RiceMultimodalX-ray / CTRootMorphology / geometry measurementPhysiological trait estimationGrowth / time-series analysisGrowth / development / phenologyRoot system architecture
Rhizosphere oxidation is a key adaptive mechanism in reductive soil environments, in which oxygen released from roots alters rhizosphere redox conditions and regulates biogeochemical processes. Rice plants possess an internal oxygen transport system, and radial oxygen loss (ROL) from roots is closely associated with root development. However, the spatial patterns of ROL in soil and their relationships with root traits remain poorly characterized. In this study, we developed a multimodal imaging system that integrates planar oxygen optodes with X-ray computed tomography to simultaneously visualize rhizosphere oxidation and root development in rice. Daily time-course tracking of individual crown roots revealed dynamic changes in the spatial distribution and magnitude of rhizosphere oxygen in relation to root elongation and aging. Root thickness was positively correlated with dissolved oxygen levels near root tips. Genotypic comparisons further identified a cultivar with reduced rhizosphere oxidation despite possessing thicker roots among the tested genotypes, thereby indicating the involvement of additional physiological processes. Overall, these findings demonstrate that rhizosphere oxidation is regulated by root growth stage and thickness and dynamically modulated during root development.
Why it matches plant phenotyping methods平面酸素オプトードとX線CTを統合したマルチモーダル画像システムを開発し、根の発達と根圏酸化を時系列・個体別に定量化しており、表現型取得手法が研究の中心である。
abstractwe developed a multimodal imaging system that integrates planar oxygen optodes with X-ray computed tomography to simultaneously visualize rhizosphere oxidation and root development in rice.
Reproduction assets foundThe paper's Data availability statement explicitly deposits the authors' RG2DO-Root analysis program together with sample optode and CT images (the paper's phenotyping inputs) in a public GitHub repository, matching the allowed URL.Code · publicing
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This work was supported by project JPNP18016, commissioned by the New Energy and
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Industrial Technology Development Organization (NEDO), JST CREST (JPMJCR17O1),
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and JST ALCA-Next (JPMJAN23D3).
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Data availability
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The source code and sample data (optode and CT images) are available from the
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GitHub repository (https://github.com/tsubasa-kawai28/RG2DO-Root).15
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References
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Aguilar EA et al. 2003. Oxygen distribution and movement, respiration and nutrient
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loading in banana roots (Musa spp. L.) subjected to aerated and oxygen-depleted
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environments. Plant Soil. 253:91–102. https://doi.org/10.1023/A:1024598319404.20
Armstrong W, Wright EJ. 1975. Radial oxygen loss fromOpen asset ↗https://github.com/tsubasa-kawai28/RG2DO-Root · RG2DO-Rootpdf-raw-page:19 lines:1-82Code / dataset availability confirmedCrossref · checked 15 Sept 2026
Abstract Background and Aims Drought reduces wheat yields, yet field-scale quantification of root water uptake (RWU) remains challenging because below-ground processes are difficult to monitor. This study developed a non-invasive hydrogeophysical framework integrating Electrical Resistivity Tomography (ERT), TDR-based soil monitoring, and depth-aware Random Forest calibration to quantify depth-resolved RWU and evaluate genotype-specific water-use strategies under terminal drought. Methods Time-lapse ERT (44 surveys, ≥ 3 week⁻ 1 ) was combined with TDR sensor measurements of soil water content (n = 278 paired ρ–θ observations) to convert resistivity measurements into depth-resolved RWU estimates across 0.1–1.0 m depth. Five petrophysical models were evaluated using date-grouped fivefold cross-validation, with the depth-aware Random Forest performing best. Three wheat genotypes with contrasting root architectures were monitored under terminal drought (142 mm available water). ERT-derived RWU were analysed alongside stomatal conductance, chlorophyll fluorescence, and grain yield. Results ERT resolved RWU strategies among genotypes. WM-203 exhibited aggressive, coordinated multi-layer water extraction across the soil profile (r = 0.80–0.98), whereas WM-140 showed a delayed uptake strategy characterized by early deep-layer dominance followed by mid- and deep-profile engagement, and IPLR-760 displayed inconsistent uptake with mid-profile hydraulic decoupling. Genotypic RWU rankings were consistent with stomatal conductance and grain yield, spanning from 7.0 t ha⁻ 1 in WM-203 to 1.5 t ha⁻ 1 in IPLR-760 despite comparable total water extraction. Conclusion ERT-based quantification of RWU provides a robust, non-invasive approach for resolving genotype-specific water-use strategies under field conditions. The framework enables characterization of water-use coordination patterns and offers a tool for phenotyping drought-resilient wheat genotypes.
Why it matches plant phenotyping methodsERT・TDR・Random Forestを統合し、圃場コムギの根系水吸収を定量化する方法を開発・検証し、乾燥耐性遺伝子型の表現型評価に用いているため、フェノタイピング手法が中心である。
abstractThis study developed a non-invasive hydrogeophysical framework integrating Electrical Resistivity Tomography (ERT), TDR-based soil monitoring, and depth-aware Random Forest calibration to quantify depth-resolved RWU and evaluate genotype-specific water-use strategies under terminal drought.
Reproduction assets foundThe paper's Data availability statement explicitly states that the code and supporting data for this ERT-based root water uptake study are publicly available on the authors' GitHub repository, which is listed in allowed_urls. This qualifies as a paper-specific public code/data asset for the phenotyping analysis.Code · publicsity of Jerusalem. This research was supported by the Chief
Scientist of the Israeli Ministry of Agriculture and Food Secu-
rity (grant no. 12–01-0056) and the Israeli Council for Higher
Education (Project: Future Crops for Carbon Farming).
Data availability The code and supporting data for this study
are publicly available at:
https://github.com/emmaiyke/ERT_RWU_Wheat_Project
Additional datasets are available from the corresponding
author upon reasonable request.
Declarations
Competing interests 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.
Open Access This article isOpen asset ↗ERT_RWU_Wheat_Project · emmaiyke/ERT_RWU_Wheat_Projectpdf-raw-page:22 lines:1-95Code / dataset availability confirmedOpenAlex · Crossref · checked 15 Sept 2026
ArabidopsisRootMorphology / geometry measurementSegmentationRoot system architecture
Introduction Plant parasitism by sedentary plant-parasitic nematodes is a dynamic and continuously evolving process, accompanied by profound remodelling of host root system architecture across distinct infection stages. However, the physiology and anisotropic growth of Arabidopsis thaliana roots under Heterodera schachtii infection, together with complex lateral root proliferation and increasingly dense, overlapping morphology, pose substantial challenges for accurate image segmentation. Methods Here, we introduce Root-TransUNet, an optimised segmentation architecture that extends TransUNet by incorporating a composite loss function to improve boundary precision and structural continuity, as well as dual-stage strip-pooling (SP) modules to enhance elongated and directional root features. These adaptations address the unique morphological complexity of the infected root system. Additionally, we integrated Root-TransUNet into a high-throughput phenotyping pipeline and applied it to an existing dataset of ~120,000 images of 362 A. thaliana MAGIC recombinant inbred lines collected over several months of infection. By extracting root system architecture traits, including root surface area and estimated root volume across infection stages, we enabled stage-specific association analyses between host root growth and nematode performance across these genotypes. Results Root-TransUNet achieved strong segmentation performance, demonstrating improved structural continuity and boundary precision compared with widely used CNN- and Transformer-based baselines, including UNet++. Stage-specific analyses revealed that the relationship between host root traits and nematode performance changed as infection progressed. During establishment, nematode number was largely independent of initial root size and varied strongly among genotypes, whereas during the reproductive phase (10-30 dpi), greater root expansion coincided with reduced estimated nematode volume accumulation. Notably, nematode burden was largely independent of host root size before infection, indicating that root quantity was generally not a limiting factor for infection in this experiment. Discussion These results demonstrate that Root-TransUNet can robustly segment infected root systems across a wide range of nematode infection densities, providing a scalable image-analysis framework for studying plant-parasitic nematode parasitism in combination with host root phenotyping.
Why it matches plant phenotyping methods感染根系の画像セグメンテーション手法を開発し、高スループット表現型解析パイプラインに統合して根系形態形質を抽出しており、表現型取得・抽出法が中心的である。
abstractHere, we introduce Root-TransUNet, an optimised segmentation architecture that extends TransUNet by incorporating a composite loss function to improve boundary precision and structural continuity, as well as dual-stage strip-pooling (SP) modules to enhance elongated and directional root features.
Reproduction assets foundThe paper analyzes a public BioImages dataset (S-BIAD2402) of ~400,000 RGB root/nematode infection images and provides authors' analysis code on GitHub; both are paper-specific, public, and actionable.Code · publicng molecular signatures, deepening our understanding of host-parasite resource allocation strategies, and establishing a foundation for the discovery of novel resistance mechanisms.
Code and data availability
Python-based source code for automating root analysis using the datasets above is accessible via our GitHub repository ( https://github.com/JieZhou1025/Root-nematode-interaction ).
Statements
Data availability statement
Publicly available datasets were analyzed in this study. This data can be found here: https://www.ebi.ac.uk/biostudies/bioimages/studies/S-BIAD2402 .
Ethics statement
The manuscript presents research on animals that do not require ethical approval for their study.
AuthorOpen asset ↗JieZhou1025/Root-nematode-interactionlines:412-424Code / dataset availability confirmedEurope PMC · Crossref · checked 15 Sept 2026
Published3 Sept 2026Methods in ecology and evolution
Growth chamberMultimodalStereoRootStem / branchTrackingGrowth / development / phenology
Understanding plant behaviour requires the integration of multiple phenotypic and physiological signals measured over time under controlled conditions. However, different plant signals are typically studied using separate experimental setups, limiting temporal alignment and integrative analyses.We present Mind(the)Plant, a modular experimental facility designed for the synchronized, long-term acquisition of multimodal plant data, including three-dimensional shoot kinematics, above- and below-ground volatile organic compounds (VOCs) and root imaging. Its modular architecture is designed to accommodate additional acquisition modules, such as electrophysiological signalling, as future extensions. The platform integrates a controlled growth environment with stereovision imaging, high-resolution time-of-flight mass spectrometry and custom rhizocameras. These components are connected through a unified network infrastructure that ensures synchronized acquisition and centralized data handling.We validate the performance of each acquisition module through multi-week recordings, demonstrating high-temporal stability, reliable stereovision synchronization, effective isolation of VOCs signals and robust operation of below-ground imaging. We further illustrate the analytical potential of the platform using a one-day continuous multimodal acquisition combining shoot kinematics, above-ground VOC emissions, rhizocameras observations and environmental data.Mind(the)Plant provides a novel methodological framework for studying plant behaviour, signalling and phenotypic plasticity in ecological and evolutionary research. By enabling coordinated measurements of multiple plant response modalities, the platform supports investigations of dynamic plant-environment and plant-plant interactions from a behavioural perspective.
Why it matches plant phenotyping methods植物の複数の表現型・生理シグナルを同期取得する施設を開発し、各取得モジュールの性能を検証しているため、表現型計測プラットフォームが研究の中心です。
abstractWe present Mind(the)Plant, a modular experimental facility designed for the synchronized, long-term acquisition of multimodal plant data, including three-dimensional shoot kinematics, above- and below-ground volatile organic compounds (VOCs) and root imaging.
Reproduction assets foundThe paper's data availability statement explicitly deposits data, code and processing pipelines (supporting the multimodal plant phenotyping measurements and analysis) in a public Zenodo archive with an authors' URL matching an allowed URL.Code · publicf Interest Statement
The authors have no conflicts of interest to declare.
Peer Review
The peer review history for this article is available at https://www.webofscience.com/api/gateway/wos/peer-review/10.1111/2041-210x.70411 .
Data availability Statement
Data, code and processing pipelines supporting this study are available at https://doi.org/10.5281/zenodo.22095454 ( Simonetti & Castiello, 2026 ).
References
Avesani S, Bonato B, Simonetti V, Guerra S, Ravazzolo L, Gjinaj G, Dadda M, Castiello U. Comparing proton transfer reaction (PTR) and adduct ionization mechanism (AIM) for the study of volatile organic compounds. Molecules. 2026;31(3):402. doi: 10.3390/molecules31030402.
Baluška F, LeOpen asset ↗zenodo · 10.5281/zenodo.22095454lines:482-508Code / dataset availability confirmedEurope PMC · checked 5 Sept 2026
Abstract Technological advances have expanded the adoption of digital technologies in agriculture, helping to reduce labour effort, increase profitability, improve crop efficiency and productivity, enhance product quality, mitigate environmental impacts, and promote human health. This context also extends to soybean farming, a sector of major economic importance in Brazil. Most importantly, Brazil has the global leadership in soybean production with biological nitrogen fixation (BNF) replacing chemical fertilisers. The research and evaluation of BNF is limited by manual counting of nodules, a time-consuming procedure. This study presents SoyNodules, designed for the automatic identification of soybean nodules, consisting of a dataset of images. The dataset includes 1,701 images acquired under controlled conditions: 1,662 images of soybean roots with nodules and 39 images of isolated nodules without roots. A total of 49,210 nodule instances are manually annotated with bounding boxes. SoyNodules was designed to promote reuse and interoperability in alignment with the FAIR principles (Findable, Accessible, Interoperable, Reusable) and to support the development, training, and evaluation of computer vision and deep learning methods for precision agriculture.
Why it matches plant phenotyping methods大豆根粒を自動識別する画像データセットであり、手作業計数の代替となる植物器官形質の抽出・評価を支援する方法論的データセット。
abstractThis study presents SoyNodules, designed for the automatic identification of soybean nodules, consisting of a dataset of images.
Reproduction assets foundThe paper is a data descriptor for SoyNodules, an annotated dataset of 1,701 soybean root/nodule images with 49,210 bounding-box annotations, publicly deposited on Zenodo with a DOI. The same repository also hosts the authors' annotation-format conversion script (AnyLabeling to Pascal VOC/COCO), per the Code AvailabilDataset · publicThe SoyNodules dataset, released as version 1.0, is publicly available on Zenodo [28]
at https://doi.org/10.5281/zenodo.22081914.Open asset ↗Zenodo · 10.5281/zenodo.22081914pdf-page:9 lines:1-43Code / dataset availability confirmedEurope PMC · checked 14 Sept 2026
Sweet potato is a nutritionally valuable crop that contributes to food security, owing to its storage roots rich in starch, sugars, and antioxidants, while requiring minimal cultivation inputs. Estimating its yield based on visible above-ground traits remains challenging due to weak and inconsistent correlations between shoot biomass and storage root development. Therefore, direct assessment of underground biomass is essential. In this study, we demonstrate the field application of ground penetrating radar (GPR) for non-destructive detection and yield estimation of sweet potato. GPR is a geophysical technique that typically transmits ultra high frequency radio waves into the soil and records reflections from subsurface objects. Electromagnetic wave simulations within the soil-root system revealed GPR signals that strongly correlate with root length, forming the basis for yield quantification. We developed an image-processing pipeline comprising static correction, gain adjustment, noise filtering, and hyperbola segmentation via the Hough transform to enable semi-automated storage root detection from GPR data. By integrating detection and quantification approaches, a linear regression model predicting sweet potato yield from GPR signals achieved moderate accuracy ( R 2 = 0.567, normalized RMSE 0.190). We established a non-destructive and low-labor approach for monitoring root systems, providing a foundation for rapid, scalable, and field-ready yield estimation in sweet potato and other root and tuber crops.
Why it matches plant phenotyping methodsGPRによる地下貯蔵根の検出・定量化と収量推定を中心に、信号処理および画像処理パイプラインを開発・評価しているため、植物フェノタイピング手法として収載する。
abstractWe developed an image-processing pipeline comprising static correction, gain adjustment, noise filtering, and hyperbola segmentation via the Hough transform to enable semi-automated storage root detection from GPR data.
Reproduction assets found保存済みの本文根拠を更新済みルールで再検証し、公開資産1件を確認しました。Code · publicThe datasets analyzed during the current study consist of GPR line scans of the field at ALRC and list of sweet potato storage root weights. These data are available together with the analysis scripts on GitHub under open access. All data and scripts are the property of NARO and are distributed under the Creative Commons Attribution-NonCommercial 4.0 International License (CC BY-NC 4.0). The repository can be accessed at: https://github.com/mtei1/GPRScript.Open asset ↗mtei1/GPRScripthtml-lines:240-264Code / dataset availability confirmedEurope PMC · checked 5 Sept 2026
Rice seedling salt-tolerance evaluation commonly relies on visual scoring or destructive assays, which are subjective, labor-intensive, and difficult to standardize for population-level analysis. This study developed a new deep-learning-assisted hydroponic RGB phenotyping framework for standardized salt-stress evaluation and genetic mapping in rice seedlings. The framework integrates controlled hydroponic cultivation, RGB imaging, RicePhenoSeg-assisted annotation and trait extraction, ELMERF-based semantic segmentation, and image-derived quantification of salt-induced shoot injury. Using this framework, we constructed the Rice Seedling-Salt RGB Dataset (RSSD), which contains green shoot tissues, yellow shoot tissues, roots, and background from hydroponically grown rice seedlings. Based on RSSD, ELMERF achieved a mean Intersection over Union of 51.4% and a mean Accuracy of 89.5%, outperforming nine representative segmentation models. We further defined shoot yellowing rate (SYR) as an image-derived quantitative trait describing visible salt-induced shoot injury. The framework was applied to 261 re-sequenced rice accessions for population-level phenotyping and genome-wide association analysis. Compared with standard evaluation score and seedling death rate, SYR showed a more continuous phenotypic distribution and detected 36 significant SNPs, including a major signal near the Saltol/OsHKT1; 5 region. Notably, 34 SYR-associated SNPs were not detected by conventional visual scores. Overall, this study provides a targeted hydroponic RGB phenotyping framework for standardized rice seedling salt-stress evaluation and genetic analysis.
Why it matches plant phenotyping methods深層学習によるRGB画像セグメンテーション、形質抽出、データセット構築、性能比較を中核とし、画像由来の塩ストレス傷害形質を定量化する植物フェノタイピング手法である。
abstractThis study developed a new deep-learning-assisted hydroponic RGB phenotyping framework for standardized salt-stress evaluation and genetic mapping in rice seedlings.
Reproduction assets foundThe paper's Data Availability statement explicitly deposits datasets, source code, and supporting data in a public GitHub repository (ELMERF), which covers the RSSD RGB image dataset, segmentation code, and phenotyping/GWAS analysis assets. RiceVarMap is a cited external SNP database, not a paper-specific asset.Code · publicThe datasets, source code, and other supporting data are openly available on the ELMERF repository (https://github.com/PhenoCodexh/ELMERF).Open asset ↗PhenoCodexh/ELMERFhtml-lines:446-478Code / dataset availability confirmedEurope PMC · OpenAlex · checked 5 Sept 2026
The co-evolutionary arms race between crops and their parasites requires continuous identification of new resistance mechanisms. Broomrape (Orobanche cumana), a root parasitic plant, poses a severe threat to sunflower (Helianthus annuus) production, yet the genetic architecture underlying host resistance remains poorly understood. To address this, we established a high-throughput phenotyping platform to quantify root infestation across a diverse sunflower association mapping (SAM) population. Combining this phenotypic resource with a dual genome-wide association study (GWAS) strategy based on both single nucleotide polymorphisms (SNPs) and k-mers, we highlight the genetic basis of broomrape resistance at unprecedented resolution. Our analyses revealed quantitative trait loci (QTLs) and identified novel candidate genes, including putative leucine-rich repeat receptor kinases potentially involved in parasite recognition and defense activation. Importantly, the k-mer approach circumvented reference genome bias and uncovered key genomic introgressions from wild Helianthus relatives that contribute substantially to resistance. These findings demonstrate the utility of integrating high-resolution phenotyping with advanced association mapping to dissect complex host-parasite interactions. Moreover, they emphasize the enduring value of wild germplasm as a reservoir of adaptive variation, providing crop breeders with crucial tools to counter the rapid evolutionary dynamics of parasitic plants.
Why it matches plant phenotyping methods根部の寄生程度を定量する高スループット表現型解析プラットフォームの確立が明示され、遺伝解析の基盤として方法が実質的に扱われている。
abstractwe established a high-throughput phenotyping platform to quantify root infestation across a diverse sunflower association mapping (SAM) population.
Reproduction assets foundThe paper's Data availability statement explicitly deposits the paper-specific raw phenotyping images on Zenodo, the k-mer genotype data on the sunflower genome database, and the authors' analysis code on the Hübner lab GitHub repository, all with public URLs.Dataset · publicAll phenotypes raw images for Gadot and Yavor are available through the Zenodo repository ( https://doi.org/10.5281/zenodo.18961268 ).Open asset ↗Zenodo · 10.5281/zenodo.18961268lines:238-238Code · publicCode is accessible through the Hübner lab github: https://github.com/hubner-lab/Sunflower-Broomrape-paper .Open asset ↗Hübner lab github · hubner-lab/Sunflower-Broomrape-paperlines:238-238Code / dataset availability confirmedCrossref · Europe PMC · checked 6 Sept 2026
RiceRootTissueMorphology / geometry measurementSegmentationRoot system architecture
Abstract Background Quantification of rice root anatomical traits such as cortical aerenchyma lacunae is key to understanding rice adaptation to diverse water regimes and to support climate-smart breeding. Aerenchyma lacunae contributes to rice internal gas transport and influences methane emissions from flooded systems and can also limit rice water conductivity. It could be an interesting anatomical trait for breeding, however, large-scale anatomical phenotyping remains limited because manual analysis of root cross-sections is labor-intensive, subjective, and difficult to scale across heterogeneous imaging conditions. Existing pipelines often require parameter tuning and do not generalize well across environments. Results We developed a deep learning pipeline based on a vision transformer architecture to automatically segment rice root cross-sections and quantify cortical aerenchyma lacunae. The model was trained on 1,760 annotated images collected across multiple countries, growth stages, cultivation systems, and experimental contexts, using a collaboratively defined annotation protocol. The final model achieved high segmentation accuracy, with mean intersection over union values exceeding 0.92 for cortical tissues and lacunae. Quantification of the lacuna-to-cortex ratio showed strong agreement with manual annotations, with a coefficient of determination of 0.98 on an independent test set. An independent expert review indicated that model predictions were at least as consistent as manual annotations and reduced large annotation inconsistencies. The pipeline is released as open-source software and includes an interactive online demonstrator, and is accompanied by an online test dataset to support testing and reproducibility. Application across six experimental use cases revealed reproducible differences in aerenchyma lacunae across genotypes, water regimes, environments, and developmental stages. Conclusions This work provides a robust, scalable, and transferable tool for automated root anatomical phenotyping under heterogeneous experimental conditions. Transformer-based segmentation enables consistent and high-throughput quantification of lacunae, facilitating integration of these anatomical traits into breeding, physiological studies, and climate-smart crop improvement programs.
Why it matches plant phenotyping methodsイネ根の通気組織空隙を画像から自動セグメンテーション・定量するTransformerベースの表現型解析パイプラインを開発し、独立データで精度検証、ソフトウェアとテストデータセットを公開しているため、植物フェノタイピング手法が中心である。
abstractWe developed a deep learning pipeline based on a vision transformer architecture to automatically segment rice root cross-sections and quantify cortical aerenchyma lacunae.
Reproduction assets foundThe paper releases its authors' phenotyping pipeline (preprocessing/training code archived on Zenodo and an interactive Hugging Face Space demonstrator with a test dataset subset) as public assets. The full multi-environment training image dataset is only available upon reasonable request, so it is not a public asset.Code · publicall code used for preprocessing and
training is released under an open-source licence on GitHub, tagged v1.0.2, and
archived with a Zenodo DOI (Atef, 2025).Open asset ↗Zenodopdf-page:46 lines:1-65Code / dataset availability confirmedEurope PMC · checked 5 Sept 2026
Root phenotypic analysis is closely related to crop yield and stress resistance. Although deep learning can improve the efficiency of root phenotype recognition, existing methods suffer from insufficient segmentation accuracy under complex soil backgrounds and focus on a single target. To address the issues of limited accuracy and operational complexity in existing root segmentation models, this paper proposes a novel wavelet-enhanced full-scale segmentation network. The WaveUNet+ model is based on U-Net3plus, replaces traditional downsampling with the Haar wavelet transform, and introduces the EMA module. The impact of the wavelet transform is validated using Grad-CAM, and HD95 is employed to evaluate the improvement in segmentation quality brought by the attention mechanism from the perspective of boundary accuracy. Transfer learning is used to improve model generalization, and the test results on diverse roots and various soils are compared. A Docker containerized root image segmentation method is designed to achieve convenient and practical operation, and the deployment feasibility of the model on edge devices is also verified. Our model effectively enhances the recognition of fine roots in soil backgrounds, leading to improvements across various metrics, achieving an Accuracy of 99.2%, while improving model accuracy with relatively low parameter count and model size. Compared with the original U-Net model, mIoU is increased by 1.52% and Recall by 2.93%. The results show that the model not only performs excellently on the original dataset but also maintains good generalization ability across different imaging modalities, crop species, and soil conditions. With Docker, users can achieve root image segmentation on their own computers without tedious program installation and environment configuration. In the future, we will attempt methods such as pruning and quantization to reduce model size, so as to better adapt to the deployment requirements of edge devices.
Why it matches plant phenotyping methods根系画像から根系形態を抽出するセグメンテーション手法を開発し、複数条件で精度・汎化性・境界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。
abstractTo address the issues of limited accuracy and operational complexity in existing root segmentation models, this paper proposes a novel wavelet-enhanced full-scale segmentation network.
Reproduction assets foundThe paper's Data Availability Statement explicitly states the analysis code is publicly available at the authors' GitHub repository (WaveUNet-), which implements the WaveUNet+ root segmentation and phenotyping analysis. The supplementary materials only contain Grad-CAM figures and parameter tables, not datasets or codeCode · publicData Availability Statement
The data are available in a publicly accessible repository. The code can be obtained from https://github.com/WLL-cyber/WaveUNet-.git (accessed on 16 June 2026).Open asset ↗WLL-cyber/WaveUNet-lines:182-213Code / dataset availability confirmedCrossref · checked 14 Sept 2026
Wild-relative introgression broadens wheat diversity, as exemplified by the Multiple Synthetic Derivatives (MSD) population, a unique hexaploid wheat resource capturing extensive genetic diversity from Aegilops tauschii. However, root system architecture (RSA), a key determinant of resource acquisition and stress adaptation, remains poorly characterized in this population. Here, we established a practical two-dimensional root phenotyping framework that enables continuous imaging to track RSA traits and their responses to heat stress. Using this framework we evaluated MSD417 as a representative genotype against its recurrent parent, Norin 61 (N61). Under control conditions, MSD417 displayed greater total root length, root system width, and convex hull area than N61 (p < 0.001), indicating enhanced early root vigor. MSD417 also exhibited larger second pair seminal root angle (p < 0.001) and length (p < 0.01) across both conditions, suggesting enhanced horizontal root exploration while maintaining similar rooting depth to N61 (p = 0.981). Heat stress reduced overall root growth and narrowed genotypic differences, limiting RSA expression. Microscopic observations revealed a lower coleorhiza height-to-width ratio in MSD417. These findings demonstrate the effectiveness of the two-dimensional platform for early-stage RSA phenotyping and highlight Aegilops tauschii-derived germplasm as a source of favorable root traits in wheat breeding.
Why it matches plant phenotyping methods二次元画像による根系構造フェノタイピング基盤を構築し、連続撮像で根形質を追跡する方法が研究の中心であるため含める。
abstractHere, we established a practical two-dimensional root phenotyping framework that enables continuous imaging to track RSA traits and their responses to heat stress.
Reproduction assets foundThe paper's data availability statement deposits the paper-specific phenotyping inputs publicly on Zenodo: root images of wheat N61 and MSD417 (the two genotypes measured for RSA traits) and microscopic coleorhiza images. These are public, paper-specific image datasets directly underlying the study's measurements. No作者Dataset · publical development in arid regions.
ORCID
Sultan Md Monwarul Islam http://orcid.org/0009-0002-7219-2104
Izzat Sidahmed Ali Tahir http://orcid.org/0000-0002-1711-6961
Kinya Akashi http://orcid.org/0000-0002-9991-5766
Data availability statement
The root images of wheat N61 and MSD417 are deposited in the Zenodo data repository under https://doi.org/10.5281/zenodo.18080159 and https://doi.org/10.5281/zenodo.18079748, respectively. The microscopic images of
coleorhiza are deposited under https://doi.org/10.5281/zenodo.18091131. The other original contributions presented
in the study are included in the article and/or supplementary material.
References
Alahmad, S., El Hassouni, K., Bassi, F. M., DiOpen asset ↗Zenodo · 10.5281/zenodo.18080159pdf-raw-page:14 lines:1-49Dataset · publicMd Monwarul Islam http://orcid.org/0009-0002-7219-2104
Izzat Sidahmed Ali Tahir http://orcid.org/0000-0002-1711-6961
Kinya Akashi http://orcid.org/0000-0002-9991-5766
Data availability statement
The root images of wheat N61 and MSD417 are deposited in the Zenodo data repository under https://doi.org/10.5281/zenodo.18080159 and https://doi.org/10.5281/zenodo.18079748, respectively. The microscopic images of
coleorhiza are deposited under https://doi.org/10.5281/zenodo.18091131. The other original contributions presented
in the study are included in the article and/or supplementary material.
References
Alahmad, S., El Hassouni, K., Bassi, F. M., Dinglasan, E., Youssef, C., Quarry, G., Aksoy,Open asset ↗Zenodo · 10.5281/zenodo.18079748pdf-raw-page:14 lines:1-49Dataset · public-6961
Kinya Akashi http://orcid.org/0000-0002-9991-5766
Data availability statement
The root images of wheat N61 and MSD417 are deposited in the Zenodo data repository under https://doi.org/10.5281/zenodo.18080159 and https://doi.org/10.5281/zenodo.18079748, respectively. The microscopic images of
coleorhiza are deposited under https://doi.org/10.5281/zenodo.18091131. The other original contributions presented
in the study are included in the article and/or supplementary material.
References
Alahmad, S., El Hassouni, K., Bassi, F. M., Dinglasan, E., Youssef, C., Quarry, G., Aksoy, A., Mazzucotelli, E., Juhász, A.,
Able, J. A., Christopher, J., Voss-Fels, K. P., & Hickey, L. T. (2019). A majOpen asset ↗Zenodo · 10.5281/zenodo.18091131pdf-raw-page:14 lines:1-49Code / dataset availability confirmedarXiv · OpenAlex · checked 11 Sept 2026
Tomographic microscopy enables three-dimensional internal imaging but often requires expensive optical or X-ray instrumentation. Here we present an ultra-low-cost continuous-wave diffusive tomography (CWDT) system for biological samples. The system uses a smartphone microscope, a white LED coupled into an optical fiber, 3D-printed micropositioners, and a physics-based forward model optimized with machine learning. We demonstrate full-color volumetric reconstructions from a tartrazine-cleared poplar section, a scattering phantom, fungal mycelium near an Arabidopsis root, and thick poplar branch imaging with an inserted side-emitting fiber. The current results are qualitative and exploratory, but they show that scanned fiber illumination and inexpensive hardware can produce useful three-dimensional reconstruction outputs for low-cost microscopy experiments.
Why it matches plant phenotyping methods低コスト三次元断層イメージング法そのものを開発し、ポプラ組織・枝やシロイヌナズナ根近傍を対象に植物の内部構造を可視化しているため、植物形態の取得法として中心的です。
abstractHere we present an ultra-low-cost continuous-wave diffusive tomography (CWDT) system for biological samples.
Reproduction assets foundThe paper's raw imaging inputs, configurations, and reconstruction outputs for Figures 2–5 are publicly deposited on Kaggle. The analysis code repository is only 'prepared for release' (no confirmed public deposit yet), so it is listed as request-only. Hardware CAD mirrors are public but are instrument designs, not theDataset · publicFigure-level raw inputs, model configurations, selected outputs, and manifests are available through the Kaggle dataset https://www.kaggle.com/datasets/alingold/continuous-wave-diffusive-tomography .Open asset ↗continuous-wave-diffusive-tomographylines:108-129Code / dataset availability confirmedEurope PMC · checked 5 Sept 2026
Clubroot disease, caused by Plasmodiophora brassicae , is one of the major constraints in rapeseed production. Breeding disease-resistant cultivars is the best way to control this devastating disease. However, breeding reliable resistant germplasm and genes is limited. Inactivation of susceptible genes has been shown to be a new and effective strategy for developing resistant crops. Therefore, we aimed to screen key candidate susceptible genes in this study. Firstly, we established a stable, high-throughput visualization method for identifying gall formation at the early stage of P.brassicae infection. At 14 days post-inoculation (dpi), the earliest time point with a clear record of scorable root swelling, remarkable variations in the speed of gall formation were observed among 85 genotypes. Secondly, genome-wide association studies (GWAS) were performed to identify genes involved in gall development. Three and two consecutive significant peaks were detected at 14 and 21 dpi, respectively. Thirdly, comparative transcriptomic analysis was conducted between 2AF195 and 2AF058 at 7 and 14 dpi; these two materials exhibit contrasting speeds of gall development. Gene clustering analysis revealed two opposite expression patterns at 14 dpi. One pattern comprised 1,383 genes downregulated in 2AF195 but upregulated in 2AF058, which were significantly enriched in 10 KEGG pathways, including Environmental Information Processing and Plant-pathogen interaction, and involved core repressors JAZ8/10 in the jasmonic acid (JA) signaling pathway, as well as nucleotide-binding site (NBS) protein-encoding genes. The opposite pattern consisted of 79 genes upregulated in 2AF195 but downregulated in 2AF058, which were enriched in an additional 10 KEGG pathways, predominantly related to Carbohydrate Metabolism and the Ubiquitin System. These genes were functionally annotated mainly as pectin methylesterases, xyloglucan endotransglucosylase/hydrolases (XTHs), and lignin biosynthesis-related enzymes. These findings demonstrated that distinct regulatory networks exist in different susceptible rapeseed genotypes. Finally, through the combined analysis of haplotype and transcriptome data, we co-localized and identified the candidate gene BnaC08g46100D , a nodulin-related gene belonging to the MtN21 transporter family. These results provide a theoretical basis for developing novel disease-resistant materials by editing the key susceptibility genes involved in root gall formation. The candidate genes identified in this study are the most promising targets for this purpose.
Why it matches plant phenotyping methods根こぶ形成を高スループットに可視化・判定する方法の確立が明示され、感染植物の病徴を測定する手法として研究の主要な技術要素になっている。
abstractwe established a stable, high-throughput visualization method for identifying gall formation at the early stage of P.brassicae infection.
Reproduction assets found本文中に内容が明示された植物フェノタイピング関連の補足表と、その公開リンクを確認しました。Supplement · publicSupplementary Table 2
Disease incidence data of 85 rapeseed accessions at various time points following inoculation with the Xinmin strain.Open asset ↗lines:502-594Code / dataset availability confirmedCrossref · OpenAlex · checked 14 Sept 2026
Abstract Warmer temperatures, permafrost thaw, and increased wildfire activity are driving rapid ecological change across the Arctic, significantly altering plant productivity and aboveground biomass (AGB). These rapid changes highlight the urgent need to improve monitoring of vegetation dynamics in the Earth’s northern ecosystems, where high spatiotemporal heterogeneity occurs at scales finer than those captured by traditional satellite observations. The growing use of unoccupied aerial systems (UASs) presents an opportunity to overcome this limitation. Yet, the diversity of UAS platforms, sensors, and data collection and processing workflows presents challenges for developing standardized, generalizable approaches. To address this challenge, we compiled 672 AGB plots co-located with 183 UAS-based structure-from-motion (SfM) or light detection and ranging (LiDAR) surveys collected across the Arctic. Here, we: (1) evaluated the generalizability of UAS-derived canopy structure derived from high-resolution SfM and LiDAR for estimating AGB, (2) assessed scaling errors and their sources in two recent satellite-based AGB products derived from Landsat and moderate resolution imaging spectroradiometer, and (3) demonstrated the use of high-resolution AGB maps to quantify biomass variation across tundra plant functional types (PFTs) and to monitor post-fire recovery. Our results show that both SfM and LiDAR accurately captured AGB and its variability across tundra PFTs using a random forest model (overall root mean squared error: 0.332 kg m –2 ), with mapping performance varying slightly by region and data source. Using UAS-derived AGB maps as a benchmark, we identified systematic biases in satellite-derived AGB products, largely attributable to the magnitude of AGB and structural heterogeneity within coarse-resolution pixels. Applying our model to repeat UAS surveys following a tundra fire on Seward Peninsula, we observed rapid AGB recovery in non-shrub patches, with biomass recovering to pre-fire levels within two years. In contrast, shrub patches recovered more slowly, with AGB gains continuing over 2–4 years through both in-patch growth and lateral expansion (via dispersal) into remaining burned areas. Overall, these findings support the generalizability of UAS-based SfM and LiDAR data for estimating tundra AGB and highlight the need for broader collection and synthesis of such data to improve ecological monitoring and model benchmarking in the Arctic.
Why it matches plant phenotyping methodsUASのSfMおよびLiDARから植物群落の地上部 biomass (AGB) を推定する手法の一般化性能を評価し、衛星推定値のベンチマークにも用いており、植物形質取得が研究の中心である。
abstractevaluated the generalizability of UAS-derived canopy structure derived from high-resolution SfM and LiDAR for estimating AGB
Reproduction assets found保存済みの本文根拠を更新済みルールで再検証し、公開資産1件を確認しました。Code · publicThe codes and training data is available on GitHub: https://github.com/Daryl-Open asset ↗pdf-page:20 lines:1-30Code / dataset availability confirmedCrossref · checked 14 Sept 2026
Given the increasing frequency, severity, and socioecological impacts of wildfires, there is an urgent need for robust frameworks to better characterize fire behavior and flammability patterns across ecosystems to support early warning, mitigation, and management strategies. However, flammability remains difficult to quantify and scale, as it involves multiple interacting components that are typically measured at the bench scale. This study aimed to establish empirical links between spectral information, plant traits, and flammability metrics, and to scale these relationships to satellite imagery to translate these metrics into a spatial context. We combined laboratory spectroscopy, plant trait measurements including leaf mass per area, carbon, and cellulose, and combustion experiments using a simple and reproducible burning device. In total, 84 samples were collected and analysed, allowing us to characterise how spectral signatures relate to vegetation traits and fire behaviour. Spectral indices were developed to estimate plant traits, which were subsequently used as predictors in flammability models. These models were then transferred to Environmental Mapping and Analysis Program (EnMAP) hyperspectral imagery to derive spatial estimates across eucalypt forests and grasslands of the Australian Capital Territory (ACT). Spectral information distinguished fuel types and captured variability of the plant traits, while these traits showed associations with combustion behaviour. Based on these links, the best-performing model predicted the rate of temperature increase, a combustibility metric, in eucalypt forests (R2 = 0.70; Root Mean Square Error = 32.48 °C/s). In contrast, grassland models showed limited predictive performance, likely due to weaker relationships between plant traits and flammability metrics. Overall, this study demonstrates a practical and scalable approach for deriving flammability maps from hyperspectral and in situ data, highlighting the potential of plant-trait-based remote sensing. The resulting maps should not be interpreted as standalone fire risk products, but rather as a characterization of the structural and biochemical drivers of flammability. The main constraint of this work is the limited sample size. Future research should expand spatial and temporal coverage to better capture vegetation variability and enable the inclusion of independent validation datasets. Exploring alternative combustion protocols and testing more advanced spectral modelling approaches for trait estimation would provide additional insights.
Why it matches plant phenotyping methods植物形質を分光情報から推定し、ハイパースペクトル画像へ展開して可燃性関連の植物状態を評価する手法が研究の中心であり、モデル性能も検証しているため。
abstractSpectral indices were developed to estimate plant traits, which were subsequently used as predictors in flammability models.
Reproduction assets foundThe paper's supplementary materials (hosted publicly by MDPI) contain the paper-specific plant phenotype measurements: sampled species lists, fractional cover, and measured vegetation traits across dates and plots, plus combustion replicate variability and trait–flammability relationship data. The raw underlying data,谱Supplement · publicbroader environmental coverage, improved plant trait retrieval meth-
ods, and independent validation. Future work should also explore non-linear modelling
frameworks to better capture the complexity of vegetation flammability across ecosystems.
Supplementary Materials: The following supporting information can be downloaded at:
https://www.mdpi.com/article/10.3390/rs18101546/s1, Supplementary Table S1 provides the list of
sampled plant species and their percentage cover across sites, paddocks, plots, and fuel types; Table
S2 presents the fractional cover of each species and litter component; Figure S1 shows the study-site
vegetation map; Figures S2–S6 show the measured vegetation traits acrosOpen asset ↗pdf-raw-page:22 lines:1-49Code / dataset availability confirmedOpenAlex · Europe PMC · Crossref · checked 15 Sept 2026
Climate change threatens global Chinese cabbage ( Brassica rapa L. ssp. pekinensis ) production, a cool-season crop essential for Asian markets. With optimal growth at 18-20°C and severe disruption above 25°C, developing heat-resilient varieties is critical. This study integrated high-throughput 3D multispectral phenotyping with multivariate analysis to characterize temporal heat stress responses in 18 Chinese cabbage genotypes. Seedlings were subjected to heat stress (setpoint 40/35°C day/night; measured 35.7/31.5°C day/night air temperature) or controls (setpoint 25/20°C day/night; measured 25.0/17.7°C day/night air temperature) for 14 days, with continuous non-destructive monitoring of 14 morphological and spectral parameters using PlantEye F600 multispectral 3D scanner. Principal component analysis of temporal phenotyping data explained 62-68% of variance, enabling quantitative assessment of phenotypic stability through Euclidean distance measurements in PC space. Temporal analysis revealed crop-specific response patterns with maximum treatment separation at 3 days after treatment (DAT) (ΔC=3.27), reflecting Chinese cabbage’s rapid heat sensitivity as a cool-season crop, followed by progressive acclimation by 14 DAT (ΔC=1.41). Early responses (3-5 DAT) were dominated by morphological parameters, transitioning to physiological adjustments (10-14 DAT) characterized by spectral indices. Under heat stress, plants prioritized evaporative cooling through increased transpiration (four-fold increase) over carbon assimilation. A critical finding was the disproportionately greater reduction in root biomass relative to shoot biomass under to heat stress, with root biomass declining 38-47% versus 20% in shoots. Strong correlations (r>0.8) between 3D imaging parameters and destructive biomass measurements validated the non-destructive approach’s reliability. Notably, image-based root surface area analysis correlated strongly with actual root biomass (R 2 =0.698, p<0.001), enabling practical assessment of root area without conventional destructive processing. Based on integration of phenotypic stability (Euclidean distances in PC space) and biomass production under heat stress, this approach identified four distinct heat tolerance strategies: stable-productive genotypes (ideal breeding targets combining phenotypic stability with high heat-stress biomass production), stable-conservative genotypes (phenotypic stability with lower production), plastic-productive genotypes (substantial phenotypic changes yet high biomass production), and plastic-sensitive genotypes (phenotypically unstable and poor biomass production). This validated framework accelerates heat-tolerant Chinese cabbage breeding through efficient high-throughput phenotyping, enabling targeted genotype selection for diverse production environments facing climate warming.
Why it matches plant phenotyping methods3Dマルチスペクトルスキャナによる非破壊・時系列表現型取得と、その解析・検証が研究の中心であり、熱ストレス下の形態・生理形質を定量化する実質的なハイスループット表現型解析研究である。
abstractThis study integrated high-throughput 3D multispectral phenotyping with multivariate analysis to characterize temporal heat stress responses in 18 Chinese cabbage genotypes.
Reproduction assets foundThe paper states its collected phenotyping data are available in the supplementary material hosted with the article (open access under CC BY-NC-ND), making the paper-specific phenotype dataset publicly actionable via the article DOI. The analysis code, however, is only available from the corresponding author uponReasonDataset · publichrough field phenotyping.) between RDA and the World Vegetable Center (WorldVeg)” and by the long-term strategic donors to the WorldVeg: Taiwan, the United States, Australia, the United Kingdom, Germany, Thailand, South Korea, Philippines, and Japan.
Footnotes
Appendix A
Supplementary data to this article can be found online at https://doi.org/10.1016/j.plaphe.2026.100221 .
Appendix A.
Supplementary data
The following is the Supplementary data to this article.
Multimedia component 1
Data availability
The data collected and used in this study are available in the supplementary material. The code used for analysis can be obtained from the corresponding author upon reasonable request.
ReferenceOpen asset ↗lines:486-514Code / dataset availability confirmedEurope PMC · checked 15 Sept 2026
Multispectral / hyperspectralLeafRootMorphology / geometry measurementLeaf traitsRoot system architecture
Premise Selective breeding over thousands of years has prioritized aboveground yield, with little regard for changes belowground. Roots underpin plant growth and resilience, but our knowledge of these critical structures lags behind that of aboveground structures. Accurately phenotyping root traits is labor-intensive, expensive, and often destructive. High-throughput, nondestructive methods are required to advance understanding of the fundamental biology of root systems and to integrate hard-to-measure root traits into breeding programs. Methods We used American licorice (Glycyrrhiza lepidota Pursh.), a perennial legume with a rich ethnobotanical history, as a model to investigate root system phenotypes. We assessed root traits across multiple populations, analyzed relationships between above- and belowground phenotypes, and tested the use of multidimensional leaf traits, including spectral reflectance, in predicting root traits. Results Root traits of American licorice varied significantly across source populations. Root traits were strongly intercorrelated and each root trait correlated with an aboveground phenotype. Leaf spectral reflectance and elemental composition predicted belowground traits; however, interpretation of some trait-specific signals were complicated by isometric scaling between plant size and root traits. Conclusions These findings demonstrate the use of high-dimensional leaf traits as a proxy for root traits, with potential applications for understanding foundational questions in plant biology and in breeding programs targeting belowground structures of perennial herbaceous species. Further optimization and larger studies are needed to improve predictive models.
Why it matches plant phenotyping methods葉の高次元形質とスペクトル反射を用いて、測定困難な根形質を非破壊・高スループットに推定する方法が研究の中心である。
abstractHigh-throughput, nondestructive methods are required to advance understanding of the fundamental biology of root systems and to integrate hard-to-measure root traits into breeding programs.
Reproduction assets foundThe paper's data availability statement points to two public, paper-specific assets: raw root scans on Zenodo and a Figshare deposit containing RhizoVision Explorer output features, CropReporter data and metadata, spectral reflectance data, elemental composition data, and all R code needed to reproduce the analyses. NoDataset · publich Center Bioanalytical Chemistry Facility (RRID:SCR_001047). Finally, we thank the reviewers for their careful evaluation of our manuscript and constructive comments, which helped us clarify the conceptual framing and strengthen the overall quality of the work.
DATA AVAILABILITY STATEMENT
Raw root scans can be found on Zenodo ( https://zenodo.org/records/18852041 ). RhizoVision Explorer output features, CropReporter and associated metadata, spectral reflectance data, elemental composition data, and all R code needed to reproduce the analyses presented in this manuscript can be found on Figshare ( https://doi.org/10.6084/m9.figshare.28742870 ).
REFERENCES
Alahmad , S.
,
D.
Smith
,
C.
KatOpen asset ↗Zenodo · 18852041lines:173-419Dataset · publicILITY STATEMENT
Raw root scans can be found on Zenodo ( https://zenodo.org/records/18852041 ). RhizoVision Explorer output features, CropReporter and associated metadata, spectral reflectance data, elemental composition data, and all R code needed to reproduce the analyses presented in this manuscript can be found on Figshare ( https://doi.org/10.6084/m9.figshare.28742870 ).
REFERENCES
Alahmad , S.
,
D.
Smith
,
C.
Katsikis
,
Z.
Aldiss
,
S. M.
Brunner
,
S. V.
Meer
,
L.
Meijer
, et al. 2025 .
Phenotyping the hidden half: combining UAV phenotyping and machine learning to predict barley root traits in the field
. Journal of Experimental Botany
76 : 5161 ‐ 5178 .
40580084
10.1093/jxb/eraf268
PMC1Open asset ↗Figshare · 10.6084/m9.figshare.28742870lines:173-419Code / dataset availability confirmedOpenAlex · Crossref · checked 5 Sept 2026
Abstract 3D models are used in plant phenotyping for non-destructive quantification and analysis of morphological characteristics. Analyzing plant structure allows breeders to select for desirable traits, associated with e.g. drought tolerance or increased productivity. In sugar beet, morphological parameters depict an essential element of the variety approval for distinguishing between genotypes. However, only a limited number of measured or scored parameters are considered at a single time point. In contrast, 4D data adds a temporal component and can depict the dynamic development of 3D parameters. To explore the potential of spatio-temporal 4D phenotyping for automated crop genotype differentiation, a greenhouse experiment was conducted by us covering twelve sugar beet genotypes. High-resolution 3D models were generated twice a week over the course of two months and both common and novel 3D morphological parameters were extracted. The importance of these parameters was assessed by us, and the dataset was analyzed using unsupervised pointwise clustering and time series clustering. Varying importance of parameters depending on the time point and significantly higher importance of plant parameters compared to leaf parameters are demonstrated by our results. Moreover, increased and more stable genotype differentiation is archived using time series clustering compared to pointwise clustering. Furthermore, taproot formation of sugar beet was found to have a crucial impact on morphological development. Substantial variations in the dynamic development of 3D morphological parameters underline the importance of 4D data for plant genotype differentiation. Thus, a novel foundation for genotype differentiation in plant phenotyping is provided by our findings.
Why it matches plant phenotyping methods3Dモデルから植物形態形質を抽出し、時系列クラスタリングで遺伝型識別を評価する4Dフェノタイピング手法が研究の中心である。
titleSpatio-temporal 4D phenotyping for automated morphological genotype differentiation of sugar beet
Reproduction assets foundThe paper publicly deposits its generated sugar beet point cloud dataset under CC BY 4.0 at a Dataverse DOI, directly reproducing the paper's phenotyping measurements. Supplementary Python codes and extracted parameter values are stated to be included with the article, but no authors' public URL for the code is presentDataset · publicThe generated point cloud dataset is available at https://doi.org/10.60507/FK2/IS8YBZ under CC BY 4.0 license.Open asset ↗10.60507/FK2/IS8YBZlines:277-363Code / dataset availability confirmedOpenAlex · Europe PMC · checked 5 Sept 2026
RootMorphology / geometry measurementSegmentationRoot system architecture
BACKGROUND: Root segmentation is a fundamental yet challenging task in image-based plant phenotyping. Accurate segmentation is a prerequisite for extracting root traits relevant to plant physiology, breeding, and agronomy. While U-Net and other convolutional neural network (ConvNet) architectures have been applied to root segmentation, no systematic comparison of multiple Transformer and ConvNet architectures has been conducted across diverse root imaging conditions. RESULTS: We evaluated 21 segmentation architectures across nine diverse root image datasets, training 1511 models to assess all combinations of architecture, dataset, pre-training strategy, and learning rate, producing over 3 million segmentations for evaluation. Transformer-based models significantly outperformed ConvNets for Dice (mean Dice 0.679 vs 0.659; [Formula: see text]). Root-diameter and root-length correlation were also higher for Transformers, but the differences were not statistically significant ([Formula: see text] and [Formula: see text] respectively). Pre-training significantly improved mean Dice from 0.623 to 0.666 ([Formula: see text]), with Transformers benefiting more from pre-training than ConvNets (Dice improvement + 0.072 vs + 0.021; [Formula: see text]), supporting the hypothesis that fine-tuned Transformers transfer more effectively across large domain gaps. MobileSAM achieved the highest Dice score (0.693) while maintaining computational efficiency. Both architecture families underestimated thin root length compared to manual annotations. Dataset choice explained 70.9% of performance variance, far exceeding model architecture (6.7%). PURPOSE: Transformer architectures significantly outperform ConvNets for root segmentation accuracy, and pre-training significantly improves performance, particularly for Transformers. Pre-trained MobileSAM offers the best accuracy at competitive computational cost. Dataset choice dominates performance variance, suggesting practitioners should prioritize data curation over architecture selection.
Why it matches plant phenotyping methods根の画像セグメンテーション手法を複数データセットで体系的に比較・検証し、根長・根径などの形質抽出性能も評価しているため、植物フェノタイピング手法が中心である。
abstractRoot segmentation is a fundamental yet challenging task in image-based plant phenotyping.
Reproduction assets foundThe paper's root image datasets (DeepRootLab, Grassland, Chicory, PRMI) are publicly available, and the authors' training code and modified RhizoVision Explorer trait-extraction fork are on GitHub with explicit availability statements.Dataset · publicImages are available from https://zenodo.org/records/15213661 .Open asset ↗Zenodo · 15213661lines:872-982Dataset · publicImages are available from https://figshare.com/ndownloader/articles/20440497/versions/2 .Open asset ↗Figshare · 20440497lines:872-982Dataset · publicImages are available from https://zenodo.org/records/3527713 .Open asset ↗Zenodo · 3527713lines:872-982Dataset · publicImages are available from https://gatorsense.github.io/PRMI/ .Open asset ↗lines:872-982Code · publicTraining code is available at https://github.com/sotlampr/seg .Open asset ↗GitHub · sotlampr/seglines:1183-1225Code · publicAll nine root image datasets used in this study are publicly available. DeepRootLab images are available from Zenodo (https://zenodo.org/records/15213661). Grassland images are available from Figshare (https://figshare.com/ndownloader/articles/20440497/versions/2). Chicory images are available from Zenodo (https://zenodo.org/records/3527713). The six PRMI datasets (Papaya, Peanut, Sesame, Sunflower, Cotton, Switchgrass) are available from https://gatorsense.github.io/PRMI/. Training code is available at https://github.com/sotlampr/seg. The modified RhizoVision Explorer fork used for trait extraction is available at https://github.com/sotlampr/RhizoVisionExplorer.Open asset ↗GitHub · sotlampr/RhizoVisionExplorerlines:1294-1347Code / dataset availability confirmedEurope PMC · checked 5 Sept 2026
Growth chamberRootMorphology / geometry measurementSegmentationGrowth / time-series analysisGrowth / development / phenologyRoot system architecture
Abstract Root system architecture and root hairs highly influence plant resource uptake, yet their simultaneous quantification at the whole-plant scale remains challenging due to the conflicting requirements of high-resolution imaging and non-destructive, repeated measurements. Here, we present a plant growth and imaging system based on A3 sized flatbed rhizotrons, combined with a non–machine-learning image analysis workflow implemented in R, that enables repeated, in situ quantification of cereal root system architecture and root hair area over three weeks of growth. The system produces integrated outputs highlighting both whole-root architecture and the spatial distribution of surrounding root hair area from single images. Validation of the image analysis algorithm showed good segmentation performance, with average Matthews Correlation Coefficient values of 0.68 for root area and 0.65 for root hair area. To demonstrate its experimental applicability, the system was used to assess root growth and root hair responses under controlled environmental conditions, combining three irrigation regimes (2, 4, and 6 irrigation events per day) with three dry bulk density levels (1.4, 1.5, and 1.6 g cm⁻³). In addition to whole-system metrics, the approach enables analysis of root hair expansion at individual root tips. This methodology provides a rapid, scalable, and training-free method for integrated analysis of root architecture and root hairs under controlled physical conditions similar to soil, facilitating studies of root–soil interactions that require both spatial resolution and temporal continuity.
Why it matches plant phenotyping methods根系画像解析システムとRベースの画像分析ワークフローを開発し、根系構造と根毛面積の定量化およびアルゴリズム性能検証を中心に扱っているため、植物フェノタイピング手法として適格です。
abstractwe present a plant growth and imaging system based on A3 sized flatbed rhizotrons, combined with a non–machine-learning image analysis workflow implemented in R, that enables repeated, in situ quantification of cereal root system architecture and root hair area over three weeks of growth.
Reproduction assets foundThe paper's authors state that the RootHairFinder C++ file, R script, and example rhizotron data will be available via a GitHub repository and Zenodo upload, and the preprint's supplementary files already include RootHairFinder.cpp and example rhizotron images (Supplimentaryfile4.tif, Supplimentaryfile5.tif). This is aCode · publicThe RootHairFinder cpp file, R script and example data files for rhizotron analysis will be made available through github https://github.com/TracyValentine/RootHairFinder and https://zenodo.org/uploads/19288922Open asset ↗TracyValentine/RootHairFinderlines:236-263Code / dataset availability confirmedEurope PMC · checked 5 Sept 2026
Metabolic processes are essential for regulating and maintaining developmental transitions. However, the distinct metabolite-driven mechanisms that are crucial for development remain poorly characterized due to inherent challenges in measuring their localization and function in situ. We applied desorption electrospray ionization mass spectrometry imaging (DESI-MSI) to generate near single-cell resolution (50-80 µm) images of metabolites in the maize root tip, which has a well-characterized longitudinal developmental gradient. We developed a new computational tool, called Developmental Imaging Mass Spectrometry Pipeline for Linear Evaluation (DIMPLE), which processes mass signatures along linear gradients and clusters metabolites based on their developmental enrichment patterns. We employed this method to compare developmental enrichment of metabolites in Oaxacan Green, a salt-resilient maize variety, to B73, which is salt sensitive. DIMPLE uncovers specific differences in individual mass signatures and overall enrichment patterns between these varieties. Further characterization of these differences revealed meristem enrichment of D-erythrose, a metabolite that can improve stress tolerance in maize. Overall, DIMPLE enables comprehensive and rapid analysis of metabolite patterns along a linear gradient, informing biological hypotheses related to plant growth and stress response.
Why it matches plant phenotyping methods植物根端の発達勾配に沿った代謝物分布を画像化・解析する計算ツールを開発しており、植物の発達状態やストレス応答に関わる表現型抽出が研究の中心である。
abstractWe developed a new computational tool, called Developmental Imaging Mass Spectrometry Pipeline for Linear Evaluation (DIMPLE), which processes mass signatures along linear gradients and clusters metabolites based on their developmental enrichment patterns.
Reproduction assets foundThe paper's authors publicly deposited the DIMPLE analysis code and raw DESI-MSI data on the Dickinson Lab GitHub and Zenodo, as stated in the Technical aspects and Data availability sections.Code · publicThe full R code analysis can be found in the Dickinson Lab Github at https://github.com/dickinsonlab.Open asset ↗dickinsonlabhtml-lines:198-204Code · publicSource code and raw data for DIMPLE are available on the Dickinson Lab GitHub (https://github.com/dickinsonlab) and at https://zenodo.org/records/17187822.Open asset ↗17187822html-lines:198-204Code / dataset availability confirmedEurope PMC · bioRxiv · checked 15 Sept 2026
1 Summary Zinc (Zn) deficiency limits rice productivity and poses a risk to human health, particularly in populations reliant on rice-based diets. Although rice germplasm exhibits wide variation in Zn-deficiency tolerance, the underlying physiological mechanisms remain poorly resolved. Evidence across the literature for Zn-deficiency–induced secretion of 2′-deoxymugineic acid (DMA) is inconsistent. This study clarifies the role of DMA secretion as a Zn-deficiency stress response. We developed and validated a sensitive LC–ESI–Q–TOF–MS method for selective detection of DMA in rice root exudates. Five rice genotypes with contrasting Zn-deficiency tolerance were grown hydroponically and DMA secretion measured. Zn-deficiency increased DMA exudation across all genotypes, with sensitive genotypes also showing higher secretion compared with control, supporting DMA’s role as a general response to Zn stress rather than being restricted to efficient genotypes. Fold-change responses exceeded previous studies, likely due to more severe stress exposure. Our results confirm that DMA secretion is induced under Zn-deficiency in rice as part of the micronutrient stress response. However, the lack of increased Zn uptake indicates that additional tolerance mechanisms are involved. These findings reconcile inconsistencies in the literature and position DMA secretion as an important, but not exclusive, component of Zn-deficiency adaptation in rice.
Why it matches plant phenotyping methodsイネ根滲出液中のDMAを選択的に検出するLC–MS法を開発・検証し、亜鉛欠乏応答という植物生理状態を測定しているため、化学分析が単なる付随測定ではなく中心的な方法貢献である。
abstractWe developed and validated a sensitive LC–ESI–Q–TOF–MS method for selective detection of DMA in rice root exudates.
Reproduction assets foundThe paper's Data availability statement points to a public Zenodo deposit containing the datasets generated and analysed in this study (DMA exudation and Zn uptake measurements in rice).Dataset · publicthe experiments, developed the
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methods and analysed the results. The experimental data were collected by C.R. assisted by
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G.L.M., C.T. and D.J.W. Data analysis and writing of paper by all authors.
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The data sets generated and/or analysed during the current study are available on Zenodo,
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CC-BY 4.0 International license
perpetuity. It is made available under a
preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in
The copyright holder for this
this version posted March 18, 2026.
;
https://doi.org/10.64898/2026.03.16.71158Open asset ↗Zenodo · 18184803pdf-raw-page:21 lines:1-47Code / dataset availability confirmedOpenAlex · Europe PMC · checked 14 Sept 2026
A detailed characterization of root system architecture (RSA) and growth dynamics is key to develop stress-resilient maize varieties. We evaluated sixty-five Mediterranean maize inbred lines using automated high-throughput phenotyping under controlled conditions. Shoot and root traits were extracted from imaging data during early vegetative development, revealing significant genotype-specific variation in root biomass-related traits (total root length, total root volume), root architecture (root angle, root system depth, root system width), and relative growth rates. Notably, lines previously classified as heat and drought stress-resilient or stress-sensitive based on above-ground development did not group according to particular root traits, indicating that multiple strategies may underlie tolerance to combined stress. We identified lines with contrasting RSA, including deeper roots, shallower roots, or overall larger root systems, that offer new opportunities for resilience breeding. Our results underscore root traits as critical yet underexploited targets for improving stress resilience and resource efficiency.
Why it matches plant phenotyping methods自動化ハイスループット画像解析により根系形態・成長形質を抽出する表現型取得が研究の主要手段であり、根系構造の実質的な応用解析に該当する。
abstractWe evaluated sixty-five Mediterranean maize inbred lines using automated high-throughput phenotyping under controlled conditions.
Reproduction assets found保存済みの本文根拠を更新済みルールで再検証し、公開資産1件を確認しました。Supplement · publicSupplementary Materials
The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/plants15060935/s1 , Figure S1: Repeatability of image-derived shoot (a) and root traits (b) of the tested 65 maize inbred lines over time.Open asset ↗lines:68-215Code / dataset availability confirmedEurope PMC · bioRxiv · Crossref · checked 15 Sept 2026
Quantitative studies of plant growth and environmental responses increasingly rely on time-series imaging, yet automated segmentation remains challenging due to continuous growth, large non-rigid morphological change, and frequent self-occlusion. Traditional image-processing pipelines and task-specific deep learning models often require extensive annotated datasets and retraining, limiting portability across species, developmental stages, and imaging conditions. Here we present SAP (Segment Any Plant), a plant-focused framework that leverages the pretrained Segment Anything Model 2 (SAM2) to enable few-shot, training-free segmentation of plant time-series imagery. SAP integrates interactive prompting, automated temporal mask propagation, and centerline extraction within a web-based interface, allowing users to move from raw images to quantitative descriptors of organ shape and dynamics without programming expertise. Across multiple systems, including Arabidopsis thaliana rosette development, root growth, sunflower gravitropism, and confocal root microscopy, SAP achieves high segmentation accuracy (mean IoU 0.89–0.93) and sub-pixel centerline precision from single-frame prompting. By reducing the need for task-specific retraining, SAP provides a transferable framework for reproducible time-series phenotyping across diverse experimental contexts.
Why it matches plant phenotyping methods植物の時系列画像から器官形状・動態を抽出するセグメンテーション手法とWeb基盤を開発し、複数系で精度検証しているため、植物フェノタイピング手法が中心である。
abstractHere we present SAP (Segment Any Plant), a plant-focused framework that leverages the pretrained Segment Anything Model 2 (SAM2) to enable few-shot, training-free segmentation of plant time-series imagery.
Reproduction assets foundThe paper's authors publicly release both the SAP analysis code (GitHub repository) and the datasets generated/analyzed in the study (Zenodo), including raw images, ground-truth and SAP-generated segmentation masks, centerline validation data, and supplementary videos. Both are paper-specific, public, and directly cit.Dataset · publicCode Availability. The code is available at
https://github.com/merozlab/plant-segmentation-app.Data Availability. The datasets generated and an-
alyzed during this study are available on Zenodo at
https://doi.org/10.5281/zenodo.18732705. This includes
raw images and segmentation masks for the sunflower
gravitropism and Arabidopsis root growth experiments,
SAP-generated masks for the Lee et al. (9) and Strauss
et al. (13) datasets, centerline validation data, and supple-
mentary videos.
Funding. Y.M. acknowledges support from the Israel Sci-
ence Foundation ResOpen asset ↗zenodo · 10.5281/zenodo.18732705pdf-raw-page:9 lines:1-74Code / dataset availability confirmedCrossref · checked 5 Sept 2026
Abstract Background Apoplastic pH is a central regulator of plant growth, development, and environmental adaptation, influencing cell expansion, nutrient uptake, and extracellular signaling. Many studies have successfully used HPTS to monitor relative changes in apoplastic pH in plants. At the same time, research increasingly targets pH-dependent biochemical and biophysical processes. Many enzymatic activities, ion binding events, and receptor–ligand interactions depend on defined proton concentrations. Accordingly, the development of reliable approaches to measure absolute pH in living tissues is gaining importance. Methods A calibration-based workflow was developed to enable quantitative assessment of absolute apoplastic pH using ratiometric HPTS imaging. The approach integrates a simplified two-point normalization strategy with an in-vitro derived sigmoidal calibration model, thereby minimizing the need for extensive in-vivo calibration curves. Confocal imaging was performed using HPTS excited at two wavelengths followed by ratiometric image processing. Data analysis is supported by a custom Fiji plugin, Ratio2pH, which converts ratiometric images into pixel-resolved maps of absolute pH. Results In vitro characterization revealed a robust, non-linear relationship between normalized HPTS ratios and pH, enabling accurate pH estimation within the physiologically relevant range of pH 5.0–7.0. When applied in-vivo to Arabidopsis thaliana roots, the workflow yielded extracellular pH estimates consistent with the pH of the incubation medium and detected reproducible pH shifts in response to pharmacological treatments. Conclusions This workflow enables reproducible, spatially resolved measurement of absolute apoplastic pH in living plant tissues. By combining a simplified calibration strategy with accessible image analysis tools, it facilitates quantitative extracellular pH measurements and their integration into biochemical and biophysical analyses.
Why it matches plant phenotyping methods生きた植物組織の絶対アポプラストpHを画像から定量する校正ワークフローを開発・検証し、Fijiプラグインも提供しているため、植物状態の取得法が中心である。
abstractA calibration-based workflow was developed to enable quantitative assessment of absolute apoplastic pH using ratiometric HPTS imaging.
Reproduction assets foundThe paper deposits its authors' analysis code and data publicly: the Ratio2pH Fiji plugin (Zenodo 10.5281/zenodo.15599805), a Python script for sigmoidal calibration curve fitting (Zenodo 10.5281/zenodo.17303477), and source data files and raw confocal images (Freidata 10.60493/t29wb-7my86). The Zenodo 15658668 ratiom�Code · publicThe Python Script for generating a user-defined sigmoidal calibration curve is available at Zenodo: https://doi.org/10.5281/zenodo.17303477Open asset ↗Zenodo · 10.5281/zenodo.17303477lines:175-235Dataset · publicSource data files and raw images are uploaded at Freidata, the data server of the University of Freiburg, available under https://doi.org/10.60493/t29wb-7my86Open asset ↗Freidata · 10.60493/t29wb-7my86lines:175-235Code / dataset availability confirmedOpenAlex · Europe PMC · checked 5 Sept 2026
BACKGROUND: Root biomass serves as a critical indicator of plant eco-physiological status and crop productivity, yet its non-destructive monitoring remains challenging because of its underground location. The use of transparent nutrient film technique (NFT) systems enables direct observation of entire root systems, rendering image-based phenotyping feasible. In this study, we investigated and compared the performance of RGB and hyperspectral imaging for predicting root dry weight in hydroponically grown spinach (Spinacia oleracea L.). RESULTS: Using 430 root segments divided from 60 plants, three models were developed: (1) an area-based regression based on root coverage, (2) a convolutional neural network (CNN) using RGB images, and (3) a partial least squares regression (PLSR) model using hyperspectral data (450-950 nm). The area-based regression exhibited limited accuracy (R² = 0.446) because of saturation at high root coverage. The CNN model improved predictive performance (R² = 0.739) but tended to overestimate sparse roots as a result of resolution constraints. The PLSR model achieved the highest accuracy (R² = 0.822, RMSE = 0.019 g/segment), with significantly lower error than RGB-based approaches (P < 0.01). Variable importance in projection analysis indicated that PLSR effectively exploited spectral signatures at 450 nm (background contrast) and 750 nm (tissue scattering), thereby maintaining stable accuracy across the full biomass range. When validated using 104 independent plants, the PLSR model achieved high predictive accuracy. Furthermore, as a proof of concept, this model successfully visualized the spatiotemporal dynamics of root biomass accumulation over 50 days, with only a 7.70% relative error at harvest. CONCLUSIONS: To our knowledge, this study is among the first to demonstrate the non-destructive monitoring of biomass distribution within entire root systems under production conditions. Hyperspectral imaging combined with PLSR outperforms RGB-based approaches by capturing spectral signatures that reflect internal tissue properties of roots, thereby overcoming limitations caused by morphological occlusion. This approach provides a robust tool for precision agriculture and high-throughput phenotyping, enabling continuous assessment of root growth through simple modifications to the existing hydroponic systems.
Why it matches plant phenotyping methodsRGB・ハイパースペクトル画像と機械学習／PLSRを用いて根乾物重を非破壊推定・検証する方法研究であり、植物表現型の取得と定量化が中心である。
abstractThe use of transparent nutrient film technique (NFT) systems enables direct observation of entire root systems, rendering image-based phenotyping feasible.
Reproduction assets foundThe paper's Data availability statement deposits the paper-specific phenotyping assets (raw hyperspectral images, RGB images, and root dry weight measurements) in a Zenodo record. The provided URL includes a token and 'preview=1', suggesting the record may not yet be fully open, but it is the authors' stated public URLDataset · publicThe datasets generated and analyzed during the model construction of the current study are available in the Zenodo repository: [https://zenodo.org/records/18072801?preview=1&token=eyJhbGciOiJIUzUxMiJ9.eyJpZCI6ImFiYTkzMzY2LTIzZjktNDlkMy1iZTBjLTk3M2E5YTUyOTFmZCIsImRhdGEiOnt9LCJyYW5kb20iOiIzNGE1ZjMxNDZhYjhiYjlhZWRiOWFjNzBkNzcwY2I3NyJ9.uR4HfosoSaVWhtSblMOS1v9bJFA5MvHwXvcW9uoNbcTWRDU4RNxZpVHjXTC3ulBM1JTlBbeHp_4T5EcILawxdg].The dataset includes:
- Raw hyperspectral images and data- RGB images
- Root dry weight measurementsOpen asset ↗Zenodo · 18072801lines:176-248Code / dataset availability confirmedEurope PMC · checked 5 Sept 2026
Volume is an important shape descriptor in postharvest quality evaluation and breeding programs of sweetpotatoes and is also valuable for other agricultural engineering applications. Traditional volume measurement methods based on water displacement are, however, laborious, destructive, and unsuitable for high-throughput online scenarios. To address this gap, this dataset was developed to support the advancement of non-destructive, automated online volume estimation using a LiDAR (light detection and ranging)-based three-dimensional (3-D) machine vision system. A total of 200 sweetpotato storage roots of the cultivar "Beauregard" were collected for constructing a 3-D multi-view imagery dataset. Each sample was imaged online using a short-range LiDAR camera (Intel RealSense™ L515) while traveling on a custom-built roller conveyor system that enables simultaneous translation and rotation for full-surface coverage. The curated dataset comprises raw color images (1280 × 720 pixels, .png format) and corresponding raw and segmented point clouds (1280 × 720 pixels, .laz format) for individual samples, alongside the reference volume measurements obtained using the standard water displacement method. In addition, to illustrate the modeling pipeline for volume prediction, the dataset provides the extracted geometric features derived from the segmented two-dimensional (2-D) masks and point clouds, and volume prediction results obtained through regression modeling. As the first publicly available LiDAR-based dataset for sweetpotato volume estimation, this dataset provides a valuable resource for developing and validating image processing pipelines, optimizing machine learning models, and advancing 3-D vision technologies for non-destructive, rapid measurement of the volume of irregularly shaped agricultural products.
Why it matches plant phenotyping methodsサツマイモ貯蔵根の体積という植物器官形質をLiDAR 3D画像から推定する公開データセットであり、取得系・参照測定・特徴抽出・予測結果を含むため、フェノタイピング手法とデータセットが中心です。
abstractthis dataset was developed to support the advancement of non-destructive, automated online volume estimation using a LiDAR (light detection and ranging)-based three-dimensional (3-D) machine vision system.
Reproduction assets foundThe paper's own LiDAR sweetpotato dataset (images, point clouds, ground-truth volumes, feature data, and Python modeling scripts) is publicly deposited on Zenodo with an explicit DOI. The librealsense GitHub link is a generic camera SDK, not a paper-specific asset.Dataset · publicDirect URL to data: https://doi.org/10.5281/zenodo.18378019Open asset ↗Zenodo · 10.5281/zenodo.18378019html-lines:90-113Code · publicThe complete Python modeling script and the associated feature datasets have been included in the public dataset repository [13] to facilitate reproducibility and provide a benchmark for future algorithm development.Open asset ↗html-lines:168-182Code / dataset availability confirmedEurope PMC · checked 5 Sept 2026
MilletSorghumRootTissueSegmentationRoot system architecture
Root anatomical features are critical for plant performance characterization, yet phenotyping at the anatomical scale remains limited by the extreme annotation burden of cellular segmentation. We present a two-stage segmentation framework that greatly reduces annotation requirements while maintaining high accuracy across diverse plant species and imaging conditions. Our approach decomposes multi-class segmentation into species-agnostic tissue identification followed by tissue type classification. By designing robust input representations invariant to imaging artifacts and morphological variations, our framework enables rapid adaptation to new species with fewer than 40 labeled images. Additionally, the first stage automatically generates tissue boundaries, transforming tedious manual tracing into simple tissue labeling. We validate our method on pearl millet, and sorghum root cross-sections from different imaging protocols, achieving state-of-the-art performance while dramatically reducing deployment time. This efficiency breakthrough enables scalable root phenotyping across diverse crop species, accelerating the development of climate-resilient varieties for global food security.
Why it matches plant phenotyping methods植物根の解剖学的形質を対象とする画像セグメンテーション手法を開発し、複数種・撮像条件で検証しているため、方法が研究の中心である。
abstractWe present a two-stage segmentation framework that greatly reduces annotation requirements while maintaining high accuracy across diverse plant species and imaging conditions.
Reproduction assets foundThe paper's Data availability statement explicitly deposits the annotated root image dataset (Zenodo 17726414), trained segmentation models (Zenodo 17737703), and the authors' source code (GitHub janetkok/Root-Segmentation-Beyond-Species-Boundaries), all directly reproducing this paper's root anatomical phenotyping andDataset · publicThe dataset and models are available at https://doi.org/10.5281/zenodo.17726414 and https://doi.org/10.5281/zenodo.17737703 , respectively.Open asset ↗Zenodo · 10.5281/zenodo.17726414lines:242-251Code · publicThe source code is hosted at https://github.com/janetkok/Root-Segmentation-Beyond-Species-Boundaries .Open asset ↗GitHub · janetkok/Root-Segmentation-Beyond-Species-Boundarieslines:242-251Code / dataset availability confirmedbioRxiv · Europe PMC · checked 5 Sept 2026
WheatGrowth chamberRootMorphology / geometry measurementRoot system architectureStress response / tolerance
The Multiple Synthetic Derivatives (MSD) population is a unique hexaploid wheat resource that captures extensive genetic diversity from Aegilops tauschii and exhibits wide variation in agronomic traits. However, root system architecture (RSA), a key determinant of resource acquisition and stress adaptation, remains poorly characterized in this population. Here, we established a practical phenotyping framework for RSA analysis and evaluated MSD417 as a representative genotype. A two-dimensional cultivation platform enabling continuous imaging of seedling root growth under controlled conditions was established to quantify RSA traits and their responses to high temperatures. MSD417 was compared with its recurrent parent, Norin 61 (N61). Under controlled conditions, MSD417 displayed greater total root length, root system width, and convex hull area than N61, indicating enhanced early root vigor. This genotype also exhibited a wider seminal root angle, suggesting improved horizontal soil exploration while maintaining root depth. High-temperature treatment reduced overall root growth and minimized genotypic differences, indicating that temperature stress constrains RSA expression. Microscopic observations further revealed a lower height-to-width ratio of coleorhiza tissue of MSD417, suggesting restricted downward expansion. Collectively, this study establishes a practical framework for RSA phenotyping and demonstrates the potential of Aegilops tauschii-derived germplasm to enhance wheat root-related adaptive traits.
Why it matches plant phenotyping methods根系構造を連続画像化して定量する2次元表現型解析プラットフォームを構築し、RSA形質の測定に実質的に適用しているため、方法が中心的である。
abstractHere, we established a practical phenotyping framework for RSA analysis and evaluated MSD417 as a representative genotype.
Reproduction assets foundThe paper deposits its paper-specific root images (N61 and MSD417) and coleorhiza microscopic images in Zenodo with explicit DOIs. The R analysis scripts are only in Supplementary Document S1 with no public URL, so they do not qualify as a public code asset.Dataset · publicThe microscopic images of coleorhiza are deposited under https://doi.org/10.5281/zenodo.18091131.Open asset ↗Zenodo · 10.5281/zenodo.18091131pdf-page:14 lines:1-71Code / dataset availability confirmedOpenAlex · Europe PMC · bioRxiv · checked 15 Sept 2026
Abstract Root segmentation is a fundamental yet challenging task in image-based plant phenotyping. We present the first systematic comparison of Transformer and Convolutional Neural Network (ConvNet) architectures for root segmentation, evaluating 21 architectures across nine diverse datasets and comparing pre-trained models to training from scratch. Transformer-based models significantly outperform ConvNets for segmentation accuracy and root-diameter agreement. Pre-training significantly improves mean Dice from 0.623 to 0.666 ( p = 3.3 × 10 −10 ). We also find that Transformers benefit more from pre-training than ConvNets, with Dice improvements of +0.072 versus +0.022 ( p = 3.7 × 10 −4 ), supporting the hypothesis that fine-tuned Transformers transfer more effectively across large domain gaps. Among evaluated models, MobileSAM achieved the highest Dice score while maintaining computational efficiency. Dataset choice explained far more performance variance (70.9%) than model architecture (6.7%), suggesting that data curation matters more than model selection.
Why it matches plant phenotyping methods根の画像セグメンテーション手法を21種類・9データセットで体系比較し、植物フェノタイピングにおける精度と根径推定を検証しているため、方法評価が中心である。
abstractRoot segmentation is a fundamental yet challenging task in image-based plant phenotyping.
Reproduction assets foundThe paper's authors explicitly state that their training/segmentation analysis code is publicly available on GitHub. The nine root image datasets evaluated are cited prior public datasets (DeepRootLab, Grassland, Chicory, PRMI), not paper-specific assets of this study, so the authors' own code repository is the only inCode · publicr of parameters, as these affect
hardware requirements, running costs, and environmental
impact. To jointly compare efficiency and accuracy, we
ranked models by the mean of their Dice, parameter count,
and FLOPs ranks, providing a simple combined metric for
practitioners balancing these trade-offs. Training code is
available at https://github.com/sotlampr/seg.Configuration selection
To prevent overfitting to the test set, model selection used
a two-stage procedure based on validation performance:
Replicate selection: For each combination of model,
dataset, learning rate, and pre-training, the replicate with
the highest validation Dice was retained, along with its
paired test result.
HyperparOpen asset ↗sotlampr/segpdf-raw-page:4 lines:1-95Code / dataset availability confirmedEurope PMC · checked 14 Sept 2026
RiceRootMorphology / geometry measurementSegmentationRoot system architecture
Root phenotypic traits such as length and number are critical indicators of plant growth and productivity. However, accurate extraction of these traits remains challenging due to the slender morphology, dense overlap, and frequent occlusion within root systems. Traditional digital image processing methods suffer from low throughput and limited robustness, while most deep learning-based approaches rely on semantic segmentation, which fails to distinguish individual roots and therefore limits their applicability in instance-level phenotypic analysis.To address these limitations, we propose Dual-Guided Asymmetric MP-Former (DGA-MP-Former), a novel instance segmentation model tailored for root phenotyping, with rice roots as a representative case. Building upon the MP-Former framework, our model introduces two key components: the Guided-Enhancement Pixel Decoder (GEPD) and the Asymmetric Dual-Query Decoder (ADQD). The GEPD enhances multi-scale feature representations via Hybrid Convolution Aggregator, Semantic-Guided Fusion Module and Frequency-Guided Feature Enhancement Module, effectively capturing fine root structures and low-contrast regions. ADQD employs asymmetric interaction between semantic and instance queries to improve long-range dependency modeling and instance separation in occluded scenarios.Additionally, we present the Rice Root Segmentation Dataset (RRSD), comprising of 343 high-resolution images with instance-level annotations. Experimental results show that DGA-MP-Former achieves state-of-the-art performance on RRSD, with 57.2% AP 0.5:0.95 and 87.4% AP 0.5 . Importantly, the accurate instance segmentation results enable reliable computation of instance-level geometric traits, such as root perimeter and area. To quantitatively assess phenotypic measurement accuracy, Relative Area Error (RAE) and Relative Perimeter Error (RPE) are further introduced, achieving 26.4% and 20.2%, respectively. These results demonstrate that the proposed method effectively bridges instance segmentation accuracy and phenotypic quantification reliability, supporting high-throughput and precise root phenotyping.
Why it matches plant phenotyping methodsイネ根の個体別セグメンテーションモデルを開発し、データセット提供、性能評価、および根の形態形質推定まで行っており、植物フェノタイピング手法が研究の中心である。
abstractwe propose Dual-Guided Asymmetric MP-Former (DGA-MP-Former), a novel instance segmentation model tailored for root phenotyping
Reproduction assets found保存済みの本文根拠を更新済みルールで再検証し、公開資産1件を確認しました。Dataset · publicThe Rice Root Segmentation Dataset is open sourced for the research community at ”https://github.com/Run-19/DGA-mpformer”.Open asset ↗Run-19/DGA-mpformerhtml-lines:442-469Code / dataset availability confirmedCrossref · Europe PMC · checked 5 Sept 2026
Understanding how plants perceive and respond to environmental and developmental cues requires tools capable of monitoring molecular signals in vivo, across whole tissues, and in real time. Genetically encoded fluorescent indicators, coupled with fluorescence microscopy, have transformed plant biology, but their application remains largely confined to small model organisms and specialized microscopy instrumentation. Here, we present MAcro Plant Projection Imaging (MAPPI), an open-source, low-cost, and modular fluorescence imaging platform for soil-grown plants beyond the model organism or seedling stage. MAPPI enables wide field-of-view, dual-projection imaging of fluorescent reporters, supporting real-time visualization of systemic signals under near-physiological conditions. We validate MAPPI by tracking calcium and l -glutamate dynamics in adult Nicotiana benthamiana plants, revealing developmentally regulated long-distance calcium waves triggered by wounding, burning, or submergence, including bidirectional shoot-to-root and root-to-shoot signaling. By democratizing access to whole-plant functional imaging, MAPPI provides a scalable tool for dissecting signal propagation, stress adaptation, and systemic communication in both model and nonmodel species.
Why it matches plant phenotyping methods植物全体の蛍光シグナルをリアルタイム取得する低コスト・オープンな画像プラットフォームを開発し、成体植物で検証しているため、植物表現型取得法が研究の中心です。
abstractHere, we present MAcro Plant Projection Imaging (MAPPI), an open-source, low-cost, and modular fluorescence imaging platform for soil-grown plants beyond the model organism or seedling stage.
Reproduction assets foundThe authors publicly deposit raw imaging data and MAPPI analysis code on Zenodo, host the MAPPI acquisition/analysis code on GitHub, and release the napari-roi-registration image registration plugin on GitHub. All are paper-specific, public, and actionable.Dataset · publicThe raw data for the images presented in the manuscript and the code to run the MAPPI system are available on Zenodo ( https://doi.org/10.5281/zenodo.15845576 ).Open asset ↗Zenodo · 10.5281/zenodo.15845576lines:170-466Code · publicThe code to run the MAPPI system is also available on the dedicated GitHub repository ( https://github.com/micropolimi/MAPPI ) along with the code used to analyze the data.Open asset ↗GitHub · micropolimi/MAPPIlines:170-466Code · publicThe software is open-source and available on GitHub ( https://github.com/GiorgiaTortora/napari-roi-registration ) and the napari-hub ( www.napari-hub.org/plugins/napari-roi-registration ).Open asset ↗GitHub · GiorgiaTortora/napari-roi-registrationlines:156-169Code / dataset availability confirmedEurope PMC · checked 5 Sept 2026
Background The plant vacuole arises by orchestrated interplay of membrane trafficking, cytoskeletal rearrangements and a variety of signaling pathways. In the root, the characteristic large central vacuole develops by endomembrane reorganization occurring mainly in the transition zone. The vacuole's bounding membrane-the tonoplast-can be visualized in vivo using fluorescent protein markers, allowing for quantitative analysis of confocal microscopy images. Tonoplast organization can thus serve as a sensitive indicator of changes to any of the processes involved in vacuole biogenesis. The Vacuolar Morphology Index (VMI) is widely accepted as a quantitative measure of vacuole structure. However, this metric has two drawbacks-it only reflects the size of the largest vacuolar compartment (missing therefore possible differences in the organization of smaller compartments), and its determination is labor intensive, limiting its use on large datasets. Results We developed an alternative metric for describing vacuole organization, named the Tonoplast Topology Index (TTI), which overcomes the above-mentioned shortcomings of the VMI. We compared the performance of our protocol with VMI on a simulated dataset and on real data. To validate the methods´ performance, we used it to confirm the previously reported differences in vacuole shape and size between Arabidopsis thaliana roots grown on the surface of an agar medium compared to those embedded inside the agar. Both VMI and TTI could efficiently detect the relatively subtle changes in vacuole organization depending on the position of the root in the agar, and provided correlated results. However, only TTI produced data with close to normal value distribution, simplifying subsequent statistical evaluation. Conclusions We present the protocol for TTI determination as a two-stage semi-automated procedure involving microscopic image analysis employing an ImageJ macro and subsequent processing of numeric data in the Jupyter Notebook environment, together with benchmarking image data. Since this implementation is freeware-based, platform-independent and (relatively) user-friendly, we hope it will find its use as a high throughput, added value alternative to the VMI metric.
Why it matches plant phenotyping methods植物液胞構造を定量化する新規指標と半自動画像解析プロトコルを開発し、シミュレーションおよび実画像で既存指標と比較・検証しているため、植物フェノタイピング手法が中心である。
abstractWe developed an alternative metric for describing vacuole organization, named the Tonoplast Topology Index (TTI)
Reproduction assets foundThe paper deposits its benchmark confocal image dataset in the EMBL-EBI BioImage Archive (S-BIAD2226) and its TTI analysis software (ImageJ macro and Jupyter/Python scripts) on GitHub, both with explicit public availability statements.Dataset · publicImage data generated and analyzed in the current study are available in the EMBL-EBI BioImage Archive repository, accession number S-BIAD2226Open asset ↗EMBL-EBI BioImage Archive · S-BIAD2226lines:141-163Code · publicArchive copy, additional sample data and possible future updates of the software tool generated here are also available at [ https://github.com/GeorgeCaldarescu/TTI-Tonoplast-Topology-Index ] .Open asset ↗GitHub · GeorgeCaldarescu/TTI-Tonoplast-Topology-Indexlines:141-163Code / dataset availability confirmedCrossref · checked 14 Sept 2026
MaizeRoot2D/3D reconstructionSegmentationRoot system architecture
Abstract Background and aims Root system architecture (RSA) shapes biogeochemical concentration patterns in the rhizosphere. Root-soil studies are often conducted on plants cultivated in rectangular rhizotrons, including when using 2D hydrochemical analysis methods. However, roots naturally expand in three dimensions, with the rhizosphere extending accordingly. Three-dimensional neutron imaging can enhance interpretation of such studies, yet imaging flat, slab-shaped rhizotrons is technically challenging. This study presents a methodological comparison between conventional neutron tomography (NT) and neutron computed laminography (NCL) to assess whether NT under high-flux conditions can achieve image quality sufficient for 3D root segmentation, comparable to NCL, without requiring tilting of the rotation axis. Methods NT and NCL were applied to maize plants grown in rectangular rhizotrons. Imaging artifacts and their impact on root segmentation were assessed for two plants representing low and high soil moisture conditions suitable for neutron imaging. Results Both methods produced 3D tomograms of comparable quality across the tested moisture range, enabling effective segmentation of primary and seminal roots. Lateral root detection was more challenging and depended on soil moisture. NCL captured a greater number of horizontally oriented lateral roots while NT was more effective in resolving vertically oriented roots. Conclusions NCL is not required to resolve 3D RSA of maize plants in flat rhizotrons. Under high-flux neutron beam conditions, NT is preferable as it simplifies sample handling, reduces plant stress, avoids soil water redistribution and enables direct integration with timeseries of 2D chemical and neutron radiographic imaging.
Why it matches plant phenotyping methods3D中性子画像法を用いた根系構造の抽出を中心に、NTとNCLを比較検証しており、植物表現型取得手法が研究の主題である。
abstractThis study presents a methodological comparison between conventional neutron tomography (NT) and neutron computed laminography (NCL) to assess whether NT under high-flux conditions can achieve image quality sufficient for 3D root segmentation, comparable to NCL
Reproduction assets foundThe paper's neutron imaging datasets (NT and NCL scans of maize in slab rhizotrons) are stated to be publicly available on the ILL Data Portal under DOI 10.5291/ILL-DATA.UGA-111. No author analysis code or trained models are explicitly deposited.Dataset · publicacknowledge funding of the
research presented here by the German Research Foundation
(DFG project numbers 396368046 and 516672636).
Data availability The datasets used in this study were gener-
ated as part of a measurement campaign on the neutron imag-
ing instrument NeXT at the ILL and are available on the ILL
Data Portal at https://doi.org/10.5291/ILL-DATA.UGA-111.Declarations
Competing interests The authors have no relevant financial
or non-financial interests to disclose.
Open Access This article is licensed under a Creative Com-
mons Attribution 4.0 International License, which permits
use, sharing, adaptation, distribution and reproduction in any
medium or format, as long as you give Open asset ↗10.5291/ILL-DATA.UGA-111pdf-raw-page:16 lines:1-92Code / dataset availability confirmedOpenAlex · Crossref · Europe PMC · checked 15 Sept 2026
BACKGROUND: Plant developmental plasticity, particularly in root system architecture, is fundamental to understanding adaptability and agricultural sustainability. Existing automated phenotyping solutions face limitations, including binary segmentation approaches, restricted structural analysis capabilities, and text-based interfaces that limit accessibility, with most focusing solely on root structures while overlooking valuable information from simultaneous analysis of multiple plant organs. FINDINGS: ChronoRoot 2.0 builds upon established low-cost hardware while significantly enhancing software capabilities and usability. The system employs nnUNet architecture for multi-class segmentation, demonstrating significant accuracy improvements while simultaneously tracking 6 distinct plant structures encompassing root, shoot, and seed components: main root, lateral roots, seed, hypocotyl, leaves, and petiole. This architecture enables easy retraining and incorporation of additional training data without requiring machine learning expertise. The platform introduces dual specialized graphical interfaces: a Standard Interface for detailed architectural analysis with novel gravitropic response parameters and a Screening Interface enabling high-throughput analysis of multiple plants through automated tracking. Functional principal component analysis integration enables discovery of novel phenotypic parameters through temporal pattern comparison. We demonstrate multi-species analysis, with Arabidopsis thaliana and Solanum lycopersicum, both morphologically distinct plant species. Three use cases in Arabidopsis thaliana and validation with tomato seedlings demonstrate enhanced capabilities: circadian growth pattern characterization, gravitropic response analysis in transgenic plants, and high-throughput etiolation screening across multiple genotypes. CONCLUSIONS: ChronoRoot 2.0 maintains the low-cost, modular hardware advantages of its predecessor while dramatically improving accessibility through intuitive graphical interfaces and expanded analytical capabilities. The open-source platform makes sophisticated temporal plant phenotyping more accessible to researchers without computational expertise. SOFTWARE AVAILABILITY: https://chronoroot.github.io.
Why it matches plant phenotyping methods根・シュート・種子を時系列追跡し、植物形態・成長・重力応答などの表現型を抽出するオープンプラットフォームの開発と検証が中心である。
titleChronoRoot 2.0: An Open AI-Powered Platform for 2D Temporal Plant Phenotyping
Reproduction assets foundThe paper publicly releases its authors' analysis code (GitHub), the annotated plant image dataset used for segmentation training/validation (HuggingFace), a pre-configured Docker image, and a project home page, all with explicit availability statements and URLs matching allowed entries.Code · publicapproach to open science will not only ensure transparency and reproducibility but also allow the system to evolve alongside the changing needs of the plant biology community.
Availability of source code and requirements
Project name: ChronoRoot 2.0.
Project home page: https://chronoroot.github.io .
Main Source Code repository: https://github.com/ChronoRoot/ChronoRoot2 .
Operating system(s): Platform independent.
Programming language: Python.
Other requirements: Conda, Apptainer, or Docker.
License: GNU GPL 3.0.
Additional files
Supplementary Text S1 : Functional PCA. Provides an intuitive explanation of functional principal component analysis (FPCA) for readers without a quantitative backgrOpen asset ↗https://github.com/ChronoRoot/ChronoRoot2lines:439-479Dataset · publicgulates LAZY genes. Plant J. 2025;121:e70016. 10.1111/tpj.70016.
19.
Gaggion N, Boccardo N A, Bonazzola R, et al. ChronoRoot 2.0 Main Source Code Repository. 2026. https://github.com/ChronoRoot/ChronoRoot2 . Accessed 25 February 2026.
20.
Gaggion N, Boccardo N A, Bonazzola R, et al. ChronoRoot 2.0 Annotated Image Dataset. 2026. https://huggingface.co/datasets/ngaggion/ChronoRoot2 . Accessed 25 February 2026.
21.
Gaggion N, Boccardo N A, Bonazzola R, et al. ChronoRoot 2.0 Docker Image. 2026. https://hub.docker.com/r/ngaggion/chronoroot . Accessed 25 February 2026.
22.
Gaggion N, Boccardo N A, Bonazzola R, et al. ChronoRoot 2.0 Project Home Page. 2026. https://chronoroot.github.io . Accessed 2Open asset ↗https://huggingface.co/datasets/ngaggion/ChronoRoot2lines:568-618Code · publicical modules, and experimental protocols. We hope that this approach to open science will not only ensure transparency and reproducibility but also allow the system to evolve alongside the changing needs of the plant biology community.
Availability of source code and requirements
Project name: ChronoRoot 2.0.
Project home page: https://chronoroot.github.io .
Main Source Code repository: https://github.com/ChronoRoot/ChronoRoot2 .
Operating system(s): Platform independent.
Programming language: Python.
Other requirements: Conda, Apptainer, or Docker.
License: GNU GPL 3.0.
Additional files
Supplementary Text S1 : Functional PCA. Provides an intuitive explanation of functional princOpen asset ↗lines:439-479Code / dataset availability confirmedEurope PMC · OpenAlex · Crossref · checked 15 Sept 2026
Background Root hairs play a key role in plant nutrient and water uptake. Historically, root hair traits have largely been quantified manually. As such, this process has been laborious and low-throughput. However, given their importance for plant health and development, high-throughput quantification of root hair morphology could help underpin rapid advances in the genetic understanding of these traits. With recent increases in the accessibility and availability of artificial intelligence (AI) and machine learning techniques, the development of tools to automate plant phenotyping processes has been greatly accelerated. Results We present pyRootHair, a high-throughput, AI-powered software application to automate root hair trait extraction from microscope images of plant roots grown on agar plates. pyRootHair is capable of batch processing over 600 images per hour without manual input from the end user. In this study, we deploy pyRootHair on a panel of 24 diverse wheat (Triticum aestivum and Triticum turgidum ssp. durum) cultivars and uncover a large, previously unresolved amount of variation in many root hair traits. We show that the overall root hair profile falls under 2 distinct shape categories and that different root hair traits often correlate with each other. We also demonstrate that pyRootHair can be deployed on a range of plant species, including oat (Avena sativa), rice (Oryza sativa), teff (Eragrostis tef), and tomato (Solanum lycopersicum). Conclusions The application of pyRootHair enables users to rapidly screen a large number of plant germplasm resources for variation in root hair morphology, supporting high-resolution measurements and high-throughput data analysis. This facilitates downstream investigation of the impacts of root hair genetic control and morphological variation on plant performance. pyRootHair is installable via PyPI (https://pypi.org/project/pyRootHair/) and can be accessed on GitHub at https://github.com/iantsang779/pyRootHair.
Why it matches plant phenotyping methods植物根毛形態を顕微鏡画像から自動抽出するAIソフトウェアを開発し、複数作物で適用・実証しており、表現型取得手法が研究の中心である。
abstractWe present pyRootHair, a high-throughput, AI-powered software application to automate root hair trait extraction from microscope images of plant roots grown on agar plates.
Reproduction assets foundThe paper's root hair phenotyping software (pyRootHair) is publicly available on GitHub and PyPI, the data and notebooks used to generate the manuscript figures are deposited in the repository's paper_data folder, and the software is annotated in the DOME-ML registry. The GigaDB deposit (10.5524/102771) is referenced,但Code · publicregression lines were computed using statsmodels (v0.14.4). Scikit-learn (v.1.5.2) was used for quality control of segmented images. nnU-Netv2 (v2.5.1) was used to create the image segmentation model with PyTorch (v.2.5.1) and CUDA (v.12.6).
Availability of Source Code and Requirements
Project name: pyRootHair
Project homepage: https://github.com/iantsang779/pyRootHair
Operating system(s): Linux, MacOS, Windows
Programming language: Python
License: MIT License
Supplementary Material
giaf141_Supplemental_File giaf141_Authors_Response_To_Reviewer_Comments_Original_Submission giaf141_GIGA-D-25-00279_Original_Submission giaf141_GIGA-D-25-00279_Revision_1 giaf141_Reviewer_1_Report_Original_SubmisOpen asset ↗github.com/iantsang779/pyRootHairlines:250-287Dataset · publicThe source jupyter notebook and data used to generate all figures in the manuscript have been deposited on GitHub [ 39 ].Open asset ↗lines:400-405Code · publiclarge number of plant germplasm resources for variation in root hair morphology, supporting high-resolution measurements and high-throughput data analysis. This facilitates downstream investigation of the impacts of root hair genetic control and morphological variation on plant performance. pyRootHair is installable via PyPI ( https://pypi.org/project/pyRootHair/ ) and can be accessed on GitHub at https://github.com/iantsang779/pyRootHair .
Keywords: root hairs, plant phenotyping, machine learning, computer vision, AI, U-Net, wheat, roots, software
status released display-pdf yes is-olf no is-manuscript no is-preprint no is-journal-matter no is-scanned no is-retracted no
Received 2025 JOpen asset ↗lines:1-34Code / dataset availability confirmedEurope PMC · OpenAlex · checked 15 Sept 2026
Background High-throughput phenotyping is addressing the current bottleneck in phenotyping within breeding programs. Imaging tools are becoming the primary resource for improving the efficiency of phenotyping processes and providing large datasets for genomic selection approaches. The advent of artificial intelligence (AI) brings new advantages by enhancing phenotyping methods using imaging, making them more accessible to breeding programs. In this context, we have developed an open Python workflow for analyzing morphology, color, and morphometric traits using AI, which can be applied to fruits and other plant organs. Results The workflow was implemented in almond (Prunus dulcis (Mill.) D. A. Webb), a species where breeding efficiency is critical due to its long breeding cycle. Over 25,000 kernels, more than 20,000 nuts, and over 600 individuals were phenotyped, making this the largest morphological study conducted in almond so far. The best segmentation and reconstruction approaches achieved error rates below 1%. Weight and area variables enabled accurate estimation of kernel thickness, with a root mean squared error of 0.47. Fifty-five heritable morphological, morphometric, and color traits were identified, highlighting their potential as target traits in breeding programs. Conclusion The proposed workflow demonstrated robust performance across diverse datasets and was effective with limited training data for fine-tuning. Its compatibility with the output of AI-based labeling tools allows users to fully leverage the advantages of these technologies-reducing manual effort, accelerating dataset preparation, and streamlining the fine-tuning process of segmentation models. This flexibility enhances the scalability and practical applicability of the workflow in real-world phenotyping scenarios, especially in the context of breeding programs.
Why it matches plant phenotyping methods植物器官の形態・色・形状特性を抽出するオープンなRGB画像解析ワークフローを開発し、分割・再構成精度も検証しているため、植物フェノタイピング手法が中心です。
abstractwe have developed an open Python workflow for analyzing morphology, color, and morphometric traits using AI, which can be applied to fruits and other plant organs.
Reproduction assets foundThe authors publicly release their almond phenotyping workflow (AlmondCV) as Python/R notebooks on GitHub and as a registered WorkflowHub workflow, covering preprocessing, segmentation model development/deployment, morphology, and morphometric analyses used for this paper's measurements.Code · publiche manual process, which is challenging to automate because of variability in shell hardness and size. This extensive dataset will facilitate future studies aimed at dissecting quantitative traits and implementing genomic selection approaches.
Availability of Source Code and Requirements
Project name: AlmondCV
Project homepage: https://github.com/jorgemasgomez/almondcv2
Operating system(s): Platform independent
Programming language: Python, R
Other requirements: see public environment file released under GNU GPL v3
RRID: SCR_027064
WorkflowHub: https://workflowhub.eu/workflows/1731
Bio.tools: https://bio.tools/almondcv2
Additional Files
Supplementary Table S1 . Article metrics studied relateOpen asset ↗https://github.com/jorgemasgomez/almondcv2lines:222-243Code · publicselection approaches.
Availability of Source Code and Requirements
Project name: AlmondCV
Project homepage: https://github.com/jorgemasgomez/almondcv2
Operating system(s): Platform independent
Programming language: Python, R
Other requirements: see public environment file released under GNU GPL v3
RRID: SCR_027064
WorkflowHub: https://workflowhub.eu/workflows/1731
Bio.tools: https://bio.tools/almondcv2
Additional Files
Supplementary Table S1 . Article metrics studied related to quantitative almond morphological traits.
Supplementary Fig. S1 . Workflow description outlining the steps involved in developing the segmentation model (green) and deploying it (purple).
Supplementary Fig. S2 . YOpen asset ↗https://workflowhub.eu/workflows/1731lines:222-243Code / dataset availability confirmedEurope PMC · checked 5 Sept 2026
WheatRootMorphology / geometry measurementRoot system architecture
Root anatomical phenotyping has become a demonstrably essential part of investigating root physiology and in acquiring a holistic understanding of plant development. However, accessible high throughput methods for root anatomical analysis are still lacking. Here, we present the Rapid Anatomics Tool (RAT), a novel, low-cost platform for high throughput root anatomical imaging with a shallow learning curve for obtaining high quality images suitable for comparative analysis across a number of plant species. Its efficiency comes from combining blockface-like imaging and stain-free imaging using near-ultraviolet (nUV) autofluorescence utilising a combination of low-cost commercial equipment, readily available mechanical components, and custom designed and 3D printed tools. Using this platform, we investigated the anatomy of mature tissue along the axis of wheat crown roots, revealing a tendency of reduction in vascular complexity (expressed through a reduction in metaxylem number, area, and mean area per metaxylem file) from the basal to the distal region of the root. This study highlights the importance of thorough sampling strategies for investigating root anatomy in relation to organ function and introduces an accessible, relatively high-throughput platform to support such research.
Why it matches plant phenotyping methods根の解剖学的形質を高スループットに画像取得する低コスト基盤を開発しており、植物フェノタイピング手法が研究の中心です。
abstractHere, we present the Rapid Anatomics Tool (RAT), a novel, low-cost platform for high throughput root anatomical imaging
Reproduction assets foundThe paper's supplementary materials (hosted at the publisher DOI page) explicitly include the 3D design files (STL) for the RAT platform and the Python script used to control image acquisition, which are paper-specific phenotyping hardware/analysis assets. The phenotype datasets generated and analysed are only 'on the'Code · public3D design files (STL) are provided in the supplementary material. The Python script used to control image acquisition using the specific USB microscope used in this study is available in the supplementary materialsOpen asset ↗lines:229-267Code / dataset availability confirmedEurope PMC · OpenAlex · checked 13 Sept 2026
PoplarField / plotRoot2D/3D reconstructionRoot system architecture
Root system architecture (RSA) is pivotal for comprehending the ecological adaptation strategies and resource acquisition mechanisms of urban flora, playing a vital role in soil stability, carbon sequestration, and ecosystem sustainability. However, the non-destructive detection and precise three-dimensional (3D) reconstruction of RSA within urban environments remain challenging. In this study, a non-destructive reconstruction method utilizing ground-penetrating radar (GPR) technology was developed to achieve 3D reconstruction and visualization of RSA, with the goal of advancing the intelligent construction and precise ecological management of urban forest parks. Field-based GPR surveys of a 9-year-old triploid poplar were conducted using a square grid and concentric circular scanning scheme. A 3D data volume (C-scan) was constructed from two-dimensional (2D) profiles, and the spatial distribution of RSA was reconstructed using instantaneous amplitude analysis. The method was validated by comparing the results with actual root structures in sandy loam environments. The research results of the 1600 MHz GPR under the square grid scanning scheme show that extracting the instantaneous amplitude isosurface of GPR can effectively reflect the spatial distribution of roots with diameters greater than 1 cm within a depth of 0.4 m subsurface. The accuracy of RSA reconstruction can reach 89 %. The results demonstrate the applicability of the proposed method for non-destructive environmental monitoring in urban forest parks, showing significant potential for the large-scale detection and reconstruction of subsurface root systems. This research provides a novel approach for RSA reconstruction with significant implications for urban ecosystem management, soil conservation, and climate resilience research. The method enhances our capability to monitor the growth and adaptation of urban roots, laying the groundwork for the large-scale, non-destructive analysis of RSA.
Why it matches plant phenotyping methodsGPRと瞬時振幅解析を用いて樹木根系構造を3D再構成する方法を開発し、実際の根構造との比較で検証しており、根系形態の取得が中心的な研究目的である。
abstracta non-destructive reconstruction method utilizing ground-penetrating radar (GPR) technology was developed to achieve 3D reconstruction and visualization of RSA
Reproduction assets foundThe paper's Data Availability statement explicitly releases the GPR root scanning data on Zenodo and the RSA reconstruction analysis code on GitHub, both with public URLs matching allowed entries.Code · publicCode is available at https://github.com/Niceguoqiu/RSA-Reconstruction-Code.git .Open asset ↗GitHub · Niceguoqiu/RSA-Reconstruction-Codelines:268-286Code / dataset availability confirmedCrossref · OpenAlex · checked 6 Sept 2026
SoybeanLaboratory / benchtopRootMorphology / geometry measurement2D/3D reconstructionSegmentationVisualization / data managementRoot system architecture
Root system analysis remains methodologically challenging in plant research: traditional soil cultivation obstructs comprehensive root observation, whereas hydroponic visualization lacks ecological relevance due to soil environment exclusion—a critical limitation for crops like soybean. This manuscript developed a cost-effective hybrid imaging system integrating transparent acrylic plates, semi-permeable membranes, and natural soil substrates with high-resolution imaging and controlled illumination, enabling non-destructive root monitoring in quasi-natural soil conditions. Complementing this hardware innovation, this manuscript proposed an unsupervised semantic segmentation algorithm that synergizes path planning with an enhanced DBSCAN framework, achieving the precise extraction of primary and lateral root architectures. Experimental validation demonstrated superior performance in soybean root analysis, with segmentation metrics reaching 0.8444 accuracy, 0.9203 recall, 0.8743 F1-score, and 0.7921 mIoU—significantly outperforming existing unsupervised methods (p 0.94) with WinRHIZO in quantifying root length, projected area, dimensional parameters, and lateral root counts confirmed system reliability. This soil-compatible phenotyping platform establishes new opportunities for root research, with future developments targeting multi-crop adaptability and complex soil condition applications through modular hardware redesign and 3D reconstruction algorithm integration.
Why it matches plant phenotyping methods根系観察用ハードウェアと画像セグメンテーション手法を開発し、根形質抽出性能を検証した、中心的な植物フェノタイピング研究である。
abstractThis manuscript developed a cost-effective hybrid imaging system
Reproduction assets foundThe paper's Data Availability Statement explicitly deposits the study's soybean root image data (the time-series NRMS dataset and scanner validation dataset used for phenotyping) in a public GitHub repository under the authors' account, matching an allowed URL. No separate analysis code availability is stated, so the资产Dataset · publicData Availability Statement: The data presented in this study are openly available in [GitHub] at
[https://github.com/xusiyue/RootPO_DBSCAN/tree/master/project_rootSystem/data (accessed
on 31 October 2025)].Open asset ↗GitHub · xusiyue/RootPO_DBSCANpdf-page:18 lines:1-58Code / dataset availability confirmedOpenAlex · Europe PMC · checked 6 Sept 2026
Cassava (Manihot esculenta Crantz) is a staple food and a key industrial crop across tropical regions, but traditional phenotyping for critical quality traits like dry matter content (DMC) and starch content (StC) is a laborious and low-throughput process. This study investigates the efficacy of a handheld near-infrared spectrometer device (NIRS) for the non-destructive, rapid prediction of these traits. The research methodology involved collecting spectral data from 2,236 cassava clones from 19 field trials in Brazil, using two sample types: fresh roots and mashed roots. Six spectral pre-processing methods and three machine learning algorithms-Partial Least Squares (PLS), Support Vector Machines (SVM), and Extreme Gradient Boosting (XGB)-were evaluated to optimize predictive models. Model performance was assessed using the coefficient of determination in calibration ([Formula: see text]), the root mean squared error of calibration ([Formula: see text]), and the Kappa index to quantify the consistency of clone selection. Results show that mashed samples consistently yielded superior predictive performance across all models. Specific preprocessing methods, such as Savitzky-Golay filtering combined with Standard Normal Variate (SG + SNV) and first-derivative transformations, significantly enhanced model accuracy. Among the algorithms, PLS demonstrated the best overall performance, with high predictive accuracy ([Formula: see text] >0.96) and low prediction errors ([Formula: see text]<1.3 for DMCo), especially with mashed samples. High Kappa index values, consistently approaching 1.0, confirmed a good alignment between NIRS-based selection and traditional phenotypic methods. This study validates a portable spectrometer as a reliable and efficient tool for high-throughput phenotyping in cassava breeding programs. The findings confirm that portable NIRS devices, when used with optimal sample preparation (mashed roots) and robust modeling (PLS), can effectively yield good predictions for plant selection. This approach can significantly accelerate breeding cycles by enabling rapid, early-stage selection decisions, thereby overcoming a major bottleneck and contributing to a more efficient and sustainable genetic improvement of cassava.
Why it matches plant phenotyping methods携帯型NIRSによるキャッサバ根の品質形質予測モデルを開発・比較・検証し、育種選抜への適用性能を評価しており、フェノタイピング手法が中心である。
abstractThis study investigates the efficacy of a handheld near-infrared spectrometer device (NIRS) for the non-destructive, rapid prediction of these traits.
Reproduction assets foundThe paper's spectral and phenotypic data (NIRS spectra from 2,236 cassava clones, DMC/StC trait measurements) are openly deposited on Figshare per the Data Availability statement. No author analysis code or trained models are explicitly shared.Dataset · publicData Availability: The data that support the findings of this study are openly available in Figshare at https://figshare.com/s/d2e947f467bd8f655ede .Open asset ↗Figsharelines:142-152Code / dataset availability confirmedCrossref · Europe PMC · checked 6 Sept 2026
Studying the mechanisms that promote deep rooting in crops is crucial for engineering plant varieties with enhanced drought resilience and increased carbon sequestration capacity. Soil compaction is a major constraint on rooting depth and, to overcome this, root system penetrability needs to be enhanced. However, because of the limitations of current methods, phenotyping root penetrability remains a bottleneck. Here, we developed RootXplorer, a computer vision-based 3D phenotyping platform for high-throughput quantification of root penetration-related traits/phenotypes across dicot and monocot species. RootXplorer integrates a novel Phytagel-based cylinder system, a 3D imaging unit, and an automated software pipeline to extract root penetration-related traits with high precision and at a large scale. We demonstrate that RootXplorer enables large-scale diversity screenings in conditions replicating soil compaction effects in multiple species, revealing species-specific strategies for overcoming mechanical impedance. These findings highlight the utility and promise of RootXplorer for accelerating research on root architectural plasticity under controlled compaction conditions, identifying genotypes with varying tolerance to mechanical impedance, and supporting data-driven breeding decisions for developing soil compaction-resilient crop varieties. This technology has important implications for future plant breeding strategies and supports ongoing climate change mitigation efforts.
Why it matches plant phenotyping methodsRoot penetrability関連形質を対象に、3D画像計測と自動ソフトウェアで抽出する高スループット表現型解析プラットフォームを開発しており、方法が研究の中心です。
abstractRootXplorer integrates a novel Phytagel-based cylinder system, a 3D imaging unit, and an automated software pipeline to extract root penetration-related traits with high precision and at a large scale.
Reproduction assets foundThe paper's Data Availability statement explicitly deposits the complete analysis pipeline and time-lapse video generation code in two public GitHub repositories under the authors' Salk Harnessing Plants Initiative organization. These directly support the paper's RootXplorer phenotyping analysis (image cropping, U-Net+Code · publicAll code for generating time-lapse videos is publicly available at https://github.com/Salk-Harnessing-Plants-Initiative/RootXplorer-cylinder-time-lapseOpen asset ↗Salk-Harnessing-Plants-Initiative/RootXplorer-cylinder-time-lapselines:167-180Code / dataset availability confirmedCrossref · OpenAlex · Europe PMC · checked 6 Sept 2026
RiceWheatField / plotMesh / voxelNeRF / 3D Gaussian SplattingLiDAR / point cloudPanicle / ear / spikeLeafRootSeed / grain
Advanced plant phenotyping technologies are vital for trait improvement and accelerating intelligent breeding. Due to the species diversity of plants, existing methods heavily rely on large-scale high-precision manually annotated data. For self-occluded objects at the grain level, unsupervised methods often prove ineffective. This study proposes IPENS, an interactive unsupervised multi-target point cloud extraction method. It utilizes radiance field information to lift 2D masks, segmented by SAM2 (Segment Anything Model 2), into 3D space for target point cloud extraction. A multi-target collaborative optimization strategy addresses the challenge of segmenting multiple targets from a single interaction. On a rice dataset, IPENS achieves a grain-level segmentation mean Intersection over Union (mIoU) of 63.72%. For phenotypic trait estimation, it achieves a grain voxel volume coefficient of determination R 2 = 0.7697 (Root Mean Square Error, RMSE = 0.0025), leaf surface area R 2 = 0.84 (RMSE = 18.93), and leaf length and width prediction accuracies of R 2 = 0.97 and R 2 = 0.87 (RMSE = 1.49 and 0.21). On a wheat dataset, IPENS further improves segmentation performance to a mIoU of 89.68%, with exceptional phenotypic estimation results: panicle voxel volume R 2 = 0.9956 (RMSE = 0.0055), leaf surface area R 2 = 1.00 (RMSE = 0.67), and leaf length and width predictions reaching R 2 = 0.99 and R 2 = 0.92 (RMSE = 0.23 and 0.15). Without requiring annotated data, IPENS rapidly extracts grain-level point clouds for multiple targets within three minutes using single-round image interactions. These features make IPENS a high-quality, non-invasive phenotypic extraction solution for rice and wheat, offering significant potential to enhance intelligent breeding.
Why it matches plant phenotyping methods植物形質抽出のためのNeRF-SAM2融合手法を開発し、作物データセットで分割性能と形質推定精度を検証しているため、方法開発・検証が中心である。
abstractThis study proposes IPENS, an interactive unsupervised multi-target point cloud extraction method.
Reproduction assets foundThe paper's analysis code is publicly available on GitHub. The rice/wheat MMR/MMW phenotype datasets (multi-view images, point clouds, annotations) are only available upon reasonable request, so they are not public.Code · publicCode is available at https://github.com/Vincent-Songwentao/IPENS-Code.git .Open asset ↗https://github.com/Vincent-Songwentao/IPENS-Code.gitlines:472-496Code / dataset availability confirmedCrossref · checked 14 Sept 2026
SoybeanLiDAR / point cloudRootMorphology / geometry measurement2D/3D reconstructionRoot system architecture
Abstract Accurate phenotyping of root traits is essential for understanding how plants respond to varying soil water treatment conditions, yet traditional phenotyping methods are often destructive and limited in capturing the full three‐dimensional (3D) complexity of root systems. Existing two‐dimensional imaging techniques and advanced 3D methods for performing root phenotyping, like magnetic resonance imaging or computed tomography, either compromise on resolution, are cost‐prohibitive, or lack scalability. To address these limitations, this study proposes fringe projection profilometry (FPP), a rapid, nondestructive 3D imaging method, for root phenotyping. Using FPP, two architectural root traits were extracted: the number of root tips and the volumetric occupancy of the root system. These traits, difficult to obtain through manual phenotyping or conventional imaging, were automatically derived from the FPP 3D point clouds and validated against expert‐assigned fibrosity scores serving as the biological reference. The study involved 36 soybean ( Glycine max (L.) Merr.) plants from six genotypes, pre‐classified as either stress‐treated or grown under rain‐fed conditions. Results showed strong alignment between FPP‐derived traits and expert evaluations. Stress‐ treated plants consistently exhibited more root tips and greater volumetric occupancy, confirming the biological relevance of these metrics. While this study does not attempt to classify drought tolerance directly, the structural variations observed under drought stress may serve as a foundation for identifying stress‐responsive phenotypes in future work. Overall, the findings demonstrate that FPP provides a fast, scalable, and accurate tool for 3D root phenotyping under variable water conditions.
Why it matches plant phenotyping methodsFPPによる根系の3次元形質取得・自動抽出を開発し、専門家評価と検証した研究であり、フェノタイピング手法が中心です。
abstractthis study proposes fringe projection profilometry (FPP), a rapid, nondestructive 3D imaging method, for root phenotyping.
Reproduction assets foundThe paper's Data Availability Statement provides a public Google Drive link to the datasets generated and/or analyzed in this soybean root FPP phenotyping study, which is an allowed URL. No author analysis code is explicitly deposited.Dataset · publicying and Overcoming Weaknesses via Breed-
ing, Genomics, Phenomics and Physiology).
C O N F L I C T O F I N T E R E S T S TAT E M E N T
The authors declare no conflicts of interest.
DATA AVA I L A B I L I T Y S TAT E M E N T
The datasets generated and/or analyzed dur-
ing the current research are available at Google
Drive link: https://drive.google.com/file/d/1BJ4yq8QEWY3E5qQIQmYcOXHhEYn1zTE-
/view?usp=sharing
O RC I D
JiaqiongLi https://orcid.org/0009-0006-2247-425X
ZengluLi https://orcid.org/0000-0003-4114-9509
BeiwenLi https://orcid.org/0000-0001-8130-7730
R E F E R E N C E S
Balasubramaniam, B., Li, J., Liu, L., & Li, B. (2023). 3D imaging with
fringe projection for food and agriculturalOpen asset ↗pdf-raw-page:17 lines:1-91Code / dataset availability confirmedCrossref · Europe PMC · checked 6 Sept 2026
In the context of precision agriculture, high-throughput phenotyping (HTP) aims to rapidly and effectively identify factors that affect crop yield, enabling timely and appropriate interventions. However, interpreting data from HTP remains challenging. We performed a proximal red-green-blue (RGB)-based HTP on several tomato genotypes exposed to abiotic stress (drought) or biotic stress induced by tomato spotted wilt virus (TSWV), Pseudopyrenochaeta lycopersici (corky root rot; CRR), or Meloidogyne incognita (root-knot nematode; RKN). We aimed to determine if RGB-based HTP is effectively able to: a) distinguish the effects of biotic from abiotic stress; b) differentiate resistant/tolerant from susceptible genotypes. Our HTP data analysis produced 12 morphometric and eight colorimetric indices. Principal Component Analysis (PCA; P < 0.0001; 83 % variation explained by three PCs) showed that factors such as shoot area solidity and certain color-based indices, including the senescence index and green area, effectively differentiated biotic from abiotic stress. Morphometric parameters, including plant height, projected shoot area, and convex hull area, proved to be applicable for identifying the stress status regardless of the type of stress. HTP effectively distinguished the genotype resistant to TSWV from the susceptible ones. This task was more challenging for below-ground stresses like CRR and RKN. Different profiles of HTP indices were observed among the genotypes assayed for drought tolerance, indicating variability in their ability to withstand drought conditions. In conclusion, our findings highlight the value of RGB-based HTP as a tool for precision farming of tomatoes, enabling the identification of both biotic and abiotic stressors.
Why it matches plant phenotyping methodsトマトのRGBベース高スループット表現型解析を用い、形態・色彩指標の抽出と、ストレス識別および遺伝子型判別への有効性を評価しており、フェノタイピング手法が研究の中心である。
abstractWe performed a proximal red-green-blue (RGB)-based HTP on several tomato genotypes exposed to abiotic stress (drought) or biotic stress induced by tomato spotted wilt virus (TSWV), Pseudopyrenochaeta lycopersici (corky root rot; CRR), or Meloidogyne incognita (root-knot nematode; RKN).
Reproduction assets foundThe paper's HTP dataset (20 indices from five stress experiments) is stated to be available in the supplementary material hosted at the article DOI, which qualifies as a paper-specific public phenotype dataset. However, the analysis code has no public deposit: it is only available from the corresponding author upon 'a'Dataset · publicThe data collected and used in this study are available in the supplementary material. The code used for analysis is available from the corresponding author, GBu, upon reasonable request.Open asset ↗lines:400-518Code / dataset availability confirmedEurope PMC · checked 14 Sept 2026
Field / plotFlowerLeafRootStem / branchClassificationDisease symptoms / severity
This dataset comprises 5452 images of durian plant parts-including leaves, flowers, branches, stems, and roots-affected by ten common disease classes. The images were captured from one family-owned durian orchard and four nearby orchards in Vinh Long Province, Vietnam. Each class contains approximately 405-427 raw images, photographed using an iPhone 14 under natural field conditions. These conditions simulate typical farmer photography practices, featuring varied angles, inconsistent lighting, and complex environmental backgrounds, resulting in significant visual noise. All raw JPEG images were manually reviewed and cropped on macOS systems using MacBook devices equipped with Apple M4 chips to focus on disease-affected regions, reduce file size, and minimize background noise. The processed, cropped images are provided in PNG format with variable dimensions. Images were resized to 224×224 pixels only during model training for machine learning experiments. Disease symptoms were verified in collaboration with plant pathologists to ensure accurate classification. This dataset is publicly available on Mendeley Data and is suitable for developing and evaluating machine learning models in plant disease classification. It is particularly valuable for testing model performance under real-world, noisy conditions and for supporting the creation of mobile or edge-based diagnostic tools in agriculture.
Why it matches plant phenotyping methods植物病徴を画像で直接捉えた公開データセットで、植物病害状態の分類モデル開発・評価を主目的とするため、表現型計測データセットとして中心的です。
abstractThis dataset comprises 5452 images of durian plant parts-including leaves, flowers, branches, stems, and roots-affected by ten common disease classes.
Reproduction assets foundThe paper is a Data in Brief describing a public durian disease image dataset (5452 field images, ten classes) deposited on Mendeley Data with an explicit DOI and direct URL, matching an allowed URL. This is a paper-specific, publicly available image dataset directly reproducing the paper's phenotyping measurements. NoDataset · publicRepository name: Mendeley Data
Data identification number: 10.17632/mhjwyb5p48
Direct URL to data: https://data.mendeley.com/datasets/mhjwyb5p48/1Open asset ↗Mendeley Data · 10.17632/mhjwyb5p48lines:47-125Code / dataset availability confirmedCrossref · Europe PMC · checked 6 Sept 2026
Floral area is a major predictor of the attractiveness of a flowering plant for pollinators, yet the measurement of floral area is time-consuming and inconsistent across studies. Here, we developed an AI-powered algorithm, FloralArea, to automate floral area measurement from an image. The FloralArea algorithm has two main components: an object segmentation module and an area estimation module. The object segmentation module extracts the pixels of flowers and the reference object in an image. The area estimation module predicts floral area based on the ratio between flower and reference object pixels. We fine-tuned two YOLOv8 segmentation models for flower and reference object segmentation. The flower segmentation model achieved moderate precision, recall, mAP0.5, and mAP0.5-0.95 of 0.794, 0.68, 0.741, and 0.455 on the test dataset, while the reference object model achieved an impressive performance of 0.907, 0.940, 0.933, and 0.832. We evaluated FloralArea using 75 images of flowering plants. We used ImageJ to calculate the actual floral area for all the images and compared them with the predicted floral area from FloralArea. The predicted floral area correlated well with the measured floral area with a coefficient of determination (R 2 ) of 0.93 and a root mean square error of 20.58 cm 2 . The FloralArea algorithm reduced the time it takes to calculate floral area from an image by 99.24% compared with traditional methods with image processing tools like ImageJ. By streamlining floral area estimation, the FloralArea algorithm provides a scalable, efficient, consistent, and accessible tool for researchers, particularly to aid in assessing plant attractiveness to different pollinator groups.
Why it matches plant phenotyping methods花画像から花の面積という植物形質を自動抽出するAI手法を開発し、実測値との比較検証と処理時間評価を行っており、植物フェノタイピング手法が研究の中心です。
abstractHere, we developed an AI-powered algorithm, FloralArea, to automate floral area measurement from an image.
Reproduction assets foundThe paper's authors publicly released the FloralArea source code on GitHub and the flower image dataset (used for fine-tuning YOLOv8 models and evaluating the algorithm) on Penn State's ScholarSphere repository, as stated in the Data Availability statement.Code · publicThe source code for the FloralArea algorithm is available on GitHub ( https://github.com/eai6/FloralArea_Web.git ).Open asset ↗GitHub · eai6/FloralArea_Weblines:137-148Dataset · publicThe image dataset used to fine-tune the YOLOv8 models and evaluate the FloralArea algorithm is on the ScholarSphere repository of the Pennsylvania State University ( https://scholarsphere.psu.edu/resources/33452dff-b807-44b0-8783-71c8c47b5242 ).Open asset ↗ScholarSphere · 33452dff-b807-44b0-8783-71c8c47b5242lines:137-148Code / dataset availability confirmedEurope PMC · OpenAlex · checked 6 Sept 2026
Understanding the genetic basis of root system architecture (RSA) in crops requires innovative approaches that enable both high-throughput and precise phenotyping in field conditions. In this study, we evaluated multiple phenotyping and analytical frameworks for quantifying RSA in mature, field-grown maize in three field experiments. We used forward and reverse genetic approaches to evaluate >1700 maize root crowns, including a diversity panel, a biparental mapping population, and maize mutant and wild-type alleles at two known RSA genes, DEEPER ROOTING 1 (DRO1) and Rootless1 (Rt1). We show the utility of increasing the dimensionality of traditional two-dimensional (2D) techniques, referred to as the "2D multi-view" method, to improve the capture of whole root system information for mapping genetic variation influencing RSA. Comparison of univariate and multivariate genome-wide association study (GWAS) approaches revealed that multivariate traits were effective at dissecting complex RSA phenotypes and identifying pleiotropic quantitative trait loci (QTLs). Overall, three-dimensional (3D) root models generated from X-ray computed tomography and digital phenotyping captured a larger proportion of RSA trait variations compared to other methods of root phenotyping, as evidenced by both genome-wide and single-gene analyses. Among the individual root traits, root pulling force emerged as a highly heritable estimate of RSA that identified the largest number of shared QTLs with 3D phenotypes. Our study shows that integrating complementary phenotyping technologies helps to provide a more comprehensive understanding of the genetic architecture of RSA in field-grown maize.
Why it matches plant phenotyping methods根系構造を定量化する複数の表現型解析法を比較・評価し、2Dマルチビュー、X線CT、デジタル表現型などの技術性能を遺伝解析で検証しており、表現型取得法が研究の中心である。
abstractwe evaluated multiple phenotyping and analytical frameworks for quantifying RSA in mature, field-grown maize
Reproduction assets foundThe paper deposits raw phenotypic metadata (root crown/RSA measurements from the field experiments) on Dryad, and uses the authors' public 3D root crown analysis pipeline (RCAP) on GitHub for the XRT feature extraction. Both are paper-specific, public, and actionable. Generic R packages and cited prior work are not.Dataset · publicRaw phenotypic metadata are available on the Dryad Digital Repository ( https://doi.org/10.5061/dryad.z34tmpgq4 , http://datadryad.org/share/HeNYoxNMdN_GrHMyZHFN3rUTN1UiG8OFhU-B107E7mM ).Open asset ↗Dryad Digital Repository · 10.5061/dryad.z34tmpgq4lines:499-731Code · publicreferred to here as the root crown analysis pipeline (RCAP). Detailed descriptions of RCAP trait implementations and related resources are available at: https://github.com/Topp‐Roots‐Lab/3d‐root‐crown‐analysis‐pipeline/ .Open asset ↗GitHub · Topp‐Roots‐Lab/3d‐root‐crown‐analysis‐pipelinelines:162-175Code / dataset availability confirmedEurope PMC · checked 6 Sept 2026
Xanthomonas oryzae pv. oryzae ( Xoo ), the causal agent of bacterial blight in rice, is primarily studied in the context of foliar infections. However, infected stubble and irrigation water may serve as reservoirs and be responsible for seedling stage root infections in the field, especially during transplanting. Here, we established a coleoptile crown root-based infection protocol to investigate whether gene-for-gene interactions between Xoo TAL effectors and SWEET sucrose uniporter susceptibility genes occur in the root xylem, and whether the disease can propagate from roots to seedling shoots. Using translational SWEET11a-GUS reporter lines under control of the native SWEET11a promoter, we observed progressive infection as indicated by accumulation of SWEET11a-GUS fusion protein in infected coleoptile crown roots. However, we did not detect progression of GUS accumulation beyond the coleoptile node, nor did we detect blight symptoms on the young leaves. Notably, the xylem, at least during early stages of infection remained functional as shown by Rhodamine B tracer, consistent with transfer of xylem constituents via living cells at the coleoptile node that did not allow bacteria to pass. Furthermore, the root infection protocol is a ∼4x faster compared to standard leaf-clipping assays (roots assay: 11 days from sowing, compared to 39 days for clip infection), enabling more rapid assessment of TAL effector repertoire and plant defense responses with translational SWEET-GUS reporter lines. Our findings expand our understanding of Xoo infection routes and provide a valuable tool for resistance testing and pathogen surveillance.
Why it matches plant phenotyping methodsイネ病害の進展・抵抗性を迅速に評価する根部感染プロトコルを確立し、従来法との速度差とレポーターによる感染進展を示しているため、病害フェノタイピング手法が中心である。
abstractHere, we established a coleoptile crown root-based infection protocol to investigate whether gene-for-gene interactions between Xoo TAL effectors and SWEET sucrose uniporter susceptibility genes occur in the root xylem, and whether the disease can propagate from roots to seedling shoots.
Reproduction assets foundThe preprint explicitly states that raw data underlying the root infection, GUS, and Rhodamine B measurements are publicly deposited at a DOI (10.60534/7s2cg-r2j06), which is an allowed URL. This is a paper-specific, publicly accessible raw data repository. No author analysis code with a public URL is stated (only use-Dataset · publicData availability:
Raw data are available at https://doi.org/10.60534/7s2cg-r2j06Open asset ↗10.60534/7s2cg-r2j06pdf-page:5 lines:1-61Code / dataset availability confirmedEurope PMC · bioRxiv · checked 14 Sept 2026
Background The plant vacuole arises by orchestrated interplay of membrane trafficking, cytoskeletal rearrangements and a variety of signalling pathways. In the root, the characteristic large central vacuole develops by endomembrane reorganization occurring mainly in the transition zone. The vacuole’s bounding membrane - the tonoplast - can be visualized in vivo using fluorescent protein markers, allowing for quantitative analysis of confocal microscopy images. Tonoplast organization can thus serve as a sensitive indicator of changes to any of the processes involved in vacuole biogenesis. The Vacuolar Morphology Index (VMI) is widely accepted as a quantitative measure of vacuole structure. However, this metric has two drawbacks - it only reflects the size of the largest vacuolar compartment (missing therefore possible differences in the organization of smaller compartments), and its determination is labor intensive, limiting its use on large datasets. Results We developed an alternative metric for describing vacuole organization, named the Tonoplast Topology Index (TTI), which overcomes the above-mentioned shortcomings of the VMI. We compared the performance of our protocol with VMI on a simulated dataset and on real data. To validate the methods’ performance, we used it to confirm the previously reported differences in vacuole shape and size between Arabidopsis thaliana roots grown on the surface of an agar medium compared to those embedded inside the agar. Both VMI and TTI could efficiently detect the relatively subtle changes in vacuole organization depending on the position of the root in the agar, and provided correlated results. However, only TTI produced data with close to normal value distribution, simplifying subsequent statistical evaluation. Conclusions We present the protocol for TTI determination as a two-stage semi-automated procedure involving microscopic image analysis employing an ImageJ macro and subsequent processing of numeric data in the Jupyter Notebook environment, together with benchmarking image data. Since this implementation is freeware-based, platform-independent and (relatively) user-friendly, we hope it will find its use as a high throughput, added value alternative to the VMI metric.
Why it matches plant phenotyping methods植物の液胞構造を定量化する新規指標と半自動画像解析プロトコルを開発し、既存指標との比較・実データおよびシミュレーションによる検証、ベンチマークデータを提示しており、表現型取得・抽出法が中心である。
abstractWe developed an alternative metric for describing vacuole organization, named the Tonoplast Topology Index (TTI), which overcomes the above-mentioned shortcomings of the VMI.
Reproduction assets found保存済みの本文根拠を更新済みルールで再検証し、公開資産1件を確認しました。Code · publicthe software tool generated here are also available at https://github.com/GeorgeCaldarescu/TTI-Open asset ↗GeorgeCaldarescu/TTI-pdf-page:9 lines:1-52Code / dataset availability confirmedEurope PMC · bioRxiv · checked 13 Sept 2026
ArabidopsisRootMorphology / geometry measurementRoot system architecture
ABSTRACT Root system architecture (RSA) is central to plant adaptation and fitness, yet the design principles and regulatory mechanisms connecting RSA to environmental adaptation are not well understood. We developed Ariadne, a semi-automated software for quantifying cost-efficiency tradeoffs of RSA by mapping root networks onto a Pareto-optimality framework, which describes the balance between resource transport efficiency and construction cost. Applying Ariadne to Arabidopsis thaliana , we found that root architectures consistently assume Pareto-optimal forms across developmental stages, genotypes, and environmental conditions. Using the Discovery Engine, an engine that combines machine learning together with interpretability techniques, we found developmental stage, the hy5/chl1-5 genotype, and manganese availability as important determinants of the cost-efficiency tradeoff, with manganese exerting a unique influence not observed for other nutrients. These results reveal that RSA plasticity is genetically constrained to cost-efficiency optimal configurations and that developmental and environmental factors shift RSA on the pareto front, with manganese acting as a strong modulator of the transport efficiency and construction cost balance.
Why it matches plant phenotyping methodsRSAのコスト効率トレードオフを定量化する半自動ソフトウェアを開発し、植物形態形質の解析に適用しており、表現型取得・抽出手法が研究の中心である。
abstractWe developed Ariadne, a semi-automated software for quantifying cost-efficiency tradeoffs of RSA by mapping root networks onto a Pareto-optimality framework
Reproduction assets foundThe paper's authors developed the Ariadne software used for all RSA phenotyping and Pareto analysis in this study, and explicitly state it is publicly available on PyPI and provide a GitHub code availability URL. Both are paper-specific, public, actionable code assets. No public phenotype dataset deposit is stated; theCode · publicCode availability : https://github.com/Salk-Harnessing-Plants-Initiative/AriadneOpen asset ↗Salk-Harnessing-Plants-Initiative/Ariadnelines:235-276Code / dataset availability confirmedOpenAlex · Europe PMC · checked 6 Sept 2026
A novel approach, the Algorithmic Root Trait (ART) extraction method, identifies and quantifies computationally-derived plant root traits, revealing latent patterns related to dense root clusters in digital images. Using an ensemble of multiple unsupervised machine learning algorithms and a custom algorithm, 27 ARTs were extracted reflecting dense root cluster size and spatial location. These ARTs were then used independently and in combination with Traditional Root Traits (TRTs) to classify wheat genotypes differing in drought tolerance. ART-based models outperformed TRT-only models in drought classification (e.g., 96.3 % vs. 85.6 % accuracy). Combining ARTs and TRTs further improved accuracy to 97.4 %. Notably, 4 selected ARTs matched the performance of all 23 TRTs, offering 5.8 × higher information density (0.213 vs. 0.037 accuracy/feature). This superiority reflects the ability of ARTs to capture richer, more complex architectural information, evidenced by higher internal variability (35.59 ± 11.41 vs. 28.91 ± 14.28 for TRTs) and distinct data structures in multivariate analyses; PERMANOVA confirmed that ARTs and TRTs provide complementary insights. Validated through experiments in controlled environments and field conditions with wheat drought-tolerant and susceptible genotypes, ART offers a scalable, customisable toolset for high-throughput phenotyping of plant roots. By bridging conventional, visually derived traits with autonomous computational analyses, this method broadens root phenotyping pipelines and underscores the value of harnessing sensor data that transcends human perception. ART thus emerges as a promising framework for revealing hidden features in plant imaging, with broader applications across plant science to deepen our understanding of crop adaptation and resilience.
Why it matches plant phenotyping methodsデジタル画像から根の潜在形質を抽出する計算法を開発し、圃場・制御環境で検証した、植物フェノタイピング手法が中心の研究。
abstractA novel approach, the Algorithmic Root Trait (ART) extraction method, identifies and quantifies computationally-derived plant root traits
Reproduction assets foundThe authors explicitly state that all code, data, and segmented root images from this study are publicly available in their GitHub repository (shoaibms/ART), which directly reproduces the paper's root phenotyping measurements and analysis.Code · publicAll code, data and segmented images are available for download at https://github.com/shoaibms/ART .Open asset ↗shoaibms/ARTlines:277-403Dataset · publicAll code and data are available for download at https://github.com/shoaibms/ART .Open asset ↗shoaibms/ARTlines:120-154Code / dataset availability confirmedEurope PMC · checked 6 Sept 2026
Laboratory / benchtopX-ray / CTRootMorphology / geometry measurementRoot system architecture
Soil compaction and escalating global drought increase soil strength and stiffness. It remains unclear which plant root biomechanical mechanisms/traits enable growth in these harsh conditions. Here, we combine synchrotron X-ray computed tomography with spatially resolved X-ray diffraction to characterize the biomechanics of a replica root-soil system. We map the strain field around the root tip analog, finding strong agreement with finite element simulations, thereby demonstrating a promising new in vivo measurement protocol.
Why it matches plant phenotyping methods根の生育に関わる土壌内の力学的状態・ひずみを、X線CTと回折で可視化・定量する新しいin vivo測定プロトコルを開発・検証しており、植物表現型取得法が中心です。
abstractWe map the strain field around the root tip analog, finding strong agreement with finite element simulations, thereby demonstrating a promising new in vivo measurement protocol.
Reproduction assets foundThe paper's Data availability and Code availability statements both deposit the study's XCT/XRD imaging and diffraction data and the processing scripts in the University of Southampton Pure repository (DOI 10.5258/SOTON/D3309), which is an allowed URL. These are paper-specific, publicly declared assets directly reproduCode · publicAll scripts used to process the data can be found in the Pure repository: https://doi.org/10.5258/SOTON/D3309 .Open asset ↗Pure · 10.5258/SOTON/D3309lines:209-251Code / dataset availability confirmedEurope PMC · checked 14 Sept 2026
ArabidopsisSoybeanCell / cellular structureRootMorphology / geometry measurementRoot system architecture
Abstract Root system architecture (RSA), the three-dimensional arrangement of roots in soil, is a critical determinant of plant productivity, resource use efficiency, and resilience to environmental stress. Despite its agronomic importance, RSA remains a largely untapped breeding target due to historical technical barriers in root phenotyping. We present RADICYL (Root Architecture 3D Cylinder), a scalable, non-invasive, gel-based platform enabling high-throughput, high-resolution quantification of 15 RSA traits in intact root systems. Applying RADICYL to a genetically diverse panel of 371 soybean accessions, we combined 3D phenotyping with genome-wide association studies (GWAS), single-nucleus RNA sequencing (snRNA-seq), and gene co-expression network (GCN) analysis to identify RCE1 and NPR3 as central regulators of RSA, suggesting auxin and salicylic acid-mediated signaling impacts RSA in specific root tissues. Functional validation in Arabidopsis mutants revealed conserved effects on root width and lateral root development. Our findings position the endodermis and metaphloem as key regulatory cell types and demonstrate how multi-omic frameworks can accelerate the discovery of functional genes underlying complex traits. This study establishes a foundation for cell-type-targeted genome editing and climate-smart crop engineering, offering actionable genetic targets to optimize root systems for improved nutrient acquisition, drought resilience, and deep carbon sequestration. By bridging genotype, cellular context, and phenotype, this work redefines RSA as a tractable and transformative trait for the future of crop improvement.
Why it matches plant phenotyping methodsRADICYLという根系構造を定量化する高スループット3Dフェノタイピング基盤の開発・適用が研究の中心であり、15形質を測定している。
abstractWe present RADICYL (Root Architecture 3D Cylinder), a scalable, non-invasive, gel-based platform enabling high-throughput, high-resolution quantification of 15 RSA traits in intact root systems.
Reproduction assets foundThe paper's Data and code availability section names public repositories containing the authors' analysis code: a GitLab repo for WGCNA/single-cell network analysis, a GitHub repo for the RADICYL root image segmentation/phenotyping pipeline, and PyGNA2 on PyPI/GitLab. These are paper-specific, publicly actionable code/Code · publicn every 5°, resulting in 72 images per plant per timepoint for subsequent 3D root
1103 reconstruction. Phenotypic traits were quantified using the same automated pipeline described
1104 above for soybean.
1105
1106 Data and code availability
1107 The code to analyze the WGCNA network and single-cell data can be found here:
1108 https://gitlab.com/salk-tm/soybean-root-gwas/. RADYCL Segmentation pipeline for image
1109 analysis can be found here: https://github.com/Salk-Harnessing-Plants-Initiative/SSRAPC-Soy-
1110 Segmentation-Root-Architecture-Phenotyping-for-Cylinder.git. PyGNA2 is available on PyPI
1111 (https://pypi.org/project/pygna2/) and GitLab (https://gitlab.com/salk-tm/pygna2).
1112Open asset ↗salk-tm/soybean-root-gwaspdf-layout-page:30 lines:1-54Code / dataset availability confirmedCrossref · checked 14 Sept 2026
Chlorophyll fluorescenceLeafRootPhysiological trait estimationPhotosynthesis / fluorescenceWater status / transpiration
The invasive aquatic macrophyte Pontederia crassipes (water hyacinth) exhibits exceptional adaptability across a wide range of light environments, yet the mechanistic basis of its photosynthetic plasticity under both high- and low-light stress remains poorly resolved. This study integrated chlorophyll fluorescence and gas-exchange analyses to evaluate three photosynthetic models—rectangular hyperbola (RH), non-rectangular hyperbola (NRH), and the Ye mechanistic model—in capturing light-response dynamics in P. crassipes. The Ye model provided superior accuracy (R2 > 0.996) in simulating the net photosynthetic rate (Pn) and electron transport rate (J), outperforming empirical models that overestimated Pnmax by 36–46% and Jmax by 1.5–24.7% and failed to predict saturation light intensity. Mechanistic analysis revealed that P. crassipes maintains high photosynthetic efficiency in low light (LUEmax = 0.030 mol mol−1 at 200 µmol photons m−2 s−1) and robust photoprotection under strong light (NPQmax = 1.375, PSII efficiency decline), supported by a large photosynthetic pigment pool (9.46 × 1016 molecules m−2) and high eigen-absorption cross-section (1.91 × 10−21 m2). Unlike terrestrial plants, its floating leaves experience enhanced irradiance due to water-surface reflection and are decoupled from water limitation via submerged root uptake, enabling flexible stomatal and energy regulation. Distinct thresholds for carboxylation efficiency (CEmax = 0.085 mol m−2 s−1) and water-use efficiency (WUEi-max = 45.91 μmol mol−1 and WUEinst = 1.96 μmol mmol−1) highlighted its flexible energy management strategies. These results establish the Ye model as a reliable tool for characterizing aquatic photosynthesis and reveal how P. crassipes balances light harvesting and dissipation to thrive in fluctuating environments. These resulting insights have implications for both understanding invasiveness and managing eutrophic aquatic systems.
Why it matches plant phenotyping methods複数の光合成モデルを実測データで比較・検証し、植物の光合成生理形質を推定するモデルの精度と適用性を中心的に評価しているため。
abstractThis study integrated chlorophyll fluorescence and gas-exchange analyses to evaluate three photosynthetic models—rectangular hyperbola (RH), non-rectangular hyperbola (NRH), and the Ye mechanistic model—in capturing light-response dynamics in P. crassipes.
Reproduction assets found保存済みの本文根拠を更新済みルールで再検証し、公開資産1件を確認しました。Supplement · publicThe following supporting information can be downloaded at https://www.mdpi.com/article/10.3390/biology14060600/s1 : Table S1. Gas-exchange measurement data.Open asset ↗lines:329-346Code / dataset availability confirmedEurope PMC · checked 14 Sept 2026
RootVisualization / data managementBiomass / plant weightRoot system architecture
Tropical ecosystems contain the world's largest biodiversity of vascular plants. Yet, our understanding of tropical functional diversity and its contribution to global diversity patterns is constrained by data availability. This discrepancy underscores an urgent need to bridge data gaps by incorporating comprehensive tropical root data into global datasets. Here, we provide a database of tropical root characteristics. This new database, TropiRoot 1.0, will be instrumental in evaluating an array of hypotheses pertaining to root functional ecology and plant biogeography, both within the tropics and relative to other global biomes. The data compilation was conducted by the TropiRoot Initiative, in partnership with the Fine-Root Ecology Database (FRED) and the Global Root Trait (GRooT) database, Colorado State University (CSU) and the Smithsonian Tropical Research Institute (STRI). Literature search and data extraction were conducted between 2020 and 2024. Literature was identified using Web of Science, Scopus, and complemented using the expert knowledge of members of TropiRoot. To provide broad environmental and geographical distributions, literature searches included root characteristics (traits) across global change drivers, natural gradients, and from different continents. We adopted FRED standardized data columns and streamlined the format to enhance accessibility for data extraction across various user groups. This optimized framework resulted in a smaller, yet comprehensive datasheet. To make the database compatible with other global root trait initiatives, column identification was standardized following the codes provided by FRED. These efforts culminated in data extracted from 104 new sources, resulting in more than 8000 rows of data (either species or community data). Most of the data in TropiRoot 1.0 include root characteristics such as root biomass, morphology, root dynamics, mass fraction, architecture, anatomy, physiology, and root chemistry. This initiative represents a 30% increase in the currently available data for tropical roots in FRED. TropiRoot 1.0 contains root characteristics from 25 different countries, where seven are located in Asia, six in South America, five in Central America and the Caribbean, four in Africa, two in North America, and 1 in Oceania. Due to the volume of data, when ancillary data were available, including soil data, these data were either extracted and included in the database or its availability was recorded in an additional column. Multiple contributors checked the entries for outliers during the collation process to ensure data quality. For text-based observations, we examined all cells to ensure that their content relates to their specific categories. For numerical observations, we ordered each numerical value from least to greatest and plotted the values, checking apparent outliers against the data in their respective sources and correcting or removing incorrect or impossible values. Some data (soil and aboveground) have different columns for the same variable presented in different units, including originally published units, but root characteristics data had units converted to match those reported in FRED. By filling a gap from global databases, TropiRoot 1.0 expands our knowledge of otherwise so far underrepresented regions and our ability to assess global trends. This advancement can be used to improve tropical forest representation in vegetation models. The data are freely available and should be cited when used.
Why it matches plant phenotyping methods熱帯植物の根形態・構造・生理などの表現型特性を標準化して収録した再利用可能なデータベースであり、データセット構築と品質管理が中心です。
abstractHere, we provide a database of tropical root characteristics.
Reproduction assets foundThe paper's core asset is the TropiRoot 1.0 root trait database itself, publicly deposited in ESS-DIVE (DOI 10.15485/2507279) and also provided as Supporting Information (Data S1). This is a paper-specific public phenotype/trait dataset directly reproducing the paper's measurements.Dataset · publich, et al. 2025. “
TropiRoot 1.0: Database of Tropical Root Characteristics across Environments.” Ecology
106(5): e70074. 10.1002/ecy.70074
Handling Editor: Simona Picardi
DATA AVAILABILITY STATEMENT
The dataset is available as Supporting Information to this Ecology data paper and is also accessible in the ESS‐DIVE repository at https://doi.org/10.15485/2507279.
Associated Data
Supplementary Materials
Data S1.
Data Availability Statement
The dataset is available as Supporting Information to this Ecology data paper and is also accessible in the ESS‐DIVE repository at https://doi.org/10.15485/2507279.Open asset ↗10.15485/2507279html-lines:63-80Code / dataset availability confirmedOpenAlex · Crossref · Europe PMC · checked 13 Sept 2026
Large-scale manual measurements of plant architectural traits in tomato growth are laborious and subjective, hindering deeper understanding of temporal variations in gene expression heterogeneity. This study develops a high-throughput approach for characterizing tomato architectural traits at different growth stages and mapping temporal broad-sense heritability using an unmanned ground vehicle-based plant phenotyping system. The SegFormer with fusion of multispectral and depth imaging modalities was employed to semantically segment plant organs from the registered RGB-D and multispectral images. Organ point clouds were then generated and clustered into instances. Finally, six key architectural traits, including fruit spacing (FS), inflorescence height (IH), stem thickness (ST), leaf spacing (LS), total leaf area (TLA), and leaf inclination angle (LIA) were extracted and the temporal broad-sense heritability folds were plotted. The root mean square errors (RMSEs) of the estimated FS, IH, ST, and LS were 0.014, 0.043, 0.003, and 0.015 m, respectively. The visualizations of the estimated TLA and LIA matched the actual growth trends. The broad-sense heritability of the extracted traits exhibited different trends across the growth stages: (i) ST, IH, and FS had a gradually increased broad-sense heritability over time, (ii) LS and LIA had a decreasing trend, and (iii) TLA showed fluctuations (i.e. an M-shaped pattern) of the broad-sense heritability throughout the growth period. The developed system and analytical approach are promising tools for accurate and rapid characterization of spatiotemporal changes of tomato plant architecture in controlled environments, laying the foundation for efficient crop breeding and precision production management in the future.
Why it matches plant phenotyping methods植物形態形質を取得するUGV型マルチモーダル画像フェノタイピングシステムと解析手法の開発・定量評価が研究の中心であり、誤差検証も行っているため。
abstractThis study develops a high-throughput approach for characterizing tomato architectural traits at different growth stages and mapping temporal broad-sense heritability using an unmanned ground vehicle-based plant phenotyping system.
Reproduction assets foundThe paper's Data availability statement explicitly deposits the authors' trait-extraction pipeline code and example data on a public GitHub repository, matching the allowed URL.Code · publicThe pipeline code and example data related to this project are available as open source on GitHub ( https://github.com/DigBigPigForU/Tomato-architectural-trait-extraction ).Open asset ↗Tomato-architectural-trait-extractionlines:822-958Code / dataset availability confirmedEurope PMC · checked 14 Sept 2026
Field / plotRootMorphology / geometry measurementGrowth / time-series analysisRoot system architectureWater status / transpiration
Summary Deep-rooted crops accessing water and nutrients from deep soil layers enhance the resource base for crop production. However, studying these roots in field conditions is labor-intensive, limiting research scope. We established a field root research facility with 48 plots for replicated experiments. The facility includes 144 six-meter-long minirhizotron tubes and an AI-based pipeline for rapid root trait analysis. We also attempted to install access-tubes and customized ingrowth-core production for less-invasive root activity determination. Our study revealed significant differences in deep root density among species, particularly at depths of 2.5 to 4.5 meters, over five years. The less invasive studies using ingrowth-cores reached depths of 4.2 meters. Nutrient tracer 15N analysis showed marked differences in deep root activity among crop species. TDR sensors indicated varying water depletion in deeper soil layers, influenced by crop species and root growth patterns. We established a field facility for studying deep root growth and function, demonstrating its effectiveness in analyzing diverse deep-rooted plant species. This facility provides an ideal platform for conducting meaningful research in deep soil layers, yielding statistically and biologically significant results for agricultural applications.
Why it matches plant phenotyping methods深根の形質取得を目的とした圃場施設であり、ミニライゾトロン画像とAIパイプラインによる根形質解析が中心的に記述されているため、植物フェノタイピング基盤として収録する。
abstractThe facility includes 144 six-meter-long minirhizotron tubes and an AI-based pipeline for rapid root trait analysis.
Reproduction assets foundThe paper's Data availability statement deposits the paper-specific root phenotyping assets — raw minirhizotron images, RootPainter training datasets, manual counts, and trained segmentation models — in a public Zenodo repository (DOI 10.5281/zenodo.15213661), which is listed in allowed_urls.Dataset · publico be studied
in well-replicated studies. The facility is an ideal
platform for conducting studies at deep soil layers in
the field with a capacity for generating statistically
and biologically meaningful results.
Data availability
The raw images, training datasets, manual counts
and models generated after training are available
http://doi.org/10.5281/zenodo.15213661.Author contributions
EH prepared the manuscript and all co-authors
contributed to writing. EH trained the model for
automatic root segmentation and conducted all the
ingrowth-core experiments. CC installed the TDR
system and conducted the experiment. AGS created
the RootPainter software and provided technical
advice on trainingOpen asset ↗zenodo · 10.5281/zenodo.15213661pdf-raw-page:18 lines:1-107Code / dataset availability confirmedarXiv · checked 6 Sept 2026
ArabidopsisTomatoLeafRootSeed / grainSegmentationGrowth / time-series analysisTrackingGrowth / development / phenologyRoot system architecture
Plant developmental plasticity, particularly in root system architecture, is fundamental to understanding adaptability and agricultural sustainability. ChronoRoot 2.0 builds upon established low-cost hardware while significantly enhancing software capabilities and usability. The system employs nnUNet architecture for multi-class segmentation, demonstrating significant accuracy improvements while simultaneously tracking six distinct plant structures encompassing root, shoot, and seed components: main root, lateral roots, seed, hypocotyl, leaves, and petiole. This architecture enables easy retraining and incorporation of additional training data without requiring machine learning expertise. The platform introduces dual specialized graphical interfaces: a Standard Interface for detailed architectural analysis with novel gravitropic response parameters, and a Screening Interface enabling high-throughput analysis of multiple plants through automated tracking. Functional Principal Component Analysis integration enables discovery of novel phenotypic parameters through temporal pattern comparison. We demonstrate multi-species analysis, with Arabidopsis thaliana and Solanum lycopersicum, both morphologically distinct plant species. Three use cases in Arabidopsis thaliana and validation with tomato seedlings demonstrate enhanced capabilities: circadian growth pattern characterization, gravitropic response analysis in transgenic plants, and high-throughput etiolation screening across multiple genotypes.ChronoRoot 2.0 maintains the low-cost, modular hardware advantages of its predecessor while dramatically improving accessibility through intuitive graphical interfaces and expanded analytical capabilities. The open-source platform makes sophisticated temporal plant phenotyping more accessible to researchers without computational expertise.
Why it matches plant phenotyping methods植物の時系列画像から根・地上部・種子などの形態形質を抽出・追跡するオープンなAI基盤を開発し、精度向上、再学習、GUI、高スループット解析、検証まで扱っており、フェノタイピング手法が研究の中心です。
titleChronoRoot 2.0: An Open AI-Powered Platform for 2D Temporal Plant Phenotyping
Reproduction assets foundThe paper explicitly releases its full analysis source code (GitHub), the annotated infrared image dataset with multiclass segmentation masks (HuggingFace), demo phenotype video datasets, and a Docker image — all paper-specific, public, and actionable.Code · publicThe complete source code of ChronoRoot 2.0, including the implementation of all analysis methods described in this paper, is freely available under the GNU General Public License v3.0 at https://github.com/ChronoRoot/ChronoRoot2Open asset ↗ChronoRoot/ChronoRoot2lines:491-523Dataset · publicThe annotated image dataset used for training and validation contains 911 infrared images of Arabidopsis thaliana seedlings and 480 images of tomato with expert annotations for multiclass segmentation. This dataset is publicly available without restrictions at https://huggingface.co/datasets/ngaggion/ChronoRoot2Open asset ↗ngaggion/ChronoRoot2lines:491-523Code / dataset availability confirmedEurope PMC · OpenAlex · checked 6 Sept 2026
The unmanned aerial vehicle (UAV) platform has emerged as a powerful tool in soybean (Glycine max (L.) Merr.) breeding phenotype research due to its high throughput and adaptability. However, previous studies have predominantly relied on statistical features like vegetation indices and textures, overlooking the crucial structural information embedded in the data. Feature fusion has often been confined to a one-dimensional exponential form, which can decouple spatial and spectral information and neglect their interactions at the data level. In this study, we leverage our team's cross-circling oblique (CCO) route photography and Structure-from-Motion with Multi-View Stereo (SfM-MVS) techniques to reconstruct the three-dimensional (3D) structure of soybean canopies. Newly point cloud deep learning models SoyNet and SoyNet-Res were further created with two novel data-level fusion that integrate spatial structure and color information. Our results reveal that incorporating RGB color and vegetation index (VI) spectral information with spatial structure information, leads to a significant reduction in root mean square error (RMSE) for yield estimation (22.55 kg ha -1 ) and an improvement in F1-score for five-class lodging discrimination (0.06) at S7 growth stage. The SoyNet-Res model employing multi-task learning exhibits better accuracy in both yield estimation (RMSE: 349.45 kg ha -1 ) when compared to the H2O-AutoML. Furthermore, our findings indicate that multi-task deep learning outperforms single-task learning in lodging discrimination, achieving an accuracy top-2 of 0.87 and accuracy top-3 of 0.97 for five-class. In conclusion, the point cloud deep learning method exhibits tremendous potential in learning multi-phenotype tasks, laying the foundation for optimizing soybean breeding programs.
Why it matches plant phenotyping methodsUAV・SfM-MVSによるダイズ群落の3D構造再構成と、収量推定・倒伏判別のための専用深層学習モデル開発が研究の中心であり、再利用可能な表現型取得・推定手法に該当する。
abstractIn this study, we leverage our team's cross-circling oblique (CCO) route photography and Structure-from-Motion with Multi-View Stereo (SfM-MVS) techniques to reconstruct the three-dimensional (3D) structure of soybean canopies.
Reproduction assets foundThe article's Data availability statement explicitly says the code and data used in the study (soybean UAV point cloud phenotyping, SoyNet/SoyNet-Res models, yield/lodging analysis) are publicly downloadable from the authors' GitLab repository.Code · publicData availability
The code and data mentioned in the article can be downloaded from https://gitlab.com/zlyzly28/plant-phenomics .Open asset ↗gitlab.com/zlyzly28/plant-phenomicslines:588-659Code / dataset availability confirmedCrossref · Europe PMC · checked 6 Sept 2026
RiceRootMorphology / geometry measurementSegmentationRoot system architectureStress response / tolerance
Summary The dissection of genetic architecture for rice root system is largely dependent on phenotyping techniques, and high‐throughput root phenotyping poses a great challenge. In this study, we established a cost‐effective root phenotyping platform capable of analysing 1680 root samples within 2 h. To efficiently process a large number of root images, we developed the root phenotyping toolbox (RPT) with an enhanced SegFormer algorithm and used it for root segmentation and root phenotypic traits. Based on this root phenotyping platform and RPT, we screened 18 candidate (quantitative trait loci) QTL regions from 219 rice recombinant inbred lines under drought stress and validated the drought‐resistant functions of gene OsIAA8 identified from these QTL regions. This study confirmed that RPT exhibited a great application potential for processing images with various sources and for mining stress‐resistance genes of rice cultivars. Our developed root phenotyping platform and RPT software significantly improved high‐throughput root phenotyping efficiency, allowing for large‐scale root trait analysis, which will promote the genetic architecture improvement of drought‐resistant cultivars and crop breeding research in the future.
Why it matches plant phenotyping methods根系画像のセグメンテーションと形質定量を行う高スループット基盤およびRPTソフトウェアの開発が研究の中心であり、植物フェノタイピング手法に該当する。
abstractwe developed the root phenotyping toolbox (RPT) with an enhanced SegFormer algorithm and used it for root segmentation and root phenotypic traits.
Reproduction assets foundThe paper explicitly states that the RPT source code is publicly available on GitHub and the root training label images are available on Google Drive, both with explicit availability language and URLs matching allowed_urls entries.Code · publicThe source code for RPT can be downloaded from https://github.com/shijiawei124/RPT.gitOpen asset ↗https://github.com/shijiawei124/RPT.gitlines:210-444Code / dataset availability confirmedEurope PMC · checked 6 Sept 2026
CowpeaSoybeanRootMorphology / geometry measurementSegmentationRoot system architecture
A simple Python algorithm was used to estimate the four major root traits: total root length (TRL), surface area (SA), average diameter (AD), and root volume (RV) of legumes (adzuki bean, mung bean, cowpea, and soybean) based on two-dimensional images. Four different thresholding methods; Otsu, Gaussian adaptive, mean adaptive and triangle threshold were used to know the effect of thresholding in root trait estimation and to optimize the accuracy of root trait estimation. The results generated by the algorithm applied to 400 legume root images were compared with those generated by two separate software (WinRHIZO and RhizoVision), and the algorithm was validated using ground truth data. Distance transform method was used for estimating SA, AD, and RV and ConnectedComponentsWithStat function for TRL estimation. Among the thresholding methods, Otsu thresholding worked well for distance transform, while triangle threshold was effective for TRL. All the traits showed a high correlation with an R² ≥0.98 (p < 0.001) with the ground truth data. The root mean square error (RMSE) and mean bias error (MBE) were also minimal when comparing the algorithm-derived values to the ground truth values, with RMSE and MBE both < 10 for TRL, < 6 for SA, and < 0.5 for AD and RV. This lower value of error metrics indicates smaller differences between the algorithm-derived values and software-derived values. Although the observed error metrics were minimal for both software, the algorithm-derived root traits were closely aligned with those derived from WinRHIZO. We provided a simple Python algorithm for easy estimation of legume root traits where the images can be analyzed without any incurring expenses, and being open source; it can be modified by an expert based on their requirements.
Why it matches plant phenotyping methods根の二次元画像から主要形質を抽出するPythonアルゴリズムを開発し、既存ソフトウェアおよびグラウンドトゥルースで検証しており、植物フェノタイピング手法が研究の中心です。
abstractA simple Python algorithm was used to estimate the four major root traits: total root length (TRL), surface area (SA), average diameter (AD), and root volume (RV) of legumes
Reproduction assets foundThe authors publicly release their Python root-trait analysis source code together with the 400 legume root images and validation images on GitHub, as stated in the article text and Data availability statement. The Zenodo DOI cited for ground-truth images is a third-party dataset from Rose and Lobet (2018), i.e., citedCode · publicThe source code along with the root images and the validation images can be downloaded from ( https://github.com/AG9843/Legume-Root-Analysis.git ).Open asset ↗AG9843/Legume-Root-Analysislines:65-75Code / dataset availability confirmedEurope PMC · checked 14 Sept 2026
Turmeric, Curcuma longa, is an economically and medicinally important crop. However, the crop has often suffered from diseases such as rhizome disease roots, leaf blotch, and dry conditions of leaves. The control of these diseases essentially requires early and accurate diagnosis to reduce losses and help farmers adopt sustainable farming methods. The conventional methods of diagnosis involve a visual examination of symptoms, which is laborious, subjective, and rather impossible in large areas. This paper proposes a new dataset consisting of 1037 originals and 4628 augmented images of turmeric plants representing five classes: healthy leaf, dry leaf, leaf blotch, rhizome disease roots, and rhizome healthy roots. The dataset was pre-processed to enhance its applicability to deep learning applications by resizing, cleaning, and augmenting the data through flipping, rotation, and brightness adjustment. The turmeric plant disease classification was conducted using the Inception-v3 model, attaining an accuracy of 97.36% with data augmentation, compared to 95.71% without augmentation. Some of the major key performance metrics are precision, recall, and F1-score, which establish the efficacy and robustness of the model. This work attempts to show the potential of AI-aided solutions towards precision farming and sustainable crop production in developing agriculture disease management. The publicly available dataset and the results obtained are expected to attract more research interest for innovations in AI-driven agriculture .
Why it matches plant phenotyping methodsウコン植物の葉・根の病徴を画像から分類する公開データセットと解析手法を構築・評価しており、植物の病害状態を推定するフェノタイピング手法が中心である。
abstractThis paper proposes a new dataset consisting of 1037 originals and 4628 augmented images of turmeric plants representing five classes: healthy leaf, dry leaf, leaf blotch, rhizome disease roots, and rhizome healthy roots.
Reproduction assets foundThe paper's turmeric plant disease image dataset (1073 original + 4628 augmented images, five classes) is publicly deposited on Mendeley Data with an explicit DOI and direct URL. No separate analysis code repository is stated.Dataset · publicels in the early detection and effective management of diseases affecting turmeric plants to support sustainable agriculture.
Data source location
Town/City/Region: Charpolisha, Jamalpur
Country: Bangladesh .
Data accessibility
Repository name: Mendeley Data.
Data identification number: 10.17632/g46dvrcvwn.2
Direct URL to data: https://data.mendeley.com/datasets/g46dvrcvwn/2
Related research article
None .
1.
Value of the Data
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This dataset consists of several images regarding turmeric plant diseases, starting from the most prevalent to the rarest. Hence, it is quite valuable in terms of scientific research and agriculture. This will act as a stepping stone to further improve the plant pathOpen asset ↗Mendeley Data · 10.17632/g46dvrcvwn.2lines:1-46Code / dataset availability confirmedEurope PMC · checked 6 Sept 2026
The soybean-cyst nematode (SCN; Heterodera glycines) is one of the most destructive pests affecting soybean crops. Effective management of SCN is imperative for the sustainability of soybean agriculture. A promising approach to achieving this goal is the development and breeding of new resistant soybean varieties. Researchers and breeders typically employ exploratory methods such as Genome-Wide Association Studies or Quantitative Trait Loci mapping to identify genes linked to resistance. These methods depend on extensive phenotypic screening. The primary phenotypic measure for assessing SCN resistance is often the number of cysts that form on a plant's root system. Manual counting hundreds of cysts on a given root system is not only laborious but also subject to variability due to individual assessor differences. Additionally, while measuring cyst size could provide valuable insights due to its correlation with cyst development, this aspect is frequently overlooked because it demands even more hands-on work. To address these challenges, we have created Nemacounter, an intuitive software designed to detect, count, and measure the size of cysts autonomously. Nemacounter boasts a user-friendly graphical interface, simplifying the process for users to obtain reliable results. It enhances productivity by delivering annotated images and compiling data into csv files for easy analysis and reporting.
Why it matches plant phenotyping methodsダイズ根上の線虫シスト数とサイズという植物病害抵抗性関連形質を、画像から自動検出・計測するソフトウェアを開発しており、表現型取得手法が研究の中心です。
abstractwe have created Nemacounter, an intuitive software designed to detect, count, and measure the size of cysts autonomously.
Reproduction assets foundThe paper's SCN cyst phenotyping assets are publicly available: the authors' Nemacounter analysis software on GitHub, two annotated cyst image datasets on Roboflow (bounding-box and segmentation/area annotations), and the authors' trained YOLOv5-xl model (cystmodel.pt) on Iowa State's Box. The SAM weights and ultralyptCode · publicThe Nemacounter software can be downloaded here: https://github.com/DjampaKozlowski/NemaCounter and we provide an installation manual and utilization manual as supplementary data.Open asset ↗DjampaKozlowski/NemaCounterlines:65-70Dataset · publicThe complete dataset is accessible on the Roboflow website at: https://universe.roboflow.com/iowa-state-university-cwvqa/cystnewboundingboxv2Open asset ↗lines:118-138Dataset · publicAll training datasets are available on Roboflow website at : https://universe.roboflow.com/iowa-state-university-cwvqa/cystnewboundingboxv2 and https://universe.roboflow.com/iowa-state-university-cwvqa/cyst-detectors-area.Open asset ↗lines:139-197Code / dataset availability confirmedEurope PMC · checked 6 Sept 2026
Radopholus similis severely damages banana roots causing significant yield losses. Field screening for resistance is labor intensive and inconsistent due to environmental variation and mixed nematode populations. The screenhouse offers a controlled environment but is limited by the time needed for root development and variation in plant growth. We developed and validated a high-throughput in vitro method for phenotyping banana resistance to R. similis using sand-Murashige and Skoog (MS) media. Tissue culture plantlets grown in sterilized sand-MS were inoculated with 50 female R. similis after root development and nematodes extracted eight weeks after inoculation to calculate the reproduction factor (RF). Although RF values were higher for in vitro than in the screenhouse, accession responses showed similar trends under both conditions. The in vitro method was rapid, cost-effective with higher throughput, accelerating phenotyping and enabling rapid assessment of banana accessions for breeding programs. Some accessions responded differently to the two methods indicating that additional methods, such as root necrosis scores are important to confirm resistance. This study is the first in vitro-based demonstration of phenotyping for nematode resistance using modified sand-MS media with improved root development and pathogen interactions.
Why it matches plant phenotyping methodsバナナの線虫抵抗性という植物状態を評価する高スループットin vitroフェノタイピング法を開発・検証し、既存のスクリーンハウス法と比較しているため、方法が研究の中心である。
abstractWe developed and validated a high-throughput in vitro method for phenotyping banana resistance to R. similis using sand-Murashige and Skoog (MS) media.
Reproduction assets foundThe paper's Data Availability statement explicitly deposits the plant entry list, raw datasets, and generated/analyzed datasets (Extended data) for this banana R. similis resistance phenotyping study on Figshare under CC-BY 4.0, matching an allowed URL. No author analysis code was deposited.Dataset · publicThe list of all plant entries, raw datasets, and datasets generated during and/or analyzed during the current study (Extended data) referred to in the manuscript text as supplementary materials are publicly available in Figshare: High-throughput resistance phenotyping of banana ( Musa spp.) against Radopholus similis . https://doi.org/10.6084/m9.figshare.28787480.v3 . The dataset has a CC-BY 4.0 license applied.Open asset ↗Figshare · 10.6084/m9.figshare.28787480.v3lines:139-144Code / dataset availability confirmedOpenAlex · Crossref · Europe PMC · checked 14 Sept 2026
With the rapid advancement of plant phenotyping research, understanding plant genetic information and growth trends has become crucial. Measuring seedling length is a key criterion for assessing seed viability, but traditional ruler-based methods are time-consuming and labor-intensive. To address these limitations, we propose an efficient deep learning approach to enhance plant seedling phenotyping analysis. We improved the DeepLabv3+ model, naming it DFMA, and introduced a novel ASPP structure, PSPA-ASPP. On our self-constructed rice seedling dataset, the model achieved a mean Intersection over Union (mIoU) of 81.72%. On publicly available datasets, including Arabidopsis thaliana, Brachypodium distachyon, and Sinapis alba, detection scores reached 87.69%, 91.07%, and 66.44%, respectively, outperforming existing models. The model generates detailed segmentation masks, capturing structures such as the embryonic shoot, axis, and root, while a seedling length measurement algorithm provides precise parameters for component development. This approach offers a comprehensive, automated solution, improving phenotyping analysis efficiency and addressing the challenges of traditional methods.
Why it matches plant phenotyping methods幼苗画像から器官を分割し、幼苗長を自動測定する深層学習ベースのフェノタイピング手法を開発・検証しており、方法が中心的です。
abstractwe propose an efficient deep learning approach to enhance plant seedling phenotyping analysis.
Reproduction assets foundThe paper uses a public Kaggle plant segmentation dataset (Arabidopsis thaliana, Brachypodium distachyon, Sinapis alba) for model validation, which is a paper-specific, publicly actionable asset. The authors' homemade rice seedling dataset and any code/models are not publicly deposited; the data availability statement仅Dataset · publicthis way, a homemade labeled dataset with the file suffix “.json” was obtained. Processed by the program, 115 sets of images were finally obtained. The sample image is shown in
Figure 1B
.
The public dataset was created using the Plant Segmentation Dataset, which was made public on the Kaggle platform by Orsolya Dobos et al. ( https://www.kaggle.com/tivadardanka/plant-segmentation ) in 2019. This dataset contains images of three seedlings, including Arabidopsis thaliana , Brachypodium distachyon , and Sinapis alba . The authors manually placed seedlings of these three plants on the surface of 1% agar plates and collected images using an EPSON PERFECTION V30 scanner. Images were saved in “.tiOpen asset ↗Kaggle · tivadardanka/plant-segmentationlines:45-67Code / dataset availability confirmedCrossref · checked 6 Sept 2026
ArabidopsisMicroscopyCell / cellular structureRootTissueVisualization / data management
Abstract Super-resolution methods provide far better spatial resolution than the optical diffraction limit of about half the wavelength of light (∼200–300 nm). Nevertheless, they have yet to attain widespread use in plants, largely due to plants' challenging optical properties. Expansion microscopy (ExM) improves effective resolution by isotropically increasing the physical distances between sample structures while preserving relative spatial arrangements and clearing the sample. However, its application to plants has been hindered by the rigid, mechanically cohesive structure of plant tissues. Here, we report on whole-mount ExM of thale cress (Arabidopsis thaliana) root tissues (PlantEx), achieving a 4-fold resolution increase over conventional microscopy. Our results highlight the microtubule cytoskeleton organization and interaction between molecularly defined cellular constituents. Combining PlantEx with stimulated emission depletion microscopy, we increase nanoscale resolution and visualize the complex organization of subcellular organelles from intact tissues by example of the densely packed COPI-coated vesicles associated with the Golgi apparatus and put these into a cellular structural context. Our results show that ExM can be applied to increase effective imaging resolution in Arabidopsis root specimens.
Why it matches plant phenotyping methods植物組織に適用可能な超解像イメージング手法を開発し、Arabidopsis根で解像度向上を実証しており、画像取得法が研究の中心である。
abstractHere, we report on whole-mount ExM of thale cress (Arabidopsis thaliana) root tissues (PlantEx), achieving a 4-fold resolution increase over conventional microscopy.
Reproduction assets foundThe paper's PlantEx expansion microscopy imaging data are deposited in ISTA's public repository, and the authors' custom analysis code (including the BigWarp-based expansion-factor script) is publicly available on GitHub. The Click-ExM repository is cited prior work whose method was adapted, not a paper-specific asset.Dataset · publicThe data that support the findings of this study are available via ISTA's data repository at https://doi.org/10.15479/AT:ISTA:18837 .Open asset ↗ISTA's data repository · 10.15479/AT:ISTA:18837lines:219-252Code · publicThe custom-written code used and described in this manuscript is available via Github ( https://github.com/danzllab/PlantEx ).Open asset ↗github.com/danzllab/PlantExlines:219-252Code · publicThe expansion factor was extracted as the linear scaling factor of the similarity transformation minimizing squared landmark residuals using the script https://github.com/danzllab/CATS/tree/master/rcats_image-analysis/bigwarp .Open asset ↗github.com/danzllab/CATSlines:154-159Code / dataset availability confirmedEurope PMC · checked 6 Sept 2026
Hormonal mechanisms associated with cell elongation play a vital role in the development and growth of plants. Here, we report Nextflow-root (nf-root), a novel best-practice pipeline for deep-learning-based analysis of fluorescence microscopy images of plant root tissue from A. thaliana. This bioinformatics pipeline performs automatic identification of developmental zones in root tissue images. This also includes apoplastic pH measurements, which is useful for modeling hormone signaling and cell physiological responses. We show that this nf-core standard-based pipeline successfully automates tissue zone segmentation and is both high-throughput and highly reproducible. In short, a deep-learning module deploys deterministically trained convolutional neural network models and augments the segmentation predictions with measures of prediction uncertainty and model interpretability, while aiming to facilitate result interpretation and verification by experienced plant biologists. We observed a high statistical similarity between the manually generated results and the output of the nf-root.
Why it matches plant phenotyping methods植物根組織の発達ゾーンを画像から自動抽出し、アポプラストpHを測定する再現可能な深層学習パイプラインを開発・検証しており、植物表現型取得が中心である。
abstractThis bioinformatics pipeline performs automatic identification of developmental zones in root tissue images.
Reproduction assets foundThe paper publicly releases the PHDFM fluorescence microscopy image dataset, a test dataset, the trained U-Net^2 segmentation model, the nf-root Nextflow pipeline, the segmentation training module, and the prediction package implementing uncertainty/interpretability, all with explicit availability statements and ZenodoDataset · publicThe PHDFM dataset is available at https://zenodo.org/record/5841376/ .Open asset ↗zenodo · 5841376lines:127-159Dataset · publicthe test dataset for the pipeline ( https://zenodo.org/record/5949352/ ) are publicly available online.Open asset ↗zenodo · 5949352lines:127-159Code · publicsoftware and hardware information are also available in the module ( https://github.com/qbic-pipelines/root-tissue-segmentation-core ). We used version 1.0.1 of the segmentation training module.Open asset ↗github · qbic-pipelines/root-tissue-segmentation-corelines:106-126Code / dataset availability confirmedCrossref · Europe PMC · checked 6 Sept 2026
Introduction Cotton, being a crucial cash crop globally, faces significant challenges due to multiple diseases that adversely affect its quality and yield. To identify such diseases is very important for the implementation of effective management strategies for sustainable agriculture. Image recognition plays an important role for the timely and accurate identification of diseases in cotton plants as it allows farmers to implement effective interventions and optimize resource allocation. Additionally, deep learning has begun as a powerful technique for to detect diseases in crops using images. Hence, the significance of this work lies in its potential to mitigate the impact of these diseases, which cause significant damage to the cotton and decrease fibre quality and promote sustainable agricultural practices. Methods This paper investigates the role of deep transfer learning techniques such as EfficientNet models, Xception, ResNet models, Inception, VGG, DenseNet, MobileNet, and InceptionResNet for cotton plant disease detection. A complete dataset of infected cotton plants having diseases like Bacterial Blight, Target Spot, Powdery Mildew, Aphids, and Army Worm along with the healthy ones is used. After pre-processing the images of the dataset, their region of interest is obtained by applying feature extraction techniques such as the generation of the biggest contour, identification of extreme points, cropping of relevant regions, and segmenting the objects using adaptive thresholding. Results and Discussion During experimentation, it is found that the EfficientNetB3 model outperforms in accuracy, loss, as well as root mean square error by obtaining 99.96%, 0.149, and 0.386 respectively. However, other models also show the good performance in terms of precision, recall, and F1 score, with high scores close to 0.98 or 1.00, except for VGG19. The findings of the paper emphasize the prospective of deep transfer learning as a viable technique for cotton plant disease diagnosis by providing a cost-effective and efficient solution for crop disease monitoring and management. This strategy can also help to improve agricultural practices by ensuring sustainable cotton farming and increased crop output.
Why it matches plant phenotyping methods綿花の画像から植物病害状態を推定する深層学習手法を比較・評価しており、病害フェノタイピング手法が中心である。
abstractThis paper investigates the role of deep transfer learning techniques such as EfficientNet models, Xception, ResNet models, Inception, VGG, DenseNet, MobileNet, and InceptionResNet for cotton plant disease detection.
Reproduction assets foundThe paper analyzed a public Kaggle cotton plant disease image dataset, explicitly linked in its data availability statement. No author code or trained models are shared.Dataset · publicPublicly available datasets were analyzed in this study. This data can be found here: https://www.kaggle.com/datasets/dhamur/cotton-plant-disease .Open asset ↗Kaggle · dhamur/cotton-plant-diseaselines:1296-1311Code / dataset availability confirmedOpenAlex · checked 6 Sept 2026
Dissecting the drought resistance (DR) mechanism and designing drought-resistant rice varieties are promising strategies to address the challenge of climate change. Here, we selected a typical drought-avoidant (DA) variety IRAT109 and drought-tolerant (DT) variety Hanhui15 as the parents to develop a stable recombinant inbred line (RIL) population (F 8 , 1,262 lines). The de novo assembled genomes of both parents were released. Through re-sequencing of the RIL population, a set of 1,189,216 reliable SNPs were obtained and used for constructing a dense genetic map. Using both aboveground and underground phenomic platforms and multimodal cameras, we captured 139,040 image-based traits (i-traits) of whole plant’s phenotypes in response to drought stress throughout entire rice growth period and identified 32,586 drought-responsive quantitative trait loci (QTLs) including 2,097 unique QTLs. The QTLs related to panicle i-traits occurred on the middle of chromosome 8 over 600 times, while the QTLs related to leaf i-traits on the 5’ end of chromosome 3 over 800 times, indicating potential effect of these QTLs on plant phenotypes. We chose three candidate genes ( OsMADS50, OsGhd8, OsSAUR11 ) related to leaf, panicle, and root traits respectively and verified their functions in resisting drought. Gene OsMADS50 was found to negatively regulate DR by modulating leaf dehydration, grain size, and root downward growth. Furthermore, a total of 18 and 21 composite QTLs significantly related to grain weight and plant biomass were screened from 597 lines in RIL population under drought conditions in field experiments, and composite QTL region was highly overlapped (76.9%) with known DR gene region. Based on three candidate DR genes, we proposed the haplotype design suitable for different environments and breeding objectives. This study provides a valuable reference for multi-modal and time-series phenomic analyses, deciphers the genetic mechanism of DA and DT rice varieties, and offers a molecular navigation map for breeding DR variety.
Why it matches plant phenotyping methods地下・地上フェノミックプラットフォームとマルチモーダルカメラで全生育期間の画像形質を大量取得しており、フェノタイピング手法の適用と技術的ワークフローが研究の中核です。
abstractUsing both aboveground and underground phenomic platforms and multimodal cameras, we captured 139,040 image-based traits (i-traits) of whole plant’s phenotypes in response to drought stress throughout entire rice growth period
Reproduction assets foundThe paper's phenome data (aboveground and belowground rice images/i-traits) and the authors' data-handling code and deep-learning model are explicitly deposited at public URLs listed in the Data Availability Statement. Genome data (riceome.hzau.edu.cn) is molecular omics and excluded.Code · publicAll the phenome data and core data-handling code have been deposited online.Open asset ↗lines:140-175Code / dataset availability confirmedbioRxiv · Europe PMC · checked 15 Sept 2026
Field / plotLaboratory / benchtopX-ray / CTRootWhole plant / canopy / plot / field
Soil compaction and escalating global drought increase soil strength and stiffness. It remains unclear which plant root biomechanical mechanisms/traits enable growth in these harsh conditions. Here, we combine synchrotron X-ray computed tomography with spatially resolved X-ray diffraction to characterize the biomechanics of a replica root-soil system. We map the strain field around the root tip, finding strong agreement with finite element simulations, thereby demonstrating a promising new in-vivo measurement protocol.
Why it matches plant phenotyping methods根周辺のひずみ場という根の力学的形質を、X線CT・回折と有限要素解析で測定・検証する新規プロトコルが研究の中心である。
abstractWe map the strain field around the root tip, finding strong agreement with finite element simulations, thereby demonstrating a promising new in-vivo measurement protocol.
Reproduction assets foundThe paper deposits its X-ray diffraction and X-ray imaging (XCT) measurements in the Southampton Pure repository (DOI 10.5258/SOTON/D3309.274) and its processing scripts in a companion deposit (DOI 10.5258/SOTON/D3309.276), both with explicit availability statements and public URLs.Dataset · public∇uT ), F(σ′) > 0,x ∈ Ω
σ′ = Cep
: (∇u+∇uT ), F(σ′) = 0,x ∈ Ω
u·ê1 = 0, x ∈ ΓAxis
u = 0, x ∈ ΓC,Top
u = [0,wstep]T , x ∈ Γbot ∪Γout
n̂·∇u = 0, x ∈ Γtop ∪ΓC,tip
n̂·σ = ppen, x ∈ (Γtop ∩Ω∩Ωc)∪(ΓC,tip ∩Ω∩Ωc)
. (29)
Data Records
273
All X-ray diffraction and X-ray imaging data used in this study can be found in the Pure repository: https://doi.org/10.5258/SOTON/D3309.274
Code availability
275
All scripts used to process the data can be found in the Pure repository: https://doi.org/10.5258/SOTON/D3309.276
References
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1. Lee, H. et al. Ipcc, 2023: Climate change 2023: Synthesis report, summary for policymakers. contribution of working
278
groups i, ii and iii to the sixth assessment report ofOpen asset ↗Pure repository · 10.5258/SOTON/D3309.274pdf-raw-page:9 lines:1-96Code · publicu = 0, x ∈ Γtop ∪ΓC,tip
n̂·σ = ppen, x ∈ (Γtop ∩Ω∩Ωc)∪(ΓC,tip ∩Ω∩Ωc)
. (29)
Data Records
273
All X-ray diffraction and X-ray imaging data used in this study can be found in the Pure repository: https://doi.org/10.5258/SOTON/D3309.274
Code availability
275
All scripts used to process the data can be found in the Pure repository: https://doi.org/10.5258/SOTON/D3309.276
References
277
1. Lee, H. et al. Ipcc, 2023: Climate change 2023: Synthesis report, summary for policymakers. contribution of working
278
groups i, ii and iii to the sixth assessment report of the intergovernmental panel on climate change [core writing team, h.
279
lee and j. romero (eds.)]. ipcc, geneva, switzerland. (2023).
2Open asset ↗Pure repository · 10.5258/SOTON/D3309.276pdf-raw-page:9 lines:1-96Code / dataset availability confirmedCrossref · Europe PMC · checked 6 Sept 2026
SoybeanX-ray / CTRootMorphology / geometry measurementRoot system architecture
Typically, root system architecture (RSA) is not visible, and realistically, high-throughput methods for RSA trait phenotyping should capture key features of developing root systems in solid substrates in 3D. In a published 2-D study using thin rhizoboxes, vermiculite as a growing medium, and photography for imaging, triplicates of 137 soybean cultivars were phenotyped for their RSA. In the transition to 3-D work using X-ray computed tomography (CT) scanning and mineral soil, two research questions are addressed: (1) how different is the soybean RSA characterization between the two phenotyping systems; and (2) is a direct comparison of the results reliable? Prior to a full-scale study in 3D, we grew, in pots filled with sand, triplicates of the Casino and OAC Woodstock cultivars that had shown the most contrasting RSAs in the 2-D study, and CT scanned them at the V1 vegetative stage of development of the shoots. Differences between soybean cultivars in RSA traits, such as total root length and fractal dimension (FD), observed in 2D, can change in 3D. In particular, in 2D, the mean FD values are 1.48 ± 0.16 (OAC Woodstock) vs. 1.31 ± 0.16 (Casino), whereas in 3D, they are 1.52 ± 0.14 (OAC Woodstock) vs. 1.24 ± 0.13 (Casino), indicating variations in RSA complexity.
Why it matches plant phenotyping methods2D写真法と3D X線CT法という根系表現型計測システムを比較・評価し、RSA形質の測定結果の信頼性を検討しているため、フェノタイピング手法が中心である。
abstracthigh-throughput methods for RSA trait phenotyping should capture key features of developing root systems in solid substrates in 3D
Reproduction assets foundThe paper's own supplement (MDPI S1) contains two videos produced in MATLAB from skeletal 3-D images of the root systems reconstructed from this study's CT scanning data — paper-specific phenotyping outputs made publicly available. The figshare links are explicitly described as 'soybean genomic data' from the prior GWASupplement · publicTwo videos (.AVI files), one per soybean cultivar, were produced in MATLAB (MathWorks, Natick, MA, USA) from skeletal 3-D images of the root systems, and are made available as a supplement to the graphical results presented for the 3-D phenotyping system in Figure 2 in the manuscript.Open asset ↗lines:100-116Code / dataset availability confirmedEurope PMC · checked 7 Sept 2026
Multispectral / hyperspectralRootClassificationRoot system architecture
Background Understanding the environmental impacts on root growth and root health is essential for effective agricultural and environmental management. Hyperspectral imaging (HSI) technology provides a non-destructive method for detailed analysis and monitoring of plant tissues and organ development, but unfortunately examples for its application to root systems and the root-soil interface are very scarce. There is also a notable lack of standardized guidelines for image acquisition and data analysis pipelines. Methods This study investigated HSI techniques for analyzing rhizobox-grown root systems across various imaging configurations, from the macro- to micro-scale, using the imec VNIR SNAPSCAN camera. Focusing on three graminoid species with different root architectures allowed us to evaluate the influence of key image acquisition parameters and data processing techniques on the differentiation of root, soil, and root-soil interface/rhizosheath spectral signatures. We compared two image classification methods, Spectral Angle Mapper (SAM) and K-Means clustering, and two machine learning approaches, Random Forest (RF) and Support Vector Machine (SVM), to assess their efficiency in automating root system image classification. Results Our study demonstrated that training a RF model using SAM classifications, coupled with wavelength reduction using the second derivative spectra with Savitzky-Golay (SG) smoothing, provided reliable classification between root, soil, and the root-soil interface, achieving 88-91% accuracy across all configurations and scales. Although the root-soil interface was not clearly resolved, it helped to improve the distinction between root and soil classes. This approach effectively highlighted spectral differences resulting from the different configurations, image acquisition settings, and among the three species. Utilizing this classification method can facilitate the monitoring of root biomass and future work investigating root adaptations to harsh environmental conditions. Conclusions Our study addressed the key challenges in HSI acquisition and data processing for root system analysis and lays the groundwork for further exploration of VNIR HSI application across various scales of root system studies. This work provides a full data analysis pipeline that can be utilized as an online Python-based tool for the semi-automated analysis of root-soil HSI data.
Why it matches plant phenotyping methods根系のハイパースペクトル画像取得・分類パイプラインを開発し、取得条件、分類法、機械学習手法を比較検証しているため、植物フェノタイピング手法が中心である。
abstractThis study investigated HSI techniques for analyzing rhizobox-grown root systems across various imaging configurations, from the macro- to micro-scale
Reproduction assets foundThe authors explicitly state that the Python scripts for the paper's HSI root-soil classification pipeline are publicly available on GitHub. No phenotype dataset or image deposit is stated.Code · publicThe scripts for data analysis are available from https://github.com/corinef/Automated-root-classification .Open asset ↗corinef/Automated-root-classificationlines:156-180Code / dataset availability confirmedEurope PMC · Crossref · checked 14 Sept 2026
Plant cover and biochemical composition are essential parameters for evaluating cover crop management. Destructive sampling or estimates with aerial imagery require substantial labor, time, expertise, or instrumentation cost. Using low-cost consumer and mobile phone cameras to estimate plant canopy coverage and biochemical composition could broaden the use of high-throughput technologies in research and crop management. Here, we estimated canopy development, tissue nitrogen, and biomass of medium red clover (Trifolium pratense L.), a perennial forage legume and common cover crop, using red-green-blue (RGB) indices collected with standard settings in non-standardized field conditions. Pixels were classified as plant or background using combinations of four RGB indices with both unsupervised machine learning and preset thresholds. The excess green minus red (ExGR) index with a preset threshold of zero was the best index and threshold combination. It correctly identified pixels as plant or background 86.25% of the time. This combination also provided accurate estimates of crop growth and quality: Canopy coverage correlated with red clover biomass (R² = 0.554, root mean square error [RMSE] = 219.29 kg ha⁻¹), and ExGR index values of vegetation pixels were highly correlated with clover nitrogen content (R² = 0.573, RMSE = 3.5 g kg⁻¹) and carbon:nitrogen ratio (R² = 0.574, RMSE = 1.29 g g⁻¹). Data collection were simple to implement and stable across imaging conditions. Pending testing across different sensors, sites, and crop species, this method contributes to a growing and open set of decision support tools for agricultural research and management.
Why it matches plant phenotyping methods低コストRGB画像と画素分類を用いて、植物被覆、バイオマス、窒素含量、C:N比を推定する手法を開発・評価しており、表現型取得が研究の中心です。
abstractUsing low-cost consumer and mobile phone cameras to estimate plant canopy coverage and biochemical composition could broaden the use of high-throughput technologies in research and crop management.
Reproduction assets foundThe paper's authors state that all referenced analysis scripts for the RGB vegetation index processing, thresholding, and canopy cover estimation are publicly available on GitHub. The phenotype/trait data (images, biomass, N, C:N measurements) are deposited at a U of M repository (hdl.handle.net/11299/263900), but thatCode · publicreferenced scripts are available at https://github.com/RTGS- of nitrogen dictated by biomass and nitrogen content, and theOpen asset ↗pdf-page:4 lines:1-49Code / dataset availability confirmedOpenAlex · checked 15 Sept 2026
Studies on the phenotypic traits and their associations in Chinese cabbage lack precise and objective digital evaluation metrics. Traditional assessment methods often rely on subjective evaluations and experience, compromising accuracy and reliability. This study develops an innovative, comprehensive trait evaluation method based on 3D point cloud technology, with the aim of enhancing the precision, reliability, and standardization of the comprehensive phenotypic traits of Chinese cabbage. By using multi-view image sequences and structure-from-motion algorithms, 3D point clouds of 50 plants from each of the 17 Chinese cabbage varieties were reconstructed. Color-based region growing and 3D convex hull techniques were employed to measure 30 agronomic traits. Comparisons between 3D point cloud-based measurements of the plant spread, plant height, leaf area, and leaf ball volume and traditional methods yielded R2 values greater than 0.97, with root mean square errors of 1.27 cm, 1.16 cm, 839.77 cm3, and 59.15 cm2, respectively. Based on the plant spread and plant height, a linear regression prediction of Chinese cabbage weights was conducted, yielding an R2 value of 0.76. Integrated optimization algorithms were used to test the parameters, reducing the measurement time from 55 min when using traditional methods to 3.2 min. Furthermore, in-depth analyses including variation, correlation, principal component analysis, and clustering analyses were conducted. Variation analysis revealed significant trait variability, with correlation analysis indicating 21 pairs of traits with highly significant positive correlations and 2 pairs with highly significant negative correlations. The top six principal components accounted for 90% of the total variance. Using the elbow method, k-means clustering determined that the optimal number of clusters was four, thus classifying the 17 cabbage varieties into four distinct groups. This study provides new theoretical and methodological insights for exploring phenotypic trait associations in Chinese cabbage and facilitates the breeding and identification of high-quality varieties. Compared with traditional methods, this system provides significant advantages in terms of accuracy, speed, and comprehensiveness, with its low cost and ease of use making it an ideal replacement for manual methods, being particularly suited for large-scale monitoring and high-throughput phenotyping.
Why it matches plant phenotyping methods中国白菜の表現型を3D点群から抽出する測定法を開発し、従来法との精度比較・検証および高速化を行っており、植物表現型測定が研究の中心である。
abstractThis study develops an innovative, comprehensive trait evaluation method based on 3D point cloud technology
Reproduction assets foundThe paper's phenotyping analysis code is explicitly deposited on a public GitHub repository with an authors' URL. The phenotype/trait measurement data themselves are only available upon request, so they do not qualify as a public asset.Code · publicapproach significantly streamlines the process, saving time and
enhancing efficiency by automating tasks which previously required extensive manual ef-
fort, thereby ensuring a more systematic and reliable method of phenotypic information
detection. The code used in this study can be accessed at the following GitHub repository:
https://github.com/chongchong123123/code (accessed on 18 October 2024).
2.4. Accuracy Analysis of Agronomic Parameter Measurements
In the course of agronomic trait measurement research, we utilized point cloud tech-
nology to measure key agronomic traits, including the plant height, plant spread, various
leaf dimensions (leaf length and leaf width), the width and thicOpen asset ↗chongchong123123/codepdf-raw-page:8 lines:1-62Code / dataset availability confirmedEurope PMC · checked 14 Sept 2026
PoplarField / plotRootCalibration / preprocessingRoot system architecture
Roots are important in agricultural and natural systems for determining plant productivity and soil carbon inputs. Sometimes, the amount of roots in a sample is too much to fit into a single scanned image, so the sample is divided among several scans, and there is no standard method to aggregate the data. Here, we describe and validate two methods for standardizing measurements across multiple scans: image concatenation and statistical aggregation. We developed a Python script that identifies which images belong to the same sample and returns a single, larger concatenated image. These concatenated images and the original images were processed with RhizoVision Explorer, a free and open-source software. An R script was developed, which identifies rows of data belonging to the same sample and applies correct statistical methods to return a single data row for each sample. These two methods were compared using example images from switchgrass, poplar, and various tree and ericaceous shrub species from a northern peatland and the Arctic. Most root measurements were nearly identical between the two methods except median diameter, which cannot be accurately computed by statistical aggregation. We believe the availability of these methods will be useful to the root biology community.
Why it matches plant phenotyping methods複数の根スキャン画像から根形質を統合する画像連結・統計集約法を開発し、比較検証した研究であり、根形質取得ワークフローが中心です。
abstractHere, we describe and validate two methods for standardizing measurements across multiple scans: image concatenation and statistical aggregation.
Reproduction assets foundThe paper's Data availability statement deposits the root scan imageset, the Python image-concatenation script, and the R statistical-aggregation/figure code on Zenodo with explicit DOIs, making the paper-specific phenotyping images and analysis code publicly actionable. Since the Zenodo deposit URLs are not among the,Dataset · publicThe imageset is available at doi: 10.5281/zenodo.12667583Open asset ↗Zenodo · 10.5281/zenodo.12667583pdf-raw-page:7 lines:1-85Code · publicthe R code for statistical aggregation along
with the figures and statistics presented here are available at doi:
10.5281/zenodo.12668177Open asset ↗Zenodo · 10.5281/zenodo.12668177pdf-raw-page:7 lines:1-85Code / dataset availability confirmedEurope PMC · checked 15 Sept 2026
Background Transcriptome-based prediction of complex phenotypes is a relatively new statistical method that links genetic variation to phenotypic variation. The selection of large-effect genes based on a priori biological knowledge is beneficial for predicting oligogenic traits; however, such a simple gene selection method is not applicable to polygenic traits because causal genes or large-effect loci are often unknown. Here, we used several gene-level features and tested whether it was possible to select a gene subset that resulted in better predictive ability than using all genes for predicting a polygenic trait. Results Using the phenotypic values of shoot and root traits and transcript abundances in leaves and roots of 57 rice accessions, we evaluated the predictive abilities of the transcriptome-based prediction models. Leaf transcripts predicted shoot phenotypes, such as plant height, more accurately than root transcripts, whereas root transcripts predicted root phenotypes, such as crown root length, more accurately than leaf transcripts. Furthermore, we used the following three features to train the prediction model: (1) tissue specificity of the transcripts, (2) ontology annotations, and (3) co-expression modules for selecting gene subsets. Although models trained by a gene subset often resulted in lower predictive abilities than the model trained by all genes, some gene subsets showed improved predictive ability. For example, using genes expressed in roots but not in leaves, the predictive ability for crown root diameter was improved by more than 10% (R 2 = 0.59 when using all genes; R 2 = 0.66, using 1,554 root-specifically expressed genes). Similarly, genes annotated as "gibberellic acid sensitivity" showed higher predictive ability than using all genes for root dry weight. Conclusions Our results highlight both the possibility and difficulty of selecting an appropriate gene subset to predict polygenic traits from transcript abundance, given the current biological knowledge and information. Further integration of multiple sources of information, as well as improvements in gene characterization, may enable the selection of an optimal gene set for the prediction of polygenic phenotypes.
Why it matches plant phenotyping methods遺伝子発現データから植物の複合形質を予測する統計的手法を開発・評価しており、形質予測モデルの性能比較が研究の中心である。
abstractTranscriptome-based prediction of complex phenotypes is a relatively new statistical method that links genetic variation to phenotypic variation.
Reproduction assets foundThe authors state that all analysis code for the transcriptome-based prediction study is publicly available on Figshare, which is a paper-specific, publicly actionable code asset. Phenotype data are only in a prior study's supplementary file and transcriptome data in GEO (GSE162313), which are cited prior deposits, notCode · publicw sequence data were deposited in the DNA Data Bank of Japan Sequence Read Archive in a previous study [27]. Transcriptome data are available from the Gene Expression Omnibus ( GSE162313 ) and phenotype data are available in the supplementary file of a previous study [28]. All codes for the data analysis are shown in Figshare ( https://doi.org/10.6084/m9.figshare.26067532.v1 ).
Declarations
Ethics approval and consent to participate
Not applicable.
Consent for publication
Not applicable.
Competing interests
The authors declare no competing interests.
Abbreviations
WRCOpen asset ↗Figshare · 10.6084/m9.figshare.26067532.v1lines:370-396Code / dataset availability confirmedEurope PMC · checked 7 Sept 2026
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
Alfalfa / lucerneRootClassificationRoot system architecture
Background: Root system architecture (RSA) is of growing interest in implementing plant improvements with belowground root traits. Modern computing technology applied to images offers new pathways forward to plant trait improvements and selection through RSA analysis (using images to discern/classify root types and traits). However, a major stumbling block to image-based RSA phenotyping is image label noise, which reduces the accuracies of models that take images as direct inputs. To address the label noise problem, this study utilized an artificial intelligence model capable of classifying the RSA of alfalfa ( Medicago sativa L.) directly from images and coupled it with downstream label improvement methods. Images were compared with different model outputs with manual root classifications, and confident machine learning (CL) and reactive machine learning (RL) methods were tested to minimize the effects of subjective labeling to improve labeling and prediction accuracies. Results: The CL algorithm modestly improved the Random Forest model's overall prediction accuracy of the Minnesota dataset (1%) while larger gains in accuracy were observed with the ResNet-18 model results. The ResNet-18 cross-population prediction accuracy was improved (~8% to 13%) with CL compared to the original/preprocessed datasets. Training and testing data combinations with the highest accuracies (86%) resulted from the CL- and/or RL-corrected datasets for predicting taproot RSAs. Similarly, the highest accuracies achieved for the intermediate RSA class resulted from corrected data combinations. The highest overall accuracy (~75%) using the ResNet-18 model involved CL on a pooled dataset containing images from both sample locations. Conclusions: ResNet-18 DNN prediction accuracies of alfalfa RSA image labels are increased when CL and RL are employed. By increasing the dataset to reduce overfitting while concurrently finding and correcting image label errors, it is demonstrated here that accuracy increases by as much as ~11% to 13% can be achieved with semi-automated, computer-assisted preprocessing and data cleaning (CL/RL).
Why it matches plant phenotyping methodsアルファルファ根系構造を画像から分類するResNet-18と、ラベルノイズを補正する機械学習手法を開発・評価しており、植物形質取得ワークフローが中心である。
abstracta major stumbling block to image-based RSA phenotyping is image label noise
Reproduction assets foundThe paper's Data Availability statement deposits two paper-specific public assets on Zenodo: the Minnesota alfalfa root crown images (with tags removed and RootPainter-segmented images) and the Oklahoma root crown images together with the R statistical analysis code generated in this study. Both are directly usable, soDataset · publicThe original images (dataset 1 from USDA-ARS at St Paul, MN) with tags removed and segmented images from RootPainter for data analysis are available on Zenodo ( https://doi.org/10.5281/zenodo.5879778 ).Open asset ↗Zenodo · 10.5281/zenodo.5879778lines:341-365Dataset · publicDataset 2 from Oklahoma: Root crown images and R statistical analysis code generated from this study are available on Zenodo ( https://doi.org/10.5281/zenodo.2172832 ).Open asset ↗Zenodo · 10.5281/zenodo.2172832lines:341-365Code / dataset availability confirmedarXiv · OpenAlex · checked 13 Sept 2026
Potato yield is an important metric for farmers to further optimize their cultivation practices. Potato yield can be estimated on a harvester using an RGB-D camera that can estimate the three-dimensional (3D) volume of individual potato tubers. A challenge, however, is that the 3D shape derived from RGB-D images is only partially completed, underestimating the actual volume. To address this issue, we developed a 3D shape completion network, called CoRe++, which can complete the 3D shape from RGB-D images. CoRe++ is a deep learning network that consists of a convolutional encoder and a decoder. The encoder compresses RGB-D images into latent vectors that are used by the decoder to complete the 3D shape using the deep signed distance field network (DeepSDF). To evaluate our CoRe++ network, we collected partial and complete 3D point clouds of 339 potato tubers on an operational harvester in Japan. On the 1425 RGB-D images in the test set (representing 51 unique potato tubers), our network achieved a completion accuracy of 2.8 mm on average. For volumetric estimation, the root mean squared error (RMSE) was 22.6 ml, and this was better than the RMSE of the linear regression (31.1 ml) and the base model (36.9 ml). We found that the RMSE can be further reduced to 18.2 ml when performing the 3D shape completion in the center of the RGB-D image. With an average 3D shape completion time of 10 milliseconds per tuber, we can conclude that CoRe++ is both fast and accurate enough to be implemented on an operational harvester for high-throughput potato yield estimation. CoRe++'s high-throughput and accurate processing allows it to be applied to other tuber, fruit and vegetable crops, thereby enabling versatile, accurate and real-time yield monitoring in precision agriculture. Our code, network weights and dataset are publicly available at https://github.com/UTokyo-FieldPhenomics-Lab/corepp.git.
Why it matches plant phenotyping methodsRGB-D画像からジャガイモ塊茎の3D形状を補完し、体積・収量を推定する手法を開発・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。
abstractwe developed a 3D shape completion network, called CoRe++, which can complete the 3D shape from RGB-D images.
Reproduction assets foundThe paper's abstract explicitly states that the authors' code, network weights, and the potato tuber RGB-D/3D point cloud dataset are publicly available at the authors' GitHub repository (UTokyo-FieldPhenomics-Lab/corepp), which is a paper-specific, public, actionable asset for the CoRe++ phenotyping analysis.Code · publicOur code, network weights and dataset are publicly available at https://github.com/UTokyo-FieldPhenomics-Lab/corepp.git .Open asset ↗UTokyo-FieldPhenomics-Lab/corepplines:1-93Code / dataset availability confirmedEurope PMC · checked 7 Sept 2026
Capitalizing on the widespread adoption of smartphones among farmers and the application of artificial intelligence in computer vision, a variety of mobile applications have recently emerged in the agricultural domain. This paper introduces GranoScan, a freely available mobile app accessible on major online platforms, specifically designed for the real-time detection and identification of over 80 threats affecting wheat in the Mediterranean region. Developed through a co-design methodology involving direct collaboration with Italian farmers, this participatory approach resulted in an app featuring: (i) a graphical interface optimized for diverse in-field lighting conditions, (ii) a user-friendly interface allowing swift selection from a predefined menu, (iii) operability even in low or no connectivity, (iv) a straightforward operational guide, and (v) the ability to specify an area of interest in the photo for targeted threat identification. Underpinning GranoScan is a deep learning architecture named efficient minimal adaptive ensembling that was used to obtain accurate and robust artificial intelligence models. The method is based on an ensembling strategy that uses as core models two instances of the EfficientNet-b0 architecture, selected through the weighted F1-score. In this phase a very good precision is reached with peaks of 100% for pests, as well as in leaf damage and root disease tasks, and in some classes of spike and stem disease tasks. For weeds in the post-germination phase, the precision values range between 80% and 100%, while 100% is reached in all the classes for pre-flowering weeds, except one. Regarding recognition accuracy towards end-users in-field photos, GranoScan achieved good performances, with a mean accuracy of 77% and 95% for leaf diseases and for spike, stem and root diseases, respectively. Pests gained an accuracy of up to 94%, while for weeds the app shows a great ability (100% accuracy) in recognizing whether the target weed is a dicot or monocot and 60% accuracy for distinguishing species in both the post-germination and pre-flowering stage. Our precision and accuracy results conform to or outperform those of other studies deploying artificial intelligence models on mobile devices, confirming that GranoScan is a valuable tool also in challenging outdoor conditions.
Why it matches plant phenotyping methods小麦の葉・穂・茎・根の病害や損傷を画像から認識するAIモバイルアプリの開発・性能評価が中心であり、植物の病害状態を直接推定するフェノタイピング手法に該当する。害虫・雑草識別も含むが、病害認識の技術的評価が明示されている。
abstractThis paper introduces GranoScan, a freely available mobile app accessible on major online platforms, specifically designed for the real-time detection and identification of over 80 threats affecting wheat in the Mediterranean region.
Reproduction assets foundThe article's data availability statement explicitly states that the authors' weed phenotyping image dataset is publicly available on Zenodo (DOI 10.5281/zenodo.7598372), a paper-specific public asset. No author analysis code or trained model checkpoints are described with a public URL.Dataset · publicThe original contributions presented in the study are publicly available (see the weed phenotyping image dataset). This data can be found here: https://doi.org/10.5281/zenodo.7598372 .Open asset ↗Zenodo · 10.5281/zenodo.7598372lines:460-508Code / dataset availability confirmedCrossref · Europe PMC · checked 14 Sept 2026
Growth chamberRGB / grayscaleRootObject detectionSegmentationGrowth / time-series analysisBiomass / plant weightGrowth / development / phenologyRoot system architecture
Abstract Flatbed scanners are commonly used for root analysis, but typical manual segmentation methods are time-consuming and prone to errors, especially in large-scale, multi-plant studies. Furthermore, the complex nature of root structures combined with noisy backgrounds in images complicates automated analysis. Addressing these challenges, this article introduces RhizoNet, a deep learning-based workflow to semantically segment plant root scans. Utilizing a sophisticated Residual U-Net architecture, RhizoNet enhances prediction accuracy and employs a convex hull operation for delineation of the primary root component. Its main objective is to accurately segment root biomass and monitor its growth over time. RhizoNet processes color scans of plants grown in a hydroponic system known as EcoFAB, subjected to specific nutritional treatments. The root detection model using RhizoNet demonstrates strong generalization in the validation tests of all experiments despite variable treatments. The main contributions are the standardization of root segmentation and phenotyping, systematic and accelerated analysis of thousands of images, significantly aiding in the precise assessment of root growth dynamics under varying plant conditions, and offering a path toward self-driving labs.
Why it matches plant phenotyping methods植物根の画像分割と根バイオマス・成長の表現型抽出ワークフローを開発・検証しており、フェノタイピング手法が中心である。
abstractthis article introduces RhizoNet, a deep learning-based workflow to semantically segment plant root scans.
Reproduction assets found保存済みの本文根拠を更新済みルールで再検証し、公開資産1件を確認しました。Code · publicPython codes for root scans segmentation enabled by RhizoNet were created by the authors and are described in this paper. These codes will be available free of charge upon acceptance, and with open source at: https://github.com/lbl-camera/rhizonet .Open asset ↗lbl-camera/rhizonetlines:154-177Code / dataset availability confirmedEurope PMC · checked 14 Sept 2026
Early sowing can help summer crops escape drought and can mitigate the impacts of climate change on them. However, it exposes them to cold stress during initial developmental stages, which has both immediate and long-term effects on development and physiology. To understand how early night-chilling stress impacts plant development and yield, we studied the reference sunflower line XRQ under controlled, semi-controlled and field conditions. We performed high-throughput imaging of the whole plant parts and obtained physiological and transcriptomic data from leaves, hypocotyls and roots. We observed morphological reductions in early stages under field and controlled conditions, with a decrease in root development, an increase in reactive oxygen species content in leaves and changes in lipid composition in hypocotyls. A long-term increase in leaf chlorophyll suggests a stress memory mechanism that was supported by transcriptomic induction of histone coding genes. We highlighted DEGs related to cold acclimation such as chaperone, heat shock and late embryogenesis abundant proteins. We identified genes in hypocotyls involved in lipid, cutin, suberin and phenylalanine ammonia lyase biosynthesis and ROS scavenging. This comprehensive study describes new phenotyping methods and candidate genes to understand phenotypic plasticity better in response to chilling and study stress memory in sunflower.
Why it matches plant phenotyping methods全身部位のハイスループット画像化と新規フェノタイピング手法が明示され、低温応答の形態評価における手法が中心的に記述されている。
abstractWe performed high-throughput imaging of the whole plant parts and obtained physiological and transcriptomic data from leaves, hypocotyls and roots.
Reproduction assets foundThe paper's plant-phenotyping measurements (morphological, chlorophyll/anthocyanin/flavonoid, root traits, yield and seed composition across 12 experiments) were deposited on Recherche Data Gouv under doi:10.57745/4HNS1J, with a specific sub-dataset (persistentId doi:10.57745/4HNS1J.2598) referenced in Table 1. No codeDataset · publicby the French National Association for Research and
Technology (ANRT).
CONFLICT OF INTEREST STATEMENT
The authors declare no conflict of interest.
DATA AVAILABILITY STATEMENT
The data that support the findings of this study are openly available
in Recherche Data Gouv at https://entrepot.recherche.data.gouv.fr/,
reference number https://doi.org/10.57745/4HNS1J.REFERENCES
Abbass, K., Qasim, M.Z., Song, H., Murshed, M., Mahmood, H. & Younis, I.Open asset ↗Recherche Data Gouvpdf-raw-page:16 lines:1-76Dataset · publicter
dynamics,
chlorophyll
content,
anthocyanin
content,
flavonoid
content,
nitrogen
balance,
fatty
acids
of
seeds
Tables
S2
and
S3
21TE01‐02
22EX01‐02
Early
and
late
Field
2
(n
=
22)
Vigour,
total
leaf
area
and
plant‐height
dynamics,
flowering
date,
yield,
yield
components
Table
S4
Note:
Data
were
submitted
to
the
public
portal
https://entrepot.recherche.data.gouv.fr/dataset.xhtml?persistentId=doi:10.57745/4HNS1J.2598 | LECONTE ET AL.
13653040,
2025,
4,
Downloaded
from
https://onlinelibrary.wiley.com/doi/10.1111/pce.14941
by
Mount
Vernon
Nazarene
University,
Wiley
Online
Library
on
[30/12/2025].
See
the
Terms
and
Conditions
(https://onlinelibrary.wiley.com/terms-and-conditions)
on
Wiley
OnliOpen asset ↗doi:10.57745/4HNS1J.2598pdf-raw-page:3 lines:1-265Code / dataset availability confirmedCrossref · Europe PMC · checked 7 Sept 2026
RiceMesh / voxelX-ray / CTRootObject detectionSegmentationRoot system architecture
Abstract Background X-ray computed tomography (CT) is a powerful tool for measuring plant root growth in soil. However, a rapid scan with larger pots, which is required for throughput-prioritized crop breeding, results in high noise levels, low resolution, and blurred root segments in the CT volumes. Moreover, while plant root segmentation is essential for root quantification, detailed conditional studies on segmenting noisy root segments are scarce. The present study aimed to investigate the effects of scanning time and deep learning-based restoration of image quality on semantic segmentation of blurry rice ( Oryza sativa ) root segments in CT volumes. Results VoxResNet, a convolutional neural network-based voxel-wise residual network, was used as the segmentation model. The training efficiency of the model was compared using CT volumes obtained at scan times of 33, 66, 150, 300, and 600 s. The learning efficiencies of the samples were similar, except for scan times of 33 and 66 s. In addition, The noise levels of predicted volumes differd among scanning conditions, indicating that the noise level of a scan time ≥ 150 s does not affect the model training efficiency. Conventional filtering methods, such as median filtering and edge detection, increased the training efficiency by approximately 10% under any conditions. However, the training efficiency of 33 and 66 s-scanned samples remained relatively low. We concluded that scan time must be at least 150 s to not affect segmentation. Finally, we constructed a semantic segmentation model for 150 s-scanned CT volumes, for which the Dice loss reached 0.093. This model could not predict the lateral roots, which were not included in the training data. This limitation will be addressed by preparing appropriate training data. Conclusions A semantic segmentation model can be constructed even with rapidly scanned CT volumes with high noise levels. Given that scanning times ≥ 150 s did not affect the segmentation results, this technique holds promise for rapid and low-dose scanning. This study offers insights into images other than CT volumes with high noise levels that are challenging to determine when annotating.
Why it matches plant phenotyping methods植物根のCT画像から根をセグメンテーションし、根成長の定量化に用いる手法の開発・技術評価が中心であるため、植物フェノタイピング方法論に該当する。
abstractX-ray computed tomography (CT) is a powerful tool for measuring plant root growth in soil.
Reproduction assets foundThe paper's own training/prediction scripts for the 3D semantic segmentation model (SStrainer3D) are publicly available on GitHub with explicit availability language. The CT volume datasets are only available upon request. RSAvis3D and RSAtrace3D are cited prior-work tools, not paper-specific assets.Code · publicThe scripts for the training and prediction of the 3D semantic segmentation are available at the GitHub repository ( https://github.com/st707311g/SStrainer3D/ , branch 1.0).Open asset ↗st707311g/SStrainer3Dlines:156-186Code / dataset availability confirmedOpenAlex · Europe PMC · checked 7 Sept 2026
The presence of Arbuscular Mycorrhizal Fungi (AMF) in vascular land plant roots is one of the most ancient of symbioses supporting nitrogen and phosphorus exchange for photosynthetically derived carbon. Here we provide a multi-scale modeling approach to predict AMF colonization of a worldwide crop from a Recombinant Inbred Line (RIL) population derived from Sorghum bicolor and S. propinquum. The high-throughput phenotyping methods of fungal structures here rely on a Mask Region-based Convolutional Neural Network (Mask R-CNN) in computer vision for pixel-wise fungal structure segmentations and mixed linear models to explore the relations of AMF colonization, root niche, and fungal structure allocation. Models proposed capture over 95% of the variation in AMF colonization as a function of root niche and relative abundance of fungal structures in each plant. Arbuscule allocation is a significant predictor of AMF colonization among sibling plants. Arbuscules and extraradical hyphae implicated in nutrient exchange predict highest AMF colonization in the top root section. Our work demonstrates that deep learning can be used by the community for the high-throughput phenotyping of AMF in plant roots. Mixed linear modeling provides a framework for testing hypotheses about AMF colonization phenotypes as a function of root niche and fungal structure allocations.
Why it matches plant phenotyping methods根内AMF構造をMask R-CNNで画素単位に分割し、AMF定着を高スループット推定する画像解析手法が研究の中心である。
abstractThe high-throughput phenotyping methods of fungal structures here rely on a Mask Region-based Convolutional Neural Network (Mask R-CNN) in computer vision for pixel-wise fungal structure segmentations
Reproduction assets foundThe paper's authors publicly release their segmentation/analysis code and summary data on GitHub, and the paper uses the public Cambridge AMF image dataset from Zenodo as training data. The >20,000 raw Georgia images are only available upon request.Code · publicCodes are available in GitHub: https://github.com/Arnold-Lab/image_seg_sorghum_am .Open asset ↗Arnold-Lab/image_seg_sorghum_amlines:222-293Dataset · publicThe publicly available Cambridge dataset (zenodo ID https://doi.org/10.5281/zenodo.5118948 ) included 15 whole slide scanning imagesOpen asset ↗10.5281/zenodo.5118948lines:185-204Code / dataset availability confirmedEurope PMC · bioRxiv · checked 15 Sept 2026
ABSTRACT Members of the Fusarium oxysporum species complex are pathogens of sugar beet causing Fusarium yellows. Fusarium yellows can reduce plant stand, yield, and extractable sugar. Improving host plant resistance against Fusarium -induced diseases, like Fusarium yellows, represents an important long-term breeding target in sugar beet breeding programs. Current methods for rating Fusarium yellows disease severity rely on an ordinal scale, which limits precision for intermediate phenotypes. In this study, we aimed to improve the accuracy and precision of rating Fusarium yellows by developing a standard area diagram (SAD). Two SAD versions were created using images of sugar beets infected with Fusarium oxysporum strain F19. Each version was tested using inexperienced raters. Comparing both the pilot and improved version showed no statistical differences in Lin’s Concordance Correlation Coefficient (LCC) values to assess accuracy and precision between the two versions (Cb = 0.99 for both versions, ρ c = 0.97 and 0.96 for version 1 and 2, respectively). In addition, five naïve Bayesian machine learning models which used pixel classification to determine disease score, were tested for congruency to human estimates in version 2. Root mean square error was lowest compared to the “true” values for the unweighted model and a model where necrotic tissue was given a 2x weight (12.4 and 12.6, respectively). The creation of this standard area diagram enables breeding programs to make consistent, accurate disease ratings regardless of personnel’s’ previous experience with Fusarium yellows. Additionally, more iterations of pixel quantification equations may overcome accuracy issues for rating Fusarium yellows.
Why it matches plant phenotyping methodsフザリウム萎黄病の植物症状を対象に、標準面積図と画像ピクセル分類による病害重症度評価法を開発・検証しており、植物フェノタイピング手法が中心である。
abstractIn this study, we aimed to improve the accuracy and precision of rating Fusarium yellows by developing a standard area diagram (SAD).
Reproduction assets foundThe paper's data availability statement explicitly deposits the authors' scripts, plant images, and excel sheets (including the RGB classifier training data) on a public GitHub repository, which is paper-specific and actionable.Code · publiceen 0-20%.
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The authors would like to acknowledge the raters’ participation in this study. Funding
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provided by USDA-ARS CRIS projects 3012-21220-011-000-D and 5050-21220-017-000-D.
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Data availability statement
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Scripts, images and excel sheets are available on the following Github page:
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https://github.com/oetodd/Fusarium_standard_area_diagram_2024
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and is also made available for use under a CC0 license.
was not certified by peer review) is the author/funder. This article is a US Government work. It is not subject to copyright under 17 USC 105
The copyright holder for this preprint (which
this version posted April 28, 2024.
;
https://doi.org/10Open asset ↗https://github.com/oetodd/Fusarium_standard_area_diagram_2024pdf-raw-page:13 lines:1-50Code / dataset availability confirmedOpenAlex · Crossref · Europe PMC · checked 7 Sept 2026
The analysis of plant phenotype parameters is closely related to breeding, so plant phenotype research has strong practical significance. This paper used deep learning to classify Arabidopsis thaliana from the macro (plant) to the micro level (organelle). First, the multi-output model identifies Arabidopsis accession lines and regression to predict Arabidopsis’s 22-day growth status. The experimental results showed that the model had excellent performance in identifying Arabidopsis lines, and the model’s classification accuracy was 99.92%. The model also had good performance in predicting plant growth status, and the regression prediction of the model root mean square error (RMSE) was 1.536. Next, a new dataset was obtained by increasing the time interval of Arabidopsis images, and the model’s performance was verified at different time intervals. Finally, the model was applied to classify Arabidopsis organelles to verify the model’s generalizability. Research suggested that deep learning will broaden plant phenotype detection methods. Furthermore, this method will facilitate the design and development of a high-throughput information collection platform for plant phenotypes.
Why it matches plant phenotyping methods深層学習による植物画像からの系統識別・生育状態推定を開発し、時間間隔データで検証しており、植物表現型取得・推定手法が研究の中心である。
abstractThis paper used deep learning to classify Arabidopsis thaliana from the macro (plant) to the micro level (organelle).
Reproduction assets foundThe paper's plant-phenotyping analysis is built on the Arabidopsis thaliana time-series image dataset from Namin et al., which the authors explicitly state is publicly available for download. No author analysis code or trained model is shared.Dataset · publicData is publicly available at: http://phenocam.anu.edu.au/cloud/a_data/_webroot/published-data/2017/2017-Namin-et-al-DeepPheno.zip (accessed on 16 April 2024).Open asset ↗phenocam.anu.edu.au · 2017-Namin-et-al-DeepPheno.ziplines:226-239Code / dataset availability confirmedEurope PMC · OpenAlex · checked 7 Sept 2026
Stem diameter is a critical phenotypic parameter for maize, integral to yield prediction and lodging resistance assessment. Traditionally, the quantification of this parameter through manual measurement has been the norm, notwithstanding its tedious and laborious nature. To address these challenges, this study introduces a non-invasive field-based system utilizing depth information from RGB-D cameras to measure maize stem diameter. This technology offers a practical solution for conducting rapid and non-destructive phenotyping. Firstly, RGB images, depth images, and 3D point clouds of maize stems were captured using an RGB-D camera, and precise alignment between the RGB and depth images was achieved. Subsequently, the contours of maize stems were delineated using 2D image processing techniques, followed by the extraction of the stem's skeletal structure employing a thinning-based skeletonization algorithm. Furthermore, within the areas of interest on the maize stems, horizontal lines were constructed using points on the skeletal structure, resulting in 2D pixel coordinates at the intersections of these horizontal lines with the maize stem contours. Subsequently, a back-projection transformation from 2D pixel coordinates to 3D world coordinates was achieved by combining the depth data with the camera's intrinsic parameters. The 3D world coordinates were then precisely mapped onto the 3D point cloud using rigid transformation techniques. Finally, the maize stem diameter was sensed and determined by calculating the Euclidean distance between pairs of 3D world coordinate points. The method demonstrated a Mean Absolute Percentage Error ( MAPE ) of 3.01%, a Mean Absolute Error ( MAE ) of 0.75 mm, a Root Mean Square Error ( RMSE ) of 1.07 mm, and a coefficient of determination ( R ²) of 0.96, ensuring accurate measurement of maize stem diameter. This research not only provides a new method of precise and efficient crop phenotypic analysis but also offers theoretical knowledge for the advancement of precision agriculture.
Why it matches plant phenotyping methodsRGB-Dカメラと画像・3D処理によりトウモロコシ茎径を非破壊測定する手法を開発し、誤差指標で精度検証しており、フェノタイピング手法が中心である。
abstractthis study introduces a non-invasive field-based system utilizing depth information from RGB-D cameras to measure maize stem diameter
Reproduction assets foundThe paper's data availability statement points to a public Figshare deposit (DOI 10.6084/m9.figshare.25450039) containing the study's datasets (RGB/depth imagery and stem diameter measurements used for the maize stem diameter phenotyping analysis). No author analysis code or trained models are explicitly deposited.Dataset · publicThe datasets presented in this study can be found in online repositories. The names of the repository/repositories and accession number(s) can be found below: http://dx.doi.org/10.6084/m9.figshare.25450039 .Open asset ↗figshare · 10.6084/m9.figshare.25450039lines:909-917Code / dataset availability confirmedEurope PMC · checked 7 Sept 2026
Image segmentation is commonly used to estimate the location and shape of plants and their external structures. Segmentation masks are then used to localize landmarks of interest and compute other geometric features that correspond to the plant's phenotype. Despite its prevalence, segmentation-based approaches are laborious (requiring extensive annotation to train) and error-prone (derived geometric features are sensitive to instance mask integrity). Here, we present a segmentation-free approach that leverages deep learning-based landmark detection and grouping, also known as pose estimation. We use a tool originally developed for animal motion capture called SLEAP (Social LEAP Estimates Animal Poses) to automate the detection of distinct morphological landmarks on plant roots. Using a gel cylinder imaging system across multiple species, we show that our approach can reliably and efficiently recover root system topology at high accuracy, few annotated samples, and faster speed than segmentation-based approaches. In order to make use of this landmark-based representation for root phenotyping, we developed a Python library ( sleap-roots ) for trait extraction directly comparable to existing segmentation-based analysis software. We show that pose-derived root traits are highly accurate and can be used for common downstream tasks including genotype classification and unsupervised trait mapping. Altogether, this work establishes the validity and advantages of pose estimation-based plant phenotyping. To facilitate adoption of this easy-to-use tool and to encourage further development, we make sleap-roots , all training data, models, and trait extraction code available at: https://github.com/talmolab/sleap-roots and https://osf.io/k7j9g/.
Why it matches plant phenotyping methods根系のランドマーク検出・形状復元・形質抽出を行う深層学習ベースの植物フェノタイピング手法を開発・検証し、専用ライブラリも提供しているため。
abstractHere, we present a segmentation-free approach that leverages deep learning-based landmark detection and grouping, also known as pose estimation.
Reproduction assets foundThe authors explicitly make all paper-specific assets public: the sleap-roots trait-extraction codebase on GitHub, a separate repository with figure-replication code, and an OSF deposit containing labeled training data, trained pose-estimation models, and analysis files for the root phenotyping measurements.Code · publicthe specific code utilized for replicating the figures presented in this study can be found in a separate GitHub repository here: https://github.com/talmolab/Berrigan_et_al_sleap-rootsOpen asset ↗talmolab/Berrigan_et_al_sleap-roots · Berrigan_et_al_sleap-rootslines:485-526Dataset · publicThe datasets generated and/or analyzed during the current study are available in the Open Science Framework (OSF) repository. This includes the labeled data, predictive models, and analysis files, which can be accessed via the following link: https://osf.io/k7j9g/Open asset ↗osf.io/k7j9g · k7j9glines:485-526Code / dataset availability confirmedEurope PMC · OpenAlex · checked 7 Sept 2026
MaizeGrowth chamberStereoRoot2D/3D reconstructionGrowth / time-series analysisRoot system architecture
Background The root of a plant is a fundamental organ for the multisensory perception of the environment. Investigating root growth dynamics as a mean of their interaction with the environment is of key importance for improving knowledge in plant behaviour, plant biology and agriculture. To date, it is difficult to study roots movements from a dynamic perspective given that available technologies for root imaging focus mostly on static characterizations, lacking temporal and three-dimensional (3D) spatial information. This paper describes a new system based on time-lapse for the 3D reconstruction and analysis of roots growing in hydroponics. Results The system is based on infrared stereo-cameras acquiring time-lapse images of the roots for 3D reconstruction. The acquisition protocol guarantees the root growth in complete dark while the upper part of the plant grows in normal light conditions. The system extracts the 3D trajectory of the root tip and a set of descriptive features in both the temporal and frequency domains. The system has been used on Zea mays L. (B73) during the first week of growth and shows good inter-reliability between operators with an Intra Class Correlation Coefficient (ICC) > 0.9 for all features extracted. It also showed measurement accuracy with a median difference of Conclusions The system and the protocol presented in this study enable accurate 3D analysis of primary root growth in hydroponics. It can serve as a valuable tool for analysing real-time root responses to environmental stimuli thus improving knowledge on the processes contributing to roots physiological and phenotypic plasticity.
Why it matches plant phenotyping methods根の3D動態を取得・解析する画像計測システムを開発し、特徴量の信頼性と精度を検証しており、植物表現型取得が中心である。
abstractThis paper describes a new system based on time-lapse for the 3D reconstruction and analysis of roots growing in hydroponics.
Reproduction assets foundThe paper's 3D root tip trajectory data (phenotyping measurements from maize root imaging) are publicly deposited on Zenodo. The analysis software and scripts are only available upon request, so they qualify as request_only.Dataset · publicData describing 3D trajectories used in this paper are available here: https://zenodo.org/record/8422242 . Software and scripts are available for research purposes upon request through the email address: mindtheplantlab@gmail.com.Open asset ↗Zenodo · 8422242lines:141-172Code / dataset availability confirmedEurope PMC · checked 15 Sept 2026
The periderm is a vital protective tissue found in the roots, stems, and woody elements of diverse plant species. It plays an important function in these plants by assuming the role of the epidermis as the outermost layer. Despite its critical role for protecting plants from environmental stresses and pathogens, research on root periderm development has been limited due to its late formation during root development, its presence only in mature root regions, and its impermeability. One of the most straightforward measurements for comparing periderm formation between different genotypes and treatments is periderm (phellem) length. We have developed PAT (Periderm Assessment Toolkit), a high-throughput user-friendly pipeline that integrates an efficient staining protocol, automated imaging, and a deep-learning-based image analysis approach to accurately detect and measure periderm length in the roots of Arabidopsis thaliana . The reliability and reproducibility of our method was evaluated using a diverse set of 20 Arabidopsis natural accessions. Our automated measurements exhibited a strong correlation with human-expert-generated measurements, achieving a 94% efficiency in periderm length quantification. This robust PAT pipeline streamlines large-scale periderm measurements, thereby being able to facilitate comprehensive genetic studies and screens. Although PAT proves highly effective with automated digital microscopes in Arabidopsis roots, its application may pose challenges with nonautomated microscopy. Although the workflow and principles could be adapted for other plant species, additional optimization would be necessary. While we show that periderm length can be used to distinguish a mutant impaired in periderm development from wild type, we also find it is a plastic trait. Therefore, care must be taken to include sufficient repeats and controls, to minimize variation, and to ensure comparability of periderm length measurements between different genotypes and growth conditions.
Why it matches plant phenotyping methods植物根の表現型（周皮長）を自動画像取得・深層学習解析で定量するパイプラインを開発し、専門家測定との相関で信頼性と再現性を検証しており、方法が研究の中心である。
abstractWe have developed PAT (Periderm Assessment Toolkit), a high-throughput user-friendly pipeline that integrates an efficient staining protocol, automated imaging, and a deep-learning-based image analysis approach to accurately detect and measure periderm length in the roots of Arabidopsis thaliana .
Reproduction assets foundThe authors publicly release the PAT pipeline (analysis code/scripts) and a test dataset of Col-0 and wox4-1 TIFF microscopy images via their GitHub repository. Full-resolution TIFF images of the 20 natural accessions are only available upon request (request_only, not listed as an allowed URL).Dataset · publicroved the manuscript.
Competing interests: W.B. is a cofounder of Cquesta, a company that works on crop root growth and carbon sequestration.
Data Availability
All raw data and datasets have been included in the Supplementary Materials. The PAT pipeline and its associated code are accessible via the following GitHub repository: https://github.com/Salk-Harnessing-Plants-Initiative/PAT-Pipeline-for-Analysis-of-Periderm . Additionally, the test dataset comprising Col-0 and wox4-1 TIFF images is available on the same GitHub repository. Full-resolution TIFF images corresponding to the natural accessions (Table 1 ) can be obtained from the corresponding author upon request.
Supplementary MaterialsOpen asset ↗Salk-Harnessing-Plants-Initiative/PAT-Pipeline-for-Analysis-of-Peridermlines:391-421Code / dataset availability confirmedEurope PMC · checked 7 Sept 2026
RadishRiceX-ray / CTRoot2D/3D reconstructionGrowth / time-series analysisRoot system architecture
Crop genetic engineering for better root systems can offer practical solutions for food security and carbon sequestration; however, soil layers prevent the direct visualization of plant roots, thus posing a challenge to effective phenotyping. Here, we demonstrate an original device with a distributed fiber-optic sensor for fully automated, real-time monitoring of underground root development. We show that spatially encoding an optical fiber with a flexible and durable polymer film in a spiral pattern can significantly enhance sensor detection. After signal processing, the resulting device can detect the penetration of a submillimeter-diameter object in the soil, indicating more than a magnitude higher spatiotemporal resolution than previously reported with underground monitoring techniques. Additionally, we also developed computational models to visualize the roots of tuber crops and monocotyledons and then applied them to radish and rice to compare the results with those of X-ray computed tomography. The device's groundbreaking sensitivity and spatiotemporal resolution enable seamless and laborless phenotyping of root systems that are otherwise invisible underground.
Why it matches plant phenotyping methods地下根系の発達を対象に、分布型光ファイバーセンサー、信号処理、根の可視化モデルを開発し、X線CTとの比較検証まで行う、植物フェノタイピング手法が中心の研究です。
abstractwe demonstrate an original device with a distributed fiber-optic sensor for fully automated, real-time monitoring of underground root development
Reproduction assets foundThe authors publicly provide MATLAB code for virtual root reconstruction and the sample datasets used in the study via their GitHub repository Fiber-RADGET, with explicit availability statements in the Methods and Data availability sections.Code · publicThe custom code for the virtual root reconstruction in MATLAB (MathWorks, Massachusetts, USA) is available at https://github.com/mtei1/Fiber-RADGET.git .Open asset ↗mtei1/Fiber-RADGETlines:126-223Code / dataset availability confirmedEurope PMC · checked 14 Sept 2026
Intelligent agriculture heavily relies on the science of agricultural disease image recognition. India is also responsible for large production of French beans, accounting for 37.25% of total production. In India from south region of Maharashtra state this crop is cultivated thrice in year. Soyabean plant is planted between the months of June through July, during the months of October and September during the rabi season, as well as in February. In the Maharashtrian regions of Pune, Satara, Ahmednagar, Solapur, and Nashik, among others, Soyabean plant is a common crop. In Maharashtra, Soyabean plant is grown over an area of around 31,050 hectares. This research presents a dataset of leaves from soyabean plants that are both insect-damaged and healthy. Images were taken over the course of fewer than two to three seasons on several farms. There are 3363 photos altogether in the seven folders that make up the dataset. Six categories comprise the dataset: I) Healthy plants II) Vein Necrosis III) Dry leaf IV) Septoria brown spot V) Root images VI) Bacterial leaf blight. This study's goal is to give academics and students accessibility to our dataset so they may use it for their studies and to build machine learning models.
Why it matches plant phenotyping methodsダイズ葉の健全・病害状態を画像データセットとして構築し、機械学習による植物病害の識別・分類に利用可能にする研究であり、表現型取得用データセットが中心である。
abstractThis research presents a dataset of leaves from soyabean plants that are both insect-damaged and healthy.
Reproduction assets foundThe paper is a Data in Brief article describing a public soyabean leaf disease image dataset (3363 images, six classes) deposited on Mendeley Data with an explicit DOI and direct URL. This is a paper-specific, publicly available plant phenotyping asset (raw and preprocessed leaf images). No separate analysis code assetDataset · publicor unhealthy.
Data source location
Goudgaon Village farm of (Sub. Major Gulab Alam Kotwal), Tal: Barshi, Dist: Solapur, Maharashtra, India.413406.
18.2157727 Latitude and 75.6680118 Longitude.
Data accessibility
Repository name: An India soyabean leaf dataset
Data identification number: 10.17632/bshkvgbzpt.1
Direct URL to data: https://data.mendeley.com/datasets/bshkvgbzpt/1
Instructions for accessing these data:
Datasets consist of Single leaf and multi-leaf folder.
Related research article
Case study:
Author: Mr.Jameer Kotwal, Dr.Ramgopal Kashyap, Dr.Shafi Pathan
Paper: https://link.springer.com/article/10.1007/s11042-023-16882
Journal: Multimedia Tools and Application [ 1 ].
1
Value of thOpen asset ↗10.17632/bshkvgbzpt.1lines:1-71Code / dataset availability confirmedEurope PMC · checked 14 Sept 2026
The root system plays a vital role in plants' ability to absorb water and nutrients. In situ root research offers an intuitive approach to exploring root phenotypes and their dynamics. Deep-learning-based root segmentation methods have gained popularity, but they require large labeled datasets for training. This paper presents an expansion method for in situ root datasets using an improved CycleGAN generator. In addition, spatial-coordinate-based target background separation method is proposed, which solves the issue of background pixel variations caused by generator errors. Compared to traditional threshold segmentation methods, this approach demonstrates superior speed, accuracy, and stability. Moreover, through time-division soil image acquisition, diverse culture medium can be replaced in in situ root images, thereby enhancing dataset versatility. After validating the performance of the Improved_UNet network on the augmented dataset, the optimal results show a 0.63% increase in mean intersection over union, 0.41% in F1, and 0.04% in accuracy. In terms of generalization performance, the optimal results show a 33.6% increase in mean intersection over union, 28.11% in F1, and 2.62% in accuracy. The experimental results confirm the feasibility and practicality of the proposed dataset augmentation strategy. In the future, we plan to combine normal mapping with rendering software to achieve more accurate shading simulations of in situ roots. In addition, we aim to create a broader range of images that encompass various crop varieties and soil types.
Why it matches plant phenotyping methods根系表現型画像のデータセット拡張、背景分離、セグメンテーション性能検証を中心とする方法研究であり、植物形態の抽出手法が主要な貢献である。
abstractThis paper presents an expansion method for in situ root datasets using an improved CycleGAN generator.
Reproduction assets foundThe paper's Data Availability statement explicitly deposits the authors' in situ root dataset and trained model on Zenodo and their analysis code (improved CycleGAN) on GitHub, both with public URLs.Code · publicterests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential competing interest.
Data Availability
The model and dataset have been uploaded to Zenodo: https://doi.org/10.5281/zenodo.10460303 . The code has been uploaded to GitHub: https://github.com/jiwd123/improved_cyclegan
Supplementary Materials
Supplementary 1
The network and corresponding weights can be viewed on GitHub ( https://github.com/jiwd123/improved_cyclegan ) and Zenodo ( https://doi.org/10.5281/zenodo.10460303 ).
References
1. Hinsinger P, Brauman A, Devau N, Gérard F, Jourdan C, Laclau J-P, Le Cadre E, Jaillard B, Plassard C.
AcOpen asset ↗GitHub · jiwd123/improved_cycleganlines:467-510Code / dataset availability confirmedOpenAlex · Crossref · checked 14 Sept 2026
MaizeField / plotGreenhouseRootSegmentationRoot system architecture
Abstract Crop root segmentation models developed through deep learning have increased the throughput of in situ crop phenotyping studies. However, models trained to identify roots in one image dataset may not accurately identify roots in another dataset, especially when the new dataset contains known differences, called domain shifts. The objective of this study was to quantify how model performance changes when models are used to segment image datasets that contain domain shifts and evaluate approaches to reduce error associated with domain shifts. We collected maize root images at two growth stages (V7 and R2) in a field experiment and manually segmented images to measure total root length (TRL). We developed five segmentation models and evaluated each model's ability to handle a temporal (growth‐stage) domain shift. For the V7 growth stage, a growth‐stage‐specific model trained only on images captured at the V7 growth stage was best suited for measuring TRL. At the R2 growth stage, combining images from both growth stages into a single dataset to train a model resulted in the most accurate TRL measurements. We applied two of the field models to images from a greenhouse experiment to evaluate how model performance changed when exposed to a cross‐site domain shift. Field models were less accurate than models trained only on the greenhouse images even when crop growth stage was identical. Although models may perform well for one experiment, model error increases when applied to images from different experiments even when crop species, growth stage, and soil type are similar.
Why it matches plant phenotyping methods根画像セグメンテーションモデルを開発・比較し、時間的および施設間ドメインシフト下での性能と根長推定精度を検証しており、植物表現型取得手法が研究の中心である。
abstractWe developed five segmentation models and evaluated each model's ability to handle a temporal (growth‐stage) domain shift.
Reproduction assets foundThe authors openly published the root images used to train their segmentation models, the trained models, and RhizoVision Explorer settings metadata in a Zenodo deposit (DOI 10.5281/zenodo.8224956), which is a paper-specific, publicly actionable asset.Dataset · publicBANET ET AL. 5 of 13
annotating for 4 h, and allowing model training to proceed
until 60/60 epochs without progress. Root images collected in
the field and the greenhouse that were used to train models are
openly published (https://doi.org/10.5281/zenodo.8224956).2.3 Image segmentation and trait
extraction
The trained models were used to generate segmentations
for the field images and greenhouse images using Root-
Painter’s “Segment folder” function from the “Network”
menu. These segmentations were then converted to binary
segmentations (i.e., black and white) using RootPainter’s
builtOpen asset ↗Zenodo · 10.5281/zenodo.8224956pdf-raw-page:5 lines:1-115Code / 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.
MaizePepper / chilliTomatoRootMorphology / geometry measurementRoot system architecture
Image-based root phenotyping technologies, including the minirhizotron (MR), have expanded our understanding of the in situ root responses to changing environmental conditions. The conventional manual methods used to analyze MR images are time-consuming, limiting their implementation. This study presents an adaptation of our previously developed convolutional neural network-based models to estimate the total (cumulative) root length (TRL) per MR image without requiring segmentation. Training data were derived from manual annotations in Rootfly, commonly used software for MR image analysis. We compared TRL estimation with 2 models, a regression-based model and a detection-based model that detects the annotated points along the roots. Notably, the detection-based model can assist in examining human annotations by providing a visual inspection of roots in MR images. The models were trained and tested with 4,015 images acquired using 2 MR system types (manual and automated) and from 4 crop species (corn, pepper, melon, and tomato) grown under various abiotic stresses. These datasets are made publicly available as part of this publication. The coefficients of determination ( R 2 ), between the measurements made using Rootfly and the suggested TRL estimation models were 0.929 to 0.986 for the main datasets, demonstrating that this tool is accurate and robust. Additional analyses were conducted to examine the effects of (a) the data acquisition system and thus the image quality on the models' performance, (b) automated differentiation between images with and without roots, and (c) the use of the transfer learning technique. These approaches can support precision agriculture by providing real-time root growth information.
Why it matches plant phenotyping methods画像から根長という植物形質を推定するCNN手法を開発し、複数データセットで精度・頑健性を検証しているため、植物フェノタイピング手法が中心です。
abstractThis study presents an adaptation of our previously developed convolutional neural network-based models to estimate the total (cumulative) root length (TRL) per MR image without requiring segmentation.
Reproduction assets foundThe authors explicitly deposited the 4,015 minirhizotron root images with Rootfly TRL and point-coordinate annotations used to train and test their CNN models in a public Zenodo repository, making it a directly qualifying paper-specific public dataset.Dataset · publicThe datasets generated and analyzed during the current study are available in the Zenodo repository, https://doi.org/10.5281/zenodo.7482146 .Open asset ↗Zenodo · 10.5281/zenodo.7482146lines:373-471Code / dataset availability confirmedOpenAlex · Europe PMC · Crossref · checked 15 Sept 2026
MaizeRootMorphology / geometry measurementRoot system architecture
A central goal of biology is to understand how genetic variation produces phenotypic variation, which has been described as a genotype to phenotype (G to P) map. The plant form is continuously shaped by intrinsic developmental and extrinsic environmental inputs, and therefore plant phenomes are highly multivariate and require comprehensive approaches to fully quantify. Yet a common assumption in plant phenotyping efforts is that a few pre-selected measurements can adequately describe the relevant phenome space. Our poor understanding of the genetic basis of root system architecture is at least partially a result of this incongruence. Root systems are complex 3D structures that are most often studied as 2D representations measured with relatively simple univariate traits. In prior work, we showed that persistent homology, a topological data analysis method that does not pre-suppose the salient features of the data, could expand the phenotypic trait space and identify new G to P relations from a commonly used 2D root phenotyping platform. Here we extend the work to entire 3D root system architectures of maize seedlings from a mapping population that was designed to understand the genetic basis of maize-nitrogen relations. Using a panel of 84 univariate traits, persistent homology methods developed for 3D branching, and multivariate vectors of the collective trait space, we found that each method captures distinct information about root system variation as evidenced by the majority of non-overlapping QTL, and hence that root phenotypic trait space is not easily exhausted. The work offers a data-driven method for assessing 3D root structure and highlights the importance of non-canonical phenotypes for more accurate representations of the G to P map.
Why it matches plant phenotyping methods3Dトウモロコシ根系の構造を、persistent homologyによって従来の形質空間を拡張して解析する計算的フェノタイピング手法が中心であり、根系構造形質の抽出・評価に該当する。
abstractpersistent homology, a topological data analysis method that does not pre-suppose the salient features of the data, could expand the phenotypic trait space
Reproduction assets foundThe paper's data availability statement points to a public figshare deposit of the 3D root models (paper-specific phenotyping inputs) and raw trait data in Supplementary Table 5 via the Frontiers supplementary material page. Matlab code is only referenced via a prior publication (Li et al., 2019) without an authors' deDataset · publicn this way, the positive value represents the QTL that have increases on the major allele, while negative values indicate QTL that have increases on minor allele.
Data availability statement
The original contributions presented in the study are included in the article/
Supplementary Material
. All the 3D models can be found at: http://dx.doi.org/10.6084/m9.figshare.23692353 . Matlab Code can be found in Li et al., 2019 . Raw trait data can be found in
Supplementary Table 5
. Further inquiries can be directed to the corresponding authors.
Author contributions
ML: Formal analysis, Investigation, Methodology, Software, Visualization, Writing – original draft, Writing – review & editing. ZL: ForOpen asset ↗figshare · 10.6084/m9.figshare.23692353lines:206-232Supplement · publicof their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.
Supplementary material
The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fpls.2023.1260005/full#supplementary-material
Supplementary Figure 1
Illustrations of persistent homology traits. (A) An example of persistence barcode. (B) The persistence diagram that is equivalent to the barcode in (A) . One example of corresponding bar-to-point is highlighted in pink color. (C) Gaussian density estimatoOpen asset ↗lines:206-232Code / dataset availability confirmedEurope PMC · checked 15 Sept 2026
Published9 Jan 2024The Plant journal : for cell and molecular biologyCited by 3 · OpenAlex ↗
CucumberGreenhouseRootSegmentationYield / biomass estimationRoot system architectureYield / yield components
The root system is important for the absorption of water and nutrients by plants. Cultivating and selecting a root system architecture (RSA) with good adaptability and ultrahigh productivity have become the primary goals of agricultural improvement. Exploring the correlation between the RSA and crop yield is important for cultivating crop varieties with high-stress resistance and productivity. In this study, 277 cucumber varieties were collected for root system image analysis and yield using germination plates and greenhouse cultivation. Deep learning tools were used to train ResNet50 and U-Net models for image classification and segmentation of seedlings and to perform quality inspection and productivity prediction of cucumber seedling root system images. The results showed that U-Net can automatically extract cucumber root systems with high quality (F1_score ≥ 0.95), and the trained ResNet50 can predict cucumber yield grade through seedling root system image, with the highest F1_score reaching 0.86 using 10-day-old seedlings. The root angle had the strongest correlation with yield, and the shallow- and steep-angle frequencies had significant positive and negative correlations with yield, respectively. RSA and nutrient absorption jointly affected the production capacity of cucumber plants. The germination plate planting method and automated root system segmentation model used in this study are convenient for high-throughput phenotypic (HTP) research on root systems. Moreover, using seedling root system images to predict yield grade provides a new method for rapidly breeding high-yield RSA in crops such as cucumbers.
Why it matches plant phenotyping methods根系画像の自動セグメンテーションと収量予測モデルを開発・評価し、高スループット表現型解析への適用を中心に扱うため。
abstractDeep learning tools were used to train ResNet50 and U-Net models for image classification and segmentation of seedlings and to perform quality inspection and productivity prediction of cucumber seedling root system images.
Reproduction assets foundThe paper reports cucumber root-image phenotyping (U-Net segmentation, ResNet50 yield-grade classification) and states that the segmentation and classification model code was uploaded to a public GitHub repository under the author's account. No public phenotype/image dataset deposit is stated in the supplied blocks.Code · publicsis of variance was used to compare trait differences
between the different yield grades. Deep learning model train-
ing and testing were conducted using the PyTorch framework,
mainly running on a cloud platform (https://www.autodl.com).The codes for the segmentation and classification models used
in this study were uploaded to https://github.com/zhucuifang/.AUTHOR CONTRIBUTIONS
Cuifang Zhu: Investigation, Data collection and analysis;
Writing – original draft; Hongjun Yu: Investigation, Data
collection, Funding acquisition; Tao Lu and Yang Li: Super-
vision; Weijei Jiang: Methodology, Guidance, Funding
acquisition; Qiang Li: Review and editing, Guidance.
Ó 2024 Society for Experimental BOpen asset ↗zhucuifangpdf-raw-page:19 lines:112-171Code / dataset availability confirmedEurope PMC · checked 8 Sept 2026
Premise Most studies of the movement of orchid fruits and roots during plant development have focused on morphological observations; however, further genetic analysis is required to understand the molecular mechanisms underlying this phenomenon. A precise tool is required to observe these movements and harvest tissue at the correct position and time for transcriptomics research. Methods We utilized three-dimensional (3D) micro-computed tomography (CT) scans to capture the movement of fast-growing Erycina pusilla roots, and built an integrated bioinformatics pipeline to process 3D images into 3D time-lapse videos. To record the movement of slowly developing E. pusilla and Phalaenopsis equestris fruits, two-dimensional (2D) photographs were used. Results The E. pusilla roots twisted and resupinated multiple times from early development. The first period occurred in the early developmental stage (77-84 days after germination [DAG]) and the subsequent period occurred later in development (140-154 DAG). While E. pusilla fruits twisted 45° from 56-63 days after pollination (DAP), the fruits of P. equestris only began to resupinate a week before dehiscence (133 DAP) and ended a week after dehiscence (161 DAP). Discussion Our methods revealed that each orchid root and fruit had an independent direction and degree of torsion from the initial to the final position. Our innovative approaches produced detailed spatial and temporal information on the resupination of roots and fruits during orchid development.
Why it matches plant phenotyping methods3DマイクロCT、2D画像、画像処理パイプラインを用いてランの根・果実のねじれ運動を時空間的に抽出する手法が研究の中心であり、植物形態表現型の取得に該当する。
abstractWe utilized three-dimensional (3D) micro-computed tomography (CT) scans to capture the movement of fast-growing Erycina pusilla roots, and built an integrated bioinformatics pipeline to process 3D images into 3D time-lapse videos.
Reproduction assets foundThe paper's authors publicly released their custom 3D time-lapse pipeline code on GitHub and the micro-CT reconstruction data (young and mature E. pusilla plants) on Figshare, both explicitly cited in the Data Availability Statement.Code · publicThe scripts of the newly built 3D time‐lapse pipeline are available from GitHub ( https://github.com/LMVaskimo/3D-Lapse-Pipeline ).Open asset ↗LMVaskimo/3D-Lapse-Pipelinelines:238-443Code / dataset availability confirmedEurope PMC · OpenAlex · checked 15 Sept 2026
PoplarLaboratory / benchtopRootMorphology / geometry measurement2D/3D reconstructionGrowth / time-series analysisGrowth / development / phenologyRoot system architecture
Root system architecture (RSA) is an important measure of how plants navigate and interact with the soil environment. However, current methods in studying RSA must make tradeoffs between precision of data and proximity to natural conditions, with root growth in germination papers providing accessibility and high data resolution. Functional-structural plant models (FSPMs) can overcome this tradeoff, though parameterization and evaluation of FSPMs are traditionally based in manual measurements and visual comparison. Here, we applied a germination paper system to study the adventitious RSA and root phenology of Populus trichocarpa stem cuttings using time-series image-based phenotyping augmented by FSPM. We found a significant correlation between timing of root initiation and thermal time at cutting collection ( P value = 0.0061, R 2 = 0.875), but little correlation with RSA. We also present a use of RhizoVision [1] for automatically extracting FSPM parameters from time series images and evaluating FSPM simulations. A high accuracy of the parameterization was achieved in predicting 2D growth with a sensitivity rate of 83.5%. This accuracy was lost when predicting 3D growth with sensitivity rates of 38.5% to 48.7%, while overall accuracy varied with phenotyping methods. Despite this loss in accuracy, the new method is amenable to high throughput FSPM parameterization and bridges the gap between advances in time-series phenotyping and FSPMs.
Why it matches plant phenotyping methods時系列画像フェノタイピングとFSPMを統合し、根系形態パラメータを自動抽出・評価する方法が中心である。
abstractwe applied a germination paper system to study the adventitious RSA and root phenology of Populus trichocarpa stem cuttings using time-series image-based phenotyping augmented by FSPM.
Reproduction assets foundThe paper's Data Availability statement deposits all data and R scripts (the paper's phenotyping measurements and analysis) on Zenodo, and the adapted CropRootBox.jl model code on GitHub. Only the Zenodo URL matches an allowed URL, so the Zenodo asset is reported; the GitHub repository is noted but its URL is not in anDataset · publicview and editing: S.P., D.B., K.Y., S.D., and S.-H.K.
Competing interests: The authors declare that there is no conflict of interest regarding the publication of this article.
Data Availability
The model is housed in Github at github.com/uwkimlab/CropRootBox.jl_propagation.jl . All data and and R scripts are housed in Zenodo at https://doi.org/10.5281/zenodo.8083525 .
Supplementary Materials
Supplementary 1
Figs. S1 to S7
Tables S1 to S2
Click here for additional data file.
References
1.
Seethepalli
A , Dhakal
K , Griffiths
M , Guo
H , Freschet
GT , York
LM
.
RhizoVision explorer: Open-source software for root image analysis and measurement standardization
. AoB PLANTS . 2021 ; 13 ( 6 ): pOpen asset ↗Zenodo · 10.5281/zenodo.8083525lines:196-345Code / dataset availability confirmedEurope PMC · checked 15 Sept 2026
RiceLaboratory / benchtopRootGrowth / time-series analysisRoot system architecture
The study of plant root growth in real time has been difficult to achieve in an automated, high-throughput, and systematic fashion. Dynamic imaging of plant roots is important in order to discover novel root growth behaviors and to deepen our understanding of how roots interact with their environments. We designed and implemented the Generating Rhizodynamic Observations Over Time (GROOT) robot, an automated, high-throughput imaging system that enables time-lapse imaging of 90 containers of plants and their roots growing in a clear gel medium over the duration of weeks to months. The system uses low-cost, widely available materials. As a proof of concept, we employed GROOT to collect images of root growth of Oryza sativa, Hudsonia montana, and multiple species of orchids including Platanthera integrilabia over six months. Beyond imaging plant roots, our system is highly customizable and can be used to collect time- lapse image data of different container sizes and configurations regardless of what is being imaged, making it applicable to many fields that require longitudinal time-lapse recording.
Why it matches plant phenotyping methods植物根の長期・高スループット画像取得システムを開発し、根成長という植物形質の時系列計測に実証適用しているため、フェノタイピング手法が中心である。
abstractWe designed and implemented the Generating Rhizodynamic Observations Over Time (GROOT) robot, an automated, high-throughput imaging system that enables time-lapse imaging of 90 containers of plants and their roots growing in a clear gel medium over the duration of weeks to months.
Reproduction assets foundThe paper's authors explicitly state that all code files (robot control, QR code generation, image sorting/preprocessing) are publicly available on their GitHub organization, with URLs given in the text. No standalone phenotype dataset deposit is stated, but the authors' analysis/control code qualifies as a paper-sphenCode · publico pmc-prop-manuscript no pmc-prop-legally-suppressed no pmc-prop-has-pdf yes pmc-prop-has-supplement yes pmc-prop-pdf-only no pmc-prop-suppress-copyright no pmc-prop-is-real-version no pmc-prop-is-scanned-article no pmc-prop-preprint no pmc-prop-in-epmc yes pmc-license-ref CC BY Data Availability All code files are available at https://github.com/the-rhizodynamics-robot .
Data Availability
All code files are available at https://github.com/the-rhizodynamics-robot .
Introduction
All organisms must perform effective environmental exploration for survival. Plants are sessile and interact with the location where they grow; therefore, they can only navigate and explore their (typically) below-groOpen asset ↗the-rhizodynamics-robotlines:71-78Code · publicprocessing and analysis of the resulting imaging data. We have included code and documentation for an image preprocessing and time-lapse movie creation system we have developed. This system uses printed QR code labels to identify the individual growth vessels from the raw images ( S3 File : QR Code Generation, code available at https://github.com/orgs/the-rhizodynamics-robot/repositories ), which are then subsequently sorted into individual subdirectories, after which (optionally) stabilized time-lapse videos are created using the open source video editing software FFMPEG [ 24 ] [ S4 File : Image Sorting, code available at https://github.com/the-rhizodynamics-robot ]. This system has been teOpen asset ↗the-rhizodynamics-robotlines:95-102Code / dataset availability confirmedEurope PMC · Crossref · checked 7 Sept 2026
Ecology and forestry sciences are using an increasing amount of data to address a wide variety of technical and research questions at the local, continental and global scales. However, one type of data remains rare: fine-grain descriptions of large landscapes. Yet, this type of data could help address the scaling issues in ecology and could prove useful for testing forest management strategies and accurately predicting the dynamics of ecosystem services. Here we present three datasets describing three large European landscapes in France, Poland and Slovenia down to the tree level. Tree diameter, height and species data were generated combining field data, vegetation maps and airborne laser scanning (ALS) data following an area-based approach. Together, these landscapes cover more than 100 000 ha and consist of more than 42 million trees of 51 different species. Alongside the data, we provide here a simple method to produce high-resolution descriptions of large landscapes using increasingly available data: inventory and ALS data. We carried out an in-depth evaluation of our workflow including, among other analyses, a leave-one-out cross validation. Overall, the landscapes we generated are in good agreement with the landscapes they aim to reproduce. In the most favourable conditions, the root mean square error (RMSE) of stand basal area (BA) and mean quadratic diameter (Dg) predictions were respectively 5.4 m2.ha-1 and 3.9 cm, and the generated main species corresponded to the observed main species in 76.2% of cases.
Why it matches plant phenotyping methods航空レーザースキャンと現地データを統合して樹木の直径・樹高・種を大規模に推定する再利用可能なワークフローを提示し、交差検証で評価しているため、植物形質取得法が中心である。
abstractTree diameter, height and species data were generated combining field data, vegetation maps and airborne laser scanning (ALS) data following an area-based approach.
Reproduction assets foundThe paper's generated tree-level dataset (42 million trees with dbh, height, species for three European landscapes) is publicly deposited on Zenodo, and the ALS point cloud input for the Bauges northern part is publicly available on Recherche Data Gouv. Other underlying data (local inventories, southern Savoie ALS, MilDataset · publicALS data in the northern part (Haute-Savoie) are available to download from the Recherche Data Gouv dataverse at
https://doi.org/10.57745/ZUT1MJ , under the Etalab open license 2.Open asset ↗Recherche Data Gouv · 10.57745/ZUT1MJlines:912-955Code / dataset availability confirmedOpenAlex · Europe PMC · checked 7 Sept 2026
Phytophthora root rot (PRR) is a major constraint to chickpea production in Australia. Management options for controlling the disease are limited to crop rotation and avoiding high risk paddocks for planting. Current Australian cultivars have partial PRR resistance, and new sources of resistance are needed to breed cultivars with improved resistance. Field- and glasshouse-based PRR resistance phenotyping methods are labour intensive, time consuming, and provide seasonally variable results; hence, these methods limit breeding programs’ abilities to screen large numbers of genotypes. In this study, we developed a new space saving (400 plants/m2), rapid (<12 days), and simplified hydroponics-based PRR phenotyping method, which eliminated seedling transplant requirements following germination and preparation of zoospore inoculum. The method also provided post-phenotyping propagation all the way through to seed production for selected high-resistance lines. A test of 11 diverse chickpea genotypes provided both qualitative (PRR symptoms) and quantitative (amount of pathogen DNA in roots) results demonstrating that the method successfully differentiated between genotypes with differing PRR resistance. Furthermore, PRR resistance hydroponic assessment results for 180 recombinant inbred lines (RILs) were correlated strongly with the field-based phenotyping, indicating the field phenotype relevance of this method. Finally, post-phenotyping high-resistance genotypes were selected. These were successfully transplanted and propagated all the way through to seed production; this demonstrated the utility of the rapid hydroponics method (RHM) for selection of individuals from segregating populations. The RHM will facilitate the rapid identification and propagation of new PRR resistance sources, especially in large breeding populations at early evaluation stages.
Why it matches plant phenotyping methods植物の根腐病抵抗性を評価する高速・高スループット水耕フェノタイピング法を開発し、遺伝子型間識別と圃場評価との相関で検証しているため、方法が研究の中心です。
abstractwe developed a new space saving (400 plants/m2), rapid (<12 days), and simplified hydroponics-based PRR phenotyping method
Reproduction assets foundThe paper's supplementary materials (MDPI S1) contain paper-specific phenotyping images (post-phenotyping propagation, genotype symptom comparisons, hydroponics setup, growth stages) and a workflow flow chart, publicly downloadable. The underlying phenotype datasets are only 'available if requested', so they do not yetSupplement · publicThe following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/plants12234069/s1 , Figure S1: Phenotypic differences between (a) plants at the time of transplanting to potting mix post-phenotyping E1 and (b) 5 weeks later showing growth and pod developmentOpen asset ↗lines:235-249Code / dataset availability confirmedCrossref · checked 7 Sept 2026
Crop height is a vital indicator of growth conditions. Traditional drone image-based crop height measurement methods primarily rely on calculating the difference between the Digital Elevation Model (DEM) and the Digital Terrain Model (DTM). The calculation often needs more ground information, which remains labour-intensive and time-consuming. Moreover, the variations of terrains can further compromise the reliability of these ground models. In response to these challenges, we introduce G-DMD, a novel method based on Gated Recurrent Units (GRUs) using DEM and multispectral drone images to calculate the crop height. Our method enables the model to recognize the relation between crop height, elevation, and growth stages, eliminating reliance on DTM and thereby mitigating the effects of varied terrains. We also introduce a data preparation process to handle the unique DEM and multispectral image. Upon evaluation using a cotton dataset, our G-DMD method demonstrates a notable increase in accuracy for both maximum and average cotton height measurements, achieving a 34% and 72% reduction in Root Mean Square Error (RMSE) when compared with the traditional method. Compared to other combinations of model inputs, using DEM and multispectral drone images together as inputs results in the lowest error for estimating maximum cotton height. This approach demonstrates the potential of integrating deep learning techniques with drone-based remote sensing to achieve a more accurate, labour-efficient, and streamlined crop height assessment across varied terrains.
Why it matches plant phenotyping methodsドローンのDEM・マルチスペクトル画像から作物高を推定する手法を開発し、従来法と精度比較・検証しており、植物表現型取得が中心である。
abstractwe introduce G-DMD, a novel method based on Gated Recurrent Units (GRUs) using DEM and multispectral drone images to calculate the crop height.
Reproduction assets foundThe paper's crop-height phenotyping analysis is built on a public cotton UAV multispectral/DEM dataset deposited by Xu et al. on Figshare, which qualifies as a paper-specific, publicly actionable phenotyping input. The authors' own G-DMD code and processed data are only available upon request, so that component is not公Dataset · public47. Xu, R.; Li, C.; Paterson, A.H. UAV Multispectral. Figshare. Dataset. 2018. Available online: https://figshare.com/articles/Open asset ↗Figsharepdf-page:22 lines:1-20Code / dataset availability confirmedEurope PMC · checked 14 Sept 2026
Why it matches plant phenotyping methodsカッサバ根の乾物含量とアミロース含量という植物器官形質を対象に、NIRS予測モデルを開発・検証しており、形質取得法が研究の中心です。
abstractnear-infrared reflectance spectroscopy (NIRS) models were developed to aid breeding and selection of DMC and AC
Reproduction assets foundThe paper's field experiment phenotyping data (cassava clones used for NIRS calibration of dry matter and amylose content) is openly available in Cassavabase at the trial 4384 URL, per the authors' explicit availability statements. No author analysis code, NIRS spectra files, or trained model/calibration equations are指Dataset · publicexperiment is available in an open access data repository at
(https://www.cassavabase.org/breeders/trial/4384?format=). The
pre-breeding set of germplasms used in the present study con-
tained genotypes that are from diverse backgrounds (from Inter-
national Institute of Tropical Agriculture (IITA), International
Center for Tropical Agriculture (CIAT) and NaCRRI), for which
diversity is important in development of NIRS calibrations. They
are cOpen asset ↗Cassavabase · trial/4384pdf-raw-page:4 lines:1-91Code / dataset availability confirmedOpenAlex · Europe PMC · bioRxiv · checked 7 Sept 2026
Abstract Image segmentation is commonly used to estimate the location and shape of plants and their external structures. Segmentation masks are then used to localize landmarks of interest and compute other geometric features that correspond to the plant’s phenotype. Despite its prevalence, segmentation-based approaches are laborious (requiring extensive annotation to train), and error-prone (derived geometric features are sensitive to instance mask integrity). Here we present a segmentation-free approach which leverages deep learning-based landmark detection and grouping, also known as pose estimation. We use a tool originally developed for animal motion capture called SLEAP (Social LEAP Estimates Animal Poses) to automate the detection of distinct morphological landmarks on plant roots. Using a gel cylinder imaging system across multiple species, we show that our approach can reliably and efficiently recover root system topology at high accuracy, few annotated samples, and faster speed than segmentation-based approaches. In order to make use of this landmark-based representation for root phenotyping, we developed a Python library ( sleap-roots ) for trait extraction directly comparable to existing segmentation-based analysis software. We show that landmark-derived root traits are highly accurate and can be used for common downstream tasks including genotype classification and unsupervised trait mapping. Altogether, this work establishes the validity and advantages of pose estimation-based plant phenotyping. To facilitate adoption of this easy-to-use tool and to encourage further development, we make sleap-roots , all training data, models, and trait extraction code available at: https://github.com/talmolab/sleap-roots and https://osf.io/k7j9g/ .
Why it matches plant phenotyping methods植物根の形態ランドマークをポーズ推定で検出し、根系形質を抽出する手法とソフトウェアを開発・検証した研究であり、植物フェノタイピング手法が中心である。
abstractHere we present a segmentation-free approach which leverages deep learning-based landmark detection and grouping, also known as pose estimation.
Reproduction assets foundThe paper makes its root phenotyping assets public: labeled training data, trained SLEAP models, and analysis files on OSF, the sleap-roots trait-extraction codebase on GitHub, and a separate figure-replication code repository.Dataset · publicThe datasets generated and/or analyzed during the current study are available in the Open Science Framework (OSF) repository. This includes the labeled data, predictive models, and analysis files which can be accessed via the following link: https://osf.io/k7j9g/ .Open asset ↗osf.io/k7j9glines:752-811Code · publicAdditionally, the specific code utilized for replicating the figures presented in this study can be found in a separate GitHub repository here: https://github.com/talmolab/Berrigan_et_al_sleap-roots .Open asset ↗talmolab/Berrigan_et_al_sleap-rootslines:752-811Code / dataset availability confirmedEurope PMC · checked 7 Sept 2026
ArabidopsisLaboratory / benchtopRGB / grayscaleRootTrackingRoot system architecture
Plant roots elongate when cells produced in the apical meristem enter a transient period of rapid expansion. To measure the dynamic process of root cell expansion in the elongation zone, we captured digital images of growing Arabidopsis roots with horizontal microscopes and analyzed them with a custom image analysis program (PatchTrack) designed to track the growth-driven displacement of many closely spaced image patches. Fitting a flexible logistics equation to patch velocities plotted versus position along the root axis produced the length of the elongation zone (mm), peak relative elemental growth rate (% h -1 ), the axial position of the peak (mm from the tip), and average root elongation rate (mm h -1 ). For a wild-type root, the average values of these kinematic traits were 0.52 mm, 23.7% h -1 , 0.35 mm, and 0.1 mm h -1 , respectively. We used the platform to determine the kinematic phenotypes of auxin transport mutants. The results support a model in which the PIN2 auxin transporter creates an area of expansion-suppressing, supraoptimal auxin concentration that ends 0.1 mm from the quiescent center (QC), and that ABCB4 and ABCB19 auxin transporters maintain expansion-limiting suboptimal auxin levels beginning approximately 0.5 mm from the QC. This study shows that PatchTrack can quantify dynamic root phenotypes in kinematic terms.
Why it matches plant phenotyping methodsPatchTrackによる画像解析で根の動的な伸長・細胞伸長形質を抽出する手法とプラットフォームを開発・実証しており、表現型取得が研究の中心です。
abstractanalyzed them with a custom image analysis program (PatchTrack) designed to track the growth-driven displacement of many closely spaced image patches.
Reproduction assets foundThe paper's Data Availability Statement explicitly deposits the authors' PatchTrack image-analysis code (used to produce the kinematic phenotyping measurements) on a public GitHub repository. No phenotype dataset or image deposit is stated.Code · publicThe computer code for PatchTrack is available at https://github.com/phytoMorph/phytoMorph_kinematics .Open asset ↗phytoMorph/phytoMorph_kinematicslines:96-111Code / dataset availability confirmedOpenAlex · checked 14 Sept 2026
RootClassificationMorphology / geometry measurementPose / keypoint estimationRoot system architecture
Image segmentation is commonly used to estimate the location and shape of plants and their external structures. Segmentation masks are then used to localize landmarks of interest and compute other geometric features that correspond to the plant’s phenotype. Despite its prevalence, segmentation-based approaches are laborious (requiring extensive annotation to train), and error-prone (derived geometric features are sensitive to instance mask integrity). Here we present a segmentation-free approach which leverages deep learning-based landmark detection and grouping, also known as pose estimation. We use a tool originally developed for animal motion capture called SLEAP (Social LEAP Estimates Animal Poses) to automate the detection of distinct morphological landmarks on plant roots. Using high-throughput phenotyping method Root Architecture 3-D Imaging Cylinder (RADICYL) across multiple species, we show that our approach can reliably and efficiently recover root system topology at greater accuracy, faster speed, and with fewer annotated samples than segmentation-based approaches. In order to make use of this landmark-based representation for root phenotyping, we developed a Python library ( sleap-roots ) for trait extraction directly comparable to existing segmentation-based analysis software. We show that landmark-derived root traits are highly accurate and can be used for common downstream tasks including genotype classification and unsupervised trait mapping. Altogether, this work establishes the validity and advantages of pose estimation-based plant phenotyping. To facilitate adoption of this easy-to-use tool and to encourage further development, we make sleap-roots , all training data, models, and trait extraction code available at: https://github.com/talmolab/sleap-roots .
Why it matches plant phenotyping methods植物根のランドマーク検出による表現型抽出法を開発・検証し、ソフトウェアと学習データも提供しているため、方法が中心的です。
abstractHere we present a segmentation-free approach which leverages deep learning-based landmark detection and grouping, also known as pose estimation.
Reproduction assets foundThe authors explicitly state they make their sleap-roots Python library, all training data, trained models, and trait extraction code publicly available on GitHub, directly supporting this paper's root pose-estimation phenotyping analysis.Code · publice classification and unsupervised
trait mapping. Altogether, this work establishes the validity and advantages of pose estimation-based plant phenotyping. To
facilitate adoption of this easy-to-use tool and to encourage further development, we make sleap-roots , all training data,
models, and trait extraction code available at: https://github.com/talmolab/sleap-roots.
Elizabeth M. Berrigan, Lin Wang, Hannah Carrillo, Kimberly Echegoyen, Mikayla Kappes, Jorge Torres,
Angel Ai-Perreira, Erica McCoy, Emily Shane, Charles Copeland, Lauren Ragel, Charidimos Georgousakis,
Sanghwa Lee, Dawn Reynolds, Avery Talgo, Juan Gonzalez, Ling Zhang, Ashish Rajurkar, Michel Ruiz,
Erin Daniels, Liezl Maree, SOpen asset ↗talmolab/sleap-rootspdf-layout-page:1 lines:1-47Code / dataset availability confirmedEurope PMC · OpenAlex · Crossref · checked 7 Sept 2026
MaizeLaboratory / benchtopStereoRootMorphology / geometry measurement2D/3D reconstructionGrowth / time-series analysisGrowth / development / phenologyRoot system architecture
Background: The root of a plant is a fundamental organ for the multisensory perception of the environment. Investigating root growth dynamics as a mean of their interaction with the environment is of key importance for improving knowledge in plant behaviour, plant biology and agriculture. To date, it is difficult to study roots movements from a dynamic perspective given that available technologies for root imaging focus mostly on static characterizations, lacking of temporal and three-dimensional (3D) spatial information. This paper describes a new system based on timelapse for the 3D reconstruction and analysis of roots growing in hydroponics. Results The system is based on infrared stereo-cameras acquiring time-lapse images of the roots for 3D reconstruction. The acquisition protocol guarantees the root growth in complete dark while the upper part of the plant grows in normal light conditions. The system extracts the 3D trajectory of the root tip and a set of descriptive features in both the temporal and frequency domains. The system has been used on Zea mays L. (B73) during the first week of growth and shows good inter-reliability between operators with an Intra Class Correlation Coefficient (ICC) > 0.9 for all features extracted. It also showed measurement accuracy with a median difference of < 1 mm between computed and manually measured root length. Conclusions The system and the protocol presented in this study enable accurate 3D analysis of primary root growth in hydroponics. It can serve as a valuable tool for analyzing real-time root responses to environmental stimuli thus improving knowledge on the processes contributing to roots physiological and phenotypic plasticity.
Why it matches plant phenotyping methods根の3D動態を画像から再構成・解析するシステムを開発し、特徴量の信頼性と測定精度を検証しており、植物表現型取得法が中心である。
abstractThis paper describes a new system based on timelapse for the 3D reconstruction and analysis of roots growing in hydroponics.
Reproduction assets foundThe preprint explicitly states that the 3D root tip trajectory data underlying its phenotyping analysis are publicly deposited on Zenodo (record 8422242), making this a paper-specific, publicly accessible phenotype dataset. No author analysis code with a public URL is stated (SPROUTS is proprietary third-party softwareDataset · publicData describing 3D trajectories used in this paper are available here: https://zenodo.org/record/8422242Open asset ↗Zenodo · 8422242lines:119-156Code / dataset availability confirmedEurope PMC · checked 15 Sept 2026
Arbuscular mycorrhizas (AM) are one of the most widespread symbiosis on earth. This plant-fungus interaction involves around 72% of plant species, including most crops. AM symbiosis improves plant nutrition and tolerance to biotic and abiotic stresses. The fungus, in turn, receives carbon compounds derived from the plant photosynthetic process, such as sugars and lipids. Most studies investigating AM and their applications in agriculture requires a precise quantification of the intensity of plant colonization. At present, the majority of researchers in the field base AM quantification analyses on manual visual methods, prone to operator errors and limited reproducibility. Here we propose a novel semi-automated approach to quantify AM fungal root colonization based on digital image analysis comparing three methods: (i) manual quantification (ii) image thresholding, (iii) machine learning. We recognize machine learning as a very promising tool for accelerating, simplifying and standardizing critical steps in analysing AM quantification, answering to an urgent need by the scientific community studying this symbiosis.
Why it matches plant phenotyping methods植物根の菌根菌コロニー形成という植物状態を、画像閾値処理と機械学習で定量する半自動手法を開発・比較しており、表現型取得・抽出法が中心である。
abstractHere we propose a novel semi-automated approach to quantify AM fungal root colonization based on digital image analysis comparing three methods: (i) manual quantification (ii) image thresholding, (iii) machine learning.
Reproduction assets foundThe paper's Data Availability statement deposits the analysed root image datasets (mycorrhizal and non-mycorrhizal) on Figshare, directly reproducing the paper's phenotyping inputs for thresholding and machine learning segmentation. The Zeiss GitHub link is a generic third-party algorithm documentation page, not a codeDataset · publicuthors have read and approved the final manuscript.
Funding
Ministero dell’Università e della Ricerca: PhD fellowship to AC; Università degli Studi di Torino: Ricerca Locale 2023 to AG.
Data availability
The analysed datasets are available from Figshare: Segmentation using thresholding and machine learning of mycorrhizal roots. https://doi.org/10.6084/m9.figshare.14679729 . Segmentation using thresholding and machine learning of non mycorrhizal roots. https://doi.org/10.6084/m9.figshare.14679684 .
Competing interests
The authors declare no competing interests.
Footnotes
Publisher's note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional aOpen asset ↗Figshare · 10.6084/m9.figshare.14679729lines:300-319Dataset · publicniversità degli Studi di Torino: Ricerca Locale 2023 to AG.
Data availability
The analysed datasets are available from Figshare: Segmentation using thresholding and machine learning of mycorrhizal roots. https://doi.org/10.6084/m9.figshare.14679729 . Segmentation using thresholding and machine learning of non mycorrhizal roots. https://doi.org/10.6084/m9.figshare.14679684 .
Competing interests
The authors declare no competing interests.
Footnotes
Publisher's note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
References
1. Rich MK, Nouri E, Courty PE, Reinhardt D. Diet of arbuscular mycorrhizal fungi: bread and butter? TOpen asset ↗Figshare · 10.6084/m9.figshare.14679684lines:300-319Code / dataset availability confirmedCrossref · checked 14 Sept 2026
WheatRootMorphology / geometry measurementPhysiological trait estimationWater status / transpiration
Abstract Background and aims Root distribution over the soil profile is important for crop resource uptake. Using machine learning (ML), this study investigated whether measured square root of planar root length density (Sqrt_pRLD) at different soil depths were related to uptake of isotope tracer (15N) and drought stress indicator (13C) in wheat, to reveal root function. Methods In the RadiMax semi-field root-screening facility 95 winter wheat genotypes were phenotyped for root growth in 2018 and 120 genotypes in 2019. Using the minirhizotron technique, root images were acquired across a depth range from 80 to 250 cm in May, June, and July and RL was extracted using a convolutional neural network. We developed ML models to explore whether the Sqrt_pRLD estimates at different soil depths were predictive of the uptake of deep soil nitrogen - using deep placement of 15N tracer as well as natural abundance of 13C isotope. We analyzed the correlations to tracer levels to both a parametrized root depth estimation and an ML approach. We further analyzed the genotypic effects on root function using mediation analysis. Results Both parametrized and ML models demonstrated clear correlations between Sqrt_pRLD distribution and resource uptake. Further, both models demonstrated that deep roots at approx. 150 to 170 cm depth were most important for explaining the plant content of 15N and 13C isotopes. The correlations were higher in 2018. Conclusions The results demonstrated that, parametrized models and ML-based analysis provided complementary insight into the importance of deep rooting for water and nitrogen uptake.
Why it matches plant phenotyping methods深根画像をCNNで解析して根長密度を抽出し、機械学習による根形質推定と技術的解析を行っており、表現型取得・抽出法が研究の中心である。
abstractUsing the minirhizotron technique, root images were acquired across a depth range from 80 to 250 cm in May, June, and July and RL was extracted using a convolutional neural network.
Reproduction assets found保存済みの本文根拠を更新済みルールで再検証し、公開資産1件を確認しました。Code · publicCodes and data are available on GitHub at https://github.com/satyasaran/CropML.git .Open asset ↗satyasaran/CropML · CropMLlines:182-216Code / dataset availability confirmedCrossref · OpenAlex · checked 15 Sept 2026
Buckwheat plant height is an important indicator for producers. Due to the decline in agricultural labor, the automatic and real-time acquisition of crop growth information will become a prominent issue for farms in the future. To address this problem, we focused on stereo vision and a regression convolutional neural network (CNN) in order to estimate buckwheat plant height. MobileNet V3 Small, NasNet Mobile, RegNet Y002, EfficientNet V2 B0, MobileNet V3 Large, NasNet Large, RegNet Y008, and EfficientNet V2 L were modified into regression CNNs. Through a five-fold cross-validation of the modeling data, the modified RegNet Y008 was selected as the optimal estimation model. Based on the depth and contour information of buckwheat depth image, the mean absolute error (MAE), root mean square error (RMSE), mean square error (MSE), and mean relative error (MRE) when estimating plant height were 0.56 cm, 0.73 cm, 0.54 cm, and 1.7%, respectively. The coefficient of determination (R2) value between the estimated and measured results was 0.9994. Combined with the LabVIEW software development platform, this method can estimate buckwheat accurately, quickly, and automatically. This work contributes to the automatic management of farms.
Why it matches plant phenotyping methodsステレオビジョンと回帰CNNにより、圃場でのソバ草丈を自動推定する手法を開発・検証しており、植物表現型の取得方法が研究の中心です。
abstractwe focused on stereo vision and a regression convolutional neural network (CNN) in order to estimate buckwheat plant height.
Reproduction assets foundThe paper's buckwheat height estimation model code (modified regression CNNs with training results) is publicly available via an authors' GitHub repository explicitly stated in the text. The phenotype dataset (depth images with height labels) is only available by contacting the authors, so it is not a public asset.Code · publicndows 11 (64 bit), and an
Nvidia GeForce RTX 3090 24 GB graphics card with Nvidia Ampere architecture. All of
the models used the processed grayscale images of 224 × 224 pixels as the input and the
estimated buckwheat height as the output. The codes of the models with training results
are available at the following GitHub link: https://github.com/18801389568/Buckwheat-height-estimation (accessed on 26 July 2023).
Figure 5. Construction method of the buckwheat crop height estimation models.
Training the models was essentially a process of continually updating the trainable
parameters of each model in order to make the crop height estimation results increasingly
accurate. Considering the quantOpen asset ↗18801389568/Buckwheat-height-estimationpdf-raw-page:6 lines:1-60Code / dataset availability confirmedOpenAlex · Europe PMC · checked 15 Sept 2026
The study of genomic control of drought tolerance in crops requires techniques to impose well defined and consistent levels of drought stress and efficiently measure single-plant water use for hundreds of experimental units over timescales of several months. Traditional gravimetric methods are extremely labor intensive or require expensive technology, and are subject to other errors. This study demonstrates a low-cost, passive, bottom-watered system that is easily scaled for high-throughput phenotyping. The soil water content in the pots is controlled by altering the water table height in an underlying wicking bed via a float valve. The resulting soil moisture profile is then maintained passively as water withdrawn by the plant is replaced by upward movement of water from the wicking bed, which is fed from a reservoir via the float valve. The single-plant water use can be directly measured over time intervals from one to several days by observing the water level in the reservoir. Using this method, four different drought stress levels were induced in pots containing soybean (Glycine max (L.) Merr.), producing four statistically distinct groups for shoot dry weight and seed yield, as well as clear treatment effects for other relevant parameters, including root:shoot dry weight ratio, pod number, cumulative water use, and water use efficiency. This system has a broad range of applications, and should increase feasibility of high-throughput phenotyping efforts for plant drought tolerance traits.
Why it matches plant phenotyping methods高スループット表現型解析のための低コスト灌水・水利用測定システムを開発・実証しており、植物の水利用と乾燥ストレス関連形質の取得が中心的な方法論的貢献である。
abstractThis study demonstrates a low-cost, passive, bottom-watered system that is easily scaled for high-throughput phenotyping.
Reproduction assets foundThe article's Data availability statement places the study's original contributions (phenotype measurements and supplementary experiment data) in the article/Supplementary Material, which is publicly available at the Frontiers supplementary-material URL. No author analysis code, scripts, models, or standalone phenotypeSupplement · publicof their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.
Supplementary material
The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fpls.2023.1201102/full#supplementary-material
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References
Araya Y. N.
Gowing D. J.
Dise N.
( 2010 ).
A controlled water-table depth system toOpen asset ↗lines:288-364Code / dataset availability confirmedOpenAlex · Europe PMC · Crossref · checked 14 Sept 2026
Premise: Higher temperatures across the globe are causing an increase in the frequency and severity of droughts. In agricultural crops, this results in reduced yields, financial losses, and increased food costs at the supermarket. Root growth maintenance in drying soils plays a major role in a plant's ability to survive and perform under drought, but phenotyping root growth is extremely difficult due to roots being under the soil. Methods and Results: RootBot is an automated high-throughput phenotyping robot that eliminates many of the difficulties and reduces the time required for performing drought-stress studies on primary roots. RootBot simulates root growth conditions using transparent plates to create a gap that is filled with soil and polyethylene glycol (PEG) to simulate low soil moisture. RootBot has a gantry system with vertical slots to hold the transparent plates, which theoretically allows for evaluating more than 50 plates at a time. Software pipelines were also co-opted, developed, tested, and extensively refined for running the RootBot imaging process, storing and organizing the images, and analyzing and extracting data. Conclusions: The RootBot platform and the lessons learned from its design and testing represent a valuable resource for better understanding drought tolerance mechanisms in roots, as well as for identifying breeding and genetic engineering targets for crop plants.
Why it matches plant phenotyping methods根の乾燥ストレス下での成長を自動撮像・解析する高スループット表現型解析ロボットの開発、試験、画像データ抽出が中心である。
abstractRootBot is an automated high-throughput phenotyping robot that eliminates many of the difficulties and reduces the time required for performing drought-stress studies on primary roots.
Reproduction assets foundThe paper's authors publicly released all RootBot source code, scripts, and CAD files on BitBucket, plus step-by-step protocols on protocols.io for the RootBot/FarmBot OS phenotype scheduling and the image scoring/analysis pipeline used to extract root measurements. No raw phenotype dataset deposit is stated beyond theCode · publicThomas S. K., Guill K. E., et al. 2023. RootBot: High‐throughput root stress phenotyping robot. Applications in Plant Sciences
11(6): e11541. 10.1002/aps3.11541
Mia Ruppel and Sven K. Nelson contributed equally to this work.
DATA AVAILABILITY STATEMENT
All source code, scripts, and CAD files are freely available on BitBucket ( https://bitbucket.org/washjake/rootbot/ ). The RootBot/FarmBot OS setup, programming, and phenotype scheduling ( https://doi.org/10.17504/protocols.io.x54v9d76zg3e/v1 ) and the image scoring protocol ( https://doi.org/10.17504/protocols.io.5jyl8j16dg2w/v1 ) are available on protocols.io (Ruppel et al., 2023a , b ).
REFERENCES
Daryanto, S.
, Wang L., and Jacinthe P.‐AOpen asset ↗bitbucket.org/washjake/rootbotlines:132-394Code · publicPlant Sciences
11(6): e11541. 10.1002/aps3.11541
Mia Ruppel and Sven K. Nelson contributed equally to this work.
DATA AVAILABILITY STATEMENT
All source code, scripts, and CAD files are freely available on BitBucket ( https://bitbucket.org/washjake/rootbot/ ). The RootBot/FarmBot OS setup, programming, and phenotype scheduling ( https://doi.org/10.17504/protocols.io.x54v9d76zg3e/v1 ) and the image scoring protocol ( https://doi.org/10.17504/protocols.io.5jyl8j16dg2w/v1 ) are available on protocols.io (Ruppel et al., 2023a , b ).
REFERENCES
Daryanto, S.
, Wang L., and Jacinthe P.‐A.. 2016. Global synthesis of drought effects on maize and wheat production. PLoS ONE
11: e0156362.
Das, A.
, SchneOpen asset ↗10.17504/protocols.io.x54v9d76zg3e/v1lines:132-394Code · publicd equally to this work.
DATA AVAILABILITY STATEMENT
All source code, scripts, and CAD files are freely available on BitBucket ( https://bitbucket.org/washjake/rootbot/ ). The RootBot/FarmBot OS setup, programming, and phenotype scheduling ( https://doi.org/10.17504/protocols.io.x54v9d76zg3e/v1 ) and the image scoring protocol ( https://doi.org/10.17504/protocols.io.5jyl8j16dg2w/v1 ) are available on protocols.io (Ruppel et al., 2023a , b ).
REFERENCES
Daryanto, S.
, Wang L., and Jacinthe P.‐A.. 2016. Global synthesis of drought effects on maize and wheat production. PLoS ONE
11: e0156362.
Das, A.
, Schneider H., Burridge J., Ascanio A. K. M., Wojciechowski T., Topp C. N., Lynch J. P., et al.Open asset ↗10.17504/protocols.io.5jyl8j16dg2w/v1lines:132-394Code / dataset availability confirmedOpenAlex · checked 14 Sept 2026
Abstract Aims Our understanding of the rhizosphere is limited by the lack of techniques for in situ live microscopy. Current techniques are either destructive or unsuitable for observing chemical changes within the pore space. To address this limitation, we have developed artificial substrates, termed smart soils, that enable the acquisition and 3D reconstruction of chemical sensors attached to soil particles. Methods The transparency of smart soils was achieved using polymer particles with refractive index matching that of water. The surface of the particles was modified both to retain water and act as a local sensor to report on pore space pH via fluorescence emissions. Multispectral signals were acquired from the particles using a light sheet microscope, and machine learning algorithms predicted the changes and spatial distribution in pH at the surface of the smart soil particles. Results The technique was able to predict pH live and in situ within ± 0.5 units of the true pH value. pH distribution could be reconstructed across a volume of several cubic centimetres around plant roots at 10 μm resolution. Using smart soils of different composition, we revealed how root exudation and pore structure create variability in chemical properties. Conclusion Smart soils captured the pH gradients forming around a growing plant root. Future developments of the technology could include the fine tuning of soil physicochemical properties, the addition of chemical sensors and improved data processing. Hence, this technology could play a critical role in advancing our understanding of complex rhizosphere processes.
Why it matches plant phenotyping methods植物根圏のpHを生体根周辺で測定・3D再構成するセンサー基盤を開発し、精度検証まで行っており、植物状態の取得方法が研究の中心である。
abstractwe have developed artificial substrates, termed smart soils, that enable the acquisition and 3D reconstruction of chemical sensors attached to soil particles.
Reproduction assets foundThe paper's Data availability statement explicitly releases the authors' software for predicting pH from light-sheet image data (the machine-learning phenotyping analysis) on the authors' public GitHub repository SENSOIL. No separate phenotype/trait dataset or image deposit is stated; supplementary material is only a 'Code · public102 Plant Soil (2024) 500:91–104
1 3
Vol:. (1234567890)
Data availability Software developped for predicting pH
from image data is available at https://github.com/LionelDu-puy/SENSOIL/tree/main/pH_Release.Declarations
Competing interest There is no competing interest.
Open Access This article is licensed under a Creative
Commons Attribution 4.0 International License, which per-
mits use, sharing, adaptation, distribution and reproduction in
any medium or format, as long as you give appropriate credit
to the original author(s) and the source,Open asset ↗SENSOILpdf-raw-page:12 lines:1-92Code / dataset availability confirmedEurope PMC · checked 14 Sept 2026
In vivo microscopy of plants with high-frequency imaging allows observation and characterization of the dynamic responses of plants to stimuli. It provides access to responses that could not be observed by imaging at a given time point. Such methods are particularly suitable for the observation of fast cellular events such as membrane potential changes. Classical measurement of membrane potential by probe impaling gives quantitative and precise measurements. However, it is invasive, requires specialized equipment, and only allows measurement of one cell at a time. To circumvent some of these limitations, we developed a method to relatively quantify membrane potential variations in Arabidopsis thaliana roots using the fluorescence of the voltage reporter DISBAC 2 (3). In this protocol, we describe how to prepare experiments for agar media and microfluidics, and we detail the image analysis. We take an example of the rapid plasma membrane depolarization induced by the phytohormone auxin to illustrate the method. Relative membrane potential measurements using DISBAC 2 (3) fluorescence increase the spatio-temporal resolution of the measurements and are non-invasive and suitable for live imaging of growing roots. Studying membrane potential with a more flexible method allows to efficiently combine mature electrophysiology literature and new molecular knowledge to achieve a better understanding of plant behaviors. Key features Non-invasive method to relatively quantify membrane potential in plant roots. Method suitable for imaging seedlings root in agar or liquid medium. Straightforward quantification.
Why it matches plant phenotyping methods植物根の膜電位を蛍光画像から定量する非侵襲的手法の開発と画像解析プロトコルが中心であり、植物表現型計測法に該当する。
abstractwe developed a method to relatively quantify membrane potential variations in Arabidopsis thaliana roots using the fluorescence of the voltage reporter DISBAC 2 (3).
Reproduction assets foundThe protocol explicitly states that the raw imaging data re-analyzed in the paper are deposited on Zenodo and that all analysis scripts (R and Python) are available in a public SourceForge repository. Both are paper-specific, public, and actionable.Dataset · publicluorescence only in the root transition zone. Moreover, we focus on the interface between the cortex and the epidermis, as the dead lateral root cap cells were strongly fluorescent (open membranes for the dye to react to). The data presented here are re-analyzed images from Serre et al. (2021). Raw data can be found on Zenodo ( https://zenodo.org/record/4922659 ). All the scripts used in this protocol can be found on the public repository https://sourceforge.net/projects/disbac2-3-data-analysis/ .
Here, we describe a method to:
Quantify DISBAC 2 (3) fluorescence in the transition zone at a given point (agar experiment) or over time (microfluidics) using the ImageJ/Fiji software.
QuantOpen asset ↗Zenodo · 4922659lines:168-214Code · publicermis, as the dead lateral root cap cells were strongly fluorescent (open membranes for the dye to react to). The data presented here are re-analyzed images from Serre et al. (2021). Raw data can be found on Zenodo ( https://zenodo.org/record/4922659 ). All the scripts used in this protocol can be found on the public repository https://sourceforge.net/projects/disbac2-3-data-analysis/ .
Here, we describe a method to:
Quantify DISBAC 2 (3) fluorescence in the transition zone at a given point (agar experiment) or over time (microfluidics) using the ImageJ/Fiji software.
Quantify root elongation either as an average growth (agar experiment) or over time (microfluidics).
Normalize the microfluidOpen asset ↗SourceForge · disbac2-3-data-analysislines:168-214Code / dataset availability confirmedEurope PMC · checked 14 Sept 2026
Image analysis is widely applied in plant science for phenotyping and monitoring botanic and agricultural species. Although a lot of software is available, tools integrating image analysis and statistical assessment of seedling growth in large groups of plants are limited or absent, and do not cover the needs of the researchers. In this study, we developed Morley, a free, open-source graphical user interface written in Python. Morley automates the following workflow: (1) group-wise analysis of a few thousand seedlings from multiple images; (2) recognition of seeds, shoots and roots in seedling images; (3) calculation of shoot and root lengths and surface areas, (4) evaluation of statistically significant differences between plant groups, (5) calculation of germination rates, (6) visualization and interpretation. Morley is designed for laboratory studies of biotic effects on seedling growth, when molecular mechanisms underlying morphometric changes are analyzed. Performance was tested using cultivars of T. aestivum, P. sativum on seedlings of up to 1 week old. Accuracy of the measured morphometric parameters was comparable with the ones obtained using ImageJ and manual measurements. Dose-dependent laboratory tests for germination affected by new bioactive compounds and fertilizers, assuming extraction of seedlings from a substrate and/or dissection are among the suggested applications.
Why it matches plant phenotyping methods植物の画像から種子・シュート・根を認識し、形態形質を自動抽出して統計評価するオープンソースツールの開発・精度検証が中心である。
abstractIn this study, we developed Morley, a free, open-source graphical user interface written in Python.
Reproduction assets foundThe paper's authors publicly released the Morley analysis code (GitHub repo dashabezik/Morley) and example data/user guide (dashabezik/plants), both explicitly stated in the Data Availability Statement and Methods. These directly support the paper's seedling image analysis and morphometric measurements.Code · publicths and plant surface areas, and figures characterizing
distributions of measured parameters, bar plots with mean values and standard deviations (95% CI),
and heatmaps visualizing the conclusions on statistical significance of the morphometric differences.
Code, graphical user interface, user guide and examples are available at
https://github.com/dashabezik/Morley and https://github.com/dashabezik/plants/, respectively.
Morley is available as a graphical user interface and a command line tool.
3. Results
3.1. Comparison of Morley with ImageJ and Manual Measurements Demonstrates Agreement between
Results
ImageJ [23] is widely applied for image analysis of plants and seedlings [24–28] andOpen asset ↗dashabezik/Morleypdf-layout-page:6 lines:1-47Dataset · publicon,
IAT; funding acquisition, IAT. All authors have read and agreed to the published version of the manuscript.
Funding: The study was supported by Russian Science Foundation, grant #22‐26‐00109.
Data Availability Statement: Program code, GUI, user guide and example data are available at
https://github.com/dashabezik/Morley and https://github.com/dashabezik/plants/.
Acknowledgments: The authors thank Dr. Olga M. Zhigalina and Dr. Dmitri N. Khmelenin (Shubnikov Institute
of Crystallography, FSRC “Crystallography and Photonics”, RAS) for collecting high‐quality TEM images of
iron nanoparticles and Dr. Nadezhda G. Berezkina (N.N. Semenov Federal Research Center for Chemical
Physics, RAS) forOpen asset ↗dashabezik/plantspdf-layout-page:13 lines:1-65Code / dataset availability confirmedEurope PMC · checked 7 Sept 2026
Background Investigations into the growth and self-organization of plant roots is subject to fundamental and applied research in various areas such as botany, agriculture, and soil science. The growth activity of the plant tissue can be investigated by isotope labeling experiments with heavy water and subsequent detection of the deuterium in non-exchangeable positions incorporated into the plant biomass. Commonly used analytical methods to detect deuterium in plants are based on mass-spectrometry or neutron-scattering and they either suffer from elaborated sample preparation, destruction of the sample during analysis, or low spatial resolution. Confocal Raman micro-spectroscopy (CRM) can be considered a promising method to overcome the aforementioned challenges. The substitution of hydrogen with deuterium results in the measurable shift of the CH-related Raman bands. By employing correlative approaches with a high-resolution technique, such as helium ion microscopy (HIM), additional structural information can be added to CRM isotope maps and spatial resolution can be further increased. For that, it is necessary to develop a comprehensive workflow from sample preparation to data processing. Results A workflow to prepare and analyze roots of hydroponically grown and deuterium labeled Zea mays by correlative HIM-CRM micro-analysis was developed. The accuracy and linearity of deuterium detection by CRM were tested and confirmed with samples of deuterated glucose. A set of root samples taken from deuterated Zea mays in a time-series experiment was used to test the entire workflow. The deuterium content in the roots measured by CRM was close to the values obtained by isotope-ratio mass spectrometry. As expected, root tips being the most actively growing root zone had incorporated the highest amount of deuterium which increased with increasing time of labeling. Furthermore, correlative HIM-CRM analysis allowed for obtaining the spatial distribution pattern of deuterium and lignin in root cross-sections. Here, more active root zones with higher deuterium incorporation showed less lignification. Conclusions We demonstrated that CRM in combination with deuterium labeling can be an alternative and reliable tool for the analysis of plant growth. This approach together with the developed workflow has the potential to be extended to complex systems such as plant roots grown in soil.
Why it matches plant phenotyping methods植物根の成長状態を測定する相関HIM-CRMワークフローを開発し、重水素検出の精度・直線性と質量分析との一致を検証しているため、植物フェノタイピング手法が中心である。
abstractA workflow to prepare and analyze roots of hydroponically grown and deuterium labeled Zea mays by correlative HIM-CRM micro-analysis was developed.
Reproduction assets foundThe paper's data availability statement deposits the datasets generated and analyzed (CRM/HIM phenotyping measurements of deuterium-labeled maize roots) in the UFZ Data Investigation Portal, a public repository with an explicit URL. No author analysis code with a public URL is stated.Dataset · publicThe datasets generated and/or analyzed during the current study are available in the UFZ Data Investigation Portal ( https://www.ufz.de/record/dmp/archive/13952 ) repository.Open asset ↗UFZ Data Investigation Portal · archive/13952lines:198-288Code / dataset availability confirmedOpenAlex · Europe PMC · Crossref · checked 7 Sept 2026
Nematode migration, feeding site formation, withdrawal of plant assimilates, and activation of plant defence responses have a significant impact on plant growth and development. Plants display intraspecific variation in tolerance limits for root-feeding nematodes. Although disease tolerance has been recognized as a distinct trait in biotic interactions of mainly crops, we lack mechanistic insights. Progress is hampered by difficulties in quantification and laborious screening methods. We turned to the model plant Arabidopsis thaliana, since it offers extensive resources to study the molecular and cellular mechanisms underlying nematode-plant interactions. Through imaging of tolerance-related parameters, the green canopy area was identified as an accessible and robust measure for assessing damage due to cyst nematode infection. Subsequently, a high-throughput phenotyping platform simultaneously measuring the green canopy area growth of 960 A. thaliana plants was developed. This platform can accurately measure cyst nematode and root-knot nematode tolerance limits in A. thaliana through classical modelling approaches. Furthermore, real-time monitoring provided data for a novel view of tolerance, identifying a compensatory growth response. These findings show that our phenotyping platform will enable a new mechanistic understanding of tolerance to below-ground biotic stress.
Why it matches plant phenotyping methods根圏線虫感染による植物の耐性を定量化するため、画像による緑色キャノピー面積の測定と、960個体を同時測定する高スループット表現型解析プラットフォームを開発しており、表現型取得法が研究の中心である。
abstractThrough imaging of tolerance-related parameters, the green canopy area was identified as an accessible and robust measure for assessing damage due to cyst nematode infection.
Reproduction assets foundThe paper's authors publicly deposited the full plant image dataset (green canopy phenotyping pictures) on figshare and the analysis code/model (SYLM and R growth analysis scripts) on a WUR GitLab repository, both explicitly linked in the Data availability statement.Dataset · publicAlso, the full picture dataset has been made available at doi: https://doi.org/10.6084/m9.figshare.23518923.v1 .Open asset ↗figshare · 10.6084/m9.figshare.23518923.v1lines:263-263Code · publicUsing these equations, the tolerance limit T SYLM and the minimum yield m were estimated (model and code available via gitlab: https://git.wur.nl/published_papers/willig_2023_camera-setup ).Open asset ↗git.wur.nl · published_papers/willig_2023_camera-setuplines:53-66Code / dataset availability confirmedEurope PMC · checked 7 Sept 2026
The root is an important organ for crops to absorb water and nutrients. Complete and accurate acquisition of root phenotype information is important in root phenomics research. The in situ root research method can obtain root images without destroying the roots. In the image, some of the roots are vulnerable to soil shading, which severely fractures the root system and diminishes its structural integrity. The methods of ensuring the integrity of in situ root identification and establishing in situ root image phenotypic restoration remain to be explored. Therefore, based on the in situ root image of cotton, this study proposes a root segmentation and reconstruction strategy, improves the UNet model, and achieves precise segmentation. It also adjusts the weight parameters of EnlightenGAN to achieve complete reconstruction and employs transfer learning to implement enhanced segmentation using the results of the former two. The research results show that the improved UNet model has an accuracy of 99.2%, mIOU of 87.03%, and F1 of 92.63%. The root reconstructed by EnlightenGAN after direct segmentation has an effective reconstruction ratio of 92.46%. This study enables a transition from supervised to unsupervised training of root system reconstruction by designing a combination strategy of segmentation and reconstruction network. It achieves the integrity restoration of in situ root system pictures and offers a fresh approach to studying the phenotypic of in situ root systems, also realizes the restoration of the integrity of the in situ root image, and provides a new method for in situ root phenotype study.
Why it matches plant phenotyping methods根の画像から表現型情報を抽出・復元するセグメンテーション／再構成手法の開発が中心であり、植物表現型計測法に該当する。
abstractthis study proposes a root segmentation and reconstruction strategy, improves the UNet model, and achieves precise segmentation.
Reproduction assets foundThe authors explicitly state their analysis code (improved UNet segmentation and EnlightenGAN reconstruction pipeline) has been uploaded to a public GitHub repository, and the wheat segmentation/reconstruction results were also uploaded there. The root image dataset itself is only available upon reasonable request fromCode · publicThe code has been uploaded to github: https://github.com/jiwd123/improved_unet .Open asset ↗https://github.com/jiwd123/improved_unetlines:248-271Code / dataset availability confirmedEurope PMC · bioRxiv · checked 7 Sept 2026
RadishRiceX-ray / CTRootObject detection2D/3D reconstructionGrowth / time-series analysisVisualization / data managementRoot system architecture
Crop genetic engineering for better root systems can offer practical solutions for food security and carbon sequestration; however, soil layers prevent direct visualization. Here, we demonstrate an original device with a distributed fiber-optic sensor for fully automated, real-time monitoring of underground root development. We demonstrate that spatially encoding an optical fiber with a flexible and durable polymer film in a spiral pattern can significantly enhance sensor detection. After signal processing, the resulting device can detect the penetration of a submillimeter-diameter object in the soil, indicating more than a magnitude higher spatiotemporal resolution than previously reported with underground monitoring techniques. We also developed computational models to visualize the roots of root crops and monocotyledons, and then applied them to radish and rice to compare the results with those of X-ray computed tomography. The device’s groundbreaking sensitivity and spatiotemporal resolution enable seamless and laborless phenotyping of root systems that are otherwise invisible underground.
Why it matches plant phenotyping methods地下根系を対象とする分布型光ファイバーセンサーと計算モデルを開発し、根系フェノタイピングへの適用・比較検証まで行うことが中心であるため。
abstractwe demonstrate an original device with a distributed fiber-optic sensor for fully automated, real-time monitoring of underground root development
Reproduction assets foundThe paper's custom MATLAB code for virtual root reconstruction from fiber-optic strain data is explicitly stated to be publicly available on the authors' GitHub repository (Fiber-RADGET). No separate public phenotype dataset deposit is mentioned; the supplementary movie is not a qualifying dataset URL.Code · publicThe
custom code for the virtual root reconstruction in MATLAB
(MathWorks, Massachusetts, USA) is available at
https://github.com/mtei1/Fiber-RADGET.git.Open asset ↗mtei1/Fiber-RADGETpdf-page:12 lines:1-24Code / dataset availability confirmedEurope PMC · checked 7 Sept 2026
Uprooting caused by flood events is a significant disturbance factor that affects the establishment, growth, and mortality of riparian vegetation. If the hydraulic drag force acting on riparian plants exceeds the peak uprooting force originate from their below-ground portion, it may result in the uprooting of these plants. Despite previous studies have documented and investigated the uprooting processes and factors influencing the peak uprooting force of plants, most of these studies have focused on how the root morphological traits of tree and shrub seedlings affect peak uprooting force or mainly collected data in indoor experiments, which may limit the extrapolation of the results to natural environments. To address these limitations, we assume that the peak uprooting force can be estimated by the morphological traits of the above-ground portion of the vegetation. In this study, we conducted in-situ vertical uprooting tests on three locally dominant species: Conyza canadensis , Daucus carota , and Leonurus sibiricus , in a typical riverine environment. The three species were found to have the highest abundance based on the outcomes of the quadrat method. We measured the peak uprooting force, plant height, stem basal diameter, shoot and root wet biomass, and shoot and root dry biomass of each plant and compared them between species. Furthermore, we quantified the influence of morphology on peak uprooting force. Our results showed significant differences in morphological traits and peak uprooting force among the three species. We found a significant positive correlation between peak uprooting force and the morphological traits of the three species. The peak uprooting force increases with plant size following a power law function which is analogous to allometric equations. The allometric equation provided a convenient and non-destructive method to estimate the peak uprooting force based on the above-ground morphological traits of the plants, which may help to overcome the limitations of measuring root morphological traits.
Why it matches plant phenotyping methods植物の地上部形態から地下部に由来する最大引抜抵抗力を推定する非破壊的なアロメトリック手法を開発・提示しており、形質取得・推定が研究の中心である。
abstractThe allometric equation provided a convenient and non-destructive method to estimate the peak uprooting force based on the above-ground morphological traits of the plants
Reproduction assets foundThe paper's data availability statement points to a public Zenodo deposit (DOI 10.5281/zenodo.6476708) containing the datasets from this study (in-situ uprooting tests and morphological trait measurements of three riparian species). This is a paper-specific, publicly accessible phenotype dataset. No author analysis代码或补Dataset · publicThe datasets presented in this study can be found in online repositories. The names of the repository/repositories and accessionnumber(s) can be found below: https://doi.org/10.5281/zenodo.6476708 .Open asset ↗zenodo · 10.5281/zenodo.6476708lines:667-709Code / dataset availability confirmedCrossref · OpenAlex · checked 14 Sept 2026
Abstract Understanding three‐dimensional (3D) root traits is essential to improve water uptake, increase nitrogen capture, and raise carbon sequestration from the atmosphere. However, quantifying 3D root traits by reconstructing 3D root models for deeper field‐grown roots remains a challenge due to the unknown tradeoff between 3D root‐model quality and 3D root‐trait accuracy. Therefore, we performed two computational experiments. We first compared the 3D model quality generated by five state‐of‐the‐art open‐source 3D model reconstruction pipelines on 12 contrasting genotypes of field‐grown maize roots. These pipelines included COLMAP, COLMAP+PMVS (Patch‐based Multi‐View Stereo), VisualSFM, Meshroom, and OpenMVG+MVE (Multi‐View Environment). The COLMAP pipeline achieved the best performance regarding 3D model quality versus computational time and image number needed. In the second test, we compared the accuracy of 3D root‐trait measurement generated by the Digital Imaging of Root Traits 3D pipeline (DIRT/3D) using COLMAP‐based 3D reconstruction with our current DIRT/3D pipeline that uses a VisualSFM‐based 3D reconstruction on the same dataset of 12 genotypes, with 5–10 replicates per genotype. The results revealed that (1) the average number of images needed to build a denser 3D model was reduced from 3000 to 3600 (DIRT/3D [VisualSFM‐based 3D reconstruction]) to around 360 for computational test 1, and around 600 for computational test 2 (DIRT/3D [COLMAP‐based 3D reconstruction]); (2) denser 3D models helped improve the accuracy of the 3D root‐trait measurement; (3) reducing the number of images can help resolve data storage problems. The updated DIRT/3D (COLMAP‐based 3D reconstruction) pipeline enables quicker image collection without compromising the accuracy of 3D root‐trait measurements.
Why it matches plant phenotyping methods3D画像再構成パイプラインを比較・検証し、更新版DIRT/3Dによる根形質推定の精度と効率を評価しており、植物フェノタイピング手法が中心である。
titleComparison of open‐source three‐dimensional reconstruction pipelines for maize‐root phenotyping
Reproduction assets foundThe paper publicly releases its analysis scripts on GitHub, demo workflows for reconstruction and trait computation, Docker/Singularity containers for DIRT/3D reconstruction and trait extraction, and manuscript data on CyVerse Data Commons via a permanent DOI.Code · publice computation of the software-
supported GPUs. The GPU model with the DELL workstation
was a GeForce RTX 2070 SUPER, NVIDIA Corporation
TU104, nvcc: NVIDIA (R) Cuda compiler driver. All the
pipelines were tested under the command-line interface to
generate related 3D root models in point cloud format. The
scripts are on GitHub (https://github.com/Computational-Plant-Science/3D_review_scripts/tree/master, folder Compu-
tational_test_1).
25782703,
2023,
1,
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[28/06/2023].
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useOpen asset ↗Computational-Plant-Science/3D_review_scriptspdf-raw-page:3 lines:1-114Code · publicm the University of Georgia to the University
of Arizona.
C O N F L I C T O F I N T E R E S T S TAT E M E N T
The authors declare no conflicts of interest.
DATA AVA I L A B I L I T Y S TAT E M E N T
GitHub link for all the scripts for running the test:
https://github.com/Computational-Plant-Science/3D_review_scripts/tree/master
https://github.com/Computational-Plant-Science/3D_model_reconstruction_demo
https://github.com/Computational-Plant-Science/3D_model_traits_demo
Permamnent DOI link to access manuscript data on
CyVerse Data Commons: https://www.doi.org/10.25739/sg2m-ky55/O RC I D
SuxingLiu https://orcid.org/0000-0001-7639-4470
WesleyPaul Bonelli https://orcid.org/0000-0002-2665-5078
PeOpen asset ↗Computational-Plant-Science/3D_model_reconstruction_demopdf-raw-page:12 lines:1-88Code · publicF I N T E R E S T S TAT E M E N T
The authors declare no conflicts of interest.
DATA AVA I L A B I L I T Y S TAT E M E N T
GitHub link for all the scripts for running the test:
https://github.com/Computational-Plant-Science/3D_review_scripts/tree/master
https://github.com/Computational-Plant-Science/3D_model_reconstruction_demo
https://github.com/Computational-Plant-Science/3D_model_traits_demo
Permamnent DOI link to access manuscript data on
CyVerse Data Commons: https://www.doi.org/10.25739/sg2m-ky55/O RC I D
SuxingLiu https://orcid.org/0000-0001-7639-4470
WesleyPaul Bonelli https://orcid.org/0000-0002-2665-5078
Peter Pietrzyk https://orcid.org/0000-0002-6794-8133
Alexander Bucksch https:/Open asset ↗Computational-Plant-Science/3D_model_traits_demopdf-raw-page:12 lines:1-88Code / dataset availability confirmedOpenAlex · Europe PMC · Crossref · bioRxiv · checked 15 Sept 2026
Abstract Nematode migration, feeding site formation, withdrawal of plant assimilates, and activation of plant defence responses have a significant impact on plant growth and development. Plants display intraspecific variation in tolerance limits for root-feeding nematodes. Although disease tolerance has been recognised as a distinct trait in biotic interactions of mainly crops, we lack mechanistic insights. Progress is hampered by difficulties in quantification and laborious screening methods. We turned to the model plant Arabidopsis thaliana , since it offers extensive resources to study the molecular and cellular mechanisms underlying nematode-plant interactions. Through imaging of tolerance-related parameters the green canopy area was identified as an accessible and robust measure for assessing damage due to cyst nematode infection. Subsequently, a high-throughput phenotyping platform simultaneously measuring the green canopy area growth of 960 A. thaliana plants was developed. This platform can accurately measure cyst- and root-knot nematode tolerance limits in A. thaliana through classical modelling of tolerance limits. Furthermore, real-time monitoring provided data for a novel view of tolerance, identifying a compensatory growth response. These findings show that our phenotyping platform will enable further studies into a mechanistic understanding of tolerance to below-ground biotic stress. Highlight The mechanisms of tolerance to root-parasitic nematodes remain unknown. We developed a high-throughput phenotyping system that enables unravelling the underlying mechanisms of tolerance to nematodes.
Why it matches plant phenotyping methods線虫耐性を評価するためのキャノピー画像計測と高スループット表現型解析プラットフォームの開発が研究の中心である。
abstractThrough imaging of tolerance-related parameters the green canopy area was identified as an accessible and robust measure for assessing damage due to cyst nematode infection.
Reproduction assets foundThe authors state that custom R scripts and functions used to analyse the high-throughput green canopy area growth data are publicly available via their GitLab repository at git.wur.nl/published_papers/willig_2023_camera-setup, and the data availability statement points to the same repository. The protocols.io link is Code · publiclimits (T) and relative minimum yield (m) were estimated for all
243
measurements.
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Plant growth analysis using the high-throughput phenotyping platform
246
To analyse the growth data of the plants obtained from the high-throughput platform, custom scripts and
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functions were written in “R” (available via gitlab:
248
https://git.wur.nl/published_papers/willig_2023_camera-setup). For analysis we used the median daily
249
leaf area (cm2), which was calculated by taking the median leaf area of the daily measurements (15 per
250
day). The data was log2-transformed before analysis for normalization. The rate of growth was
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determined per day per plant by
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𝑅𝑥,𝑡 = log2(𝐴𝑥,𝑡−1 − 𝐴𝑥,𝑡Open asset ↗git.wur.nl/published_papers/willig_2023_camera-setuppdf-raw-page:11 lines:1-60Code / dataset availability confirmedEurope PMC · Crossref · checked 7 Sept 2026
Field / plotRootWhole plant / canopy / plot / fieldObject detectionSegmentationRoot system architecture
Plant roots influence many ecological and biogeochemical processes, such as carbon, water and nutrient cycling. Because of difficult accessibility, knowledge on plant root growth dynamics in field conditions, however, is fragmentary at best. Minirhizotrons, i.e. transparent tubes placed in the substrate into which specialized cameras or circular scanners are inserted, facilitate the capture of high-resolution images of root dynamics at the soil-tube interface with little to no disturbance after the initial installation. Their use, especially in field studies with multiple species and heterogeneous substrates, though, is limited by the amount of work that subsequent manual tracing of roots in the images requires. Furthermore, the reproducibility and objectivity of manual root detection is questionable. Here, we use a Convolutional Neural Network (CNN) for the automatic detection of roots in minirhizotron images and compare the performance of our RootDetector with human analysts with different levels of expertise. Our minirhizotron data come from various wetlands on organic soils, i.e. highly heterogeneous substrates consisting of dead plant material, often times mainly roots, in various degrees of decomposition. This may be seen as one of the most challenging soil types for root segmentation in minirhizotron images. RootDetector showed a high capability to correctly segment root pixels in minirhizotron images from field observations (F1 = 0.6044; r 2 compared to a human expert = 0.99). Reproducibility among humans, however, depended strongly on expertise level, with novices showing drastic variation among individual analysts and annotating on average more than 13-times higher root length/cm 2 per image compared to expert analysts. CNNs such as RootDetector provide a reliable and efficient method for the detection of roots and root length in minirhizotron images even from challenging field conditions. Analyses with RootDetector thus save resources, are reproducible and objective, and are as accurate as manual analyses performed by human experts.
Why it matches plant phenotyping methodsCNNによるミニライゾトロン画像からの根の自動検出・根長推定を開発し、人間専門家との性能比較と再現性評価を行っており、植物表現型取得手法が研究の中心である。
abstractHere, we use a Convolutional Neural Network (CNN) for the automatic detection of roots in minirhizotron images and compare the performance of our RootDetector with human analysts with different levels of expertise.
Reproduction assets foundThe paper's authors publicly released the RootDetector CNN analysis code on GitHub, explicitly stated in the Data availability section. No separate public image/phenotype dataset deposit is stated; the minirhizotron images themselves are not explicitly deposited.Code · publicRootDetector is supplied as readily usable
code on GitHub, enabling easy use by ecologists without the need of advanced programming skills.Open asset ↗pdf-page:8 lines:1-75Code / dataset availability confirmedEurope PMC · checked 7 Sept 2026
Common beanRootSegmentationStress / disease detectionDisease symptoms / severity
Premise Plant disease severity assessments are used to quantify plant-pathogen interactions and identify disease-resistant lines. One common method for disease assessment involves scoring tissue manually using a semi-quantitative scale. Automating assessments would provide fast, unbiased, and quantitative measurements of root disease severity, allowing for improved consistency within and across large data sets. However, using traditional Root System Markup Language (RSML) software in the study of root responses to pathogens presents additional challenges; these include the removal of necrotic tissue during the thresholding process, which results in inaccurate image analysis. Methods Using PlantCV, we developed a Python-based pipeline, herein called RootDS, with two main objectives: (1) improving disease severity phenotyping and (2) generating binary images as inputs for RSML software. We tested the pipeline in common bean inoculated with Fusarium root rot. Results Quantitative disease scores and root area generated by this pipeline had a strong correlation with manually curated values ( R 2 = 0.92 and 0.90, respectively) and provided a broader capture of variation than manual disease scores. Compared to traditional manual thresholding, images generated using our pipeline did not affect RSML output. Discussion Overall, the RootDS pipeline provides greater functionality in disease score data sets and provides an alternative method for generating image sets for use in available RSML software.
Why it matches plant phenotyping methodsPlantCVを用いて根の病害重症度と根面積を自動画像推定するRootDSパイプラインを開発し、手動評価との相関で検証しており、植物表現型取得法が研究の中心です。
abstractUsing PlantCV, we developed a Python-based pipeline, herein called RootDS, with two main objectives: (1) improving disease severity phenotyping and (2) generating binary images as inputs for RSML software.
Reproduction assets foundThe authors publicly released the RootDS Python analysis code and a subset of the root images on GitHub; the full dataset is available only upon request.Code · publicThe available code and a subset of the images are available on GitHub ( https://github.com/HausMJ/RootDS_PythonCode ).Open asset ↗HausMJ/RootDS_PythonCodelines:95-140Code / dataset availability confirmedbioRxiv · Europe PMC · Crossref · checked 14 Sept 2026
Field / plotRootWhole plant / canopy / plot / fieldMorphology / geometry measurementGrowth / time-series analysisGrowth / development / phenologyRoot system architecture
1Roots have a central role in plant resource capture and are the interface between the plant and the soil that affect multiple ecosystem processes. Field pennycress (Thlaspi arvense L.) is a diploid annual cover crop species that has potential utility for reducing soil erosion and nutrient losses; and has rich seeds (30-35% oil) amenable to biofuel production and as a protein animal feed. The objective of this research was to (1) precisely characterize root system architecture and development, (2) understand adaptive responses of pennycress roots to nitrate nutrition, (3) and determine genotypic variance available in root development and nitrate plasticity. Using a root imaging and analysis pipeline, 4D pennycress root system architecture was characterized under four nitrate regimes (from zero to high nitrate concentration) across four time points (days 5, 9, 13, and 17 after sowing). Significant nitrate condition response and genotype interactions were identified for many root traits with greatest impact on lateral root traits. In trace nitrate conditions a greater lateral root count, length, interbranch density, and a steeper lateral root angle was observed compared to high nitrate conditions. Genotype-by-nitrate condition interaction was observed for root width, width:depth ratio, mean lateral root length, and lateral root density. These results illustrate root trait variance available in pennycress accessions that could be useful targets for breeding of improved nitrate responsive cover crops for greater productivity, resilience, and ecosystem service.
Why it matches plant phenotyping methods根系画像・解析パイプラインを用いた4D根系構造の取得と多数の根形質の抽出が研究の中心であり、植物フェノタイピング手法の実質的な応用に該当する。
abstractUsing a root imaging and analysis pipeline, 4D pennycress root system architecture was characterized under four nitrate regimes
Reproduction assets found保存済みの本文根拠を更新済みルールで再検証し、公開資産1件を確認しました。Code · public151 codes including the packages needed are available https://doi.org/10.5281/zenodo.7536940. A total of 44Open asset ↗zenodo · 10.5281/zenodo.7536940pdf-page:5 lines:1-52Code / dataset availability confirmedEurope PMC · Crossref · checked 8 Sept 2026
SoybeanField / plotRootSeed / grainCountingGrowth / development / phenologyRoot system architecture
The symbiotic relationship between soybean [ Glycine max L. (Merr.)] roots and bacteria ( Bradyrhizobium japonicum ) lead to the development of nodules, important legume root structures where atmospheric nitrogen (N 2 ) is fixed into bio-available ammonia (NH 3 ) for plant growth and development. With the recent development of the Soybean Nodule Acquisition Pipeline (SNAP), nodules can more easily be quantified and evaluated for genetic diversity and growth patterns across unique soybean root system architectures. We explored six diverse soybean genotypes across three field year combinations in three early vegetative stages of development and report the unique relationships between soybean nodules in the taproot and non-taproot growth zones of diverse root system architectures of these genotypes. We found unique growth patterns in the nodules of taproots showing genotypic differences in how nodules grew in count, size, and total nodule area per genotype compared to non-taproot nodules. We propose that nodulation should be defined as a function of both nodule count and individual nodule area resulting in a total nodule area per root or growth regions of the root. We also report on the relationships between the nodules and total nitrogen in the seed at maturity, finding a strong correlation between the taproot nodules and final seed nitrogen at maturity. The applications of these findings could lead to an enhanced understanding of the plant- Bradyrhizobium relationship and exploring these relationships could lead to leveraging greater nitrogen use efficiency and nodulation carbon to nitrogen production efficiency across the soybean germplasm.
Why it matches plant phenotyping methodsSNAPという機械学習ベースの表現型取得・解析パイプラインを用いて、根粒数・サイズ・面積を定量化することが研究の中心的手法として明示されている。
titleUsing machine learning enabled phenotyping to characterize nodulation in three early vegetative stages in soybean
Reproduction assets foundThe paper states that all data and code will be shared through the authors' Singh group GitHub. This is the only paper-specific availability statement; it points to a lab-level GitHub organization rather than a specific repository, and uses future tense ('will be shared'), so the exact deposit for this paper's nod phenCode · publicAll data and code will be shared through the Singh group GitHub https://github.com/SoylabSingh .Open asset ↗SoylabSinghlines:1171-1263Code / dataset availability confirmedEurope PMC · OpenAlex · Crossref · checked 15 Sept 2026
Current methods of root sampling typically only obtain small or incomplete sections of root systems and do not capture their true complexity. To facilitate the visualization and analysis of full-sized plant root systems in 3-dimensions, we developed customized mesocosm growth containers. While highly scalable, the design presented here uses an internal volume of 45 ft 3 (1.27 m 3 ), suitable for large crop and bioenergy grass root systems to grow largely unconstrained. Furthermore, they allow for the excavation and preservation of 3-dimensional root system architecture (RSA), and facilitate the collection of time-resolved subterranean environmental data. Sensor arrays monitoring matric potential, temperature and CO 2 levels are buried in a grid formation at various depths to assess environmental fluxes at regular intervals. Methods of 3D data visualization of fluxes were developed to allow for comparison with root system architectural traits. Following harvest, the recovered root system can be digitally reconstructed in 3D through photogrammetry, which is an inexpensive method requiring only an appropriate studio space and a digital camera. We developed a pipeline to extract features from the 3D point clouds, or from derived skeletons that include point cloud voxel number as a proxy for biomass, total root system length, volume, depth, convex hull volume and solidity as a function of depth. Ground-truthing these features with biomass measurements from manually dissected root systems showed a high correlation. We evaluated switchgrass, maize, and sorghum root systems to highlight the capability for species wide comparisons. We focused on two switchgrass ecotypes, upland (VS16) and lowland (WBC3), in identical environments to demonstrate widely different root system architectures that may be indicative of core differences in their rhizoeconomic foraging strategies. Finally, we imposed a strong physiological water stress and manipulated the growth medium to demonstrate whole root system plasticity in response to environmental stimuli. Hence, these new "3D Root Mesocosms" and accompanying computational analysis provides a new paradigm for study of mature crop systems and the environmental fluxes that shape them.
Why it matches plant phenotyping methods3Dルートメソコスム、フォトグラメトリ、点群解析による根系形態形質の取得・検証が研究の中心であり、植物フェノタイピング手法に該当する。
abstractTo facilitate the visualization and analysis of full-sized plant root systems in 3-dimensions, we developed customized mesocosm growth containers.
Reproduction assets foundThe paper's supplementary videos on figshare are photogrammetry-generated 3D point clouds of the paper's own root system phenotyping measurements (sorghum, maize, and switchgrass root systems, including stress-conditioned and sensor-flux coaligned visualizations), publicly downloadable. The OpenCV link is a generic, unDataset · publice, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.
Supplementary material
The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fpls.2022.1041404/full#supplementary-material . Videos can be found for viewing and download at https://doi.org/10.6084/m9.figshare.21335898.v1 .
Supplementary Figure 1
Interpolation of 3-dimensional environmental sensor data.
Click here for additional data file.
Supplementary Figure 2
Time course of shoot morphological responses of switchgrass in different growth media.
Click here for additional data file.
Supplementary Figure 3
Manual post-process cleaning of Open asset ↗figshare · 10.6084/m9.figshare.21335898.v1lines:327-356Code / dataset availability confirmedOpenAlex · Europe PMC · bioRxiv · checked 15 Sept 2026
ABSTRACT Advancements in hyperspectral imaging (HSI) and establishment of dedicated plant phenotyping facilities have enabled researchers to gather large quantities of plant spectral images with the aim of inferring target phenotypes non-destructively. However, large volumes of data that result from HSI and corequisite specialized methods for analysis may prevent plant scientists from taking full advantage of these systems. Here, we explore estimation of physiological traits in 23 rice accessions using an automated HSI system. Under contrasting nitrogen conditions, HSI data are used to classify treatment groups with ≥ 83% accuracy by utilizing support vector machines. Out of the 14 physiological traits collected, leaf-level nitrogen content (N, %) and carbon to nitrogen ratio (C:N) could also be predicted from the hyperspectral imaging data with normalized root mean square error of predictions smaller than 14% (R 2 of 0.88 for N and 0.75 for C:N). This study demonstrates the potential of using an automated HSI system to analyze genotypic variation for physiological traits in a diverse panel of rice; to help lower barriers of application of hyperspectral imaging in the greater plant science research community, analysis scripts used in this study are carefully documented and made publicly available. HIGHLIGHT Data from an automated hyperspectral imaging system are used to classify nitrogen treatment and predict leaf-level nitrogen content and carbon to nitrogen ratio during vegetative growth in rice.
Why it matches plant phenotyping methods自動ハイパースペクトル画像を用いてイネの生理形質を非破壊推定し、予測精度を評価しているため、フェノタイピング手法が中心的です。
abstractHere, we explore estimation of physiological traits in 23 rice accessions using an automated HSI system.
Reproduction assets foundThe paper's collected/analyzed datasets (hyperspectral imaging and physiological trait data) are publicly deposited in the Purdue University Research Repository, and the authors' analysis code is publicly available on GitHub. Both are paper-specific, public, and actionable.Dataset · publicand/or edits.
657 CONFLICT OF INTEREST
658 The authors declare no conflict of interest.
659 FUNDING
660 This work was partially funded by a grant from USDA NIFA to DRW (#2022-67013-36205).
661 DATA AVAILABILITY
662 The datasets collected and analyzed for this study can be found in the Purdue University Research
663 Repository [https://purr.purdue.edu/publications/4079/1].
664
665 REFERENCES
666 Al Makdessi, N., Ecarnot, M., Roumet, P., and Rabatel, G. (2019). A spectral correction method for
667 multi-scattering effects in close range hyperspectral imagery of vegetation scenes: application
668 to nitrogen content assessment in wheat. Precision Agric 20, 237–259. doi: 10.1007/s11119-
669 018-Open asset ↗pdf-layout-page:30 lines:1-64Code · public249
250 Data analysis
251 Data were formatted and analyzed in R 4.1.1 (R Core Team, 2021) with packages dplyr
252 (Wickham et al., 2021) and reshape2 (Wickham, 2007). Plots were made with package ggplot2
253 (Wickham, 2016) or in base R environment. The code for each physiological trait model can be
254 accessed through GitHub (https://github.com/To-Chia/rice_imaging_ms).
255 Physiological trait collection: From the physiological trait measurements, we derived specific
256 leaf area (SLA, cm2g-1), CN ratio (C:N), specific leaf area with respect to carbon (SLA_C (cm2
257 mg-1 (C)) and specific leaf nitrogen (SLN, mg (N) cm-2). The summary statistics are in Table S3.
258 Histograms and normal Open asset ↗GitHub · To-Chia/rice_imaging_mspdf-layout-page:12 lines:1-64Code / dataset availability confirmedEurope PMC · checked 8 Sept 2026
Background Cyst nematodes are one of the major groups of plant-parasitic nematode, responsible for considerable crop losses worldwide. Improving genetic resources, and therefore resistant cultivars, is an ongoing focus of many pest management strategies. One of the major bottlenecks in identifying the plant genes that impact the infection, and thus the yield, is phenotyping. The current available screening method is slow, has unidimensional quantification of infection limiting the range of scorable parameters, and does not account for phenotypic variation of the host. The ever-evolving field of computer vision may be the solution for both the above-mentioned issues. To utilise these tools, a specialised imaging platform is required to take consistent images of nematode infection in quick succession. Results Here, we describe an open-source, easy to adopt, imaging hardware and trait analysis software method based on a pre-existing nematode infection screening method in axenic culture. A cost-effective, easy-to-build and -use, 3D-printed imaging device was developed to acquire images of the root system of Arabidopsis thaliana infected with the cyst nematode Heterodera schachtii, replacing costly microscopy equipment. Coupling the output of this device to simple analysis scripts allowed the measurement of some key traits such as nematode number and size from collected images, in a semi-automated manner. Additionally, we used this combined solution to quantify an additional trait, root area before infection, and showed both the confounding relationship of this trait on nematode infection and a method to account for it. Conclusion Taken together, this manuscript provides a low-cost and open-source method for nematode phenotyping that includes the biologically relevant nematode size as a scorable parameter, and a method to account for phenotypic variation of the host. Together these tools highlight great potential in aiding our understanding of nematode parasitism.
Why it matches plant phenotyping methods根系感染像の取得プラットフォームと画像解析ソフトウェアを開発し、線虫数・サイズや感染前根面積を測定する手法が研究の中心であるため。
abstractwe describe an open-source, easy to adopt, imaging hardware and trait analysis software method
Reproduction assets foundThe paper's authors publicly release the imaging tower hardware design (STL files) and all ImageJ/Python analysis scripts used for nematode counting, root area quantification, and colour normalization in a GitHub repository, explicitly stated in the Availability of data and materials section.Code · publicAll scripts used in this experiment are available under the following github repository: https://github.com/OlafKranse/A_low_cost_imaging_tower .Open asset ↗OlafKranse/A_low_cost_imaging_towerlines:141-197Code / dataset availability confirmedEurope PMC · checked 8 Sept 2026
RiceLaboratory / benchtopX-ray / CTRootImage / point-cloud registrationGrowth / time-series analysisTrackingGrowth / development / phenologyRoot system architecture
Background Root system architecture (RSA) is an essential characteristic for efficient water and nutrient absorption in terrestrial plants; its plasticity enables plants to respond to different soil environments. Better understanding of root plasticity is important in developing stress-tolerant crops. Non-invasive techniques that can measure roots in soils nondestructively, such as X-ray computed tomography (CT), are useful to evaluate RSA plasticity. However, although RSA plasticity can be measured by tracking individual root growth, only a few methods are available for tracking individual roots from time-series three-dimensional (3D) images. Results We developed a semi-automatic workflow that tracks individual root growth by vectorizing RSA from time-series 3D images via two major steps. The first step involves 3D alignment of the time-series RSA images by iterative closest point registration with point clouds generated by high-intensity particles in potted soils. This alignment ensures that the time-series RSA images overlap. The second step consists of backward prediction of vectorization, which is based on the phenomenon that the root length of the RSA vector at the earlier time point is shorter than that at the last time point. In other words, when CT scanning is performed at time point A and again at time point B for the same pot, the CT data and RSA vectors at time points A and B will almost overlap, but not where the roots have grown. We assumed that given a manually created RSA vector at the last time point of the time series, all RSA vectors except those at the last time point could be automatically predicted by referring to the corresponding RSA images. Using 21 time-series CT volumes of a potted plant of upland rice (Oryza sativa), this workflow revealed that the root elongation speed increased with age. Compared with a workflow that does not use backward prediction, the workflow with backward prediction reduced the manual labor time by 95%. Conclusions We developed a workflow to efficiently generate time-series RSA vectors from time-series X-ray CT volumes. We named this workflow 'RSAtrace4D' and are confident that it can be applied to the time-series analysis of RSA development and plasticity.
Why it matches plant phenotyping methods根系形態を時系列X線CT画像から抽出・追跡する半自動ワークフローを開発しており、植物フェノタイピング手法が研究の中心である。
abstractWe developed a semi-automatic workflow that tracks individual root growth by vectorizing RSA from time-series 3D images via two major steps.
Reproduction assets foundThe paper's authors publicly released RSAtrace4D, the software implementing the backward-prediction workflow for time-series X-ray CT root system architecture analysis, on GitHub, and state that the datasets used are available via their GitHub account and project homepage.Code · publicThe implementation of this workflow, which is specified for rice, was named RSAtrace4D and is available at the GitHub repository ( https://github.com/st707311g/RSAtrace4D ).Open asset ↗st707311g/RSAtrace4Dlines:116-124Dataset · publicThe datasets used in this study are available at the GitHub repository ( https://github.com/st707311g/ ) and the project homepage ( https://rootomics.dna.affrc.go.jp/en/ ).Open asset ↗st707311glines:136-191Code / dataset availability confirmedOpenAlex · Europe PMC · Crossref · checked 8 Sept 2026
BACKGROUND: High-throughput phenotyping is crucial for the genetic and molecular understanding of adaptive root system development. In recent years, imaging automata have been developed to acquire the root system architecture of many genotypes grown in Petri dishes to explore the Genetic x Environment (GxE) interaction. There is now an increasing interest in understanding the dynamics of the adaptive responses, such as the organ apparition or the growth rate. However, due to the increasing complexity of root architectures in development, the accurate description of the topology, geometry, and dynamics of a growing root system remains a challenge. RESULTS: We designed a high-throughput phenotyping method, combining an imaging device and an automatic analysis pipeline based on registration and topological tracking, capable of accurately describing the topology and geometry of observed root systems in 2D + t. The method was tested on a challenging Arabidopsis seedling dataset, including numerous root occlusions and crossovers. Static phenes are estimated with high accuracy ([Formula: see text] and [Formula: see text] for primary and second-order roots length, respectively). These performances are similar to state-of-the-art results obtained on root systems of equal or lower complexity. In addition, our pipeline estimates dynamic phenes accurately between two successive observations ([Formula: see text] for lateral root growth). CONCLUSIONS: We designed a novel method of root tracking that accurately and automatically measures both static and dynamic parameters of the root system architecture from a novel high-throughput root phenotyping platform. It has been used to characterise developing patterns of root systems grown under various environmental conditions. It provides a solid basis to explore the GxE interaction controlling the dynamics of root system architecture adaptive responses. In future work, our approach will be adapted to a wider range of imaging configurations and species.
Why it matches plant phenotyping methods根系の静的・動的形質を画像から自動抽出する高スループット手法と解析パイプラインを開発・検証しており、表現型取得法が研究の中心である。
abstractWe designed a high-throughput phenotyping method, combining an imaging device and an automatic analysis pipeline based on registration and topological tracking
Reproduction assets foundThe paper's root reconstruction/phenotyping pipeline (RootSystemTracker) is released as open-source code on GitHub with an ImageJ plugin documentation page; an example time-lapse movie of the reconstruction is also available on YouTube. No public dataset of the 1000 time-lapse images or RSML outputs is stated in thesupCode · publicThe architecture reconstruction pipeline is supplied as an ImageJ plugin with online documentation (Plugin page: https://imagej.net/plugins/rootsystemtracker [ 9 ]) and as open-source code on GitHub ( https://github.com/Rocsg/RootSystemTracker ).Open asset ↗Rocsg/RootSystemTrackerlines:208-277Code / dataset availability confirmedOpenAlex · Europe PMC · Crossref · checked 14 Sept 2026
High temperatures inhibit plant growth. A proposed strategy for improving plant productivity under elevated temperatures is the use of plant growth-promoting rhizobacteria (PGPR). While the effects of PGPR on plant shoots have been extensively explored, roots—particularly their spatial and temporal dynamics—have been hard to study, due to their below-ground nature. Here, we characterized the time- and tissue-specific morphological changes in bacterized plants using a novel non-invasive high-resolution plant phenotyping and imaging platform—GrowScreen-Agar II. The platform uses custom-made agar plates, which allow air exchange to occur with the agar medium and enable the shoot to grow outside the compartment. The platform provides light protection to the roots, the exposure of it to the shoots, and the non-invasive phenotyping of both organs. Arabidopsis thaliana, co-cultivated with Paraburkholderia phytofirmans PsJN at elevated and ambient temperatures, showed increased lengths, growth rates, and numbers of roots. However, the magnitude and direction of the growth promotion varied depending on root type, timing, and temperature. The root length and distribution per depth and according to time was also influenced by bacterization and the temperature. The shoot biomass increased at the later stages under ambient temperature in the bacterized plants. The study offers insights into the timing of the tissue-specific, PsJN-induced morphological changes and should facilitate future molecular and biochemical studies on plant–microbe–environment interactions.
Why it matches plant phenotyping methodsGrowScreen-Agar IIという非侵襲的な高解像度フェノタイピング・イメージングプラットフォームを用い、根とシュートの形態を時空間的に測定することが研究の中心的手法です。
abstractwe characterized the time- and tissue-specific morphological changes in bacterized plants using a novel non-invasive high-resolution plant phenotyping and imaging platform—GrowScreen-Agar II.
Reproduction assets found保存済みの本文根拠を更新済みルールで再検証し、公開資産1件を確認しました。Supplement · publicThe following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/plants11212927/s1 , Figure S1: WinRhizo analyzed root lengths and root and shoot biomass; Figure S2: Root sampling and bacterial colonization confirmation; Figure S3: Sample root images generated by the GrowScreen-Agar II; Figure S4: Agar plates for GrowScreen-Agar II; Figure S5: Magazines for GrowScreen-Agar II; Figure S6: Imaging station of GrowScreen-Agar II; Table S1: Mean values and standard error of different root type morphological traits; Table S2: Mean values and standard error of different root system traits describing distribution and spread.Open asset ↗lines:106-120Code / dataset availability confirmedEurope PMC · OpenAlex · checked 15 Sept 2026
CassavaRootMorphology / geometry measurementGrowth / time-series analysisGrowth / development / phenologyRoot system architecture
Storage roots of cassava plants crops are one of the main providers of starch in many South American, African, and Asian countries. Finding varieties with high yields is crucial for growing and breeding. This requires a better understanding of the dynamics of storage root formation, which is usually done by repeated manual evaluation of root types, diameters, and their distribution in excavated roots. We introduce a newly developed method that is capable to analyze the distribution of root diameters automatically, even if root systems display strong variations in root widths and clustering in high numbers. An application study was conducted with cassava roots imaged in a video acquisition box. The root diameter distribution was quantified automatically using an iterative ridge detection approach, which can cope with a wide span of root diameters and clustering. The approach was validated with virtual root models of known geometries and then tested with a time-series of excavated root systems. Based on the retrieved diameter classes, we show plausibly that the dynamics of root type formation can be monitored qualitatively and quantitatively. We conclude that this new method reliably determines important phenotypic traits from storage root crop images. The method is fast and robustly analyses complex root systems and thereby applicable in high-throughput phenotyping and future breeding.
Why it matches plant phenotyping methods根系画像から根径分布などの表現型形質を自動抽出する新手法を開発し、仮想モデルで検証しており、表現型取得・解析が研究の中心である。
abstractWe introduce a newly developed method that is capable to analyze the distribution of root diameters automatically
Reproduction assets foundThe paper's root diameter analysis software is publicly available on the authors' GitLab (grow-screen-field). The phenotype/image data are deposited on Zenodo (doi: 10.5281/zenodo.5883368), but no Zenodo URL is in the allowed list, so only the code asset is reported. Paraview and Detectron2 are generic third-party toolCode · publicthe Helmholtz. We thank Alexander Putz for his technical support and N. Punyasu for allowing us to use her parametrization of the OpenSimRoot cassava model.
Data Availability
The data presented in this study are openly available in Zenodo.org (doi: 10.5281/zenodo.5883368 ) [ 39 ]. The software has been published in Gitlab under https://gitlab-public.fz-juelich.de/grow-screen-field . The parameters of the OSR-models are available from the authors upon request.
Authors’ Contributions
J.W. did the software implementation and compiled all algorithms and methods into a software with graphical user interface. He also helped developing the methodology. T.W. provided the data for the real root case Open asset ↗gitlab-public.fz-juelich.de/grow-screen-fieldlines:110-132Code / dataset availability confirmedEurope PMC · checked 8 Sept 2026
Capturing cell-to-cell signals in a three-dimensional (3D) environment is key to studying cellular functions. A major challenge in the current culturing methods is the lack of accurately capturing multicellular 3D environments. In this study, we established a framework for 3D bioprinting plant cells to study cell viability, cell division, and cell identity. We established long-term cell viability for bioprinted Arabidopsis and soybean cells. To analyze the generated large image datasets, we developed a high-throughput image analysis pipeline. Furthermore, we showed the cell cycle reentry of bioprinted cells for which the timing coincides with the induction of core cell cycle genes and regeneration-related genes, ultimately leading to microcallus formation. Last, the identity of bioprinted Arabidopsis root cells expressing endodermal markers was maintained for longer periods. The framework established here paves the way for a general use of 3D bioprinting for studying cellular reprogramming and cell cycle reentry toward tissue regeneration.
Why it matches plant phenotyping methods植物細胞の3Dバイオプリンティング枠組みと、大規模画像から生存性・細胞分裂・細胞同一性を解析する高スループット画像解析パイプラインを開発しており、表現型取得・解析が研究の中心的な技術的貢献である。
abstractIn this study, we established a framework for 3D bioprinting plant cells to study cell viability, cell division, and cell identity.
Reproduction assets foundThe paper's authors publicly released their high-throughput confocal z-stack cell quantification pipeline (Python scripts wrapped in an R Shiny GUI), used to analyze the paper's bioprinted plant cell imaging datasets, on GitHub with a Zenodo deposit (10.5281/zenodo.7012765). The Zenodo record 5537065 in allowed_urls isCode · publicScripts for our high-throughput and automatic image analysis are available at https://github.com/LisaVdB/Confocal-z-stack-cell-detection and 10.5281/zenodo.7012765 .Open asset ↗LisaVdB/Confocal-z-stack-cell-detection · 10.5281/zenodo.7012765lines:247-261Code / dataset availability confirmedOpenAlex · Europe PMC · Crossref · checked 15 Sept 2026
RootAnnotation / quality controlSegmentationRoot system architecture
Accurate segmentation of root system architecture (RSA) from 2D images is an important step in studying phenotypic traits of root systems. Various approaches to image segmentation exist but many of them are not well suited to the thin and reticulated structures characteristic of root systems. The findings presented here describe an approach to RSA segmentation that takes advantage of the inherent structural properties of the root system, a segmentation network architecture we call ITErRoot. We have also generated a novel 2D root image dataset which utilizes an annotation tool developed for producing high quality ground truth segmentation of root systems. Our approach makes use of an iterative neural network architecture to leverage the thin and highly branched properties of root systems for accurate segmentation. Rigorous analysis of model properties was carried out to obtain a high-quality model for 2D root segmentation. Results show a significant improvement over other recent approaches to root segmentation. Validation results show that the model generalizes to plant species with fine and highly branched RSA's, and performs particularly well in the presence of non-root objects.
Why it matches plant phenotyping methods植物根系画像からRSAを抽出するセグメンテーション手法を開発し、データセット作成と他手法との検証・比較を行っており、植物フェノタイピング手法が中心です。
abstractAccurate segmentation of root system architecture (RSA) from 2D images is an important step in studying phenotypic traits of root systems.
Reproduction assets foundThe paper's Data availability statement provides public GitHub repositories for the authors' ITErRoot training code and the Friendly Ground Truth annotation tool used to create the paper's root segmentation ground truth. Both are paper-specific, public, and actionable. No separate phenotype image dataset deposit URL isCode · publicada First Research Excellence Fund. https://www.cfref-apogee.gc.ca/program-programme/communication_guidelines-lignes_directrices-eng.aspx . This work was also supported by the Google Cloud Platform (GCP) Research Credits Program.
Data availability
The code used to train the neural networks in this study is available on Github ( https://github.com/p2irc/ITErRoot ). The annotation tool used to create ground truth segmentations for training is available on Github ( https://github.com/p2irc/friendly_ground_truth ).
Competing interests
The authors declare no competing interests.
References
1.
Clark RT
Three-dimensional root phenotyping with a novel imaging and software platform
Plant PhysiOpen asset ↗p2irc/ITErRootlines:1379-1497Code · publicby volunteer Computer Science students with experience with other annotation tools. Friendly Ground Truth was successfully employed to generate a dataset of root images that were used to train and evaluate the segmentation network structure proposed in this work. The annotation tool has been made publicly available on GitHub ( https://github.com/p2irc/friendly_ground_truth ) for use by the community to generate root segmentation datasets.
Iterative neural network architectureOpen asset ↗p2irc/friendly_ground_truthlines:70-78Code / dataset availability confirmedOpenAlex · Europe PMC · Crossref · checked 8 Sept 2026
Currently, plant phenomics is considered the key to reducing the genotype-to-phenotype knowledge gap in plant breeding. In this context, breakthrough imaging technologies have demonstrated high accuracy and reliability. The X-ray computed tomography (CT) technology can noninvasively scan roots in 3D; however, it is urgently required to implement high-throughput phenotyping procedures and analyses to increase the amount of data to measure more complex root phenotypic traits. We have developed a spatial-temporal root architectural modeling software tool based on 4D data from temporal X-ray CT scans. Through a cylinder fitting, we automatically extract significant root architectural traits, distribution, and hierarchy. The open-source software tool is named 4DRoot and implemented in MATLAB. The source code is freely available at https://github.com/TIDOP-USAL/4DRoot. In this research, 3D root scans from the black walnut tree were analyzed, a punctual scan for the spatial study and a weekly time-slot series for the temporal one. 4DRoot provides breeders and root biologists an objective and useful tool to quantify carbon sequestration throw trait extraction. In addition, 4DRoot could help plant breeders to improve plants to meet the food, fuel, and fiber demands in the future, in order to increase crop yield while reducing farming inputs.
Why it matches plant phenotyping methodsX線CTの時系列3D画像から根系形態形質を自動抽出するソフトウェア開発が研究の中心であり、植物フェノタイピング手法に該当する。
abstractWe have developed a spatial-temporal root architectural modeling software tool based on 4D data from temporal X-ray CT scans.
Reproduction assets foundThe paper's authors explicitly state that the 4DRoot source code (the software performing the root phenotyping analysis) is freely available on GitHub. The X-ray CT scan data themselves are not deposited in a public repository; only the code is. TreeQSM is a cited prior-work dependency, not a paper-specific asset.Code · publicThe open-source software tool is named 4DRoot and implemented in MATLAB. The source code is freely available at https://github.com/TIDOP-USAL/4DRoot .Open asset ↗TIDOP-USAL/4DRootlines:225-297Code / dataset availability confirmedEurope PMC · checked 8 Sept 2026
ArabidopsisRootMorphology / geometry measurementGrowth / time-series analysisGrowth / development / phenologyRoot system architecture
The plant kingdom contains a stunning array of complex morphologies easily observed above-ground, but more challenging to visualize below-ground. Understanding the magnitude of diversity in root distribution within the soil, termed root system architecture (RSA), is fundamental in determining how this trait contributes to species adaptation in local environments. Roots are the interface between the soil environment and the shoot system and therefore play a key role in anchorage, resource uptake, and stress resilience. Previously, we presented the GLO-Roots (Growth and Luminescence Observatory for Roots) system to study the RSA of soil-grown Arabidopsis thaliana plants from germination to maturity (Rellán-Álvarez et al., 2015). In this study, we present the automation of GLO-Roots using robotics and the development of image analysis pipelines in order to examine the temporal dynamic regulation of RSA and the broader natural variation of RSA in Arabidopsis , over time. These datasets describe the developmental dynamics of two independent panels of accessions and reveal highly complex and polygenic RSA traits that show significant correlation with climate variables of the accessions' respective origins.
Why it matches plant phenotyping methodsロボティクスによる根系画像取得の自動化と画像解析パイプライン開発が中心で、根系構造・成長動態という植物形質を抽出するフェノタイピング基盤を提示している。
abstractwe present the automation of GLO-Roots using robotics and the development of image analysis pipelines
Reproduction assets foundThe paper deposits its root phenotyping imaging data, image analysis pipelines/scripts, RShiny exploration apps, and rhizotron build files on Zenodo, plus robotics software on GitHub — all paper-specific, public, and actionable.Dataset · publicThe raw data is available through Zenodo at https://doi.org/10.5281/zenodo.5709009 .Open asset ↗Zenodo · 10.5281/zenodo.5709009lines:160-163Code · publicImage analysis pipelines and scripts are available through Zenodo, DOI: https://doi.org/10.5281/zenodo.5708430 .Open asset ↗Zenodo · 10.5281/zenodo.5708430lines:224-389Code · publicGeneral code for software operating robotics available: GitHub: https://github.com/rhizolab/rhizo-server .Open asset ↗GitHub · rhizolab/rhizo-serverlines:224-389Code / dataset availability confirmedEurope PMC · OpenAlex · checked 13 Sept 2026
Growth traits, such as fresh weight, diameter, and leaf area, are pivotal indicators of growth status and the basis for the quality evaluation of lettuce. The time-consuming, laborious and inefficient method of manually measuring the traits of lettuce is still the mainstream. In this study, a three-stage multi-branch self-correcting trait estimation network (TMSCNet) for RGB and depth images of lettuce was proposed. The TMSCNet consisted of five models, of which two master models were used to preliminarily estimate the fresh weight (FW), dry weight (DW), height (H), diameter (D), and leaf area (LA) of lettuce, and three auxiliary models realized the automatic correction of the preliminary estimation results. To compare the performance, typical convolutional neural networks (CNNs) widely adopted in botany research were used. The results showed that the estimated values of the TMSCNet fitted the measurements well, with coefficient of determination ( R 2 ) values of 0.9514, 0.9696, 0.9129, 0.8481, and 0.9495, normalized root mean square error (NRMSE) values of 15.63, 11.80, 11.40, 10.18, and 14.65% and normalized mean squared error (NMSE) value of 0.0826, which was superior to compared methods. Compared with previous studies on the estimation of lettuce traits, the performance of the TMSCNet was still better. The proposed method not only fully considered the correlation between different traits and designed a novel self-correcting structure based on this but also studied more lettuce traits than previous studies. The results indicated that the TMSCNet is an effective method to estimate the lettuce traits and will be extended to the high-throughput situation. Code is available at https://github.com/lxsfight/TMSCNet.git.
Why it matches plant phenotyping methodsRGB・深度画像からレタスの複数形質を推定する新規ネットワークを開発し、既存手法と性能比較しており、植物フェノタイピング手法が研究の中心である。
abstracta three-stage multi-branch self-correcting trait estimation network (TMSCNet) for RGB and depth images of lettuce was proposed
Reproduction assets foundThe paper uses the public Autonomous Greenhouses Challenge 3 dataset (RGB/depth lettuce images with FW/DW/H/D/LA measurements) and states author code availability on GitHub.Code · publicCode is available at https://github.com/lxsfight/TMSCNet.git .Open asset ↗github.com/lxsfight/TMSCNetlines:1-41Code / dataset availability confirmedEurope PMC · checked 14 Sept 2026
WheatField / plotMicroscopyRootMorphology / geometry measurementRoot system architecture
Root hairs play an important role in absorbing water and nutrients in crop plants. Here we optimized high-throughput root hair length (RHL) and root hair density (RHD) phenotyping in wheat using a portable Dinolite™ microscope. A collection of 24 century wide spring wheat cultivars released between 1911 and 2016 were phenotyped for RHL and RHD. The results revealed significant variations for both traits with five and six-fold variation for RHL and RHD, respectively. RHL ranged from 1.01 mm to 1.77 mm with an average of 1.39 mm, and RHD ranged from 17.08 mm -2 to 20.8 mm -2 with an average of 19.6 mm -2 . Agronomic and physiological traits collected from five different environments and their best linear unbiased predictions (BLUPs) were correlated with RHL and RHD, and results revealed that relative-water contents (RWC), biomass and grain per spike (GpS) were positively correlated with RHL in both water-limited and well-watered conditions. While RHD was negatively correlated with grain yield (GY) in four environments and their BLUPs. Both RHL and RHD had positive correlation indicating the possibility of simultaneous selection of both phenotypes during wheat breeding. The expression pattern of TaRSL4 gene involved in regulation of root hair length was determined in all 24 wheat cultivars based on RNA-seq data, which indicated the differentially higher expression of the A- and D- homeologues of the gene in roots, while B-homeologue was consistently expressed in both leaf and roots. The results were validated by qRT-PCR and the expression of TaRSL4 was consistently high in rainfed cultivars such as Chakwal-50, Rawal-87, and Margallah-99. Overall, the new phenotyping method for RHL and RHD along with correlations with morphological and physiological traits in spring wheat cultivars improved our understanding for selection of these phenotypes in wheat breeding.
Why it matches plant phenotyping methods携帯型顕微鏡を用いたコムギ根毛長・密度のハイスループット表現型測定法を最適化し、品種で実証しているため、根形態フェノタイピング手法が中心である。
abstractHere we optimized high-throughput root hair length (RHL) and root hair density (RHD) phenotyping in wheat using a portable Dinolite™ microscope.
Reproduction assets found保存済みの本文根拠を更新済みルールで再検証し、公開資産1件を確認しました。Supplement · publicTable S1: Name of the cultivars, pedigree, year of release and raw phenotypic data used in this study.Open asset ↗lines:71-196Code / dataset availability confirmedEurope PMC · OpenAlex · checked 8 Sept 2026
Fresh weight is a widely used growth indicator for quantifying crop growth. Traditional fresh weight measurement methods are time-consuming, laborious, and destructive. Non-destructive measurement of crop fresh weight is urgently needed in plant factories with high environment controllability. In this study, we proposed a multi-modal fusion based deep learning model for automatic estimation of lettuce shoot fresh weight by utilizing RGB-D images. The model combined geometric traits from empirical feature extraction and deep neural features from CNN. A lettuce leaf segmentation network based on U-Net was trained for extracting leaf boundary and geometric traits. A multi-branch regression network was performed to estimate fresh weight by fusing color, depth, and geometric features. The leaf segmentation model reported a reliable performance with a mIoU of 0.982 and an accuracy of 0.998. A total of 10 geometric traits were defined to describe the structure of the lettuce canopy from segmented images. The fresh weight estimation results showed that the proposed multi-modal fusion model significantly improved the accuracy of lettuce shoot fresh weight in different growth periods compared with baseline models. The model yielded a root mean square error (RMSE) of 25.3 g and a coefficient of determination ( R 2 ) of 0.938 over the entire lettuce growth period. The experiment results demonstrated that the multi-modal fusion method could improve the fresh weight estimation performance by leveraging the advantages of empirical geometric traits and deep neural features simultaneously.
Why it matches plant phenotyping methodsRGB-D画像からレタスの生体重を非破壊推定する画像解析・深層学習手法の開発が研究の中心であり、植物表現型取得法に該当する。
abstractA lettuce leaf segmentation network based on U-Net was trained for extracting leaf boundary and geometric traits.
Reproduction assets foundThe paper's phenotyping inputs (top-view RGB and aligned depth images of 388 lettuces with destructively measured traits) come from the publicly available 3rd Autonomous Greenhouse Challenge Online Challenge Lettuce Images dataset, with an explicit public URL in the data availability statement. No author analysis code,Dataset · publicPublicly available datasets were analyzed in this study. This data can be found here: https://data.4tu.nl/articles/dataset/3rd_Autonomous_Greenhouse_Challenge_Online_Challenge_Lettuce_Images/15023088 .Open asset ↗data.4tu.nl · 15023088lines:657-691Code / dataset availability confirmedEurope PMC · checked 8 Sept 2026
RootCountingMorphology / geometry measurementSegmentationRoot system architecture
Convolutional neural networks (CNNs) are a powerful tool for plant image analysis, but challenges remain in making them more accessible to researchers without a machine-learning background. We present RootPainter, an open-source graphical user interface based software tool for the rapid training of deep neural networks for use in biological image analysis. We evaluate RootPainter by training models for root length extraction from chicory (Cichorium intybus L.) roots in soil, biopore counting, and root nodule counting. We also compare dense annotations with corrective ones that are added during the training process based on the weaknesses of the current model. Five out of six times the models trained using RootPainter with corrective annotations created within 2 h produced measurements strongly correlating with manual measurements. Model accuracy had a significant correlation with annotation duration, indicating further improvements could be obtained with extended annotation. Our results show that a deep-learning model can be trained to a high accuracy for the three respective datasets of varying target objects, background, and image quality with < 2 h of annotation time. They indicate that, when using RootPainter, for many datasets it is possible to annotate, train, and complete data processing within 1 d.
Why it matches plant phenotyping methodsRootPainterは植物画像から根長・バイオポア・根粒を抽出する深層学習ソフトウェアであり、補正アノテーション、精度、手動測定との相関を評価しているため、植物フェノタイピング手法が中心です。
abstractWe present RootPainter, an open-source graphical user interface based software tool for the rapid training of deep neural networks for use in biological image analysis.
Reproduction assets foundThe paper's Data availability statement explicitly deposits the authors' analysis software (client/server source code and installers on GitHub) and a Colab notebook, all with public URLs. The paper-specific phenotype/training datasets (nodules, biopores, roots) and trained models are on Zenodo (DOIs 10.5281/zenodo.3755Code · publicThe source code for both client and server is available from https://github.com/Abe404/root_painter .Open asset ↗github.com/Abe404/root_painterlines:332-520Code / dataset availability confirmedEurope PMC · checked 15 Sept 2026
Submergence during germination impedes aerobic metabolisms and limits the growth of most higher plants. However, some wetland plants including rice can germinate under submerged conditions. It has long been hypothesized that the first elongating shoot tissue, the coleoptile, acts as a snorkel to acquire atmospheric oxygen (O 2 ) to initiate the first leaf elongation and seminal root emergence. Here, we obtained direct evidence for this hypothesis by visualizing the spatiotemporal O 2 dynamics during submerged germination in rice using a planar O 2 optode system. In parallel with the O 2 imaging, we tracked the anatomical development of shoot and root tissues in real-time using an automated flatbed scanner. Three hours after the coleoptile tip reached the water surface, O 2 levels around the embryo transiently increased. At this time, the activity of alcohol dehydrogenase (ADH), an enzyme critical for anaerobic metabolism, was significantly reduced, and the coleorhiza covering the seminal roots in the embryo was broken. Approximately 10 h after the transient burst in O 2 , seminal roots emerged. A transient O 2 burst around the embryo was shown to be essential for seminal root emergence during submerged rice germination. The parallel application of a planar O 2 optode system and automated scanning system can be a powerful tool for examining how environmental conditions affect germination in rice and other plants.
Why it matches plant phenotyping methods水中イネ発芽時の酸素動態と器官発達を、平面O2オプトードおよび自動スキャナーで時空間的に可視化・追跡する手法が研究の中心であり、植物の生理状態・形態発達を測定する実質的なフェノタイピング手法である。
abstractvisualizing the spatiotemporal O 2 dynamics during submerged germination in rice using a planar O 2 optode system.
Reproduction assets foundThe paper's phenotyping measurements (time-lapse growth images and planar optode O2 imaging of submerged rice germination) are publicly available as Supplementary Videos 1–3 hosted at the Frontiers supplementary material URL. No author analysis code or trained models are deposited; ImageJ and UWSC are generic third‑dayDataset · publicof their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.
Supplementary material
The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fpls.2022.946776/full#supplementary-material
Supplementary Video 1
Time-lapse images showing the germination process of submerged rice with normoxic or anoxic atmospheres.
Click here for additional data file.
Supplementary Video 2
Time-lapse images of the growth process following a shift from an anoxic to a normoxic atmosphOpen asset ↗lines:107-237Code / dataset availability confirmedEurope PMC · OpenAlex · checked 13 Sept 2026
Growth indices can quantify crop productivity and establish optimal environmental, nutritional, and irrigation control strategies. A convolutional neural network (CNN)-based model is presented for estimating various growth indices (i.e., fresh weight, dry weight, height, leaf area, and diameter) of four varieties of greenhouse lettuce using red, green, blue, and depth (RGB-D) data obtained using a stereo camera. Data from an online autonomous greenhouse challenge (Wageningen University, June 2021) were employed in this study. The data were collected using an Intel RealSense D415 camera. The developed model has a two-stage CNN architecture based on ResNet50V2 layers. The developed model provided coefficients of determination from 0.88 to 0.95, with normalized root mean square errors of 6.09%, 6.30%, 7.65%, 7.92%, and 5.62% for fresh weight, dry weight, height, diameter, and leaf area, respectively, on unknown lettuce images. Using red, green, blue (RGB) and depth data employed in the CNN improved the determination accuracy for all five lettuce growth indices due to the ability of the stereo camera to extract height information on lettuce. The average time for processing each lettuce image using the developed CNN model run on a Jetson SUB mini-PC with a Jetson Xavier NX was 0.83 s, indicating the potential for the model in fast real-time sensing of lettuce growth indices.
Why it matches plant phenotyping methodsRGB-D画像とCNNを用いてレタスの複数の生育形質を推定する手法を開発・検証しており、表現型取得が研究の中心である。
abstractA convolutional neural network (CNN)-based model is presented for estimating various growth indices (i.e., fresh weight, dry weight, height, leaf area, and diameter) of four varieties of greenhouse lettuce using red, green, blue, and depth (RGB-D) data obtained using a stereo camera.
Reproduction assets foundThe paper's phenotyping inputs (388 RGB-D lettuce image pairs with destructive growth-index measurements from the Third Autonomous Greenhouse Challenge) are a third-party public dataset explicitly stated to be publicly available at 4TU.ResearchData, with the DOI 10.4121/15023088.v1 cited in the text and figure captionsDataset · publicThe dataset is available in online: https://doi.org/10.4121/15023088.v1 [ 30 ].Open asset ↗10.4121/15023088.v1lines:518-697Code / dataset availability confirmedEurope PMC · bioRxiv · checked 15 Sept 2026
Background Cyst nematodes are one of the major groups of plant-parasitic nematode, responsible for considerable crop losses worldwide. Improving genetic resources, and therefore resistant cultivars, is an ongoing focus of many pest management strategies. One of the major bottlenecks in identifying the plant genes that impact the infection, and thus the yield, is phenotyping. The current available screening method is slow, has unidimensional quantification of infection limiting the range of scorable parameters, and does not account for phenotypic variation of the host. The ever-evolving field of computer vision may be the solution for both the above-mentioned issues. To utilise these tools, a specialised imaging platform is required to take consistent images of nematode infection in quick succession. Results Here, we describe an open-source, easy to adopt, imaging hardware and trait analysis software method based on a pre-existing nematode infection screening method in axenic culture. A cost-effective, easy-to-build and -use, 3D-printed imaging device was developed to acquire images of the root system of Arabidopsis thaliana infected with the cyst nematode Heterodera schachtii , replacing costly microscopy equipment. Coupling the output of this device to simple analysis scripts allowed the measurement of some key traits such as nematode number and size from collected images, in a semi-automated manner. Additionally, we used this combined solution to quantify an additional trait, root area before infection, and showed both the confounding relationship of this trait on nematode infection and a method to account for it. Conclusion Taken together, this manuscript provides a low-cost and open-source method for nematode phenotyping that includes the biologically relevant nematode size as a scorable parameter, and a method to account for phenotypic variation of the host. Together these tools highlight great potential in aiding our understanding of nematode parasitism.
Why it matches plant phenotyping methods植物寄生性線虫感染の画像取得・解析を自動化する低コストの装置とソフトウェアを開発し、線虫数・サイズおよび根面積を測定する手法が中心であるため。
abstractHere, we describe an open-source, easy to adopt, imaging hardware and trait analysis software method
Reproduction assets foundThe paper's ImageJ analysis scripts (root surface area, colored-agar variant, leaf surface count) and a custom Python color-normalization script are explicitly deposited in the authors' public GitHub repository (OlafKranse/A_low_cost_imaging_tower), directly reproducing this paper's phenotyping analysis. No phenotype/тCode · publici.org/10.1101/2022.07.14.500020; this version posted July 15, 2022. The copyright holder for this preprint
(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is
made available under a CC-BY 4.0 International license.
described in the script
(https://github.com/OlafKranse/A_low_cost_imaging_tower/blob/main/Imaging and
Analyses/automated_root_surface_area.ijm). A slightly adjusted script was used for plates
containing dye (https://github.com/OlafKranse/A_low_cost_imaging_tower/blob/main/Imaging and
Analyses/automated_root_surface_area_colored_agar.ijm). The root surface area for all the images
in the folderOpen asset ↗OlafKranse/A_low_cost_imaging_towerpdf-layout-page:6 lines:1-37Code · publicing and quantifiable traits
Automatic counting was performed on images taken as described above. Depending on the
treatment a different script was used to calculate the number and size of females. Before isolation,
the colour histogram for all images was normalised to the first image in the dataset using a custom
python script (https://github.com/OlafKranse/A_low_cost_imaging_tower/tree/main/Imaging and
Analyses/Normalise colour). The images were then processed in ImageJ for two different nematode
life stages: i) tanned cyst nematodes; ii) female nematodes.Open asset ↗OlafKranse/A_low_cost_imaging_towerpdf-layout-page:6 lines:1-37Code / dataset availability confirmedEurope PMC · checked 8 Sept 2026
MaizeField / plotRootPhysiological trait estimationRoot system architectureStress response / tolerance
High-throughput, field-based characterization of root systems for hundreds of genotypes in thousands of plots is necessary for breeding and identifying loci underlying variation in root traits and their plasticity. We designed a large-scale sampling of root pulling force, the vertical force required to extract the root system from the soil, in a maize diversity panel under differing irrigation levels for two growing seasons. We then characterized the root system architecture of the extracted root crowns. We found consistent patterns of phenotypic plasticity for root pulling force for a subset of genotypes under differential irrigation, suggesting that root plasticity is predictable. Using genome-wide association analysis, we identified 54 SNPs as statistically significant for six independent root pulling force measurements across two irrigation levels and four developmental timepoints. For every significant GWAS SNP for any trait in any treatment and timepoint we conducted post hoc tests for genotype-by-environment interaction, using a mixed model ANOVA. We found that 8 of the 54 SNPs showed significant GxE. Candidate genes underlying variation in root pulling force included those involved in nutrient transport. Although they are often treated separately, variation in the ability of plant roots to sense and respond to variation in environmental resources including water and nutrients may be linked by the genes and pathways underlying this variation. While functional validation of the identified genes is needed, our results expand the current knowledge of root phenotypic plasticity at the whole plant and gene levels, and further elucidate the complex genetic architecture of maize root systems.
Why it matches plant phenotyping methods数百遺伝子型・数千区画を対象とする高スループットな圃場根系表現型測定を設計・適用し、根抜き力と根系構造を取得している。主目的は遺伝解析だが、表現型取得手法の大規模適用が実質的に記述されているため採録する。
abstractHigh-throughput, field-based characterization of root systems for hundreds of genotypes in thousands of plots is necessary for breeding and identifying loci underlying variation in root traits and their plasticity.
Reproduction assets found本文中に内容が明示された植物フェノタイピング関連の補足表と、その公開リンクを確認しました。Supplement · publicSupplementary Table 5 ); however, we saw no overlap in hits between our root traits and flowering, consistent with the lack of correlation in Figure 4 .Open asset ↗lines:330-340Code / dataset availability confirmedCrossref · OpenAlex · checked 14 Sept 2026
Arctic vegetation communities are rapidly changing with climate warming, which impacts wildlife, carbon cycling and climate feedbacks. Accurately monitoring vegetation change is thus crucial, but scale mismatches between field and satellite-based monitoring cause challenges. Remote sensing from unmanned aerial vehicles (UAVs) has emerged as a bridge between field data and satellite-based mapping. We assess the viability of using high resolution UAV imagery and UAV-derived Structure from Motion (SfM) to predict cover, height and aboveground biomass (henceforth biomass) of Arctic plant functional types (PFTs) across a range of vegetation community types. We classified imagery by PFT, estimated cover and height, and modeled biomass from UAV-derived volume estimates. Predicted values were compared to field estimates to assess results. Cover was estimated with root-mean-square error (RMSE) 6.29-14.2% and height was estimated with RMSE 3.29-10.5 cm, depending on the PFT. Total aboveground biomass was predicted with RMSE 220.5 g m -2 , and per-PFT RMSE ranged from 17.14-164.3 g m -2 . Deciduous and evergreen shrub biomass was predicted most accurately, followed by lichen, graminoid, and forb biomass. Our results demonstrate the effectiveness of using UAVs to map PFT biomass, which provides a link towards improved mapping of PFTs across large areas using earth observation satellite imagery.
Why it matches plant phenotyping methodsUAV画像とSfMから植物機能タイプの被覆、草丈、地上部バイオマスを推定し、現地推定値との比較で精度評価を行うことが研究の中心であるため。
abstractWe assess the viability of using high resolution UAV imagery and UAV-derived Structure from Motion (SfM) to predict cover, height and aboveground biomass (henceforth biomass) of Arctic plant functional types (PFTs) across a range of vegetation community types.
Reproduction assets foundThe paper's plant-phenotyping data (UAV-derived PFT cover, canopy height, biomass, and field validation measurements) are explicitly stated to be publicly archived at the NSF Arctic Data Center under DOI 10.18739/A2R785Q5B. No author analysis code or trained model checkpoints are described with a public deposit.Dataset · publicns Attribution 4.0 International License (CC BY 4.0),
which permits unrestricted use, distribution, and reproduc-
tion in any medium, provided the original author(s) and
source are credited.
Data availability
Data supporting the results in this paper are publicly
archived at the National Science Foundation Arctic Data Cen-
ter: https://doi.org/10.18739/A2R785Q5B.Author information
Author ORCIDs
Kathleen M. Orndahlhttps://orcid.org/0000-0002-4873-4375
Author contributions
KMO and SJG conceived the ideas; KMO, LPWE, and JDH de-
signed the methodology; KMO, LPWE, JDH, and REP collected
the data; KMO, LPWE, and REP processed and curated the
data; KMO analyzed the data with input from MH; KMO ledOpen asset ↗10.18739/A2R785Q5Bpdf-raw-page:14 lines:1-99Code / dataset availability confirmedEurope PMC · checked 8 Sept 2026
Alfalfa / lucerneField / plotRootClassificationRoot system architecture
Active breeding programs specifically for root system architecture (RSA) phenotypes remain rare; however, breeding for branch and taproot types in the perennial crop alfalfa is ongoing. Phenotyping in this and other crops for active RSA breeding has mostly used visual scoring of specific traits or subjective classification into different root types. While image-based methods have been developed, translation to applied breeding is limited. This research is aimed at developing and comparing image-based RSA phenotyping methods using machine and deep learning algorithms for objective classification of 617 root images from mature alfalfa plants collected from the field to support the ongoing breeding efforts. Our results show that unsupervised machine learning tends to incorrectly classify roots into a normal distribution with most lines predicted as the intermediate root type. Encouragingly, random forest and TensorFlow-based neural networks can classify the root types into branch-type, taproot-type, and an intermediate taproot-branch type with 86% accuracy. With image augmentation, the prediction accuracy was improved to 97%. Coupling the predicted root type with its prediction probability will give breeders a confidence level for better decisions to advance the best and exclude the worst lines from their breeding program. This machine and deep learning approach enables accurate classification of the RSA phenotypes for genomic breeding of climate-resilient alfalfa.
Why it matches plant phenotyping methodsアルファルファ根系構造を対象に、画像増強と機械学習・深層学習による表現型分類手法を開発・比較しており、フェノタイピング手法が研究の中心である。
abstractThis research is aimed at developing and comparing image-based RSA phenotyping methods using machine and deep learning algorithms
Reproduction assets foundThe paper's root images (originals with tags removed and RootPainter segmentations) used for the alfalfa RSA phenotyping/ML analysis are publicly deposited on Zenodo (doi: 10.5281/zenodo.5879778), as stated in the Data Availability section. No allowed URL in the supplied list matches this deposit, so no URL is providedDataset · publicThe original images with tags removed and segmented images from RootPainter for data analysis are available on Zenodo doi: 10.5281/zenodo.5879778 [ 85 ].Zenodo · 10.5281/zenodo.5879778lines:627-653Code / dataset availability confirmedEurope PMC · checked 15 Sept 2026
Capturing cell-to-cell and cell-to-environment signals in a defined 3 dimensional (3D) microenvironment is key to study cellular functions, including cellular reprogramming towards tissue regeneration. A major challenge in current culturing methods is that these methods cannot accurately capture this multicellular 3D microenvironment. In this study, we established the framework of 3D bioprinting with plant cells to study cell viability, cell division, and cell identity. We established long-term cell viability for bioprinted Arabidopsis root cells and soybean meristematic cells. To analyze the large image datasets generated during these long-term viability studies, we developed an open source high-throughput image analysis pipeline. Furthermore, we showed the cell cycle re-entry of the isolated Arabidopsis and soybean cells leading to the formation of microcalli. Finally, we showed that the identity of isolated cells of Arabidopsis roots expressing endodermal markers maintained longer periods of time. The framework established in this study paves the way for a general use of 3D bioprinting for studying cellular reprogramming and cell cycle re-entry towards tissue regeneration.
Why it matches plant phenotyping methods植物細胞の3Dバイオプリンティング系と、長期画像データから細胞生存性・分裂・同一性を抽出するオープンソース解析パイプラインを開発しており、植物状態の取得・解析手法が中心である。
abstractIn this study, we established the framework of 3D bioprinting with plant cells to study cell viability, cell division, and cell identity.
Reproduction assets foundThe paper's authors publicly released their in-house confocal z-stack cell-counting image analysis pipeline (Python scripts wrapped in an R Shiny GUI) on GitHub, which directly reproduces the paper's computational analysis of bioprinted plant cell images. Generic dependencies (pyimageJ, OpenCV, ComDet, R shiny) and theCode · publicpipeline
contained in Python and further developed into an R Shiny application (44) can be easily accessed, along
with the usage instructions, from the Github repository at https://github.com/LisaVdB/Confocal-z-stack-
cell-detection.
Data availability
Scripts for our high-throughput and automatic image analysis are available at
https://github.com/LisaVdB/Confocal-z-stack-cell-detection.Open asset ↗LisaVdB/Confocal-z-stack-cell-detectionpdf-layout-page:10 lines:1-49Code / dataset availability confirmedEurope PMC · Crossref · checked 8 Sept 2026
Field / plotRootStem / branchWhole plant / canopy / plot / fieldObject detectionSegmentationRoot system architecture
Plant roots influence many ecological and biogeochemical processes, such as carbon, water and nutrient cycling. Because of difficult accessibility, knowledge on plant root dynamics in field conditions, however, is fragmentary at best. Minirhizotrons, i.e. transparent tubes placed in the substrate into which specialized cameras are inserted, facilitate the capture of high-resolution images of root dynamics at the soil-tube interface with little to no disturbance after the initial installation. Their use, especially in field studies with multiple species and heterogeneous substrates, though, is limited by the amount of work that subsequent manual tracing of roots in the images requires. Furthermore, the reproducibility and objectivity of manual root detection is questionable. Here, we use a Convolutional Neural Network (CNN) for the automatic detection of roots in minirhizotron images and compare the performance of our RootDetector with human analysts with different levels of expertise. The minirhizotron data stem from various wetland types on organic soils. RootDetector showed a high capability to correctly segmenting root pixels in minirhizotron images from field observations (F1 = 0.6044; r² compared to a human expert = 0.99). Reproducibility among humans, however, depended strongly on expertise level, with novices showing drastic variation among individual analysts and annotating on average almost 3-times higher root length/cm² per image compared to expert analysts. Analyses with RootDetector save resources, are reproducible and objective, and are as accurate as manual analyses performed by human experts.
Why it matches plant phenotyping methodsミニライゾトロン画像から根を自動検出・セグメンテーションするCNN手法を開発し、人間の専門家と性能・再現性を比較しており、植物形態形質の取得方法が中心です。
abstractHere, we use a Convolutional Neural Network (CNN) for the automatic detection of roots in minirhizotron images and compare the performance of our RootDetector with human analysts with different levels of expertise.
Reproduction assets foundThe authors state RootDetector is supplied as usable code on GitHub, with the Data Accessibility section giving the repository URL, which matches an allowed URL.Code · publicRootDetector is supplied as readily usable code on GitHub, enabling easy use byOpen asset ↗pdf-page:20 lines:1-51Code / dataset availability confirmedEurope PMC · checked 14 Sept 2026
ArabidopsisLaboratory / benchtopRootMorphology / geometry measurementTrackingGrowth / development / phenologyRoot system architecture
Directional root growth control is crucial for plant fitness. The degree of root growth deviation depends on several factors, whereby exogenous growth conditions have a profound impact. The perception of mechanical impedance by wild-type roots results in the modulation of root growth traits, and it is known that gravitropic stimulus influences distinct root movement patterns in concert with mechanoadaptation. Mutants with reduced shootward auxin transport are described as being numb towards mechanostimulus and gravistimulus, whereby different growth conditions on agar-supplemented medium have a profound effect on how much directional root growth and root movement patterns differ between wild types and mutants. To reduce the impact of unilateral mechanostimulus on roots grown along agar-supplemented medium, we compared the root movement of Col-0 and auxin resistant 1-7 in a root penetration assay to test how both lines adjust the growth patterns of evenly mechanostimulated roots. We combined the assay with the D-root system to reduce light-induced growth deviation. Moreover, the impact of sucrose supplementation in the growth medium was investigated because exogenous sugar enhances root growth deviation in the vertical direction. Overall, we observed a more regular growth pattern for Col-0 but evaluated a higher level of skewing of aux1-7 compared to the wild type than known from published data. Finally, the tracking of the growth rate of the gravistimulated roots revealed that Col-0 has a throttling elongation rate during the bending process, but aux1-7 does not.
Why it matches plant phenotyping methodsD-rootシステムと根貫通アッセイを組み合わせ、根の成長パターン・伸長速度を追跡して評価する測定ワークフローが研究の中心であるため。
abstractWe combined the assay with the D-root system to reduce light-induced growth deviation.
Reproduction assets found保存済みの本文根拠を更新済みルールで再検証し、公開資産1件を確認しました。Supplement · publicThe following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/plants11050650/s1 , Table S1: raw data.Open asset ↗lines:48-103Code / dataset availability confirmedCrossref · Europe PMC · checked 8 Sept 2026
Abstract Background In recent years, there has been an increase of interest in plant behaviour as represented by growth-driven responses. These are generally classified into nastic (internally driven) and tropic (environmentally driven) movements. Nastic movements include circumnutations, a circular movement of plant organs commonly associated with search and exploration, while tropisms refer to the directed growth of plant organs toward or away from environmental stimuli, such as light and gravity. Tracking these movements is therefore fundamental for the study of plant behaviour. Convolutional neural networks, as used for human and animal pose estimation, offer an interesting avenue for plant tracking. Here we adopted the Social LEAP Estimates Animal Poses (SLEAP) framework for plant tracking. We evaluated it on time-lapse videos of cases spanning a variety of parameters, such as: (i) organ types and imaging angles (e.g., top-view crown leaves vs. side-view shoots and roots), (ii) lighting conditions (full spectrum vs. IR), (iii) plant morphologies and scales (100 μm-scale Arabidopsis seedlings vs. cm-scale sunflowers and beans), and (iv) movement types (circumnutations, tropisms and twining). Results Overall, we found SLEAP to be accurate in tracking side views of shoots and roots, requiring only a low number of user-labelled frames for training. Top views of plant crowns made up of multiple leaves were found to be more challenging, due to the changing 2D morphology of leaves, and the occlusions of overlapping leaves. This required a larger number of labelled frames, and the choice of labelling “skeleton” had great impact on prediction accuracy, i.e., a more complex skeleton with fewer individuals (tracking individual plants) provided better results than a simpler skeleton with more individuals (tracking individual leaves). Conclusions In all, these results suggest SLEAP is a robust and versatile tool for high-throughput automated tracking of plants, presenting a new avenue for research focusing on plant dynamics.
Why it matches plant phenotyping methods植物の成長運動を抽出するため、SLEAPを植物追跡へ適応し、多様な器官・撮像条件・形態・運動で精度を評価している。植物表現型取得手法が中心である。
abstractHere we adopted the Social LEAP Estimates Animal Poses (SLEAP) framework for plant tracking.
Reproduction assets foundThe paper's Availability of data and materials statement points to a public Zenodo deposit containing the paper-specific time-lapse videos, SLEAP .slp labelled training files, and predicted output analysis files used in this study.Dataset · publicThe datasets during and/or analysed during the current study available at: https://zenodo.org/record/5764169#.YbCK0_FBxqt , https://doi.org/10.5281/zenodo.5764169 , which includes: (1) raw videos of the timelapse for each analysis. (2) The.slp files for each video analysis, which can be loaded into SLEAP and contain the 5, 10 or 20 labelled training frames.Open asset ↗zenodo · 10.5281/zenodo.5764169lines:134-177Code / dataset availability confirmedOpenAlex · Crossref · checked 14 Sept 2026
MaizeField / plotMesh / voxelLiDAR / point cloudRootWhole plant / canopy / plot / field2D/3D reconstructionRoot system architecture
Understanding root traits is essential to improve water uptake, increase nitrogen capture and accelerate carbon sequestration from the atmosphere. High-throughput phenotyping to quantify root traits for deeper field-grown roots remains a challenge, however. Recently developed open-source methods use 3D reconstruction algorithms to build 3D models of plant roots from multiple 2D images and can extract root traits and phenotypes. Most of these methods rely on automated image orientation (Structure from Motion)[1] and dense image matching (Multiple View Stereo) algorithms to produce a 3D point cloud or mesh model from 2D images. Until now the performance of these methods when applied to field-grown roots has not been compared tested commonly used open-source pipelines on a test panel of twelve contrasting maize genotypes grown in real field conditions[2-6]. We compare the 3D point clouds produced in terms of number of points, computation time and model surface density. This comparison study provides insight into the performance of different open-source pipelines for maize root phenotyping and illuminates trade-offs between 3D model quality and performance cost for future high-throughput 3D root phenotyping.
Why it matches plant phenotyping methods3D画像再構成パイプラインを比較・評価し、圃場トウモロコシ根の表現型取得性能を検証する研究であり、フェノタイピング手法が中心です。
titleComparison of open-source image-based reconstruction pipelines for 3D root phenotyping of field-grown maize
Reproduction assets foundThe paper's data availability statement provides two public, paper-specific assets: a GitHub repository with the scripts used to run the 3D reconstruction pipeline comparison, and a Cyverse archive containing all 60 resulting 3D root point cloud models from the twelve field-grown maize genotypes.Code · publicDATA AVAILABILITY STATEMENT
GitHub link for all the scripts for running the test:
https://github.com/Computational-Plant-Science/3D_review_scripts/tree/master
Cyverse link to all the 3D model results:
https://data.cyverse.org/dav-anon/iplant/home/lsx1980/3D_model_compare.zip
ACKNOWLEDGMENTS
The research was supported by the NSF CAREER Award No. 1845760 and USDOE ARPA-E ROOTS
Award Number DE-AR0000821 to A.B. Any Opinions, findings, and conclusions or recommendations
expressed in thisOpen asset ↗Computational-Plant-Science/3D_review_scriptspdf-raw-page:6 lines:1-40Code / dataset availability confirmedEurope PMC · Crossref · checked 13 Sept 2026
Arabidopsis (Arabidopsis thaliana) primary and lateral roots (LRs) are well suited for 3D and 4D microscopy, and their development provides an ideal system for studying morphogenesis and cell proliferation dynamics. With fast-advancing microscopy techniques used for live-imaging, whole tissue data are increasingly available, yet present the great challenge of analyzing complex interactions within cell populations. We developed a plugin "Live Plant Cell Tracking" (LiPlaCeT) coupled to the publicly available ImageJ image analysis program and generated a pipeline that allows, with the aid of LiPlaCeT, 4D cell tracking and lineage analysis of populations of dividing and growing cells. The LiPlaCeT plugin contains ad hoc ergonomic curating tools, making it very simple to use for manual cell tracking, especially when the signal-to-noise ratio of images is low or variable in time or 3D space and when automated methods may fail. Performing time-lapse experiments and using cell-tracking data extracted with the assistance of LiPlaCeT, we accomplished deep analyses of cell proliferation and clonal relations in the whole developing LR primordia and constructed genealogical trees. We also used cell-tracking data for endodermis cells of the root apical meristem (RAM) and performed automated analyses of cell population dynamics using ParaView software (also publicly available). Using the RAM as an example, we also showed how LiPlaCeT can be used to generate information at the whole-tissue level regarding cell length, cell position, cell growth rate, cell displacement rate, and proliferation activity. The pipeline will be useful in live-imaging studies of roots and other plant organs to understand complex interactions within proliferating and growing cell populations. The plugin includes a step-by-step user manual and a dataset example that are available at https://www.ibt.unam.mx/documentos/diversos/LiPlaCeT.zip.
Why it matches plant phenotyping methods植物の4Dライブイメージングから細胞系譜・位置・長さ・成長率などの形態・成長表現型を抽出する解析プラグインとパイプラインの開発が中心である。
abstractWe developed a plugin "Live Plant Cell Tracking" (LiPlaCeT) coupled to the publicly available ImageJ image analysis program and generated a pipeline that allows, with the aid of LiPlaCeT, 4D cell tracking and lineage analysis of populations of dividing and growing cells.
Reproduction assets foundThe paper's LiPlaCeT Fiji plugin for 4D plant cell tracking is publicly available: source code on GitHub and an ImageJ plugin package including a dataset example and user manual on the authors' IBT-UNAM site.Code · publicThe source code is freely available at https://github.com/paul-hernandez-herrera/LiPlaCeT and the ImageJ plugin including a dataset example and the User Manual can be downloaded from https://www.ibt.unam.mx/documentos/diversos/LiPlaCeT.zip .Open asset ↗paul-hernandez-herrera/LiPlaCeTlines:203-225Code / dataset availability confirmedEurope PMC · bioRxiv · checked 8 Sept 2026
Field / plotRootWhole plant / canopy / plot / fieldRoot system architecture
Root phenotyping describes methods for measuring root properties, or traits. While root phenotyping can be challenging, it is advancing quickly. In order for the field to move forward, it is essential to understand the current state and challenges of root phenotyping, as well as the pressing needs of the root biology community. In this letter, we present and discuss the results of a survey that was created and disseminated by members of the Graduate Student and Postdoc Ambassador Program at the 11th symposium of the International Society of Root Research. This survey aimed to (1) provide an overview of the objectives, biological models and methodological approaches used in root phenotyping studies, and (2) identify the main limitations currently faced by plant scientists with regard to root phenotyping. Our survey highlighted that (1) monocotyledonous crops dominate the root phenotyping landscape, (2) root phenotyping is mainly used to quantify morphological and architectural root traits, (3) 2D root scanning/imaging is the most widely used root phenotyping technique, (4) time-consuming tasks are an important barrier to root phenotyping, (5) there is a need for standardised, high-throughput methods to sample and phenotype roots, particularly under field conditions, and to improve our understanding of trait-function relationships.
Why it matches plant phenotyping methods根系フェノタイピングの手法、利用状況、限界、標準化ニーズを調査・整理したレビュー的研究であり、フェノタイピング方法論が中心です。
abstractRoot phenotyping describes methods for measuring root properties, or traits.
Reproduction assets foundThe paper's Data availability statement explicitly deposits the survey raw data and R analysis code on Zenodo (DOI 10.5281/zenodo.5901959), a public, paper-specific, actionable asset. The Nottingham Hidden Half maize image URL is only a credited Figure 1 image source, not a paper-specific dataset, and is not listed asaCode · publicRaw data and R code are available on Zenodo at https://doi.org/10.5281/zenodo.5901959.Open asset ↗Zenodo · 10.5281/zenodo.5901959pdf-page:10 lines:1-39Code / dataset availability confirmedEurope PMC · OpenAlex · Crossref · checked 13 Sept 2026
Field / plotPhotogrammetry / SfM / MVSLiDAR / point cloudRootStem / branch2D/3D reconstructionSkeletonization / topologyArchitecture / morphology / geometry
Living architecture, changing in structure with annual growth, requires precise, regular characterisation. However, its geometric irregularity and topological complexity make documentation using traditional methods difficult and presents challenges in creating useful models for mechanical and physiological analyses. Two kinds of living architecture are examined: historic living root bridges grown in Meghalaya, India, and contemporary 'Baubotanik' structures designed and grown in Germany. These structures exhibit common features, in particular network-like structures of varying complexity that result from inosculations between shoots or roots. As an answer to this modelling challenge, we present the first extensive documentation of living architecture using photogrammetry and a subsequent skeleton extraction workflow that solves two problems related to the anastomoses and varying nearby elements specific to living architecture. Photogrammetry was used as a low cost method, supplying detailed point clouds of the structures' visible surfaces. A workflow based on voxel-thinning (using deletion templates and adjusted p-simplicity criteria) provides efficient, accurate skeletons. A volume reconstruction method is derived from the thinning process. The workflow is assessed on seven characteristics beneficial in representing living architecture in comparison with alternative skeleton extraction methods. The resulting models are ready for use in analytical tools, necessary for functional, responsible design.
Why it matches plant phenotyping methods植物の生体構造をフォトグラメトリで取得し、点群から骨格・体積を再構成するワークフロー自体が中心的な方法開発であり、植物構造の表現・解析に用いるため。
abstractwe present the first extensive documentation of living architecture using photogrammetry and a subsequent skeleton extraction workflow
Reproduction assets foundThe paper's Data availability statement explicitly provides public access to the authors' skeletonisation source code on GitHub and the photogrammetric point clouds (Freiburg pavilion, Ficus joint, Baubotanik joint) on the TUM media repository. Both are paper-specific, public, and actionable.Code · publicThe source code is available at: https://github.com/QiguanShu/skeleton-abstraction-of-point-cloud-by-voxel-thinningOpen asset ↗QiguanShu/skeleton-abstraction-of-point-cloud-by-voxel-thinninglines:141-214Code / dataset availability confirmedCrossref · Europe PMC · checked 8 Sept 2026
Plants are often attacked by various pathogens during their growth, which may cause environmental pollution, food shortages, or economic losses in a certain area. Integration of high throughput phenomics data and computer vision (CV) provides a great opportunity to realize plant disease diagnosis in the early stage and uncover the subtype or stage patterns in the disease progression. In this study, we proposed a novel computational framework for plant disease identification and subtype discovery through a deep-embedding image-clustering strategy, Weighted Distance Metric and the t-stochastic neighbor embedding algorithm (WDM-tSNE). To verify the effectiveness, we applied our method on four public datasets of images. The results demonstrated that the newly developed tool is capable of identifying the plant disease and further uncover the underlying subtypes associated with pathogenic resistance. In summary, the current framework provides great clustering performance for the root or leave images of diseased plants with pronounced disease spots or symptoms.
Why it matches plant phenotyping methods植物病斑画像を対象とした疾患識別・サブタイプ発見のための深層埋め込み画像クラスタリング手法を開発し、複数公開データセットで検証しているため、病害表現型の取得・解析が中心である。
abstractwe proposed a novel computational framework for plant disease identification and subtype discovery through a deep-embedding image-clustering strategy, Weighted Distance Metric and the t-stochastic neighbor embedding algorithm (WDM-tSNE).
Reproduction assets foundThe paper's data availability statement provides public links to the raw plant lesion images (data.rar) and the authors' WDM-tSNE source code on GitHub, both paper-specific and directly actionable.Dataset · publicAll the raw images involved in this study can be accessed through the links: https://xf-data-bucket.oss-cn-hangzhou.aliyuncs.com/data.rarOpen asset ↗lines:523-539Code · publicSource code is available at GitHub: https://github.com/JakeJiUThealth/WDM1.0Open asset ↗JakeJiUThealth/WDM1.0lines:523-539Code / dataset availability confirmedOpenAlex · Europe PMC · checked 8 Sept 2026
The root system of a plant provides vital functions including resource uptake, storage, and anchorage in soil. The uptake of macro-nutrients like nitrogen (N), phosphorus (P), potassium (K), and sulphur (S) from the soil is critical for plant growth and development. Small signaling peptide (SSP) hormones are best known as potent regulators of plant growth and development with a few also known to have specialized roles in macronutrient utilization. Here we describe a high throughput phenotyping platform for testing SSP effects on root uptake of multiple nutrients. The SSP, CEP1 (C-TERMINALLY ENCODED PEPTIDE) enhanced nitrate uptake rate per unit root length in Medicago truncatula plants deprived of N in the high-affinity transport range. Single structural variants of M. truncatula and Arabidopsis thaliana specific CEP1 peptides, MtCEP1D1:hyp4,11 and AtCEP1:hyp4,11, enhanced uptake not only of nitrate, but also phosphate and sulfate in both model plant species. Transcriptome analysis of Medicago roots treated with different MtCEP1 encoded peptide domains revealed that hundreds of genes respond to these peptides, including several nitrate transporters and a sulfate transporter that may mediate the uptake of these macronutrients downstream of CEP1 signaling. Likewise, several putative signaling pathway genes including LEUCINE-RICH REPEAT RECPTOR-LIKE KINASES and Myb domain containing transcription factors, were induced in roots by CEP1 treatment. Thus, a scalable method has been developed for screening synthetic peptides of potential use in agriculture, with CEP1 shown to be one such peptide.
Why it matches plant phenotyping methods植物の栄養吸収率を測定する高スループット表現型解析プラットフォームを開発し、合成ペプチドのスクリーニングに適用しているため、測定手法が研究の中心である。
abstractHere we describe a high throughput phenotyping platform for testing SSP effects on root uptake of multiple nutrients.
Reproduction assets foundThe paper's nutrient uptake rate calculations were performed with R code publicly available on Zenodo (Griffiths et al., 2021), which the authors state they used with minor modifications for this paper's phenotyping analysis. The NCBI BioProject (PRJNA764762) is an RNA-seq omics deposit and is excluded per criteria; TrCode · publicdata processing to determine specific nutrient uptake rates was conducted using R version 3.6.0 (Team, 2020)( R Core Team, 2020 ) with minor modification to the R code available at https://doi.org/10.5281/zenodo.3893945 ( Griffiths et al., 2021 )Open asset ↗zenodo · 10.5281/zenodo.3893945lines:318-326Code / dataset availability confirmedEurope PMC · OpenAlex · checked 15 Sept 2026
The three-dimensional (3D) arrangement of cells in tissues provides an anatomical basis for analyzing physiological and biochemical aspects of plant and animal cellular development and function. In this study, we established a protocol for tissue clearing and 3D imaging in rice. Our protocol is based on three improvements: clearing with iTOMEI (clearing solution suitable for plants), developing microscopic conditions in which the Z step is optimized for 3D reconstruction, and optimizing cell-wall staining. Our protocol successfully 3D imaged rice shoot apical meristems, florets, and root apical meristems at cellular resolution throughout whole tissues. Using fluorescent reporters of auxin signaling in rice root tips, we also revealed the 3D distribution of auxin signaling events that are activated in the columella, quiescent center, and multiple rows of cells in the stele of the root apical meristem. Examination of cells with higher levels of auxin signaling revealed that only the central row of cells was connected to the quiescent center. Our method provides opportunities to observe the 3D arrangement of cells in rice tissues.
Why it matches plant phenotyping methodsイネ組織を対象に、組織透明化・最適化した3D顕微鏡撮像・細胞壁染色による細胞配置の取得法を開発しており、植物表現型の画像取得が中心的な技術貢献である。
abstractIn this study, we established a protocol for tissue clearing and 3D imaging in rice.
Reproduction assets foundThe paper's 3D imaging datasets (supplementary videos S1–S6 of rice SAMs, florets, anthers, and root tips, plus figure data) are publicly available via the MDPI supplementary materials link. No separate analysis code repository is mentioned.Dataset · publiccquisition, which took approximately 2 h for 150 μm in depth, the images were processed using LASX software (Leica Microsystems, Tokyo, Japan).
Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Supplementary Materials
The following are available online at https://www.mdpi.com/article/10.3390/ijms23010040/s1 .
Click here for additional data file.
Author Contributions
M.S. and H.T. designed the research; M.S., H.A., Y.S. and S.M. performed the research; M.S. and H.T. analyzed the data; M.S. and H.T. wrote the paper. All authors have read and agreed to the published version of the manuscript.
Funding
This study was supporOpen asset ↗lines:70-202Code / dataset availability confirmedEurope PMC · OpenAlex · Crossref · checked 9 Sept 2026
Growing evaluation in the early stages of crop development can be critical to eventual yield. Point clouds have been used for this purpose in tasks such as detection, characterization, phenotyping, and prediction on different crops with terrestrial mapping platforms based on laser scanning. 3D model generation requires the use of specialized measurement equipment, which limits access to this technology because of their complex and high cost, both hardware elements and data processing software. An unmanned 3D reconstruction mapping system of orchards or small crops has been developed to support the determination of morphological indices, allowing the individual calculation of the height and radius of the canopy of the trees to monitor plant growth. This paper presents the details on each development stage of a low-cost mapping system which integrates an Unmanned Ground Vehicle UGV and a 2D LiDAR to generate 3D point clouds. The sensing system for the data collection was developed from the design in mechanical, electronic, control, and software layers. The validation test was carried out on a citrus crop section by a comparison of distance and canopy height values obtained from our generated point cloud concerning the reference values obtained with a photogrammetry method. A 3D crop map was generated to provide a graphical view of the density of tree canopies in different sections which led to the determination of individual plant characteristics using a Python-assisted tool. Field evaluation results showed plant individual tree height and crown diameter with a root mean square error of around 30.8 and 45.7 cm between point cloud data and reference values.
Why it matches plant phenotyping methods低コストUGV・LiDARによる3D植物計測システムを開発し、樹冠形態指標を抽出・検証しており、植物フェノタイピング手法が研究の中心である。
abstractAn unmanned 3D reconstruction mapping system of orchards or small crops has been developed to support the determination of morphological indices, allowing the individual calculation of the height and radius of the canopy of the trees to monitor plant growth.
Reproduction assets foundThe paper's Data Availability Statement provides an authors' public GitHub repository containing their code implementation for the UGV-LiDAR citrus crop mapping/phenotyping system. No separate phenotype dataset or point cloud deposit is stated.Code · publicOur code implementation is available online at https://github.com/HaroldMurcia/miniRover_LiDAR_citrush_crop.git , accessed on 25 November 2021.Open asset ↗HaroldMurcia/miniRover_LiDAR_citrush_croplines:356-358Code / dataset availability confirmedEurope PMC · checked 15 Sept 2026
Background 3D imaging, such as X-ray CT and MRI, has been widely deployed to study plant root structures. Many computational tools exist to extract coarse-grained features from 3D root images, such as total volume, root number and total root length. However, methods that can accurately and efficiently compute fine-grained root traits, such as root number and geometry at each hierarchy level, are still lacking. These traits would allow biologists to gain deeper insights into the root system architecture. Results We present TopoRoot, a high-throughput computational method that computes fine-grained architectural traits from 3D images of maize root crowns or root systems. These traits include the number, length, thickness, angle, tortuosity, and number of children for the roots at each level of the hierarchy. TopoRoot combines state-of-the-art algorithms in computer graphics, such as topological simplification and geometric skeletonization, with customized heuristics for robustly obtaining the branching structure and hierarchical information. TopoRoot is validated on both CT scans of excavated field-grown root crowns and simulated images of root systems, and in both cases, it was shown to improve the accuracy of traits over existing methods. TopoRoot runs within a few minutes on a desktop workstation for images at the resolution range of 400^3, with minimal need for human intervention in the form of setting three intensity thresholds per image. Conclusions TopoRoot improves the state-of-the-art methods in obtaining more accurate and comprehensive fine-grained traits of maize roots from 3D imaging. The automation and efficiency make TopoRoot suitable for batch processing on large numbers of root images. Our method is thus useful for phenomic studies aimed at finding the genetic basis behind root system architecture and the subsequent development of more productive crops.
Why it matches plant phenotyping methods3D画像からトウモロコシ根系の階層別形態形質を抽出する計算手法を開発し、既存法と精度比較・検証しており、植物表現型取得が中心です。
abstractWe present TopoRoot, a high-throughput computational method that computes fine-grained architectural traits from 3D images of maize root crowns or root systems.
Reproduction assets foundThe paper's authors publicly distribute the TopoRoot analysis software (C++ pipeline with GUI) together with the 45 X-ray CT scans of maize root crowns, per-image threshold values, and hand-measured nodal root counts in a GitHub repository. The synthetic OpenSimRoot images and ground-truth traits are only available on.Code · publicto a Euclidean distance field (e.g., using [ 29 ]). Fig. 12
Hierarchies of sorghum roots computed by TopoRoot, showing one tiller ( A ), two tillers ( B ), and four tillers ( C ). Hierarchy levels 0, 1, 2, 3 and 4 are colored dark blue, light blue, green, orange, and red.
Software availability
TopoRoot is available for free at: https://github.com/danzeng8/TopoRoot .
Included in the page are instructions to run the software, and details on the formats of the input and output files. Currently, the accepted inputs are either image slices (suffixed with.png) or.raw files, with a.dat accompanying the.raw file to specify the dimensions. The output consists of a skeleton, a hierarchy annotationOpen asset ↗https://github.com/danzeng8/TopoRootlines:2051-2060Dataset · public\usepackage{amsfonts}
\usepackage{amssymb}
\usepackage{amsbsy}
\usepackage{mathrsfs}
\usepackage{upgreek}
\setlength{\oddsidemargin}{-69pt}
\begin{document}$$t_{low} ,t_{mid} ,t_{high}$$\end{document} t low , t mid , t high ) and hand measurements of nodal roots for each sample, are available in the TopoRoot Github repository: https://github.com/danzeng8/TopoRoot . The synthetic images of simulated roots and associated ground truth trait measurements are available from the corresponding author upon request.
Declarations
Ethics approval and consent to participate
Not applicable.
Consent for publication
Not applicable.
Competing interests
The authors declare that they have no competing inOpen asset ↗https://github.com/danzeng8/TopoRootlines:2061-2116Code / dataset availability confirmedCrossref · checked 9 Sept 2026
Significance The lack of suitable approaches for studying root–microbe interactions, live and in situ, has severely limited our ability to understand the rhizosphere. In this study, we overcome this major limitation with an imaging system that combines transparent soils with cutting edge light sheet microscopy. The study revealed that the root cap is a point of first contact for microbes before establishment and reveals how the pore structure influences the patterns of interactions between the microbe and the plant. With the combined use of light sheet microscopy and transparent soils, we shed light on previously unseen interaction phenomena and accelerate the understanding of how rhizospheres are formed.
Why it matches plant phenotyping methods透明土壌とライトシート顕微鏡を組み合わせたライブ・インサイチュ画像システムの開発が研究の中心で、根と微生物の相互作用という植物状態を可視化している。
abstractwe overcome this major limitation with an imaging system that combines transparent soils with cutting edge light sheet microscopy.
Reproduction assets foundThe paper deposits its phenotyping data (light-sheet microscopy volumes of root–soil–bacteria interactions) on Zenodo, makes its image analysis software (MATLAB/MeVisLab segmentation and quantification pipeline) publicly available on GitHub, and hosts supplementary materials on PNAS.Dataset · publicThe data in this study is available at https://doi.org/10.5281/zenodo.5650962 .Open asset ↗zenodo · 10.5281/zenodo.5650962lines:90-123Code · publicImage processing methods were programmed using MATLAB using the Image Processing Toolbox (MathWorks). Segmentation and extraction of geometrical features were performed using MeVisLab (MeVis Medical Solutions AG). All software is freely available from https://github.com/LionelDupuy/SENSOIL .Open asset ↗github · LionelDupuy/SENSOILlines:80-89Code / dataset availability confirmedEurope PMC · checked 15 Sept 2026
ArabidopsisRootMorphology / geometry measurementGrowth / time-series analysisGrowth / development / phenologyRoot system architecture
The plant kingdom contains a stunning array of complex morphologies easily observed above ground, but largely unexplored below-ground. Understanding the magnitude of diversity in root distribution within the soil, termed root system architecture (RSA), is fundamental to determining how this trait contributes to species adaptation in local environments. Roots are the interface between the soil environment and the shoot system and therefore play a key role in anchorage, resource uptake, and stress resilience. Previously, we presented the GLO-Roots (Growth and Luminescence Observatory for Roots) system to study the RSA of soil-grown Arabidopsis thaliana plants from germination to maturity (Rellán-Álvarez et al. 2015). In this study, we present the automation of GLO-Roots using robotics and the development of image analysis pipelines in order to examine the natural variation of RSA in Arabidopsis over time. This dataset describes the developmental dynamics of 93 accessions and reveals highly complex and polygenic RSA traits that show significant correlation with climate variables.
Why it matches plant phenotyping methodsロボティクスによる表現型取得の自動化と画像解析パイプライン開発が中心で、根系構造の時系列形質を抽出するフェノタイピング基盤を提示している。
abstractIn this study, we present the automation of GLO-Roots using robotics and the development of image analysis pipelines in order to examine the natural variation of RSA in Arabidopsis over time.
Reproduction assets foundThe paper's data availability statement deposits the GLORIAv2 phenotyping robot hardware, the image analysis pipelines/scripts used to extract root traits, the RShiny RSA exploration app, and the raw imaging data/images on Zenodo, all directly reproducing this paper's root phenotyping measurements and analysis.Dataset · public10.5281/zenodo.5574925
Image analysis pipelines and scripts are available through Zenodo, DOI:
https://doi.org/10.5281/zenodo.5708430
RShiny App for exploring root system architecture of accessions is available through Zenodo, DOI:
https://doi.org/10.5281/zenodo.5708422
Imaging data and images are available through Zenodo, DOI: https://doi.org/10.5281/zenodo.5709009
Previously published datasets used: WORLCLIM2: Fick SE, Hijmans RJ, 2017, https://worldclim.org/,
https://doi.org/10.1002/joc.5086
Acknowledgements:
Work in the JRD lab was funded by the U.S. Department of Energy’s Office of Biological and
Environmental Research (DE-SC0008769 and DE-SC0018277) and the Carnegie Institution for
SOpen asset ↗Zenodo · 10.5281/zenodo.5709009pdf-raw-page:13 lines:1-35Code · publicData availability:
GLORIAv2 is available through Zenodo, DOI: https://doi.org/10.5281/zenodo.5574925
Image analysis pipelines and scripts are available through Zenodo, DOI:
https://doi.org/10.5281/zenodo.5708430
RShiny App for exploring root system architecture of accessions is available through Zenodo, DOI:
https://doi.org/10.5281/zenodo.5708422
Imaging data and images are available through Zenodo, DOI: https://doi.org/10.5281/zenodo.5709009
Previously published datasets used: WORLCLIM2: Fick SE, Hijmans RJ, 2017, https://worldclim.org/,
https://doi.org/10.1002/joc.5086
Acknowledgements:
Work in the JRD lab was funded by the U.S. Department of Energy’s Office of BiologOpen asset ↗Zenodo · 10.5281/zenodo.5708422pdf-raw-page:13 lines:1-35Code · publicData availability:
GLORIAv2 is available through Zenodo, DOI: https://doi.org/10.5281/zenodo.5574925
Image analysis pipelines and scripts are available through Zenodo, DOI:
https://doi.org/10.5281/zenodo.5708430
RShiny App for exploring root system architecture of accessions is available through Zenodo, DOI:
https://doi.org/10.5281/zenodo.5708422
Imaging data and images are available through Zenodo, DOI: https://doi.org/10.528Open asset ↗Zenodo · 10.5281/zenodo.5574925pdf-raw-page:13 lines:1-35Code / dataset availability confirmedEurope PMC · checked 9 Sept 2026
MaizeX-ray / CTRootMorphology / geometry measurement2D/3D reconstructionRoot system architecture
The root system is critical for the survival of nearly all land plants and a key target for improving abiotic stress tolerance, nutrient accumulation, and yield in crop species. Although many methods of root phenotyping exist, within field studies, one of the most popular methods is the extraction and measurement of the upper portion of the root system, known as the root crown, followed by trait quantification based on manual measurements or 2D imaging. However, 2D techniques are inherently limited by the information available from single points of view. Here, we used X-ray computed tomography to generate highly accurate 3D models of maize root crowns and created computational pipelines capable of measuring 71 features from each sample. This approach improves estimates of the genetic contribution to root system architecture and is refined enough to detect various changes in global root system architecture over developmental time as well as more subtle changes in root distributions as a result of environmental differences. We demonstrate that root pulling force, a high-throughput method of root extraction that provides an estimate of root mass, is associated with multiple 3D traits from our pipeline. Our combined methodology can therefore be used to calibrate and interpret root pulling force measurements across a range of experimental contexts or scaled up as a stand-alone approach in large genetic studies of root system architecture.
Why it matches plant phenotyping methodsトウモロコシ根系のX線CT画像から3Dモデルを構築し、71形質を抽出する計算パイプラインを開発・適用しており、表現型取得手法が研究の中心です。
abstractHere, we used X-ray computed tomography to generate highly accurate 3D models of maize root crowns and created computational pipelines capable of measuring 71 features from each sample.
Reproduction assets foundThe paper's custom image-processing and feature-extraction scripts are publicly available in the Topp-Roots-Lab GitHub repository, explicitly linked by the authors for reproducing the work. The phenotype data (Data File S1) is in supplements without a direct URL, and image volumes are only available upon request.Code · publicA more extensive description of trait implementations, all scripts used for image processing and feature extraction, and links to repositories required to reproduce the work are available at https://github.com/Topp-Roots-Lab/3d-root-crown-analysis-pipeline/Open asset ↗Topp-Roots-Lab/3d-root-crown-analysis-pipelinelines:42-50Code / dataset availability confirmedCrossref · checked 14 Sept 2026
MaizeRoot2D/3D reconstructionSegmentationRoot system architecture
Abstract Purpose Root growth, respiration, water uptake as well as root exudation induce biogeochemical patterns in the rhizosphere that can change dynamically over time. Our aim is to develop a method that provides complementary information on 3D root system architecture and biogeochemical gradients around the roots needed for the quantitative description of rhizosphere processes. Methods We captured for the first time the root system architecture of maize plants grown in rectangular rhizotrons in 3D using neutron computed laminography (NCL). Simultaneously, we measured pH and oxygen concentration using fluorescent optodes and the 2D soil water distribution by means of neutron radiography. We co-registered the 3D laminography data with the 2D oxygen and pH maps to analyze the sensor signal as a function of the distance between the roots and the optode. Results The 3D root system architecture was successfully segmented from the laminographic data. We found that exudation of roots in up to 2 mm distance to the pH optode induced patterns of local acidification or alkalization. Over time, oxygen gradients in the rhizosphere emerged for roots up to a distance of 7.5 mm. Conclusion Neutron computed laminography allows for a three-dimensional investigation of root systems grown in laterally extended rhizotrons as the ones designed for 2D optode imaging studies. The 3D information on root position within the rhizotrons derived by NCL explained measured 2D oxygen and pH distribution. The presented new combination of 3D and 2D imaging methods facilitates systematical investigations of a wide range of dynamic processes in the rhizosphere.
Why it matches plant phenotyping methodsNCLを用いた3D根系構造の取得・セグメンテーションが研究の中心であり、根系アーキテクチャという植物表現型を抽出する新しい画像計測法を開発・適用している。
abstractOur aim is to develop a method that provides complementary information on 3D root system architecture
Reproduction assets foundThe paper's Data availability statement deposits the raw and reconstructed 3D neutron computed laminography dataset of one maize root sample in the datacite repository at Helmholtz-Zentrum Berlin (DOI 10.5442/ND000004), a public, paper-specific phenotyping asset. No author analysis code or trained models are disclosed;Dataset · publicacknowledge funding of the
research presented here by the German Research Foundation
(DFG) under Grant Numbers OS 351/8-1 and TO 949/2-1.
Data availability The raw data and reconstructed 3D dataset
from neutron computed laminography of one maize sample is
available at the datacite repository from Helmholtz Centre Ber-
lin under http://doi.org/10.5442/ND000004.Declarations
Conflicts of interest The authors have no conflicts of interest
to declare that are relevant to the content of this article.
Open Access This article is licensed under a Creative Com-
mons Attribution 4.0 International License, which permits
use, sharing, adaptation, distribution and reproduction in any
medium or format, asOpen asset ↗datacite · 10.5442/ND000004pdf-raw-page:11 lines:1-94Code / dataset availability confirmedEurope PMC · checked 9 Sept 2026
Laboratory / benchtopRootMorphology / geometry measurementRoot system architecture
Roots are central to the function of natural and agricultural ecosystems by driving plant acquisition of soil resources and influencing the carbon cycle. Root characteristics like length, diameter and volume are critical to measure to understand plant and soil functions. RhizoVision Explorer is an open-source software designed to enable researchers interested in roots by providing an easy-to-use interface, fast image processing and reliable measurements. The default broken roots mode is intended for roots sampled from pots and soil cores, washed and typically scanned on a flatbed scanner, and provides measurements like length, diameter and volume. The optional whole root mode for complete root systems or root crowns provides additional measurements such as angles, root depth and convex hull. Both modes support providing measurements grouped by defined diameter ranges, the inclusion of multiple regions of interest and batch analysis. RhizoVision Explorer was successfully validated against ground truth data using a new copper wire image set. In comparison, the current reference software, the commercial WinRhizo™, drastically underestimated volume when wires of different diameters were in the same image. Additionally, measurements were compared with WinRhizo™ and IJ_Rhizo using a simulated root image set, showing general agreement in software measurements, except for root volume. Finally, scanned root image sets acquired in different labs for the crop, herbaceous and tree species were used to compare results from RhizoVision Explorer with WinRhizo™. The two software showed general agreement, except that WinRhizo™ substantially underestimated root volume relative to RhizoVision Explorer. In the current context of rapidly growing interest in root science, RhizoVision Explorer intends to become a reference software, improve the overall accuracy and replicability of root trait measurements and provide a foundation for collaborative improvement and reliable access to all.
Why it matches plant phenotyping methods根画像から長さ・直径・体積などの植物形質を抽出するオープンソースソフトウェアを開発し、グラウンドトゥルースおよび既存ソフトウェアとの比較検証を行っており、植物フェノタイピング手法が中心である。
abstractRhizoVision Explorer is an open-source software designed to enable researchers interested in roots by providing an easy-to-use interface, fast image processing and reliable measurements.
Reproduction assets foundThe paper's own phenotyping assets are all publicly available: the RhizoVision Explorer source code (GitHub) and Windows binaries (Zenodo 3747697), the copper wire validation image set (Zenodo 4677546), the scanned root image sets from maize, wheat, herbaceous and tree species (Zenodo 4677751), and the R statistical/分析Code · publicThe open-source code for RhizoVision Explorer written in C++ is available at https://github.com/noble-research-institute/RhizoVisionExplorer on GitHub.Open asset ↗https://github.com/noble-research-institute/RhizoVisionExplorerlines:300-393Dataset · publicThe copper wire image set used here is available in a public repository and can be downloaded at http://doi.org/10.5281/zenodo.4677546 ( Dhakal et al. 2021a ).Open asset ↗zenodo · 10.5281/zenodo.4677546lines:86-101Dataset · publicThese four image sets of roots from several plant species are available in a public repository and can be downloaded at http://doi.org/10.5281/zenodo.4677751 ( Dhakal et al. 2021b ).Open asset ↗zenodo · 10.5281/zenodo.4677751lines:105-118Code / dataset availability confirmedOpenAlex · Crossref · Europe PMC · checked 8 Sept 2026
Phosphorus is one of the second most important nutrients for plant growth and development, and its importance has been realised from its role in various chains of reactions leading to better crop dynamics accompanied by optimum yield. However, the injudicious use of phosphorus (P) and non-renewability across the globe severely limit the agricultural production of crops, such as rice. The development of P-efficient cultivar can be achieved by screening genotypes either by destructive or non-destructive approaches. Exploring image-based phenotyping (shoot and root) and tolerant indices in conjunction under low P conditions was the first report, the epicentre of this study. Eighteen genotypes were selected for hydroponic study from the soil-based screening of 68 genotypes to identify the traits through non-destructive (geometric traits by imaging) and destructive (morphology and physiology) techniques. Geometric traits such as minimum enclosing circle, convex hull, and calliper length show promising responses, in addition to morphological and physiological traits. In 28-day-old seedlings, leaves positioned from third to fifth played a crucial role in P mobilisation to different plant parts and maintained plant architecture under P deficient conditions. Besides, a reduction in leaf angle adjustment due to a decline in leaf biomass was observed. Concomitantly, these geometric traits facilitate the evaluation of low P-tolerant rice cultivars at an earlier stage, accompanying several stress indices. Out of which, Mean Productivity Index, Mean Relative Performance, and Relative Efficiency index utilising image-based traits displayed better responses in identifying tolerant genotypes under low P conditions. This study signifies the importance of image-based phenotyping techniques to identify potential donors and improve P use efficiency in modern rice breeding programs.
Why it matches plant phenotyping methods低リン耐性イネの選抜において、画像から幾何学的形質を抽出するイメージベース表現型解析を中心的に適用・評価しており、単なる生物学的実験のルーチン測定ではない。
abstractExploring image-based phenotyping (shoot and root) and tolerant indices in conjunction under low P conditions was the first report, the epicentre of this study.
Reproduction assets found本文中に内容が明示された植物フェノタイピング関連の補足表と、その公開リンクを確認しました。Supplement · publicSupplementary Table 1 ) were selected based on the level of tolerance from all quarters of principal component analysis (PCA) to evaluate further under hydroponics and identify the traits through destructive (morphology and physiology) and non-destructive (geometric traits by imaging) techniques in a low phosphorus regime.Open asset ↗lines:313-319Code / dataset availability confirmedEurope PMC · bioRxiv · checked 15 Sept 2026
Background 3D imaging, such as X-ray CT and MRI, has been widely deployed to study plant root structures. Many computational tools exist to extract coarse-grained features from 3D root images, such as total volume, root number and total root length. However, methods that can accurately and efficiently compute fine-grained root traits, such as root number and geometry at each hierarchy level, are still lacking. These traits would allow biologists to gain deeper insights into the root system architecture (RSA). Results We present TopoRoot, a high-throughput computational method that computes fine-grained architectural traits from 3D X-ray CT images of field-excavated maize root crowns. These traits include the number, length, thickness, angle, tortuosity, and number of children for the roots at each level of the hierarchy. TopoRoot combines state-of-the-art algorithms in computer graphics, such as topological simplification and geometric skeletonization, with customized heuristics for robustly obtaining the branching structure and hierarchical information. TopoRoot is validated on both real and simulated root images, and in both cases it was shown to improve the accuracy of traits over existing methods. We also demonstrate TopoRoot in differentiating a maize root mutant from its wild type segregant using fine-grained traits. TopoRoot runs within a few minutes on a desktop workstation for volumes at the resolution range of 400^3, without need for human intervention. Conclusions TopoRoot improves the state-of-the-art methods in obtaining more accurate and comprehensive fine-grained traits of maize roots from 3D CT images. The automation and efficiency makes TopoRoot suitable for batch processing on a large number of root images. Our method is thus useful for phenomic studies aimed at finding the genetic basis behind root system architecture and the subsequent development of more productive crops.
Why it matches plant phenotyping methodsX線CT画像からトウモロコシ根系の階層的形態形質を抽出する計算手法を開発し、実画像・シミュレーション画像で検証しているため、植物フェノタイピング手法が中心である。
abstractWe present TopoRoot, a high-throughput computational method that computes fine-grained architectural traits from 3D X-ray CT images of field-excavated maize root crowns.
Reproduction assets foundThe paper's TopoRoot phenotyping software (C++ pipeline computing root hierarchy and fine-grained traits from X-ray CT volumes) and the datasets generated/analysed in the study (including the test dataset) are publicly released on the authors' GitHub repository.Code · publicduce a
697
probability density field (e.g., deep learning). Since TopoRoot requires a gray-scale intensity
698
volume with three thresholds (shape, kernel and neighborhood), a binary segmentation will first
699
need to be converted into a Euclidean distance field.
700
Software availability
701
TopoRoot is available for free at: https://github.com/danzeng8/TopoRoot
702
.
CC-BY 4.0 International license
available under a
(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made
The copyright holder for this preprint
this version posted August 28, 2021.
;
https://doi.org/10.1101/2021.08.24.457522
doi:
bOpen asset ↗danzeng8/TopoRootpdf-raw-page:37 lines:1-53Dataset · public39
CT: Computed Tomography
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Declarations
724
Ethics approval and consent to participate
725
Not applicable
726
Consent for publication
727
Not applicable
728
Availability of data and materials
729
The datasets generated and analysed during the current study are available in the TopoRoot
730
Github repository: https://github.com/danzeng8/TopoRoot
731
Competing interests
732
The authors declare that they have no competing interests.
733
Funding
734
This material is based upon work supported by the National Science Foundation under award
735
numbers DBI-1759836, DBI-1759807, DBI-1759796, EF-1971728, CCF-1907612, CCF-
736
2106672, and IOS-1638507. DZ is funded in part by aOpen asset ↗danzeng8/TopoRootpdf-raw-page:39 lines:1-45Code / dataset availability confirmedCrossref · Europe PMC · OpenAlex · checked 9 Sept 2026
The Early Devonian Rhynie chert preserves the earliest terrestrial ecosystem and informs our understanding of early life on land. However, our knowledge of the 3D structure, and development of these plants is still rudimentary. Here we used digital 3D reconstruction techniques to produce the first well-evidenced reconstruction of the structure and development of the rooting system of the lycopsid Asteroxylon mackiei , the most complex plant in the Rhynie chert. The reconstruction reveals the organisation of the three distinct axis types – leafy shoot axes, root-bearing axes, and rooting axes – in the body plan. Combining this reconstruction with developmental data from fossilised meristems, we demonstrate that the A. mackiei rooting axis – a transitional lycophyte organ between the rootless ancestral state and true roots – developed from root-bearing axes by anisotomous dichotomy. Our discovery demonstrates how this unique organ developed and highlights the value of evidence-based reconstructions for understanding the development and evolution of the first complex vascular plants on Earth.
Why it matches plant phenotyping methods化石植物の根系構造と発生をデジタル3D再構成で推定する手法が研究の中心であり、植物形態の取得・再構成に該当する。
abstractHere we used digital 3D reconstruction techniques to produce the first well-evidenced reconstruction of the structure and development of the rooting system of the lycopsid Asteroxylon mackiei
Reproduction assets foundThe authors deposited photographs of the serial thick sections and peels used for phenotyping-style 3D reconstruction, plus the 3D reconstructions themselves, on Zenodo (DOI 10.5281/zenodo.4287297), which is an allowed URL and is explicitly cited as the generated dataset.Dataset · publicImages of the full series of thick sections were deposited on Zenodo ( http://doi.org/10.5281/zenodo.4287297 ).Open asset ↗Zenodo · 10.5281/zenodo.4287297lines:155-186Code / dataset availability confirmedEurope PMC · checked 15 Sept 2026
TomatoRootClassificationMorphology / geometry measurementRoot system architectureStress response / tolerance
The root architecture of wild tomato, Solanum pimpinellifolium , can be viewed as a network connecting the main root to various lateral roots. Several constraints have been proposed on the structure of such biological networks, including minimizing the total amount of wire necessary for constructing the root architecture (wiring cost), and minimizing the distances (and by extension, resource transport time) between the base of the main root and the lateral roots (conduction delay). For a given set of lateral root tip locations, these two objectives compete with each other — optimizing one results in poorer performance on the other — raising the question how well S. pimpinellifolium root architectures balance this network design trade-off in a distributed manner. Here, we describe how well S. pimpinellifolium roots resolve this trade-off using the theory of Pareto optimality. We describe a mathematical model for characterizing the network structure and design trade-offs governing the structure of S. pimpinellifolium root architecture. We demonstrate that S. pimpinellifolium arbors construct architectures that are more optimal than would be expected by chance. Finally, we use this framework to quantify structural differences between arbors grown in the presence of salt stress, classify arbors into four distinct architectural ideotypes, and test for heritability of variation in root architecture structure.
Why it matches plant phenotyping methods根系アーキテクチャをネットワークとして定量化・分類する数学的解析フレームワークが研究の中心であり、植物表現型の構造差とイデオタイプを抽出しているため。
abstractWe describe a mathematical model for characterizing the network structure and design trade-offs governing the structure of S. pimpinellifolium root architecture.
Reproduction assets foundThe paper's root-architecture analysis code is publicly available on GitHub. The phenotype/root-image data itself is only available upon request, so it is listed as a request-only asset.Code · publicpeer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made
available under a CC-BY-ND 4.0 International license.
222 Data availability
223 We will make data available upon request. Our code for analyzing arbors and performing statistical
224 analysis can be found here https://github.com/arjunc12/Plant-Architecture.
7Open asset ↗arjunc12/Plant-Architecturepdf-layout-page:7 lines:1-15Code / dataset availability confirmedCrossref · checked 13 Sept 2026
Abstract A key impediment to studying water-related mechanisms in plants is the inability to non-invasively image water fluxes in cells at high temporal and spatial resolution. Here, we report that Raman microspectroscopy, complemented by hydrodynamic modelling, can achieve this goal - monitoring hydrodynamics within living root tissues at cell- and sub-second-scale resolutions. Raman imaging of water-transporting xylem vessels in Arabidopsis thaliana mutant roots reveals faster xylem water transport in endodermal diffusion barrier mutants. Furthermore, transverse line scans across the root suggest water transported via the root xylem does not re-enter outer root tissues nor the surrounding soil when en-route to shoot tissues if endodermal diffusion barriers are intact, thereby separating ‘two water worlds’.
Why it matches plant phenotyping methodsRaman顕微分光と流体力学モデリングを組み合わせ、根組織内の水輸送を非侵襲・細胞解像度で測定する手法が研究の中心であり、植物の生理状態を定量化している。
abstractRaman microspectroscopy, complemented by hydrodynamic modelling, can achieve this goal - monitoring hydrodynamics within living root tissues at cell- and sub-second-scale resolutions.
Reproduction assets foundThe paper's MECHA 3D solute advection-diffusion model and MATLAB inverse modeling code are openly available on GitHub under a GPL.2 license. Other deposits (Raman data, hydraulic conductivity data, custom RMS analysis code on FigShare) exist but their URLs are not in the allowed list, so only the GitHub code asset is aCode · publicThe latest code of MECHA working in 3D with solute advection-diffusion and associated Matlab codes for inverse modeling schemes are openly available online under GPL.2 open-source licence at FigShare [10.6084/m9.figshare.14892408.v2] or GitHub [ https://github.com/MECHARoot/MECHA/blob/master/MECHA_4Dsolute.zip ].Open asset ↗MECHARoot/MECHA · MECHA_4Dsolute.ziplines:111-143Code / dataset availability confirmedbioRxiv · Europe PMC · OpenAlex · Crossref · checked 15 Sept 2026
The here-on presented SimpleForest is written in C++ and published under GPL v3. As input data SimpleForest utilizes forestry scenes recorded as terrestrial laser scan clouds. SimpleForest provides a fully automated pipeline to model the ground as a digital terrain model, then segment the vegetation and finally build quantitative structure models of trees (QSMs) consisting of up to thousands of topologically ordered cylinders. These QSMs allow us to calculate traditional forestry metrics such as diameter at breast height, but also volume and other structural metrics that are hard to measure in the field. Our volume evaluation on three data sets with destructive volumes show high prediction qualities with concordance correlation coefficient CCC [Formula] of 0.91 (0.87), 0.94 (0.92) and 0.97 (0.93) for each data set respectively. We combine two common assumptions in plant modeling "The sum of cross sectional areas after a branch junction equals the one before the branch junction" (Pipe Model Theory) and "Twigs are self-similar" (West, Brown and Enquist model). As even sized twigs correspond to even sized cross sectional areas for twigs we define the Reverse Pipe Radius Branchorder (RPRB) as the square root of the number of supported twigs. The prediction model radius = B0 * RPRB relies only on correct topological information and can be used to detect and correct overestimated cylinders. In QSM building the necessity to handle overestimated cylinders is well known. The RPRB correction performs better with a CCC [Formula] of 0.97 (0.93) than former published ones 0.80 (0.88) and 0.86 (0.85) in our validation. We encourage forest ecologists to analyze output parameters such as the GrowthVolume published in earlier works, but also other parameters such as the GrowthLength, VesselVolume and RPRB which we define in this manuscript. Upload statementSelf-uploaded pre-print for peer-review submitted manuscript. The manuscript was submitted on 26th of July 2021 to Plos Computational Biology: I, Jan Hackenberg uploaded this manuscript because the automated journal upload was rejected for the following reason: Thank you for considering posting your manuscript "SimpleForest - a comprehensive tool for 3d reconstruction of tree from forest plot point clouds." as a preprint. Your manuscript does not meet bioRxivs criteria and therefore we will not be sending it for posting as a preprint. For more information about our checks, see link. We have noted that it contains material that is potentially subject to copyright. In particular, screenshot in Figure 1. Preprints posted to bioRxiv following submission to PLOS journals are done so under the CC BY license. To avoid a potential breach of the copyright that applies to the material listed above, we are unable to make the manuscript publicly available. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=62 SRC="FIGDIR/small/454344v1_fig1.gif" ALT="Figure 1"> View larger version (11K): org.highwire.dtl.DTLVardef@1094b17org.highwire.dtl.DTLVardef@120d7a3org.highwire.dtl.DTLVardef@12d2fbcorg.highwire.dtl.DTLVardef@1990a4d_HPS_FORMAT_FIGEXP M_FIG O_FLOATNOFig 1.C_FLOATNO Submission system screenshot. C_FIG Please note that this decision does not affect the editorial process at PLOS Computational Biology. Your manuscript is being separately assessed with regards to sending for peer review. From section Abstract on, the pdf you see is same as submitted one.
Why it matches plant phenotyping methods森林プロットの点群から樹木を3D再構成し、DBH・体積などの植物構造形質を定量化するソフトウェアと自動解析パイプラインを開発・検証しており、フェノタイピング手法が中心である。
abstractSimpleForest provides a fully automated pipeline to model the ground as a digital terrain model, then segment the vegetation and finally build quantitative structure models of trees (QSMs) consisting of up to thousands of topologically ordered cylinders.
Reproduction assets foundThe paper explicitly publishes its TLS point cloud datasets (5 datasets with harvested ground-truth volumes), SimpleForest processing/QSM scripts, R validation scripts, combined results table, and GPL v3 source code in a public Zenodo repository (10.5281/zenodo.5131717) and GitLab repository, all directly reproducing QCode · publicipts SimpleForest scripts to process S1 Dataset. 75
• Erythrophleum fordii denoising scripts: 76
https://zenodo.org/record/5131717/files/hackenbergErythrophleumDenoisingScripts.zip 77
• Pinus massoniana denoising scripts: 78
https://zenodo.org/record/5131717/files/hackenbergPinusDenoisingScripts.zip 79
• QSM modeling script: 80
https://zenodo.org/record/5131717/files/hackenbergQsm.xsct2 81
S2 Processing scripts SimpleForest scripts to process S2 Dataset. 82
• Denoising scripts: 83
https://zenodo.org/record/5131717/files/deTanagoDenoisingScriptsDenoisedClouds.zip 84
• Poisson reconstruction buttress script: 85
https://zenodo.org/record/5131717/files/deTanagoButtressPoisson.xsct2 86
• QSM modeOpen asset ↗zenodopdf-raw-page:4 lines:1-48Code · publicipt: 109
https://zenodo.org/record/5131717/files/wythamAnalysis.R 110
S5 Validation scripts SimpleForest scripts to validate results of S1 Processing scripts, S2 Processing 111
scripts, S3 Processing scripts. 112
• Combined results data table: 113
https://zenodo.org/record/5131717/files/tableAll.csv 114
• Volume validation: 115
https://zenodo.org/record/5131717/files/ValidationScriptAll.R 116
1.2 Software 117
S1 Software Software code repository. 118
• Under the GPL version 3 license: 119
https://gitlab.com/SimpleForest/computree/-/blob/master/pluginSimpleForest/GPL_v3_template 120
• we provide source code with compilation instructions for the here presented SimpleForestv5.3.1 plugin publishOpen asset ↗zenodopdf-raw-page:5 lines:1-46Dataset · publicripts to validate results of S4 Processing scripts. 108
• Statistical plotting script: 109
https://zenodo.org/record/5131717/files/wythamAnalysis.R 110
S5 Validation scripts SimpleForest scripts to validate results of S1 Processing scripts, S2 Processing 111
scripts, S3 Processing scripts. 112
• Combined results data table: 113
https://zenodo.org/record/5131717/files/tableAll.csv 114
• Volume validation: 115
https://zenodo.org/record/5131717/files/ValidationScriptAll.R 116
1.2 Software 117
S1 Software Software code repository. 118
• Under the GPL version 3 license: 119
https://gitlab.com/SimpleForest/computree/-/blob/master/pluginSimpleForest/GPL_v3_template 120
• we provide source code withOpen asset ↗zenodopdf-raw-page:5 lines:1-46Code · publiconScriptAll.R 116
1.2 Software 117
S1 Software Software code repository. 118
• Under the GPL version 3 license: 119
https://gitlab.com/SimpleForest/computree/-/blob/master/pluginSimpleForest/GPL_v3_template 120
• we provide source code with compilation instructions for the here presented SimpleForestv5.3.1 plugin published: 121
https://gitlab.com/SimpleForest/computree/-/commits/v5.3.1. 122
• Inside a subfolder this repository contains a Win10 compiled executable : 123
https://gitlab.com/SimpleForest/computree/-/tree/master/bin. 124
• Persistent 5.1.3: 125
https://doi.org/10.5281/zenodo.5138255 126
5/25
.
CC-BY-NC 4.0 International license
available under a
was not certified by peer review) Open asset ↗gitlab · SimpleForest/computreepdf-raw-page:5 lines:1-46Code / dataset availability confirmedCrossref · checked 9 Sept 2026
Laboratory / benchtopChlorophyll fluorescenceMicroscopyRootGrowth / time-series analysisVisualization / data management
Fabricated ecosystems (EcoFABs) offer an innovative approach to in situ examination of microbial establishment patterns around plant roots using nondestructive, high-resolution microscopy. Previously high-resolution imaging was challenging because the roots were not constrained to a fixed distance from the objective. Here, we describe a new ‘Imaging EcoFAB’ and the use of this device to image the entire root system of growing Brachypodium distachyon at high resolutions (20×, 40×) over a 3-week period. The device is capable of investigating root–microbe interactions of multimember communities. We examined nine strains of Pseudomonas simiae with different fluorescent constructs to B. distachyon and individual cells on root hairs were visible. Succession in the rhizosphere using two different strains of P. simiae was examined, where the second addition was shown to be able to establish in the root tissue. The device was suitable for imaging with different solid media at high magnification, allowing for the imaging of fungal establishment in the rhizosphere. Overall, the Imaging EcoFAB could improve our ability to investigate the spatiotemporal dynamics of the rhizosphere, including studies of fluorescently-tagged, multimember, synthetic communities.
Why it matches plant phenotyping methods植物根系全体を高解像度・経時的に撮像するための新規チャンバーを開発し、その撮像性能と用途を示しており、表現型取得法が研究の中心である。
abstractHere, we describe a new ‘Imaging EcoFAB’ and the use of this device to image the entire root system of growing Brachypodium distachyon at high resolutions (20×, 40×) over a 3-week period.
Reproduction assets foundThe paper's computational analysis (K-means clustering and segmentation/cell counting of the 40× multispectral root image) is explicitly stated to have its environment, code, and parent data file available in the Supplementary Materials, hosted at the MDPI S1 link. Additionally, the 3D-printing-ready Imaging EcoFAB 3D-Code · publicThe environment, code, and parent data file are available in the Supplementary Materials .Open asset ↗lines:65-81Code / dataset availability confirmedEurope PMC · checked 9 Sept 2026
MaizeRootMorphology / geometry measurementPhysiological trait estimationWater status / transpiration
Root hydraulic properties play a central role in the global water cycle, in agricultural systems productivity, and in ecosystem survival as they impact the canopy water supply. However, the existing experimental methods to quantify root hydraulic conductivities, such as the root pressure probing, are particularly challenging, and their applicability to thin roots and small root segments is limited. Therefore, there is a gap in methods enabling easy estimations of root hydraulic conductivities in diverse root types. Here, we present a new pipeline to quickly estimate root hydraulic conductivities across different root types, at high resolution along root axes. Shortly, free-hand root cross-sections were used to extract a selected number of key anatomical traits. We used these traits to parametrize the Generator of Root Anatomy in R (GRANAR) model to simulate root anatomical networks. Finally, we used these generated anatomical networks within the Model of Explicit Cross-section Hydraulic Anatomy (MECHA) to compute an estimation of the root axial and radial hydraulic conductivities ( k x and k r , respectively). Using this combination of anatomical data and computational models, we were able to create a root hydraulic conductivity atlas at the root system level, for 14-day-old pot-grown Zea mays (maize) plants of the var. B73 . The altas highlights the significant functional variations along and between different root types. For instance, predicted variations of radial conductivity along the root axis were strongly dependent on the maturation stage of hydrophobic barriers. The same was also true for the maturation rates of the metaxylem vessels. Differences in anatomical traits along and across root types generated substantial variations in radial and axial conductivities estimated with our novel approach. Our methodological pipeline combines anatomical data and computational models to turn root cross-section images into a detailed hydraulic atlas. It is an inexpensive, fast, and easily applicable investigation tool for root hydraulics that complements existing complex experimental methods. It opens the way to high-throughput studies on the functional importance of root types in plant hydraulics, especially if combined with novel phenotyping techniques such as laser ablation tomography.
Why it matches plant phenotyping methods根の断面画像と計算モデルを組み合わせ、根の解剖形質から油圧伝導性を推定する新規パイプラインを開発しており、植物表現型の取得・推定手法が研究の中心である。
abstractHere, we present a new pipeline to quickly estimate root hydraulic conductivities across different root types, at high resolution along root axes.
Reproduction assets foundThe paper provides two paper-specific public assets: the GRANAR–MECHA coupling workflow (Jupyter notebook via Binder, GitHub repo HeymansAdrien/GranarMecha, Zenodo DOI 10.5281/zenodo.4316762) and the Rmarkdown script plus all input/output data used to compute the B73 root hydraulic atlas (GitHub repo granar/B73_HydraulCode · publicThe whole script that was used to compute the root hydraulic atlas from the root anatomical measurement is presented as a Rmarkdown script stored in a GitHub repository ( https://github.com/granar/B73_HydraulicMap doi: https://doi.org/10.5281/zenodo.4320861 ). All input and output data of this study are stored in the same repository.Open asset ↗granar/B73_HydraulicMap · 10.5281/zenodo.4320861lines:267-339Code · publicThe whole script that was used to compute the root hydraulic atlas from the root anatomical measurement is presented as a Rmarkdown script stored in a GitHub repository ( https://github.com/granar/B73_HydraulicMap doi: https://doi.org/10.5281/zenodo.4320861 ). All input and output data of this study are stored in the same repository.Open asset ↗granar/B73_HydraulicMap · 10.5281/zenodo.4320861lines:267-339Code / dataset availability confirmedbioRxiv · Europe PMC · checked 15 Sept 2026
Plants respond to the surrounding environment in countless ways. One of these responses is their ability to sense and orient their root growth toward the gravity vector. Root gravitropism is studied in many laboratories as a hallmark of auxin-related phenotypes. However, manual analysis of images and microscopy data is known to be subjected to human bias. This is particularly the case for manual measurements of root bending as the selection lines to calculate the angle are set subjectively. Therefore, it is essential to develop and use automated or semi-automated image analysis to produce reproducible and unbiased data. Moreover, the increasing usage of vertical-stage microscopy in plant root biology yields gravitropic experiments with an unprecedented spatiotemporal resolution. To this day, there is no available solution to measure root bending angle over time for vertical-stage microscopy. To address these problems, we developed ACORBA (Automatic Calculation Of Root Bending Angles), a fully automated software to measure root bending angle over time from vertical-stage microscope and flatbed scanner images. Moreover, the software can be used semi-automated for camera, mobile phone or stereomicroscope images. ACORBA represents a flexible approach based on both traditional image processing and deep machine learning segmentation to measure root angle progression over time. By its automated nature, the workflow is limiting human interactions and has high reproducibility. ACORBA will support the plant biologist community by reducing time and labor and by producing quality results from various kinds of inputs. Significance statementACORBA is implementing an automated and semi-automated workflow to quantify root bending and waving angles from images acquired with a microscope, a scanner, a stereomicroscope or a camera. It will support the plant biology community by reducing time and labor and by producing trustworthy and reproducible quantitative data.
Why it matches plant phenotyping methods根の屈曲角度を画像から自動抽出するソフトウェアとワークフローの開発が研究の中心であり、植物形態表現型の定量手法に該当する。
abstractwe developed ACORBA (Automatic Calculation Of Root Bending Angles), a fully automated software to measure root bending angle over time from vertical-stage microscope and flatbed scanner images.
Reproduction assets foundThe paper explicitly releases the ACORBA software (source code, trained models, annotated training libraries, notebooks, user manual) on SourceForge and the raw microscopy/scanner image stacks used for the root-angle measurements on Zenodo (DOI 10.5281/zenodo.5105719). Both are paper-specific, public, and actionable.Code · publicand
online Python image analysis and machine learning tutorials.
Availability of data and materials
The latest versions of ACORBA software training annotated libraries, source code, examples,
image pre-processing scripts, deep machine learning model training Jupyter notebooks and user
manual maintained by NBCS are available at https://sourceforge.net/projects/acorba/. The raw
microscopy and scanner stacks used in this paper are available at ZENODO
(https://doi.org/10.5281/zenodo.5105719). The analyzed results are supplemented (Supplemental
data).
Competing interests
The authors declare that they have no competing interests.
Funding
This work was supported by the European Research Council (GOpen asset ↗sourceforge.net/projects/acorbapdf-raw-page:17 lines:1-45Dataset · publicACORBA software training annotated libraries, source code, examples,
image pre-processing scripts, deep machine learning model training Jupyter notebooks and user
manual maintained by NBCS are available at https://sourceforge.net/projects/acorba/. The raw
microscopy and scanner stacks used in this paper are available at ZENODO
(https://doi.org/10.5281/zenodo.5105719). The analyzed results are supplemented (Supplemental
data).
Competing interests
The authors declare that they have no competing interests.
Funding
This work was supported by the European Research Council (Grant No. 803048), Charles
University Primus (Grant No. PRIMUS/19/SCI/09).
Author contributions
NBCS and MF conceived the pOpen asset ↗ZENODO · 10.5281/zenodo.5105719pdf-raw-page:17 lines:1-45Code / dataset availability confirmedbioRxiv · Europe PMC · checked 15 Sept 2026
Soil fungi establish mutualistic interactions with the roots of most vascular land plants. Arbuscular mycorrhizal (AM) fungi are among the most extensively characterised mycobionts to date. Current approaches to quantifying the extent of root colonisation and the abundance of hyphal structures in mutant roots rely on staining and human scoring involving simple, yet repetitive tasks prone to variations between experimenters. We developed AMFinder which allows for automatic computer vision-based identification and quantification of AM fungal colonisation and intraradical hyphal structures on ink-stained root images using convolutional neural networks. AMFinder delivered high-confidence predictions on image datasets of roots of multiple plant hosts (Nicotiana benthamiana, Medicago truncatula, Lotus japonicus, Oryza sativa) and captured the altered colonisation in ram1-1, str and smax1 mutants. A streamlined protocol for sample preparation and imaging allowed us to quantify mycobionts from the genera Rhizophagus, Claroideoglomus, Rhizoglomus and Funneliformis via flatbed scanning or digital microscopy including dynamic increases in colonisation in whole root systems over time. AMFinder adapts to a wide array of experimental conditions. It enables accurate, reproducible analyses of plant root systems and will support better documentation of AM fungal colonisation analyses. AMFinder can be accessed here: https://github.com/SchornacklabSLCU/amfinder.git
Why it matches plant phenotyping methods植物根の菌根菌感染状態を画像から自動識別・定量する手法とソフトウェアを開発しており、表現型取得・抽出が研究の中心です。
abstractWe developed AMFinder which allows for automatic computer vision-based identification and quantification of AM fungal colonisation and intraradical hyphal structures on ink-stained root images using convolutional neural networks.
Reproduction assets foundThe paper's AMFinder analysis software (amf/amfbrowser) and pre-trained CNN models are publicly available on the authors' GitHub repository under the MIT license. The training image datasets are not public and must be requested from the authors.Code · publicmanuscript. All authors
have read and approved the manuscript.
Data Availability
AMFinder is released under the terms of the open-source MIT license
(https://opensource.org/licenses/MIT) allowing unrestricted usage. Source code, pre-trained models
and detailed installation instructions are available on AMFinder GitHub webpage
(https://github.com/SchornacklabSLCU/amfinder.git). Training datasets are available upon request.
References
Abadi M, Agarwal A, Barham P, Brevdo E, Chen Z, Citro C, Corrado GS, Davis A, Dean J,
Devin M, et al. 2016. TensorFlow: Large-Scale Machine Learning on Heterogeneous Distributed
Systems. arXiv.
Bally J, Jung H, Mortimer C, Naim F, Philips JG, Hellens R, BombarelyOpen asset ↗SchornacklabSLCU/amfinderpdf-raw-page:21 lines:1-72Code / dataset availability confirmedOpenAlex · Europe PMC · Crossref · checked 14 Sept 2026
The development of crops with deeper roots holds substantial promise to mitigate the consequences of climate change. Deeper roots are an essential factor to improve water uptake as a way to enhance crop resilience to drought, to increase nitrogen capture, to reduce fertilizer inputs, and to increase carbon sequestration from the atmosphere to improve soil organic fertility. A major bottleneck to achieving these improvements is high-throughput phenotyping to quantify root phenotypes of field-grown roots. We address this bottleneck with Digital Imaging of Root Traits (DIRT)/3D, an image-based 3D root phenotyping platform, which measures 18 architecture traits from mature field-grown maize (Zea mays) root crowns (RCs) excavated with the Shovelomics technique. DIRT/3D reliably computed all 18 traits, including distance between whorls and the number, angles, and diameters of nodal roots, on a test panel of 12 contrasting maize genotypes. The computed results were validated through comparison with manual measurements. Overall, we observed a coefficient of determination of r2>0.84 and a high broad-sense heritability of Hmean2> 0.6 for all but one trait. The average values of the 18 traits and a developed descriptor to characterize complete root architecture distinguished all genotypes. DIRT/3D is a step toward automated quantification of highly occluded maize RCs. Therefore, DIRT/3D supports breeders and root biologists in improving carbon sequestration and food security in the face of the adverse effects of climate change.
Why it matches plant phenotyping methods画像ベースの3D根形態フェノタイピング基盤を開発し、18形質を算出して手動測定と検証しているため、フェノタイピング手法が研究の中心である。
abstractWe address this bottleneck with Digital Imaging of Root Traits (DIRT)/3D, an image-based 3D root phenotyping platform, which measures 18 architecture traits from mature field-grown maize (Zea mays) root crowns (RCs) excavated with the Shovelomics technique.
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 · publicOur open-source software is available to the whole plant science community on GitHub and can be deployed within a platform-agnostic Singularity/Docker container to be executed independently of the operating system ( Supplemental Data S D3 ; https://github.com/Computational-Plant-Science )Open asset ↗Computational-Plant-Sciencelines:184-191Code / dataset availability confirmedOpenAlex · Crossref · Europe PMC · checked 8 Sept 2026
Laboratory / benchtopRoot2D/3D reconstructionSegmentationGrowth / time-series analysisGrowth / development / phenologyRoot system architecture
BACKGROUND: Deep learning methods have outperformed previous techniques in most computer vision tasks, including image-based plant phenotyping. However, massive data collection of root traits and the development of associated artificial intelligence approaches have been hampered by the inaccessibility of the rhizosphere. Here we present ChronoRoot, a system that combines 3D-printed open-hardware with deep segmentation networks for high temporal resolution phenotyping of plant roots in agarized medium. RESULTS: We developed a novel deep learning-based root extraction method that leverages the latest advances in convolutional neural networks for image segmentation and incorporates temporal consistency into the root system architecture reconstruction process. Automatic extraction of phenotypic parameters from sequences of images allowed a comprehensive characterization of the root system growth dynamics. Furthermore, novel time-associated parameters emerged from the analysis of spectral features derived from temporal signals. CONCLUSIONS: Our work shows that the combination of machine intelligence methods and a 3D-printed device expands the possibilities of root high-throughput phenotyping for genetics and natural variation studies, as well as the screening of clock-related mutants, revealing novel root traits.
Why it matches plant phenotyping methods根系画像の深層セグメンテーションと3D装置を開発し、画像から根系形態・成長動態を自動抽出する方法が研究の中心である。
abstractHere we present ChronoRoot, a system that combines 3D-printed open-hardware with deep segmentation networks for high temporal resolution phenotyping of plant roots in agarized medium.
Reproduction assets foundThe paper publicly releases its root segmentation image/annotation datasets, hardware files, and analysis code via GitHub repositories, plus supporting data in GigaDB. Three qualifying paper-specific assets with allowed URLs are listed; the GigaDB deposit (10.5524/100911) is paper-specific but its URL is not in the允许edCode · publicThe source code corresponding to ChronoRoot imaging controller, namely, the web interface to check and set up the image acquisition parameters:
Project name: ChronoRoot: Module Controller
Project home page: https://github.com/ThomasBlein/ChronoRootControlOpen asset ↗https://github.com/ThomasBlein/ChronoRootControllines:185-222Dataset · publicThe 2 datasets of images and annotations described in the Datasets section, as well as the 3D printing and laser cutting files, are publicly available at https://github.com/ThomasBlein/ChronoRootModuleHardware under the CERN Open Hardware License Version 2—Strongly Reciprocal licence.Open asset ↗https://github.com/ThomasBlein/ChronoRootModuleHardwarelines:223-262Code / dataset availability confirmedCrossref · checked 14 Sept 2026
RiceRootMorphology / geometry measurementSegmentationRoot system architecture
Research on rice (Oryza sativa) roots demands the automatic analysis of root architecture during image processing. It is challenging for a digital filter to identify the roots from the obscure and cluttered background. The original Frangi algorithm, presented by Alejandro F. Frangi in 1998, is a successful low-pass filter dedicated to blood vessel image enhancement. Considering the similarity between vessels and roots, the Frangi filter algorithm is applied to outline the roots. However, the original Frangi only enhances the tube-like primary roots but erases the lateral roots during filtering. In this paper, an improved Frangi filtering algorithm (IFFA), designed for plant roots, is proposed. Firstly, an automatic root phenotyping system is designed to fulfill the high-throughput root image acquisition. Secondly, multilevel image thresholding, connected components labeling, and width correction are used to optimize the output binary image. Thirdly, to enhance the local structure, the Gaussian filtering operator in the original Frangi is redesigned with a truncated Gaussian kernel, resulting in more discernible lateral roots. Compared to the original Frangi filter and commercially available software, IFFA is faster and more accurate, achieving a pixel accuracy of 97.48%. IFFA is an effective morphological filtering approach to enhance the roots of rice for segmentation and further biological research. It is convincing that IFFA is suitable for different 2-D plant root image processing and morphological analysis.
Why it matches plant phenotyping methodsイネ根の画像取得・分割・形態解析のための自動フェノタイピングシステムと改良画像フィルタを開発し、既存手法・商用ソフトと精度比較しているため、植物フェノタイピング手法が中心である。
abstractan automatic root phenotyping system is designed to fulfill the high-throughput root image acquisition
Reproduction assets foundThe paper's Data Availability statement points to a public GitHub repository containing the IFFA algorithm code, original rice root images, and processed images used in this study, making the paper-specific phenotyping assets publicly actionable.Code · publicty using two different Image analyses systems,” Plant and Soil,
vol. 260, no. 1/2, pp. 111–120, 2004.
All additional files, containing the algorithm, original im-
[5] T. C. Kaspar and R. P. Ewing, “ROOTEDGE: software for
ages, and processed images, are provided in the repository measuring root length from desktop scanner images,”
https://github.com/gitDux/IFFA. Agronomy Journal, vol. 89, no. 6, pp. 932–940, 1997.
[6] A. F. Frangi, W. J. Niessen, K. L. Vincken, and
Conflicts of Interest M. A. Viergever, “Multiscale vessel enhancement filtering,”
Medical Image Computing and Computer-Assisted Interven-
The authors declare that there are no conflicts of interest tion-MICCAI’98, pp. 130–137Open asset ↗gitDux/IFFApdf-layout-page:13 lines:1-47Code / dataset availability confirmedOpenAlex · Crossref · Europe PMC · checked 15 Sept 2026
CarrotRGB / grayscaleRootMorphology / geometry measurementRoot system architecture
), which ranges from long and tapered to short and blunt, has been used for at least several centuries to classify carrot cultivars. The subjectivity involved in determining market class hinders the establishment of metric-based standards and is ill-suited to dissecting the genetic basis of such quantitative phenotypes. Advances in digital image acquisition and analysis has enabled new methods for quantifying sizes of plant structures and shapes, but in order to dissect the genetic control of the shape features that define market class in carrot, a tool is required that quantifies the specific shape features used by humans in distinguishing between classes. This study reports the construction and demonstration of the first such platform, which facilitates rapid phenotyping of traits that are measurable by hand, such as length and width, as well as principal component analysis (PCA) of the root contour and its curvature. This latter approach is of particular interest, as it enabled the detection of a novel and significant quantitative trait, defined here as root fill, which accounts for 85% of the variation in root shape. Curvature analysis was demonstrated to be an effective method for precise measurement of the broadness of the carrot shoulder, and degree of tip fill; the first principal component of the respective curvature profiles captured 87% and 84% of the total variance. This platform's performance was validated in two experimental panels. First, a diverse, global collection of germplasm was used to assess its capacity to identify market classes through clustering analysis. Second, a diallel mating design between inbred breeding lines of differing market classes was used to estimate the heritability of the key phenotypes that define market class, which revealed significant variation in the narrow-sense heritability of size and shape traits, ranging from 0.14 for total root size, to 0.84 for aspect ratio. These results demonstrate the value of high-throughput digital phenotyping in characterizing the genetic control of complex quantitative phenotypes.
Why it matches plant phenotyping methodsニンジン根形状の画像取得・輪郭解析・曲率解析を行うデジタル表現型解析プラットフォームを開発し、複数パネルで性能検証しており、表現型取得手法が研究の中心である。
abstractThis study reports the construction and demonstration of the first such platform, which facilitates rapid phenotyping of traits that are measurable by hand, such as length and width, as well as principal component analysis (PCA) of the root contour and its curvature.
Reproduction assets foundThe paper explicitly provides two public author repositories containing the phenotyping analysis code: a Python image-acquisition/mask-generation platform and MATLAB algorithms for mask straightening and contour/curvature PCA. No standalone phenotype dataset deposit is stated; the supplementary material link is genericCode · publicAs such, this metric ranges from 0 (in the case of all variance being attributed to SCA) to 1 (in the case of all variance being attributed to GCA) ( Baker, 1978 ).
Software Availability
Python code for the image acquisition platform and scripts for producing binary masks are available at: https://github.com/shbrainard/carrot-phenotyping . MATLAB algorithms for straightening binary masks and performing PCA on contours or curvature values are available at: https://github.com/jbustamante35/carrotsweeper .
Results
Accuracy of Image-Derived Phenotypes
Prior to a rigorous evaluation of any experimental populations, it is critical to confirm that a newly developed phOpen asset ↗https://github.com/shbrainard/carrot-phenotypinglines:75-85Code · publicttributed to GCA) ( Baker, 1978 ).
Software Availability
Python code for the image acquisition platform and scripts for producing binary masks are available at: https://github.com/shbrainard/carrot-phenotyping . MATLAB algorithms for straightening binary masks and performing PCA on contours or curvature values are available at: https://github.com/jbustamante35/carrotsweeper .
Results
Accuracy of Image-Derived Phenotypes
Prior to a rigorous evaluation of any experimental populations, it is critical to confirm that a newly developed phenotyping platform produces accurate and reliable phenotypes. Scatter plots of the root phenotypes obtained from digital images vs. hand measurements confirms thOpen asset ↗https://github.com/jbustamante35/carrotsweeperlines:75-85Code / dataset availability confirmedEurope PMC · checked 15 Sept 2026
Motivation Arbuscular mycorrhizas are the most widespread plant symbioses and involve the majority of crop plants. The beneficial interaction between plant roots and a group of soil fungi (Glomeromycotina) grants the green host a preferential access to soil mineral nutrients and water, supporting plant health, biomass production and resistance to both abiotic and biotic stresses. The nutritional exchanges at the core of this symbiosis take place inside the living root cells, which are diffusely colonized by specialized fungal structures called arbuscules. For this reason, the vast majority of studies investigating arbuscular mycorrhizas and their applications in agriculture require a precise quantification of the intensity of root colonization. To this aim, several manual methods have been used for decades to estimate the extension of intraradical fungal structures, mostly based on optical microscopy observations and individual assessment of fungal abundance in the root tissues. Results: Here we propose a novel semi-automated approach to quantify AM colonization based on digital image analysis and compare two methods based on image thresholding and machine learning. Our results indicate in machine learning a very promising tool for accelerating, simplifying and standardizing this critical type of analysis, with a direct potential interest for applicative and basic research. Contact ivan.sciascia@unito.it; andrea.genre@unito.it
Why it matches plant phenotyping methods植物根の菌根コロニー形成という植物状態を、画像解析・画像しきい値処理・機械学習で定量する手法の提案と比較検証が中心である。
abstractHere we propose a novel semi-automated approach to quantify AM colonization based on digital image analysis and compare two methods based on image thresholding and machine learning.
Reproduction assets foundThe authors deposited the microscopy image datasets used for binary, thresholding, and machine-learning segmentation of mycorrhizal Medicago truncatula roots in three public Figshare repositories, explicitly listed under 'Availability of data and material'. These are paper-specific phenotype image datasets directly支撑本.Dataset · public22
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Thresholding and machine learning segmentation - mycorrhized roots DOI
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Thresholding and machine learning segmentation – non mycorrhized roots DOI
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The authors declare that they have no competiOpen asset ↗Figshare · 10.6084/m9.figshare.14679642pdf-raw-page:23 lines:1-85Dataset · publicNot applicable
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Thresholding and machine learning segmentation - mycorrhized roots DOI
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https://doi.org/10.6084/m9.figshare.14679729
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Thresholding and machine learning segmentation – non mycorrhized roots DOI
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https://doi.org/10.6084/m9.figshare.14679684
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The authors declare that they have no competing interests.
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Ministero dell’Istruzione, dell’Università e della Ricerca: PhD fellowship to AC UniveOpen asset ↗Figshare · 10.6084/m9.figshare.14679729pdf-raw-page:23 lines:1-85Dataset · publicavailable in the Figshare
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Thresholding and machine learning segmentation - mycorrhized roots DOI
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https://doi.org/10.6084/m9.figshare.14679729
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Thresholding and machine learning segmentation – non mycorrhized roots DOI
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https://doi.org/10.6084/m9.figshare.14679684
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The authors declare that they have no competing interests.
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Ministero dell’Istruzione, dell’Università e della Ricerca: PhD fellowship to AC Università degli
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IS designed the image analysis approach, performed image anOpen asset ↗Figshare · 10.6084/m9.figshare.14679684pdf-raw-page:23 lines:1-85Code / dataset availability confirmedEurope PMC · checked 14 Sept 2026
Field / plotRootMorphology / geometry measurementSegmentationRoot system architecture
The scale of root quantification in research is often limited by the time required for sampling, measurement, and processing samples. Recent developments in convolutional neural networks (CNNs) have made faster and more accurate plant image analysis possible, which may significantly reduce the time required for root measurement, but challenges remain in making these methods accessible to researchers without an in-depth knowledge of machine learning. We analyzed root images acquired from three destructive root samplings using the RootPainter CNN software that features an interface for corrective annotation for easier use. Root scans with and without non-root debris were used to test if training a model (i.e. learning from labeled examples) can effectively exclude the debris by comparing the end results with measurements from clean images. Root images acquired from soil profile walls and the cross-section of soil cores were also used for training, and the derived measurements were compared with manual measurements. After 200 min of training on each dataset, significant relationships between manual measurements and RootPainter-derived data were noted for monolith (R2=0.99), profile wall (R2=0.76), and core-break (R2=0.57). The rooting density derived from images with debris was not significantly different from that derived from clean images after processing with RootPainter. Rooting density was also successfully calculated from both profile wall and soil core images, and in each case the gradient of root density with depth was not significantly different from manual counts. Differences in root-length density (RLD) between crops with contrasting root systems were captured using automatic segmentation at soil profiles with high RLD (1-5 cm cm-3) as well with low RLD (0.1-0.3 cm cm-3). Our results demonstrate that the proposed approach using CNN can lead to substantial reductions in root sample processing workloads, increasing the potential scale of future root investigations.
Why it matches plant phenotyping methodsRoot画像から根長密度などの植物形質を抽出するCNNソフトウェアを検証し、手動測定との比較や異物除去性能を評価しており、フェノタイピング手法が研究の中心です。
abstractWe analyzed root images acquired from three destructive root samplings using the RootPainter CNN software that features an interface for corrective annotation for easier use.
Reproduction assets foundThe paper's Data availability statement deposits the study's root image dataset with manual counts, the created training dataset and final trained models, and a Python analysis script on Zenodo, all with explicit public URLs.Dataset · publicptualization; EH: investigation, data curation, formal
analysis; EH, AGS, RK, R
W, JK, and MA: methodology; EH: writing—
original draft; EH, MA, and KTK: funding acquisition; EH, AGS, RK,
R
W, JK, KTK, and MA: writing—review and editing.
Data availability
The dataset and manual counts used in the study are available on-
line at http://doi.org/10.5281/zenodo.3754081, the created training
dataset and final trained models are available at http://doi.org/10.5281/zenodo.4300127, and the Python script for splitting the segmenta-
tion on profile wall images is available at http://doi.org/10.5281/zenodo.4299944.References
Böhm W. 1976. In situ estimation of root length at natural soil profiles.
JOpen asset ↗zenodo · 10.5281/zenodo.3754081pdf-raw-page:10 lines:1-88Model / weights · public: writing—
original draft; EH, MA, and KTK: funding acquisition; EH, AGS, RK,
R
W, JK, KTK, and MA: writing—review and editing.
Data availability
The dataset and manual counts used in the study are available on-
line at http://doi.org/10.5281/zenodo.3754081, the created training
dataset and final trained models are available at http://doi.org/10.5281/zenodo.4300127, and the Python script for splitting the segmenta-
tion on profile wall images is available at http://doi.org/10.5281/zenodo.4299944.References
Böhm W. 1976. In situ estimation of root length at natural soil profiles.
Journal of Agricultural Science 87, 365.
Dodge S, Karam L. 2016. Understanding how image quality affects deep
nOpen asset ↗zenodo · 10.5281/zenodo.4300127pdf-raw-page:10 lines:1-88Code / dataset availability confirmedOpenAlex · Crossref · checked 14 Sept 2026
Unoccupied aerial system (UAS; i.e., drone equipped with sensors) field-based high-throughput phenotyping (HTP) platforms are used to collect high quality images of plant nurseries to screen genetic materials (e.g., hybrids and inbreds) throughout plant growth at relatively low cost. In this study, a set of 100 advanced breeding maize (Zea mays L.) hybrids were planted at optimal (OHOT trial) and delayed planting dates (DHOT trial). Twelve UAS surveys were conducted over the trials throughout the growing season. Fifteen vegetative indices (VIs) and the 99th percentile canopy height measurement (CHMs) were extracted from processed UAS imagery (orthomosaics and point clouds) which were used to predict plot-level grain yield, days to anthesis (DTA), and silking (DTS). A novel statistical approach utilizing a nested design was fit to predict temporal best linear unbiased predictors (TBLUP) for the combined temporal UAS data. Our results demonstrated machine learning-based regressions (ridge, lasso, and elastic net) had from 4- to 9-fold increases in the prediction accuracies and from 13- to 73-fold reductions in root mean squared error (RMSE) compared to classical linear regression in prediction of grain yield or flowering time. Ridge regression performed best in predicting grain yield (prediction accuracy = ~0.6), while lasso and elastic net regressions performed best in predicting DTA and DTS (prediction accuracy = ~0.8) consistently in both trials. We demonstrated that predictor variable importance descended towards the terminal stages of growth, signifying the importance of phenotype collection beyond classical terminal growth stages. This study is among the first to demonstrate an ability to predict yield in elite hybrid maize breeding trials using temporal UAS image-based phenotypes and supports the potential benefit of phenomic selection approaches in estimating breeding values before harvest.
Why it matches plant phenotyping methodsUAS画像から植生指数と草冠高を抽出し、機械学習で収量・開花期を推定する高スループット表現型解析が研究の中心です。
abstractUnoccupied aerial system (UAS; i.e., drone equipped with sensors) field-based high-throughput phenotyping (HTP) platforms are used to collect high quality images of plant nurseries to screen genetic materials
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 · publicand PLSR regression. All R codes are available in Github repository (https://github.com/Open asset ↗pdf-page:8 lines:1-175Code / dataset availability confirmedCrossref · checked 14 Sept 2026
Abstract Fractional vegetation cover (FVC) is the key trait of interest for characterizing crop growth status in crop breeding and precision management. Accurate quantification of FVC among different breeding lines, cultivars, and growth environments is challenging, especially because of the large spatiotemporal variability in complex field conditions. This study presents an ensemble modeling strategy for phenotyping crop FVC from unmanned aerial vehicle (UAV)-based multispectral images by coupling the PROSAIL model with a gap probability model (PROSAIL-GP). Seven field experiments for four main crops were conducted, and canopy images were acquired using a UAV platform equipped with RGB and multispectral cameras. The PROSAIL-GP model successfully retrieved FVC in oilseed rape (Brassica napus L.) with coefficient of determination, root mean square error (RMSE), and relative RMSE (rRMSE) of 0.79, 0.09, and 18%, respectively. The robustness of the proposed method was further examined in rice (Oryza sativa L.), wheat (Triticum aestivum L.), and cotton (Gossypium hirsutum L.), and a high accuracy of FVC retrieval was obtained, with rRMSEs of 12%, 6%, and 6%, respectively. Our findings suggest that the proposed method can efficiently retrieve crop FVC from UAV images at a high spatiotemporal domain, which should be a promising tool for precision crop breeding.
Why it matches plant phenotyping methodsUAVマルチスペクトル画像から作物のFVCという形態・生育形質を推定するモデルを開発し、複数作物・圃場実験で精度と頑健性を検証しており、表現型取得手法が研究の中心である。
abstractThis study presents an ensemble modeling strategy for phenotyping crop FVC from unmanned aerial vehicle (UAV)-based multispectral images by coupling the PROSAIL model with a gap probability model (PROSAIL-GP).
Reproduction assets foundThe paper's Data availability statement explicitly deposits the authors' PROSAIL-GP model code and all datasets (UAV-derived canopy reflectance/FVC measurements) in a public GitHub repository, plus detailed protocols on protocols.io. The PROSAIL model itself is a generic prior tool and is excluded.Code · publicle.
Conflict of interest
The authors declare no conflict of interest.
Data availability
Data supporting this work,such as details and source code of the PROSAIL
model used in this study,are openly available at http://teledetection.ipgp.jussieu.fr/prosail/.The code of the PROSAIL-GP model and all of the
datasets are available at https://github.com/WanLiangZJU/Crop-FVC-retrieval. The detailed protocols can be found at protocols.io (https://
dx.doi.org/10.17504/protocols.io.btmynk7w).
References
Aballa A, Cen H, Wan L, Mehmood K, He Y. 2020. Nutrient status diag-
nosis of infield oilseed rape via deep learning-enabled dynamic model. IEEE
Transactions on Industrial Informatics 17, 4379–4389.
BacOpen asset ↗WanLiangZJU/Crop-FVC-retrievalpdf-raw-page:15 lines:1-89Code / dataset availability confirmedEurope PMC · bioRxiv · checked 9 Sept 2026
ABSTRACT Over 800 million people across the tropics rely on cassava as a major source of calories. While the root dry matter content (RDMC) of this starchy root crop is important for both producers and consumers, characterization of RDMC by traditional methods is time-consuming and laborious for breeding programs. Alternate phenotyping methods have been proposed but lack the accuracy, cost, or speed ultimately needed for cassava breeding programs. For this reason, we investigated the use of a low-cost, handheld NIR spectrometer for field-based RDMC prediction in cassava. Oven-dried measurements of RDMC were paired with 21,044 scans of roots of 376 diverse clones from 10 field trials in Nigeria and grouped into training and test sets based on cross-validation schemes relevant to plant breeding programs. Mean partial least squares regression model performance ranged from R 2 p = 0.62 - 0.89 for within-trial predictions, which is within the range achieved with laboratory-grade spectrometers in previous studies. Relative to other factors, model performance was highly impacted by the inclusion of samples from the same environment in both the training and test sets. Random forest variable importance analysis of root spectra revealed increased importance in a region previously identified as predictive of water content in plants (~950 - 990 nm). With appropriate model calibration, the tested spectrometer will allow for field-based collection of spectral data with a smartphone for accurate RDMC prediction and potentially other quality traits, a step that could be easily integrated into existing harvesting workflows of cassava breeding programs. CORE IDEAS A low-cost, handheld near-infrared spectrometer was tested for phenotyping of cassava roots Plant breeding-relevant cross-validation schemes were used for predictions High prediction accuracies were achieved for cassava root dry matter content A spectral region predictive of plant water content was identified as important
Why it matches plant phenotyping methodsカッサバ根の乾物含量という植物形質を対象に、低コスト携帯型NIR分光計と予測モデルを開発・検証しており、フェノタイピング手法が研究の中心である。
abstractwe investigated the use of a low-cost, handheld NIR spectrometer for field-based RDMC prediction in cassava.
Reproduction assets foundThe paper's raw RDMC and NIR spectral data (21,044 SCiO scans paired with oven-dry RDMC from 10 Nigerian field trials) are publicly deposited on Cyverse under the GoreLab shared directory, as stated in the Data Availability section. The analysis R code on GitHub (GoreLab/CassavaNIRS) is paper-specific but its URL is anDataset · publicRaw RDMC and spectral data are available for download on Cyverse atOpen asset ↗Cyversepdf-page:20 lines:1-54Code / dataset availability confirmedEurope PMC · checked 9 Sept 2026
Roots are at the core of plant water dynamics. Nonetheless, root morphology and functioning are not easily assessable without destructive approaches. Nuclear Magnetic Resonance (NMR), and particularly low-field NMR (LF-NMR), is an interesting noninvasive method to study water in plants, as measurements can be performed outdoors and independent of sample size. However, as far as we know, there are no reported studies dealing with the water dynamics in plant roots using LF-NMR. Thus, the aim of this study is to assess the feasibility of using LF-NMR to characterize root water status and water dynamics non-invasively. To achieve this goal, a proof-of-concept study was designed using well-controlled environmental conditions. NMR and ecophysiological measurements were performed continuously over one week on three herbaceous species grown in rhizotrons. The NMR parameters measured were either the total signal or the transverse relaxation time T 2 . We observed circadian variations of the total NMR signal in roots and in soil and of the root slow relaxing T 2 value. These results were consistent with ecophysiological measurements, especially with the variation of fluxes between daytime and nighttime. This study assessed the feasibility of using LF-NMR to evaluate root water status in herbaceous species.
Why it matches plant phenotyping methodsLF-NMRによる根の水分状態・水動態の非破壊的測定可能性を中心に検証した proof-of-concept 研究であり、植物生理状態の取得手法が主要な貢献である。
abstractthe aim of this study is to assess the feasibility of using LF-NMR to characterize root water status and water dynamics non-invasively.
Reproduction assets foundThe authors deposited the paper's NMR and ecophysiological measurement data openly in Data INRAE (DOI 10.15454/NWRHDA), and supplementary materials at MDPI contain the CPMG decay curves and NNLS processing methods used for the T2 analysis.Dataset · publicThe data presented in this study are openly available in Data INRAE ( https://data.inrae.fr/ , accessed on 12 March 2021) repository at https://doi.org/10.15454/NWRHDA (accessed on 12 March 2021).Open asset ↗Data INRAE · 10.15454/NWRHDAlines:115-137Code / dataset availability confirmedEurope PMC · bioRxiv · checked 9 Sept 2026
Laboratory / benchtopRootMorphology / geometry measurementSegmentationRoot system architecture
Roots are central to the function of natural and agricultural ecosystems by driving plant acquisition of soil resources and influencing the carbon cycle. Root characteristics like length, diameter, and volume are critical to measure to understand plant and soil functions. RhizoVision Explorer is an open-source software designed to enable researchers interested in roots by providing an easy-to-use interface, fast image processing, and reliable measurements. The default broken roots mode is intended for roots sampled from pots or soil cores, washed, and typically scanned on a flatbed scanner, and provides measurements like length, diameter, and volume. The optional whole root mode for complete root systems or root crowns provides additional measurements such as angles, root depth, and convex hull. Both modes support providing measurements grouped by defined diameter ranges, the inclusion of multiple regions of interest, and batch analysis. RhizoVision Explorer was successfully validated against ground truth data using a novel copper wire image set. In comparison, the current reference software, the commercial WinRhizo™, drastically underestimated volume when wires of different diameters were in the same image. Additionally, measurements were compared with WinRhizo™ and IJ_Rhizo using a simulated root image set, showing general agreement in software measurements, except for root volume. Finally, scanned root image sets acquired in different labs for the crop, herbaceous, and tree species were used to compare results from RhizoVision Explorer with WinRhizo™. The two software showed general agreement, except that WinRhizo™ substantially underestimated root volume relative to RhizoVision Explorer. In the current context of rapidly growing interest in root science, RhizoVision Explorer intends to become a reference software, improve the overall accuracy and replicability of root trait measurements, and provide a foundation for collaborative improvement and reliable access to all. Abstract Figure
Why it matches plant phenotyping methods根画像から長さ・直径・体積などの植物形質を抽出するオープンソースソフトウェアの開発と、基準データおよび既存ソフトウェアとの技術検証が中心である。
abstractRhizoVision Explorer is an open-source software designed to enable researchers interested in roots by providing an easy-to-use interface, fast image processing, and reliable measurements.
Reproduction assets foundThe paper's copper wire validation image set is publicly deposited on Zenodo, and the authors' software binaries (Zenodo) and cvutil code library (GitHub) are explicitly released with public URLs. The simulated root image set (Zenodo 1159845) is cited prior work (Rose and Lobet 2018), not a paper-specific asset, and isDataset · publicThe copper wire image set used here is available in a public repository
and can be downloaded at http://doi.org/10.5281/zenodo.4677546 (Dhakal et al. 2021a).Open asset ↗zenodo · 10.5281/zenodo.4677546pdf-page:12 lines:1-49Code / dataset availability confirmedOpenAlex · Europe PMC · checked 9 Sept 2026
Root rot in common bean is a disease that causes serious damage to grain production, particularly in the upland areas of Eastern and Central Africa where significant losses occur in susceptible bean varieties. Pythium spp. and Fusarium spp. are among the soil pathogens causing the disease. In this study, a panel of 228 lines, named RR for root rot disease, was developed and evaluated in the greenhouse for Pythium myriotylum and in a root rot naturally infected field trial for plant vigor, number of plants germinated, and seed weight. The results showed positive and significant correlations between greenhouse and field evaluations, as well as high heritability (0.71–0.94) of evaluated traits. In GWAS analysis no consistent significant marker trait associations for root rot disease traits were observed, indicating the absence of major resistance genes. However, genomic prediction accuracy was found to be high for Pythium , plant vigor and related traits. In addition, good predictions of field phenotypes were obtained using the greenhouse derived data as a training population and vice versa. Genomic predictions were evaluated across and within further published data sets on root rots in other panels. Pythium and Fusarium evaluations carried out in Uganda on the Andean Diversity Panel showed good predictive ability for the root rot response in the RR panel. Genomic prediction is shown to be a promising method to estimate tolerance to Pythium, Fusarium and root rot related traits, indicating a quantitative resistance mechanism. Quantitative analyses could be applied to other disease-related traits to capture more genetic diversity with genetic models.
Why it matches plant phenotyping methods根腐病抵抗性や植物生育を遺伝情報から推定するゲノム予測を中心に、温室・圃場データ間および複数集団で予測性能を評価しており、単なる生物学的測定ではなく植物形質推定法の検証・応用である。
abstractGenomic predictions were evaluated across and within further published data sets on root rots in other panels.
Reproduction assets foundThe paper's data availability statement explicitly deposits the SNP marker matrix and raw and modeled phenotypic data of the RR panel (root rot phenotyping measurements) on Harvard Dataverse, a public, paper-specific, actionable asset. No author analysis code repository is mentioned.Dataset · publicThe SNP marker matrix, the raw and modeled phenotypic data of the RR panel used in this study are available for download at Harvard Dataverse: https://doi.org/10.7910/DVN/SVA5CJ .Open asset ↗Harvard Dataverse · 10.7910/DVN/SVA5CJlines:500-547Code / dataset availability confirmedEurope PMC · checked 9 Sept 2026
Background Phosphorus (P) is an important in ensuring plant morphogenesis and grain quality, therefore an efficient root system is crucial for P-uptake. Identification of useful loci for root morphological and P uptake related traits at seedling stage is important for wheat breeding. The aims of this study were to evaluate phenotypic diversity of Yangmai 16/Zhongmai 895 derived doubled haploid (DH) population for root system architecture (RSA) and biomass related traits (BRT) in different P treatments at seedling stage using hydroponic culture, and to identify QTL using 660 K SNP array based high-density genetic map. Results All traits showed significant variations among the DH lines with high heritabilities (0.76 to 0.91) and high correlations (r = 0.59 to 0.98) among all traits. Inclusive composite interval mapping (ICIM) identified 34 QTL with 4.64-20.41% of the phenotypic variances individually, and the log of odds (LOD) values ranging from 2.59 to 10.43. Seven QTL clusters (C1 to C7) were mapped on chromosomes 3DL, 4BS, 4DS, 6BL, 7AS, 7AL and 7BL, cluster C5 on chromosome 7AS (AX-109955164 - AX-109445593) with pleiotropic effect played key role in modulating root length (RL), root tips number (RTN) and root surface area (ROSA) under low P condition, with the favorable allele from Zhongmai 895. Conclusions This study carried out an imaging pipeline-based rapid phenotyping of RSA and BRT traits in hydroponic culture. It is an efficient approach for screening of large populations under different nutrient conditions. Four QTL on chromosomes 6BL (2) and 7AL (2) identified in low P treatment showed positive additive effects contributed by Zhongmai 895, indicating that Zhongmai 895 could be used as parent for P-deficient breeding. The most stable QTL QRRS.caas-4DS for ratio of root to shoot dry weight (RRS) harbored the stable genetic region with high phenotypic effect, and QTL clusters on 7A might be used for speedy selection of genotypes for P-uptake. SNPs closely linked to QTLs and clusters could be used to improve nutrient-use efficiency.
Why it matches plant phenotyping methods水耕条件下の根系形態とバイオマスを画像パイプラインで迅速に測定し、大規模集団・異なる栄養条件のスクリーニングに用いる方法が明示されており、表現型取得が実質的な役割を持つ。
abstractThis study carried out an imaging pipeline-based rapid phenotyping of RSA and BRT traits in hydroponic culture.
Reproduction assets foundThe paper deposits its phenotype dataset (root system architecture and biomass-related trait measurements of the Yangmai 16/Zhongmai 895 DH population under three phosphorus treatments) in a Dryad repository with an explicit public sharing link and DOI. No author analysis code or trained models are reported.Dataset · publicThe datasets are available in the “Dataset Yang et al.” repository at Dryad data bank. Data can be accessed using following link; https://datadryad.org/stash/share/BTR6YCbZX1mr-vH5QojHRYlPHe4uZ5vWSsGmVE2jbPkOpen asset ↗Dryadlines:146-205Code / dataset availability confirmedCrossref · checked 15 Sept 2026
Abstract. Root water uptake by plants is a vital process that influences terrestrial energy, water, and carbon exchanges. At the soil, vegetation, and atmosphere interfaces, root water uptake and solar radiation predominantly regulate the dynamics and health of vegetation growth, which can be remotely monitored by satellites, using the soil–plant relationship proxy – solar-induced chlorophyll fluorescence. However, most current canopy photosynthesis and fluorescence models do not account for root water uptake, which compromises their applications under water-stressed conditions. To address this limitation, this study integrated photosynthesis, fluorescence emission, and transfer of energy, mass, and momentum in the soil–plant–atmosphere continuum system, via a simplified 1D root growth model and a resistance scheme linking soil, roots, leaves, and the atmosphere. The coupled model was evaluated with field measurements of maize and grass canopies. The results indicated that the simulation of land surface fluxes was significantly improved by the coupled model, especially when the canopy experienced moderate water stress. This finding highlights the importance of enhanced soil heat and moisture transfer, as well as dynamic root growth, on simulating ecosystem functioning.
Why it matches plant phenotyping methods植物の光合成・蛍光などの生理状態を推定する結合モデルを開発し、トウモロコシおよび草本キャノピーで評価しており、モデル開発と検証が研究の中心です。
abstractTo address this limitation, this study integrated photosynthesis, fluorescence emission, and transfer of energy, mass, and momentum in the soil–plant–atmosphere continuum system, via a simplified 1D root growth model and a resistance scheme linking soil, roots, leaves, and the atmosphere.
Reproduction assets foundThe paper's Code and data availability section explicitly archives the exact STEMMUS–SCOPE model version on Zenodo and publishes the Yangling eddy-covariance validation dataset on 4TU, both with public DOIs.Dataset · publicdoi.org/10.5281/zenodo.3839092 , Wang et al., 2020). The original source of the SCOPE model and STEMMUS model was obtained from Van der Tol et al. (2009) and Zeng et al. (2011a, b), respectively. The tower-based eddy-covariance measurements used for model validation were provided by the authors for the Yangling station, China ( https://doi.org/10.4121/uuid:aa0ed483-701e-4ba0-b7b0-674695f5f7a7 , Wang et al., 2019), and were obtained from the FLUXNET2015 Dataset and PLUMBER2 program for the Vaira Ranch (US-Var) FLUXNET site.
Author contributions
YW, YZ, HC, and ZS designed the study. YW developed the code, conducted the analysis, and wrote the paper. YW and HC collected and shared their eddy-cOpen asset ↗10.4121/uuid:aa0ed483-701e-4ba0-b7b0-674695f5f7a7lines:657-684Code / dataset availability confirmedOpenAlex · Crossref · Europe PMC · checked 8 Sept 2026
One of the objectives of many studies conducted by breeding programs is to characterize and select rootstocks well-adapted to drought conditions. In recent years, field high-throughput phenotyping methods have been developed to characterize plant traits and to identify the most water use efficient varieties and rootstocks. However, none of these studies have been able to quantify the behavior of crop evapotranspiration in almond rootstocks under different water regimes. In this study, remote sensing phenotyping methods were used to assess the evapotranspiration of almond cv. “Marinada” grafted onto a rootstock collection. In particular, the two-source energy balance and Shuttleworth and Wallace models were used to, respectively, estimate the actual and potential evapotranspiration of almonds grafted onto 10 rootstock under three different irrigation treatments. For this purpose, three flights were conducted during the 2018 and 2019 growing seasons with an aircraft equipped with a thermal and multispectral camera. Stem water potential (Ψstem) was also measured concomitant to image acquisition. Biophysical traits of the vegetation were firstly assessed through photogrammetry techniques, spectral vegetation indices and the radiative transfer model PROSAIL. The estimates of canopy height, leaf area index and daily fraction of intercepted radiation had root mean square errors of 0.57 m, 0.24 m m–1 and 0.07%, respectively. Findings of this study showed significant differences between rootstocks in all of the evaluated parameters. Cadaman® and Garnem® had the highest canopy vigor traits, evapotranspiration, Ψstem and kernel yield. In contrast, Rootpac® 20 and Rootpac® R had the lowest values of the same parameters, suggesting that this was due to an incompatibility between plum-almond species or to a lower water absorption capability of the rooting system. Among the rootstocks with medium canopy vigor, Adesoto and IRTA 1 had a lower evapotranspiration than Rootpac® 40 and Ishtara®. Water productivity (WP) (kg kernel/mm water evapotranspired) tended to decrease with Ψstem, mainly in 2018. Cadaman® and Garnem® had the highest WP, followed by INRA GF-677, IRTA 1, IRTA 2, and Rootpac® 40. Despite the low Ψstem of Rootpac® R, the WP of this rootstock was also high.
Why it matches plant phenotyping methodsリモートセンシングによる植物形質・蒸発散の推定が研究の中心で、熱・マルチスペクトル画像、フォトグラメトリ、モデルを用いた推定精度も評価している。
abstractIn recent years, field high-throughput phenotyping methods have been developed to characterize plant traits and to identify the most water use efficient varieties and rootstocks.
Reproduction assets foundThe paper's data availability statement points to the author's public GitHub profile (Héctor Nieto, pyTSEB developer) as the location of the datasets analyzed, which include the remote sensing phenotyping measurements (thermal/multispectral imagery-derived ETa, LAI, fiPAR, Ψstem relationships) and the TSEB-based model.Dataset · publicPublicly available datasets were analyzed in this study. This data can be found here: https://github.com/hectornieto .Open asset ↗hectornietolines:1046-1107Code / dataset availability confirmedOpenAlex · Crossref · Europe PMC · checked 8 Sept 2026
Summary The root economics space is a useful framework for plant ecology but is rarely considered for crop ecophysiology. In order to understand root trait integration in winter wheat, we combined functional phenomics with trait economic theory, utilizing genetic variation, high‐throughput phenotyping, and multivariate analyses. We phenotyped a diversity panel of 276 genotypes for root respiration and architectural traits using a novel high‐throughput method for CO 2 flux and the open‐source software RhizoVision Explorer to analyze scanned images. We uncovered substantial variation in specific root respiration (SRR) and specific root length (SRL), which were primary indicators of root metabolic and structural costs. Multiple linear regression analysis indicated that lateral root tips had the greatest SRR, and the residuals from this model were used as a new trait. Specific root respiration was negatively correlated with plant mass. Network analysis, using a Gaussian graphical model, identified root weight, SRL, diameter, and SRR as hub traits. Univariate and multivariate genetic analyses identified genetic regions associated with SRR, SRL, and root branching frequency, and proposed gene candidates. Combining functional phenomics and root economics is a promising approach to improving our understanding of crop ecophysiology. We identified root traits and genomic regions that could be harnessed to breed more efficient crops for sustainable agroecosystems.
Why it matches plant phenotyping methods根の呼吸と構造を対象に、CO2フラックスの新規ハイスループット法と画像解析ソフトウェアを用いた機能的フェノミクスを中心的に実施しており、植物形質取得法が研究の主要部分である。
abstractWe phenotyped a diversity panel of 276 genotypes for root respiration and architectural traits using a novel high‐throughput method for CO 2 flux and the open‐source software RhizoVision Explorer to analyze scanned images.
Reproduction assets foundThe paper explicitly deposits its trait data, GEMMA GWAS output, and R analysis scripts at Zenodo (10.5281/zenodo.4247894), and separately deposits the root respiration measurement protocol and flux-calculation R scripts at Zenodo (10.5281/zenodo.4247873). Both are paper-specific, public, and actionable. The Triticeae-Dataset · publicAll trait data, gemma output, and R analysis scripts necessary for the statistical analysis and plotting are publicly available at https://doi.org/10.5281/zenodo.4247894 (Guo et al.,
2020b ).Open asset ↗Zenodo · 10.5281/zenodo.4247894lines:608-654Code · publicThe protocol for the root respiration measurements and the R script for calculating total flux from a directory of text files are available at https://doi.org/10.5281/zenodo.4247873 (Guo et al.,
2020a ).Open asset ↗Zenodo · 10.5281/zenodo.4247873lines:85-97Code / dataset availability confirmedEurope PMC · bioRxiv · checked 15 Sept 2026
ABSTRACT The root system is critical for the survival of nearly all land plants and a key target for improving abiotic stress tolerance, nutrient accumulation, and yield in crop species. Although many methods of root phenotyping exist, within field studies one of the most popular methods is the extraction and measurement of the upper portion of the root system, known as the root crown, followed by trait quantification based on manual measurements or 2D imaging. However, 2D techniques are inherently limited by the information available from single points of view. Here, we used X-ray computed tomography to generate highly accurate 3D models of maize root crowns and created computational pipelines capable of measuring 71 features from each sample. This approach improves estimates of the genetic contribution to root system architecture, and is refined enough to detect various changes in global root system architecture over developmental time as well as more subtle changes in root distributions as a result of environmental differences. We demonstrate that root pulling force, a high-throughput method of root extraction that provides an estimate of root biomass, is associated with multiple 3D traits from our pipeline. Our combined methodology can therefore be used to calibrate and interpret root pulling force measurements across a range of experimental contexts, or scaled up as a stand-alone approach in large genetic studies of root system architecture.
Why it matches plant phenotyping methodsトウモロコシ根系を対象に、X線CTによる3Dモデル化と計算パイプラインで71形質を抽出し、根引抜き力との較正・解釈まで行う、中心的な表現型計測手法研究である。
abstractHere, we used X-ray computed tomography to generate highly accurate 3D models of maize root crowns and created computational pipelines capable of measuring 71 features from each sample.
Reproduction assets foundThe paper states that the authors' scripts for X-ray CT image processing and root feature extraction (batch-segmentation, batch-skeleton) are publicly available in the Topp-Roots-Lab GitHub repository. Raw phenotype data is said to be in Supplemental File 1, but no public URL for it is provided in the supplied blocks.Code · publicestimated by taking the 2D projection of the 3D volume, then
185 calculated using a similar approach to that described in Grift et al., 2011. DensityS features are
186 computationally similar to plant compactness traits described in Yang et al., 2014. Scripts used
187 for image processing and feature extraction are available at https://github.com/Topp-Roots-Lab/
188
189 Statistical Analysis
190
191 All downstream (i.e. post feature extraction) analysis was performed in the R statistical
192 computing environment. Initially, principal component analysis using all 71 3D roots traits was
193 used to identify large outliers, leading to the removal of 2 samples in the G2F 2017 data and 3
19Open asset ↗Topp-Roots-Labpdf-layout-page:5 lines:1-56Code / dataset availability confirmedOpenAlex · Crossref · checked 15 Sept 2026
Automated machine learning (AutoML) has been heralded as the next wave in artificial intelligence with its promise to deliver high-performance end-to-end machine learning pipelines with minimal effort from the user. However, despite AutoML showing great promise for computer vision tasks, to the best of our knowledge, no study has used AutoML for image-based plant phenotyping. To address this gap in knowledge, we examined the application of AutoML for image-based plant phenotyping using wheat lodging assessment with unmanned aerial vehicle (UAV) imagery as an example. The performance of an open-source AutoML framework, AutoKeras, in image classification and regression tasks was compared to transfer learning using modern convolutional neural network (CNN) architectures. For image classification, which classified plot images as lodged or non-lodged, transfer learning with Xception and DenseNet-201 achieved the best classification accuracy of 93.2%, whereas AutoKeras had a 92.4% accuracy. For image regression, which predicted lodging scores from plot images, transfer learning with DenseNet-201 had the best performance (R2 = 0.8303, root mean-squared error (RMSE) = 9.55, mean absolute error (MAE) = 7.03, mean absolute percentage error (MAPE) = 12.54%), followed closely by AutoKeras (R2 = 0.8273, RMSE = 10.65, MAE = 8.24, MAPE = 13.87%). In both tasks, AutoKeras models had up to 40-fold faster inference times compared to the pretrained CNNs. AutoML has significant potential to enhance plant phenotyping capabilities applicable in crop breeding and precision agriculture.
Why it matches plant phenotyping methodsAutoMLと画像解析を用いた植物表現型測定手法を、コムギ倒伏評価で比較・検証しており、表現型取得・推定手法が研究の中心である。
titleAutomated Machine Learning for High-Throughput Image-Based Plant Phenotyping
Reproduction assets foundThe paper's Data Availability Statement points to a public GitHub repository containing the authors' source code to replicate the AutoML/transfer-learning phenotyping analyses and the best AutoKeras models. A Zenodo deposit with the wheat plot images and lodging ground-truth CSV is also referenced, but no Zenodo URL isCode · publicSource codes required to replicate the analyses in this article
and the best performing models reported for AutoKeras are provided in a GitHub repository [58].Open asset ↗pdf-raw-page:16 lines:1-53Code / dataset availability confirmedEurope PMC · checked 9 Sept 2026
RootMorphology / geometry measurementRoot system architecture
High throughput phenotyping is important to bridge the gap between genotype and phenotype. The methods used to describe the phenotype therefore should be robust to measurement errors, relatively stable over time, and most importantly, provide a reliable estimate of elementary phenotypic components. In this study, we use functional-structural modeling to evaluate quantitative phenotypic metrics used to describe root architecture to determine how they fit these criteria. Our results show that phenes such as root number, root diameter, and lateral root branching density are stable, reliable measures and are not affected by imaging method or plane. Metrics aggregating multiple phenes such as total length , total volume , convex hull volume , and bushiness index estimate different subsets of the constituent phenes; they however do not provide any information regarding the underlying phene states. Estimates of phene aggregates are not unique representations of underlying constituent phenes: multiple phenotypes having phenes in different states could have similar aggregate metrics. Root growth angle is an important phene which is susceptible to measurement errors when 2D projection methods are used. Metrics that aggregate phenes which are complex functions of root growth angle and other phenes are also subject to measurement errors when 2D projection methods are used. These results support the hypothesis that estimates of phenes are more useful than metrics aggregating multiple phenes for phenotyping root architecture. We propose that these concepts are broadly applicable in phenotyping and phenomics.
Why it matches plant phenotyping methods根系アーキテクチャの定量的表現型指標を機能構造モデルで評価し、測定誤差、安定性、信頼性を比較しており、表現型測定法の技術的検証が中心です。
abstractIn this study, we use functional-structural modeling to evaluate quantitative phenotypic metrics used to describe root architecture to determine how they fit these criteria.
Reproduction assets foundThe paper's Data Availability statement explicitly deposits the executable SimRoot code used in this study, the parameters used to generate the simulated root phenotypes, and the raw simulated root data on a public figshare link, which is an allowed URL.Code · publicThe executable code of the version of SimRoot employed in this study, parameters used to generate these data, and the raw data are all available at https://figshare.com/s/58c7599752bcb75fbd76 .Open asset ↗figsharelines:450-462Code / dataset availability confirmedOpenAlex · Europe PMC · checked 8 Sept 2026
The physical presence of roots and the compounds they release affect the cohesion between roots and their environment. However, the plant traits that are important for these interactions are unknown and most methods that quantify the contributions of these traits are time-intensive and require specialist equipment and complex substrates. Our lab developed an inexpensive, high-throughput phenotyping assay that quantifies root-substrate adhesion in Arabidopsis thaliana. We now report that this method has high sensitivity and versatility for identifying different types of traits affecting root-substrate adhesion including root hair morphology, vesicle trafficking pathways, and root exudate composition. We describe a practical protocol for conducting this assay and introduce its use in a forward genetic screen to identify novel genes affecting root-substrate interactions. This assay is a powerful tool for identifying and quantifying genetic contributions to cohesion between roots and their environment.
Why it matches plant phenotyping methods根—基質接着を定量する高スループット表現型測定法を開発・検証し、遺伝子スクリーニングへの応用も示すため、手法が研究の中心である。
abstractOur lab developed an inexpensive, high-throughput phenotyping assay that quantifies root-substrate adhesion in Arabidopsis thaliana.
Reproduction assets foundThe paper's data availability statement points to a public University of Bristol (data.bris) repository deposit containing the study's centrifuge assay datasets (root-gel detachment measurements and associated analyses). No author analysis code or trained models are explicitly deposited.Dataset · publicThe datasets presented in this study can be found in online repositories. The names of the repository/repositories and accession number(s) can be found at: https://doi.org/10.5523/bris.21loiw3fpw372g99l93meaja1 .Open asset ↗10.5523/bris.21loiw3fpw372g99l93meaja1lines:526-555Code / dataset availability confirmedOpenAlex · Crossref · Europe PMC · checked 8 Sept 2026
Plant height (PH) is an essential trait in the screening of most crops. While in crops such as wheat, medium stature helps reduce lodging, tall plants are preferred to increase total above-ground biomass. PH is an easy trait to measure manually, although it can be labor-intense depending on the number of plots. There is an increasing demand for alternative approaches to estimate PH in a higher throughput mode. Crop surface models (CSMs) derived from dense point clouds generated via aerial imagery could be used to estimate PH. This study evaluates PH estimation at different phenological stages using plot-level information from aerial imaging-derived 3D CSM in wheat inbred lines during two consecutive years. Multi-temporal and high spatial resolution images were collected by fixed-wing (PlatFW) and multi-rotor (PlatMR) unmanned aerial vehicle (UAV) platforms over two wheat populations (50 and 150 lines). The PH was measured and compared at four growth stages (GS) using ground-truth measurements (PHground) and UAV-based estimates (PHaerial). The CSMs generated from the aerial imagery were validated using ground control points (GCPs) as fixed reference targets at different heights. The results show that PH estimations using PlatFW were consistent with those obtained from PlatMR, showing some slight differences due to image processing settings. The GCPs heights derived from CSM showed a high correlation and low error compared to their actual heights (R2 ≥ 0.90, RMSE ≤ 4 cm). The coefficient of determination (R2) between PHground and PHaerial at different GS ranged from 0.35 to 0.88, and the root mean square error (RMSE) from 0.39 to 4.02 cm for both platforms. In general, similar and higher heritability was obtained using PHaerial across different GS and years and ranged according to the variability, and environmental error of the PHground observed (0.06–0.97). Finally, we also observed high Spearman rank correlations (0.47–0.91) and R2 (0.63–0.95) of PHaerial adjusted and predicted values against PHground values. This study provides an example of the use of UAV-based high-resolution RGB imagery to obtain time-series estimates of PH, scalable to tens-of-thousands of plots, and thus suitable to be applied in plant wheat breeding trials.
Why it matches plant phenotyping methodsUAV-RGB画像と3D作物表面モデルによるコムギ草丈推定法を開発・検証し、地上測定との比較、精度評価、複数プラットフォーム間の検証を行っており、表現型取得手法が研究の中心である。
abstractThis study evaluates PH estimation at different phenological stages using plot-level information from aerial imaging-derived 3D CSM in wheat inbred lines during two consecutive years.
Reproduction assets foundThe paper's authors publicly deposited the R scripts used for UAV image analysis and plant-height trait extraction on GitHub. The raw phenotyping data are only available on request. Pix4D support articles and the R boot package are generic third-party resources, not paper-specific assets.Code · publicThe PHaerial scripts used to perform the image analyses and trait extract are available at https://github.com/volpatoo/HTP-via-drone-imagery/tree/master/UAV-HTP_PlantHeightOpen asset ↗volpatoo/HTP-via-drone-imagery · UAV-HTP_PlantHeightlines:519-573Code / dataset availability confirmedCrossref · Europe PMC · checked 9 Sept 2026
Abstract Arbuscular mycorrhiza fungi (AMF) are beneficial soil fungi that can promote the growth of their host plants. Accurate quantification of AMF in plant roots is important because the level of colonization is often indicative of the activity of these fungi. Root colonization is traditionally measured with microscopy methods which visualize fungal structures inside roots. Microscopy methods are labor-intensive, and results depend on the observer. In this study, we present a relative qPCR method to quantify AMF in which we normalized the AMF qPCR signal relative to a plant gene. First, we validated the primer pair AMG1F and AM1 in silico, and we show that these primers cover most AMF species present in plant roots without amplifying host DNA. Next, we compared the relative qPCR method with traditional microscopy based on a greenhouse experiment with Petunia plants that ranged from very high to very low levels of AMF root colonization. Finally, by sequencing the qPCR amplicons with MiSeq, we experimentally confirmed that the primer pair excludes plant DNA while amplifying mostly AMF. Most importantly, our relative qPCR approach was capable of discriminating quantitative differences in AMF root colonization and it strongly correlated (Spearman Rho = 0.875) with quantifications by traditional microscopy. Finally, we provide a balanced discussion about the strengths and weaknesses of microscopy and qPCR methods. In conclusion, the tested approach of relative qPCR presents a reliable alternative method to quantify AMF root colonization that is less operator-dependent than traditional microscopy and offers scalability to high-throughput analyses.
Why it matches plant phenotyping methods植物根のAMF菌根 colonization を定量する相対qPCR法を開発・検証し、顕微鏡法との比較で性能を評価しているため、植物状態の取得手法が中心である。
abstractIn this study, we present a relative qPCR method to quantify AMF in which we normalized the AMF qPCR signal relative to a plant gene.
Reproduction assets foundThe paper provides two paper-specific public assets: the authors' analysis code repository on GitHub (R/DADA2/qPCR analysis workflow) and raw amplicon sequencing data deposited in the European Nucleotide Archive under study accession PRJEB20127 (sample SAMEA103939171), which contains the qPCR amplicon sequences used toCode · publicAll code is available under https://github.com/PMI-Basel/Bodenhausen_et_al_AMF_qPCR .Open asset ↗PMI-Basel/Bodenhausen_et_al_AMF_qPCRlines:90-98Code / dataset availability confirmedEurope PMC · checked 9 Sept 2026
Crop improvement for Nitrogen Use Efficiency (NUE) requires a well-defined phenotype and genotype, especially for different N-forms. As N-supply enhances growth, we comprehensively evaluated 25 commonly measured phenotypic parameters for N response using 4 N treatments in six indica rice genotypes. For this, 32 replicate potted plants were grown in the green-house on nutrient-depleted sand. They were fertilized to saturation with media containing either nitrate or urea as the sole N source at normal (15 mM N) or low level (1.5 mM N). The variation in N-response among genotypes differed by N form/dose and increased developmentally from vegetative to reproductive parameters. This indicates survival adaptation by reinforcing variation in every generation. Principal component analysis segregated vegetative parameters from reproduction and germination. Analysis of variance revealed that relative to low level, normal N facilitated germination, flowering and vegetative growth but limited yield and NUE. Network analysis for the most connected parameters, their correlation with yield and NUE, ranking by Feature selection and validation by Partial least square discriminant analysis enabled shortlisting of eight parameters for NUE phenotype. It constitutes germination and flowering, shoot/root length and biomass parameters, six of which were common to nitrate and urea. Field-validation confirmed the NUE differences between two genotypes chosen phenotypically. The correspondence between multiple approaches in shortlisting parameters for NUE makes it a novel and robust phenotyping methodology of relevance to other plants, nutrients or other complex traits. Thirty-Four N-responsive genes associated with the phenotype have also been identified for genotypic characterization of NUE.
Why it matches plant phenotyping methodsNUEの複合形質を定義・選抜するため、複数形質の評価、特徴選択、統計解析、フィールド検証を統合したフェノタイピング手法が中心的に開発・検証されている。
abstractNetwork analysis for the most connected parameters, their correlation with yield and NUE, ranking by Feature selection and validation by Partial least square discriminant analysis enabled shortlisting of eight parameters for NUE phenotype.
Reproduction assets found本文中に内容が明示された植物フェノタイピング関連の補足表と、その公開リンクを確認しました。Supplement · publicSupplementary Table 3
Mean values of the measured 25 phenotypic parameters in nitrate/urea sources and normal (15 mM) or low (1.5 mM) doses.Open asset ↗lines:557-644Code / dataset availability confirmedOpenAlex · Europe PMC · Crossref · checked 14 Sept 2026
Nutrient uptake is critical for crop growth and is determined by root foraging in soil. Growth and branching of roots lead to effective root placement to acquire nutrients, but relatively little is known about absorption of nutrients at the root surface from the soil solution. This knowledge gap could be alleviated by understanding sources of genetic variation for short-term nutrient uptake on a root length basis. A modular platform called RhizoFlux was developed for high-throughput phenotyping of multiple ion-uptake rates in maize (Zea mays L.). Using this system, uptake rates were characterized for the crop macronutrients nitrate, ammonium, potassium, phosphate, and sulfate among the Nested Association Mapping (NAM) population founder lines. The data revealed substantial genetic variation for multiple ion-uptake rates in maize. Interestingly, specific nutrient uptake rates (nutrient uptake rate per length of root) were found to be both heritable and distinct from total uptake and plant size. The specific uptake rates of each nutrient were positively correlated with one another and with specific root respiration (root respiration rate per length of root), indicating that uptake is governed by shared mechanisms. We selected maize lines with high and low specific uptake rates and performed an RNA-seq analysis, which identified key regulatory components involved in nutrient uptake. The high-throughput multiple ion-uptake kinetics pipeline will help further our understanding of nutrient uptake, parameterize holistic plant models, and identify breeding targets for crops with more efficient nutrient acquisition.
Why it matches plant phenotyping methods根の複数イオン吸収速度を高スループットで測定するRhizoFluxプラットフォームを開発し、性能・遺伝的変異を評価しており、植物表現型取得法が研究の中心です。
abstractA modular platform called RhizoFlux was developed for high-throughput phenotyping of multiple ion-uptake rates in maize (Zea mays L.).
Reproduction assets found保存済みの本文根拠を更新済みルールで再検証し、公開資産1件を確認しました。Code · publicthe statistical analysis R codes including the packages needed are available at https://doi.org/10.5281/zenodo.3893944 ( Griffiths and York, 2020b )Open asset ↗Zenodo · 10.5281/zenodo.3893944lines:83-89Code / dataset availability confirmedbioRxiv · checked 15 Sept 2026
Root hydraulic properties play a central role in the global water cycle, agricultural systems productivity, and ecosystem survival as they impact the global canopy water supply. However, the available experimental methods to quantify root hydraulic conductivities, such as the root pressure probing, are particularly challenging and their applicability on thin roots and small root segments is limited. There is a gap in methods enabling easy estimations of root hydraulic conductivities across a diversity of root types and at high resolution along root axes. In this case study, we analysed Zea mays (maize) plants of the var. B73 that were grown in pots for 14 days. Root cross-section data were used to extract anatomical measurements. We used the Generator of Root Anatomy in R (GRANAR) model to generate root anatomical networks from anatomical features. Then we used the Model of Explicit Cross-section Hydraulic Anatomy (MECHA) to compute an estimation of the root axial and radial hydraulic conductivities (kx and kr, respectively), based on the generated anatomical networks and cell hydraulic properties from the literature. The root hydraulic conductivity maps obtained from the root cross-sections suggest significant functional variations along and between different root types. Predicted variations of kr along the root axis were strongly dependent on the maturation stage of hydrophobic barriers. The same was also true for the maturation rates of the metaxylem. The different anatomical features, as well as their evolution along the root type add significant variation to the kr estimation in between root type and along the root axe. Under the prism of root types, anatomy, and hydrophobic barriers, our results highlight the diversity of root radial and axial hydraulic conductivities, which may be veiled under low-resolution measurements of the root system hydraulic conductivity. While predictions of our root hydraulic maps match the range and trend of measurements reported in the literature, future studies could focus on the quantitative validation of hydraulic maps. From now on, a novel method, which turns root cross-section images into hydraulic maps will offer an inexpensive and easily applicable investigation tool for root hydraulics, in parallel to root pressure probing experiments. One-Sentence summaryThe use of cross-section images and modelling tools to generate a map the axial and radial hydraulic conductivity along different root types for the maize cultivar B73.
Why it matches plant phenotyping methods根の断面画像から解剖学的形質を抽出し、モデルで軸方向・半径方向の根 hydraulic conductivity を推定する手法が研究の中心であるため、植物表現型計測手法として収録する。
abstractWe used the Generator of Root Anatomy in R (GRANAR) model to generate root anatomical networks from anatomical features. Then we used the Model of Explicit Cross-section Hydraulic Anatomy (MECHA) to compute an estimation of the root axial and radial hydraulic conductivities (kx and kr, respectively)
Reproduction assets foundThe paper provides two public, paper-specific assets: the GRANAR-MECHA coupling workflow (Jupyter/R repository with Zenodo DOI) and the B73_HydraulicMap repository containing the Rmarkdown script used to compute the root hydraulic maps plus all input and output data of the study.Code · publicection can be visualized through different figures that show the
186 proportion of the water fluxes in each compartiment (apoplastic and symplastic fluxes).
The whole script that was used to compute the root hydraulic maps from the root anatomical
188 measurement is presented as a Rmarkdown script stored in a GitHub repository
(https://github.com/granar/B73_HydraulicMap doi: 10.5281/zenodo.4320861). In the same
190 repository are stored all input and output data of this study.
192
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CC-BY 4.0 International license
perpetuity. It is made available under a
preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint Open asset ↗granar/B73_HydraulicMap · 10.5281/zenodo.4320861pdf-raw-page:10 lines:1-20Code / dataset availability confirmedEurope PMC · checked 9 Sept 2026
Phenomics technologies allow quantitative assessment of phenotypes across a larger number of plant genotypes compared to traditional phenotyping approaches. The utilization of such technologies has enabled the generation of multidimensional plant traits creating big datasets. However, to harness the power of phenomics technologies, more sophisticated data analysis methods are required. In this study, Aphanomyces root rot (ARR) resistance in 547 lentil accessions and lines was evaluated using Red-Green-Blue (RGB) images of roots. We created a dataset of 6,460 root images that were annotated by a plant breeder based on the disease severity. Two approaches, generalized linear model with elastic net regularization (EN) and convolutional neural network (CNN), were developed to classify disease resistance categories into three classes: resistant, partially resistant, and susceptible. The results indicated that the selected image features using EN models were able to classify three disease categories with an accuracy of up to 0.91 ± 0.004 (0.96 ± 0.005 resistant, 0.82 ± 0.009 partially resistant, and 0.92 ± 0.007 susceptible) compared to CNN with an accuracy of about 0.84 ± 0.009 (0.96 ± 0.008 resistant, 0.68 ± 0.026 partially resistant, and 0.83 ± 0.015 susceptible). The resistant class was accurately detected using both classification methods. However, partially resistant class was challenging to detect as the features (data) of the partially resistant class often overlapped with those of resistant and susceptible classes. Collectively, the findings provided insights on the use of phenomics techniques and machine learning approaches to provide quantitative measures of ARR resistance in lentil.
Why it matches plant phenotyping methodsレンティル根のRGB画像から根腐病の重症度・抵抗性を推定する画像解析および機械学習手法を開発・比較しており、植物表現型取得が中心的です。
abstractTwo approaches, generalized linear model with elastic net regularization (EN) and convolutional neural network (CNN), were developed to classify disease resistance categories into three classes: resistant, partially resistant, and susceptible.
Reproduction assets foundThe paper's Data Availability statement points to a Zenodo deposit (DOI 10.5281/zenodo.4018168) containing the paper-specific lentil root rot image dataset (6,460 annotated RGB root images) used for the EN and CNN phenotyping analyses. No separate author analysis code URL is given; the R project URL is a generic tool, Dataset · publicData available at: https://doi.org/10.5281/zenodo.4018168 .Open asset ↗zenodo · 10.5281/zenodo.4018168lines:121-134Code / dataset availability confirmedOpenAlex · Europe PMC · bioRxiv · checked 8 Sept 2026
WheatRootMorphology / geometry measurementPhysiological trait estimationRoot system architecture
Summary The root economics space is a useful framework for plant ecology, but rarely considered for crop ecophysiology. In order to understand root trait integration in winter wheat, we combined functional phenomics with trait economic theory utilizing genetic variation, high-throughput phenotyping, and multivariate analyses. We phenotyped a diversity panel of 276 genotypes for root respiration and architectural traits using a novel high-throughput method for CO 2 flux and the open-source software RhizoVision Explorer for analyzing scanned images. We uncovered substantial variation for specific root respiration (SRR) and specific root length (SRL), which were primary indicators of root metabolic and construction costs. Multiple linear regression estimated that lateral root tips had the greatest SRR, and the residuals of this model were used as a new trait. SRR was negatively correlated with plant mass. Network analysis using a Gaussian graphical model identified root weight, SRL, diameter, and SRR as hub traits. Univariate and multivariate genetic analyses identified genetic regions associated with aspects of the root economics space, with underlying gene candidates. Combining functional phenomics and root economics is a promising approach to understand crop ecophysiology. We identified root traits and genomic regions that could be harnessed to breed more efficient crops for sustainable agroecosystems.
Why it matches plant phenotyping methods根の呼吸と形態を対象に、CO2フラックスの新規ハイスループット測定法と画像解析ソフトウェアを用いたフェノタイピングが研究の中心である。
abstractWe phenotyped a diversity panel of 276 genotypes for root respiration and architectural traits using a novel high-throughput method for CO 2 flux and the open-source software RhizoVision Explorer for analyzing scanned images.
Reproduction assets foundThe paper explicitly deposits two paper-specific public assets: (1) the root respiration measurement protocol and R script for computing CO2 flux from LI-850 text files (Zenodo 4247873), and (2) all trait data, GEMMA output, and R analysis scripts for the statistical analysis and plotting (Zenodo 4247894). Both are theCode · publicThe protocol for the root
respiration measurements and the R script for calculating total flux from a directory of text files
are available at https://doi.org/10.5281/zenodo.4247873 (Guo et al., 2020a).Open asset ↗Zenodo · 10.5281/zenodo.4247873pdf-page:8 lines:1-41Dataset · publicAll trait data, GEMMA output, and R analysis scripts necessary for doing the statistical
analysis and plotting are available at https://doi.org/10.5281/zenodo.4247894 (Guo et al.,
2020b).Open asset ↗Zenodo · 10.5281/zenodo.4247894pdf-page:12 lines:1-35Code / dataset availability confirmedCrossref · checked 9 Sept 2026
Abstract Plant interactions are as important belowground as aboveground. Belowground plant interactions are however inherently difficult to quantify, as roots of different species are difficult to disentangle. Although for a couple of decades molecular techniques have been successfully applied to quantify root abundance, root identification and quantification in multispecies plant communities remains particularly challenging. Here we present a novel methodology, multispecies genotyping by sequencing (msGBS), as a next step to tackle this challenge. First, a multispecies meta‐reference database containing thousands of gDNA clusters per species is created from GBS derived High Throughput Sequencing (HTS) reads. Second, GBS derived HTS reads from multispecies root samples are mapped to this meta‐reference which, after a filter procedure to increase the taxonomic resolution, allows the parallel quantification of multiple species. The msGBS signal of 111 mock‐mixture root samples, with up to 8 plant species per sample, was used to calculate the within‐species abundance. Optional subsequent calibration yielded the across‐species abundance. The within‐ and across‐species abundances highly correlated ( R 2 range 0.72–0.94 and 0.85–0.98, respectively) to the biomass‐based species abundance. Compared to a qPCR based method which was previously used to analyse the same set of samples, msGBS provided similar results. Additional data on 11 congener species groups within 105 natural field root samples showed high taxonomic resolution of the method. msGBS is highly scalable in terms of sensitivity and species numbers within samples, which is a major advantage compared to the qPCR method and advances our tools to reveal hidden belowground interactions.
Why it matches plant phenotyping methods根サンプル中の各植物種の相対 abundance（バイオマスに対応する植物状態）を定量する分子ベースの測定法を開発し、模擬試料および自然試料で検証しているため、方法中心の植物フェノタイピング研究と判断します。
abstractHere we present a novel methodology, multispecies genotyping by sequencing (msGBS), as a next step to tackle this challenge.
Reproduction assets foundThe paper's msGBS analysis scripts are publicly available on GitHub, and metadata underlying the study are deposited on Dryad; both are explicitly stated in the Data Availability Statement with authors' public URLs.Code · publicAll scripts used were made available on GitHub ( https://github.com/NielsWagemaker/scripts_msGBS/tree/msGBS-1.0 ).Open asset ↗GitHub · NielsWagemaker/scripts_msGBSlines:804-868Code / dataset availability confirmedEurope PMC · checked 9 Sept 2026
Common beanMaizeField / plotRootClassificationMorphology / geometry measurementRoot system architecture
A soil coring protocol was developed to cooptimize the estimation of root length distribution (RLD) by depth and detection of functionally important variation in root system architecture (RSA) of maize and bean. The functional-structural model OpenSimRoot was used to perform in silico soil coring at six locations on three different maize and bean RSA phenotypes. Results were compared to two seasons of field soil coring and one trench. Two one-sided T -test (TOST) analysis of in silico data suggests a between-row location 5 cm from plant base (location 3), best estimates whole-plot RLD/D of deep, intermediate, and shallow RSA phenotypes, for both maize and bean. Quadratic discriminant analysis indicates location 3 has ~70% categorization accuracy for bean, while an in-row location next to the plant base (location 6) has ~85% categorization accuracy in maize. Analysis of field data suggests the more representative sampling locations vary by year and species. In silico and field studies suggest location 3 is most robust, although variation is significant among seasons, among replications within a field season, and among field soil coring, trench, and simulations. We propose that the characterization of the RLD profile as a dynamic rhizo canopy effectively describes how the RLD profile arises from interactions among an individual plant, its neighbors, and the pedosphere.
Why it matches plant phenotyping methods根系長分布と根系構造を推定する土壌コア採取プロトコルを開発し、シミュレーション・圃場データ・トレンチで比較検証しており、植物表現型取得法が研究の中心である。
abstractA soil coring protocol was developed to cooptimize the estimation of root length distribution (RLD) by depth and detection of functionally important variation in root system architecture (RSA) of maize and bean.
Reproduction assets foundThe authors publicly deposited the field and simulation phenotype data, OpenSimRoot parameterizations/outputs, Voronoi R code, and analysis scripts on Zenodo (DOI 10.5281/zenodo.3952179), explicitly stated in the Data Availability section and Methods.Dataset · publicThe field and simulation data, model parameterization, R package to calculate Voronoi-adjusted root length distribution, and R scripts used to analyze data are available at Zenodo ( https://doi.org/10.5281/zenodo.3952179 ).Open asset ↗Zenodo · 10.5281/zenodo.3952179lines:95-122Code / dataset availability confirmedEurope PMC · checked 9 Sept 2026
MaizeField / plotRootStem / branchMorphology / geometry measurementGrowth / development / phenologyRoot system architectureStress response / tolerance
Mechanical failure, known as lodging, negatively impacts yield and grain quality in crops. Limiting crop loss from lodging requires an understanding of the plant traits that contribute to lodging-resistance. In maize, specialized aerial brace roots are reported to reduce root lodging. However, their direct contribution to plant biomechanics has not been measured. In this manuscript, we use a non-destructive field-based mechanical test on plants before and after the removal of brace roots. This precisely determines the contribution of brace roots to establish a rigid base (i.e. stalk anchorage) that limits plant deflection in maize. These measurements demonstrate that the more brace root whorls that contact the soil, the greater their overall contribution to anchorage, but that the contributions of each whorl to anchorage were not equal. Previous studies demonstrated that the number of nodes that produce brace roots is correlated with flowering time in maize. To determine if flowering time selection alters the brace root contribution to anchorage, a subset of the Hallauer's Tusón tropical population was analyzed. Despite significant variation in flowering time and anchorage, selection neither altered the number of brace root whorls in the soil nor the overall contribution of brace roots to anchorage. These results demonstrate that brace roots provide a rigid base in maize and that the contribution of brace roots to anchorage was not linearly related to flowering time.
Why it matches plant phenotyping methodsトウモロコシの茎基部アンカレッジという植物力学形質を、非破壊の野外機械試験で定量する測定法が研究の中心であり、単なるルーチン測定ではない。
abstractwe use a non-destructive field-based mechanical test on plants before and after the removal of brace roots. This precisely determines the contribution of brace roots to establish a rigid base (i.e. stalk anchorage) that limits plant deflection in maize.
Reproduction assets foundThe paper's data availability statement explicitly deposits all raw data, processing code, and analyzed data (DARLING force-deflection phenotyping measurements) in a public authors' GitHub repository.Code · publicAll raw data, the code used to process data, and the analyzed data are available at: https://github.com/EESparksL/ab/Reneau_et_al_2020 .Open asset ↗EESparksL/ab/Reneau_et_al_2020lines:132-295Code / dataset availability confirmedOpenAlex · Europe PMC · checked 14 Sept 2026
OatRootSeed / grainMorphology / geometry measurementGrowth / development / phenologyRoot system architecture
Abstract Seed vigor is crucial for crop early establishment in the field and is particularly important for forage crop production. Oat (Avena sativa L.) is a nutritious food crop and also a valuable forage crop. However, little is known about the genetics of seed vigor in oats. To investigate seed vigor-related traits and their genetic architecture in oats, we developed an easy-to-implement image-based phenotyping pipeline and applied it to 650 elite oat lines from the Collaborative Oat Research Enterprise (CORE). Root number, root surface area, and shoot length were measured in two replicates. Variables such as growth rate were derived. Using a genome-wide association (GWA) approach, we identified 34 and 16 unique loci associated with root traits and shoot traits, respectively, which corresponded to 41 and 16 unique SNPs at a false discovery rate < 0.1. Nine root-associated loci were organized into four sets of homeologous regions, while nine shoot-associated loci were organized into three sets of homeologous regions. The context sequences of five trait-associated markers matched to the sequences of rice, Brachypodium and maize (E-value < 10−10), including three markers matched to known gene models with potential involvement in seed vigor. These were a glucuronosyltransferase, a mitochondrial carrier protein domain containing protein, and an iron-sulfur cluster protein. This study presents the first GWA study on oat seed vigor and data of this study can provide guidelines and foundation for further investigations.
Why it matches plant phenotyping methods画像ベースの表現型取得パイプラインを開発し、根・シュート形質を抽出して大規模適用しており、フェノタイピング手法が中心的です。
abstractwe developed an easy-to-implement image-based phenotyping pipeline and applied it to 650 elite oat lines from the Collaborative Oat Research Enterprise (CORE).
Reproduction assets found保存済みの本文根拠を更新済みルールで再検証し、公開資産1件を確認しました。Dataset · publicPhenotypic data collected in the study have been uploaded to T3/Oat: https://triticeaetoolbox.org/oat/ .Open asset ↗T3/Oatlines:89-100Code / dataset availability confirmedEurope PMC · checked 9 Sept 2026
Arbuscular mycorrhizal fungi function as conduits for underground nutrient transport. While the fungal partner is dependent on the plant host for its carbon (C) needs, the amount of nutrients that the fungus allocates to hosts can vary with context. Because fungal allocation patterns to hosts can change over time, they have historically been difficult to quantify accurately. We developed a technique to tag rock phosphorus (P) apatite with fluorescent quantum-dot (QD) nanoparticles of three different colors, allowing us to study nutrient transfer in an in vitro fungal network formed between two host roots of different ages and different P demands over a 3-week period. Using confocal microscopy and raster image correlation spectroscopy, we could distinguish between P transfer from the hyphae to the roots and P retention in the hyphae. By tracking QD-apatite from its point of origin, we found that the P demands of the younger root influenced both: (1) how the fungus distributed nutrients among different root hosts and (2) the storage patterns in the fungus itself. Our work highlights that fungal trade strategies are highly dynamic over time to local conditions, and stresses the need for precise measurements of symbiotic nutrient transfer across both space and time.
Why it matches plant phenotyping methods量子ドット標識と共焦点画像解析を開発し、植物根へのリン移行および菌根内保持を時空間的に定量する手法が研究の中心であるため、植物の栄養生理状態を測定するフェノタイピング手法として含める。
abstractWe developed a technique to tag rock phosphorus (P) apatite with fluorescent quantum-dot (QD) nanoparticles of three different colors
Reproduction assets foundThe paper's authors publicly deposited all data, scripts, and analysis for this study in a GitHub repository, explicitly stated in the Methods. This is a paper-specific, publicly actionable code/data asset reproducing the paper's QD-apatite phenotyping measurements and statistical analysis.Code · publicWe performed all statistical analysis in R version 3.6.1 [ 48 ]. All data, scripts, and analysis are available at: https://github.com/anoukvantpadje/Two_roots .Open asset ↗anoukvantpadje/Two_rootslines:57-192Code / dataset availability confirmedCrossref · checked 9 Sept 2026
Field / plotRootPhysiological trait estimationWater status / transpiration
Abstract Background and aims Monitoring root water uptake dynamics under water deficit (WD) conditions in fields are crucial to assess plant drought tolerance. In this study, we investigate the ability of Electrical Resistivity Tomography (ERT) to capture specific soil water depletion induced by root water uptake. Methods A combination of surface and depth electrodes with a high spatial resolution (10 cm) was used to map 2-D changes of bulk soil electrical conductivity (EC) in an agronomic trial with different herbaceous species. A synthetic experiment was performed with a mechanistic model to assess the ability of the electrode configuration to discriminate abstraction patterns due to roots. The impact of root segments was incorporated in the forward electrical model using the power-law mixing model. Results The time-lapse analysis of the synthetic ERT experiment shows that different root water uptake patterns can be delineated for measurements collected under WD conditions but not under wet conditions. Three indices were found (depletion amount, maximum depth, and spread), which allow capturing plant-specific water signatures based moisture profile changes derived from EC profiles. When root electrical properties were incorporated in the synthetic experiments, it led to the wrong estimation of the amount of water depletion, but a correct ranking of plants depletion depth. When applied to the filed data, our indices showed that Cocksfoot and Ryegrass had shallower soil water depletion zones than white clover and white clover combined with Ryegrass. However, in terms of water depletion amount, Cocksfoot consumed the largest amount of water, followed by White Clover, Ryegrass+White Clover mixture, and Ryegrass. Conclusion ERT is a well-suited method for phenotyping root water uptake ability in field trials under WD conditions.
Why it matches plant phenotyping methodsERTによる根の吸水動態・耐乾性の表現型取得法を、合成実験と圃場データで検証・適用しており、フェノタイピング手法が中心である。
abstractA synthetic experiment was performed with a mechanistic model to assess the ability of the electrode configuration to discriminate abstraction patterns due to roots.
Reproduction assets foundThe paper's Data availability statement explicitly deposits the field datasets acquired and analyzed (ERT/TDR soil water depletion measurements used for plant phenotyping) on Zenodo, with a public URL matching an allowed entry.Dataset · publicThe field datasets acquired and analyzed in the current study are available at https://zenodo.org/record/3750199#.XpS-bVwzY2x , https://doi.org/10.5281/zenodo.3750199Open asset ↗zenodo · 10.5281/zenodo.3750199lines:218-250Code / dataset availability confirmedEurope PMC · checked 9 Sept 2026
Plasmodesmata are small channels that connect plant cells. While recent technological advances have facilitated analysis of the ultrastructure of these channels, there are limitations to efficiently addressing their presence over an entire cellular interface. Here, we highlight the value of serial block electron microscopy for this purpose. We developed a computational pipeline to study plasmodesmata distributions and detect the presence/absence of plasmodesmata clusters, or pit fields, at the phloem unloading interfaces of Arabidopsis ( Arabidopsis thaliana ) roots. Pit fields were visualized and quantified. As the wall environment of plasmodesmata is highly specialized, we also designed a tool to extract the thickness of the extracellular matrix at and outside of plasmodesmata positions. We detected and quantified clear wall thinning around plasmodesmata with differences between genotypes, including the recently published plm-2 sphingolipid mutant. Our tools open avenues for quantitative approaches in the analysis of symplastic trafficking.
Why it matches plant phenotyping methods植物組織の電子顕微鏡画像から原形質連絡の分布、クラスター、細胞壁厚を定量化する計算パイプラインとツールを開発しており、植物形態・構造形質の取得が中心です。
abstractWe developed a computational pipeline to study plasmodesmata distributions and detect the presence/absence of plasmodesmata clusters, or pit fields
Reproduction assets foundThe paper's authors publicly released their Matlab plugins for plasmodesmata distribution and cell-wall thickness analysis on GitHub, a guided R analysis pipeline tutorial, and the Col-0 SB-EM data sets with segmented wall models and PD annotations on Figshare. Generic tools (MIB, matGeom, CRAN packages) and the EMPIARCode · publicA guided tutorial with all the necessary code for this analysis is available at https://andreapaterlini.github.io/Plasmodesmata_dist_wall/ (last accessed March 2020).Open asset ↗lines:148-159Dataset · publicThe Col-0 data sets used in this article, with corresponding models and annotations, are available on Figshare ( https://doi.org/10.6084/m9.figshare.12488702.v1 ). They can be used as example data sets to test our pipeline.Open asset ↗figshare · 10.6084/m9.figshare.12488702.v1lines:148-159Code / 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 · Europe PMC · checked 15 Sept 2026
PREMISE: High-resolution cameras are very helpful for plant phenotyping as their images enable tasks such as target vs. background discrimination and the measurement and analysis of fine above-ground plant attributes. However, the acquisition of high-resolution images of plant roots is more challenging than above-ground data collection. An effective super-resolution (SR) algorithm is therefore needed for overcoming the resolution limitations of sensors, reducing storage space requirements, and boosting the performance of subsequent analyses. METHODS: We propose an SR framework for enhancing images of plant roots using convolutional neural networks. We compare three alternatives for training the SR model: (i) training with non-plant-root images, (ii) training with plant-root images, and (iii) pretraining the model with non-plant-root images and fine-tuning with plant-root images. The architectures of the SR models were based on two state-of-the-art deep learning approaches: a fast SR convolutional neural network and an SR generative adversarial network. RESULTS: In our experiments, we observed that the SR models improved the quality of low-resolution images of plant roots in an unseen data set in terms of the signal-to-noise ratio. We used a collection of publicly available data sets to demonstrate that the SR models outperform the basic bicubic interpolation, even when trained with non-root data sets. DISCUSSION: The incorporation of a deep learning-based SR model in the imaging process enhances the quality of low-resolution images of plant roots. We demonstrate that SR preprocessing boosts the performance of a machine learning system trained to separate plant roots from their background. Our segmentation experiments also show that high performance on this task can be achieved independently of the signal-to-noise ratio. We therefore conclude that the quality of the image enhancement depends on the desired application.
Why it matches plant phenotyping methods植物根の低解像度画像を高解像度化する深層学習手法を開発・比較し、根画像の分割性能への効果も検証しており、表現型取得・抽出法が中心である。
abstractAn effective super-resolution (SR) algorithm is therefore needed for overcoming the resolution limitations of sensors
Reproduction assets foundThe paper's authors explicitly state that their source code and pre-trained SR models are publicly available on GitHub and Zenodo, and they used five publicly available image datasets (DIV2K, 91-Image, Arabidopsis thaliana root data, wheat seedling roots, 3D MRI barley roots) plus a SegRoot soybean test set as phenotypCode · publicbidopsis thaliana data set ( https://zenodo.org/record/50831#.XjIAPVNKhQI ), wheat seedling data set ( http://gigadb.org/dataset/100346 ), and barley data set ( https://www.quantitative‐plant.org/dataset/3d‐magnetic‐resonance‐images‐of‐barley‐roots ). The source code and pre‐trained SR models are available at GitHub and Zenodo (https://github.com/GatorSense/SRrootimaging; https://doi.org/10.5281/zenodo.3940562 ; Ruiz‐Munoz, 2020 ).
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Cairns
. 2014
FielOpen asset ↗GatorSense/SRrootimaginglines:155-276Dataset · publicWinRHIZO images of the barley roots.
In our experiments, we grouped the three plant‐root data sets into a single data set named “Roots.” Figure 3 shows examples of the plant‐root data sets used for training the SR model. To test the performance of the SR models, we used a data set of 65 soybean ( Glycine max (L.) Merr.) roots ( https://github.com/wtwtwt0330/SegRoot [accessed 11 June 2020]) (Wang et al., 2019 ).
SR model training
Many CNN architectures that enable the mapping of LR images into SR images can be found in the machine learning literature. In this study, we used two state‐of‐the‐art CNN‐based models, FSRCNN and SRGAN, to convert LR root images to SR images. FSRCNN is a model thOpen asset ↗wtwtwt0330/SegRootlines:93-102Dataset · publicIn this study, we used five publicly available data sets to train the SR models. We used two non‐plant‐root data sets, DIV2K ( https://data.vision.ee.ethz.ch/cvl/DIV2K/ [accessed 11 June 2020]) and 91‐Image ( https://www.kaggle.com/ll01dm/t91‐image‐dataset [accessed 11 June 2020]). DIV2K is a data set of natural images that has been used by others to train and test SR algorithms (Timofte et al., 2017 ). We trained our models on the grayscale version of this training data set (800 images). TheOpen asset ↗lines:93-102Dataset · publicIn this study, we used five publicly available data sets to train the SR models. We used two non‐plant‐root data sets, DIV2K ( https://data.vision.ee.ethz.ch/cvl/DIV2K/ [accessed 11 June 2020]) and 91‐Image ( https://www.kaggle.com/ll01dm/t91‐image‐dataset [accessed 11 June 2020]). DIV2K is a data set of natural images that has been used by others to train and test SR algorithms (Timofte et al., 2017 ). We trained our models on the grayscale version of this training data set (800 images). The 91‐Image information is a classical data set commonly used in SR studies. We also used tOpen asset ↗lines:93-102Code / dataset availability confirmedEurope PMC · checked 9 Sept 2026
ArabidopsisGrowth chamberLeafRootWhole plant / canopy / plot / fieldMorphology / geometry measurementGrowth / development / phenologyLeaf traitsRoot system architecture
Background Root system architecture and especially its plasticity in acclimation to variable environments play a crucial role in the ability of plants to explore and acquire efficiently soil resources and ensure plant productivity. Non-destructive measurement methods are indispensable to quantify dynamic growth traits. For closing the phenotyping gap, we have developed an automated phenotyping platform, GrowScreen - Agar , for non-destructive characterization of root and shoot traits of plants grown in transparent agar medium. Results The phenotyping system is capable to phenotype root systems and correlate them to whole plant development of up to 280 Arabidopsis plants within 15 min. The potential of the platform has been demonstrated by quantifying phenotypic differences within 78 Arabidopsis accessions from the 1001 genomes project. The chosen concept 'plant-to-sensor' is based on transporting plants to the imaging position, which allows for flexible experimental size and design. As transporting causes mechanical vibrations of plants, we have validated that daily imaging, and consequently, moving plants has negligible influence on plant development. Plants are cultivated in square Petri dishes modified to allow the shoot to grow in the ambient air while the roots grow inside the Petri dish filled with agar. Because it is common practice in the scientific community to grow Arabidopsis plants completely enclosed in Petri dishes, we compared development of plants that had the shoot inside with that of plants that had the shoot outside the plate. Roots of plants grown completely inside the Petri dish grew 58% slower, produced a 1.8 times higher lateral root density and showed an etiolated shoot whereas plants whose shoot grew outside the plate formed a rosette. In addition, the setup with the shoot growing outside the plate offers the unique option to accurately measure both, leaf and root traits, non-destructively, and treat roots and shoots separately. Conclusions Because the GrowScreen - Agar system can be moved from one growth chamber to another, plants can be phenotyped under a wide range of environmental conditions including future climate scenarios. In combination with a measurement throughput enabling phenotyping a large set of mutants or accessions, the platform will contribute to the identification of key genes.
Why it matches plant phenotyping methods自動画像計測による根・シュート形質の非破壊取得プラットフォームを開発・検証しており、表現型取得法が研究の中心である。
abstractThe phenotyping system is capable to phenotype root systems and correlate them to whole plant development of up to 280 Arabidopsis plants within 15 min.
Reproduction assets foundThe paper's phenotypic datasets (root/shoot trait measurements of 78 Arabidopsis accessions and experiments 1-2) are publicly deposited in the e!DAL research data publication system. The analysis software is only available upon request from the corresponding author, so it is not a public asset. AraPheno and cited worksDataset · publicThe datasets generated and analysed during the current study are available in the e!DAL research data publication system, https://doi.org/10.25622/FZJ/2020/0 .Open asset ↗e!DAL research data publication system · 10.25622/FZJ/2020/0lines:157-166Code / dataset availability confirmedCrossref · checked 9 Sept 2026
One of the essential factors in the root zone environment that affects plant growth is temperature. Determining the optimal root zone temperature condition in a hydroponic system during cultivation could lead to an improvement in plant growth. An optimal control strategy can be determined by identifying the eco-physiological process using a dynamic model. However, it is difficult to develop a dynamic model of the responses of plant growth to root zone temperature because the eco-physiological processes of plants are quite complicated. We propose an intelligent approach that can deal with this complex system. Non-linear autoregressive with exogenous input (NARX) neural networks were used to develop a dynamic model of the responses of plant growth to root zone temperature. The responses of chili pepper plant growth as affected by root zone temperature were measured during 60 days of cultivation inside a growth chamber using a non-destructive and continuous system based on a load cell. Five datasets of dynamic responses of plant growth were obtained for system identification. The results suggest that the application of a neural network is useful for modeling the dynamic response of plant growth to root zone temperature in hydroponic cultivation, with promising performance.
Why it matches plant phenotyping methods植物成長を連続・非破壊に測定するロードセル系と、成長応答を推定するNARXニューラルネットワーク動的モデルが研究の中心であり、植物成長という形質の取得・モデル化手法に該当する。
abstractWe propose an intelligent approach that can deal with this complex system. Non-linear autoregressive with exogenous input (NARX) neural networks were used to develop a dynamic model of the responses of plant growth to root zone temperature.
Reproduction assets foundThe paper cites the authors' own public Matlab program script for the NARX modeling of plant growth response to root zone temperature, hosted on GitHub (reference 47), which qualifies as a paper-specific public analysis code asset. No public phenotype dataset deposit is stated; the five measurement datasets are not明确lyCode · publicAji, G. K.; Hatou, K.; Morimoto, T. Matlab Program Script for Modeling the Dynamic Response of Plant
Growth to Root Zone Temperature in Hydroponic Chili Pepper Plant using Neural Network Available
online: https://github.com/mradjie/narx‐plant‐growthOpen asset ↗mradjie/narx‐plant‐growthpdf-page:14 lines:1-47Code / dataset availability confirmedOpenAlex · Europe PMC · bioRxiv · checked 15 Sept 2026
MaizeRootPhysiological trait estimationGrowth / development / phenologyRoot system architecture
Nutrient uptake is critical for crop growth and determined by root foraging in soil. Growth and branching of roots lead to effective root placement to acquire nutrients, but relatively less is known about absorption of nutrients at the root surface from the soil solution. This knowledge gap could be alleviated by understanding sources of genetic variation for short-term nutrient uptake on a root length basis. A new modular platform for high-throughput phenotyping of multiple ion uptake kinetics was designed to determine nutrient uptake rates in Zea mays . Using this system, uptake rates were characterized for the crop macronutrients nitrate, ammonium, potassium, phosphate and sulfate among the Nested Association Mapping (NAM) population founder lines. The data revealed that substantial genetic variation exists for multiple ion uptake rates in maize. Interestingly, specific nutrient uptake rates (nutrient uptake rate per length of root) were found to be both heritable and distinct from total uptake and plant size. The specific uptake rates of each nutrient were positively correlated with one another and with specific root respiration (root respiration rate per length of root), indicating that uptake is governed by shared mechanisms. We selected maize lines with high and low specific uptake rates and performed an RNA-seq analysis, which identified key regulatory components involved in nutrient uptake. The high-throughput multiple ion uptake kinetics pipeline will help further our understanding of nutrient uptake, parameterize holistic plant models, and identify breeding targets for crops with more efficient nutrient acquisition. Significance Statement Nutrient uptake is among the most limiting factors for plant growth and yet has not been used as a selection criterion in breeding. This is partly due to the lack of high-throughput phenotyping methods for measuring nutrient uptake. Here we describe a novel high-throughput phenotyping pipeline for quantification of multiple ion uptake rates. Using this new phenotyping system, our results demonstrate that specific ion uptake performance by maize plants is positively correlated among the macronutrients nitrogen, phosphorus, potassium and sulfur, and that substantial variation exists within a genetically diverse population. The findings reveal components of regulatory pathways possibly related with enhanced uptake, and confirm that nutrient uptake itself is a potential target for breeding of nutrient-efficient crops.
Why it matches plant phenotyping methods複数イオンの吸収速度を定量する高スループット植物表現型解析プラットフォーム自体の設計・記述が中心であり、植物の生理形質を測定する方法論研究に該当する。
abstractA new modular platform for high-throughput phenotyping of multiple ion uptake kinetics was designed to determine nutrient uptake rates in Zea mays .
Reproduction assets foundThe paper's RhizoFlux phenotyping analysis R scripts and statistical analysis code are explicitly deposited on Zenodo with an authors' public URL (https://doi.org/10.5281/zenodo.3893945), directly reproducing the paper's ion-uptake and trait analysis. No public phenotype dataset or image deposit is stated in the blocksCode · publics quantified based on the ∆ ∆Ct method using normal-
681
ized geo-metric means of the two reference genes (Zm00001d002944,
682
Zm00001d020826; (59)).
683
Statistical analysis. Statistical analyses were conducted using R ver-
684
sion 3.6.0 (60); the statistical analysis R codes including the pack-
685
ages needed are available (https://doi.org/10.5281/zenodo.3893945).686
The depletion rate of a nutrient from a solution is commonly
687
accepted as equal to the net uptake rate by roots (assuming both
688
influx and efflux). Therefore, the following equation was used
689
to determine the total net influx rates for nitrate, ammonium,
690
potassium, phosphate and sulfate:
691
In =
(Ct − C0)
(t0Open asset ↗zenodo · 10.5281/zenodo.3893945pdf-raw-page:9 lines:1-156Code / dataset availability confirmedOpenAlex · Crossref · Europe PMC · checked 15 Sept 2026
High-throughput plant phenotyping in controlled environments (growth chambers and glasshouses) is often delivered via large, expensive installations, leading to limited access and the increased relevance of "affordable phenotyping" solutions. We present two robot vectors for automated plant phenotyping under controlled conditions. Using 3D-printed components and readily-available hardware and electronic components, these designs are inexpensive, flexible and easily modified to multiple tasks. We present a design for a thermal imaging robot for high-precision time-lapse imaging of canopies and a Plate Imager for high-throughput phenotyping of roots and shoots of plants grown on media plates. Phenotyping in controlled conditions requires multi-position spatial and temporal monitoring of environmental conditions. We also present a low-cost sensor platform for environmental monitoring based on inexpensive sensors, microcontrollers and internet-of-things (IoT) protocols.
Why it matches plant phenotyping methods植物の熱画像ロボット、根・シュート用プレートイメージャ、環境センサープラットフォームを開発しており、表現型取得基盤が研究の中心である。
abstractWe present two robot vectors for automated plant phenotyping under controlled conditions.
Reproduction assets foundThe paper is a hardware/software design paper for low-cost phenotyping vectors and an IoT environmental sensor platform. It contains no public phenotype/trait datasets or plant images, but the authors explicitly deposit all 3D-printed hardware files, microcontroller sketches, LabVIEW control software, PCB schematics/fcCode · publiccontrolled by a program written in the LabVIEW development environment [ 18 ] running on the host computer. This provides a user-friendly graphical interface for control of the vector (distances moved, time-lapse parameters, etc.) and imaging sensor ( Figure 3 ). The microcontroller sketch and LabVIEW software are available at https://github.com/UoNMakerSpace/themal-imager-software . Once acquired, image sets are processed for leaf temperature values at multiple points on each rosette using macros written for the ImageJ/FIJI image analysis platforms [ 19 , 20 ].
2.1.4. Performance and Results
Operating characteristics of the Thermal Imager are given in Table 2 . For comparison, characteristiOpen asset ↗UoNMakerSpace/themal-imager-softwarelines:39-47Code · publicvidual directories for each plate position with unique filenames including acquisition time and date. Experimental settings can be saved as a configuration file and re-loaded on subsequent experimental runs, ensuring that image sets are appended to the same directory. The microcontroller sketch and LabVIEW code are available at https://github.com/UoNMakerSpace/plate-imager-software .
2.2.4. Performance
Characteristics of the Plate Imager are given in Table 3 . For comparison, characteristics of a previously published research system [ 26 ] and a typical commercially-available actuator are also given.
The Plate Imager outperforms the CPIB Imaging Robot (see Table 2 ) in all measured parameterOpen asset ↗UoNMakerSpace/plate-imager-softwarelines:48-56Code / dataset availability confirmedEurope PMC · Crossref · checked 9 Sept 2026
Rapeseed is an important oil crop in China. Timely estimation of rapeseed stand count at early growth stages provides useful information for precision fertilization, irrigation, and yield prediction. Based on the nature of rapeseed, the number of tillering leaves is strongly related to its growth stages. However, no field study has been reported on estimating rapeseed stand count by the number of leaves recognized with convolutional neural networks (CNNs) in unmanned aerial vehicle (UAV) imagery. The objectives of this study were to provide a case for rapeseed stand counting with reference to the existing knowledge of the number of leaves per plant and to determine the optimal timing for counting after rapeseed emergence at leaf development stages with one to seven leaves. A CNN model was developed to recognize leaves in UAV-based imagery, and rapeseed stand count was estimated with the number of recognized leaves. The performance of leaf detection was compared using sample sizes of 16, 24, 32, 40, and 48 pixels. Leaf overcounting occurred when a leaf was much bigger than others as this bigger leaf was recognized as several smaller leaves. Results showed CNN-based leaf count achieved the best performance at the four- to six-leaf stage with F-scores greater than 90% after calibration with overcounting rate. On average, 806 out of 812 plants were correctly estimated on 53 days after planting (DAP) at the four- to six-leaf stage, which was considered as the optimal observation timing. For the 32-pixel patch size, root mean square error (RMSE) was 9 plants with relative RMSE (rRMSE) of 2.22% on 53 DAP, while the mean RMSE was 12 with mean rRMSE of 2.89% for all patch sizes. A sample size of 32 pixels was suggested to be optimal accounting for balancing performance and efficiency. The results of this study confirmed that it was feasible to estimate rapeseed stand count in field automatically, rapidly, and accurately. This study provided a special perspective in phenotyping and cultivation management for estimating seedling count for crops that have recognizable leaves at their early growth stage, such as soybean and potato.
Why it matches plant phenotyping methodsUAV画像とCNNを用いて rapeseed の葉を認識し、植物体数（stand count）を自動推定する手法の開発・性能評価が研究の中心であるため、植物フェノタイピング方法論に該当します。
abstractA CNN model was developed to recognize leaves in UAV-based imagery, and rapeseed stand count was estimated with the number of recognized leaves.
Reproduction assets foundThe paper's data availability statement explicitly deposits the 'Rapeseed_seedling_counting' data (supporting the UAV imagery-based stand count findings) in a public GitHub repository with an authors' URL, qualifying as a paper-specific public asset.Dataset · publicThe “Rapeseed_seedling_counting” data that support the findings of this study are available in “LARSC-Lab/Rapeseed_seedling_counting” in GitHub, which can be found at https://github.com/LARSC-Lab/Rapeseed_seedling_counting .Open asset ↗LARSC-Lab/Rapeseed_seedling_countinglines:590-664Code / dataset availability confirmedEurope PMC · checked 9 Sept 2026
RootAnnotation / quality controlRoot system architecture
Motivation Trait data are fundamental to quantitatively describe plant form and function. Although root traits capture key dimensions related to plant responses to changing environmental conditions and effects on ecosystem processes, they have rarely been included in large-scale comparative studies and global models. For instance, root traits remain absent from nearly all studies that define the global spectrum of plant form and function. Thus, to overcome conceptual and methodological roadblocks preventing a widespread integration of root trait data into large-scale analyses we created the Global Root Trait (GRooT) Database. GRooT provides ready-to-use data by combining the expertise of root ecologists with data mobilization and curation. Specifically, we (i) determined a set of core root traits relevant to the description of plant form and function based on an assessment by experts, (ii) maximized species coverage through data standardization within and among traits, and (iii) implemented data quality checks. Main types of variables contained GRooT contains 114,222 trait records on 38 continuous root traits. Spatial location and grain Global coverage with data from arid, continental, polar, temperate, and tropical biomes. Data on root traits derived from experimental studies and field studies. Time period and grain Data recorded between 1911 and 2019 Major taxa and level of measurement GRooT includes root trait data for which taxonomic information is available. Trait records vary in their taxonomic resolution, with sub-species or varieties being the highest and genera the lowest taxonomic resolution available. It contains information for 184 sub-species or varieties, 6,214 species, 1,967 genera and 254 families. Due to variation in data sources, trait records in the database include both individual observations and mean values. Software format GRooT includes two csv file. A GitHub repository contains the csv files and a script in R to query the database.
Why it matches plant phenotyping methods植物の根形質を大規模に標準化・品質管理して提供する再利用可能なデータベースであり、植物フェノタイピング用データセットとして中心的な貢献である。
abstractwe created the Global Root Trait (GRooT) Database
Reproduction assets foundThe paper's core asset is the GRooT root trait database (two csv files) plus the authors' R script (GRooTExtraction) for querying/error-risk calculation, explicitly deposited in a public GitHub repository with a project website.Dataset · publicGRooT is public and will be maintained in a GitHub repository
(https://github.com/GRooT-Database/GRooT-Data).Open asset ↗GRooT-Database/GRooT-Datapdf-page:8 lines:1-48Code / dataset availability confirmedCrossref · checked 14 Sept 2026
CottonMaizeLaboratory / benchtopRootStem / branch2D/3D reconstructionRoot system architecture
Abstract Aims The flow of electric current in the root-soil system relates to the pathways of water and solutes, its characterization provides information on the root architecture and functioning. We developed a current source density approach with the goal of non-invasively image the current pathways in the root-soil system. Methods A current flow is applied from the plant stem to the soil, the proposed geoelectrical approach images the resulting distribution and intensity of the electric current in the root-soil system. The numerical inversion procedure underlying the approach was tested in numerical simulations and laboratory experiments with artificial metallic roots. We validated the method using rhizotron laboratory experiments on maize and cotton plants. Results Results from numerical and laboratory tests showed that our inversion approach was capable of imaging root-like distributions of the current source. In maize and cotton, roots acted as “leaky conductors”, resulting in successful imaging of the root crowns and negligible contribution of distal roots to the current flow. In contrast, the electrical insulating behavior of the cotton stems in dry soil supports the hypothesis that suberin layers can affect the mobility of ions and water. Conclusions The proposed approach with rhizotrons studies provides the first direct and concurrent characterization of the root-soil current pathways and their relationship with root functioning and architecture. This approach fills a major gap toward non-destructive imaging of roots in their natural soil environment.
Why it matches plant phenotyping methods根圏の電流経路を非侵襲的に画像化し、根の構造・機能を推定する新規手法を開発・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。
abstractWe developed a current source density approach with the goal of non-invasively image the current pathways in the root-soil system.
Reproduction assets found保存済みの本文根拠を更新済みルールで再検証し、公開資産2件を確認しました。Code · publicERT and iCSD codes, with data from this study, are maintained at https://github.com/Peruz/ERTpm and https://github.com/Peruz/icsd .Open asset ↗Peruz/ERTpmlines:342-431Code · publicERT and iCSD codes, with data from this study, are maintained at https://github.com/Peruz/ERTpm and https://github.com/Peruz/icsd .Open asset ↗Peruz/icsdlines:342-431Code / dataset availability confirmedEurope PMC · checked 14 Sept 2026
Field / plotLaboratory / benchtopRootMorphology / geometry measurementRoot system architecture
Premise We developed a novel low-cost method to visually phenotype belowground structures in the plant rhizosphere. We devised the method introduced here to address the difficulties encountered growing plants in seed germination pouches for long-term experiments and the high cost of other mini-rhizotron alternatives. Methods and results The method described here took inspiration from homemade ant farms commonly used as an educational tool in elementary schools. Using compact disc (CD) cases, we developed mini-rhizotrons for use in the field and laboratory using the burclover Medicago lupulina . Conclusions Our method combines the benefits of pots and germination pouches. In CD mini-rhizotrons, plants grew significantly larger than in germination pouches, and unlike pots, it is possible to measure roots without destructive sampling. Our protocol is a cheaper, widely available alternative to more destructive methods, which could facilitate the study of belowground phenotypes and processes by scientists with fewer resources.
Why it matches plant phenotyping methods植物根系表現型を非破壊的に観察・測定する低コスト mini-rhizotron 法の開発が研究の中心であるため、収載対象。
abstractWe developed a novel low-cost method to visually phenotype belowground structures in the plant rhizosphere.
Reproduction assets foundThe paper's DATA AVAILABILITY statement deposits the root-phenotyping measurement data and the authors' R analysis scripts on Figshare. The analysis code DOI (10.6084/m9.figshare.12021084) matches an allowed URL; the data DOI (12021075) does not appear verbatim in the allowed URL list, so only the analysis asset is audCode · publics Davis, and Nick Mihailoff provided vital logistical
support at the Pymatuning Laboratory of Ecology; Laurie Follweiler
assisted with the growth chambers.
DATA AVAILABILITY
The data and associated R scripts are available through the open ac-
cess repository Figshare (data: https://doi.org/10.6084/m9.figsh
are.12021075;analysis:https://doi.org/10.6084/m9.figshare.12021084).LITERATURE CITED
Atamian, H. S., P. A. Roberts, and I. Kaloshian. 2012. High and low through-
put screens with root-knot nematodes Meloidogyne spp. JoVE (Journal ofOpen asset ↗Figshare · 10.6084/m9.figshare.12021084pdf-raw-page:5 lines:1-79Code / dataset availability confirmedEurope PMC · checked 9 Sept 2026
Plasmodesmata are small channels that connect plant cells. While recent technological advances have facilitated the analysis of the ultrastructure of these channels, there are limitations to efficiently addressing their presence over an entire cellular interface. Here, we highlight the value of serial block electron microscopy for this purpose. We developed a computational pipeline to study plasmodesmata distributions and we detect presence/absence of plasmodesmata clusters, pit fields, at the phloem unloading interfaces of Arabidopsis thaliana roots. Pit fields can be visualised and quantified. As the wall environment of plasmodesmata is highly specialised we also designed a tool to extract the thickness of the extracellular matrix at and outside plasmodesmata positions. We show and quantify clear wall thinning around plasmodesmata with differences between genotypes, namely in the recently published plm-2 sphingolipid mutant. Our tools open new avenues for quantitative approaches in the analysis of symplastic trafficking. Sentence summary We developed computational tools for serial block electron microscopy datasets to extract information on the spatial distribution of plasmodesmata over an entire cellular interface and on the wall environment the plasmodesmata are in.
Why it matches plant phenotyping methods植物組織の電子顕微鏡画像から原形質連絡の分布や細胞壁厚を定量抽出する計算ツールとパイプラインが研究の中心であり、植物形態状態の測定法に該当する。
abstractWe developed a computational pipeline to study plasmodesmata distributions
Reproduction assets foundThe paper publicly releases its authors' MIB plugins for plasmodesmata distribution and wall-thickness analysis (GitHub), a guided R analysis tutorial/pipeline (GitHub Pages), and the Col-0 SB-EM datasets with segmented wall models and PD annotations (Google Drive), all with explicit availability statements and URLs.Code · publicA guided tutorial with all the necessary
code for this analysis is available at https://andreapaterlini.github.io/Plasmodesmata_dist_wall/Open asset ↗pdf-page:6 lines:1-49Dataset · publicThe Col-0 datasets used in this paper, with corresponding models and
annotation are available from https://drive.google.com/file/d/1g-Open asset ↗pdf-page:6 lines:1-49Code / dataset availability confirmedEurope PMC · checked 14 Sept 2026
Autophagy is the main catabolic process in eukaryotes and plays a key role in cell homeostasis. In vivo measurement of autophagic activity (flux) is a powerful tool for investigating the role of the pathway in organism development and stress responses. Here we describe a significant optimization of the tandem tag assay for detection of autophagic flux in planta in epidermal root cells of Arabidopsis thaliana seedlings. The tandem tag consists of TagRFP and mWasabi fluorescent proteins fused to ATG8a, and is expressed in wildtype or autophagy-deficient backgrounds to obtain reporter and control lines, respectively. Upon autophagy activation, the TagRFP-mWasabi-ATG8a fusion protein is incorporated into autophagosomes and delivered to the lytic vacuole. Ratiometric quantification of the low pH-tolerant TagRFP and low pH-sensitive mWasabi fluorescence in the vacuoles of control and reporter lines allows for a reliable estimation of autophagic activity. We provide a step by step protocol for plant growth, imaging and semi-automated data analysis. The protocol presents a rapid and robust method that can be applied for any studies requiring in planta quantification of autophagic flux.
Why it matches plant phenotyping methods植物体内のオートファジー活性という生理状態を、蛍光イメージングと半自動解析で定量する手法を最適化し、プロトコルとして提示しているため。
abstractHere we describe a significant optimization of the tandem tag assay for detection of autophagic flux in planta in epidermal root cells of Arabidopsis thaliana seedlings.
Reproduction assets foundThe paper's semi-automated autophagic flux analysis pipeline (ImageJ macros and R scripts) is publicly available in the authors' AuTToFlux GitHub repository, which also contains demo data for validating the analysis.Code · public20-22 °C, 50-70% humidity, 150 µM light
Confocal Laser Scanning Microscope (CLSM; Zeiss, LSM 800)
Software
Fiji, the version of ImageJ with included set of plugins ( https://fiji.sc/ , for this study, we utilized versions 1.51s and 2.0.0-rc-69/1.52i).
AuTToFlux repository containing three ImageJ macro and three R script files ( https://github.com/jonasoh/AuTToFlux/archive/master.zip ):
CalibrateThreshold.ijm
ImageProcessor.ijm
FluorescenceIntensity.ijm
EvaluateCalibration.R
Control-vs-Reporter.R
Flux-vs-Time.R
R ( https://www.r-project.org , we used 3.5.2 and 3.5.1)
RStudio ( https://www.rstudio.com/ , we used versions 1.1.453 and 1.2.1186).
Git ( https://git-scm.com/downloads , we usedOpen asset ↗jonasoh/AuTToFluxlines:121-191Code / dataset availability confirmedEurope PMC · checked 14 Sept 2026
In vascular plants, lignin is deposited during morphogenesis but also under stress conditions. Assessing the degree of stress-induced lignin deposition is complicated because it occurs locally and irregularly in plant tissues. In this study, we developed a macro program, LigninJ, for the open-source software ImageJ to automatically and efficiently determine areas and levels of lignification after Wiesner (phloroglucinol-HCl) staining. We used the CIELAB color space for detection of red color following the Wiesner reaction. In addition, LigninJ has a function for adjusting the background level and its white balance to reduce biases that are inherent to individual color images. Furthermore, LigninJ can be used for batch analyses of multiple images, taking about 2 s per image. In this study, we analyzed wound-induced lignin deposition in cotyledons of the Arabidopsis thaliana ecotypes Landsberg erecta and Columbia and assessed ectopic lignin depositions in roots of lignescence ( lig ) mutants of Arabidopsis . Our results confirmed that this method is efficient for evaluating the degree of stress-induced lignin deposition.
Why it matches plant phenotyping methods植物組織のリグニン沈着量を画像から自動定量するImageJマクロを開発しており、表現型取得・抽出法が研究の中心である。
abstractwe developed a macro program, LigninJ, for the open-source software ImageJ to automatically and efficiently determine areas and levels of lignification
Reproduction assets foundThe paper's authors publicly distribute the LigninJ ImageJ macro program, an Excel macro file, and sample microscopic images via their lab website, directly supporting this paper's lignin-deposition image analysis.Code · publicas in the a * stack image, measurement and record of selected areas, and mean values of L *, a *, and b * components. A description of the practical application of LigninJ is shown below. Samples of microscopic pictures, the LigninJ macro program file and the macro file of Microsoft Excel are provided from the author’s website (http://bio.sci.ehime-u.ac.jp/morphol/SatoLab).
Save a set of color images (e.g., in JPEG or TIFF format) at the same magnification in one working directory.
Open an image of a microscale or an image including a scale bar at the same magnification as the sample pictures in ImageJ, and calculate the length of a known distance in pixels. For instance, use the “straight lOpen asset ↗lines:85-97Code / dataset availability confirmedOpenAlex · Europe PMC · checked 14 Sept 2026
Root system architecture has received increased attention in recent years; however, significant knowledge gaps remain for physiological phenes, or units of phenotype, that have been relatively less studied. Ion uptake kinetics studies have been invaluable in uncovering distinct nutrient uptake systems in plants with the use of Michaelis-Menten kinetic modeling. This review outlines the theoretical framework behind ion uptake kinetics, provides a meta-analysis for macronutrient uptake parameters, and proposes new strategies for using uptake kinetics parameters as selection criteria for breeding crops with improved resource acquisition capability. Presumably, variation in uptake kinetics is caused by variation in type and number of transporters, assimilation machinery, and anatomical features that can vary greatly within and among species. Critically, little is known about what determines transporter properties at the molecular level or how transporter properties scale to the entire root system. A meta-analysis of literature containing measures of crop nutrient uptake kinetics provides insights about the need for standardization of reporting, the differences among crop species, and the relationships among various uptake parameters and experimental conditions. Therefore, uptake kinetics parameters are proposed as promising target phenes that integrate several processes for functional phenomics and genetic analysis, which will lead to a greater understanding of this fundamental plant process. Exploiting this genetic and phenotypic variation has the potential to greatly advance breeding efforts for improved nutrient use efficiency in crops.
Why it matches plant phenotyping methods植物の栄養吸収速度を表現型（phene）として扱い、理論枠組み、メタ解析、測定報告の標準化、育種利用を検討する方法論的レビューであり、表現型測定が中心です。
abstractThis review outlines the theoretical framework behind ion uptake kinetics, provides a meta-analysis for macronutrient uptake parameters, and proposes new strategies for using uptake kinetics parameters as selection criteria for breeding crops with improved resource acquisition capability.
Reproduction assets foundThe authors deposited the meta-analysis data and statistical analysis code for this paper's ion uptake kinetics meta-analysis on Zenodo, with an explicit availability statement and public DOI link. The same Zenodo DOI also hosts the supplemental materials.Code · publicthat the K m values were relatively low, so nitrate can be reduced to very low concentrations by plants. Here, a new meta-analysis is presented for uptake kinetics across multiple crop species and for multiple nutrient types: nitrate, phosphate, and potassium. The meta-analysis data and statistical analysis code are available ( https://doi.org/10.5281/zenodo.3605654 ).
To summarize the current state of crop ion uptake kinetic research, maize is the most widely characterized crop for ion uptake kinetics, with approximately half of all studies focusing on maize; however, there are also a substantial number of studies for barley and rice ( Fig. 3A ). By comparison, wheat ( Triticum aestivum )Open asset ↗Zenodo · 10.5281/zenodo.3605654lines:121-127Code / dataset availability confirmedEurope PMC · checked 15 Sept 2026
RiceRootMorphology / geometry measurement2D/3D reconstructionRoot system architectureWater status / transpiration
Background and aims Upland rice is often grown where water and phosphorus (P) are limited and these two factors interact on P bioavailability. To better understand this interaction, mechanistic models representing small-scale nutrient gradients and water dynamics in the rhizosphere of full-grown root systems are needed. Methods Rice was grown in large columns using a P-deficient soil at three different P supplies in the topsoil (deficient, suboptimal, non-limiting) in combination with two water regimes (field capacity versus drying periods). Root architectural parameters and P uptake were determined. Using a multiscale model of water and nutrient uptake, in-silico experiments were conducted by mimicking similar P and water treatments. First, 3D root systems were reconstructed by calibrating an architecure model with observed phenological root data, such as nodal root number, lateral types, interbranch distance, root diameters, and root biomass allocation along depth. Secondly, the multiscale model was informed with these 3D root architectures and the actual transpiration rates. Finally, water and P uptake were simulated. Key results The plant P uptake increased over threefold by increasing P and water supply, and drying periods reduced P uptake at high but not at low P supply. Root architecture was significantly affected by the treatments. Without calibration, simulation results adequately predicted P uptake, including the different effects of drying periods on P uptake at different P levels. However, P uptake was underestimated under P deficiency, a process likely related to an underestimated affinity of P uptake transporters in the roots. Both types of laterals (i.e. S- and L-type) are shown to be highly important for both water and P uptake, and the relative contribution of each type depend on both soil P availability and water dynamics. Key drivers in P uptake are growing root tips and the distribution of laterals. Conclusions This model-data integration demonstrates how multiple co-occurring single root phene responses to environmental stressors contribute to the development of a more efficient root system. Further model improvements such as the use of Michaelis constants from buffered systems and the inclusion of mycorrhizal infections and exudates are proposed.
Why it matches plant phenotyping methods3D根系アーキテクチャを観測データで再構成・較正し、根形態と吸水・リン吸収を統合モデルで推定する手法適用が研究の中心である。
abstractFirst, 3D root systems were reconstructed by calibrating an architecure model with observed phenological root data
Reproduction assets foundThe paper explicitly states that the multiscale soil-root model code used for the water and phosphorus uptake simulations is publicly shared on GitHub at the Plant-Root-Soil-Interactions-Modelling/dumux-rosi repository (pub/Mai2019 branch), which is the authors' computational analysis code for this study. No public rawCode · publictrient transport models, the
20
implementation of the dynamic root growth in the flow and transport model, the root growth model, the
21
mathematical equations, and the multiscale coupling method are presented in Supplementary Information
22
(Text S1) and can be found in Mai et al. (2018). The model code is shared on GitHub
23
(https://github.com/Plant-Root-Soil-Interactions-Modelling/dumux-rosi/tree/pub/Mai2019).24
25
Virtual experiment setup
26
(which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission.
The copyright holder for this preprint
this version posted January 27, 2020.
;
https://doi.org/10.1101/2020.01.27.921247
doi:
biOpen asset ↗Plant-Root-Soil-Interactions-Modelling/dumux-rosi · pub/Mai2019pdf-raw-page:10 lines:1-58Code / dataset availability confirmedCrossref · OpenAlex · Europe PMC · checked 9 Sept 2026
SoybeanRootMorphology / geometry measurementSegmentationGrowth / time-series analysisGrowth / development / phenologyRoot system architecture
Abstract Background Root system architecture (RSA) traits are of interest for breeding selection; however, measurement of these traits is difficult, resource intensive, and results in large variability. The advent of computer vision and machine learning (ML) enabled trait extraction and measurement has renewed interest in utilizing RSA traits for genetic enhancement to develop more robust and resilient crop cultivars. We developed a mobile, low-cost, and high-resolution root phenotyping system composed of an imaging platform with computer vision and ML based segmentation approach to establish a seamless end-to-end pipeline - from obtaining large quantities of root samples through image based trait processing and analysis. Results This high throughput phenotyping system, which has the capacity to handle hundreds to thousands of plants, integrates time series image capture coupled with automated image processing that uses optical character recognition (OCR) to identify seedlings via barcode, followed by robust segmentation integrating convolutional auto-encoder (CAE) method prior to feature extraction. The pipeline includes an updated and customized version of the Automatic Root Imaging Analysis (ARIA) root phenotyping software. Using this system, we studied diverse soybean accessions from a wide geographical distribution and report genetic variability for RSA traits, including root shape, length, number, mass, and angle. Conclusions This system provides a high-throughput, cost effective, non-destructive methodology that delivers biologically relevant time-series data on root growth and development for phenomics, genomics, and plant breeding applications. This phenotyping platform is designed to quantify root traits and rank genotypes in a common environment thereby serving as a selection tool for use in plant breeding. Root phenotyping platforms and image based phenotyping are essential to mirror the current focus on shoot phenotyping in breeding efforts.
Why it matches plant phenotyping methods画像取得、機械学習による根のセグメンテーション、形質抽出を統合した高スループット根系フェノタイピング基盤の開発であり、方法が研究の中心です。
abstractWe developed a mobile, low-cost, and high-resolution root phenotyping system composed of an imaging platform with computer vision and ML based segmentation approach to establish a seamless end-to-end pipeline
Reproduction assets foundThe paper publicly releases ARIA 2.0 phenotyping software on Bitbucket and analysis code on GitHub; raw images and segmented masks are only available upon request.Code · publicAnalysis code is freely available at the address: https://github.com/mighster/ARIA2.0 .Open asset ↗mighster/ARIA2.0lines:176-226Code / 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
MaizeSorghumField / plotRootMorphology / geometry measurementRoot system architecture
Determining the genetic control of root system architecture (RSA) in plants via large-scale genome-wide association study (GWAS) requires high-throughput pipelines for root phenotyping. We developed Core Root Excavation using Compressed-air (CREAMD), a high-throughput pipeline for the cleaning of field-grown roots, and Core Root Feature Extraction (COFE), a semiautomated pipeline for the extraction of RSA traits from images. CREAMD-COFE was applied to diversity panels of maize ( Zea mays ) and sorghum ( Sorghum bicolor ), which consisted of 369 and 294 genotypes, respectively. Six RSA-traits were extracted from images collected from >3,300 maize roots and >1,470 sorghum roots. Single nucleotide polymorphism (SNP)-based GWAS identified 87 TAS (trait-associated SNPs) in maize, representing 77 genes and 115 TAS in sorghum. An additional 62 RSA-associated maize genes were identified via expression read depth GWAS. Among the 139 maize RSA-associated genes (or their homologs), 22 (16%) are known to affect RSA in maize or other species. In addition, 26 RSA-associated genes are coregulated with genes previously shown to affect RSA and 51 (37% of RSA-associated genes) are themselves transe-quantitative trait locus for another RSA-associated gene. Finally, the finding that RSA-associated genes from maize and sorghum included seven pairs of syntenic genes demonstrates the conservation of regulation of morphology across taxa.
Why it matches plant phenotyping methods根系形態の画像取得・特徴抽出パイプライン（CREAMD-COFE）の開発が研究の中心であり、RSA形質を大規模に抽出しているため。
abstractWe developed Core Root Excavation using Compressed-air (CREAMD), a high-throughput pipeline for the cleaning of field-grown roots, and Core Root Feature Extraction (COFE), a semiautomated pipeline for the extraction of RSA traits from images.
Reproduction assets foundThe paper's COFE root-image analysis software is explicitly stated to be publicly available on Bitbucket, and the paper's RSA phenotype measurements (maize BLUP trait values and sorghum trait values) are released as supplemental tables accessible with the article.Code · publiche Maize273 and SAM273
panels is a subset of the data used for the root-GWAS of the SAM Diversity
Panel. GWAS was conducted with the same protocol as in comparative GWAS
between maize and sorghum (see above section), except an arbitrarily relaxed
window of 100 kb, centered on the TAS was used here.
COFE Software is available at https://bitbucket.org/baskargroup/cofe/src/master/.Accession Numbers
The maize sequence data from this article can be found in the GenBank/
EMBL data libraries under accession numbers SRP055871. The sorghum SNP
data were downloaded from https://www.morrislab.org/data.Supplemental Data
The following supplemental materials are available.
Supplemental Text S1. CREAMD-COOpen asset ↗baskargroup/cofepdf-raw-page:12 lines:1-84Code / dataset availability confirmedEurope PMC · Crossref · checked 15 Sept 2026
Field / plotGrowth chamberRootWhole plant / canopy / plot / fieldClassificationMorphology / geometry measurementArchitecture / morphology / geometryRoot system architecture
Plant root systems are essential for sustainable agriculture, conveying resource-efficient genotypes and species with benefits to soil ecosystem functions. Targeted selection of species/genotypes depends on available root system information. Currently there is no standardized approach for comprehensive root system characterization, suggesting the need for data integration across methods and sources. Here, we combine field measured root descriptors from the classical Root Atlas series with traits from controlled-environment root imaging for 10 cover crop species to (i) detect descriptors scaling between distant experimental methods, (ii) provide traits for species classification, and (iii) discuss implications for cover crop ecosystem functions. Results revealed relation of single axes measures from root imaging (convex hull, primary-lateral length ratio) to Root Atlas field descriptors (depth, branching order). Using composite root variables (principal components) for branching, morphology, and assimilate investment traits, cover crops were classified into species with (i) topsoil-allocated large diameter rooting type, (ii) low-branched primary/shoot-born axes-dominated rooting type, and (iii) highly branched dense rooting type, with classification trait-dependent distinction according to depth distribution. Data integration facilitated identification of root classification variables to derive root-related cover crop distinction, indicating their agro-ecological functions.
Why it matches plant phenotyping methods根系画像計測と既存Root Atlas記述子を統合し、異なる計測法間の対応を評価して根系形態形質による分類を行っており、植物表現型の取得・統合が研究の中心である。
abstractHere, we combine field measured root descriptors from the classical Root Atlas series with traits from controlled-environment root imaging for 10 cover crop species to (i) detect descriptors scaling between distant experimental methods
Reproduction assets foundThe paper's rhizobox imaging measurements and Root Atlas trait tables are presented in-text, and the authors point to a public MDPI supplementary file (Figure S1: root length distribution over diameter for the ten cover crop species from rhizobox imaging) as the only explicitly deposited paper-specific asset. No authorSupplement · publicd. PCA was performed using SAS procedure PROC FACTOR and clustering was done using PROC CLUSTER with Ward’s minimum-variance method. The dendrogram was constructed with PROC TREE.
Acknowledgments
Publication was supported by BOKU Vienna’s Open Access Publishing Fund.
Supplementary Materials
The following are available online at https://www.mdpi.com/2223-7747/8/11/514/s1 , Figure S1: Root length distribution over diameter for ten different cover crop species from rhizobox imaging.
Click here for additional data file.
Author Contributions
G.B., W.L., E.E., W.H. and M.S. commonly conceptualized the manuscript. Evaluation of the data and writing of the original draft were done by G.B. Data and dOpen asset ↗MDPIlines:311-336Code / dataset availability confirmedEurope PMC · checked 14 Sept 2026
MaizeRootMorphology / geometry measurementPhysiological trait estimationRoot system architectureWater status / transpiration
Functional-structural root system models combine functional and structural root traits to represent the growth and development of root systems. In general, they are characterized by a large number of growth, architectural and functional root parameters, generating contrasted root systems evolving in a highly nonlinear environment (soil, atmosphere), which makes unclear what impact of each single root system on root system functioning actually is. On the other end of the root system modelling continuum, macroscopic root system models associate to each root system instance a set of plant-scale, easily interpretable parameters. However, as of today, it is unclear how these macroscopic parameters relate to root-scale traits and whether the upscaling of local root traits are compatible with macroscopic parameter measurements. The aim of this study was to bridge the gap between these two modelling approaches by providing a fast and reliable tool, which eventually can help performing plant virtual breeding. We describe here the MAize Root System Hydraulic Architecture soLver (MARSHAL), a new efficient and user-friendly computational tool that couples a root architecture model (CRootBox) with fast and accurate algorithms of water flow through hydraulic architectures and plant-scale parameter calculations, and a review of architectural and hydraulic parameters of maize. To illustrate the tool’s potential, we generated contrasted maize hydraulic architectures that we compared with architectural (root length density) and hydraulic (root system conductance) observations. Observed variability of these traits was well captured by model ensemble runs We also analyzed the multivariate sensitivity of mature root system conductance, mean depth of uptake, root system volume and convex hull to the input parameters to highlight the key parameters to vary for efficient virtual root system breeding. MARSHAL enables inverse optimisations, sensitivity analyses and virtual breeding of maize hydraulic root architecture. It is available as an R package, an RMarkdown pipeline, and a web application. One-sentence summary We developed a dynamic hydraulic-architectural model of the root system, parameterized for maize, to generate contrasted hydraulic architectures, compatible with field and lab observations and that can be further analyzed in soil-root system models for virtual breeding. Authors contributions F.M., X.D., M.J. and G.L. designed the study and defined its scope; F.M. and G.L. developed the model while associated tools were created by A.H. and G.L.; F.M. ran the model simulations and analyzed the results together with M.J and G.L.; F.M. and M.J. wrote the first version of this manuscript; all co-authors critically revised it.
Why it matches plant phenotyping methodsトウモロコシ根系の水理・構造形質を仮想生成・推定する計算ツールの開発が研究の中心であり、観測形質による比較検証も行っている。
abstractWe describe here the MAize Root System Hydraulic Architecture soLver (MARSHAL), a new efficient and user-friendly computational tool
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 9 Sept 2026
RootMorphology / geometry measurementGrowth / development / phenologyRoot system architecture
Background The root is an important organ for water and nutrient uptake, and soil anchorage. It is equipped with root hairs (RHs) which are elongated structures increasing the exchange surface with the soil. RHs are also studied as a model for plant cellular development, as they represent a single cell with specific and highly regulated polarized elongation. For these reasons, it is useful to be able to accurately quantify RH length employing standardized procedures. Methods commonly employed rely on manual steps and are therefore time consuming and prone to errors, restricting analysis to a short segment of the root tip. Few partially automated methods have been reported to increase measurement efficiency. However, none of the reported methods allow an accurate and standardized definition of the position along the root for RH length measurement, making data comparison difficult. Results We developed an image analysis algorithm that semi-automatically detects RHs and measures their length along the whole differentiation zone of roots. This method, implemented as a simple automated script in ImageJ/ Fiji software that we termed Root Hair Sizer, slides a rectangular window along a binarized and straightened image of root tips to estimate the maximal RH length in a given measuring interval. This measure is not affected by heavily bent RHs and any bald spots. RH length data along the root are then modelled with a sigmoidal curve, generating several biologically significant parameters such as RH length, positioning of the root differentiation zone and, under certain conditions, RH growth rate. Conclusions Image analysis with Root Hair Sizer and subsequent sigmoidal modelling of RH length data provide a simple and efficient way to characterize RH growth in different conditions, equally suitable to small and large scale phenotyping experiments.
Why it matches plant phenotyping methods根毛長などの植物形質を画像から半自動抽出・モデル化するアルゴリズムを開発し、大規模フェノタイピングへの適用を明示しているため。
abstractWe developed an image analysis algorithm that semi-automatically detects RHs and measures their length along the whole differentiation zone of roots.
Reproduction assets foundThe paper's Root Hair Sizer analysis scripts (ImageJ/Fiji macros for Medicago, Brachypodium, and Arabidopsis) are published as open-access supplementary files (Additional files 1, 3, 4, 6) of this article, along with a demonstration movie and example root images. No standalone repository URL is given in the supplied; 1Code · publicThe image processing steps are implemented as an automated procedure in the Root Hair Sizer (RHS) script for ImageJ , available as Additional file 1 : Script 1.Open asset ↗lines:86-98Code / dataset availability confirmedEurope PMC · OpenAlex · Crossref · checked 15 Sept 2026
Background Characterization and quantification of visual plant traits is often limited to the use of tools and software that were developed to address a specific context, making them unsuitable for other applications. CoverageTool is flexible multi-purpose software capable of area calculation in cm 2 , as well as coverage area in percentages, suitable for a wide range of applications. Results Here we present a novel, semi-automated and robust tool for detailed characterization of visual plant traits. We demonstrate and discuss the application of this tool to quantify a broad spectrum of plant phenotypes/traits such as: tissue culture parameters, ground surface covered by annual plant canopy, root and leaf projected surface area, and leaf senescence area ratio. The CoverageTool software provides easy to use functions to analyze images. While use of CoverageTool involves subjective operator color selections, applying them uniformly to full sets of samples makes it possible to provide quantitative comparison between test subjects. Conclusion The tool is simple and straightforward, yet suitable for the quantification of biological and environmental effects on a wide variety of visual plant traits. This tool has been very useful in quantifying different plant phenotypes in several recently published studies, and may be useful for many applications.
Why it matches plant phenotyping methods植物画像から面積や被覆率などの形質を定量化する半自動ソフトウェアが論文の中心であり、植物フェノタイピング手法に該当する。
abstractThe CoverageTool software provides easy to use functions to analyze images.
Reproduction assets foundThe paper's own phenotyping software CoverageTool is publicly released on GitHub with an explicit project home page, license, and availability statement. Supplementary image datasets (Additional files 5-8, 10-11) are described but only available via the article's supplementary material, not via an allowed URL.Code · publicsigned the phenotyping protocol and the tissue culture experiment. All authors read and approved the final manuscript.
Funding
Not applicable.
Availability of data and materials
CoverageTool software and it’s additional files are in Additional files 1 , 2 , 3 , 4 , 5 , 6 , 7 and 8 .
Project name: CoverageTool
Project home page: https://github.com/lianneovnat/CoverageTool.git
Operating system(s): MS Windows: XP, Win7, Win10 etc.
Programming language: “C” with WIN32 (Visual Studio 2008 Express Edition)
Other requirements: Visual Studio 2008 Redistributal (or above)
License: GNU.
Ethics approval and consent to participate
Not applicable.
Consent for publication
Not applicable.
Competing interesOpen asset ↗lianneovnat/CoverageToollines:346-425Code / dataset availability confirmedEurope PMC · checked 9 Sept 2026
RootMorphology / geometry measurementGrowth / development / phenologyRoot system architecture
Plant root systems play vital roles in the biosphere, environment and agriculture, but the quantitative principles governing their growth and architecture remain poorly understood. The 'forward problem' of what root forms can arise from given models and parameters has been well studied through modelling and simulation, but comparatively little attention has been given to the 'inverse problem': what models and parameters are responsible for producing an experimentally observed root system? Here, we propose the use of approximate Bayesian computation (ABC) to infer mechanistic parameters governing root growth and architecture, allowing us to learn and quantify uncertainty in parameters and model structures using observed root architectures. We demonstrate the use of this platform on synthetic and experimental root data and show how it may be used to identify growth mechanisms and characterize growth parameters in different mutants. Our highly adaptable framework can be used to gain mechanistic insight into the generation of observed root system architectures.
Why it matches plant phenotyping methods観測された根系構造から成長モデルとパラメータを推定するABCベースの計算フレームワークを提案・実証しており、根系形態という植物表現型の解析手法が中心である。
abstractHere, we propose the use of approximate Bayesian computation (ABC) to infer mechanistic parameters governing root growth and architecture, allowing us to learn and quantify uncertainty in parameters and model structures using observed root architectures.
Reproduction assets foundThe authors explicitly state that the data and code used for the root-architecture ABC SMC inference (including experimental Arabidopsis root measurements and analysis scripts) are freely available in a public GitHub repository, and the electronic supplementary material (containing additional posterior figures) is公开ly Code · publicData accessibility
The data and code used are freely available in Github repository https://github.com/StochasticBiology/root-inference .Open asset ↗StochasticBiology/root-inferencelines:120-196Code / dataset availability confirmedCrossref · checked 9 Sept 2026
Abstract Afforestation projects for mitigating CO 2 emissions require to monitor the carbon fixation and plant growth as key indicators. We proposed a monitoring method for predicting carbon fixation in afforestation projects, combining a process‐based ecosystem model and field data and addressed the uncertainty of predicted carbon fixation and ecophysiological characteristics with plant growth. Carbon pools were simulated using the Biome‐BGC model tuned by parameter optimization using measured carbon density of biomass pools on an 11‐year‐old Eucommia ulmoides plantation on Loess Plateau, China. The allocation parameters fine root carbon to leaf carbon (FRC:LC) and stem carbon to leaf carbon (SC:LC), along with specific leaf area (SLA) and maximum stomatal conductance ( g smax ) strongly affected aboveground woody (AC) and leaf carbon (LC) density in sensitivity analysis and were selected as adjusting parameters. We assessed the uncertainty of carbon fixation and plant growth predictions by modeling three growth phases with corresponding parameters: (i) before afforestation using default parameters, (ii) early monitoring using parameters optimized with data from years 1 to 5, and (iii) updated monitoring at year 11 using parameters optimized with 11‐year data. The predicted carbon fixation and optimized parameters differed in the three phases. Overall, 30‐year average carbon fixation rate in plantation (AC, LC, belowground woody parts and soil pools) was ranged 0.14–0.35 kg‐C m −2 y −1 in simulations using parameters of phases (i)–(iii). Updating parameters by periodic field surveys reduced the uncertainty and revealed changes in ecophysiological characteristics with plant growth. This monitoring method should support management of afforestation projects by carbon fixation estimation adapting to observation gap, noncommon species and variable growing conditions such as climate change, land use change.
Why it matches plant phenotyping methods植物器官・生態系の炭素固定と成長を推定する監視手法を、プロセスモデルと圃場データ、パラメータ最適化で構築・不確実性評価しており、植物の生理状態推定が中心です。
abstractWe proposed a monitoring method for predicting carbon fixation in afforestation projects, combining a process‐based ecosystem model and field data
Reproduction assets foundThe paper's Data Availability Statement explicitly deposits the authors' biometric data for E. ulmoides allometric relationships and the files related to parameter optimization and simulation results on Zenodo, a public repository with a DOI. This is a paper-specific, publicly actionable asset. The NCDC GSOD meteoricalDataset · publicThe biometric data for allometric relationships of E. ulmoides and the files related to optimization and simulation results are available on Zenodo ( https://doi.org/10.5281/zenodo.2815612 ).Open asset ↗Zenodo · 10.5281/zenodo.2815612lines:393-549Code / dataset availability confirmedEurope PMC · checked 15 Sept 2026
Field / plotRaman / spectroscopyRootPhysiological trait estimation
1. Root lignin is a key driver of root decomposition, which in turn is a fundamental component of the terrestrial carbon cycle and increasingly in the focus of ecologists and global climate change research. However, measuring lignin content is labor-intensive and therefore not well-suited to handle the large sample sizes of most ecological studies. To overcome this bottleneck, we explored the applicability of high-throughput near infrared spectroscopy (NIRS) measurements to predict fine root lignin content. 2. We measured fine root lignin content in 73 plots of a field biodiversity experiment containing a pool of 60 grassland species using the Acetylbromid (AcBr) method. To predict lignin content, we established NIRS calibration and prediction models based on partial least square regression (PLSR) resulting in moderate prediction accuracies (RPD = 1.96, R 2 = 0.74, RMSE = 3.79). 3. In a second step, we combined PLSR with spectral variable selection. This considerably improved model performance (RPD = 2.67, R 2 = 0.86, RMSE = 2.78) and enabled us to identify chemically meaningful wavelength regions for lignin prediction. 4. We identified 38 case studies in a literature survey and quantified median model performance parameters from these studies as a benchmark for our results. Our results show that the combination Acetylbromid extracted lignin and NIR spectroscopy is well suited for the rapid analysis of root lignin contents in herbaceous plant species even if the amount of sample is limited.
Why it matches plant phenotyping methods近赤外分光とPLSRによる植物細根リグニン含量の高速推定法を開発・検証し、性能比較とベンチマークも行っており、植物形質取得が中心である。
abstractwe explored the applicability of high-throughput near infrared spectroscopy (NIRS) measurements to predict fine root lignin content.
Reproduction assets foundThe paper's fine root lignin/NIR spectral dataset is publicly deposited in PANGAEA. The carspls, pls, baseline, and prospectr R packages are generic third-party libraries, not authors' analysis code, and no author code or trained model is deposited.Dataset · publicltivation.
Author Contributions
A.W. designed the experiment. O.E. collected the data. R.R. and O.E. analyzed the data with input of M.V. O.E., R.R. and A.W. wrote the manuscript with input from M.V. and all authors provided input on the final written manuscript.
Data Availability
The data used in this article is accessible via https://doi.pangaea.de/10.1594/PANGAEA.895501 .
Competing Interests
The authors declare no competing interests.
Footnotes
Publisher’s note: Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Oliver Elle and Ronny Richter contributed equally.
Supplementary information
Supplementary information accompanOpen asset ↗PANGAEA · 10.1594/PANGAEA.895501lines:233-256Code / dataset availability confirmedEurope PMC · checked 10 Sept 2026
Premise of the study The key to increased cassava production is balancing the trade-off between marketable roots and traits that drive nutrient and water uptake. However, only a small number of protocols have been developed for cassava roots. Here, we introduce a set of new variables and methods to phenotype cassava roots and enhance breeding pipelines. Methods Different cassava genotypes were planted in pot and field conditions under well-watered and drought treatments. We developed cassava shovelomics and used digital imaging of root traits (DIRT) to evaluate geometrical root traits in addition to common traits (e.g., length, number). Results Cassava shovelomics and DIRT were successfully implemented to extract root phenotypes, and a large phenotypic variation for root traits was observed. Significant correlations were found among root traits measured manually and by DIRT. Drought significantly decreased shoot dry weight, total root number, and root length by 84%, 30%, and 25%, respectively. High adventitious root number was associated with increased shoot dry weight ( r = 0.44) under drought. Discussion Our methods allow for high-throughput cassava root phenotyping, which makes a breeding program targeting root traits feasible. We suggest that root number is a breeding target for improved cassava production under drought.
Why it matches plant phenotyping methodsキャッサバ根の表現型取得法（shovelomicsとデジタル画像解析DIRT）の開発・適用・相関検証が研究の中心であり、根形質を抽出する高スループット手法として明示されている。
abstractHere, we introduce a set of new variables and methods to phenotype cassava roots and enhance breeding pipelines.
Reproduction assets foundThe authors explicitly deposit the root images and phenotype data supporting this cassava phenotyping study on CyVerse Data Commons under the identifier Saengwilai_Cassava_2019, with a public DOI link. This is a paper-specific, publicly accessible dataset of the plant images and trait measurements used in the analysis.Dataset · publicThe images and data that support the findings of this study are openly available on CyVerse Data Commons (as Saengwilai_Cassava_2019; https://doi.org/10.25739/ej8x-3b24 ).Open asset ↗CyVerse Data Commons · Saengwilai_Cassava_2019lines:798-1004Code / dataset availability confirmedEurope PMC · checked 15 Sept 2026
ArabidopsisLaboratory / benchtopRootMorphology / geometry measurementRoot system architecture
Root analysis is essential for both academic and agricultural research. Despite the great advances in root phenotyping and imaging, calculating root length is still performed manually and involves considerable amounts of labor and time. To overcome these limitations, we developed MyROOT, a software for the semiautomatic quantification of root growth of seedlings growing directly on agar plates. Our method automatically determines the scale from the image of the plate, and subsequently measures the root length of the individual plants. To this aim, MyROOT combines a bottom-up root tracking approach with a hypocotyl detection algorithm. At the same time as providing accurate root measurements, MyROOT also significantly minimizes the user intervention required during the process. Using Arabidopsis, we tested MyROOT with seedlings from different growth stages and experimental conditions. When comparing the data obtained from this software with that of manual root measurements, we found a high correlation between both methods (R 2 = 0.997). When compared with previous developed software with similar features (BRAT and EZ-Rhizo), MyROOT offered an improved accuracy for root length measurements. Therefore, MyROOT will be of great use to the plant science community by permitting high-throughput root length measurements while saving both labor and time.
Why it matches plant phenotyping methods根長を画像から半自動抽出するソフトウェアを開発し、手動測定および既存ソフトウェアと比較検証しており、植物フェノタイピング手法が研究の中心である。
abstractwe developed MyROOT, a software for the semiautomatic quantification of root growth of seedlings growing directly on agar plates.
Reproduction assets foundThe authors deposited the MyROOT standalone executable application together with the root-length datasets generated in the study (Figures 3, 5 and S4) in a Zenodo repository with an explicit public DOI, making it a paper-specific, publicly actionable asset.Dataset · publiclable to the plant sciences community through the Plant Image Analysis website (plant‐image‐analysis.org; Lobet et al ., 2013 ) as a standalone executable application. The executable application together with the datasets generated during the current study (from Figures 3 , 5 and S4 ) are available in the [Zenodo] repository, [ https://doi.org/10.5281/zenodo.2552250 ].
Conflict of Interest
The authors declare no conflicts of interest.
Author Contributions
AIC‐D conceived the idea. AG and XS developed the algorithms for the method. AG, XS, IB‐P and DB‐E performed the validation experiments. IB‐P and DB‐E acquired the dataset. XS and AIC‐D designed and supervised the study. IB‐P, AG, XS andOpen asset ↗Zenodo · 10.5281/zenodo.2552250lines:139-168Code / dataset availability confirmedOpenAlex · Europe PMC · bioRxiv · checked 15 Sept 2026
SoybeanWheatRGB / grayscaleRootMorphology / geometry measurementRoot system architecture
ABSTRACT Root crown phenotyping measures the top portion of crop root systems and can be used for marker-assisted breeding, genetic mapping, and understanding how roots influence soil resource acquisition. Several imaging protocols and image analysis programs exist, but they are not optimized for high-throughput, repeatable, and robust root crown phenotyping. The RhizoVision Crown platform integrates an imaging unit, image capture software, and image analysis software that are optimized for reliable extraction of measurements from large numbers of root crowns. The hardware platform utilizes a back light and a monochrome machine vision camera to capture root crown silhouettes. RhizoVision Imager and RhizoVision Analyzer are free, open-source software that streamline image capture and image analysis with intuitive graphical user interfaces. RhizoVision Analyzer was physically validated using copper wire and features were extensively validated using 10,464 ground-truth simulated images of dicot and monocot root systems. This platform was then used to phenotype soybean and wheat root crowns. A total of 2,799 soybean ( Glycine max ) root crowns of 187 lines and 1,753 wheat ( Triticum aestivum ) root crowns of 186 lines were phenotyped. Principal component analysis indicated similar correlations among features in both species. The maximum heritability was 0.74 in soybean and 0.22 in wheat, indicating differences in species and populations need to be considered. The integrated RhizoVision Crown platform facilitates high-throughput phenotyping of crop root crowns, and sets a standard by which open plant phenotyping platforms can be benchmarked.
Why it matches plant phenotyping methods根冠形質を高スループットに取得するハードウェア、画像取得・解析ソフトウェアを開発し、物理的・シミュレーション画像で検証しているため、植物フェノタイピング手法が中心である。
abstractThe RhizoVision Crown platform integrates an imaging unit, image capture software, and image analysis software that are optimized for reliable extraction of measurements from large numbers of root crowns.
Reproduction assets foundThe paper's data availability statement deposits the wire and root crown image sets, tabular phenotype data, and R analysis code on Zenodo (10.5281/zenodo.3380473), and the authors' RhizoVision Imager and Analyzer software are publicly available on Zenodo (10.5281/zenodo.2585882 and 10.5281/zenodo.2585892). These are直接Dataset · public6953), the
520 Department of Energy ARPA-E ROOTS program (DE-AR0000822), and the United Soybean
521 Board (1420-532-5613).
522 Competing interests: The authors declare no competing interests.
523 Data availability: The wire and root crown image sets, tabular data, and R code for statistics and
524 graphing are available online: http://doi.org/10.5281/zenodo.3380473. The simulated root images
30Open asset ↗Zenodo · 10.5281/zenodo.3380473pdf-layout-page:30 lines:1-56Code / dataset availability confirmedEurope PMC · checked 15 Sept 2026
Accurate quantification of below-ground biomass (BGB) of woody vegetation is critical to understanding ecosystem function and potential for climate change mitigation from sequestration of biomass carbon. We compiled 2054 measurements of planted and natural individual tree and shrub biomass from across different regions of Australia (arid shrublands to tropical rainforests) to develop allometric models for prediction of BGB. We found that the relationship between BGB and stem diameter was generic, with a simple power-law model having a BGB prediction efficiency of 72–93% for four broad plant functional types: (i) shrubs and Acacia trees, (ii) multi-stemmed mallee eucalypts, (iii) other trees of relatively high wood density, and; (iv) a species of relatively low wood density, Pinus radiata D. Don. There was little improvement in accuracy of model prediction by including variables (e.g. climatic characteristics, stand age or management) in addition to stem diameter alone. We further assessed the generality of the plant functional type models across 11 contrasting stands where data from whole-plot excavation of BGB were available. The efficiency of model prediction of stand-based BGB was 93%, with a mean absolute prediction error of only 6.5%, and with no improvements in validation results when species-specific models were applied. Given the high prediction performance of the generalised models, we suggest that additional costs associated with the development of new species-specific models for estimating BGB are only warranted when gains in accuracy of stand-based predictions are justifiable, such as for a high-biomass stand comprising only one or two dominant species. However, generic models based on plant functional type should not be applied where stands are dominated by species that are unusual in their morphology and unlikely to conform to the generalised plant functional group models.
Why it matches plant phenotyping methods植物の地下部バイオマスという明示的な形質を推定する汎用アロメトリーモデルを開発し、複数の機能型・林分で予測性能を検証しており、形質測定法が研究の中心です。
abstractWe compiled 2054 measurements of planted and natural individual tree and shrub biomass from across different regions of Australia (arid shrublands to tropical rainforests) to develop allometric models for prediction of BGB.
Reproduction assets foundThe paper's below-ground biomass allometry is built from the authors' Australian Individual Tree Biomass Library (Paul et al. 2017b), a public dataset deposited with a DOI and ÆKOS portal URL, which qualifies as a paper-specific public phenotype dataset. The ecoregions map and Dryad wood density database are generic/cdDataset · publicH, England JR, Davies MJ, Luck H (2017a) Measurements of stem diameter:
786 implications for individual- and stand-level errors. Environmental Monitoring and Assessment, 189, 416, 1-
787 14.
788 Paul KI, Larmour, J., Zerihun, A., et al. (2017b) Australian Individual Tree Biomass Library, Version 3.
789 10.4227/05/566629ADA95DA. http://www.aekos.org.au/dataset/223706. Obtained from Australian
790 Ecological Knowledge and Observation System Data Portal (ÆKOS, http://www.portal. aekos.org.au/), ,
Generic allometrics 38Open asset ↗aekos.org.au · 10.4227/05/566629ADA95DApdf-layout-page:38 lines:1-43Code / dataset availability confirmedbioRxiv · Europe PMC · checked 15 Sept 2026
Root system architecture (RSA) is critical for plant growth, which is influenced by several edaphic, environmental, genetic and biotic factors including beneficial and pathogenic microbes. Studying root architecture and the dynamic changes that occur during a plants lifespan, especially for perennial crops growing over multiple growing seasons, is still a challenge because of the nature of their growing environment in soil. We describe the utility of an imaging platform called RhizoVision Crown to study RSA of alfalfa, a perennial forage crop affected by Phymatotrichopsis Root Rot (PRR) disease. Phymatotrichopsis omnivora is the causal agent of PRR disease that reduces alfalfa stand longevity. During the lifetime of the stand, PRR disease rings enlarge and the field can be categorized into three zones based upon plant status: asymptomatic, disease front and survivor. To study root architectural changes associated with PRR, a four-year old 25.6-hectare alfalfa stand infested with PRR was selected at the Red River Farm, Burneyville, OK during October 2017. Line transect sampling was conducted from four actively growing PRR disease rings. At each disease ring, six line transects were positioned spanning 15 m on either side of the disease front with one alfalfa root sampled at every 3 m interval. Each alfalfa root was imaged with the RhizoVision Crown platform using a backlight and a high-resolution monochrome CMOS camera enabling preservation of the natural root architectural integrity. The platforms image analysis software, RhizoVision Analyzer, automatically segmented images, skeletonized, and extracted a suite of features. Data indicated that the survivor plants compensated for damage or loss to the taproot through the development of more lateral and crown roots, and that a suite of multivariate features could be used to automatically classify roots as from survivor or asymptomatic zones. Root growth is a dynamic process adapting to ever changing interactions among various phytobiome components, by utilizing a low-cost, efficient and high-throughput Rhizo-Vision Crown platform we showed quantification of these changes occurring in a mature perennial forage crop.
Why it matches plant phenotyping methodsRhizoVision CrownとRhizoVision Analyzerによる根系形態の画像取得・自動解析が研究の中心であり、根系構造特徴の抽出と分類を実施しているため、植物フェノタイピング手法として含める。
abstractWe describe the utility of an imaging platform called RhizoVision Crown to study RSA of alfalfa
Reproduction assets foundThe paper's Data Availability section explicitly deposits the root crown images and R statistical analysis code on Zenodo (doi 10.5281/zenodo.2172832), a paper-specific public asset containing the phenotyping images and analysis code.Dataset · publicical analysis code generated from this study are available on
382
Zenodo.
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York, Larry M., Young, Carolyn A., Mattupalli, Chakradhar, & Seethepalli, Anand. (2018). Images
384
and statistical analysis of alfalfa root crowns from inside and outside disease rings caused by
385
cotton root rot (Version 1.0.0) [Data set]. Zenodo. http://doi.org/10.5281/zenodo.2172832
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ACKNOWLEDGEMENTS. We thank the Noble Research Institute, LLC for funding this project.
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LITERATURE CITED.
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Arias, M. M. D., Leandro, L. F., and Munkvold, G. P. 2013. Aggressiveness of Fusarium species and
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impact of root infection on growth and yield of soybeans. Phytopathology 103:822-832.
392
Arif, M., FlOpen asset ↗Zenodo · 10.5281/zenodo.2172832pdf-raw-page:18 lines:1-49Code / dataset availability confirmedEurope PMC · checked 10 Sept 2026
Carrot is a globally important crop, yet efficient and accurate methods for quantifying its most important agronomic traits are lacking. To address this problem, we developed an automated image analysis platform that extracts components of size and shape for carrot shoots and roots, which are necessary to advance carrot breeding and genetics. This method reliably measured variation in shoot size and shape, petiole number, petiole length, and petiole width as evidenced by high correlations with hundreds of manual measurements. Similarly, root length and biomass were accurately measured from the images. This platform also quantified shoot and root shapes in terms of principal components, which do not have traditional, manually measurable equivalents. We applied the pipeline in a study of a six-parent diallel population and an F 2 mapping population consisting of 316 individuals. We found high levels of repeatability within a growing environment, with low to moderate repeatability across environments. We also observed co-localization of quantitative trait loci for shoot and root characteristics on chromosomes 1, 2, and 7, suggesting these traits are controlled by genetic linkage and/or pleiotropy. By increasing the number of individuals and phenotypes that can be reliably quantified, the development of a rapid, automated image analysis pipeline to measure carrot shoot and root morphology will expand the scope and scale of breeding and genetic studies.
Why it matches plant phenotyping methodsニンジンのシュート・根の形態を画像から自動抽出する解析プラットフォームを開発し、手動測定との相関や反復性を検証しており、表現型取得手法が研究の中心である。
abstractwe developed an automated image analysis platform that extracts components of size and shape for carrot shoots and roots
Reproduction assets foundThe paper's data availability statement explicitly deposits the authors' analysis scripts on GitHub and the carrot images plus unfiltered F2 SNP calls on FigShare, both with public URLs.Code · publicScripts for data processing, visualization, and QTL mapping are available on GitHub at https://github.com/mishaploid/carrot-image-analysis .Open asset ↗mishaploid/carrot-image-analysislines:960-975Dataset · publicUnfiltered SNPs from the F 2 mapping population (variant call format) and images are deposited on FigShare at https://doi.org/10.6084/m9.figshare.c.4300439.v1 .Open asset ↗10.6084/m9.figshare.c.4300439.v1lines:960-975Code / dataset availability confirmedCrossref · checked 10 Sept 2026
Background and aims Non- or minimally invasive methods are urgently needed to characterize and monitor crop root systems to foster progress in phenotyping and general system understanding. Electrical methods have come into focus due to their unique sensitivity to various structural and functional root characteristics. The aim of this study is to highlight imaging capabilities of these methods with regard to crop root systems and to investigate changes in electrical signals caused by physiological reactions. Methods Spectral electrical impedance tomography (sEIT) and electrical impedance spectroscopy (EIS) were used in three laboratory experiments to characterize oilseed root systems embedded in nutrient solution. Two experiments imaged the root extension with sEIT, including one experiment monitoring a nutrient stress situation. In the third experiment electrical signatures were observed over the diurnal cycle using EIS. Results Root system extension was imaged using sEIT under static conditions. During continuous nutrient deprivation, electrical polarization signals decreased steadily. Systematic changes were observed over the diurnal cycle, indicating further sensitivity to associated physiological processes. Spectral parameters suggest polarization processes at the μm scale. Conclusions Electrical imaging methods are able to non-invasively characterize crop root systems in controlled laboratory conditions, thereby offering links to root structure and function. The methods have the potential to be upscaled to the field scale.
Why it matches plant phenotyping methods電気インピーダンス画像化・分光法を用いて作物根系の構造と生理状態を非侵襲的に測定する方法が研究の中心であり、根系伸長や栄養ストレス・日周生理変化の表現型取得を実証している。
abstractNon- or minimally invasive methods are urgently needed to characterize and monitor crop root systems to foster progress in phenotyping and general system understanding.
Reproduction assets foundThe paper's Data Availability statement explicitly deposits the sEIT/EIS measurement data and analysis scripts in a public Zenodo repository, which directly reproduces this paper's root-phenotyping measurements and computational analysis.Dataset · publicData Availability
Measurement data and analysis scripts are available under the https://doi.org/10.5281/zenodo.1320755Open asset ↗zenodo · 10.5281/zenodo.1320755lines:233-271Code / dataset availability confirmedCrossref · checked 14 Sept 2026
Abstract. The investigation of plant roots is inherently difficult and often neglected. Being out of sight, roots are often out of mind. Nevertheless, roots play a key role in the exchange of mass and energy between soil and the atmosphere, in addition to the many practical applications in agriculture. In this paper, we propose a method for roots imaging based on the joint use of two electrical noninvasive methods: electrical resistivity tomography (ERT) and mise-à-la-masse (MALM). The approach is based on the key assumption that the plant root system acts as an electrically conductive body, so that injecting electrical current into the plant stem will ultimately result in the injection of current into the subsoil through the root system, and particularly through the root terminations via hair roots. Evidence from field data, showing that voltage distribution is very different whether current is injected into the tree stem or in the ground, strongly supports this hypothesis. The proposed procedure involves a stepwise inversion of both ERT and MALM data that ultimately leads to the identification of electrical resistivity (ER) distribution and of the current injection root distribution in the three-dimensional soil space. This, in turn, is a proxy to the active (hair) root density in the ground. We tested the proposed procedure on synthetic data and, more importantly, on field data collected in a vineyard, where the estimated depth of the root zone proved to be in agreement with literature on similar crops. The proposed noninvasive approach is a step forward towards a better quantification of root structure and functioning.
Why it matches plant phenotyping methods植物根系の三次元画像化と活動根密度の推定を目的とした非侵襲的センシング手法を提案し、合成データおよび圃場データで検証しているため、植物フェノタイピング手法が中心である。
abstractIn this paper, we propose a method for roots imaging based on the joint use of two electrical noninvasive methods: electrical resistivity tomography (ERT) and mise-à-la-masse (MALM).
Reproduction assets found保存済みの本文根拠を更新済みルールで再検証し、公開資産1件を確認しました。Dataset · publicMeasured and simulated raw data, electrical imaging, and MALM data used to generate the
figures can be accessed at https://doi.org/10.5281/zenodo.1464825 (Mary et al., 2018).Open asset ↗Zenodo · 10.5281/zenodo.1464825lines:872-946Code / dataset availability confirmedEurope PMC · bioRxiv · checked 15 Sept 2026
Cell / cellular structureRootTissueMorphology / geometry measurementObject detectionRoot system architecture
The aboveground plant efficiency has improved significantly in recent years, and the improvement has led to a steady increase in global food production. The improvement of belowground plant efficiency has the potential to further increase food production. However, the belowground plant roots are harder to study, due to inherent challenges presented by root phenotyping. Several tools for identifying root anatomical features in root cross-section images have been proposed. However, the existing tools are not fully automated and require significant human effort to produce accurate results. To address this limitation, we propose a fully automated approach, called Deep Learning for Root Anatomy (DL-RootAnatomy), for identifying anatomical traits in root cross-section images. Using the Faster Region-based Convolutional Neural Network (Faster R-CNN), the DL-RootAnatomy models detect objects such as root, stele and late metaxylem, and predict rectangular bounding boxes around such objects. Subsequently, the bounding boxes are used to estimate the root diameter, stele diameter, and late metaxylem number and average diameter. Experimental evaluation using standard object detection metrics, such as intersection-over-union and mean average precision, has shown that our models can accurately detect the root, stele and late metaxylem objects. Furthermore, the results have shown that the measurements estimated based on predicted bounding boxes have very small root mean square error when compared with the corresponding ground truth values, suggesting that DL-RootAnatomy can be used to accurately detect anatomical features. Finally, a comparison with existing approaches, which involve some degree of human interaction, has shown that the proposed approach is more accurate than existing approaches on a subset of our data. A webserver for performing root anatomy using our deep learning pre-trained models is available at https://rootanatomy.org, together with a link to a GitHub repository that contains code that can be used to re-train or fine-tune our network with other types of root-cross section images. The labeled images used for training and evaluating our models are also available from the GitHub repository.
Why it matches plant phenotyping methods根横断面画像から根径・中心柱径・後期後生木部の数と平均径を自動推定する深層学習手法を開発し、既存手法との比較および精度検証を行っており、植物フェノタイピング手法が研究の中心である。
abstractwe propose a fully automated approach, called Deep Learning for Root Anatomy (DL-RootAnatomy), for identifying anatomical traits in root cross-section images.
Reproduction assets foundThe authors publicly release the labeled rice root cross-section image dataset (with ground truth measurements), the source code, and the pre-trained Faster R-CNN models via a GitHub repository linked from the paper's Data Availability Statement and webserver description.Dataset · publicthe preliminary version. CW
815 designed and developed the webserver. All authors read and approved the
816 final manuscript.
817 Funding
818 Contribution No. 19-072-J from Kansas Agriculture Experiment Station.
819 Data Availability Statement
820 The image datasets used in this study can be found in a GitHub repository
821 at https://github.com/cwang16/Root-Anatomy-Using-Faster-RCNN.
822 Acknowledgments
823 An earlier version of this manuscript has been released as a Pre-Print at
824 https://www.biorxiv.org/content/10.1101/442244v2.article-info [65].
825 References
826 [1] J. L. Araus, G. A. Slafer, C. Royo, M. D. Serret, Breeding for yield
827 potential and stress adaptation in cereals, CrOpen asset ↗https://github.com/cwang16/Root-Anatomy-Using-Faster-RCNNpdf-layout-page:50 lines:1-47Code / dataset availability confirmedOpenAlex · Europe PMC · checked 15 Sept 2026
Boron (B) is an essential micronutrient for seed plants. Information on B-efficiency mechanisms and B-efficient crop and model plant genotypes is very scarce. Studies evaluating the basis and consequences of B-deficiency and B-efficiency are limited by the facts that B occurs as a trace contaminant essentially everywhere, its bioavailability is difficult to control and soil-based B-deficiency growth systems allowing a high-throughput screening of plant populations have hitherto been lacking. The crop plant Brassica napus shows a very high sensitivity towards B-deficient conditions. To reduce B-deficiency-caused yield losses in a sustainable manner, the identification of B-efficient B. napus genotypes is indispensable. We developed a soil substrate-based cultivation system which is suitable to study plant growth in automated high-throughput phenotyping facilities under defined and repeatable soil B conditions. In a comprehensive screening, using this system with soil B concentrations below 0.1 mg B (kg soil)-1, we identified three highly B-deficiency tolerant B. napus cultivars (CR2267, CR2280 and CR2285) amongst a genetically diverse collection comprising 590 accessions from all over the world. The B-efficiency classification of cultivars was based on a detailed assessment of various physical and high-throughput imaging-based shoot and root growth parameters in soil substrate or in in vitro conditions, respectively. We identified cultivar-specific patterns of B-deficiency-responsive growth dynamics. Elemental analysis revealed striking differences only in B contents between contrasting genotypes when grown under B-deficient but not under standard conditions. Results indicate that B-deficiency tolerant cultivars can grow with a very limited amount of B which is clearly below previously described critical B-tissue concentration values. These results suggest a higher B utilization efficiency of CR2267, CR2280 and CR2285 which would represent a unique trait amongst so far identified B-efficient B. napus cultivars which are characterized by a higher B-uptake capacity. Testing various other nutrient deficiency treatments, we demonstrated that the tolerance is specific for B-deficient conditions and is not conferred by a general growth vigor at the seedling stage. The identified B-deficiency tolerant cultivars will serve as genetic and physiological ‘tools’ to further understand the mechanisms regulating the B nutritional status in rapeseed and to develop B-efficient elite genotypes.
Why it matches plant phenotyping methods土壌B条件を制御した自動ハイスループット表現型解析システムを開発し、画像ベースの生長形質で590系統を評価しており、表現型取得基盤が研究の中心的役割を担う。
abstractWe developed a soil substrate-based cultivation system which is suitable to study plant growth in automated high-throughput phenotyping facilities under defined and repeatable soil B conditions.
Reproduction assets foundThe paper's phenotyping measurements (590-accession B-efficiency screen, root cessation assay, imaging-derived traits, substrate nutrient quantification) are distributed as Supplementary Data Sheets S1–S5, publicly available via the Frontiers article's supplementary material page. No author analysis code or trained模型的专Supplement · publiccation number: 031A053).
1
www.fao.org
2
https://gbis.ipk-gatersleben.de/gbis2i/
3
https://gbis.ipk-gatersleben.de/gbis2i/
4
http://www.ipk-gatersleben.de/en/dept-genebank/satellite-collections-north/
5
http://apps.fas.usda.gov/psdonline/
Supplementary Material
The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fpls.2018.01142/full#supplementary-material
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Click here for additioOpen asset ↗lines:224-299Code / dataset availability confirmedEurope PMC · checked 15 Sept 2026
Carrot is a globally important crop, yet efficient and accurate methods for quantifying its most important agronomic traits are lacking. To address this problem, we developed an automated analysis platform that extracts components of size and shape for carrot shoots and roots, which are necessary to advance carrot breeding and genetics. This method reliably measured variation in shoot size and shape, leaf number, petiole length, and petiole width as evidenced by high correlations with hundreds of manual measurements. Similarly, root length and biomass were accurately measured from the images. This platform quantified shoot and root shapes in terms of principal components, which do not have traditional, manually-measurable equivalents. We applied the pipeline in a study of a six-parent diallel population and an F 2 mapping population consisting of 316 individuals. We found high levels of repeatability within a growing environment, with low to moderate repeatability across environments. We also observed co-localization of quantitative trait loci for shoot and root characteristics on chromosomes 1, 2, and 7, suggesting these traits are controlled by genetic linkage and/or pleiotropy. By increasing the number of individuals and phenotypes that can be reliably quantified, the development of a high-throughput image analysis pipeline to measure carrot shoot and root morphology will expand the scope and scale of breeding and genetic studies.
Why it matches plant phenotyping methodsニンジンのシュート・根の形態形質を画像から自動抽出する高スループット解析基盤を開発し、手動測定との相関や反復性で検証しているため、表現型取得法が研究の中心である。
abstractwe developed an automated analysis platform that extracts components of size and shape for carrot shoots and roots
Reproduction assets foundThe paper's Data Availability statement provides public, paper-specific assets: carrot plant images via a CyVerse download link, and authors' scripts for data processing, visualization, and QTL mapping on GitHub. Both are directly tied to this paper's phenotyping measurements and analysis.Dataset · publicAutomated image analysis for genetic studies of carrot shoot and root shape
14
5 Data Availability
538
All images, scripts, and sequence data used in this study are publicly available. Images are available
539
at https://de.cyverse.org/dl/d/2F1B4398-9D2E-4BF4-BFFF-65F507DB6865/sampleCarrotImages.zip
540
and will also be deposited in the Dryad digital repository (https://datadryad.org/). Custom algorithms
541
for image analysis are accessible on CyVerse as part of the PhytoMorph ToolKit. Scripts for data
542
processing, visualization, and QTL mapping are available on GitHub at
543
https://github.com/mishaploid/carrot-image-Open asset ↗CyVersepdf-raw-page:14 lines:1-65Code · publicF4-BFFF-65F507DB6865/sampleCarrotImages.zip
540
and will also be deposited in the Dryad digital repository (https://datadryad.org/). Custom algorithms
541
for image analysis are accessible on CyVerse as part of the PhytoMorph ToolKit. Scripts for data
542
processing, visualization, and QTL mapping are available on GitHub at
543
https://github.com/mishaploid/carrot-image-analysis. SNPs from the F2 mapping population will be
544
deposited as VCF files on FigShare.
545
6 Conflict of Interest
546
The authors declare that the research was conducted in the absence of any commercial or financial
547
relationships that could be construed as a potential conflict of interest.
548
7 Author ContributionOpen asset ↗GitHub · mishaploid/carrot-image-analysispdf-raw-page:14 lines:1-65Code / dataset availability confirmedbioRxiv · Europe PMC · checked 10 Sept 2026
Root systems are branched networks that develop from simple growth properties of their individual roots. Yet a mature maize root system has many thousands of roots that each interact with soil structures, water and nutrient patches, and microbial ecologies in the micro-environments surrounding each root tip. Although the plasticity of root growth to these and other environmental factors is well known, how the many local processes contribute over time to global features of root system architecture is hardly understood. We employ an automated 3D root imaging pipeline to capture the growth of maize roots every four hours throughout seven days of seedling development. We model the contrasting architectures of two maize inbred genotypes and their hybrid to derive key parameters that distinguish complex growth patterns as a function of time. The statistical characteristics of local root growth defined the global system properties despite a large range of trait values. \"Computational dissection\" of a single root from each root system identified differences in the size of the root branching zone and lateral branching densities, but not radial patterns, that drove the contrasting root architectures from seedling to maturity. X-ray imaging of mature field-grown root crowns showed that seedling growth trajectories persisted throughout development and could predict eventual architectures, suggesting a strong genetic basis. The work connects individual and systemwide scales of root growth dynamics, providing the means for a function-valued approach to understanding the genetic and genetic x environment conditioning of root growth that will enable breeding for enhanced root traits.\n\nSIGNIFICANCE STATEMENTWhen and where roots grow determines their ability to capture short-lived and patchy water and nutrient resources to support the aboveground organs of the plant. Roots have no known long-distance external sensing mechanisms, but form branched networks that blindly explore the soil and respond to encountered local stimuli. How global architectures form from the many thousands of these local responses, and how they are controlled genetically are major open questions. Here we quantify differences in local root growth patterns of two inbred genotypes of maize that control contrasting systemwide properties. Measurements at the seedling stage were highly correlated with the complex architectures of mature root systems, paving the way for the development of crops with greater resource uptake capacity.
Why it matches plant phenotyping methods自動3D根画像パイプラインで根系成長を4時間ごとに取得・解析し、局所成長パラメータから根系構造形質を推定しており、フェノタイピング手法が研究の中心である。
abstractWe employ an automated 3D root imaging pipeline to capture the growth of maize roots every four hours throughout seven days of seedling development.
Reproduction assets foundThe paper explicitly states that the custom R code used to extract and analyze dynamic root traits from the 4D time-series phenotyping data is publicly available on the authors' GitHub (Topp-Roots-Lab/timeseries_analysis). This is a paper-specific, publicly actionable analysis code asset. No public phenotype dataset orCode · publicpaired it with DynamicRoots
software (46), and custom R code (available on Github: https://github.com/Topp-Roots-Open asset ↗Topp-Roots-pdf-page:5 lines:1-27Code / dataset availability confirmedOpenAlex · Europe PMC · Crossref · checked 15 Sept 2026
Geometric dimensions of plants are significant parameters for showing plant dynamic responses to environmental variations. An image-based high-throughput phenotyping platform was developed to automatically measure geometric dimensions of plants in a greenhouse. The goal of this paper was to evaluate the accuracy in geometric measurement using the Structure from Motion (SfM) method from images acquired using the automated image-based platform. Images of nine artificial objects of different shapes were taken under 17 combinations of three different overlaps in x and y directions, respectively, and two different spatial resolutions (SRs) with three replicates. Dimensions in x, y and z of these objects were measured from 3D models reconstructed using the SfM method to evaluate the geometric accuracy. A metric power of unit (POU) was proposed to combine the effects of image overlap and SR. Results showed that measurement error of dimension in z is the least affected by overlap and SR among the three dimensions and measurement error of dimensions in x and y increased following a power function with the decrease of POU (R2 = 0.78 and 0.88 for x and y respectively). POUs from 150 to 300 are a preferred range to obtain reasonable accuracy and efficiency for the developed image-based high-throughput phenotyping system. As a study case, the developed system was used to measure the height of 44 plants using an optimal POU in greenhouse environment. The results showed a good agreement (R2 = 92% and Root Mean Square Error = 9.4 mm) between the manual and automated method.
Why it matches plant phenotyping methods温室画像型ハイスループット表現型解析プラットフォームの3D幾何計測精度をSfMで評価し、植物体高への適用も検証しており、表現型取得手法が研究の中心である。
abstractAn image-based high-throughput phenotyping platform was developed to automatically measure geometric dimensions of plants in a greenhouse.
Reproduction assets foundThe paper's image-derived measurement dataset (images of nine objects under 17 POUs in three replicates) is explicitly deposited as online Supplementary Materials at the MDPI URL, which is an allowed URL. No author analysis code or trained models are stated as publicly available.Dataset · publicersity of Missouri for providing experimental materials and supplies. We also would like to thank colleagues Chin Nee Vong and Aijing Feng from Precision and Automated Agriculture Laboratory at the University of Missouri for their kind help in conducting experiments.
Supplementary Materials
The following are available online at http://www.mdpi.com/1424-8220/18/7/2270/s1 .
Click here for additional data file.
Author Contributions
J.Z. (Jing Zhou) conducted the experiment, developed the software, analyzed the data, and wrote the paper. X.F. developed the platform and facilities in greenhouse, supervised J.Z. (Jing Zhou)’s experimental work, and revised the manuscript. L.S. and J.Z. (Jianfeng ZOpen asset ↗lines:86-189Code / dataset availability confirmedbioRxiv · Europe PMC · Crossref · checked 14 Sept 2026
ArabidopsisLaboratory / benchtopRootMorphology / geometry measurementObject detectionTrackingRoot system architecture
Root analysis is essential for both academic and agricultural research. Despite the great advances in root phenotyping and imaging however, calculating root length is still performed manually and involves considerable amounts of labor and time. To overcome these limitations, we have developed MyROOT, a novel software for the semi-automatic quantification of root growth of seedlings growing directly in agar plates. Our method automatically determines the scale from the image of the plate, and subsequently measures the root length of the individual plants. To this aim, MyROOT combines a bottom-up root tracking approach with a hypocotyl detection algorithm. At the same time as providing accurate root measurements, MyROOT also significantly minimizes the user intervention required during the process. Using Arabidopsis, we tested MyROOT with seedlings from different growth stages. Upon comparing the data obtained using this software with that of manual root measurements, we found that there are no significant differences (t-test, p-value < 0.05). Thus, MyROOT will be of great aid to the plant science community by permitting high-throughput root length measurements while saving on both labor and time.
Why it matches plant phenotyping methods根長という植物形質の半自動画像計測ソフトウェアを開発し、手動測定との比較で検証しており、フェノタイピング手法が研究の中心です。
abstractwe have developed MyROOT, a novel software for the semi-automatic quantification of root growth of seedlings growing directly in agar plates.
Reproduction assets found保存済みの本文根拠を更新済みルールで再検証し、公開資産1件を確認しました。Code · publicMyROOT software is available at https://www.cragenomica.es/research-Open asset ↗pdf-page:3 lines:1-45Code / dataset availability confirmedbioRxiv · Europe PMC · checked 15 Sept 2026
BackgroundRoot crown phenotyping has linked root properties to shoot mass, nutrient uptake, and yield in the field, which increases the understanding of soil resource acquisition and presents opportunities for breeding. The original methods using manual measurements have been largely supplanted by image-based approaches. However, most image-based systems have been limited to one or two perspectives and rely on segmentation from grayscale images. An efficient high-throughput root crown phenotyping system is introduced that takes images from five perspectives simultaneously, constituting the Multi-Perspective Imaging Platform (M-PIP). A segmentation procedure using the Expectation-Maximization Gaussian Mixture Model (EM-GMM) algorithm was developed to distinguish plant root pixels from background pixels in color images and using hardware acceleration (CPU and GPU). Phenes were extracted using MatLab scripts. Placement of excavated root crowns for image acquisition was standardized and is ergonomic. The M-PIP was tested on 24 soybean [Glycine max (L.) Merr.] cultivars released between 1930 and 2005.\n\nResultsRelative to previous reports of imaging throughput, this system provides greater throughput with sustained rates of 1.66 root crowns min-1. The EM-GMM segmentation algorithm with hardware acceleration was able to segment images in 10 s, faster than previous methods, and the output images were consistently better connected with less loss of fine detail. Image-based phenes had similar heritabilities as manual measures with the greatest effect sizes observed for Maximum Radius and Fine Radius Frequency. Correlations were also noted, especially among the manual Complexity score and phenes such as number of roots and Total Root Length. Averaging phenes across perspectives generally increased heritability, and no single perspective consistently performed better than others. Angle-based phenes, Fineness Index, Maximum Width, Holes, Solidity and Width-to-Depth Ratio were the most sensitive to perspective with decreased correlations among perspectives.\n\nConclusionThe substantial heritabilities measured for many phenes suggest that they are potentially useful for breeding. Multiple perspectives together often produced the greatest heritabilities, and no single perspective consistently performed better than others. Thus, as illustrated here for soybean, multiple perspectives may be beneficial for root crown phenotyping systems. This system can contribute to breeding efforts that incorporate under-utilized root phenotypes to increase food security and sustainability.
Why it matches plant phenotyping methods根冠形質を高スループットに取得する多視点画像プラットフォーム、画像セグメンテーション、形質抽出を開発・評価しており、植物フェノタイピング手法が研究の中心です。
abstractAn efficient high-throughput root crown phenotyping system is introduced that takes images from five perspectives simultaneously, constituting the Multi-Perspective Imaging Platform (M-PIP).
Reproduction assets foundThe paper's EM-GMM segmentation and MATLAB phene-extraction software for the M-PIP root crown phenotyping platform is publicly available on GitHub with a Zenodo DOI. Raw images and segmented masks are only available upon request, so they do not qualify as public assets.Code · publicnder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under
a CC-BY-NC 4.0 International license.
559 Availability of data and materials
560 Raw images and/or segmented masks are available upon request. Software code is available on github
561 (DOI: 10.5281/zenodo.1213805 | website: https://github.com/GatorSense/MPIP).
562 Competing Interests
563 The authors declare no competing interests.
564 Restrictions or Required Licenses
565 No restrictions on this research are known under local or national laws.
566 Funding
567 The authors gratefully acknowledge partial funding for the research from the United Soybean Board to
568 FBF.
569 Authors' cOpen asset ↗GatorSense/MPIP · 10.5281/zenodo.1213805pdf-layout-page:38 lines:1-44Code / dataset availability confirmedCrossref · checked 15 Sept 2026
Most effective nematicides for the control of root-knot nematodes are banned, which demands a better understanding of the plant-nematode interaction. Understanding how gene expression in the nematode-feeding sites relates to morphological features may assist a better characterization of the interaction. However, nematode-induced galls resulting from cell-proliferation and hypertrophy hinders such observation, which would require tissue sectioning or clearing. We demonstrate that a method based on the green auto-fluorescence produced by glutaraldehyde and the tissue-clearing properties of benzyl-alcohol/benzyl-benzoate preserves the structure of the nematode-feeding sites and the plant-nematode interface with unprecedented resolution quality. This allowed us to obtain detailed measurements of the giant cells’ area in an Arabidopsis line overexpressing CHITINASE-LIKE-1 (CTL1) from optical sections by confocal microscopy, assigning a role for CTL1 and adding essential data to the scarce information of the role of gene repression in giant cells. Furthermore, subcellular structures and features of the nematodes body and tissues from thick organs formed after different biotic interactions, i.e., galls, syncytia, and nodules, were clearly distinguished without embedding or sectioning in different plant species (Arabidopsis, cucumber or Medicago). The combination of this method with molecular studies will be valuable for a better understanding of the plant-biotic interactions.
Why it matches plant phenotyping methods根こぶ線虫摂食部位の構造を共焦点画像から高解像度に取得し、巨大細胞面積を測定する植物フェノタイピング法の開発が中心である。
titleA Phenotyping Method of Giant Cells from Root-Knot Nematode Feeding Sites by Confocal Microscopy Highlights a Role for CHITINASE-LIKE 1 in Arabidopsis
Reproduction assets foundThe paper describes a confocal-microscopy phenotyping method for nematode-induced giant cells. The only paper-specific public asset referenced is the authors' supplementary material (hosted at MDPI), which per the text contains Table S1 (gene filtering results) and Videos S6–S9 of the confocal optical sections used forSupplement · public(PEII-2014-020-P to Carmen Fenoll). Javier Cabrera is supported by a Cytema-Santander contract from Universidad de Castilla-La Mancha. Christian Hermans is a research associate from Fonds de la Recherche Scientifique—National Fund for Scientific Research (Belgium).
Supplementary Materials
Supplementary materials can be found at http://www.mdpi.com/1422-0067/19/2/429/s1 and www.mdpi.com/1422-0067/19/2/429/s2 .
Click here for additional data file.
Click here for additional data file.
Author Contributions
Javier Cabrera, Rocio Olmo, Virginia Ruiz-Ferrer, and Christian Hermans conceived and designed the experiments; Javier Cabrera, Rocio Olmo, Virginia Ruiz-Ferrer, Christian Hermans, and Isabel Open asset ↗lines:51-66Code / dataset availability confirmedEurope PMC · checked 10 Sept 2026
RootMorphology / geometry measurementSkeletonization / topologyRoot system architecture
Quantifying plant morphology is a very challenging task that requires methods able to capture the geometry and topology of plant organs at various spatial scales. Recently, the use of persistent homology as a mathematical framework to quantify plant morphology has been successfully demonstrated for leaves, shoots, and root systems. In this paper, we present a new data analysis pipeline implemented in the R package archiDART to analyse root system architectures using persistent homology. In addition, we also show that both geometric and topological descriptors are necessary to accurately compare root systems and assess their natural complexity.
Why it matches plant phenotyping methods植物根系の形態・トポロジーを定量化する解析パイプラインとRパッケージを開発しており、植物表現型の抽出手法が中心である。
abstractIn this paper, we present a new data analysis pipeline implemented in the R package archiDART to analyse root system architectures using persistent homology.
Reproduction assets foundThe paper's use-case data and R analysis code are publicly deposited on Zenodo (data/R codes for the use cases; archived archiDART 3.0 source; archiShiny app code), with live code on GitHub and a public web application. These directly reproduce the paper's root-system phenotyping and persistent homology analysis.Dataset · publicThe data and R codes used for the use cases presented in this manuscript are available:
https://doi.org/10.5281/zenodo.1117836Open asset ↗Zenodo · 10.5281/zenodo.1117836lines:223-267Code · publicSource code available from:
https://github.com/archidart/archidartOpen asset ↗GitHub · archidart/archidartlines:223-267Code · publicThe data and codes used to make the web application are available:
https://github.com/archidart/archishinyOpen asset ↗GitHub · archidart/archishinylines:223-267Code / 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 confirmedEurope PMC · OpenAlex · checked 10 Sept 2026
RootMorphology / geometry measurementRoot system architecture
Genetic analyses of plant root systems require large datasets of extracted architectural traits. To quantify such traits from images of root systems, researchers often have to choose between automated tools (that are prone to error and extract only a limited number of architectural traits) or semi-automated ones (that are highly time consuming). We trained a Random Forest algorithm to infer architectural traits from automatically extracted image descriptors. The training was performed on a subset of the dataset, then applied to its entirety. This strategy allowed us to (i) decrease the image analysis time by 73% and (ii) extract meaningful architectural traits based on image descriptors. We also show that these traits are sufficient to identify the quantitative trait loci that had previously been discovered using a semi-automated method. We have shown that combining semi-automated image analysis with machine learning algorithms has the power to increase the throughput of large-scale root studies. We expect that such an approach will enable the quantification of more complex root systems for genetic studies. We also believe that our approach could be extended to other areas of plant phenotyping.
Why it matches plant phenotyping methods根系画像から建築形質を抽出する半自動画像解析と機械学習手法の開発であり、表現型取得の高速化と形質推定を中心に扱っているため含める。
abstractTo quantify such traits from images of root systems, researchers often have to choose between automated tools (that are prone to error and extract only a limited number of architectural traits) or semi-automated ones (that are highly time consuming).
Reproduction assets foundThe paper's root image datasets, RSML annotations, and genotype mapping data are openly deposited in GigaScience's GigaDB (DOI 10.5524/100346), and the authors' PRIMAL Random Forest analysis application is publicly available at https://plantmodelling.github.io/primal/. Both are paper-specific, public, and actionable.Dataset · publicThe following supporting data are open and available from the GigaScience repository, Giga DB [ 22 ]:
Root system image dataset #1. Images of root systems of plants tagged with genotype information; 1665 images from [ 5 ].
Root system image dataset #2. Training images without genotype information; 969 images.
Root System Markup Language files for both image datasets.Open asset ↗lines:73-137Code / dataset availability confirmedCrossref · Europe PMC · OpenAlex · checked 10 Sept 2026
Field / plotRootWhole plant / canopy / plot / fieldObject detectionRoot system architecture
Abstract In plant phenotyping, it has become important to be able to measure many features on large image sets in order to aid genetic discovery. The size of the datasets, now often captured robotically, often precludes manual inspection, hence the motivation for finding a fully automated approach. Deep learning is an emerging field that promises unparalleled results on many data analysis problems. Building on artificial neural networks, deep approaches have many more hidden layers in the network, and hence have greater discriminative and predictive power. We demonstrate the use of such approaches as part of a plant phenotyping pipeline. We show the success offered by such techniques when applied to the challenging problem of image-based plant phenotyping and demonstrate state-of-the-art results (>97% accuracy) for root and shoot feature identification and localization. We use fully automated trait identification using deep learning to identify quantitative trait loci in root architecture datasets. The majority (12 out of 14) of manually identified quantitative trait loci were also discovered using our automated approach based on deep learning detection to locate plant features. We have shown deep learning–based phenotyping to have very good detection and localization accuracy in validation and testing image sets. We have shown that such features can be used to derive meaningful biological traits, which in turn can be used in quantitative trait loci discovery pipelines. This process can be completely automated. We predict a paradigm shift in image-based phenotyping bought about by such deep learning approaches, given sufficient training sets.
Why it matches plant phenotyping methods深層学習による画像ベース植物表現型取得・特徴同定を開発し、検証画像で精度を評価しているため、表現型測定手法が研究の中心です。
abstractWe demonstrate the use of such approaches as part of a plant phenotyping pipeline.
Reproduction assets foundThe paper's root/shoot image datasets, trained Caffe models, and analysis scripts are publicly deposited in GigaDB (doi:10.5524/100343), with methods on protocols.io.Code · publicOur CNN models, learned parameters, and all the related scripts for training and validation will be made publically available [ 12 ].Open asset ↗lines:47-102Code / dataset availability confirmedEurope PMC · checked 10 Sept 2026
RootMorphology / geometry measurementCalibration / preprocessingRoot system architecture
Background There are numerous systems and techniques to measure the growth of plant roots. However, phenotyping large numbers of plant roots for breeding and genetic analyses remains challenging. One major difficulty is to achieve high throughput and resolution at a reasonable cost per plant sample. Here we describe a cost-effective root phenotyping pipeline, on which we perform time and accuracy benchmarking to identify bottlenecks in such pipelines and strategies for their acceleration. Results Our root phenotyping pipeline was assembled with custom software and low cost material and equipment. Results show that sample preparation and handling of samples during screening are the most time consuming task in root phenotyping. Algorithms can be used to speed up the extraction of root traits from image data, but when applied to large numbers of images, there is a trade-off between time of processing the data and errors contained in the database. Conclusions Scaling-up root phenotyping to large numbers of genotypes will require not only automation of sample preparation and sample handling, but also efficient algorithms for error detection for more reliable replacement of manual interventions.
Why it matches plant phenotyping methods根系表現型取得パイプラインを開発し、カスタムソフトウェア、画像からの形質抽出、処理時間と精度のベンチマークを中心に評価しているため、方法論が中心である。
abstractHere we describe a cost-effective root phenotyping pipeline, on which we perform time and accuracy benchmarking to identify bottlenecks in such pipelines and strategies for their acceleration.
Reproduction assets foundThe paper's ArchiPhen software, analysis scripts, and supporting root phenotyping data are publicly available at archiroot.org.uk and mirrored on the authors' GitHub repository (linked to Zenodo).Code · publicSoftware files are also stored on Github repository https://github.com/LionelDupuy/ARCHI_PHEN and linked to Zenodo (DOI: 10.5281/zenodo.399222).Open asset ↗https://github.com/LionelDupuy/ARCHI_PHEN · ARCHI_PHENlines:126-268Code · publicThe source code is freely available at www.archiroot.org.uk .Open asset ↗lines:99-104Code / dataset availability confirmedEurope PMC · checked 14 Sept 2026
Flooding is a devastating abiotic stress that endangers crop production in the twenty-first century. Because of the severe susceptibility of common bean ( Phaseolus vulgaris L.) to flooding, an understanding of the genetic architecture and physiological responses of this crop will set the stage for further improvement. However, challenging phenotyping methods hinder a large-scale genetic study of flooding tolerance in common bean and other economically important crops. A greenhouse phenotyping protocol was developed to evaluate the flooding conditions at early stages. The Middle-American diversity panel ( n = 272) of common bean was developed to capture most of the diversity exits in North American germplasm. This panel was evaluated for seven traits under both flooded and non-flooded conditions at two early developmental stages. A subset of contrasting genotypes was further evaluated in the field to assess the relationship between greenhouse and field data under flooding condition. A genome-wide association study using ~150 K SNPs was performed to discover genomic regions associated with multiple physiological responses. The results indicate a significant strong correlation ( r > 0.77) between greenhouse and field data, highlighting the reliability of greenhouse phenotyping method. Black and small red beans were the least affected by excess water at germination stage. At the seedling stage, pinto and great northern genotypes were the most tolerant. Root weight reduction due to flooding was greatest in pink and small red cultivars. Flooding reduced the chlorophyll content to the greatest extent in the navy bean cultivars compared with other market classes. Races of Durango/Jalisco and Mesoamerica were separated by both genotypic and phenotypic data indicating the potential effect of eco-geographical variations. Furthermore, several loci were identified that potentially represent the antagonistic pleiotropy. The GWAS analysis revealed peaks at Pv08/1.6 Mb and Pv02/41 Mb that are associated with root weight and germination rate, respectively. These regions are syntenic with two QTL reported in soybean ( Glycine max L.) that contribute to flooding tolerance, suggesting a conserved evolutionary pathway involved in flooding tolerance for these related legumes.
Why it matches plant phenotyping methods洪水耐性を評価する温室フェノタイピングプロトコルを開発し、圃場データとの相関で信頼性を検証しており、表現型取得法が研究の中心である。
abstractA greenhouse phenotyping protocol was developed to evaluate the flooding conditions at early stages.
Reproduction assets found保存済みの本文根拠を更新済みルールで再検証し、公開資産1件を確認しました。Supplement · publicThe phenotypic responses of seven traits were measured in both non-flooded and flooded conditions (Supplementary Material, Data Sheet 1).Open asset ↗lines:55-103Code / dataset availability confirmedEurope PMC · checked 15 Sept 2026
Root-knot nematodes induce galls that contain giant-feeding cells harboring multiple enlarged nuclei within the roots of host plants. It is recognized that the cell cycle plays an essential role in the set-up of a peculiar nuclear organization that seemingly steers nematode feeding site induction and development. Functional studies of a large set of cell cycle genes in transgenic lines of the model host Arabidopsis thaliana have contributed to better understand the role of the cell cycle components and their implication in the establishment of functional galls. Mitotic activity mainly occurs during the initial stages of gall development and is followed by an intense endoreduplication phase imperative to produce giant-feeding cells, essential to form vigorous galls. Transgenic lines overexpressing particular cell cycle genes can provoke severe nuclei phenotype changes mainly at later stages of feeding site development. This can result in chaotic nuclear phenotypes affecting their volume. These aberrant nuclear organizations are hampering gall development and nematode maturation. Herein we report on two nuclear volume assessment methods which provide information on the complex changes occurring in nuclei during giant cell development. Although we observed that the data obtained with AMIRA tend to be more detailed than Volumest (Image J), both approaches proved to be highly versatile, allowing to access 3D morphological changes in nuclei of complex tissues and organs. The protocol presented here is based on standard confocal optical sectioning and 3-D image analysis and can be applied to study any volume and shape of cellular organelles in various complex biological specimens. Our results suggest that an increase in giant cell nuclear volume is not solely linked to increasing ploidy levels, but might result from the accumulation of mitotic defects.
Why it matches plant phenotyping methods根こぶ線虫誘導巨大細胞の核体積・3D形態を取得する画像解析手法を提示し、AMIRAとVolumestを比較検証しているため、植物表現型計測法が中心です。
abstractHerein we report on two nuclear volume assessment methods which provide information on the complex changes occurring in nuclei during giant cell development.
Reproduction assets foundThe paper's nuclear volume measurements (individual GC and NGC nuclear volumes for Col-0, KRP3 OE, and KRP5 OE lines) are deposited in the article's public Supplementary File 1, available via the Frontiers supplementary-material URL. The Volumest plugin URL is a generic third-party tool, not a paper-specific asset, andSupplement · publicS-COFECUB (n°. sv 683/10 2011) project. RC has been supported by a doctoral scholarship in Brazil from CNPq (process number: 143030/2009-4) and in France from CAPES (process number: 6585-11-6).
1
http://lepo.it.da.ut.ee/~markkom/volumest/
Supplementary Material
The Supplementary Material for this article can be found online at: http://journal.frontiersin.org/article/10.3389/fpls.2017.00961/full#supplementary-material
Click here for additional data file.
References
Banora M. Y. Rodiuc N. Baldacci-Cresp F. Smertenko A. Bleve-Zacheo T. Mellilo M. T.
( 2011 ).
Feeding cells induced by phytoparasitic nematodes require gamma-tubulin ring complex for microtubule reorganization.
PLoS Pathog.
7 : e10Open asset ↗lines:88-182Code / 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 · Crossref · OpenAlex · checked 14 Sept 2026
RootMorphology / geometry measurementRoot system architecture
Background Computer-based phenotyping of plants has risen in importance in recent years. Whilst much software has been written to aid phenotyping using image analysis, to date the vast majority has been only semi-automatic. However, such interaction is not desirable in high throughput approaches. Here, we present a system designed to analyse plant images in a completely automated manner, allowing genuine high throughput measurement of root traits. To do this we introduce a new set of proxy traits. Results We test the system on a new, automated image capture system, the Microphenotron, which is able to image many 1000s of roots/h. A simple experiment is presented, treating the plants with differing chemical conditions to produce different phenotypes. The automated imaging setup and the new software tool was used to measure proxy traits in each well. A correlation matrix was calculated across automated and manual measures, as a validation. Some particular proxy measures are very highly correlated with the manual measures (e.g. proxy length to manual length, r 2 > 0.9). This suggests that while the automated measures are not directly equivalent to classic manual measures, they can be used to indicate phenotypic differences (hence the term, proxy ). In addition, the raw discriminative power of the new proxy traits was examined. Principal component analysis was calculated across all proxy measures over two phenotypically-different groups of plants. Many of the proxy traits can be used to separate the data in the two conditions. Conclusion The new proxy traits proposed tend to correlate well with equivalent manual measures, where these exist. Additionally, the new measures display strong discriminative power. It is suggested that for particular phenotypic differences, different traits will be relevant, and not all will have meaningful manual equivalent measures. However, approaches such as PCA can be used to interrogate the resulting data to identify differences between datasets. Select images can then be carefully manually inspected if the nature of the precise differences is required. We suggest such flexible measurement approaches are necessary for fully automated, high throughput systems such as the Microphenotron.
Why it matches plant phenotyping methods植物画像から根形質を完全自動抽出するソフトウェアと撮像プラットフォームを開発・検証した研究であり、フェノタイピング手法が中心です。
abstractwe present a system designed to analyse plant images in a completely automated manner, allowing genuine high throughput measurement of root traits.
Reproduction assets found保存済みの本文根拠を更新済みルールで再検証し、公開資産1件を確認しました。Code · publicThe AutoRoot software is open source and available from http://dx.doi.org/10.5281/zenodo.60433 .Open asset ↗zenodo · 10.5281/zenodo.60433lines:250-369Code / dataset availability confirmedEurope PMC · Crossref · OpenAlex · checked 14 Sept 2026
Background Chemical genetics provides a powerful alternative to conventional genetics for understanding gene function. However, its application to plants has been limited by the lack of a technology that allows detailed phenotyping of whole-seedling development in the context of a high-throughput chemical screen. We have therefore sought to develop an automated micro-phenotyping platform that would allow both root and shoot development to be monitored under conditions where the phenotypic effects of large numbers of small molecules can be assessed. Results The 'Microphenotron' platform uses 96-well microtitre plates to deliver chemical treatments to seedlings of Arabidopsis thaliana L. and is based around four components: (a) the 'Phytostrip', a novel seedling growth device that enables chemical treatments to be combined with the automated capture of images of developing roots and shoots; (b) an illuminated robotic platform that uses a commercially available robotic manipulator to capture images of developing shoots and roots; (c) software to control the sequence of robotic movements and integrate these with the image capture process; (d) purpose-made image analysis software for automated extraction of quantitative phenotypic data. Imaging of each plate (representing 80 separate assays) takes 4 min and can easily be performed daily for time-course studies. As currently configured, the Microphenotron has a capacity of 54 microtitre plates in a growth room footprint of 2.1 m 2 , giving a potential throughput of up to 4320 chemical treatments in a typical 10 days experiment. The Microphenotron has been validated by using it to screen a collection of 800 natural compounds for qualitative effects on root development and to perform a quantitative analysis of the effects of a range of concentrations of nitrate and ammonium on seedling development. Conclusions The Microphenotron is an automated screening platform that for the first time is able to combine large numbers of individual chemical treatments with a detailed analysis of whole-seedling development, and particularly root system development. The Microphenotron should provide a powerful new tool for chemical genetics and for wider chemical biology applications, including the development of natural and synthetic chemical products for improved agricultural sustainability.
Why it matches plant phenotyping methodsロボット撮像、画像解析、定量的形質抽出を統合した植物表現型解析プラットフォームの開発・検証が中心である。
abstractWe have therefore sought to develop an automated micro-phenotyping platform that would allow both root and shoot development to be monitored
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 · publicAutoRoot, the software for automated analysis of the images [ 19 ], is Open Source and can be downloaded from https://zenodo.org/ , and the Phytostrips are available to purchase by contacting the corresponding author.Open asset ↗zenodolines:107-110Code / 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.
1
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
Click here for additional data file.
Click here for additional data file.
Click here for additional data file.
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 confirmedEurope PMC · checked 15 Sept 2026
A major challenge in plant systems biology is the development of robust, predictive multiscale models for organ growth. In this context it is important to bridge the gap between the, rather well-documented molecular scale and the organ scale by providing quantitative methods to study within-organ growth patterns. Here, we describe a simple method for the analysis of the evolution of growth patterns within rod-shaped organs that does not require adding markers at the organ surface. The method allows for the simultaneous analysis of root and hypocotyl growth, provides spatio-temporal information on curvature, growth anisotropy and relative elemental growth rate and can cope with complex organ movements. We demonstrate the performance of the method by documenting previously unsuspected complex growth patterns within the growing hypocotyl of the model species Arabidopsis thaliana during normal growth, after treatment with a growth-inhibiting drug or in a mechano-sensing mutant. The method is freely available as an intuitive and user-friendly Matlab application called KymoRod.
Why it matches plant phenotyping methods植物器官の成長パターン、曲率、成長異方性、相対元素成長率を自動抽出する手法とソフトウェアを開発しており、植物表現型取得が研究の中心です。
abstractwe describe a simple method for the analysis of the evolution of growth patterns within rod-shaped organs
Reproduction assets foundThe paper's KymoRod Matlab application for automated kinematic analysis of rod-shaped plant organs is explicitly stated to be freely available on the authors' public GitHub repository (ijpb/KymoRod). This is the authors' analysis code implementing the paper's phenotyping method. No public phenotype dataset or image de-Code · publicwe have presented a simple and robust
method for the analysis of sub-organ growth patterns in
plant seedlings. Its performance exceeds that of previous
methods that are mostly too laborious for the analysis of
large numbers of samples. The method is packaged in
KymoRod, a user-friendly application freely available on
internet (http://github.com/ijpb/KymoRod), which should
facilitate the study of the cellular basis of organ growth for
non-specialist users.
EXPERIMENTAL PROCEDURES
Plant growth, image acquisition and pre-treatment
Arabidopsis seeds (genotypes Col-0 and fer-4; Duan et al., 2010)
were surface sterilized (Santoni et al., 1994), plated on Arabidop-
sis medium (Santoni etOpen asset ↗ijpb/KymoRodpdf-raw-page:6 lines:1-205Code / dataset availability confirmedCrossref · checked 15 Sept 2026
Summary Spectroscopy has recently emerged as an effective method to accurately characterize leaf biochemistry in living tissue through the application of chemometric approaches to foliar optical data, but this approach has not been widely used for plant secondary metabolites. Here, we examine the ability of reflectance spectroscopy to quantify specific phenolic compounds in trembling aspen ( Populus tremuloides ) and paper birch ( Betula papyrifera ) that play influential roles in ecosystem functioning related to trophic‐level interactions and nutrient cycling. Spectral measurements on live aspen and birch leaves were collected, after which concentrations of condensed tannins (aspen and birch) and salicinoids (aspen only) were determined using standard analytical approaches in the laboratory. Predictive models were then constructed using jackknifed, partial least squares regression ( PLSR ). Model performance was evaluated using coefficient of determination ( R 2 ), root‐mean‐square error ( RMSE ) and the per cent RMSE of the data range (% RMSE ). Condensed tannins of aspen and birch were well predicted from both combined ( R 2 = 0·86, RMSE = 2·4, % RMSE = 7%)‐ and individual‐species models (aspen: R 2 = 0·86, RMSE = 2·4, % RMSE = 6%; birch: R 2 = 0·81, RMSE = 1·9, % RMSE = 10%). Aspen total salicinoids were better predicted than individual salicinoids (total: R 2 = 0·76, RMSE = 2·4, % RMSE = 8%; salicortin: R 2 = 0·57, RMSE = 1·9, % RMSE = 11%; tremulacin: R 2 = 0·72, RMSE = 1·1, % RMSE = 11%), and spectra collected from dry leaves produced better models for both aspen tannins ( R 2 = 0·92, RMSE = 1·7, % RMSE = 5%) and salicinoids ( R 2 = 0·84, RMSE = 1·4, % RMSE = 5%) compared with spectra from fresh leaves. The decline in prediction performance from total to individual salicinoids and from dry to fresh measurements was marginal, however, given the increase in detailed salicinoid information acquired and the time saved by avoiding drying and grinding leaf samples. Reflectance spectroscopy can successfully characterize specific secondary metabolites in living plant tissue and provide detailed information on individual compounds within a constituent group. The ability to simultaneously measure multiple plant traits is a powerful attribute of reflectance spectroscopy because of its potential for in situ – in vivo field deployment using portable spectrometers. The suite of traits currently estimable, however, needs to expand to include specific secondary metabolites that play influential roles in ecosystem functioning if we are to advance the integration of chemical, landscape and ecosystem ecology.
Why it matches plant phenotyping methods生葉の反射分光とPLSRにより二次代謝産物を定量する測定・予測手法を構築し、モデル性能を評価しており、植物形質取得法が研究の中心である。
abstractSpectroscopy has recently emerged as an effective method to accurately characterize leaf biochemistry in living tissue through the application of chemometric approaches to foliar optical data
Reproduction assets foundThe paper's Data Accessibility statement explicitly archives both the spectral data used in the study and the PLSR model-building code in EcoSIS, with a public URL matching an allowed URL.Dataset · publico PAT and RLL, and USDA NIFA McIntire-Stennis projects
WIS01651 to RLL and WIS01531 and WIS01599 to PAT.
Data Accessibility
Spectral data used in this study and the partial least squares regression code used for model
building are archived in the Ecosystem Spectral Information System (EcoSIS;
www.ecosis.org) and can be found at https://ecosis.org/#result/d5445eb9-f334-4ee7-90a9-1fe07e67a20c.Open asset ↗EcoSIS · d5445eb9-f334-4ee7-90a9-1fe07e67a20cpdf-raw-page:23 lines:1-25Code / dataset availability confirmedOpenAlex · Europe PMC · checked 11 Sept 2026
BACKGROUND: In this study we carried out a genome-wide association analysis for plant and grain morphology and root architecture in a unique panel of temperate rice accessions adapted to European pedo-climatic conditions. This is the first study to assess the association of selected phenotypic traits to specific genomic regions in the narrow genetic pool of temperate japonica. A set of 391 rice accessions were GBS-genotyped yielding-after data editing-57000 polymorphic and informative SNPS, among which 54% were in genic regions. RESULTS: In total, 42 significant genotype-phenotype associations were detected: 21 for plant morphology traits, 11 for grain quality traits, 10 for root architecture traits. The FDR of detected associations ranged from 3 · 10-7 to 0.92 (median: 0.25). In most cases, the significant detected associations co-localised with QTLs and candidate genes controlling the phenotypic variation of single or multiple traits. The most significant associations were those for flag leaf width on chromosome 4 (FDR = 3 · 10-7) and for plant height on chromosome 6 (FDR = 0.011). CONCLUSIONS: We demonstrate the effectiveness and resolution of the developed platform for high-throughput phenotyping, genotyping and GWAS in detecting major QTLs for relevant traits in rice. We identified strong associations that may be used for selection in temperate irrigated rice breeding: e.g. associations for flag leaf width, plant height, root volume and length, grain length, grain width and their ratio. Our findings pave the way to successfully exploit the narrow genetic pool of European temperate rice and to pinpoint the most relevant genetic components contributing to the adaptability and high yield of this germplasm. The generated data could be of direct use in genomic-assisted breeding strategies.
Why it matches plant phenotyping methods高スループット表現型解析プラットフォームの開発・適用が明示され、植物形態・根系・穀粒形質の測定とGWASを結び付けているため、表現型取得基盤が研究の主要部分と判断する。
abstractWe demonstrate the effectiveness and resolution of the developed platform for high-throughput phenotyping, genotyping and GWAS in detecting major QTLs for relevant traits in rice.
Reproduction assets foundThe authors state that all relevant data (phenotypic and genotypic data underlying the GWAS) are publicly available in a Zenodo repository, which qualifies as a paper-specific public data asset.Dataset · publicData Availability All relevant data are publicly available in a Zenodo repository at the following URL: https://zenodo.org/record/50803#.VytVnrp97CI .Open asset ↗Zenodo · record/50803lines:48-55Code / dataset availability confirmedOpenAlex · Europe PMC · checked 15 Sept 2026
Robustness in lettuce, defined as the ability to produce stable yields across a wide range of environments, may be associated with below-ground traits such as water and nitrate capture. In lettuce, research on the role of root traits in resource acquisition has been rather limited. Exploring genetic variation for such traits and shoot performance in lettuce across environments can contribute to breeding for robustness. A population of 142 lettuce cultivars was evaluated during two seasons (spring and summer) in two different locations under organic cropping conditions, and water and nitrate capture below-ground and accumulation in the shoots were assessed at two sampling dates. Resource capture in each soil layer was measured using a volumetric method based on fresh and dry weight difference in the soil for soil moisture, and using an ion-specific electrode for nitrate. We used these results to carry out an association mapping study based on 1170 single nucleotide polymorphism markers. We demonstrated that our indirect, high-throughput phenotyping methodology was reliable and capable of quantifying genetic variation in resource capture. QTLs for below-ground traits were not detected at early sampling. Significant marker-trait associations were detected across trials for below-ground and shoot traits, in number and position varying with trial, highlighting the importance of the growing environment on the expression of the traits measured. The difficulty of identifying general patterns in the expression of the QTLs for below-ground traits across different environments calls for a more in-depth analysis of the physiological mechanisms at root level allowing sustained shoot growth.
Why it matches plant phenotyping methods水・硝酸の資源捕捉を定量する間接的な高スループット表現型計測法を明示的に評価し、信頼性と遺伝変異の定量能力を検証しているため、方法が研究の中心的要素です。
abstractWe demonstrated that our indirect, high-throughput phenotyping methodology was reliable and capable of quantifying genetic variation in resource capture.
Reproduction assets foundThe paper's phenotypic measurements (soil water/nitrate capture, shoot traits) and genotype scores/linkage map are stated to be available as Supplementary Material (Tables S1 and S2) hosted at the article's public Frontiers URL. No author analysis code or trained models are mentioned.Supplement · publicey also thank Jan Velema, Marcel van Diemen and Pieter Schwegman, Vitalis Organic Seeds, for providing seeds, advice, and insight. The project was financially supported through the Top Institute Green Genetics (project number: 2CFD024RP).
Supplementary material
The Supplementary Material for this article can be found online at: http://journal.frontiersin.org/article/10.3389/fpls.2016.00343
Click here for additional data file.
Click here for additional data file.
Click here for additional data file.
References
Biddington N. L., Dearman A. S. (1985). The effects of mechanically-induced stress on water loss and drought resistance in lettuce, cauliflower and celery seedlings. Ann. Bot.
56, 795–8Open asset ↗lines:840-871Code / dataset availability confirmedCrossref · Europe PMC · checked 14 Sept 2026
Field / plotRootWhole plant / canopy / plot / fieldMorphology / geometry measurementObject detection2D/3D reconstructionRoot system architectureYield / yield components
Abstract A plant's ability to maintain or improve its yield under limiting conditions, such as nutrient deficiency or drought, can be strongly influenced by root system architecture (RSA), the three‐dimensional distribution of the different root types in the soil. The ability to image, track and quantify these root system attributes in a dynamic fashion is a useful tool in assessing desirable genetic and physiological root traits. Recent advances in imaging technology and phenotyping software have resulted in substantive progress in describing and quantifying RSA. We have designed a hydroponic growth system which retains the three‐dimensional RSA of the plant root system, while allowing for aeration, solution replenishment and the imposition of nutrient treatments, as well as high‐quality imaging of the root system. The simplicity and flexibility of the system allows for modifications tailored to the RSA of different crop species and improved throughput. This paper details the recent improvements and innovations in our root growth and imaging system which allows for greater image sensitivity (detection of fine roots and other root details), higher efficiency, and a broad array of growing conditions for plants that more closely mimic those found under field conditions.
Why it matches plant phenotyping methods根系の3D構造を高品質に撮像・追跡・定量する成長・イメージングシステムの改良が中心であり、植物表現型取得基盤に該当する。
abstractRecent advances in imaging technology and phenotyping software have resulted in substantive progress in describing and quantifying RSA.
Reproduction assets foundThe paper describes its RootReader 3D-based imaging/analysis software as freely available, with visualization tools hosted at the authors' USDA URL (http://foo.ars.usda.gov.Root). This is a paper-specific, publicly actionable analysis software asset. No phenotype datasets, raw images, or trained models are explicitlydeCode · publicimages are processed by RootReader 3D to obtain a 3D
reconstruction (Figure 2F) and associated root traits. The
voxels in the reconstruction can be visualized as a point cloud
(Figure 2G) or animated as a movie (Movie 1D) using software
tools available at http://foo.ars.usda.gov.Root system growth and imaging in hydroponics
A hydroponic-based system significantly improves experimen-
tal flexibility in that plants can be grown with a constant
supply of a well-defined nutrient composition and the solution
can be easily replaced or replenished. In addition, a different
nutrient composition (i.e., treatments) canOpen asset ↗pdf-raw-page:4 lines:89-121Code / 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