmentation and adaptive mean shift sapling segmentation methods used in this ALS-based phenotyping analysis.Code · publicThe relevant code and data of this research are available at https://github.com/limingado/NSC/tree/v1.0.0 and https://github.com/limingado/Adaptive-mean-shift .Open asset ↗limingado/NSC · v1.0.0lines:137-242Code · publicThe relevant code and data of this research are available at https://github.com/limingado/NSC/tree/v1.0.0 and https://github.com/limingado/Adaptive-mean-shift .Open asset ↗limingado/Adaptive-mean-shiftlines:137-242Code / dataset availability confirmedEurope PMC · checked 7 Sept 2026
Diets consisting of greater quantity/diversity of phytochemicals are correlated with reduced risk of disease. This understanding guides policy development increasing awareness of the importance of consuming fruits, grains, and vegetables. Enacted policies presume uniform concentrations of phytochemicals across crop varieties regardless of production/harvesting methods. A growing body of research suggests that concentrations of phytochemicals can fluctuate within crop varieties. Improved awareness of how cropping practices influence phytochemical concentrations are required, guiding policy development improving human health. Reliable, inexpensive laboratory equipment represents one of several barriers limiting further study of the complex interactions influencing crop phytochemical accumulation. Addressing this limitation our study validated the capacity of a low-cost Reflectometer ($500) to measure phytochemical content in selected crops, against a commercial grade laboratory spectrophotometer. Our correlation results ranged from r 2 = 0.81 for protein in wheat and oats to r 2 = 0.99 for polyphenol content in lettuce in both the Reflectometer and laboratory spectrophotometer assessment, suggesting the Reflectometer provides an accurate accounting of phytochemical content within evaluated crops. Repeatability evaluation demonstrated good reproducibility of the Reflectometer to assess crop phytochemical content. Additionally, we confirmed large variation in phytochemical content within specific crop varieties, suggesting that cultivar is but one of multiple drivers of phytochemical accumulation. Our findings indicate dramatic nutrient variations could exist across the food supply, a point whose implications are not well understood. Future studies should investigate the interactions between crop phytochemical accumulation and farm management practices that influence specific soil characteristics.
Why it matches plant phenotyping methods作物の植物化学成分量を測定する低コスト反射計を、実験室用分光光度計と比較して精度・再現性検証しており、植物形質の取得手法の技術的検証が中心である。
abstractour study validated the capacity of a low-cost Reflectometer ($500) to measure phytochemical content in selected crops, against a commercial grade laboratory spectrophotometer.
Reproduction assets foundThe paper explicitly states that all Bionutrient Institute data (reflectometer/spectrometer phytochemical measurements used in this study) are publicly available in the authors' GitLab repository, and the authors' data-processing pipeline code is also publicly hosted on GitLab.Dataset · publicAll data derived from the Bionutrient Institute methods are available publicly from our repository: https://gitlab.com/our-sci/bionutrient-institute/dataset . The data used in this manuscript covers samples submitted up to 7/31/2022.Open asset ↗our-sci/bionutrient-institute/datasetlines:156-212Code · publicAn automated data pipeline was built using SurveyStacks API’s to merge data from each completed survey and mongoDB scripts ( https://gitlab.com/our-sci/real-food-campaign/lab-data-review-dashboard/-/tree/main ) calculated measurement outcomes.Open asset ↗our-sci/real-food-campaign/lab-data-review-dashboardlines:132-143Code / dataset availability confirmedCrossref · checked 15 Sept 2026
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 · 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 confirmedOpenAlex · Europe PMC · Crossref · checked 15 Sept 2026
MaizeRootMorphology / geometry measurementRoot system architecture
A central goal of biology is to understand how genetic variation produces phenotypic variation, which has been described as a genotype to phenotype (G to P) map. The plant form is continuously shaped by intrinsic developmental and extrinsic environmental inputs, and therefore plant phenomes are highly multivariate and require comprehensive approaches to fully quantify. Yet a common assumption in plant phenotyping efforts is that a few pre-selected measurements can adequately describe the relevant phenome space. Our poor understanding of the genetic basis of root system architecture is at least partially a result of this incongruence. Root systems are complex 3D structures that are most often studied as 2D representations measured with relatively simple univariate traits. In prior work, we showed that persistent homology, a topological data analysis method that does not pre-suppose the salient features of the data, could expand the phenotypic trait space and identify new G to P relations from a commonly used 2D root phenotyping platform. Here we extend the work to entire 3D root system architectures of maize seedlings from a mapping population that was designed to understand the genetic basis of maize-nitrogen relations. Using a panel of 84 univariate traits, persistent homology methods developed for 3D branching, and multivariate vectors of the collective trait space, we found that each method captures distinct information about root system variation as evidenced by the majority of non-overlapping QTL, and hence that root phenotypic trait space is not easily exhausted. The work offers a data-driven method for assessing 3D root structure and highlights the importance of non-canonical phenotypes for more accurate representations of the G to P map.
Why it matches plant phenotyping methods3Dトウモロコシ根系の構造を、persistent homologyによって従来の形質空間を拡張して解析する計算的フェノタイピング手法が中心であり、根系構造形質の抽出・評価に該当する。
abstractpersistent homology, a topological data analysis method that does not pre-suppose the salient features of the data, could expand the phenotypic trait space
Reproduction assets foundThe paper's data availability statement points to a public figshare deposit of the 3D root models (paper-specific phenotyping inputs) and raw trait data in Supplementary Table 5 via the Frontiers supplementary material page. Matlab code is only referenced via a prior publication (Li et al., 2019) without an authors' deDataset · publicn this way, the positive value represents the QTL that have increases on the major allele, while negative values indicate QTL that have increases on minor allele.
Data availability statement
The original contributions presented in the study are included in the article/
Supplementary Material
. All the 3D models can be found at: http://dx.doi.org/10.6084/m9.figshare.23692353 . Matlab Code can be found in Li et al., 2019 . Raw trait data can be found in
Supplementary Table 5
. Further inquiries can be directed to the corresponding authors.
Author contributions
ML: Formal analysis, Investigation, Methodology, Software, Visualization, Writing – original draft, Writing – review & editing. ZL: ForOpen asset ↗figshare · 10.6084/m9.figshare.23692353lines:206-232Supplement · publicof their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.
Supplementary material
The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fpls.2023.1260005/full#supplementary-material
Supplementary Figure 1
Illustrations of persistent homology traits. (A) An example of persistence barcode. (B) The persistence diagram that is equivalent to the barcode in (A) . One example of corresponding bar-to-point is highlighted in pink color. (C) Gaussian density estimatoOpen asset ↗lines:206-232Code / dataset availability confirmedEurope PMC · checked 14 Sept 2026
In the dataset presented in this article, samples belonging to one of the following crops, apple, broccoli, leek, and mushroom, were measured by hyperspectral cameras in the visible/near-infrared spectral domain (430-900 nm). The dataset was compiled by putting together measurements from different calibrated hyperspectral imaging cameras and crops to facilitate the training of artificial intelligence models, helping to overcome the generalization problem of hyperspectral models. In particular, this dataset focuses on estimating dry matter content across various crops by a single model in a non-destructive way using hyperspectral measurements. This dataset contains extracted mean reflectance spectra for each sample (n=1028) and their respective dry matter content (%).
Why it matches plant phenotyping methods複数作物の果実・器官について、ハイパースペクトル画像から乾物含量を非破壊推定するデータセットを構築しており、形質取得・推定手法と再利用可能なベンチマークが研究の中心である。
abstractThe dataset was compiled by putting together measurements from different calibrated hyperspectral imaging cameras and crops to facilitate the training of artificial intelligence models, helping to overcome the generalization problem of hyperspectral models.
Reproduction assets foundThe paper is a data descriptor for the SpectroFood hyperspectral dataset; all five Zenodo deposits (meta-dataset plus per-crop hyperspectral image data) are public, paper-specific phenotype/trait datasets with direct URLs in the Specifications Table.Dataset · publicce), Rc: corrected hyperspectral image.
Data source location
Data are stored at Agricultural University of Athens (AUA) premises. Iera Odos 75, 11855 Athens, Greece, Department of Horticultural Engineering
Data accessibility
Repository name:Zenodo
Table data
Data identification number: 10.5281/zenodo.8362947
Direct URL to data: https://zenodo.org/record/8362947
Hyperspectral image data
1) Data identification number: 10.5281/zenodo.10301753
Direct URL to data: https://zenodo.org/records/10301753
2) Data identification number: 10.5281/zenodo.10302438
Direct URL to data: https://zenodo.org/records/10302438
3) Data identification number: 10.5281/zenodo.10302426
Direct URL to data: https:/Open asset ↗Zenodo · 10.5281/zenodo.8362947lines:1-65Dataset · publicOdos 75, 11855 Athens, Greece, Department of Horticultural Engineering
Data accessibility
Repository name:Zenodo
Table data
Data identification number: 10.5281/zenodo.8362947
Direct URL to data: https://zenodo.org/record/8362947
Hyperspectral image data
1) Data identification number: 10.5281/zenodo.10301753
Direct URL to data: https://zenodo.org/records/10301753
2) Data identification number: 10.5281/zenodo.10302438
Direct URL to data: https://zenodo.org/records/10302438
3) Data identification number: 10.5281/zenodo.10302426
Direct URL to data: https://zenodo.org/records/10302426
4) Data identification number: 10.5281/zenodo.10302386
Direct URL to data: https://zenodo.org/records/10302Open asset ↗Zenodo · 10.5281/zenodo.10301753lines:1-65Dataset · publicdo
Table data
Data identification number: 10.5281/zenodo.8362947
Direct URL to data: https://zenodo.org/record/8362947
Hyperspectral image data
1) Data identification number: 10.5281/zenodo.10301753
Direct URL to data: https://zenodo.org/records/10301753
2) Data identification number: 10.5281/zenodo.10302438
Direct URL to data: https://zenodo.org/records/10302438
3) Data identification number: 10.5281/zenodo.10302426
Direct URL to data: https://zenodo.org/records/10302426
4) Data identification number: 10.5281/zenodo.10302386
Direct URL to data: https://zenodo.org/records/10302386
1.
Value of the Data
•
Spectra were acquired using calibrated hyperspectral imaging systems under the sameOpen asset ↗Zenodo · 10.5281/zenodo.10302438lines:1-65Dataset · public8362947
Hyperspectral image data
1) Data identification number: 10.5281/zenodo.10301753
Direct URL to data: https://zenodo.org/records/10301753
2) Data identification number: 10.5281/zenodo.10302438
Direct URL to data: https://zenodo.org/records/10302438
3) Data identification number: 10.5281/zenodo.10302426
Direct URL to data: https://zenodo.org/records/10302426
4) Data identification number: 10.5281/zenodo.10302386
Direct URL to data: https://zenodo.org/records/10302386
1.
Value of the Data
•
Spectra were acquired using calibrated hyperspectral imaging systems under the same controlled conditions for four crops with high variation of dry matter values amongst the same crop and acrossOpen asset ↗Zenodo · 10.5281/zenodo.10302426lines:1-65Dataset · publicps://zenodo.org/records/10301753
2) Data identification number: 10.5281/zenodo.10302438
Direct URL to data: https://zenodo.org/records/10302438
3) Data identification number: 10.5281/zenodo.10302426
Direct URL to data: https://zenodo.org/records/10302426
4) Data identification number: 10.5281/zenodo.10302386
Direct URL to data: https://zenodo.org/records/10302386
1.
Value of the Data
•
Spectra were acquired using calibrated hyperspectral imaging systems under the same controlled conditions for four crops with high variation of dry matter values amongst the same crop and across all four.
•
The dry matter content of the four crops is the common variable when considering the quality of theOpen asset ↗Zenodo · 10.5281/zenodo.10302386lines:1-65Code / dataset availability confirmedEurope PMC · checked 15 Sept 2026
Globally, wheat ( Triticum aestivum L.) is a major source of proteins in human nutrition despite its unbalanced amino acid composition. The low lysine content in the protein fraction of wheat can lead to protein-energy-malnutrition prominently in developing countries. A promising strategy to overcome this problem is to breed varieties which combine high protein content with high lysine content. Nevertheless, this requires the incorporation of yet undefined donor genotypes into pre-breeding programs. Genebank collections are suspected to harbor the needed genetic diversity. In the 1970s, a large-scale screening of protein traits was conducted for the wheat genebank collection in Gatersleben; however, this data has been poorly mined so far. In the present study, a large historical dataset on protein content and lysine content of 4,971 accessions was curated, strictly corrected for outliers as well as for unreplicated data and consolidated as the corresponding adjusted entry means. Four genomic prediction approaches were compared based on the ability to accurately predict the traits of interest. High-quality phenotypic data of 558 accessions was leveraged by engaging the best performing prediction model, namely EG-BLUP. Finally, this publication incorporates predicted phenotypes of 7,651 accessions of the winter wheat collection. Five accessions were proposed as donor genotypes due to the combination of outstanding high protein content as well as lysine content. Further investigation of the passport data suggested an association of the adjusted lysine content with the elevation of the collecting site. This publicly available information can facilitate future pre-breeding activities.
Why it matches plant phenotyping methods小麦のタンパク質・リジン含量という植物形質について、歴史的表現型データを整理し、複数のゲノム予測法を比較して大規模コレクションの予測表現型を生成しており、計算的な形質推定とデータセット活用が研究の中心です。
abstracta large historical dataset on protein content and lysine content of 4,971 accessions was curated, strictly corrected for outliers as well as for unreplicated data and consolidated as the corresponding adjusted entry means.
Reproduction assets foundThe authors deposited the paper's curated historical protein/lysine phenotype data (ISA-Tab), the R code for BLUE calculation and genomic prediction with all input files, and key output files (BLUEs and predicted phenotypes) in the public e!DAL repository under DOI 10.5447/ipk/2023/20. This is a paper-specific, public,Dataset · publicn with all input files, and the most important output files of the analysis. The output files include BLUEs of protein and lysine content as well as the predictions of protein content, lysine content and adjusted lysine content. The aforementioned information is available via the e!DAL ( Arend et al., 2014 ) online repository ( https://dx.doi.org/10.5447/ipk/2023/20 ).
Author contributions
MB: Conceptualization, Formal Analysis, Investigation, Methodology, Software, Visualization, Writing – original draft. SW: Data curation, Writing – review & editing. JR: Conceptualization, Methodology, Supervision, Writing – review & editing. AS: Conceptualization, Methodology, Supervision, Validation, WOpen asset ↗e!DAL · 10.5447/ipk/2023/20lines:302-323Code / dataset availability confirmedEurope PMC · checked 15 Sept 2026
Leaf stomata facilitate the exchange of water and CO 2 during photosynthetic gas exchange. The shape, size, and density of leaf pores have not been constant over geologic time, and each morphological trait has potentially been impacted by changing environmental and climatic conditions, especially by changes in the concentration of atmospheric carbon dioxide. As such, stomatal parameters have been used in simple regressions to reconstruct ancient carbon dioxide, as well as incorporated into more complex gas-exchange models that also leverage plant carbon isotope ecology. Most of these proxy relationships are measured on chemically cleared leaves, although newer techniques such as creating stomatal impressions are being increasingly employed. Additionally, many of the proxy relationships use angiosperms with broad leaves, which have been increasingly abundant in the last 130 million years but are absent from the fossil record before this. We focus on the methodology to define stomatal parameters for paleo-CO 2 studies using two separate methodologies (one corrosive, one non-destructive) to prepare leaves on both scale- and broad-leaves collected from herbaria with known global atmospheric CO 2 levels. We find that the corrosive and non-corrosive methodologies give similar values for stomatal density, but that measurements of stomatal sizes, particularly guard cell width (GCW), for the two methodologies are not comparable. Using those measurements to reconstruct CO 2 via the gas exchange model, we found that reconstructed CO 2 based on stomatal impressions (due to inaccurate measurements in GCW) far exceeded measured CO 2 for modern plants. This bias was observed in both coniferous (scale-shaped) and angiosperm (broad) leaves. Thus, we advise that applications of gas exchange models use cleared leaves rather than impressions.
Why it matches plant phenotyping methods葉の気孔形態（密度・サイズ）を測定する2手法を比較・検証し、CO2推定への影響を評価しており、植物表現型取得法が研究の中心です。
abstractWe focus on the methodology to define stomatal parameters for paleo-CO 2 studies using two separate methodologies (one corrosive, one non-destructive) to prepare leaves on both scale- and broad-leaves collected from herbaria with known global atmospheric CO 2 levels.
Reproduction assets foundThe paper's stomatal phenotyping measurements (stomatal density, guard cell length/width, CO2 reconstructions) are publicly deposited as supplemental data tables on Mendeley Data (DOI 10.17632/gs6rn9tjxn.1), explicitly linked in the Supplementary Materials and Data Availability sections. No author analysis code or phenDataset · publicSupplemental Data Tables: All Measurements—Data are available at Mendeley Data: 10.17632/gs6rn9tjxn.1 (accessed on 13 November 2023).Open asset ↗Mendeley Data · 10.17632/gs6rn9tjxn.1lines:58-75Code / dataset availability confirmedCrossref · checked 14 Sept 2026
Abstract The potential of single date fully Polarimetric RADARSAT‐2 data in retrieving crop biophysical parameters using Machine Learning techniques was investigated. Various polarimetric parameters along with coherent and incoherent decomposition techniques were assessed for its sensitivity toward crop parameters like Wet and Dry Biomass, Crop Height, Leaf Area Index and Vegetation Water Content. A set of 39 polarimetric observables extracted from the Quad‐Pol data were used for regression analysis. In this study two Machine Learning techniques Random Forest Regression (RFR) and Multiple Linear Regression (MLR) models were assessed for the prediction of Wet Biomass (gm −2 ) and Height (cm). The most significant (6 out of 39) variables were applied for prediction. The results revealed that RFR algorithm performed better than MLR. The coefficient of determination ( R 2 ) and root‐mean‐square‐error of estimating wet biomass and height were 0.646, 655.65 (gm −2 ) and 0.71, 14.5 (cm) respectively in RFR and 0.566, 683.86 (gm −2 ) and 0.65, 16.14 (cm) respectively in MLR. Thus this study explored the effective application of quad‐pol data for assessing sensitivity and accurate retrieval of parameters using optimum PolSAR observables.
Why it matches plant phenotyping methodsPolSARセンサーデータと機械学習により、作物のバイオマスおよび草丈を推定し、回帰手法の性能を比較・評価しているため、表現型取得手法が中心である。
abstractThe potential of single date fully Polarimetric RADARSAT‐2 data in retrieving crop biophysical parameters using Machine Learning techniques was investigated.
Reproduction assets foundThe paper's Data Availability Statement deposits two paper-specific public assets: the ground-truth field campaign dataset (GT Points, Zenodo 10.5281/zenodo.10403380) and the authors' RFR/MLR prediction code (Zenodo 10.5281/zenodo.10403352). Generic ESA software (PolSARpro, SNAP) is excluded as a general library.Dataset · publicEarth and Space Science
THULASIRAMAN ET AL.
10.1029/2022EA002799
17 of 20
Appendix A: Supplementary Data
Supplementary data to this article, ground truth points collected during 12 August 2019, campaign is provided in
.xlsx format (https://doi.org/10.5281/zenodo.10403380).Appendix B: RFR and MLR Algorithm
The algorithm applied for Random Forest and MLR is displayed below in Figure B1.
Data Availability Statement
The fully polarimetric RADARSAT-2 data was purchased from MDA corporation. The field data collected
during the study can be accessed from Supporting Information section (Appendix A) (htOpen asset ↗Zenodo · 10.5281/zenodo.10403380pdf-raw-page:17 lines:1-77Code / dataset availability confirmedEurope PMC · bioRxiv · checked 15 Sept 2026
MaizeLiDAR / point cloudLeafStem / branchWhole plant / canopy / plot / fieldMorphology / geometry measurementLeaf traitsWater status / transpiration
Advanced smartphone technology now integrates sophisticated sensors, increasing access to high-precision data acquisition. This study tested the hypothesis that the iPhone 13-Pro camera, with LiDAR technology, can accurately estimate maize leaf surface area (Zea mays). 3D point cloud models enabled non-destructive data collection, and four methods for canopy area extraction were evaluated in relation to plant transpiration rates. Results showed a strong correlation (R 2 =0.92, RMSE=49.78) between manually scanned and iPhone-estimated plant surface areas. Additionally, the stem-to-plant surface area ratio was found to be 12.3% (R 2 =0.9, RMSE=28.42). Using this ratio to predict canopy area showed a significant correlation (R 2 =0.83) with actual canopy measurements. The iPhone’s surface area measurement tool offers an advantage by scanning the entire plant surface, unlike traditional leaf area index measurements, which often cannot penetrate the canopy. Moreover, real-size surface measurement of the canopy correlated strongly (R 2 =0.83) with whole canopy transpiration rates measured gravimetrically. This study introduces a novel method for analyzing 3D plant traits using a portable, affordable, and accurate tool, which has the potential to enhance plant breeding and agricultural practices. 0. How to Use This Template The template details the sections that can be used in a manuscript. Note that each section has a corresponding style, which can be found in the “Styles” menu of Word. Sections that are not mandatory are listed as such. The section titles given are for articles. Review papers and other article types have a more flexible structure. Remove this paragraph and start section numbering with 1. For any questions, please contact the editorial office of the journal or support@mdpi.com .
Why it matches plant phenotyping methodsiPhoneのLiDARと3D点群を用いてトウモロコシの葉・植物表面積を推定する手法を開発・検証しており、植物形質の取得が研究の中心である。
abstractThis study tested the hypothesis that the iPhone 13-Pro camera, with LiDAR technology, can accurately estimate maize leaf surface area (Zea mays).
Reproduction assets foundThe paper states that all statistical code and data files for the maize 3D leaf phenotyping analysis are publicly available in the authors' GitHub repository.Code · publicconducted using the “scipy” package’s “f_oneway”
12 function [18]. The Python packages “pandas” [19] and “numpy” [20] were used to arrange the
13 data before plotting. The Python packages “matplotlib”, “seaborn” [21] were used for data
14 visualization. All statistical code and data files needed are available to download
15 at https://github.com/gavrielbs/3D_Corn_Phenotype.
16
17 PlantArray System by Plant-DiTech LTD
18 PlantArray is a high-throughput, multi-sensor physiological phenotyping gravimetric
19 platform. This plant phenotyping system performs quick plant screening based on precise
20 physiology traits measurements that are great indicators for yield potential with proven high
21 cOpen asset ↗gavrielbs/3D_Corn_Phenotypepdf-layout-page:4 lines:1-44Code / dataset availability confirmedCrossref · checked 14 Sept 2026
Laboratory / benchtopSeed / grainTissueVisualization / data management
Abstract Motivation The propensity of plant tissues to burn (i.e. their flammability) is a key trait to understand fire regimes in many ecosystems across the globe. Measuring plant flammability under laboratory conditions allows us to improve both our understanding of plant evolutionary processes and modelling tools for simulating fire hazard and behaviour. Plant flammability has been studied from different but complementary disciplines (e.g. physics, chemistry, ecology, evolution, forestry). However, information is scattered and standardized terminology is lacking, which slows down the progress of research on plant flammability. Here we provide an open access global database on plant flammability traits measured under laboratory conditions aiming to: (a) identify the diversity of methodologies to measure plant flammability under laboratory conditions; (b) standardize the associated terminology; and (c) find geographical, ecological, and taxonomic gaps in our knowledge on plant flammability. We hope this database will stimulate transdisciplinary research and provide useful information to better cope with an increasingly flammable planet. Main Types of Variables Contained The FLAMITS database contains 19,972 records of 40 flammability variables (classified according to the measured component of flammability). For each record, relevant details of the flammability experiment are given, such as the burning device, the ignition source, and the burnt plant part. In addition, FLAMITS compiles taxonomic and functional data of the studied species and information on the study site (i.e. locality, geographic coordinates, biome, biogeographic realm, and fire activity). Spatial Location and Grain We compiled data from 295 studies in 39 countries and distributed across 12 biomes worldwide. Time Period and Grain The last 62.5 years (1961 to 15th May 2023). Major Taxa and Level of Measurement 1790 plant taxa from 186 families, 883 genera, and 1784 species. Software Format Five text files (.csv), relationally linked.
Why it matches plant phenotyping methods植物の可燃性という観察可能な形質を対象に、測定法の多様性を整理したグローバルデータベースを構築しており、形質取得・方法標準化が中心です。
abstractHere we provide an open access global database on plant flammability traits measured under laboratory conditions aiming to: (a) identify the diversity of methodologies to measure plant flammability under laboratory conditions; (b) standardize the associated terminology
Reproduction assets foundThe paper's core asset is the FLAMITS database itself: five text files (Data, Taxa, Synonymy, Site, Source) containing 19,972 flammability trait records for 1790 taxa. The Data Availability Statement explicitly deposits these files openly in DRYAD (DOI 10.5061/dryad.h18931zr3). The exact Dryad URL is not among the whitDataset · publicDATA AVAILABILITY STATEMENT
The five text files composing the database are openly available in
DRYAD at https://
doi.
org/
10.
5061/
dryad.
h1893
1zr3.Open asset ↗DRYADpdf-raw-page:11 lines:1-102Code / dataset availability confirmedEurope PMC · checked 14 Sept 2026
Conventional methods of crop yield estimation are costly, inefficient, and prone to error resulting in poor yield estimates. This affects the ability of farmers to appropriately plan and manage their crop production pipelines and market processes. There is therefore a need to develop automated methods of crop yield estimation. However, the development of accurate machine-learning methods for crop yield estimation depends on the availability of appropriate datasets. There is a lack of such datasets, especially in sub-Saharan Africa. We present curated image datasets of coffee and cashew nuts acquired in Uganda during two crop harvest seasons. The datasets were collected over nine months, from September 2022 to May 2023. The data was collected using a high-resolution camera mounted on an Unmanned Aerial Vehicle . The datasets contain 3000 coffee and 3086 cashew nut images, constituting 6086 images. Annotated objects of interest in the coffee dataset consist of five classes namely: unripe, ripening, ripe, spoilt, and coffee_tree. Annotated objects of interest in the cashew nut dataset consist of six classes namely: tree, flower, premature, unripe, ripe, and spoilt. The datasets may be used for various machine-learning tasks including flowering intensity estimation, fruit maturity stage analysis, disease diagnosis, crop variety identification, and yield estimation.
Why it matches plant phenotyping methodsコーヒーとカシューナッツの画像データセットを構築し、開花強度、成熟段階、収量などの植物形質・状態推定に利用する方法基盤を提供しており、表現型取得用データセットが研究の中心である。
abstractWe present curated image datasets of coffee and cashew nuts acquired in Uganda during two crop harvest seasons.
Reproduction assets foundThe paper's own UAV coffee and cashew image datasets with YOLO annotations are publicly deposited on Mendeley Data (DOI 10.17632/r46c6bpfpf.1), directly reproducing the paper's phenotyping measurements. Annotation tools (Makesense AI, VGG Image Annotator) are generic third-party tools, not paper-specific assets.Dataset · publicre of f/1.7 and focus range of 1 m to ∞, shutter speed of 2-1/8000s and ISO range of 100-6400 (Auto and Manual)
Data source location
Institution: Makerere University
City: Kampala
Country: Uganda
Data accessibility
Repository name: Mendely Data
Data identification number: http://doi.org/10.17632/r46c6bpfpf.1
Direct URL to data:
https://data.mendeley.com/datasets/r46c6bpfpf/1
1.
Value of the Data
•
Flowering intensity estimation. Flowering represents an important stage in coffee and cashew farming since it affects crop yield. It has a significant impact on yield in that flowering intensity is positively correlated with the amount of crop yield. Therefore, flowering intensity could be an imporOpen asset ↗10.17632/r46c6bpfpf.1lines:1-51Code / dataset availability confirmedEurope PMC · checked 14 Sept 2026
Leaf area estimation is a critical component in the study of plant growth and productivity within agricultural systems. This research introduces the LeafArea package, a specialized tool designed to calculate the leaf area of six distinct Andean fruit species: S. quitoense, S. betaceum, P. peruviana, R. fruticosus, P. ligularis and P. edulis. Leveraging response variables such as species type, leaf length and width, the package employs advanced machine learning algorithms to estimate leaf area accurately. The primary focus of the study is to identify the most effective model for describing the relationship between leaf width, length, and area for each plant species. Currently, the LeafArea package utilizes four different machine learning algorithms, namely generalized linear model (GLM), generalized linear mixed model (GLMM), Random Forest and XGBoost. Among these, XGBoost stands out as a top-performing algorithm, exhibiting exceptional predictive accuracy. The evaluation metrics employed in the program provide valuable insights for researchers, aiding in informed decision-making. Specifically, XGBoost demonstrates significantly lower prediction errors and approaches a near-perfect R2 value, emphasizing its potential to enhance predictive accuracy. These results underscore the efficacy of machine learning techniques, as a compelling choice for researchers seeking precise and robust predictions in leaf area estimation. The LeafArea package thus represents a valuable tool for advancing our understanding of plant growth dynamics, resource allocation, and overall productivity within agricultural ecosystems.
Why it matches plant phenotyping methods葉面積という植物形質を機械学習で推定するソフトウェアパッケージを開発・評価しており、形質取得・推定手法が研究の中心である。
abstractThis research introduces the LeafArea package, a specialized tool designed to calculate the leaf area of six distinct Andean fruit species
Reproduction assets foundThe paper's leaf photographs dataset is deposited on figshare (CC-BY) and the LeafArea R analysis package is open-source on GitHub; both are paper-specific, public, and actionable.Code · publiccurrently for six plant species. We encourage researchers to provide sufficient data to expand both
the number of species and the number of observations, thereby continually enhancing the predictive
power of our models. This includes broadening the range of plant species that can be studied. The
LeafArea package is open-source (https://github.com/velasquez-vasconez/LeafArea), and any
contributions to the database or code will be greatly appreciated.
Conclusions
The LeafArea package introduces four invaluable functions for precise leaf area estimation in six
Andean fruit species. It incorporates the optimal GLM and GLMM models, alongside the powerful
Random Forest and XGBoost algorithms, resuOpen asset ↗github · velasquez-vasconez/LeafAreapdf-raw-page:7 lines:1-52Code / dataset availability confirmedEurope PMC · checked 14 Sept 2026
The Plumbago Zeylanica ( Chitrak ) Leaf Image Dataset is a valuable resource for botanical studies, herbal medicine research, and environmental analyses. Comprising a total of 10,660 high-resolution leaf images, the dataset is meticulously categorized into three distinct classes: Unhealthy leaves (3343 images), Healthy leaves (5288 images), and Dried leaves (2029 images). These images were captured from the medicinal plant Chitrak , a species of paramount importance in traditional medicine and environmental contexts. Researchers and practitioners can benefit from this dataset's richness in terms of both quantity and quality, using it to develop and test algorithms for leaf classification and health assessment. The Chitrak leaf image dataset holds the potential to foster innovative investigations and applications within the domains of botany, medicine, and environmental sciences.
Why it matches plant phenotyping methods植物の葉画像を用いた健康状態分類のための大規模データセットであり、表現型（葉の健全性・乾燥状態）の取得と評価を主目的とするため、方法文献の対象に含める。
abstractComprising a total of 10,660 high-resolution leaf images
Reproduction assets foundThe paper is a data descriptor for the authors' own Plumbago Zeylanica (Chitrak) leaf image dataset (10,660 images), publicly deposited on Mendeley Data with an explicit direct URL and DOI, matching an allowed URL.Dataset · publicthe dataset and preserve the important details
Data source location
Vishwakarma University,
Kondhwa Budruk, Maharashtra, Pune, India
Latitude: 18.4605° N Longitude: 73.8837° E
Data accessibility
Repository name: Plumbago Zeylanica (Chitrak) Leaf Image Dataset
Data identification number: 10.17632/twpv7hhmgb.2
Direct URL to data: https://data.mendeley.com/datasets/twpv7hhmgb/2
1
Value of the Data
•
This dataset comprises a substantial collection of 10,660 high-resolution leaf images, making it a comprehensive resource for researchers in various fields. The dataset size greatly enhances the potential for diverse analyses and model development.
•
Data scientists and machine learning practitionerOpen asset ↗10.17632/twpv7hhmgb.2lines:1-57Code / dataset availability confirmedEurope PMC · checked 15 Sept 2026
The analysis of telomere length is an important component of many studies aiming to characterize the role of telomere maintenance mechanisms in cellular lifespan, disease, or in general chromosome protection and DNA replication pathways. Several powerful methods to accurately measure the telomere length from Southern blots have been developed, but their utility for large-scale genomic studies has not been previously evaluated. Here, we performed a comparative analysis of two recently developed programs, TeloTool and WALTER, for the extraction of mean telomere length values from Southern blots. Using both software packages, we measured the telomere length in two extensive experimental datasets for the model plant Arabidopsis thaliana , consisting of 537 natural accessions and 65 T-DNA (transfer DNA for insertion mutagenesis) mutant lines in the reference Columbia (Col-0) genotype background. We report that TeloTool substantially overestimates the telomere length in comparison to WALTER, especially for values over 4500 bp. Importantly, the TeloTool- and WALTER-calculated telomere length values correlate the most in the 2100-3500 bp range, suggesting that telomeres in this size interval can be estimated by both programs equally well. We further show that genome-wide association studies using datasets from both telomere length analysis tools can detect the most significant SNP candidates equally well. However, GWAS analysis with the WALTER dataset consistently detects fewer significant SNPs than analysis with the TeloTool dataset, regardless of the GWAS method used. These results imply that the telomere length data generated by WALTER may represent a more stringent approach to GWAS and SNP selection for the downstream molecular screening of candidate genes. Overall, our work reveals the unanticipated impact of the telomere length analysis method on the outcomes of large-scale genomic screens.
Why it matches plant phenotyping methods植物のテロメア長という形質をSouthern blotから抽出する2つの解析ツールを大規模データで比較・評価しており、測定法の技術的妥当性が研究の中心である。
abstractHere, we performed a comparative analysis of two recently developed programs, TeloTool and WALTER, for the extraction of mean telomere length values from Southern blots.
Reproduction assets foundThe paper's plant-phenotyping measurements (TeloTool- and WALTER-derived telomere length datasets for 537 Arabidopsis accessions and 65 T-DNA mutant lines, used for GWAS) are stated to be contained in Supplemental Data S1 and S2, publicly downloadable from the MDPI supplement URL listed in the allowed URLs. No author-cSupplement · publicvant for telomere biology studies and the functional analysis of candidate genes in other systems, including large-scale genomic screens in other models and in humans.
Acknowledgments
We thank Jae Choi (University of Kansas) for the insightful discussions.
Supplementary Materials
The supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/ijms242417194/s1 .
Click here for additional data file.
Author ContributionsOpen asset ↗lines:273-287Code / dataset availability confirmedEurope PMC · bioRxiv · checked 7 Sept 2026
ABSTRACT Background The prediction of desirable traits in wheat from imaging data is an area of growing interest thanks to the increasing accessibility of remote sensing technology. However, as the amount of data generated continues to grow, it is important that the most appropriate models are used to make sense of this information. Here, the performance of neural network models in predicting grain asparagine content is assessed against the performance of other models. Results Neural networks had greater accuracies than partial least squares regression models and gaussian naïve Bayes models for prediction of grain asparagine content, yield, genotype, and fertiliser treatment. Genotype was also more accurately predicted from seed data than from canopy data. Conclusion Using wheat canopy spectral data and combinations of wheat seed morphology and spectral data, neural networks can provide improved accuracies over other models for the prediction of agronomically important traits.
Why it matches plant phenotyping methods画像・スペクトルデータから穀粒成分や収量などの植物形質を予測するニューラルネットワークを他手法と比較評価しており、形質推定法の性能検証が中心である。
abstractHere, the performance of neural network models in predicting grain asparagine content is assessed against the performance of other models.
Reproduction assets foundThe preprint states that the data and code used in this study (neural network/PLSR/GNB modelling of wheat canopy spectral and seed imaging data) are publicly available in the author's GitHub repository, which matches an allowed URL.Code · publicData and code used in this study are available at: https://github.com/JosephOddy/wheat-Open asset ↗JosephOddy/wheat-pdf-page:7 lines:1-50Code / 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 confirmedCrossref · checked 7 Sept 2026
Crop height is a vital indicator of growth conditions. Traditional drone image-based crop height measurement methods primarily rely on calculating the difference between the Digital Elevation Model (DEM) and the Digital Terrain Model (DTM). The calculation often needs more ground information, which remains labour-intensive and time-consuming. Moreover, the variations of terrains can further compromise the reliability of these ground models. In response to these challenges, we introduce G-DMD, a novel method based on Gated Recurrent Units (GRUs) using DEM and multispectral drone images to calculate the crop height. Our method enables the model to recognize the relation between crop height, elevation, and growth stages, eliminating reliance on DTM and thereby mitigating the effects of varied terrains. We also introduce a data preparation process to handle the unique DEM and multispectral image. Upon evaluation using a cotton dataset, our G-DMD method demonstrates a notable increase in accuracy for both maximum and average cotton height measurements, achieving a 34% and 72% reduction in Root Mean Square Error (RMSE) when compared with the traditional method. Compared to other combinations of model inputs, using DEM and multispectral drone images together as inputs results in the lowest error for estimating maximum cotton height. This approach demonstrates the potential of integrating deep learning techniques with drone-based remote sensing to achieve a more accurate, labour-efficient, and streamlined crop height assessment across varied terrains.
Why it matches plant phenotyping methodsドローンのDEM・マルチスペクトル画像から作物高を推定する手法を開発し、従来法と精度比較・検証しており、植物表現型取得が中心である。
abstractwe introduce G-DMD, a novel method based on Gated Recurrent Units (GRUs) using DEM and multispectral drone images to calculate the crop height.
Reproduction assets foundThe paper's crop-height phenotyping analysis is built on a public cotton UAV multispectral/DEM dataset deposited by Xu et al. on Figshare, which qualifies as a paper-specific, publicly actionable phenotyping input. The authors' own G-DMD code and processed data are only available upon request, so that component is not公Dataset · public47. Xu, R.; Li, C.; Paterson, A.H. UAV Multispectral. Figshare. Dataset. 2018. Available online: https://figshare.com/articles/Open asset ↗Figsharepdf-page:22 lines:1-20Code / dataset availability confirmedEurope PMC · checked 14 Sept 2026
Why it matches plant phenotyping methodsカッサバ根の乾物含量とアミロース含量という植物器官形質を対象に、NIRS予測モデルを開発・検証しており、形質取得法が研究の中心です。
abstractnear-infrared reflectance spectroscopy (NIRS) models were developed to aid breeding and selection of DMC and AC
Reproduction assets foundThe paper's field experiment phenotyping data (cassava clones used for NIRS calibration of dry matter and amylose content) is openly available in Cassavabase at the trial 4384 URL, per the authors' explicit availability statements. No author analysis code, NIRS spectra files, or trained model/calibration equations are指Dataset · publicexperiment is available in an open access data repository at
(https://www.cassavabase.org/breeders/trial/4384?format=). The
pre-breeding set of germplasms used in the present study con-
tained genotypes that are from diverse backgrounds (from Inter-
national Institute of Tropical Agriculture (IITA), International
Center for Tropical Agriculture (CIAT) and NaCRRI), for which
diversity is important in development of NIRS calibrations. They
are cOpen asset ↗Cassavabase · trial/4384pdf-raw-page:4 lines:1-91Code / dataset availability confirmedEurope PMC · checked 14 Sept 2026
LeafPhysiological trait estimationPhotosynthesis / fluorescencePlant / canopy temperature
We present the Fast Assimilation-Temperature Response (FAsTeR) method, a new method for measuring plant assimilation-temperature (AT) response that reduces measurement time and increases data density compared with conventional methods. The FAsTeR method subjects plant leaves to a linearly increasing temperature ramp while taking rapid, nonequilibrium measurements of gas exchange variables. Two postprocessing steps are employed to correct measured assimilation rates for nonequilibrium effects and sensor calibration drift. Results obtained with the new method are compared with those from two conventional stepwise methods. Our new method accurately reproduces results obtained from conventional methods, reduces measurement time by a factor of c. 3.3 (from c. 90 to 27 min), and increases data density by a factor of c. 55 (from c. 10 to c. 550 observations). Simulation results demonstrate that increased data density substantially improves confidence in parameter estimates and drastically reduces the influence of noise. By improving measurement speed and data density, the FAsTeR method enables users to ask fundamentally new kinds of ecological and physiological questions, expediting data collection in short-field campaigns, and improving the representativeness of data across species in the literature.
Why it matches plant phenotyping methods葉レベル光合成の温度応答を高速・高密度に測定する新手法を開発し、従来法と比較検証しているため、植物フェノタイピング手法が中心です。
abstractWe present the Fast Assimilation-Temperature Response (FAsTeR) method, a new method for measuring plant assimilation-temperature (AT) response that reduces measurement time and increases data density compared with conventional methods.
Reproduction assets foundThe paper's Data availability statement explicitly provides full data and R code (postmeasurement corrections, analyses, figures, and FAsTeR protocol) at the authors' public GitHub repository.Code · publicSTM. JCG wrote the first
draft of the manuscript, and JCG and STM revised the manu-
script.
ORCID
Josef C. Garen https://orcid.org/0000-0002-3338-6662
Sean T. Michaletz https://orcid.org/0000-0003-2158-6525
Data availability
Full data and code used for the production of figures and statis-
tics in this article are available at https://github.com/garenj/Faster-method.New Phytologist (2024) 241: 1361–1372
www.newphytologist.com
Ó 2023 The Authors
New Phytologist Ó 2023 New Phytologist Foundation
Research Methods
New
Phytologist
1370
14698137,
2024,
3,
Downloaded
from
https://nph.onlinelibrary.wiley.com/doi/10.1111/nph.19405
by
Mount
Vernon
Nazarene
University,
Wiley
Online
Library
on
[20/12/Open asset ↗garenj/Faster-methodpdf-raw-page:10 lines:89-144Code / dataset availability confirmedOpenAlex · Crossref · Europe PMC · checked 7 Sept 2026
Within-host spread of pathogens is an important process for the study of plant-pathogen interactions. However, the development of plant-pathogen lesions remains practically difficult to characterize beyond the common traits such as lesion area. Here, we address this question by combining image-based phenotyping with mathematical modelling. We consider the spread of Peyronellaea pinodes on pea stipules that were monitored daily with visible imaging. We assume that pathogen propagation on host-tissues can be described by the Fisher-KPP model where lesion spread depends on both a logistic growth and an homogeneous diffusion. Model parameters are estimated using a variational data assimilation approach on sets of registered images. This modelling framework is used to compare the spread of an aggressive isolate on two pea cultivars with contrasted levels of partial resistance. We show that the expected slower spread on the most resistant cultivar is actually due to a significantly lower diffusion coefficient. This study shows that combining imaging with spatial mechanistic models can offer a mean to disentangle some processes involved in host-pathogen interactions and further development may allow a better identification of quantitative traits thereafter used in genetics and ecological studies.
Why it matches plant phenotyping methods画像ベースの病斑追跡と時空間モデルを組み合わせ、病斑拡散パラメータを推定する手法が研究の中心であるため。
abstractHere, we address this question by combining image-based phenotyping with mathematical modelling.
Reproduction assets foundThe article cites two public Recherche Data Gouv deposits containing this paper's own phenotyping assets: the image sequences of growing lesions on pea stipules used for monitoring, and the segmentation outputs used for image-based phenotyping. Both are explicitly referenced with DOIs in the reference list.Dataset · publicImage sequences of growing lesions—Ascochyta blight of pea. Recherche Data Gouv; 2022. Available from: https://doi.org/10.57745/MQXKCP .Open asset ↗Recherche Data Gouv · 10.57745/MQXKCPlines:296-384Dataset · publicSegmentation of ascochyta blight symptoms on pea stipules. Recherche Data Gouv; 2022. Available from: https://doi.org/10.57745/5B1XGU .Open asset ↗Recherche Data Gouv · 10.57745/5B1XGUlines:296-384Code / 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
1.
Carvalho
LC , Goncalves
EF ,
da
Silva
JM , Costa
JM
.
Potential phOpen asset ↗10.57745/PETTGY · 10.57745/PETTGYlines:825-1015Code / dataset availability confirmedEurope PMC · checked 7 Sept 2026
We present a machine vision-based database named GrainSet for the purpose of visual quality inspection of grain kernels. The database contains more than 350K single-kernel images with experts' annotations. The grain kernels used in the study consist of four types of cereal grains including wheat, maize, sorghum and rice, and were collected from over 20 regions in 5 countries. The surface information of each kernel is captured by our custom-built device equipped with high-resolution optic sensor units, and corresponding sampling information and annotations include collection location and time, morphology, physical size, weight, and Damage & Unsound grain categories provided by senior inspectors. In addition, we employed a commonly used deep learning model to provide classification results as a benchmark. We believe that our GrainSet will facilitate future research in fields such as assisting inspectors in grain quality inspections, providing guidance for grain storage and trade, and contributing to applications of smart agriculture.
Why it matches plant phenotyping methods穀粒画像と形態・サイズ・重量・損傷状態の注釈を大規模に整備した再利用可能なデータセットで、カスタム撮像装置とベンチマーク分類も含むため、植物器官の表現型取得・解析が中心です。
abstractWe present a machine vision-based database named GrainSet for the purpose of visual quality inspection of grain kernels.
Reproduction assets foundThe paper's GrainSet database (annotated single-kernel grain images with DU-grain, weight, size, and mask annotations) is publicly deposited on Figshare under CC BY 4.0, split into four species sub-datasets plus tiny/raw previews, and the authors' validation code and trained models are released on GitHub.Code · publicThe validation code and models are released in the Github repository https://github.com/GrainSpace/GrainSet.Open asset ↗GitHub · GrainSpace/GrainSetpdf-page:10 lines:1-57Code / dataset availability confirmedEurope PMC · bioRxiv · checked 7 Sept 2026
Field / plotLeafStomata / guard-cell complexPhysiological trait estimation2D/3D reconstructionPhotosynthesis / fluorescenceStomatal traitsWater status / transpiration
ABSTRACT Premise of the study The adaptive significance of stomata on both upper and lower leaf surfaces, called amphistomy, is unresolved. A widespread association between amphistomy and open, sunny habitats suggests the adaptive benefit of amphistomy may be greatest in these contexts, but this hypothesis has not been tested experimentally. Understanding why amphistomy evolves can inform its potential as a target for crop improvement and paleoenvironment reconstruction. Methods We developed a new method to quantify “amphistomy advantage”, AA, as the log-ratio of photosynthesis in an amphistomatous leaf to that of the same leaf but with gas exchange blocked through the upper (adaxial) surface, which we term “pseudohypostomy”. We used humidity to modulate stomatal conductance and thus compare photosynthetic rates at the same total stomatal conductance. We estimated AA and related physiological and anatomical traits in 12 populations, six coastal (open, sunny) and six montane (closed, shaded), of the indigenous Hawaiian species ‘ilima ( Sida fallax ). Key results Coastal ‘ilima leaves benefit 4.04 times more from amphistomy compared to their montane counterparts. Our evidence was equivocal with respect to two hypotheses – that coastal leaves benefit more because 1) they are thicker and therefore have lower CO 2 conductance through the internal airspace, and 2) that they benefit more because they have similar conductance on each surface, as opposed to most of the conductance being on the lower (abaxial) surface. Conclusions This is the first direct experimental evidence that amphistomy per se increases photosynthesis, consistent with the hypothesis that parallel pathways through upper and lower mesophyll increase the supply of CO 2 to chloroplasts. The prevalence of amphistomatous leaves in open, sunny habitats can partially be explained the increased benefit of amphistomy in ‘sun’ leaves, but the mechanistic basis of this observation is an area for future research.
Why it matches plant phenotyping methods葉の両面気孔性が光合成に与える効果を定量化する新しい生理的測定法を開発し、複数集団で比較検証しており、表現型取得が研究の中心である。
abstractWe developed a new method to quantify “amphistomy advantage”, AA, as the log-ratio of photosynthesis in an amphistomatous leaf to that of the same leaf but with gas exchange blocked through the upper (adaxial) surface, which we term “pseudohypostomy”.
Reproduction assets foundThe paper's Data Availability Statement explicitly provides a public GitHub repository containing the custom analysis scripts for this study's amphistomy advantage measurements. Raw data are only promised for future Dryad deposit (not yet available), so only the code asset qualifies.Code · publicCustom scripts are available on a GitHub repository (https://github.com/cdmuir/stomata-ilima) and will
be archived on Zenodo with a DOI and stable URL upon publication.Open asset ↗cdmuir/stomata-ilimapdf-page:16 lines:1-52Code / dataset availability confirmedEurope PMC · 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 · checked 14 Sept 2026
Tomato, a fruiting plant species within the Solanaceae family, is a widely used ingredient in culinary dishes due to its sweet and acidic flavor profile, as well as its rich nutritional content. Recognized for its potential health benefits, including reducing the risk of coronary artery disease and specific types of cancer, tomatoes have become a staple in global cuisine. Traditional methods for tomato maturity assessment, harvesting, quality grading, and packaging are often labor-intensive and economically inefficient. This paper introduces an extensive dataset of high-resolution tomato images collected over an eight-month period from the demonstration fields of Sher-E-Bangla Agricultural University in Dhaka, Bangladesh, in collaboration with plant breeding experts of the same university. The dataset was meticulously curated to ensure precision and consistency, encompassing various stages of tomato maturity, including images of both fresh and defective tomatoes. This dataset is a valuable resource for researchers, stakeholders, and individuals interested in tomato production in Bangladesh, providing a robust foundation for leveraging computer vision and deep learning techniques in the agriculture sector. The dataset's potential applications extend to automating tasks such as robotic harvesting, quality assessment, and packaging systems, ultimately enhancing the efficiency of tomato production processes.
Why it matches plant phenotyping methodsトマト果実の成熟段階と欠陥を対象とする大規模画像データセットを構築しており、植物状態の画像ベース評価が研究の中心です。
abstractThis paper introduces an extensive dataset of high-resolution tomato images
Reproduction assets foundThis Data in Brief article describes its own public tomato image dataset (maturity detection and quality grading) deposited on Mendeley Data, with explicit direct URL and DOI. The dataset is the paper's plant-phenotyping image asset and is publicly actionable. No separate analysis code repository is provided.Dataset · publict this dataset is entirely new, and no prior research has been conducted using it.
Data source location
Location: Sher-E-Bangla Agricultural University
Zone: Sher-E-Bangla Nagar, Dhaka-1207
Country: Bangladesh
Data accessibility
Repository name: Mendeley Data
Data identification number: 10.17632/s42kpg8h37.1
Direct URL to data: https://data.mendeley.com/datasets/s42kpg8h37/1
Instructions for accessing these data: Adhering to the appropriate citation guidelines is crucial when utilizing these datasets.
1
Value of the Data
•
Robotic harvesting represents an advanced agricultural technology that offers the potential for substantial enhancements in both quality and productivity, while concurrentOpen asset ↗Mendeley Data · 10.17632/s42kpg8h37.1lines:1-51Code / dataset availability confirmedCrossref · checked 8 Sept 2026
Abstract Remote sensing of vegetation by spectroscopy is increasingly used to characterize trait distributions in plant communities. How leaves interact with electromagnetic radiation is determined by their structure and contents of pigments, water, and abundant dry matter constituents like lignins, phenolics, and proteins. High-resolution (“hyperspectral”) spectroscopy can characterize trait variation at finer scales, and may help to reveal underlying genetic variation—information important for assessing the potential of populations to adapt to global change. Here, we use a set of 360 inbred genotypes of the wild coyote tobacco Nicotiana attenuata : wild accessions, recombinant inbred lines (RILs), and transgenic lines (TLs) with targeted changes to gene expression, to dissect genetic versus non-genetic influences on variation in leaf spectra across three experiments. We calculated leaf reflectance from hand-held field spectroradiometer measurements covering visible to short-wave infrared wavelengths of electromagnetic radiation (400–2500 nm) using a standard radiation source and backgrounds, resulting in a small and quantifiable measurement uncertainty. Plants were grown in more controlled (glasshouse) or more natural (field) environments, and leaves were measured both on- and off-plant with the measurement set-up thus also in more to less controlled environmental conditions. Entire spectra varied across genotypes and environments. We found that the greatest variance in leaf reflectance was explained by between-experiment and non-genetic between-sample differences, with subtler and more specific variation distinguishing groups of genotypes. The visible spectral region was most variable, distinguishing experimental settings as well as groups of genotypes within experiments, whereas parts of the short-wave infrared may vary more specifically with genotype. Overall, more genetically variable plant populations also showed more varied leaf spectra. We highlight key considerations for the application of field spectroscopy to assess genetic variation in plant populations.
Why it matches plant phenotyping methods葉の反射スペクトルを用いて植物の遺伝的変異を評価する測定法を、異なる環境・測定条件で検証・評価しており、植物フェノタイピング手法が中心です。
titleEvaluating potential of leaf reflectance spectra to monitor plant genetic variation
Reproduction assets foundThe paper's leaf reflectance spectral measurements and analysis code are publicly available: processed spectral data, metadata, and code are on the authors' GitHub repository, and the raw spectral measurement dataset is published in SPECCHIO.Code · publicAll processed spectral data, metadata and code are provided at the GitHub repository: https://github.com/licheng1221/How-leaves-reflect-genetic-variation .Open asset ↗https://github.com/licheng1221/How-leaves-reflect-genetic-variationlines:218-235Code / dataset availability confirmedEurope PMC · checked 15 Sept 2026
Measurements of rice physical traits, such as length, width, and percentage of filled/unfilled grains, are essential steps of rice breeding. A new approach for measuring the physical traits of rice grains for breeding purposes was presented in this study, utilizing image processing techniques. Backlight photography was used to capture a grayscale image of a group of rice grains, which was then analyzed using a clustering algorithm to differentiate between filled and unfilled grains based on their grayscale values. The impact of backlight intensity on the accuracy of the method was also investigated. The results show that the proposed method has excellent accuracy and high efficiency. The mean absolute percentage error of the method was 0.24% and 1.36% in calculating the total number of grain particles and distinguishing the number of filled grains, respectively. The grain size was also measured with a little margin of error. The mean absolute percentage error of grain length measurement was 1.11%, while the measurement error of grain width was 4.03%. The method was found to be highly accurate, non-destructive, and cost-effective when compared to conventional methods, making it a promising approach for characterizing physical traits for crop breeding.
Why it matches plant phenotyping methodsイネ籾の長さ・幅・充実度を画像処理で測定する方法を開発し、精度とバックライト条件の影響を検証しており、植物表現型取得が中心です。
abstractA new approach for measuring the physical traits of rice grains for breeding purposes was presented in this study, utilizing image processing techniques.
Reproduction assets foundThe paper's phenotype reference measurements (grain counts, filled/unfilled counts, and grain sizes for the validation experiments) are reported in Appendices A–C, which are included in the article's supplementary material, publicly available at the Frontiers supplementary-material URL. No author analysis code or imageSupplement · publicof their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.
Supplementary material
The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fpls.2023.1213486/full#supplementary-material
Click here for additional data file.
References
Al-Tam F., Adam H., Anjos A. D., Lorieux M., Larmande P., Ghesquière A., et al. (2013). P-TRAP: a panicle trait phenotyping tool. BMC Plant Biol.
13 (1), 1–14. doi: 10.1186/1471-2229-13-122Open asset ↗lines:182-208Code / dataset availability confirmedEurope PMC · checked 15 Sept 2026
Diets consisting of greater quantity/diversity of phytochemicals are correlated with reduced risk of disease. This understanding guides policy development increasing awareness of the importance of consuming fruits, grains, and vegetables. Enacted policies presume uniform concentrations of phytochemicals across crop varieties regardless of production/harvesting methods. A growing body of research suggests that concentrations of phytochemicals can fluctuate within crop varieties. Improved awareness of how cropping practices influence phytochemical concentrations are required, guiding policy development improving human health. Reliable, inexpensive laboratory equipment represents one of several barriers limiting further study of the complex interactions influencing crop phytochemical accumulation. Addressing this limitation our study validated the capacity of a low-cost Reflectometer ($500) to measure phytochemical content in selected crops, against a commercial grade laboratory spectrophotometer. Our results suggest the Reflectometer provides an accurate accounting of phytochemical content within evaluated crops. Additionally, we confirmed large variation in phytochemical content within specific crop varieties, suggesting that cultivar is but one of multiple drivers of phytochemical accumulation. Our findings indicate dramatic nutrient variations could exist across the food supply, a point whose implications are not well understood. Future studies should investigate the interactions between crop phytochemical accumulation and farm management practices that influence specific soil characteristics.
Why it matches plant phenotyping methods作物中の植物化学物質量を測定する低コスト反射計を商用分光光度計と比較検証しており、植物の化学的形質取得が研究の中心である。
abstractour study validated the capacity of a low-cost Reflectometer ($500) to measure phytochemical content in selected crops, against a commercial grade laboratory spectrophotometer.
Reproduction assets foundThe paper explicitly states that all data derived from the Bionutrient Institute methods (the crop phytochemical measurements underlying this study) are publicly available in the authors' GitLab repository, and that the automated data pipeline scripts used to calculate, merge, and QC the sample data are also publicly可用Dataset · publicd redefined extraction protocols utilized for crop phytochemical assessment, developed protocols, and provided technical oversight for the usage of reflectometer and offered editorial review of the manuscript.
3 Data Availability
All data derived from the Bionutrient Institute methods are available publicly from our repository: https://gitlab.com/our-sci/bionutrient-institute/dataset. The data used in this manuscript covers samples submitted up to 7/31/2022 . Our Sci seeks to increase transparency and access to research via open-source hardware and software and open-access data [19].
4 Competing Interests Statement
Author G. Austic and D. Ter Avest are co-founders of OurSci, LLC, the companyOpen asset ↗our-sci/bionutrient-institute/datasetlines:108-124Code · publice created to guide users through each aspect of the protocol, including Reflectometer measurements, instructions, and questions for entering metadata (ex: crop type, amount of extractant used, etc). An automated data pipeline was built using SurveyStacks API’s to merge data from each completed survey and mongoDB scripts ( https://gitlab.com/our-sci/real-food-campaign/lab-data-review-dashboard/-/tree/main ) calculated measurement outcomes.
4.2 Crop Phytochemical Variability Study
4.2.1 Crop Sample characteristics
Crop samples were submitted from both producer and consumer volunteers from 2019–2022 representing 10,000 unique samples with accompanying geographical and management datOpen asset ↗our-sci/real-food-campaign/lab-data-review-dashboardlines:86-97Code · publiccation was built using NodeJS/express with mongoDB on the Server and Vue with Vuetify on the Client. Hardware integration between SurveyStack and the Reflectometer occurs via the SurveyStack Kit mobile application written in Kotlin for Android devices. The source code for all applications is available and documented on Gitlab ( https://gitlab.com/our-sci/software/surveystack ). The Android application is available for download over the Google Play store ( https://play.google.com/store ). SurveyStack and SurveyStack Kit are licensed under the GNU General Public License v3.0.
All data collection was completed using SurveyStack forms. For each data collection activity, forms were created to guiOpen asset ↗our-sci/software/surveystacklines:86-97Code / dataset availability confirmedEurope PMC · checked 7 Sept 2026
Stomata play an essential role in regulating water and carbon dioxide levels in plant leaves, which is important for photosynthesis. Previous deep learning-based plant stomata detection methods are based on horizontal detection. The detection anchor boxes of deep learning model are horizontal, while the angle of stomata is randomized, so it is not possible to calculate stomata traits directly from the detection anchor boxes. Additional processing of image (e.g., rotating image) is required before detecting stomata and calculating stomata traits. This paper proposes a novel approach, named DeepRSD (deep learning-based rotating stomata detection), for detecting rotating stomata and calculating stomata basic traits at the same time. Simultaneously, the stomata conductance loss function is introduced in the DeepRSD model training, which improves the efficiency of stomata detection and conductance calculation. The experimental results demonstrate that the DeepRSD model reaches 94.3% recognition accuracy for stomata of maize leaf. The proposed method can help researchers conduct large-scale studies on stomata morphology, structure, and stomata conductance models.
Why it matches plant phenotyping methods植物葉の気孔を対象に、回転を考慮した検出と気孔形質・コンダクタンス算出を一体化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。
abstractThis paper proposes a novel approach, named DeepRSD (deep learning-based rotating stomata detection), for detecting rotating stomata and calculating stomata basic traits at the same time.
Reproduction assets foundThe paper's DeepRSD stomata detection and conductance calculation code is explicitly stated to be publicly hosted on GitHub at the authors' repository URL. No public dataset of the 2,192 maize stomata images is mentioned.Code · publicThe code of anchor-free stomata detection and stomata conductance calculation has been hosted to GitHub and is available at https://github.com/sswangbo159357/Rotating-stomata-detection .Open asset ↗sswangbo159357/Rotating-stomata-detectionlines:288-533Code / dataset availability confirmedEurope PMC · OpenAlex · checked 15 Sept 2026
Accurate estimates of above-ground tree biomass within forest inventories are essential for calibration and validation of biomass mapping products based on Earth observation data. Terrestrial laser scanning (TLS) enables detailed and non-destructive volume estimation of individual trees, which can be converted to biomass with wood basic density. Existing TLS-based approaches range from simple geometrical features to virtual 3D reconstruction of entire trees. Validating such approaches with weight measurements is a key step before the integration of TLS or other close-range technologies into operational applications such as forest inventories. In this study, we firstly evaluate individual tree volume estimation approaches based on 3D reconstruction through quantitative structure models (QSM) against destructive reference data of 60 trees and compare them to operational allometric scaling models (ASM). Secondly, we determine the explanatory power of TLS-derived geometric parameters regarding total wood, stem, coarse wood and fine branch volume. We observe similar accuracy in merchantable (¿7 cm) wood compartments for ASMs (NRMSE = 25 %) and QSMs (NRMSE = 29 %), with QSMs showing better results for broadleaves than conifers and generally overestimating fine branch volume. Feature selection shows that a combination of stem diameters and volume of convex hulls around tree crowns has the most potential to model the entire tree volume including branches, especially for conifers. In cases where the quality of available point clouds is insufficient for QSMs, 3D information can thus still be utilised by deriving geometric parameters. The integration of crown dimension parameters into new allometric models could substantially improve the estimation of branch wood volume.
Why it matches plant phenotyping methodsTLSによる個体樹木の体積という植物形質の推定手法を、破壊的実測データと比較検証しており、測定・推定法が研究の中心である。
abstractTerrestrial laser scanning (TLS) enables detailed and non-destructive volume estimation of individual trees
Reproduction assets foundThe paper's Data availability statement points to a public EnviDat deposit (DOI 10.16904/envidat.403) containing the data associated with this TLS-based tree volume estimation study, including the TLS point clouds and destructive reference measurements collected in the SwissBiomass project. No author analysis code URL,Dataset · publicvolume by integrating them into new allometric models.
Declaration of competing interest
The authors declare that they have no known competing finan-
cial interests or personal relationships that could have appeared to
influence the work reported in this paper.
Data availability
Data associated with this article is available at http://dx.doi.org/10.16904/envidat.403.Acknowledgements
The authors would like to thank everyone involved in the WSL
project ‘‘SwissBiomass’’, in the course of which the data used in this
study were collected. In particular, we thank Marina Beck for project
coordination, as well as all those who helped with the field and lab-
oratory measurements. We also thank DanielOpen asset ↗envidat · 10.16904/envidat.403pdf-raw-page:11 lines:1-93Code / dataset availability confirmedEurope PMC · checked 14 Sept 2026
Nitrogen (N) is one of the key inputs in maize production applied in the form of fertilizers. Nitrogen deficiency during the vegetation period leads to lower yields since N is utilized in proteins and enzymes that enable important biochemical processes such as photosynthesis. Nitrogen deficiency leads to specific symptoms that eventually become visible to the naked eye during vegetation. Our hypothesis was that N deficiency can be detected from maize RGB images in parametric process such as a deep neural network. The aim of the reported dataset is to optimize the usage of N in the farmer's fields and accordingly, reduce its environmental footprint. This dataset contains 1200 images of maize canopy from field trials, annotated by an expert from an agricultural institution. The field trials included three levels of N fertilization: N0 without N fertilization, N75 with 75 kg of added N fertilizer, and NFull with 136 kg of added N fertilizer. For each fertilizer level, 400 plots were created with 238 different maize genotypes, resulting in a total of 1200 plots. Images were taken with a tripod mounted DSLR camera, aperture priority set to f/8 and sensor sensitivity set to ISO400. Images were taken at a 45° angle to each plot. This dataset can be useful to both researchers, data scientists and agronomists, especially in the context of emerging technologies in precision agriculture, such as robotics, 5G networks and unmanned aerial vehicle (UAV). The dataset is one of the first publicly accessible datasets of maize canopy images under different N fertilization levels and represents a valuable public resource for development of machine learning models for in-season detection of N deficiency in maize.
Why it matches plant phenotyping methodsトウモロコシの画像から窒素欠乏という植物状態を検出するための注釈付き公開画像データセットであり、機械学習による表現型抽出の基盤として方法論的に中心的です。
abstractThis dataset contains 1200 images of maize canopy from field trials, annotated by an expert from an agricultural institution.
Reproduction assets foundThe paper is a Data in Brief describing a public Mendeley Data deposit of 1200 annotated maize canopy RGB images across three N fertilization levels, plus a preprocessing iPython notebook (TensorFlow_preprocessing.ipynb) included in the same repository. This is a paper-specific, publicly and freely downloadable phenopyDataset · publicers are not. Images at different field rows were taken randomly between 7:30 and 11:00 a.m.
Data source location
•
Institution: Agricultural Institute Osijek (AIO)
•
City/Town/Region: Osijek
•
Country: Croatia
Data accessibility
Repository name: Mendeley Data
Data identification number: 10.17632/g7xnn2bm4g.1
Direct URL to data: https://data.mendeley.com/datasets/g7xnn2bm4g/1
Instructions for accessing these data: Data are freely and anonymously downloadable from the link. Images are compressed into a single .zip file. Additionally, iPython notebook ‘TensorFlow_preprocessing.ipynb’ and ‘requirements.txt’ cover data preprocessing and required libraries to run the scripts.
1.
Value of the Data
Open asset ↗Mendeley Data · 10.17632/g7xnn2bm4g.1lines:1-58Code / dataset availability confirmedEurope PMC · Crossref · checked 7 Sept 2026
Rice is a vital food crop that feeds most of the global population. Cultivating high-yielding and superior-quality rice varieties has always been a critical research direction. Rice grain-related traits can be used as crucial phenotypic evidence to assess yield potential and quality. However, the analysis of rice grain traits is still mainly based on manual counting or various seed evaluation devices, which incur high costs in time and money. This study proposed a high-precision phenotyping method for rice panicles based on visible light scanning imaging and deep learning technology, which can achieve high-throughput extraction of critical traits of rice panicles without separating and threshing rice panicles. The imaging of rice panicles was realized through visible light scanning. The grains were detected and segmented using the Faster R-CNN-based model, and an improved Pix2Pix model cascaded with it was used to compensate for the information loss caused by the natural occlusion between the rice grains. An image processing pipeline was designed to calculate fifteen phenotypic traits of the on-panicle rice grains. Eight varieties of rice were used to verify the reliability of this method. The R 2 values between the extraction by the method and manual measurements of the grain number, grain length, grain width, grain length/width ratio and grain perimeter were 0.99, 0.96, 0.83, 0.90 and 0.84, respectively. Their mean absolute percentage error (MAPE) values were 1.65%, 7.15%, 5.76%, 9.13% and 6.51%. The average imaging time of each rice panicle was about 60 seconds, and the total time of data processing and phenotyping traits extraction was less than 10 seconds. By randomly selecting one thousand grains from each of the eight varieties and analyzing traits, it was found that there were certain differences between varieties in the number distribution of thousand-grain length, thousand-grain width, and thousand-grain length/width ratio. The results show that this method is suitable for high-throughput, non-destructive, and high-precision extraction of on-panicle grains traits without separating. Low cost and robust performance make it easy to popularize. The research results will provide new ideas and methods for extracting panicle traits of rice and other crops.
Why it matches plant phenotyping methodsイネ穂上粒の形態形質を画像・深層学習で抽出する手法を開発し、手動測定との比較で検証しており、表現型取得が研究の中心である。
abstractThis study proposed a high-precision phenotyping method for rice panicles based on visible light scanning imaging and deep learning technology
Reproduction assets foundThe paper's data availability statement explicitly points to a public GitHub repository (BME-PhenoTeam/Method-for-on-panicle-rice-grain-detection) hosting the study's datasets, which per the statement contain the paper's rice panicle images and phenotyping resources. No separate trained-model checkpoint or analysis URLDataset · 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/BME-PhenoTeam/Method-for-on-panicle-rice-grain-detection .Open asset ↗BME-PhenoTeam/Method-for-on-panicle-rice-grain-detectionlines:521-553Code / dataset availability confirmedEurope PMC · checked 14 Sept 2026
Detailed and precise knowledge of production parameters (yield, quality, health status, etc.) in agriculture is the basis for analyzing the effect of any agricultural practice. Fine mapping of production parameters makes it possible to identify the origin of observed variability, whether associated with environmental factors or with agricultural practices. In viticulture, in real commercial context, these data are rare because monitoring systems embedded on harvesting machines for grape yield and quality are not yet available. As a result, they are costly and/or cumbersome to acquire manually. As an alternative, a research project has been proposed to test low-cost methods using GNSS tracking devices for yield and harvest quality mapping in viticulture. The data set was acquired as part of this research. The methodology was applied on a commercial vineyard of 30 ha during the whole 2022 harvest season. The method has identified harvest sectors (HS) associated to measured production parameters (grape mass and harvest quality parameters: sugar content, total acidity, pH, yeast assimilable nitrogen, organic nitrogen) and calculated production parameters (potential alcohol of grapes, yield, yield per plant, percentage of unproductive plants) over the entire vineyard. The grape mass was measured at the vineyard cellar or at the wine-growing cooperative by calibrated scales. The harvest quality parameters were measured from samples on grape must at a commercial laboratory specialized in oenological analysis (Institut Coopératif du Vin, Montpellier, France) with standardized protocols. The percentage of unproductive plants of a harvest sector was calculated from the manually geolocation of each unproductive plants (dead plants + missing plants) over the entire vineyard, the plantation density of blocks, and the geolocalization of the harvest sector. The mean area of these harvest sectors is 0.3 ha. The data set is supplemented by climatic data from a weather station deployed in the center of the vineyard. It provided three climatic parameters (relative humidity, rainfall, air temperature) every 15 min, for the 2020, 2021 and 2022 years. It was also supplemented by a complete description of the vineyard blocks (grape variety, plantation year, area, inter-row distance and vine distance). The proposed data set constitutes a unique and interesting resource for research in agronomy, vine ecophysiology and remote sensing. It can be used for any research in vine ecophysiology aimed at identifying potential relationships between yield and harvest quality parameters for different grape varieties. The data set only covers one year, which is a limitation for studying inter-annual variability of the parameters measured. Another limitation of the method concerns the footprint (0.3 ha on average) of the parameters measured.
Why it matches plant phenotyping methodsGNSSを用いた低コストのブドウ収量・収穫品質マッピング手法と、その大規模データセットが研究の中心であり、収量や不生産株割合などの植物・圃場形質を抽出している。
abstracta research project has been proposed to test low-cost methods using GNSS tracking devices for yield and harvest quality mapping in viticulture.
Reproduction assets foundThe article is a Data in Brief describing the authors' own public Zenodo deposit containing the vineyard phenotyping measurements (block, agronomic/harvest-sector, and weather data as .shp and .csv files), plus an example analysis script (Yield_vs_variety.py) and yield map, all hosted at the stated Zenodo DOI.Dataset · publicce), as well as the geolocation of unproductive wines were obtained from the vineyard Farm Management Information System.
Data source location
Institution: Institut Agro Montpellier
City: Montpellier
Country: France
Data accessibility
Repository name: Zenodo
Data identification number: 10.5281/zenodo.8328384
Direct URL to data: https://doi.org/10.5281/zenodo.8328384
Related research article
J-P. Gras, S. Moinard, T. Crestey and B. Tisseyre. Mapping grape yield with low-cost vehicle tracking devices, In Precision agriculture’23 , Wageningen Academic Publishers. (2023) 555-561. https://doi.org/10.3920/978-90-8686-947-3_70
1.
Value of the Data
The dataset presented in this paper is a particulOpen asset ↗Zenodo · 10.5281/zenodo.8328384lines:32-61Code / dataset availability confirmedEurope PMC · checked 15 Sept 2026
In greenhouse production, grey mould caused by Botrytis cinerea Pers. is one of the most widespread and damaging diseases affecting medicinal cannabis (MC). Fungicide options to control this disease are extremely limited due to the regulations surrounding fungicides and chemical residues as the product end users are medical patients, often with compromised immune systems. Screening for alternative disease control options, such as biological and organic products, can be time-consuming and costly. Here, we optimise and validate a detached leaf assay as a quick and non-destructive method to evaluate interactions between plants and pathogens, allowing the assessment of potential pathogens' infectivity and product efficacy. We tested eight industrial hemp varieties for susceptibility to B. cinerea infection. Using detached leaves from a susceptible variety, we screened a variety of chemical or organic products for efficacy in controlling the lesion development caused by B. cinerea . A consistent reduction in lesion growth was observed using treatments containing Tau-fluvalinate and Myclobutanil, as well as the softer chemical alternatives containing potassium salts. The performance of treatments was pH-dependent, emphasizing the importance of applying them at optimal pH levels to maximise their effectiveness. The detached leaf assay differentiated varietal susceptibility and was an effective method for screening treatment options for diseases caused by Botrytis. The results from the detached leaf assays gave comparable results to responses tested on whole plants.
Why it matches plant phenotyping methodsボトリティス感染による葉の病斑進展を測定するデタッチドリーフアッセイを最適化・検証し、品種感受性と製品効果のスクリーニングに用いているため、植物病害状態の取得手法が中心です。
abstractHere, we optimise and validate a detached leaf assay as a quick and non-destructive method to evaluate interactions between plants and pathogens, allowing the assessment of potential pathogens' infectivity and product efficacy.
Reproduction assets foundThe paper's detached leaf assay lesion measurements are contained in the manuscript and its Supplementary Materials (Tables S1 and S2 with average daily lesion length data), which are publicly downloadable from the MDPI article's supplementary file URL. No author analysis code or trained models are mentioned.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/plants12183278/s1 , Table S1: Average daily lesion length results from four repeated experiments screening the efficacy of products against B. cinerea .; Table S2: Detached leaf assay results for four repeated experiments screening the efficacy of products with amended pH against B. cinerea .
Click here for additional data file.
AuOpen asset ↗lines:67-140Code / dataset availability confirmedOpenAlex · Europe PMC · Crossref · checked 15 Sept 2026
Among seed attributes, weight is one of the main factors determining the soybean harvest index. Recently, the focus of soybean breeding has shifted to improving seed size and weight for crop optimization in terms of seed and oil yield. With recent technological advancements, there is an increasing application of imaging sensors that provide simple, real-time, non-destructive, and inexpensive image data for rapid image-based prediction of seed traits in plant breeding programs. The present work is related to digital image analysis of seed traits for the prediction of hundred-seed weight (HSW) in soybean. The image-based seed architectural traits (i-traits) measured were area size (AS), perimeter length (PL), length (L), width (W), length-to-width ratio (LWR), intersection of length and width (IS), seed circularity (CS), and distance between IS and CG (DS). The phenotypic investigation revealed significant genetic variability among 164 soybean genotypes for both i-traits and manually measured seed weight. Seven popular machine learning (ML) algorithms, namely Simple Linear Regression (SLR), Multiple Linear Regression (MLR), Random Forest (RF), Support Vector Regression (SVR), LASSO Regression (LR), Ridge Regression (RR), and Elastic Net Regression (EN), were used to create models that can predict the weight of soybean seeds based on the image-based novel features derived from the Red-Green-Blue (RGB)/visual image. Among the models, random forest and multiple linear regression models that use multiple explanatory variables related to seed size traits (AS, L, W, and DS) were identified as the best models for predicting seed weight with the highest prediction accuracy (coefficient of determination, R 2= 0.98 and 0.94, respectively) and the lowest prediction error, i.e., root mean square error (RMSE) and mean absolute error (MAE). Finally, principal components analysis (PCA) and a hierarchical clustering approach were used to identify IC538070 as a superior genotype with a larger seed size and weight. The identified donors/traits can potentially be used in soybean improvement programs
Why it matches plant phenotyping methodsRGB画像から種子形態形質を抽出し、機械学習で種子重量を予測する画像ベース表現型解析が研究の中心である。
abstractThe present work is related to digital image analysis of seed traits for the prediction of hundred-seed weight (HSW) in soybean.
Reproduction assets foundThe paper's image-based seed architectural trait (i-trait) measurements and hundred-seed weight data for 164 soybean accessions are stated to be included in the article's Supplementary Material (e.g., Supplementary Table 1 of genotypes and trait data), publicly available at the Frontiers supplementary-material URL. No专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.2023.1206357/full#supplementary-material
Click here for additional data file.
Click here for additional data file.
References
Abdelhakim L. O. A., Rosenqvist E., Wollenweber B., Spyroglou I., Ottosen C. O., Panzarová K. (2021). Investigating combined drought-and heat stress effects in wheat under controlled conditions Open asset ↗lines:434-465Code / 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
Plant breeding is gaining importance as a sustainable tool to address the challenges posed by a growing global population and enhance food security. Advanced high-throughput omics technologies are utilized to accelerate crop improvement and develop resilient varieties with higher yield performance. These technologies generate vast genetic data, which can be exploited to manipulate key plant characteristics for crop improvement. The integration of big data and AI in plant breeding has the potential to revolutionize the field and increase food security. By using branching data (phenotype) of 1918 soybean accessions and 42k SNP polymorphic data (genotype), this study systematically compared 11 non-linear regression AI models, including four deep learning models (DBN regression, ANN regression, Autoencoders regression, and MLP regression) and seven machine learning models (e.g., SVR, XGBoost regression, Random Forest regression, LightGBM regression, GPS regression, Decision Tree regression, and Polynomial regression). After being evaluated by four valuation metrics: R2 (R-squared), MAE (Mean Absolute Error), MSE (Mean Squared Error), and MAPE (Mean Absolute Percentage Error), it was found that the SVR, ANN, and Autoencoder outperformed other models and could obtain a better prediction accuracy if they were used for phenotype prediction. To support the evaluation of deep learning methods, feature importance and GO enrichment analyses were conducted. After comprehensively comparing four feature importance algorithms, there was no significant difference among the feature importance ranking score among these four algorithms, but the SHAP value could provide rich information on genes with negative contributions, and SHAP importance was chosen for feature selection. The genes identified by the SVR model plus SHAP importance combination clearly grouped into three clusters on the soybean whole genome. Our GO enrichment results also confirmed the prediction accuracy of this methods combination. The results of this study offer valuable insights for AI-mediated plant breeding, addressing challenges faced by traditional breeding programs. The method developed has broad applicability in phenotype prediction, minor QTL mining, and plant smart-breeding systems, contributing significantly to the advancement of AI-based breeding practices and transitioning from experience-based to data-based breeding.
Why it matches plant phenotyping methodsダイズ分枝形質の予測を対象に、11種類の非線形回帰モデルを比較・評価し、SHAPによる特徴量選択も行っており、植物表現型推定手法が研究の中心である。
abstractAfter being evaluated by four valuation metrics: R2 (R-squared), MAE (Mean Absolute Error), MSE (Mean Squared Error), and MAPE (Mean Absolute Percentage Error)
Reproduction assets foundThe paper states that the datasets generated and/or analyzed (soybean branching phenotype and SNP genotype data used for the 11 AI models) are publicly available in a ScienceDB repository, with an explicit authors' URL matching an allowed URL. This is a paper-specific, publicly actionable data asset. No author analysisDataset · publicThe datasets generated and/or analyzed during the current study are available in the ScienceDB repository,
https://www.scidb.cn/en/anonymous/MkFWdklm.Open asset ↗ScienceDBpdf-page:14 lines:1-58Code / dataset availability confirmedCrossref · OpenAlex · checked 15 Sept 2026
Buckwheat plant height is an important indicator for producers. Due to the decline in agricultural labor, the automatic and real-time acquisition of crop growth information will become a prominent issue for farms in the future. To address this problem, we focused on stereo vision and a regression convolutional neural network (CNN) in order to estimate buckwheat plant height. MobileNet V3 Small, NasNet Mobile, RegNet Y002, EfficientNet V2 B0, MobileNet V3 Large, NasNet Large, RegNet Y008, and EfficientNet V2 L were modified into regression CNNs. Through a five-fold cross-validation of the modeling data, the modified RegNet Y008 was selected as the optimal estimation model. Based on the depth and contour information of buckwheat depth image, the mean absolute error (MAE), root mean square error (RMSE), mean square error (MSE), and mean relative error (MRE) when estimating plant height were 0.56 cm, 0.73 cm, 0.54 cm, and 1.7%, respectively. The coefficient of determination (R2) value between the estimated and measured results was 0.9994. Combined with the LabVIEW software development platform, this method can estimate buckwheat accurately, quickly, and automatically. This work contributes to the automatic management of farms.
Why it matches plant phenotyping methodsステレオビジョンと回帰CNNにより、圃場でのソバ草丈を自動推定する手法を開発・検証しており、植物表現型の取得方法が研究の中心です。
abstractwe focused on stereo vision and a regression convolutional neural network (CNN) in order to estimate buckwheat plant height.
Reproduction assets foundThe paper's buckwheat height estimation model code (modified regression CNNs with training results) is publicly available via an authors' GitHub repository explicitly stated in the text. The phenotype dataset (depth images with height labels) is only available by contacting the authors, so it is not a public asset.Code · publicndows 11 (64 bit), and an
Nvidia GeForce RTX 3090 24 GB graphics card with Nvidia Ampere architecture. All of
the models used the processed grayscale images of 224 × 224 pixels as the input and the
estimated buckwheat height as the output. The codes of the models with training results
are available at the following GitHub link: https://github.com/18801389568/Buckwheat-height-estimation (accessed on 26 July 2023).
Figure 5. Construction method of the buckwheat crop height estimation models.
Training the models was essentially a process of continually updating the trainable
parameters of each model in order to make the crop height estimation results increasingly
accurate. Considering the quantOpen asset ↗18801389568/Buckwheat-height-estimationpdf-raw-page:6 lines:1-60Code / dataset availability confirmedOpenAlex · Europe PMC · checked 15 Sept 2026
The study of genomic control of drought tolerance in crops requires techniques to impose well defined and consistent levels of drought stress and efficiently measure single-plant water use for hundreds of experimental units over timescales of several months. Traditional gravimetric methods are extremely labor intensive or require expensive technology, and are subject to other errors. This study demonstrates a low-cost, passive, bottom-watered system that is easily scaled for high-throughput phenotyping. The soil water content in the pots is controlled by altering the water table height in an underlying wicking bed via a float valve. The resulting soil moisture profile is then maintained passively as water withdrawn by the plant is replaced by upward movement of water from the wicking bed, which is fed from a reservoir via the float valve. The single-plant water use can be directly measured over time intervals from one to several days by observing the water level in the reservoir. Using this method, four different drought stress levels were induced in pots containing soybean (Glycine max (L.) Merr.), producing four statistically distinct groups for shoot dry weight and seed yield, as well as clear treatment effects for other relevant parameters, including root:shoot dry weight ratio, pod number, cumulative water use, and water use efficiency. This system has a broad range of applications, and should increase feasibility of high-throughput phenotyping efforts for plant drought tolerance traits.
Why it matches plant phenotyping methods高スループット表現型解析のための低コスト灌水・水利用測定システムを開発・実証しており、植物の水利用と乾燥ストレス関連形質の取得が中心的な方法論的貢献である。
abstractThis study demonstrates a low-cost, passive, bottom-watered system that is easily scaled for high-throughput phenotyping.
Reproduction assets foundThe article's Data availability statement places the study's original contributions (phenotype measurements and supplementary experiment data) in the article/Supplementary Material, which is publicly available at the Frontiers supplementary-material URL. No author analysis code, scripts, models, or standalone phenotypeSupplement · publicof their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.
Supplementary material
The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fpls.2023.1201102/full#supplementary-material
Click here for additional data file.
Click here for additional data file.
Click here for additional data file.
Click here for additional data file.
Click here for additional data file.
References
Araya Y. N.
Gowing D. J.
Dise N.
( 2010 ).
A controlled water-table depth system toOpen asset ↗lines:288-364Code / dataset availability confirmedEurope PMC · checked 14 Sept 2026
Soy is the main product of Brazilian agriculture and the fourth most cultivated bean globally. Since soy cultivation tends to increase and due to this large market, the guarantee of product quality is an indispensable factor for enterprises to stay competitive. Industries perform vigor tests to acquire information and evaluate the quality of soy planting. The tetrazolium test, for example, provides information about moisture damage, bedbugs, or mechanical damage. However, the verification of the damage reason and its severity are done by an analyst, one by one. Since this is massive and exhausting work, it is susceptible to mistakes. Proposals involving different supervised learning approaches, including active learning strategies, have already been used, and have brought significant results. Therefore, this paper analyzes the performance of non-supervised techniques for classifying soybeans. An extensive experimental evaluation was performed, considering (9) different clustering algorithms (partitional, hierarchical, and density-based) applied to 5 image datasets of soybean seeds submitted to the tetrazolium test, including different damages and/or their levels. To describe those images, we considered 18 extractors of traditional features. We also considered four metrics (accuracy, FOWLKES, DAVIES, and CALINSKI) and two-dimensionality reduction techniques (principal component analysis and t-distributed stochastic neighbor embedding) for validation. Results show that this paper presents essential contributions since it makes it possible to identify descriptors and clustering algorithms that shall be used as preprocessing in other learning processes, accelerating and improving the classification process of key agricultural problems.
Why it matches plant phenotyping methods大豆種子画像から損傷とその程度を分類するため、複数のクラスタリング手法・特徴量・評価指標を比較検証しており、種子状態の表現・抽出手法が中心である。
titleAssessment of clustering techniques to support the analyses of soybean seed vigor.
Reproduction assets foundThe paper's Data Availability statement points to the authors' public GitHub repository containing the soybean seed image datasets and feature files used in the clustering experiments. JFeatureLib is a generic third-party library, not a paper-specific asset.Dataset · publiclf no pmc-prop-manuscript no pmc-prop-legally-suppressed no pmc-prop-has-pdf yes pmc-prop-has-supplement no pmc-prop-pdf-only no pmc-prop-suppress-copyright no pmc-prop-is-real-version no pmc-prop-is-scanned-article no pmc-prop-preprint no pmc-prop-in-epmc yes pmc-license-ref CC BY Data Availability All files are available from https://github.com/BioinfoCP/visual-features-soybean-vigor .
Data Availability
All files are available from https://github.com/BioinfoCP/visual-features-soybean-vigor .
1 Introduction
Soy is the fourth most cultivated bean globally and the main product in Brazilian agriculture. In 2021/22, Brazil estimates a production record of 142,009 million tons of soybeans. This Open asset ↗BioinfoCP/visual-features-soybean-vigorlines:48-65Code / 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
1038
Data Availability
1039
An annotated script of the R code used in this research can be accessed via GitHub
1040
(https://github.com/ajdesalvio/Maize-NIRS-GBS.git). Supplementary Data 1
1041
(Supplementary_Data_1.xlsx) contains prediction results, GWAS results, and variable importance
1042
scores for NIRS bands. Files necessary to run the R script and reproduce the prediction results are
1043
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 confirmedOpenAlex · checked 15 Sept 2026
Premise: Many plant communities across the world are undergoing changes due to climate change, human disturbance, and other threats. These community-level changes are often tracked with the use of permanent vegetative plots, but this approach is not always feasible. As an alternative, we propose using photogrammetry, specifically photograph-based digital surface models (DSMs) developed using structure-from-motion, to establish virtual permanent plots in plant communities where the use of permanent structures may not be possible. Methods: plots distributed across alpine communities in the northeastern United States. We then compared field estimates of percent coverage with coverage estimated using DSMs. Results: Digital surface models can provide effective, minimally invasive, and permanent records of plant species presence and percent coverage, while also allowing managers to mark survey locations virtually for long-term monitoring. We found that percent coverage estimated from DSMs did not differ from field estimates for most species and substrates. Discussion: In order to continue surveying efforts in areas where permanent structures or other surveying methods are not feasible, photogrammetry and structure-from-motion methods can provide a low-cost approach that allows agencies to accurately survey and record sensitive plant communities through time.
Why it matches plant phenotyping methods植物群落の種存在と被覆率を、写真測量・SfMによるDSMから推定する手法を開発し、現地推定値と比較検証しているため、植物フェノタイピング手法が中心です。
abstractwe propose using photogrammetry, specifically photograph-based digital surface models (DSMs) developed using structure-from-motion, to establish virtual permanent plots in plant communities
Reproduction assets foundThe authors publicly deposited the 3D image models (DSMs/virtual permanent plots) created in this study on FigShare, as stated in the Data Availability Statement. Other URLs (Agisoft, QGIS, R Metrics, GLORIA) are generic tools or cited prior work, not paper-specific assets.Dataset · publiccreate virtual permanent plots in rare and threatened plant communities. Applications in Plant Sciences
11(5): e11534. 10.1002/aps3.11534
This article is part of the special issue “Advances in Plant Imaging across Scales.”
DATA AVAILABILITY STATEMENT
3D image models created during this project can be found online on FigShare ( https://figshare.com/s/03a500bf7717afe3a9a6 ).
REFERENCES
Agisoft Helpdesk Portal
. 2022. 3D Model Reconstruction. Website: https://agisoft.freshdesk.com/support/solutions/articles/31000152092 [accessed 12 June 2023].
Barros, A.
, Aschero V., Mazzolari A., Cavieres L. A., and Pickering C. M.. 2020. Going off trails: How dispersed visitor use affects alpine vegetation. Open asset ↗FigSharelines:99-133Code / dataset availability confirmedEurope PMC · Crossref · checked 7 Sept 2026
Wheat stripe rust disease (WRD) is extremely detrimental to wheat crop health, and it severely affects the crop yield, increasing the risk of food insecurity. Manual inspection by trained personnel is carried out to inspect the disease spread and extent of damage to wheat fields. However, this is quite inefficient, time-consuming, and laborious, owing to the large area of wheat plantations. Artificial intelligence (AI) and deep learning (DL) offer efficient and accurate solutions to such real-world problems. By analyzing large amounts of data, AI algorithms can identify patterns that are difficult for humans to detect, enabling early disease detection and prevention. However, deep learning models are data-driven, and scarcity of data related to specific crop diseases is one major hindrance in developing models. To overcome this limitation, in this work, we introduce an annotated real-world semantic segmentation dataset named the NUST Wheat Rust Disease (NWRD) dataset. Multileaf images from wheat fields under various illumination conditions with complex backgrounds were collected, preprocessed, and manually annotated to construct a segmentation dataset specific to wheat stripe rust disease. Classification of WRD into different types and categories is a task that has been solved in the literature; however, semantic segmentation of wheat crops to identify the specific areas of plants and leaves affected by the disease remains a challenge. For this reason, in this work, we target semantic segmentation of WRD to estimate the extent of disease spread in wheat fields. Sections of fields where the disease is prevalent need to be segmented to ensure that the sick plants are quarantined and remedial actions are taken. This will consequently limit the use of harmful fungicides only on the targeted disease area instead of the majority of wheat fields, promoting environmentally friendly and sustainable farming solutions. Owing to the complexity of the proposed NWRD segmentation dataset, in our experiments, promising results were obtained using the UNet semantic segmentation model and the proposed adaptive patching with feedback (APF) technique, which produced a precision of 0.506, recall of 0.624, and F1 score of 0.557 for the rust class.
Why it matches plant phenotyping methods病害植物画像のセマンティックセグメンテーションデータセットを構築し、罹病範囲の推定を評価することが研究の中心であり、植物病害状態の画像ベース表現型計測に該当する。
abstractin this work, we introduce an annotated real-world semantic segmentation dataset named the NUST Wheat Rust Disease (NWRD) dataset.
Reproduction assets foundThe authors explicitly make the NWRD segmentation dataset, implementation, and pretrained models publicly available on GitHub.Dataset · publicWe make our dataset, implementation, and pretrained models publicly available at https://github.com/dll-ncai/NUST-Wheat-Rust-Disease-NWRDOpen asset ↗dll-ncai/NUST-Wheat-Rust-Disease-NWRDlines:23-28Code / dataset availability confirmedEurope PMC · checked 7 Sept 2026
Abstract This research presents an innovative approach called Tenacious Fish Swarm Optimization based Hidden Markov Model (TFSO-HMM) for augmented accurate cotton leaf disease identification and yield prediction. Cotton leaf diseases significantly threaten crop productivity, requiring timely detection and precise prediction for effective disease management. The proposed TFSO-HMM framework combines the strengths of Tenacious Fish Swarm Optimization (TFSO) and the Hidden Markov Model (HMM) to address the challenges associated with disease identification and yield prediction in cotton plants. TFSO, a nature-inspired optimization algorithm, optimizes the classification process, enhancing the accuracy of disease identification. By harnessing the collective intelligence of fish swarms, TFSO intelligently explores the search space to identify the optimal solution. The selected information is then incorporated into the HMM framework, which captures the temporal dependencies in disease progression and yield prediction. HMM's sequential modelling approach facilitates understanding the dynamic behaviour of cotton leaf diseases over time, leading to more accurate predictions. Experimental results on a comprehensive dataset demonstrate the superior performance of the TFSO-HMM method over existing approaches in terms of accuracy and predictive capability. The augmented accuracy achieved through TFSO-HMM enables early detection and precise prediction of cotton leaf diseases, enabling timely interventions for disease management and maximizing crop yield.
Why it matches plant phenotyping methods綿花葉の病害状態を対象に、TFSO-HMMという計算手法を開発・評価して病害識別を行っており、植物の病害表現型の抽出が中心的です。
abstractThis research presents an innovative approach called Tenacious Fish Swarm Optimization based Hidden Markov Model (TFSO-HMM) for augmented accurate cotton leaf disease identification and yield prediction.
Reproduction assets foundThe paper uses the public Kaggle 'Cotton Plant Disease Dataset' as its phenotyping image dataset, with an explicit public URL. The authors' analysis code is only available on request, so it does not qualify as a public asset.Dataset · publicThe “Cotton Plant Disease Dataset” available at
https://www.kaggle.com/datasets/dhamur/cotton-plant-diseaseOpen asset ↗kaggle.com · dhamur/cotton-plant-diseasepdf-page:44 lines:1-24Code / 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 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 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:53-66