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 confirmedOpenAlex · checked 14 Sept 2026
Early crop establishment strongly influences plant performance and yield, making seedling emergence an important trait in crop phenotyping, breeding, and stress physiology studies. However, emergence monitoring is still commonly performed manually and typically records only the final emergence percentage, limiting the analysis to other dynamic observations. Automated image-based approaches are promising but remain challenging due to the small size of plant structures, heterogeneous soil backgrounds, and variability across imaging systems. Here, we present SPROUT (AI- based S eedling PR edicti O n and trait extraction U sing RGB T ime-series), a low-cost RGB imaging pipeline for automated prediction of crop emergence dynamics and trait extraction. The system integrates instance segmentation, object-detection–based data reduction, and temporal deep learning to estimate the emergence time of individual seedlings from RGB image sequences. The pipeline then automatically reconstructs emergence curves and extracts associated traits, including final emergence percentage, EC50, and emergence synchronicity. SPROUT was developed and evaluated using barley and wheat datasets acquired with different RGB cameras under controlled growth-chamber conditions. In the development and retraining settings, the best-performing TCN model achieved 90.0% per-well accuracy with a ± 2h tolerance, supporting accurate emergence curve reconstruction. In an independent inference-only dataset, the model still captured approximate emergence dynamics, although accuracy decreased to 59.3%, indicating that SPROUT is best used as a modular pipeline that can be retrained or fine-tuned for new crop, camera, or experimental domains. A cadmium-stress case study in two contrasting wheat genotypes showed that SPROUT-derived traits captured genotype-specific establishment strategies associated with growth and metabolic responses.
Why it matches plant phenotyping methodsRGB時系列画像から出芽動態を推定し、出芽率・EC50・同時性などの形質を抽出するパイプラインを開発・評価しており、植物フェノタイピング手法が中心である。
abstractHere, we present SPROUT (AI- based S eedling PR edicti O n and trait extraction U sing RGB T ime-series), a low-cost RGB imaging pipeline for automated prediction of crop emergence dynamics and trait extraction.
Reproduction assets foundThe paper's SPROUT emergence-prediction pipeline code is publicly available on GitHub with explicit availability language, and raw images plus morphology/metabolic data are deposited on Zenodo (10.5281/zenodo.18889863). The GitHub URL is in allowed_urls; the Zenodo DOI is not, so only the code asset is listed as an ad-Code · publiccan be found online at https://doi.org/10.1016/j.compag.2026.112184.Data availability
The raw images and raw data for the morphology and metabolic
profiling on the case study are available in ZENODO (10.5281/zen
odo.18889863), and the code for the machine learning pipeline and
emergence curve analysis are available on GitHub (https://github.com/kit-pef-czu-cz/sprout-emergence-prediction).References
Albarenque, S., Basso, B., Davidson, O., Maestrini, B., Melchiori, R., 2023. Plant
emergence and maize (Zea mays L.) yield across multiple farmers’ fields. Field Crops
Res. 302. https://doi.org/10.1016/j.fcr.2023.109090.Arsovski, A.A., Galstyan, A., Guseman, J.M., Nemhauser, J.L., 2012.
PhotomorpOpen asset ↗kit-pef-czu-cz/sprout-emergence-predictionpdf-raw-page:13 lines:78-112Code / dataset availability confirmedOpenAlex · Europe PMC · checked 5 Sept 2026
Traditional deep learning-based plant computed tomography (CT) image segmentation methods require a large amount of high-quality manually labeled data for model training specific to each species, leading to substantial labor costs and poor adaptability to new species. These limitations hinder the application of CT imaging in large-scale cross-species plant phenotyping analysis. Therefore, developing annotation-free and training-free plant CT image segmentation methods is of significant research and application value in reducing research costs and promoting the efficiency of cross-species analysis. To achieve this, we introduce an unsupervised zero-shot segmentation framework for cross-species plant CT images, DiffPlantCT. It is a 2D-to-3D framework that first segments all 2D slices and then assembles them in their original order to generate a 3D CT segmentation. For each slice, this framework directly constructs discriminative clustering features by combining the general semantic priors provided by the self-attention layers in a pre-trained stable diffusion model with the intrinsic grayscale distribution of original image, thereby completely avoiding the need for manual annotations. The method ultimately outputs segmentation results solely through unsupervised clustering, achieving zero-shot generalization without any model training or fine-tuning. To evaluate the feasibility of DiffPlantCT in cross-species segmentation, we benchmark the segmentation performance on two public datasets (walnut fruit and barley spike) and two self-collected datasets (wheat spike and rice panicle). The results show that DiffPlantCT achieved the best performance, with a 41.6% improvement in overall mIoU compared to the state-of-the-art unsupervised method. For the first time, we demonstrate annotation-free, training-free segmentation of cross-species plant CT images successfully.
Why it matches plant phenotyping methods植物CT画像から3D形状を抽出する、アノテーション不要・学習不要の分割手法を開発し、複数作物データセットで性能評価しており、表現型取得手法が研究の中心である。
abstractwe introduce an unsupervised zero-shot segmentation framework for cross-species plant CT images, DiffPlantCT.
Reproduction assets foundThe paper open-sources the DiffPlantCT implementation code on GitHub and benchmarks on two public plant CT datasets (walnut fruit via figshare; barley spike via Plant Methods), all with explicit availability statements and matching allowed URLs.Code · publicThe datasets and implementation code of the DiffPlantCT framework are open-sourced on GitHub at https://github.com/WeizhenLiuBioinform/DiffPlantCT_Zero-Shot_Plant_CT_Segmentation .Open asset ↗WeizhenLiuBioinform/DiffPlantCT_Zero-Shot_Plant_CT_Segmentationlines:220-287Code / dataset availability confirmedOpenAlex · Europe PMC · bioRxiv · checked 5 Sept 2026
Abstract Waterlogging is a major constraint on barley productivity, yet its dynamic, multi-phase nature makes it challenging to dissect using traditional phenotyping approaches. High-throughput phenotyping (HTP) platforms address this by enabling temporal, multi-sensor imaging of large populations, but generate complex datasets that demand new analytical frameworks. Here, we imaged 230 barley accessions over 14 days of waterlogging stress and seven days of recovery using visible, chlorophyll fluorescence, and hyperspectral sensors. Explainable AI was applied to classify stress responses into early stress, late stress, and recovery phases, achieving 86% classification accuracy, and to identify the hyperspectral indices most informative for each phase. Water index (WATER1) and structure insensitive pigment index (SIPI) emerged as primary predictors of stress response. Longitudinal genome-wide association studies (GWAS), using a treatment-by-marker interaction model, identified 236 significant loci across 12 linkage disequilibrium blocks, implicating candidate genes involved in oxidative stress regulation, transcriptional control, and auxin transport. MYB transcription factors were consistently identified across all stress phases, underscoring their central role in waterlogging adaptation. To support interpretation of longitudinal GWAS results, we developed 3D-QTLVis, an interactive visualisation tool that extends Manhattan plots across time, enabling clearer identification of dynamic genomic regions underlying stress tolerance.
Why it matches plant phenotyping methods長期マルチセンサー画像による水ストレス応答の表現型取得と、AIによるフェーズ分類・指標抽出が研究の中心であり、3D-QTLVisも開発している。
abstractHigh-throughput phenotyping (HTP) platforms address this by enabling temporal, multi-sensor imaging of large populations
Reproduction assets foundThe paper's authors publicly release their GWAS Interaction model R scripts and the 3D-QTLVis Shiny visualization tool on GitHub; no public phenotype dataset or trained model deposit is stated (phenotypic data only as summary statistics in supplements).Code · publicCode used for running the GWAS interaction model in R and the 3D-QTLVis tool are available at https://github.com/Walshj73/3D-QTLVis .Open asset ↗Walshj73/3D-QTLVislines:216-267Code / dataset availability confirmedOpenAlex · Europe PMC · bioRxiv · Crossref · checked 15 Sept 2026
BarleyCommon beanCowpeaGrowth chamberMesh / voxelLiDAR / point cloudMultispectral / hyperspectralLeafWhole plant / canopy / plot / fieldAnnotation / quality control
Abstract High-throughput 3D multispectral plant phenotyping platforms generate large volumes of point cloud files, but trait extraction is typically performed by sensor-bundled software whose internal algorithms are not publicly documented, which limits reproducibility and integration into custom research pipelines. Here we present PhytoScan3D, an open-source Python pipeline that extracts morphological and spectral phenotypic traits, spanning plant height, 3D leaf area, digital biomass, convex hull volume, leaf inclination, canopy geometry, NDVI, hue, and vegetation indices, from both PLY and PCD point cloud files generated by Phenospex PlantEye F500 and F600 sensors, and is portable to point clouds from any acquisition platform. PhytoScan3D was validated against HortControl (PhenoSpex) ground-truth measurements on 936 barley ( Hordeum vulgare ) pot-date observations from the growth chamber trial (20 Norwegian cultivars, 12 scan dates, Septemenr 2025 to January 2026), achieving Pearson r = 0.913 to 0.999 and ratio approximately 1.000 for Plant Height Max, 3D Leaf Area, and NDVI Average. A vectorised mesh face filtering implementation achieved a 120x speed improvement, increasing valid 3D Leaf Area coverage from 0.6% to 100% of files. Cross-format validation on 223 PlantEye F600 PCD files from the ICRISAT LeasyScan platform (four legume species: mungbean, cowpea, lima bean, and common bean; 1,523 plant observations) yielded r = 0.884 against independent cuboid annotation heights. The systematic positive bias (mean +27.2 mm, ratio = 1.44) is attributable to PhytoScan3D computing height from raw point cloud Z-range while cuboid annotations are fitted to segmented plant points only, with the offset consistent across all four species (per-species r = 0.880 to 0.888). Cross-dataset processing of 1,180 PLY files from the Crops3D benchmark (8 species, 3 acquisition methods) confirmed zero extraction errors. PhytoScan3D is available at “github.com/kovimallik/phytoscan3d” under the MIT licence and processes 1,651 files across three independent datasets in under 12 minutes on GPU hardware. Highlights PhytoScan3D is the first open-source Python pipeline for batch extraction of phenotypic traits, including plant height, 3D leaf area, digital biomass, convex hull volume, leaf inclination, NDVI, and excess green index, from both PLY and PCD point cloud files generated by Phenospex PlantEye sensors. Primary validation against HortControl ground-truth measurements on 936 barley pot-date observations achieved Pearson r = 0.913-0.999 for Plant Height Max, 3D Leaf Area, and NDVI Average. A 120x computational speedup in mesh face filtering (vectorised NumPy vs. set-based loop) increased the coverage of valid 3D Leaf Area extraction from 0.6% to 100% of files. Cross-format validation on 223 PlantEye F600 PCD files from ICRISAT LeasyScan (four legume species, 1,523 plants) achieved r = 0.884 against independent cuboid annotation heights. The systematic +27.2 mm bias reflects a methodological difference (raw Z-range vs. soil-segmented annotations), is consistent and predictable across all four species (per-species r = 0.880-0.888), and is correctable by a single linear factor. Cross-dataset processing of 1,180 PLY files from the Crops3D benchmark (8 species, 3 acquisition methods) confirmed zero extraction errors. Significant scan-unit variation was detected for Plant Height Max (F = 5.71, p < 0.001, η 2 = 0.138) and Canopy Width X (F = 6.32, p < 0.001, η 2 = 0.150), demonstrating the biological utility of extracted traits.
Why it matches plant phenotyping methods植物の3D点群・マルチスペクトルデータから形態・スペクトル形質を抽出するオープンソース手法を開発し、複数データセットで技術検証・ベンチマークしているため、植物フェノタイピング手法が中心である。
abstractHere we present PhytoScan3D, an open-source Python pipeline that extracts morphological and spectral phenotypic traits
Reproduction assets foundThe paper's own analysis code (PhytoScan3D pipeline) is publicly released on GitHub under the MIT licence, and the two external 3D point cloud datasets used for validation (Crops3D and ICRISAT LeasyScan) are publicly available on figshare. The primary barley PLY dataset is not yet public (to be deposited in NVA upon).Code · publicditing, Funding acquisition.
Declaration of Competing Interest
The authors declare that they have no known competing financial interests or personal
relationships that could have appeared to influence the work reported in this paper.
Data Availability
PhytoScan3D source code, documentation, and example datasets are available at
https://github.com/kovimallik/phytoscan3d under the MIT licence. The barley PLY dataset
will be deposited in the Norwegian Research Information Repository (NVA) upon acceptance.
The Crops3D benchmark dataset is publicly available at
https://doi.org/10.6084/m9.figshare.27313272 (Zhu et al. 2024). The ICRISAT LeasyScan
dataset is publicly available at https://doi.org/10Open asset ↗github.com/kovimallik/phytoscan3dpdf-raw-page:15 lines:1-36Dataset · publicData Availability
PhytoScan3D source code, documentation, and example datasets are available at
https://github.com/kovimallik/phytoscan3d under the MIT licence. The barley PLY dataset
will be deposited in the Norwegian Research Information Repository (NVA) upon acceptance.
The Crops3D benchmark dataset is publicly available at
https://doi.org/10.6084/m9.figshare.27313272 (Zhu et al. 2024). The ICRISAT LeasyScan
dataset is publicly available at https://doi.org/10.6084/m9.figshare.28270742 (Galba et al.
2025).
Acknowledgements
This work was supported by the PheNo, DLT-Farming and Soil2Milk from Research Council
of Norway and TWIN-NUE from Norwegian University of Life Sciences (NMBU). The
authoOpen asset ↗figshare · 10.6084/m9.figshare.27313272pdf-raw-page:15 lines:1-36Dataset · publicimallik/phytoscan3d under the MIT licence. The barley PLY dataset
will be deposited in the Norwegian Research Information Repository (NVA) upon acceptance.
The Crops3D benchmark dataset is publicly available at
https://doi.org/10.6084/m9.figshare.27313272 (Zhu et al. 2024). The ICRISAT LeasyScan
dataset is publicly available at https://doi.org/10.6084/m9.figshare.28270742 (Galba et al.
2025).
Acknowledgements
This work was supported by the PheNo, DLT-Farming and Soil2Milk from Research Council
of Norway and TWIN-NUE from Norwegian University of Life Sciences (NMBU). The
authors thank Sara Catarina Costa Laranjeira, Min Lin and other NMBU growth facility staff
for plant care and scanning operOpen asset ↗figshare · 10.6084/m9.figshare.28270742pdf-raw-page:15 lines:1-36Code / dataset availability confirmedEurope PMC · checked 5 Sept 2026
Genomic selection (GS) can accelerate crop breeding and enhance selection efficiency. However, accurately predicting genomic estimated breeding values (GEBVs) for complex traits and applying GS in diverse environments remains challenging. To address these issues, we developed a novel hybrid method capable of modelling gene-gene and gene-environment interactions. This method offers precise predictions of phenotypic performance for complex traits, identifies haplotypes associated with desirable phenotypes, and enables prediction of optimal haplotypes tailored to specific environments. We evaluated the approach using a dataset of 855 barley lines, with phenotypic data for grain yield and flowering time collected across multiple environments. The model incorporated 30,543 SNPs, nine soil parameters, and six daily environmental variables, achieving high prediction accuracies, with correlation coefficients of 0.93 for flowering time and 0.82 for grain yield. Our method identified 10 haplotype blocks significantly associated with flowering time and 13 blocks with grain yield, collectively accounting for over 90% of the total genetic variance. Additionally, we predicted the phenotypic effects of each haplotype and identified elite varieties carrying the most favourable haplotypes for crossing design and selection. The method also allows prediction of untested genotype × environment combinations, enabling selection of optimal genotypes for targeted environments. To facilitate its application, we developed a web-based interface (accessible at [https://penghaowang.shinyapps.io/shinygui/]), which enables breeders to identify optimal haplotypes and the varieties that carry them, streamlining the process of haplotype-based, environment-informed breeding. We note that the reverse prediction framework is currently applied on a single-trait basis and does not resolve multi-trait trade-offs such as between flowering time and yield, which remains a topic for future extensions.
Why it matches plant phenotyping methods複雑形質の表現型性能を遺伝子型・環境情報から予測する新規計算手法を開発し、オオムギの収量・開花期で評価している。ウェブインターフェースも提供され、形質推定ワークフローが中心である。
abstractwe developed a novel hybrid method capable of modelling gene-gene and gene-environment interactions.
Reproduction assets foundThe paper deposits its barley genotype, phenotype, and environmental datasets at three DOI repositories, and its analysis source code on GitHub, plus a public Shiny web tool.Dataset · publicDetailed information on all experimental lines, including their genotypes, phenotypic, and environmental data, is available at https://doi.org/10.60867/00000010 , https://doi.org/10.60867/00000003 , and https://doi.org/10.60867/00000011 , respectively.Open asset ↗10.60867 · 10.60867/00000010lines:31-42Dataset · publicDetailed information on all experimental lines, including their genotypes, phenotypic, and environmental data, is available at https://doi.org/10.60867/00000010 , https://doi.org/10.60867/00000003 , and https://doi.org/10.60867/00000011 , respectively.Open asset ↗10.60867 · 10.60867/00000003lines:31-42Dataset · publicDetailed information on all experimental lines, including their genotypes, phenotypic, and environmental data, is available at https://doi.org/10.60867/00000010 , https://doi.org/10.60867/00000003 , and https://doi.org/10.60867/00000011 , respectively.Open asset ↗10.60867 · 10.60867/00000011lines:31-42Code · publicAll the data and source codes have been uploaded to GitHub and can be accessed under the GNU Open License at: https://github.com/pwang2019/GxE_Model .Open asset ↗github.com/pwang2019/GxE_Modellines:196-205Code / dataset availability confirmedEurope PMC · checked 5 Sept 2026
Motivation The AGENT project established a network of actively cooperating European genebanks, integrating genomic and phenotypic data from accessions of wheat and barley. Due to specific storage demands for phenotypic and genotypic data, the project used separate database instances and backend technologies to manage integrated phenotypic and genotypic data. Results We discuss the challenges encountered when integrating dispersed data to serve through a single interface such as the Plant Breeding Application Programming Interface, BrAPI. We examine how the consistent mappability of genebank data to the BrAPI model can enable the implementation of effective services. The advantages of BrAPI in transparently linking distributed data entities through embedded, unique identifiers are highlighted. We present a technical solution involving a BrAPI proxy, which combines and merges separate BrAPI endpoints. Finally, we demonstrate the AGENT BrAPI implementation with an illustrative example that validates a suggested SNP for a trait from the literature by linking phenotypic, genotypic and passport data. Availability and implementation The BrAPI proxy implementation and documentation is available at the Python Package Index (https://pypi.org/project/brapi-proxy) and archived in Zenodo (doi: 10.5281/zenodo.19436445). Supplementary information A Jupyter Notebook file for the validation example using a marker-trait relationship found in the literature.
Why it matches plant phenotyping methods植物の表現型データを含む分散データを統合・提供するBrAPIプロキシの技術実装が中心であり、表現型データ基盤・再利用可能なソフトウェアとして対象に含める。
abstractWe discuss the challenges encountered when integrating dispersed data to serve through a single interface such as the Plant Breeding Application Programming Interface, BrAPI.
Reproduction assets foundThe paper's authors publicly released the BrAPI proxy software used to merge the AGENT project's phenotypic/genotypic BrAPI endpoints, available on PyPI and archived in Zenodo. The supplementary Jupyter Notebook for the marker-trait validation example is mentioned but no public URL is provided, so it is not listed as aCode · publicThe BrAPI proxy implementation and documentation is available at the Python Package Index ( https://pypi.org/project/brapi-proxy ) and archived in Zenodo (doi: 10.5281/zenodo.19436445).Open asset ↗brapi-proxylines:1-44Code / dataset availability confirmedEurope PMC · checked 5 Sept 2026
Background Accurate segmentation of foliar diseases under field conditions is essential for large-scale phenotyping, as breeding programs rely on reliable severity estimates to identify genotypes with improved resistance. However, most deep learning approaches have been developed as pathogen-specific models, which limits scalability in field-grown barley where multiple diseases naturally co-occur and exhibit substantial visual similarity. Results We evaluated whether a multiclass segmentation model can simultaneously detect and distinguish two fungal diseases of barley, Puccinia hordei and Ramularia collo-cygni, and compared its performance with two disease-specific binary models. Using 336 high-resolution leaf scans collected in the field with naturally occurring co-infections, the multiclass model achieved higher Dice scores for brown rust (0.59 vs 0.40; +47.5% relative improvement) and ramularia (0.60 vs 0.53; +13.2% relative improvement). It also captured a greater proportion of individual lesions across both classes. At the genotype level, the model-predicted disease area percentages were highly consistent with those from ground truth annotations ([Formula: see text]). Conclusions A unified multiclass framework can more effectively segment visually similar foliar diseases than separate binary models, while simplifying the computational workflow. This provides a scalable basis for automated resistance assessment within breeding pipelines. Code and data are publicly available at https://github.com/grimmlab/BarleyDiseaseSegmentation, with Mendeley Data dataset DOI 10.17632/4ny92p2r8f.1.
Why it matches plant phenotyping methods圃場画像から葉面病害面積をセグメンテーションし、遺伝子型レベルの病害重症度を推定する手法を開発・比較・検証しており、植物フェノタイピングが中心です。
abstractAccurate segmentation of foliar diseases under field conditions is essential for large-scale phenotyping
Reproduction assets foundThe paper's annotated barley leaf disease segmentation dataset (Mendeley Data DOI 10.17632/4ny92p2r8f.1) and the authors' analysis/segmentation code (GitHub grimmlab/BarleyDiseaseSegmentation) are explicitly declared publicly available, directly reproducing this paper's phenotyping measurements and computational modelsDataset · publicThe annotated dataset and the code implementing our machine learning–based model are publicly available on Mendeley Data (https://doi.org/10.17632/4ny92p2r8f.1) and GitHub (https://github.com/grimmlab/BarleyDiseaseSegmentation).Open asset ↗Mendeley Data · 10.17632/4ny92p2r8f.1lines:133-140Code · publicCode and data are publicly available at https://github.com/grimmlab/BarleyDiseaseSegmentation, with Mendeley Data dataset DOI 10.17632/4ny92p2r8f.1.Open asset ↗GitHub · grimmlab/BarleyDiseaseSegmentationlines:1-70Code / dataset availability confirmedOpenAlex · checked 5 Sept 2026
Abstract Modern agriculture faces the dual challenge of sustainably increasing food production while mitigating the environmental impact of intensive monocultures. Mixed cropping, which is the cultivation of multiple species or varieties, may provide ecological benefits that address productivity and environmental sustainability challenges. However, evaluating its multifunctionality in conventional agricultural field experiments is costly and labour-intensive, and small sample sizes and high spatial variability often make it difficult to detect the statistical significance of mixed cropping effects. This study aims to introduce and validate a high-throughput field phenotyping (HTP) framework that integrates aerial imagery obtained from unmanned aerial vehicles (UAVs) to efficiently assess the multifunctionality of mixed cropping systems. We conducted a field experiment comparing monocultures of oat, rye, and barley; intraspecific mixed cropping combining three oat varieties; and interspecific mixed cropping combining oat, rye, and barley. Using UAV-derived data across the entire field, including vegetation cover, plant height, and the normalised difference vegetation index, we evaluated five multifunctionalities (biomass production, spatial variability in biomass production, early canopy closure, lodging resistance, and lodging resilience). This framework reveals that mixed cropping outperforms monocropping in several key ecological functions. The proposed UAV-based HTP approach enables cost-effective, robust, and scalable evaluation of mixed cropping systems, facilitating their optimisation for multifunctionality and contributing to the advancement of sustainable agriculture.
Why it matches plant phenotyping methodsUAV画像を用いた高スループット圃場フェノタイピング枠組みを導入・検証し、植生被覆、草丈、NDVIから複数の植物形質・状態を抽出しており、フェノタイピング手法が中心的です。
abstractThis study aims to introduce and validate a high-throughput field phenotyping (HTP) framework that integrates aerial imagery obtained from unmanned aerial vehicles (UAVs)
Reproduction assets foundThe paper's Data availability statement explicitly deposits the datasets generated and analysed (UAV-derived phenotyping measurements) in a public Zenodo repository with a DOI matching an allowed URL.Dataset · publicThe datasets generated and analysed during the current study are available in the Zenodo repository, https://doi.org/10.5281/zenodo.17042273.Open asset ↗Zenodo · 10.5281/zenodo.17042273lines:197-235Code / dataset availability confirmedEurope PMC · checked 5 Sept 2026
Published11 Jan 2026TAG. Theoretical and applied genetics. Theoretische und angewandte GenetikCited by 2 · OpenAlex ↗
Utilizing the diversity preserved in genebank collections is essential for accelerating crop improvement, yet information is often limited to selected core collections. Genome-wide prediction (GWP) offers a promising approach to large-scale phenotypic imputation, with proven utility in practical pre-breeding contexts. In this study, we leveraged GWP to expand the German Federal ex situ barley core collection (core1000) with a focus on resistance to Puccinia hordei, Blumeria graminis hordei, and Rhynchosporium commune. Using the barley core1000 collection, which was originally selected to maximize molecular diversity, we trained genomic prediction models and imputed resistance scores for 20,458 genebank accessions based on sequence data encompassing 306,049 high-quality SNPs. To empirically validate prediction accuracy, we selected 300 spring and winter barley genotypes for field evaluation across four environments, resulting in moderate-to-strong correlations between predicted and observed resistance levels. Genome-wide association mapping in this set revealed five marker-trait associations that were not detected in the original core1000 collection. These results demonstrate that prediction-informed sampling can effectively expand trait-relevant genetic diversity and increase the frequency of resistance-associated alleles, thereby improving the power to detect loci that may be overlooked in conventional panels. Accordingly, GWP supports the targeted inclusion of accessions with trait-relevant variation and enhances the value of genebank resources for trait discovery and pre-breeding applications.
Why it matches plant phenotyping methodsゲノム情報から病害抵抗性という植物形質を大規模に推定・補完する方法を開発し、圃場評価で予測精度を検証しているため、方法論が中心である。
abstractGenome-wide prediction (GWP) offers a promising approach to large-scale phenotypic imputation
Reproduction assets foundThe authors deposited the paper's raw disease-resistance phenotypic data, BLUEs for the spring/winter validation set, and the R script for curation/heritability/BLUE computation in the public e!DAL-PGP repository (Yuan 2025). The companion IPK/2024/7 dataset belongs to cited prior work (Yuan et al. 2025 training data).Dataset · publicThe raw phenotypic data described here as well as the ready-to-use phenotypic values (BLUEs) for spring and winter validation set, and the R script to import and curate the raw phenotypic data to compute heritability and BLUEs are available in the e!DAL-PGP Repository (Arend et al. 2016 ) and can be directly accessed here (Yuan 2025 ).Open asset ↗e!DAL-PGP Repositorylines:157-182Code / dataset availability confirmedCrossref · Europe PMC · checked 6 Sept 2026
Early plant disease detection is crucial for sustainable crop production and food security. Stem rust, caused by Puccinia graminis f. sp. tritici, poses a major threat to wheat and barley. This study evaluates the feasibility of using hyperspectral imaging and machine learning for early detection of stem rust and examines the cross-crop transferability of diagnostic models. Hyperspectral datasets of wheat (Triticum aestivum L.) and barley (Hordeum vulgare L.) were collected under controlled conditions, before visible symptoms appeared. Multi-stage preprocessing, including spectral normalization and standardization, was applied to enhance data quality. Feature engineering focused on spectral curve morphology using first-order derivatives, categorical transformations, and extrema-based descriptors. Models based on Support Vector Machines, Logistic Regression, and Light Gradient Boosting Machine were optimized through Bayesian search. The best-performing feature set achieved F1-scores up to 0.962 on wheat and 0.94 on barley. Cross-crop transferability was evaluated using zero-shot cross-domain validation. High model transferability was confirmed, with F1 > 0.94 and minimal false negatives (
Why it matches plant phenotyping methods植物の病徴が現れる前の茎さび病状態を、ハイパースペクトル画像と機械学習で検出・推定する方法の開発および転移性検証が中心である。
abstractThis study evaluates the feasibility of using hyperspectral imaging and machine learning for early detection of stem rust and examines the cross-crop transferability of diagnostic models.
Reproduction assets foundThe paper's Data Availability Statement provides a public Google Drive link to the hyperspectral imaging datasets (864 hyperspectral cubes of wheat and barley, control and stem-rust-inoculated) used for the phenotyping and machine learning analysis. No code or model checkpoints are explicitly deposited.Dataset · publicData is available via online download link https://drive.google.com/drive/folders/1qAgWEAH5BruTNmT0bmSm4HTbXe_iitap (accessed on 21 October 2025).Open asset ↗1qAgWEAH5BruTNmT0bmSm4HTbXe_iitaplines:233-282Code / dataset availability confirmedOpenAlex · Crossref · Europe PMC · checked 6 Sept 2026
Developing crop varieties that maintain productivity under drought is essential for future food security. Here, we investigated the potential of time-resolved high-throughput phenotyping to predict harvest-related traits and identify drought-stressed plants. Six barley lines ( Hordeum vulgare ) were grown in a greenhouse environment with well-watered and drought treatments, and dynamically phenotyped using RGB, thermal infrared, chlorophyll fluorescence, and hyperspectral imaging sensors. A temporal phenomic classification model accurately distinguished between drought-treated and control plants, achieving high accuracy (classification accuracy ≥0.97) even when relying solely on predictors from the early drought response phase. Canopy temperature depression at the early stage and RGB-derived plant size estimates at the late stage emerged as key classification features. A temporal phenomic prediction model of harvest-related traits achieved particularly high mean R 2 values for total biomass dry weight (0.97) and total spike weight (0.93), with RGB plant size estimators emerging as important predictors. Importantly, prediction accuracy for these traits remained high (R 2 ≥ 0.84) even when restricted to early developmental phase data, including the stem elongation stage. Models trained on pooled drought and control data outperformed single-treatment models and maintained high predictive power across treatments. Together, these findings highlight the value of integrating high-throughput phenotyping with temporal modeling to enable earlier, more cost-effective selection of drought-resilient genotypes and demonstrate the broader potential of phenomics-driven strategies for accelerating crop improvement under stress-prone environments.
Why it matches plant phenotyping methodsRGB・熱赤外・蛍光・ハイパースペクトルによる時系列表現型取得と、収穫形質予測モデルの構築・評価が研究の中心であるため。
abstractdynamically phenotyped using RGB, thermal infrared, chlorophyll fluorescence, and hyperspectral imaging sensors
Reproduction assets foundThe authors explicitly state that the data and analysis pipeline code for this barley phenotyping study is publicly available on GitHub at https://github.com/hatiez/barley-TPP-pipeline. This is a paper-specific computational asset (the temporal phenomic classification/prediction pipeline) with an authors' public URL. DCode · publicThe data and analysis pipeline code is available on https://github.com/hatiez/barley-TPP-pipeline .Open asset ↗https://github.com/hatiez/barley-TPP-pipelinelines:390-415Code / dataset availability confirmedEurope PMC · checked 6 Sept 2026
Abstract Modern agriculture faces the dual challenge of sustainably increasing food production while mitigating the environmental impact of intensive monocultures. Mixed cropping, which is the cultivation of multiple species or varieties, may provide ecological benefits that address productivity and environmental sustainability challenges. However, evaluating its multifunctionality in conventional agricultural field experiments is costly and labour-intensive, and small sample sizes and high spatial variability often make it difficult to detect the statistical significance of mixed cropping effects. This study aims to introduce and validate a high-throughput field phenotyping (HTP) framework that integrates aerial imagery obtained from unmanned aerial vehicles (UAVs) to efficiently assess the multifunctionality of mixed cropping systems. We conducted a field experiment comparing monocultures of oat, rye, and barley; intraspecific mixed cropping combining three oat varieties; and interspecific mixed cropping combining oat, rye, and barley. Using UAV-derived data across the entire field, including vegetation cover, plant height, and the normalised difference vegetation index, we evaluated five multifunctionalities (biomass production, spatial variability in biomass production, early canopy closure, lodging resistance, and lodging resilience). This framework reveals that mixed cropping outperforms monocropping in several key ecological functions. The proposed UAV-based HTP approach enables cost-effective, robust, and scalable evaluation of mixed cropping systems, facilitating their optimisation for multifunctionality and contributing to the advancement of sustainable agriculture.
Why it matches plant phenotyping methodsUAV画像から植被率・草高・NDVIなどの植物形質を取得する高スループット表現型解析フレームワークを導入・検証しており、フェノタイピング手法が研究の中心です。
abstractThis study aims to introduce and validate a high-throughput field phenotyping (HTP) framework that integrates aerial imagery obtained from unmanned aerial vehicles (UAVs)
Reproduction assets foundThe preprint's data availability statement deposits the datasets generated and analysed in the study (UAV-derived phenotypic measurements and field data) on Zenodo with a DOI that appears verbatim in the allowed URL list. No author analysis code or trained models are explicitly deposited.Dataset · publicThe datasets generated and analysed during the current study are available in the Zenodo repository, https://doi.org/10.5281/zenodo.17042273.Open asset ↗Zenodo · 10.5281/zenodo.17042273lines:135-161Code / dataset availability confirmedCrossref · Europe PMC · checked 15 Sept 2026
The integration of nanotechnology in agriculture allows for more precise nutrient delivery through nanoparticles (NPs), particularly via foliar application. To mature this technology for enhancing fertilizer efficiency, it is essential to shed new light on the transport and dissolution of NPs in plants. Available analytical methods struggle to address this challenge in a direct manner. We introduce correlative X-ray imaging as a novel analytical tool capable of tracking NP pathways, dissolution and hence nutrient release in plants. By utilizing three complementary X-ray techniques, we offer a unique insight into the plant processes associated with foliar fertilization. We demonstrate that small-angle X-ray scattering enables the characterization of NP size and concentration, while X-ray fluorescence imaging, maps the distribution of elements within the sample. Finally, micro-computed tomography integrates these findings into a complete three-dimensional digital representation of the plant’s microstructure, revealing regions of apparent densification associated with NP accumulation. Using freeze-dried barley plants infiltrated with nano-hydroxyapatite (nHAP), we observed rapid dissolution of NPs, and we are able to associate time and space attributes to the translocation process of nutrients up to three days following foliar application of NPs. With the first pilot study of applying correlative X-ray imaging to live plants, we sought to indicate the potential of this new analytical approach for future nano-enabled agricultural research.
Why it matches plant phenotyping methods植物内のナノ粒子経路・溶解・栄養輸送を可視化する相関X線イメージング手法の導入と実証が中心であり、植物の状態・生理過程を測定する方法論的研究である。
abstractWe introduce correlative X-ray imaging as a novel analytical tool capable of tracking NP pathways, dissolution and hence nutrient release in plants.
Reproduction assets foundThe article's data availability statement points to a public figshare repository hosting the study's datasets (X-ray imaging/phenotyping measurements). No author analysis code with explicit public deposit language was identified; Dragonfly is a commercial visualization tool, not a paper-specific asset.Dataset · publicng Wan , Hainan University, China
Zhansheng Li , Chinese Academy of Agricultural Sciences, China
Firozeh Solimani , Politecnico di Bari, Italy
Data availability statement
The datasets presented in this study can be found in online repositories. The names of the repository/repositories and accession number(s) can be found below: https://figshare.com/s/33ee8388a36600fe98d5 .
Author contributionsOpen asset ↗figsharelines:191-206Code / dataset availability confirmedEurope PMC · checked 6 Sept 2026
Accurate and efficient assessment of highland barley (Hordeum vulgare L.) density is crucial for optimizing cultivation and management practices. However, challenges such as overlapping spikes in unmanned aerial vehicle (UAV) images and the computational requirements for high-resolution image analysis hinder real-time detection capabilities. To address these issues, this study proposes an improved lightweight YOLOv5 model for highland barley spike detection. We chose depthwise separable convolution (DSConv) and ghost convolution (GhostConv) for the backbone and neck networks, respectively, to reduce the parameter and computational complexity. In addition, the integration of convolutional block attention module (CBAM) enhances the model's ability to focus on target object in complex backgrounds. The results show that the improved YOLOv5 model has a significant improvement in detection performance. Precision and recall increased by 3.1% to 92.2% and 86.2%, respectively, with an F1 score of 0.892. The AP0.5 reaches 92.7% and 93.5% for highland barley in the growth and maturation stages, respectively, and the overall mAP0.5 improved to 93.1%. Compared to the baseline YOLOv5n model, the number of parameters and floating-point operations (FLOPs) were reduced by 70.6% and 75.6%, respectively, enabling lightweight deployment without compromising accuracy. In addition,the proposed model outperformed mainstream object detection algorithms such as Faster R-CNN, Mask R-CNN, RetinaNet, YOLOv7, and YOLOv8, in terms of detection accuracy and computational efficiency. Although this study also suffers from limitations such as insufficient generalization under varying lighting conditions and reliance on rectangular annotations, it provides valuable support and reference for the development of real-time highland barley spike detection systems, which can help to improve agricultural management.
Why it matches plant phenotyping methodsUAV画像からハダンオオムギの穂を検出し密度評価に用いる軽量化画像解析モデルを開発・比較しており、植物形質状態の取得手法が中心である。
abstractthis study proposes an improved lightweight YOLOv5 model for highland barley spike detection
Reproduction assets foundThe paper's Data availability statement explicitly provides the authors' highland barley UAV spike-detection dataset on ModelScope and their analysis code on GitHub, both paper-specific and publicly actionable.Dataset · publicThe dataset can be available from https://modelscope.cn/datasets/Cai121/highland_barley and the code can be available from https://github.com/trangle666ddd/YOLOv5-highland-barley-detection .Open asset ↗Cai121/highland_barleylines:182-211Code · publicThe dataset can be available from https://modelscope.cn/datasets/Cai121/highland_barley and the code can be available from https://github.com/trangle666ddd/YOLOv5-highland-barley-detection .Open asset ↗trangle666ddd/YOLOv5-highland-barley-detectionlines:182-211Code / dataset availability confirmedEurope PMC · Crossref · checked 6 Sept 2026
Evaluation of relevant seed traits is an essential part of most plant breeding and biotechnology programmes. There is a need for non-destructive, three-dimensional assessment of the morphometry, composition, and internal features of seeds. Here, we introduce a novel tool, MRI-Seed-Wizard, which integrates deep learning algorithms with non-invasive magnetic resonance imaging (MRI) for use in a new domain-plant MRI. The tool enabled in vivo quantification of 23 grain traits, including volumetric parameters of inner seed structure. Several of these features cannot be assessed using conventional techniques, including X-ray computed tomography. MRI-Seed-Wizard was designed to automate the manual processes of identifying, labeling, and analysing digital MRI data. We further provide advanced MRI protocols that allow the evaluation of multiple seeds simultaneously to increase throughput. The versatility of MRI-Seed-Wizard in seed phenotyping is demonstrated for wheat (Triticum aestivum) and barley (Hordeum vulgare) grains, and it is applicable to a wide range of crop seeds. Thus, artificial intelligence, combined with the most versatile imaging modality, MRI, opens up new perspectives in seed phenotyping and crop improvement.
Why it matches plant phenotyping methodsMRIと深層学習を統合した種子表現型解析ツールを開発し、多数の種子形質を自動・非破壊・高スループットに定量化する中心的な方法論研究である。
abstractHere, we introduce a novel tool, MRI-Seed-Wizard, which integrates deep learning algorithms with non-invasive magnetic resonance imaging (MRI) for use in a new domain-plant MRI.
Reproduction assets foundThe paper's MRI-Seed-Wizard segmentation/phenotyping pipeline (Python/PyTorch scripts, nnU-Net/U-Net models) and demonstration data are explicitly published online by the authors at the GitHub repository akvilonBrown/mri-wizard, matching an allowed URL.Code · publicCode and demonstration data are available at: https://github.com/akvilonBrown/mri-wizard .Open asset ↗akvilonBrown/mri-wizardlines:227-303Code / dataset availability confirmedEurope PMC · checked 7 Sept 2026
This study introduces a comprehensive approach for classifying individual malting barley kernels, involving dual-sided kernel imaging, a specifically designed image processing algorithm, an optimized deep neural network architecture, and a mechanical sorting system. The proposed method achieves precise classification into multiple classes, aligning with quality standards for malting material assessment. Throughout the study, various image analysis techniques were assessed, including traditional feature engineering, established transfer learning deep neural network architectures, and our custom-designed convolutional neural network tailored for barley kernel image analysis. Comparative analysis underscores the superior performance of our network model. The study reveals that our proposed deep learning network achieves a 94% accuracy in classifying barley kernel defects and varieties, outperforming well-established transfer learning models to complex architectures that attain 93% accuracy. Additionally, it surpasses the traditional machine learning approach involving feature extraction and support vector machine classifiers, which achieve accuracy below 90% in detecting defective kernels and below 70% in varietal classification. However, we also noted the traditional approach's advantage in morphological feature recognition. This observation guides new research toward integrating morphological feature extraction techniques with modern convolutional networks. This paper presents a deep neural network designed specifically for the analysis of cereal kernel images in two applications: defect and variety classification. It emphasizes the importance of standardizing kernel orientation and merging images from both sides of the kernel, and introduces a device for image acquisition that fulfills this need.
Why it matches plant phenotyping methods麦芽大麦粒の欠陥・品種という植物器官の状態・属性を、両面画像、画像処理、深層学習、画像取得装置で分類する方法が研究の中心であり、比較検証も行っている。
abstractThis study introduces a comprehensive approach for classifying individual malting barley kernels, involving dual-sided kernel imaging, a specifically designed image processing algorithm, an optimized deep neural network architecture, and a mechanical sorting system.
Reproduction assets foundThe paper's dual-sided malting barley kernel image dataset (MaBaKI) is publicly deposited in a repository with an explicit DOI, matching an allowed URL. No author analysis code or trained model checkpoints are stated as publicly available.Dataset · publicThe datasets generated and/or analysed during the current study are available in the Malting Barley Kernel Images (MaBaKI) database repository. The MaBaKI dataset is available at https://doi.org/10.34658/RDB.MMLNNX.Open asset ↗MaBaKI · 10.34658/RDB.MMLNNXlines:170-191Code / dataset availability confirmedEurope PMC · checked 7 Sept 2026
BarleyRootMorphology / geometry measurementSegmentationRoot system architecture
Measuring seminal root angle is an important aspect of root phenotyping, yet automated methods are lacking. We introduce SeminalRootAngle, a novel open-source automated method that measures seminal root angles from images. To ensure our method is flexible and user-friendly we build on an established corrective annotation training method for image segmentation. We tested SeminalRootAngle on a heterogeneous dataset of 662 spring barley rhizobox images, which presented challenges in terms of image clarity and root obstruction. Validation of our new automated pipeline against manual measurements yielded a Pearson correlation coefficient of 0.71. We also measure inter-annotator agreement, obtaining a Pearson correlation coefficient of 0.68, indicating that our new pipeline provides similar root angle measurement accuracy to manual approaches. We use our new SeminalRootAngle tool to identify single nucleotide polymorphisms (SNPs) significantly associated with angle and length, shedding light on the genetic basis of root architecture.
Why it matches plant phenotyping methods根の角度を画像から自動抽出するオープンソース手法を開発し、手動測定との相関で検証しているため、植物表現型取得法が中心です。
abstractWe introduce SeminalRootAngle, a novel open-source automated method that measures seminal root angles from images.
Reproduction assets foundThe paper's rhizobox root image dataset is publicly deposited on Zenodo, the SeminalRootAngle analysis code/installer is open-sourced on GitHub, and the BVS QTL analysis materials are on a second authors' GitHub repository. All are paper-specific, public, and actionable.Dataset · publicTo promote transparency and reproducibility, we makeour image dataset freely available under a CreativeCommons license at https://zenodo.org/records/7870965#.ZEp5iXZByUkOpen asset ↗zenodo · 7870965lines:30-44Code · publicwe open-source our code and make our downloadable installer available at https://github.com/Abe404/SeminalRootAngleOpen asset ↗github · Abe404/SeminalRootAnglelines:30-44Code / dataset availability confirmedEurope PMC · checked 7 Sept 2026
Abstract This study introduces a comprehensive approach for classifying individual malting barley kernels, involving dual-sided kernel imaging, a specifically designed image processing algorithm, an optimized deep neural network architecture, and a mechanical sorting system. The proposed method achieves precise classification into multiple classes, aligning with quality standards for malting material assessment. Throughout the study, various image analysis techniques were assessed, including traditional feature engineering, established transfer learning deep neural network architectures, and our custom-designed convolutional neural network tailored for barley kernel image analysis. Comparative analysis underscores the superior performance of our network model. The study reveals that our proposed deep learning network achieves a 94% accuracy in classifying barley kernel defects and varieties, outperforming well-established transfer learning models with complex architectures that attain 93% accuracy. Additionally, it surpasses the traditional machine learning approach involving feature extraction and support vector machine classifiers, which achieve accuracy below 90% in detecting defective kernels and below 70% in varietal classification. However, we also noted the traditional approach's advantage in morphological feature recognition. This observation guides new research toward integrating morphological feature extraction techniques with modern convolutional networks. This paper presents a deep neural network designed specifically for the analysis of cereal kernel images in two applications: defect and variety classification. It emphasizes the importance of standardizing kernel orientation and merging images from both sides of the kernel, and introduces a device for image acquisition that fulfills this need.
Why it matches plant phenotyping methods大麦穀粒の画像取得装置、画像処理、深層学習によって品種と欠陥を分類する手法を開発・比較しており、植物器官の形態・状態の取得が研究の中心である。
abstractThis study introduces a comprehensive approach for classifying individual malting barley kernels, involving dual-sided kernel imaging, a specifically designed image processing algorithm, an optimized deep neural network architecture, and a mechanical sorting system.
Reproduction assets foundThe paper's barley kernel image datasets (MaBaKI) are explicitly stated as publicly available in a repository with a DOI matching an allowed URL. No analysis code or trained models are reported as available.Dataset · publicThe datasets generated and/or analysed during the current study are available in the Malting Barley Kernel Images (MaBaKI) database repository. The MaBaKI dataset is available at https://doi.org/10.34658/RDB.MMLNNX.Open asset ↗10.34658/RDB.MMLNNXlines:230-256Code / dataset availability confirmedEurope PMC · checked 7 Sept 2026
Analysis of hyperspectral images is of great interest in plant studies. Nowadays, this analysis is used more and more widely, so the development of hyperspectral image processing methods is an urgent task. This paper presents a hyperspectral image processing pipeline that includes: preprocessing, basic statistical analysis, visualization of a multichannel hyperspectral image, and solving classification and clustering problems using machine learning methods. The current version of the package implements the following methods: construction of a confidence interval of an arbitrary level for the difference of sample averages; verification of the similarity of intensity distributions of spectral lines for two sets of hyperspectral images on the basis of the Mann-Whitney U-criterion and Pearson's criterion of agreement; visualization in two-dimensional space using dimensionality reduction methods PCA, ISOMAP and UMAP; classification using linear or ridge regression, random forest and catboost; clustering of samples using the EM-algorithm. The software pipeline is implemented in Python using the Pandas, NumPy, OpenCV, SciPy, Sklearn, Umap, CatBoost and Plotly libraries. The source code is available at: https://github.com/igor2704/Hyperspectral_images. The pipeline was applied to identify melanin pigment in the shell of barley grains based on hyperspectral data. Visualization based on PCA, UMAP and ISOMAP methods, as well as the use of clustering algorithms, showed that a linear separation of grain samples with and without pigmentation could be performed with high accuracy based on hyperspectral data. The analysis revealed statistically significant differences in the distribution of median intensities for samples of images of grains with and without pigmentation. Thus, it was demonstrated that hyperspectral images can be used to determine the presence or absence of melanin in barley grains with great accuracy. The flexible and convenient tool created in this work will significantly increase the efficiency of hyperspectral image analysis.
Why it matches plant phenotyping methods植物のハイパースペクトル画像から穀粒のメラニン着色状態を抽出する解析パイプラインとソフトウェアを開発・適用しており、表現型取得・解析手法が中心である。
abstractThis paper presents a hyperspectral image processing pipeline
Reproduction assets foundThe paper's authors publicly release the hyperspectral image processing pipeline (Python source code) used for the barley grain melanin analysis, and the supplementary material lists the 313 barley accessions with their pigmentation (melanin-containing vs. non-containing) phenotype labels used in the study.Code · publicThe source code is available at: https://github.com/igor2704/Hyperspectral_images.Open asset ↗https://github.com/igor2704/Hyperspectral_imageslines:1-42Code / dataset availability confirmedOpenAlex · Europe PMC · bioRxiv · checked 15 Sept 2026
Abstract Background Automating stomatal trait measurement has gained popularity because of their inherent importance for field phenotyping application as stomata are critical for both carbon capture and water use efficiency in plants. Such tool has been reported for rice, wheat, tomato, barley and oil palm. However, none exist yet for canola, which is an important economic and agronomic crop globally. Results We developed a new toolkit called Stomatal Comprehensive Automated Neural Network or SCAN by combining the use of high-resolution portable digital microscopy with machine learning based on You Only Look Once algorithm (YOLOv8). Digital micrographs of leaf surfaces enter the SCAN pipeline, which includes stomata detection, stomata segmentation and stomatal pore segmentation models, to measure stomatal density, stomatal size and stomatal pore area, respectively. In addition to SCAN’s ability to measure leaf stomatal traits in canola at 89 to 94% accuracy, we also showed that SCAN can be used to predict stomatal density even in species not included in the training set such as Arabidopsis, tobacco, rice, wheat, maize and proso millet. SCAN was designed for the biological science community with the premise that users are not required to possess advanced programming capabilities to manage dependency prerequisites, execute the models, and integrate the analysis. This was achieved by packaging the models into a desktop application system that can be accessed offline. Conclusion Overall, SCAN provides a non-destructive, real-time, portable, and high-throughput measurement of leaf stomatal traits in canola. The minimised hardware requirement and user-friendly desktop application system make SCAN suitable for field phenotyping application.
Why it matches plant phenotyping methodsカノーラ葉の気孔形質を画像と機械学習で自動抽出するツールを開発し、精度検証と他種での適用性評価を行っており、フェノタイピング手法が中心である。
abstractWe developed a new toolkit called Stomatal Comprehensive Automated Neural Network or SCAN by combining the use of high-resolution portable digital microscopy with machine learning based on You Only Look Once algorithm (YOLOv8).
Reproduction assets foundThe paper's authors publicly deposit the SCAN pipeline's model weights, hyperparameters, training scripts, and datasets in the FD_detection GitHub repository, and provide the SCAN application itself (with download and demonstration) in a second GitHub repository. Both are paper-specific, public, and actionable.Code · publicin Table S1.
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The training tasks were carried out on an Ubuntu 20.04 Linux server at the Research School of Biology in
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Australian National University, using two Nvidia A30 (24G) Graphic Processing Units (GPUs). The full
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details of models’ weights, hyperparameters, training scripts and datasets can be found at
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https://github.com/William-Yao0993/FD_detection.128
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Model evaluation
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Mean Average Precision (mAP, Fig. 3) and F1 score were used to assess model ability (Fig. 4). mAP is
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calculated as the mean value of each class area under the precision-recall curve over thresholds, and the
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F1 score is the harmonic mean of precision and recall. The formulas are deOpen asset ↗William-Yao0993/FD_detectionpdf-raw-page:4 lines:1-81Code / dataset availability confirmedCrossref · Europe PMC · checked 14 Sept 2026
Repeated measurements of crop height to observe plant growth dynamics in real field conditions represent a challenging task. Although there are ways to collect data using sensors on UAV systems, proper data processing and analysis are the key to reliable results. As there is need for specialized software solutions for agricultural research and breeding purposes, we present here a fast algorithm ALFA for the processing of UAV LiDAR derived point-clouds to extract the information on crop height at many individual cereal field-plots at multiple time points. Seven scanning flights were performed over 3 blocks of experimental barley field plots between April and June 2021. Resulting point-clouds were processed by the new algorithm ALFA. The software converts point-cloud data into a digital image and extracts the traits of interest–the median crop height at individual field plots. The entire analysis of 144 field plots of dimension 80 x 33 meters measured at 7 time points (approx. 100 million LiDAR points) takes about 3 minutes at a standard PC. The Root Mean Square Deviation of the software-computed crop height from the manual measurement is 5.7 cm. Logistic growth model is fitted to the measured data by means of nonlinear regression. Three different ways of crop-height data visualization are provided by the software to enable further analysis of the variability in growth parameters. We show that the presented software solution is a fast and reliable tool for automatic extraction of plant height from LiDAR images of individual field-plots. We offer this tool freely to the scientific community for non-commercial use.
Why it matches plant phenotyping methodsUAV LiDAR点群から圃場区画ごとの作物高を自動抽出するソフトウェアと処理アルゴリズムを開発・検証しており、植物形質取得が研究の中心である。
abstractwe present here a fast algorithm ALFA for the processing of UAV LiDAR derived point-clouds to extract the information on crop height at many individual cereal field-plots at multiple time points.
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 · publicsoftware (available freely for non-commercial use here: https://github.com/PalackyUniversity/Open asset ↗pdf-page:9 lines:1-59Code / dataset availability confirmedOpenAlex · Europe PMC · checked 7 Sept 2026
BACKGROUND: Spike is the grain-bearing organ in cereal crops, which is a key proxy indicator determining the grain yield and quality. Machine learning methods for image analysis of spike-related phenotypic traits not only hold the promise for high-throughput estimating grain production and quality, but also lay the foundation for better dissection of the genetic basis for spike development. Barley (Hordeum vulgare L.) is one of the most important crops globally, ranking as the fourth largest cereal crop in terms of cultivated area and total yield. However, image analysis of spike-related traits in barley, especially based on CT-scanning, remains elusive at present. RESULTS: In this study, we developed a non-invasive, high-throughput approach to quantitatively measuring the multitude of spike architectural traits in barley through combining X-ray computed tomography (CT) and a deep learning model (UNet). Firstly, the spikes of 11 barley accessions, including 2 wild barley, 3 landraces and 6 cultivars were used for X-ray CT scanning to obtain the tomographic images. And then, an optimized 3D image processing method was used to point cloud data to generate the 3D point cloud images of spike, namely 'virtual' spike, which is then used to investigate internal structures and morphological traits of barley spikes. Furthermore, the virtual spike-related traits, such as spike length, grain number per spike, grain volume, grain surface area, grain length and grain width as well as grain thickness were efficiently and non-destructively quantified. The virtual values of these traits were highly consistent with the actual value using manual measurement, demonstrating the accuracy and reliability of the developed model. The reconstruction process took 15 min approximately, 10 min for CT scanning and 5 min for imaging and features extraction, respectively. CONCLUSIONS: This study provides an efficient, non-invasive and useful tool for dissecting barley spike architecture, which will contribute to high-throughput phenotyping and breeding for high yield in barley and other crops.
Why it matches plant phenotyping methodsX線CT、深層学習、3D画像処理を組み合わせ、オオムギ穂の形態・構造形質を定量化する高スループット表現型計測法を開発し、手動測定で検証しているため。
abstractwe developed a non-invasive, high-throughput approach to quantitatively measuring the multitude of spike architectural traits in barley through combining X-ray computed tomography (CT) and a deep learning model (UNet).
Reproduction assets foundThe authors explicitly state that all code and datasets for deep learning segmentation, prediction, and barley spike trait extraction are open-sourced on GitHub at the authors' repository, which is an allowed URL.Code · publicAll code and datasets pertaining to deep learning segmentation training, predicting and barley spike traits extraction is open-sourced on Github at https://github.com/zerosky010/CT_barley_spike_detection .Open asset ↗zerosky010/CT_barley_spike_detectionlines:160-268Code / dataset availability confirmedEurope PMC · checked 7 Sept 2026
Background: The cereal spike is the main harvested plant organ determining the grain yield and quality, and its dissection provides the basis to estimate yield- and quality-related traits, such as grain number per spike and kernel weight. Phenotypic detection of spike architecture has potential for genetic improvement of yield and quality. However, manual collection and analysis of phenotypic data is laborious, time-consuming, low-throughput and destructive. Results We used a barley model to develop a non-invasive, high-throughput approach through combining X-ray computed tomography (CT) and deep learning model (UNet) to phenotype spike architectural traits. We used an optimized 3D image processing methods by point cloud for analyzing internal structure and quantifying morphological traits of barley spikes. The volume and surface area of grains per spike can be determined efficiently, which is hard to be measured manually. The UNet model was trained based on two types of spikes (wheat cultivar D3 and two-row barley variety S17350), and the best model accurately predicted grain characteristics from CT images. The spikes of ten barley varieties were analyzed and classified into three categories, namely wild barley, barley cultivars and barley landraces. The results showed that modern cultivated barley has shorter but thicker grains with larger volume and higher yield compared to wild barley. The X-ray CT reconstruction and phenotype extraction pipeline needed only 5 minutes per spike for imaging and traits extracting. Conclusions The combination of X-ray CT scans and a deep learning model could be a useful tool in breeding for high yield in cereal crops, and optimized 3D image processing methods could be valuable means of phenotypic traits calculation.
Why it matches plant phenotyping methodsX線CT、深層学習、3D画像処理を組み合わせ、オオムギ穂の内部形態・粒形質を非破壊かつ高スループットに抽出する手法を開発しており、フェノタイピング手法が研究の中心である。
abstractWe used a barley model to develop a non-invasive, high-throughput approach through combining X-ray computed tomography (CT) and deep learning model (UNet) to phenotype spike architectural traits.
Reproduction assets foundThe authors explicitly state that all code and datasets for deep learning segmentation, prediction, and barley spike trait extraction are open-sourced on GitHub at the allowed URL.Code · publicAvailability of data and materials: All code and datasets pertaining to deep learning segmentation training, predicting and barley spike traits extraction is open-sourced on Github at https://github.com/zerosky010/CT_detection_barley_spike_python.Open asset ↗zerosky010/CT_detection_barley_spike_pythonlines:99-131Code / dataset availability confirmedEurope PMC · checked 14 Sept 2026
Many studies proposed the use of stable carbon isotope ratio (δ 13 C) as a predictor of abiotic stresses in plants, considering only drought and nitrogen deficiency without further investigating the impact of other nutrient deficiencies, that is, phosphorus (P) and/or iron (Fe) deficiencies. To fill this knowledge gap, we assessed the δ 13 C of barley ( Hordeum vulgare L.), cucumber ( Cucumis sativus L.), maize ( Zea mays L.), and tomato ( Solanum lycopersicon L.) plants suffering from P, Fe, and combined P/Fe deficiencies during a two-week period using an isotope-ratio mass spectrometer. Simultaneously, plant physiological status was monitored with an infra-red gas analyzer. Results show clear contrasting time-, treatment-, species-, and tissue-specific variations. Furthermore, physiological parameters showed limited correlation with δ 13 C shifts, highlighting that the plants' δ 13 C, does not depend solely on photosynthetic carbon isotope fractionation/discrimination (Δ). Hence, the use of δ 13 C as a predictor is highly discouraged due to its inability to detect and discern different nutrient stresses, especially when combined stresses are present.
Why it matches plant phenotyping methodsδ13Cを用いた栄養ストレス予測法の有効性を複数作物で評価・検証しており、植物状態の推定手法の技術的妥当性が中心である。
titleδ 13 C as a tool for iron and phosphorus deficiency prediction in crops.
Reproduction assets foundThe paper's data availability statement points to a public GitHub repository containing the raw δ13C/physiology data and the analysis scripts used to generate figures, which is a paper-specific, publicly actionable asset.Code · publick Dr. Christian Ceccon for providing support for the isotope analysis.
DATA AVAILABILITY STATEMENT
The following information was supplied regarding data and code availability: the raw data, the version of the individual packages and scripts used to analyze the data and generate the figures of this study are available at GitHub: https://github.com/Fabio-Trevisan/13C-Experiment.git .
REFERENCES
Andaluz , S.
,
López‐Millán , A. F.
,
Peleato , M. L.
,
Abadía , J.
, &
Abadía , A.
( 2002 ).
Increases in phosphoenolpyruvate carboxylase activity in iron‐deficient sugar beet roots: Analysis of spatial localization and post‐translational modification
. Plant and Soil , 241 ( 1 ), 43 – 48 .
10.1023/A:1Open asset ↗Fabio-Trevisan/13C-Experiment · 13C-Experimentlines:309-505Code / dataset availability confirmedEurope PMC · checked 15 Sept 2026
In cereals with hollow internodes, lodging resistance is influenced by morphological characteristics such as internode diameter and culm wall thickness. Despite their relevance, knowledge of the genetic control of these traits and their relationship with lodging is lacking in temperate cereals such as barley. To fill this gap, we developed an image analysis-based protocol to accurately phenotype culm diameters and culm wall thickness across 261 barley accessions. Analysis of culm trait data collected from field trials in seven different environments revealed high heritability values (>50%) for most traits except thickness and stiffness, as well as genotype-by-environment interactions. The collection was structured mainly according to row-type, which had a confounding effect on culm traits as evidenced by phenotypic correlations. Within both row-type subsets, outer diameter and section modulus showed significant negative correlations with lodging (<-0.52 and <-0.45, respectively), but no correlation with plant height, indicating the possibility of improving lodging resistance independent of plant height. Using 50k iSelect SNP genotyping data, we conducted multi-environment genome-wide association studies using mixed model approach across the whole panel and row-type subsets: we identified a total of 192 quantitative trait loci (QTLs) for the studied traits, including subpopulation-specific QTLs and 21 main effect loci for culm diameter and/or section modulus showing effects on lodging without impacting plant height. Providing insights into the genetic architecture of culm morphology in barley and the possible role of candidate genes involved in hormone and cell wall-related pathways, this work supports the potential of loci underpinning culm features to improve lodging resistance and increase barley yield stability under changing environments.
Why it matches plant phenotyping methods大麦の穂軸径と穂軸壁厚を測定する画像解析プロトコルを開発し、多数アクセッション・複数環境で適用しており、表現型取得法が研究の中心的要素である。
abstractwe developed an image analysis-based protocol to accurately phenotype culm diameters and culm wall thickness across 261 barley accessions.
Reproduction assets foundThe paper's culm morphology phenotype measurements (BLUEs per genotype, trait statistics, heritability inputs) are distributed via the article's public supplementary material, which also contains the image-analysis phenotyping protocol (Supplementary Method 1) and analysis methods (Supplementary Methods 3-4). No authorSupplement · publicwe initially obtained the best linear unbiased estimates (BLUEs; Supplementary Table 1 ) of each genotype from the analysis of individual environmentsOpen asset ↗lines:49-59Code / dataset availability confirmedCrossref · checked 14 Sept 2026
Monitoring within-field crop variability at fine spatial and temporal resolution can assist farmers in making reliable decisions during their agricultural management; however, it traditionally involves a labor-intensive and time-consuming pointwise manual process. To the best of our knowledge, few studies conducted a comparison of Sentinel-2 with UAV data for crop monitoring in the context of precision agriculture. Therefore, prospects of crop monitoring for characterizing biophysical plant parameters and leaf nitrogen of wheat and barley crops were evaluated from a more practical viewpoint closer to agricultural routines. Multispectral UAV and Sentinel-2 imagery was collected over three dates in the season and compared with reference data collected at 20 sample points for plant leaf nitrogen (N), maximum plant height, mean plant height, leaf area index (LAI), and fresh biomass. Higher correlations of UAV data to the agronomic parameters were found on average than with Sentinel-2 data with a percentage increase of 6.3% for wheat and 22.2% for barley. In this regard, VIs calculated from spectral bands in the visible part performed worse for Sentinel-2 than for the UAV data. In addition, large-scale patterns, formed by the influence of an old riverbed on plant growth, were recognizable even in the Sentinel-2 imagery despite its much lower spatial resolution. Interestingly, also smaller features, such as the tramlines from controlled traffic farming (CTF), had an influence on the Sentinel-2 data and showed a systematic pattern that affected even semivariogram calculation. In conclusion, Sentinel-2 imagery is able to capture the same large-scale pattern as can be derived from the higher detailed UAV imagery; however, it is at the same time influenced by management-driven features such as tramlines, which cannot be accurately georeferenced. In consequence, agronomic parameters were better correlated with UAV than with Sentinel-2 data. Crop growers as well as data providers from remote sensing services may take advantage of this knowledge and we recommend the use of UAV data as it gives additional information about management-driven features. For future perspective, we would advise fusing UAV with Sentinel-2 imagery taken early in the season as it can integrate the effect of agricultural management in the subsequent absence of high spatial resolution data to help improve crop monitoring for the farmer and to reduce costs.
Why it matches plant phenotyping methodsUAVおよびSentinel-2マルチスペクトル画像から草丈、LAI、バイオマス、葉窒素などの植物形質を推定し、参照データとの相関比較・技術評価を行っているため、植物フェノタイピング手法の検証・応用が中心です。
abstractprospects of crop monitoring for characterizing biophysical plant parameters and leaf nitrogen of wheat and barley crops were evaluated
Reproduction assets found保存済みの本文根拠を更新済みルールで再検証し、公開資産1件を確認しました。Supplement · publicSupplementary Materials: The following supporting information can be downloaded at:
https://www.mdpi.com/article/10.3390/rs14174426/s1, Table S1: Summary statistics of the plant trait
variables measured at 20 sample points in field A; Table S2: Summary statistics of the plant trait
variables measured at 20 sample points in field G; Table S3: Absolute correlation results of plant
maximum height and mean height in field AOpen asset ↗pdf-page:20 lines:1-58Code / dataset availability confirmedEurope PMC · checked 8 Sept 2026
We investigated prediction of malting quality (MQ) phenotypes in different locations using metabolomic spectra, and compared the prediction ability of different models, and training population (TP) sizes. Data of five MQ traits was measured on 2667 individual plots of 564 malting spring barley lines from three years and two locations. A total of 24,018 metabolomic features (MFs) were measured on each wort sample. Two statistical models were used, a metabolomic best linear unbiased prediction (MBLUP) and a partial least squares regression (PLSR). Predictive ability within location and across locations were compared using cross-validation methods. For all traits, more than 90% of the total variance in MQ traits could be explained by MFs. The prediction accuracy increased with increasing TP size and stabilized when the TP size reached 1000. The optimal number of components considered in the PLSR models was 20. The accuracy using leave-one-line-out cross-validation ranged from 0.722 to 0.865 and using leave-one-location-out cross-validation from 0.517 to 0.817. In conclusion, the prediction accuracy of metabolomic prediction of MQ traits using MFs was high and MBLUP is better than PLSR if the training population is larger than 100. The results have significant implications for practical barley breeding for malting quality.
Why it matches plant phenotyping methodsメタボロームスペクトルから大麦の麦芽品質形質を予測し、複数モデルと交差検証で予測精度を比較・検証しており、育種利用可能な形質推定法が中心です。
abstractWe investigated prediction of malting quality (MQ) phenotypes in different locations using metabolomic spectra, and compared the prediction ability of different models, and training population (TP) sizes.
Reproduction assets foundThe article states that all data used (malting quality trait records and metabolomic features for 2667 plots of 564 spring barley lines) are deposited in a public Mendeley Data repository with a direct link, making the paper's phenotyping measurements publicly available.Dataset · publicAll the data used are available in a public accessible repository with the direct link as https://data.mendeley.com/datasets/s3s4ft92wj/1 .Open asset ↗s3s4ft92wjlines:160-241Code / dataset availability confirmedEurope PMC · checked 9 Sept 2026
Determining the grain yield potential contributed by grain number is a step towards advancing the yield of cereal crops. To achieve this aim, it is pivotal to recognize the maximum yield potential (MYP) of the crop. In barley (Hordeum vulgare L.), the MYP is defined as the maximum spikelet primordia number of a spike. Many barley studies assumed the awn primordium (AP) stage to be the MYP stage regardless of genotypes and growth conditions. From our spikelet-tracking experiments using the two-rowed cultivar Bowman, we found that the MYP stage can be different from the AP stage. Importantly, we find that the occurrence of inflorescence meristem deformation and its loss of activity coincided with the MYP stage, indicating the end of further spikelet initiation. Thus, we recommend validating the barley MYP stage with the shape of the inflorescence meristem and propose this approach (named 'spikelet stop') for MYP staging. To clarify the relevance of AP and MYP stages, we compared the MYP stage and the MYP in 27 barley accessions (two- and six-rowed accessions) grown in the greenhouse and in the field. Our results reveal that the MYP stage can be reached at various developmental stages, which greatly depend on the genotype and growth conditions. Furthermore, we propose that the MYP stage and the time to reach the MYP stage can be used to determine yield potential in barley. Based on our findings, we suggest key steps for the identification of the MYP stage in barley that may also be applied in a related crop such as wheat.
Why it matches plant phenotyping methods花序メリステムの形状と活動停止から最大収量ポテンシャル段階を判定する「spikelet stop」手法を提案・検証しており、植物発達形質の取得法が中心である。
abstractThus, we recommend validating the barley MYP stage with the shape of the inflorescence meristem and propose this approach (named 'spikelet stop') for MYP staging.
Reproduction assets foundThe paper's spikelet-tracking phenotype data (spikelet ridge numbers, Waddington stages, GDDs, grain numbers for Bowman experiments and the 27-accession panel) are openly deposited in the Dryad Digital Repository, as stated in the Data availability statement.Dataset · publicThe data that support the findings of this study are openly available in Dryad Digital Repository at https://doi.org/10.5061/dryad.ffbg79cth ; Thirulogachandar and Schnurbusch, (2021) .Open asset ↗Dryad Digital Repository · 10.5061/dryad.ffbg79cthlines:71-101Code / dataset availability confirmedEurope PMC · checked 14 Sept 2026
Plant genebanks constitute a key resource for breeding to ensure crop yield under changing environmental conditions. Because of their roles in a range of stress responses, phenylpropanoids are promising targets. Phenylpropanoids comprise a wide array of metabolites; however, studies regarding their diversity and the underlying genes are still limited for cereals. The assessment of barley diversity via genotyping-by-sequencing is in rapid progress. Exploring these resources by integrating genetic association studies to in-depth metabolomic profiling provides a valuable opportunity to study barley phenylpropanoid metabolism; but poses a challenge by demanding large-scale approaches. Here, we report an LC-PDA-MS workflow for barley high-throughput metabotyping. Without prior construction of a species-specific library, this method produced phenylpropanoid-enriched metabotypes with which the abundance of putative metabolic features was assessed across hundreds of samples in a single-processed data matrix. The robustness of the analytical performance was tested using a standard mix and extracts from two selected cultivars: Scarlett and Barke. The large-scale analysis of barley extracts showed (1) that barley flag leaf profiles were dominated by glycosylation derivatives of isovitexin, isoorientin, and isoscoparin; (2) proved the workflow's capability to discriminate within genotypes; (3) highlighted the role of glycosylation in barley phenylpropanoid diversity. Using the barley S42IL mapping population, the workflow proved useful for metabolic quantitative trait loci purposes. The protocol can be readily applied not only to explore the barley phenylpropanoid diversity represented in genebanks but also to study species whose profiles differ from those of cereals: the crop Helianthus annuus (sunflower) and the model plant Arabidopsis thaliana.
Why it matches plant phenotyping methods植物試料の代謝状態を大規模に抽出するLC-PDA-MSメタボタイピング・ワークフローが研究の中心で、分析性能の検証と遺伝型識別・代謝QTLへの適用まで示しているため、単なる生物学的測定ではない。
abstractHere, we report an LC-PDA-MS workflow for barley high-throughput metabotyping.
Reproduction assets foundThe paper's LC–MS metabotyping data (barley, sunflower, Arabidopsis phenylpropanoid profiles) are openly deposited in the IPK e!DAL archive, per the Data Availability Statement. No author analysis code is described; MetaboScape is proprietary software.Dataset · publics: Adriana Garibay-Hern
andez, Nikolas Kessler; Adriana
Garibay-Hern
andez and Hans-Peter Mock wrote the manuscript. All
authors reviewed and approved the final manuscript.
DATA AVAILABILITY STATEMENT
The data supporting this study are openly available through the e!DAL
electronic data archive library (Arend et al., 2014) at: https://doi.org/10.5447/ipk/2021/11
694 GARIBAY-HERN
ANDEZ ET AL.
Physiologia Plantarum
13993054,
2021,
3,
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governOpen asset ↗e!DAL · 10.5447/ipk/2021/11pdf-raw-page:15 lines:80-149Code / dataset availability confirmedOpenAlex · Europe PMC · checked 15 Sept 2026
Abstract Image-based plant phenotyping is the major approach to quantitative assessment of important plant properties. For automated analysis of a large amount of image data from high-throughput greenhouse measurements, efficient techniques for image segmentation are required. However, conventional approaches to whole plant and plant organ segmentation are hampered by high variability of plant and background illumination, and naturally occurring changes in geometry and colors of growing plants. Consequently, application of advanced machine learning techniques for automated image segmentation is required. Here, we investigate six advanced neural network (NN) methods for detection and segmentation of grain spikes in RGB images including three detection deep NNs (SSD, Faster-RCNN, YOLOv3/v4), two deep (U-Net, DeepLabv3+) and one shallow segmentation NNs. Our experimental results show superior performance of deep learning NNs that achieve in average more than 90% accuracy by detection and segmentation of wheat as well as barley and rye spikes. However, different methods demonstrate different performance on matured, emergent and occluded spikes. In addition to comprehensive comparison of six NN methods, a GUI-based tool (SpikeApp) provided with this work demonstrates the application of detection and segmentation NNs to fully automated spike phenotyping. Further improvements of evaluated NN approaches are discussed.
Why it matches plant phenotyping methods穀粒穂の画像検出・セグメンテーション手法を比較評価し、SpikeAppによる自動フェノタイピングを実証しており、植物表現型取得手法が中心である。
abstractImage-based plant phenotyping is the major approach to quantitative assessment of important plant properties.
Reproduction assets foundThe paper's data availability statement explicitly provides demo software (SpikeApp with pre-trained U-Net, YOLOv3, and shallow ANN models) and example spike images for public download from the authors' IPK page. The DeepLabv3+ GitHub link is a cited third-party library, and psi.cz is the imaging facility, not a paper-Code · publics work was also supported by the project
of specific research provided by the Masaryk University.
Conflict of interest statement
The authors declare no competing interests.
Data availability statement
In addition to data presented in the main text, demo software as well as examples of spike images are provided for download
from https://ag-ba.ipk-gatersleben.de/spikeapp.html.11/14Open asset ↗spikeapp.htmlpdf-raw-page:12 lines:1-30Code / dataset availability confirmedEurope PMC · checked 15 Sept 2026
The importance of fluorescence light microscopy for understanding cellular and sub-cellular structures and functions is undeniable. However, the resolution is limited by light diffraction (~200-250 nm laterally, ~500-700 nm axially). Meanwhile, super-resolution microscopy, such as structured illumination microscopy (SIM), is being applied more and more to overcome this restriction. Instead, super-resolution by stimulated emission depletion (STED) microscopy achieving a resolution of ~50 nm laterally and ~130 nm axially has not yet frequently been applied in plant cell research due to the required specific sample preparation and stable dye staining. Single-molecule localization microscopy (SMLM) including photoactivated localization microscopy (PALM) has not yet been widely used, although this nanoscopic technique allows even the detection of single molecules. In this study, we compared protein imaging within metaphase chromosomes of barley via conventional wide-field and confocal microscopy, and the sub-diffraction methods SIM, STED, and SMLM. The chromosomes were labeled by DAPI (4',6-diamidino-2-phenylindol), a DNA-specific dye, and with antibodies against topoisomerase IIα (Topo II), a protein important for correct chromatin condensation. Compared to the diffraction-limited methods, the combination of the three different super-resolution imaging techniques delivered tremendous additional insights into the plant chromosome architecture through the achieved increased resolution.
Why it matches plant phenotyping methods植物染色体の構造を対象に、複数の顕微鏡法を比較評価し、解像度向上による表現型（染色体アーキテクチャ）取得を中心課題としている。
abstractIn this study, we compared protein imaging within metaphase chromosomes of barley via conventional wide-field and confocal microscopy, and the sub-diffraction methods SIM, STED, and SMLM.
Reproduction assets foundThe paper's super-resolution microscopy measurements (SIM/STED/PALM imaging of barley metaphase chromosomes) are supported by supplementary materials, including PALM movies (Movies S1–S4) and supplementary figures, publicly available at the MDPI supplement URL. The main datasets themselves are only 'available from the Supplement · publicphotoactivated localization microscopy
rb
rabbit
RT
room temperature
SIM
structured illumination microscopy
SMLM
single molecule localization microscopy
STED
stimulated emission depletion
STORM
stochastic optical reconstruction microscopy
Topo II
topoisomerase IIα
Supplementary Materials
Supplementary Materials can be found at https://www.mdpi.com/1422-0067/22/4/1903/s1 .
Click here for additional data file.
Author Contributions
V.S. conceived the project. I.K., A.N., V.S. and K.W. conducted the study and processed the data. I.K., A.N. and V.S. wrote the manuscript. I.K., A.N., K.W., E.H. and V.S. discussed the results and contributed to manuscript writing. All authors have read and agreed tOpen asset ↗lines:237-298Code / dataset availability confirmedEurope PMC · checked 9 Sept 2026
Genome-wide predictions are a powerful tool for predicting trait performance. Against this backdrop we aimed to evaluate the potential and limitations of genome-wide predictions to inform the barley collection of the Federal ex situ Genebank for Agricultural and Horticultural Crops with phenotypic data on complex traits including flowering time, plant height, thousand grain weight, as well as on growth habit and row type. We used previously published sequence data, providing information on 306,049 high-quality SNPs for 20,454 barley accessions. The prediction abilities of the two unordered categorical traits row type and growth type as well as the quantitative traits flowering time, plant height and thousand grain weight were investigated using different cross validation scenarios. Our results demonstrate that the unordered categorical traits can be predicted with high precision. In this way genome-wide prediction can be routinely deployed to extract information pertinent to the taxonomic status of gene bank accessions. In addition, the three quantitative traits were also predicted with high precision, thereby increasing the amount of information available for genotyped but not phenotyped accessions. Deeply phenotyped core collections, such as the barley 1,000 core set of the IPK Gatersleben, are a promising training population to calibrate genome-wide prediction models. Consequently, genome-wide predictions can substantially contribute to increase the attractiveness of gene bank collections and help evolve gene banks into bio-digital resource centers.
Why it matches plant phenotyping methodsゲノムワイド予測によって未表現型化アクセスionsの農業形質を推定し、交差検証で予測性能を評価しており、植物表現型推定手法が研究の中心である。
abstractThe prediction abilities of the two unordered categorical traits row type and growth type as well as the quantitative traits flowering time, plant height and thousand grain weight were investigated using different cross validation scenarios.
Reproduction assets foundThe paper reuses previously published, publicly deposited phenotypic (FT, PH, TGW) and genomic (306,049 SNPs for 20,454 barley accessions) datasets, explicitly linked in its data availability statement via two DOIs. No author analysis code or trained model checkpoints are disclosed; supplementary material is not shown.Dataset · publicThe datasets used for this study were published and are available at https://doi.org/10.1038/sdata.2018.278 and https://doi.org/10.1038/s41588-018-0266-x .Open asset ↗10.1038/sdata.2018.278lines:750-813Code / dataset availability confirmedEurope PMC · checked 14 Sept 2026
Malting barley (Hordeum vulgare) requires precise nitrogen (N) fertilizer management to achieve a narrow range of grain protein content (∼9–10.5%) while maintaining yields, but practical tools to accomplish this are lacking. This study hypothesized that canopy reflectance (Normalized Difference Vegetation Index (NDVI)) measured at tillering (Feekes 2–3) and expressed as a sufficiency index (SI), can estimate the likelihood of a site‐specific response to in‐season N fertilizer in malting barley. Canopy reflectance was measured from plots at tillering with a GreenSeeker and unmanned aerial vehicle (UAV) borne multispectral cameras in trials across heterogeneous California agroecosystems. Field experiments included a range of N fertilizer application rates (0–168 kg N ha⁻¹) and timings (pre‐plant, tillering, or evenly split), and resulted in a range of crop N sufficiency/deficiency. NDVI‐based SI measurements were categorized into one of three quantitative categories (low, medium, and high) without additional experimental context using Gaussian mixture modeling. Despite that 85% of variation in protein yield was due to site‐year, the reflectance‐based categories indicated whether N fertilizer applied in‐season would increase protein yield (p < .01). Nitrogen application at tillering increased yield and protein for plots in the “low” and “medium” SI categories (45 and 4% for yield and 16 and 12% for protein, respectively) (p < .05), while “high” SI plots had neither yield (p = .23) nor protein (p = .26) increases. Importantly, the broader agronomic conditions of a site primarily determined whether response to in‐season N manifested as increased yield or protein.
Why it matches plant phenotyping methods圃場の作物キャノピー反射をGreenSeekerおよびUAVマルチスペクトルカメラで取得し、NDVI由来の指標を混合モデルで分類して、作物の窒素充足状態と施肥応答を推定・検証している。反射計測と解析ワークフローが研究の中心であり、単なる日常的形質測定ではない。
abstractCanopy reflectance was measured from plots at tillering with a GreenSeeker and unmanned aerial vehicle (UAV) borne multispectral cameras
Reproduction assets foundThe article's data availability statement explicitly deposits the data and code used to produce the manuscript at a public DOI (https://doi.org/10.25338/B8633H), which is an allowed URL. This covers the paper's NDVI/SI measurements, yield/protein outcomes, and mixture-model analysis. A GitHub reference (Nelsen 2019, 'DDataset · publicis
project was provided by the University of California Divi-
sion of Agriculture and Natural Resources, the University
of California, Davis Department of Plant Sciences, and the
California Crop Improvement Association.
DATA AVA I L A B I L I T Y S TAT E M E N T
The data and code used to produce this manuscript are
available at https://doi.org/10.25338/B8633H
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 do not have any conflicts of interest to declare.
O RC I D
TaylorS. Nelsen https://orcid.org/0000-0003-1467-5204
MarkE. Lundy https://orcid.org/0000-0003-4043-0841
R E F E R E N C E S
Arnall, D. B., & Raun, B. (2014). Applying nitrogen-rich strips (CR-
2277). StillOpen asset ↗pdf-raw-page:16 lines:1-92Code / dataset availability confirmedCrossref · Europe PMC · checked 14 Sept 2026
BarleyChickpeaGreenhouseRootGrowth / time-series analysisGrowth / development / phenologyRoot system architecture
SUMMARY The phenotypic analysis of root system growth is important to inform efforts to enhance plant resource acquisition from soils; however, root phenotyping remains challenging because of the opacity of soil, requiring systems that facilitate root system visibility and image acquisition. Previously reported systems require costly or bespoke materials not available in most countries, where breeders need tools to select varieties best adapted to local soils and field conditions. Here, we report an affordable soil‐based growth (rhizobox) and imaging system to phenotype root development in glasshouses or shelters. All components of the system are made from locally available commodity components, facilitating the adoption of this affordable technology in low‐income countries. The rhizobox is large enough (approximately 6000 cm 2 of visible soil) to avoid restricting vertical root system growth for most if not all of the life cycle, yet light enough (approximately 21 kg when filled with soil) for routine handling. Support structures and an imaging station, with five cameras covering the whole soil surface, complement the rhizoboxes. Images are acquired via the Phenotiki sensor interface, collected, stitched and analysed. Root system architecture (RSA) parameters are quantified without intervention. The RSAs of a dicot species ( Cicer arietinum , chickpea) and a monocot species ( Hordeum vulgare , barley), exhibiting contrasting root systems, were analysed. Insights into root system dynamics during vegetative and reproductive stages of the chickpea life cycle were obtained. This affordable system is relevant for efforts in Ethiopia and other low‐ and middle‐income countries to enhance crop yields and climate resilience sustainably.
Why it matches plant phenotyping methods土壌栽培植物の根系構造を画像取得・解析する、低コストのrhizoboxおよび多カメラ撮像システムを開発しており、根系形態の定量化が研究の中心である。
abstractHere, we report an affordable soil‐based growth (rhizobox) and imaging system to phenotype root development in glasshouses or shelters.
Reproduction assets foundThe paper's data availability statement deposits software, test data, and rhizobox CAD files publicly at the Edinburgh DataShare DOI 10.7488/ds/2841, and materials are also linked at chickpearoots.org/resourcesandlinks. The analysis pipeline code itself is only available on request.Dataset · public, TB, CC and IR developed the growth conditions
for chickpea growth in rhizoboxes. TB, CC, VG, IR, ST and
PD wrote the paper.
CONFLICTS OF INTEREST
The authors declare no conflicts of interest.
DATA AVAILABILITY STATEMENT
Software, test data for its evaluation and CAD files to con-
struct rhizoboxes have been made available at: https://doi.org/10.7488/ds/2841. Data and code implementing the anal-
ysis pipeline is available on request by emailing the senior/
co-corresponding authors.
SUPPORTING INFORMATION
Additional Supporting Information may be found in the online ver-
sion of this article.
Figure S1. Imaging station for imaging of a rhizobox.
Figure S2. Diagram of image capture anOpen asset ↗10.7488/ds/2841pdf-raw-page:13 lines:1-98Code / dataset availability confirmedOpenAlex · Crossref · Europe PMC · checked 9 Sept 2026
In order to train the neural network for plant phenotyping, a sufficient amount of training data must be prepared, which requires time-consuming manual data annotation process that often becomes the limiting step. Here, we show that an instance segmentation neural network aimed to phenotype the barley seed morphology of various cultivars, can be sufficiently trained purely by a synthetically generated dataset. Our attempt is based on the concept of domain randomization, where a large amount of image is generated by randomly orienting the seed object to a virtual canvas. The trained model showed 96% recall and 95% average Precision against the real-world test dataset. We show that our approach is effective also for various crops including rice, lettuce, oat, and wheat. Constructing and utilizing such synthetic data can be a powerful method to alleviate human labor costs for deploying deep learning-based analysis in the agricultural domain.
Why it matches plant phenotyping methods合成データとインスタンスセグメンテーションによる種子形態フェノタイピング手法を開発し、実画像で性能検証しているため、方法が研究の中心である。
abstractan instance segmentation neural network aimed to phenotype the barley seed morphology of various cultivars
Reproduction assets foundThe authors publicly release both the synthetic and real-world seed image datasets and the analysis code (Mask R-CNN deployment and multivariate analysis notebooks) via their GitHub repository, explicitly stated in Data availability and Code availability sections.Dataset · publicSynthetically generated and real-world datasets can be obtained from the following GitHub repository ( https://github.com/totti0223/crop_seed_instance_segmentation ).Open asset ↗https://github.com/totti0223/crop_seed_instance_segmentationlines:149-171Code · publicCode to reproduce the deployment of the trained Mask R-CNN and multivariate analysis is formatted as IPython notebooks and can also be obtained from the GitHub repository ( https://github.com/totti0223/crop_seed_instance_segmentation ).Open asset ↗https://github.com/totti0223/crop_seed_instance_segmentationlines:149-171Code / dataset availability confirmedEurope PMC · checked 14 Sept 2026
Conventional methods for screening for stress-tolerant cereal varieties rely on expensive, labour-intensive field testing and molecular biology techniques. Here, we use the root hair assay (RHA) as a rapid screening tool to identify stress-tolerant varieties at the early seedling stage. Wheat and barley seedlings had stress applied, and the response quantified in terms of programmed cell death (PCD), viability and necrosis. Heat shock experiments of seven barley varieties showed that winter and spring barley varieties could be partitioned into their two distinct seasonal groups based on their PCD susceptibility, allowing quick data-driven evaluation of their thermotolerance at an early seedling stage. In addition, evaluating the response of eight wheat varieties to heat and salt stress allowed identification of their PCD inflection points (35°C and 150 mM NaCl), where the largest differences in PCD levels arise. Using the PCD inflection points as a reference, we compared different stress effects and found that heat-susceptible wheat varieties displayed similar vulnerabilities to salt stress. Stress-induced PCD levels also facilitated the assessment of the basal, induced and cross-stress tolerance of wheat varieties using single, combined and multiple individual stress exposures by applying concurrent heat and salt stress in a time-course experiment. Two stress-susceptible varieties were found to have low constitutive resistance as illustrated by their high PCD levels in response to single and combined stress exposure. However, both varieties had a fast, adaptive response as PCD levels declined at the other time-points, showing that even with low constitutive resistance, the initial stress cue primes cross-stress tolerance adaptations for enhanced resistance even to a second, different stress type. Here, we demonstrate the RHA's suitability for high-throughput analysis (∼4 days from germination to data collection) of multiple cereal varieties and stress treatments. We also showed the versatility of using stress-induced PCD levels to investigate the role of constitutive and adaptive resistance by exploring the temporal progression of cross-stress tolerance. Our results show that by identifying suboptimal PCD levels in vivo in a laboratory setting, we can preliminarily identify stress-susceptible cereal varieties and this information can guide further, more efficiently targeted, field-scale experimental testing.
Why it matches plant phenotyping methods根毛アッセイ（RHA）を用いてストレス誘導性PCD・生存性・壊死を定量し、作物品種の耐性を迅速かつハイスループットにスクリーニングする手法を実証しており、表現型取得法が研究の中心である。
abstractHere, we use the root hair assay (RHA) as a rapid screening tool to identify stress-tolerant varieties at the early seedling stage.
Reproduction assets found保存済みの本文根拠を更新済みルールで再検証し、公開資産1件を確認しました。Supplement · publicAll datasets generated for this study are included in the article/ Supplementary Material .Open asset ↗lines:703-766Code / dataset availability confirmedEurope PMC · checked 15 Sept 2026
Soil biota have important effects on crop productivity, but can be difficult to study in situ. Laser ablation tomography (LAT) is a novel method that allows for rapid, three-dimensional quantitative and qualitative analysis of root anatomy, providing new opportunities to investigate interactions between roots and edaphic organisms. LAT was used for analysis of maize roots colonized by arbuscular mycorrhizal fungi, maize roots herbivorized by western corn rootworm, barley roots parasitized by cereal cyst nematode, and common bean roots damaged by Fusarium. UV excitation of root tissues affected by edaphic organisms resulted in differential autofluorescence emission, facilitating the classification of tissues and anatomical features. Samples were spatially resolved in three dimensions, enabling quantification of the volume and distribution of fungal colonization, western corn rootworm damage, nematode feeding sites, tissue compromised by Fusarium, and as well as root anatomical phenotypes. Owing to its capability for high-throughput sample imaging, LAT serves as an excellent tool to conduct large, quantitative screens to characterize genetic control of root anatomy and interactions with edaphic organisms. Additionally, this technology improves interpretation of root-organism interactions in relatively large, opaque root segments, providing opportunities for novel research investigating the effects of root anatomical phenes on associations with edaphic organisms.
Why it matches plant phenotyping methodsレーザーアブレーショントモグラフィーを用いて根の解剖学的形質と病害・生物相互作用による損傷を三次元定量化する手法を開発・実証しており、表現型取得が研究の中心である。
abstractLaser ablation tomography (LAT) is a novel method that allows for rapid, three-dimensional quantitative and qualitative analysis of root anatomy
Reproduction assets foundThe paper deposits its LAT scan videos and 3D reconstructions of root colonization (AMF, WCR, nematode, Fusarium) in a public Zenodo repository, which directly reproduces this paper's phenotyping imaging data. Supplementary figures/tables are hosted at JXB, not at an allowed URL, so only the Zenodo deposit qualifies.Dataset · publicereo-microscope.
Fig. S4. Comparison of images of common bean ( Phaseolus vulgaris ) roots damaged by Fusarium ( Fusarium virguliforme ) taken with a stereo-microscope and LAT.
erz271_suppl_Supplementary_Figures_S1-S4_Tables_S1-S4
Click here for additional data file.
Data deposition
The following videos are available at Zenodo: http://doi.org/10.5281/zenodo.1479847 .
Video S1. LAT scan of maize ( Zea mays ) root segment colonized with AMF.
Video S2. Three-dimensional reconstruction of AMF colonization in a maize ( Zea mays ) root segment, highlighting the spatial relationship between AMF (yellow) and aerenchyma (green).
Video S3. LAT scan of maize ( Zea mays ) root segment colonized withOpen asset ↗Zenodo · 10.5281/zenodo.1479847lines:158-220Code / dataset availability confirmedEurope PMC · checked 10 Sept 2026
The scarce knowledge on phenotypic characterization restricts the usage of genetic diversity of plant genetic resources in research and breeding. We describe original and ready-to-use processed data for approximately 60% of ~22,000 barley accessions hosted at the Federal ex situ Genebank for Agricultural and Horticultural Plant Species. The dataset gathers records for three traits with agronomic relevance: flowering time, plant height and thousand grain weight. This information was collected for seven decades for winter and spring barley during the seed regeneration routine. The curated data represent a source for research on genetics and genomics of adaptive and yield related traits in cereals due to the importance of barley as model organism. This data could be used to predict the performance of non-phenotyped individuals in other collections through genomic prediction. Moreover, the dataset empowers the utilization of phenotypic diversity of genetic resources for crop improvement.
Why it matches plant phenotyping methods大規模な植物表現型データセットの構築・整理と再利用を主題としており、形質測定自体は再生時の routine だが、データセット提供が中心的な貢献である。
abstractWe describe original and ready-to-use processed data for approximately 60% of ~22,000 barley accessions
Reproduction assets foundThe paper is a data descriptor publishing its own barley phenotypic dataset (FT, PH, TGW; original, outlier-corrected, and BLUEs) in ISA-Tab format at the IPK PGP repository, together with the authors' R/ASReml-R scripts for outlier detection and BLUE estimation, deposited under DOI 10.5447/IPK/2018/10.Code · publicScripts used for outlier detection and estimating BLUEs are included together with the
dataset in the public repository described below (Data Citation 1).Open asset ↗pdf-page:4 lines:1-60Code / dataset availability confirmedCrossref · checked 10 Sept 2026
Abstract. Ground reference data are a prerequisite for the calibration, update, and validation of retrieval models facilitating the monitoring of land parameters based on Earth Observation data. Here, we describe the acquisition of a comprehensive ground reference database which was created to test and validate the recently developed Earth Observation Land Data Assimilation System (EO-LDAS) and products derived from remote sensing observations in the visible and infrared range. In situ data were collected for seven crop types (winter barley, winter wheat, spring wheat, durum, winter rape, potato, and sugar beet) cultivated on the agricultural Gebesee test site, central Germany, in 2013 and 2014. The database contains information on hyperspectral surface reflectance factors, the evolution of biophysical and biochemical plant parameters, phenology, surface conditions, atmospheric states, and a set of ground control points. Ground reference data were gathered at an approximately weekly resolution and on different spatial scales to investigate variations within and between acreages. In situ data collected less than 1 day apart from satellite acquisitions (RapidEye, SPOT 5, Landsat-7 and -8) with a cloud coverage ≤ 25 % are available for 10 and 15 days in 2013 and 2014, respectively. The measurements show that the investigated growing seasons were characterized by distinct meteorological conditions causing interannual variations in the parameter evolution. Here, the experimental design of the field campaigns, and methods employed in the determination of all parameters, are described in detail. Insights into the database are provided and potential fields of application are discussed. The data will contribute to a further development of crop monitoring methods based on remote sensing techniques. The database is freely available at PANGAEA (https://doi.org/10.1594/PANGAEA.874251).
Why it matches plant phenotyping methods複数作物の植物パラメータ、表現型、ハイパースペクトル反射を体系的に取得した地上基準データベースであり、取得設計と各パラメータの測定法を詳細に記述して、リモートセンシングモデルの校正・検証に用いる点が中心的です。
abstractwe describe the acquisition of a comprehensive ground reference database which was created to test and validate the recently developed Earth Observation Land Data Assimilation System (EO-LDAS) and products derived from remote sensing observations
Reproduction assets foundThis is a data descriptor paper whose plant-phenotyping measurements (biophysical/biochemical plant parameters, phenology, hyperspectral reflectance, FVC/PSM, soil moisture, photos, survey data) are explicitly deposited as public PANGAEA datasets with DOIs listed in the text. Multiple paper-specific public assets are直接Dataset · publicThe database is freely available at PANGAEA
(https://doi.org/10.1594/PANGAEA.874251).Open asset ↗PANGAEA · 10.1594/PANGAEA.874251pdf-page:1 lines:1-54Code / dataset availability confirmedCrossref · OpenAlex · Europe PMC · checked 10 Sept 2026
Abstract Background Image-based high-throughput phenotyping technologies have been rapidly developed in plant science recently, and they provide a great potential to gain more valuable information than traditionally destructive methods. Predicting plant biomass is regarded as a key purpose for plant breeders and ecologists. However, it is a great challenge to find a predictive biomass model across experiments. Results In the present study, we constructed 4 predictive models to examine the quantitative relationship between image-based features and plant biomass accumulation. Our methodology has been applied to 3 consecutive barley (Hordeum vulgare) experiments with control and stress treatments. The results proved that plant biomass can be accurately predicted from image-based parameters using a random forest model. The high prediction accuracy based on this model will contribute to relieving the phenotyping bottleneck in biomass measurement in breeding applications. The prediction performance is still relatively high across experiments under similar conditions. The relative contribution of individual features for predicting biomass was further quantified, revealing new insights into the phenotypic determinants of the plant biomass outcome. Furthermore, methods could also be used to determine the most important image-based features related to plant biomass accumulation, which would be promising for subsequent genetic mapping to uncover the genetic basis of biomass. Conclusions We have developed quantitative models to accurately predict plant biomass accumulation from image data. We anticipate that the analysis results will be useful to advance our views of the phenotypic determinants of plant biomass outcome, and the statistical methods can be broadly used for other plant species.
Why it matches plant phenotyping methods画像由来特徴量から植物バイオマスを推定する予測モデルを開発し、複数実験で性能評価しており、表現型取得・推定手法が研究の中心である。
abstractwe constructed 4 predictive models to examine the quantitative relationship between image-based features and plant biomass accumulation.
Reproduction assets foundThe paper's authors publicly deposited their analysis code (HTPmod R package on GitHub), raw image datasets in the IPK PGP repository under three DOIs, and supporting data (metadata, raw images, archival code copy) in the GigaScience GigaDB repository. All are paper-specific, public, and actionable.Dataset · publicThe raw image datasets, as well as analyzed data supporting the results of this article, are available in the PGP repository [ 44 ] under https://dx.doi.org/10.5447/IPK/2017/24 , https://dx.doi.org/10.5447/IPK/2017/25 , and https://dx.doi.org/10.5447/IPK/2017/26Open asset ↗PGP repository · 10.5447/IPK/2017/24lines:102-163Dataset · publicThe raw image datasets, as well as analyzed data supporting the results of this article, are available in the PGP repository [ 44 ] under https://dx.doi.org/10.5447/IPK/2017/24 , https://dx.doi.org/10.5447/IPK/2017/25 , and https://dx.doi.org/10.5447/IPK/2017/26Open asset ↗PGP repository · 10.5447/IPK/2017/25lines:102-163Dataset · publicThe raw image datasets, as well as analyzed data supporting the results of this article, are available in the PGP repository [ 44 ] under https://dx.doi.org/10.5447/IPK/2017/24 , https://dx.doi.org/10.5447/IPK/2017/25 , and https://dx.doi.org/10.5447/IPK/2017/26Open asset ↗PGP repository · 10.5447/IPK/2017/26lines:102-163Dataset · publicSupporting data, including metadata tables, raw image files, and an archival copy of HTPmod are also available via the GigaScience repository, Giga DB [ 46 ].Open asset ↗lines:102-163Code / dataset availability confirmedEurope PMC · checked 10 Sept 2026
BarleyLaboratory / benchtopMRI / PETRootMorphology / geometry measurementRoot system architecture
Background Root systems are highly plastic and adapt according to their soil environment. Studying the particular influence of soils on root development necessitates the adaptation and evaluation of imaging methods for multiple substrates. Non-invasive 3D root images in soil can be obtained using magnetic resonance imaging (MRI). Not all substrates, however, are suitable for MRI. Using barley as a model plant we investigated the achievable image quality and the suitability for root phenotyping of six commercially available natural soil substrates of commonly occurring soil textures. The results are compared with two artificially composed substrates previously documented for MRI root imaging. Results In five out of the eight tested substrates, barley lateral roots with diameters below 300 µm could still be resolved. In two other soils, only the thicker barley seminal roots were detectable. For these two substrates the minimal detectable root diameter was between 400 and 500 µm. Only one soil did not allow imaging of the roots with MRI. In the artificially composed substrates, soil moisture above 70% of the maximal water holding capacity (WHC max ) impeded root imaging. For the natural soil substrates, soil moisture had no effect on MRI root image quality in the investigated range of 50-80% WHC max . Conclusions Almost all tested natural soil substrates allowed for root imaging using MRI. Half of these substrates resulted in root images comparable to our current lab standard substrate, allowing root detection down to a diameter of 300 µm. These soils were used as supplied by the vendor and, in particular, removal of ferromagnetic particles was not necessary. With the characterization of different soils, investigations such as trait stability across substrates are now possible using noninvasive MRI.
Why it matches plant phenotyping methodsMRIによる土壌中の根の非侵襲的画像化について、異なる土壌基質への適用性と画像品質を評価し、根径の検出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ある。
abstractStudying the particular influence of soils on root development necessitates the adaptation and evaluation of imaging methods for multiple substrates.
Reproduction assets foundThe authors state that the 3D MRI root images and excavated root images from this study are publicly available under a DOI (IPK repository), directly reproducing the paper's root phenotyping measurements.Dataset · public3D root images and excavated root images are available at: http://dx.doi.org/10.5447/IPK/2017/10 .Open asset ↗IPK · 10.5447/IPK/2017/10lines:196-230Code / dataset availability confirmedCrossref · checked 13 Sept 2026
Abstract. Ground reference data are a prerequisite for the calibration, update and validation of retrieval models facilitating the monitoring of land parameters based on Earth Observation data. Here, we describe the acquisition of a comprehensive ground reference database which was elaborated to test and validate the recently developed Earth Observation Land Data Assimilation System (EO-LDAS). In situ data was collected for seven crop types (winter barley, winter wheat, spring wheat, durum, winter rape, potato and sugar beet) cultivated on the agricultural Gebesee test site, central Germany, in 2013 and 2014. The database contains information on hyperspectral surface reflectance, the evolution of biophysical and biochemical plant parameters, phenology, surface conditions, atmospheric states, and a set of ground control points. Ground reference data was gathered with an approximately weekly resolution and on different spatial scales to investigate variations within and between acreages. In situ data collected less than 1 day apart from satellite acquisitions (RapidEye, SPOT5, Landsat-7 and -8) with a cloud coverage ≤ 25 % is available for 10 and 16 days in 2013 and 2014, respectively. The measurements show that the investigated growing seasons were characterized by distinct meteorological conditions causing interannual variations in the parameter evolution. In the article, the experimental design of the field campaigns, and methods employed in the determination of all parameters are described in detail. Insights into the database are provided and potential fields of application are discussed. We hope these data will contribute to a further development of crop monitoring methods based on remote sensing techniques. The database is freely available at PANGAEA (doi:10.1594/PANGAEA.874251).
Why it matches plant phenotyping methods作物の生育・生物物理／生化学的パラメータと反射スペクトル等を反復取得する地上観測データベースを構築し、衛星リモートセンシング検索モデルの校正・検証に用いる方法とデータが中心であるため、植物フェノタイピングのデータセット／測定基盤として含める。
abstractHere, we describe the acquisition of a comprehensive ground reference database which was elaborated to test and validate the recently developed Earth Observation Land Data Assimilation System (EO-LDAS).
Reproduction assets foundThis is a data descriptor paper whose entire contribution is a public ground-reference plant-phenotyping database (biophysical/biochemical plant parameters, phenology, hyperspectral reflectance, FVC/PSM imagery-derived traits) hosted on PANGAEA. The article text explicitly provides public PANGAEA DOIs for the main 2013Dataset · publicThe database is freely available at PANGAEA
(https://doi.org/10.1594/PANGAEA.874251).Open asset ↗PANGAEA · 10.1594/PANGAEA.874251pdf-page:1 lines:1-54Code / dataset availability confirmedEurope PMC · checked 10 Sept 2026
BarleyChickpeaWheatLaboratory / benchtopX-ray / CTRootMorphology / geometry measurementSegmentationRoot system architecture
The objective of this study was to develop a flexible and free image processing and analysis solution, based on the Public Domain ImageJ platform, for the segmentation and analysis of complex biological plant root systems in soil from x-ray tomography 3D images. Contrasting root architectures from wheat, barley and chickpea root systems were grown in soil and scanned using a high resolution micro-tomography system. A macro (Root1) was developed that reliably identified with good to high accuracy complex root systems (10% overestimation for chickpea, 1% underestimation for wheat, 8% underestimation for barley) and provided analysis of root length and angle. In-built flexibility allowed the user interaction to (a) amend any aspect of the macro to account for specific user preferences, and (b) take account of computational limitations of the platform. The platform is free, flexible and accurate in analysing root system metrics.
Why it matches plant phenotyping methods植物根系の3D画像から根長・根角度を抽出する画像解析ツールの開発と精度評価が研究の中心であるため。
abstractThe objective of this study was to develop a flexible and free image processing and analysis solution
Reproduction assets foundThe paper's μCT root image data and analysis files (including the Root1 macro workflow) are stated to be publicly deposited in a Harvard Dataverse dataset with an explicit DOI, directly supporting this paper's root phenotyping measurements and analysis.Dataset · publicAll files are available from the database https://dataverse.harvard.edu/dataset.xhtml?persistentId=doi:10.7910/DVN/DXG4AH .Open asset ↗doi:10.7910/DVN/DXG4AHlines:45-53Code / dataset availability confirmedEurope PMC · checked 15 Sept 2026
As integral parts of plant signaling networks, phytohormones are involved in the regulation of plant metabolism and growth under adverse environmental conditions, including salinity. Globally, salinity is one of the most severe abiotic stressors with an estimated 800 million hectares of arable land affected. Roots are the first plant organ to sense salinity in the soil, and are the initial site of sodium (Na + ) exposure. However, the quantification of phytohormones in roots is challenging, as they are often present at extremely low levels compared to other plant tissues. To overcome this challenge, we developed a high-throughput LC-MS method to quantify ten endogenous phytohormones and their metabolites of diverse chemical classes in roots of barley. This method was validated in a salinity stress experiment with six barley varieties grown hydroponically with and without salinity. In addition to phytohormones, we quantified 52 polar primary metabolites, including some phytohormone precursors, using established GC-MS and LC-MS methods. Phytohormone and metabolite data were correlated with physiological measurements including biomass, plant size and chlorophyll content. Root and leaf elemental analysis was performed to determine Na + exclusion and K + retention ability in the studied barley varieties. We identified distinct phytohormone and metabolite signatures as a response to salinity stress in different barley varieties. Abscisic acid increased in the roots of all varieties under salinity stress, and elevated root salicylic acid levels were associated with an increase in leaf chlorophyll content. Furthermore, the landrace Sahara maintained better growth, had lower Na + levels and maintained high levels of the salinity stress linked metabolite putrescine as well as the phytohormone metabolite cinnamic acid, which has been shown to increase putrescine concentrations in previous studies. This study highlights the importance of root phytohormones under salinity stress and the multi-variety analysis provides an important update to analytical methodology, and adds to the current knowledge of salinity stress responses in plants at the molecular level.
Why it matches plant phenotyping methods根の植物ホルモンを定量するLC-MS法の開発と検証が中心で、塩ストレス状態に関連する植物表現型・生理状態の抽出法として扱われているため。
abstractwe developed a high-throughput LC-MS method to quantify ten endogenous phytohormones and their metabolites of diverse chemical classes in roots of barley.
Reproduction assets foundThe article reports LC-MS/GC-MS phytohormone and metabolite quantification plus physiological measurements (biomass, lengths, chlorophyll, Na+/K+) for six barley varieties under salinity stress. No author analysis code, models, or image/sensor datasets are described. The only paper-specific public asset is the article'Supplement · publicr providing barley seeds and advice. We also want to thank Mrs. Nirupama Jayasinghe, Mrs. Natalie Pereira, and Mrs. Himasha Mendis (Metabolomics Australia) for primary metabolite quantification and analysis.
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http://www.metaboanalyst.ca/
Supplementary Material
The Supplementary Material for this article can be found online at: http://journal.frontiersin.org/article/10.3389/fpls.2016.02070/full#supplementary-material
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References
Achard P. Cheng H. De Grauwe L. Decat J. Schoutteten H. Moritz T.
( 2006 ).
Integration of plant responses to environmentally activated phytohormonal signalsOpen asset ↗lines:623-682Code / dataset availability confirmedOpenAlex · Crossref · Europe PMC · checked 5 Sept 2026
Modern phenotyping and plant disease detection methods, based on optical sensors and information technology, provide promising approaches to plant research and precision farming. In particular, hyperspectral imaging have been found to reveal physiological and structural characteristics in plants and to allow for tracking physiological dynamics due to environmental effects. In this work, we present an approach to plant phenotyping that integrates non-invasive sensors, computer vision, as well as data mining techniques and allows for monitoring how plants respond to stress. To uncover latent hyperspectral characteristics of diseased plants reliably and in an easy-to-understand way, we "wordify" the hyperspectral images, i.e., we turn the images into a corpus of text documents. Then, we apply probabilistic topic models, a well-established natural language processing technique that identifies content and topics of documents. Based on recent regularized topic models, we demonstrate that one can track automatically the development of three foliar diseases of barley. We also present a visualization of the topics that provides plant scientists an intuitive tool for hyperspectral imaging. In short, our analysis and visualization of characteristic topics found during symptom development and disease progress reveal the hyperspectral language of plant diseases.
Why it matches plant phenotyping methods非侵襲的ハイパースペクトル画像から植物の病害進展・生理状態を抽出し、トピックモデルで自動追跡する新しい表現・解析手法が研究の中心である。
abstractwe present an approach to plant phenotyping that integrates non-invasive sensors, computer vision, as well as data mining techniques
Reproduction assets foundThe paper's authors publicly released the Python implementation of online regularized LDA used for their hyperspectral plant phenotyping analysis on GitHub. No public phenotype dataset or image deposit is stated; the hyperspectral data itself is only described, not deposited.Code · publicThe Python implementation of online regularized LDA is freely available at https://github.com/mirwaes/sclda .Open asset ↗mirwaes/scldalines:78-87Code / dataset availability confirmedOpenAlex · Crossref · Europe PMC · checked 14 Sept 2026
Precise measurements of root system architecture traits are an important requirement for plant phenotyping. Most of the current methods for analyzing root growth require either artificial growing conditions (e.g. hydroponics), are severely restricted in the fraction of roots detectable (e.g. rhizotrons), or are destructive (e.g. soil coring). On the other hand, modalities such as magnetic resonance imaging (MRI) are noninvasive and allow high-quality three-dimensional imaging of roots in soil. Here, we present a plant root imaging and analysis pipeline using MRI together with an advanced image visualization and analysis software toolbox named NMRooting. Pots up to 117 mm in diameter and 800 mm in height can be measured with the 4.7 T MRI instrument used here. For 1.5 l pots (81 mm diameter, 300 mm high), a fully automated system was developed enabling measurement of up to 18 pots per day. The most important root traits that can be nondestructively monitored over time are root mass, length, diameter, tip number, and growth angles (in two-dimensional polar coordinates) and spatial distribution. Various validation measurements for these traits were performed, showing that roots down to a diameter range between 200 μm and 300 μm can be quantitatively measured. Root fresh weight correlates linearly with root mass determined by MRI. We demonstrate the capabilities of MRI and the dedicated imaging pipeline in experimental series performed on soil-grown maize (Zea mays) and barley (Hordeum vulgare) plants.
Why it matches plant phenotyping methodsMRIによる土壌中根系の3D画像取得・解析パイプラインと専用ソフトウェアを開発し、根形態形質を検証しており、植物フェノタイピング手法が研究の中心である。
abstractHere, we present a plant root imaging and analysis pipeline using MRI together with an advanced image visualization and analysis software toolbox named NMRooting.
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 · publicAutomated image analysis was performed using an in-house developed software tool, named NMRooting (available at http://www.nmrooting.de ), which was written in the programming language PythonOpen asset ↗NMRootinglines:169-172