MultimodalLiDAR / point cloudAnnotation / quality controlClassificationObject detectionCalibration / preprocessingSegmentationTracking
Plant phenomics, the comprehensive study of plant phenotypes, has gained prominence as a vital tool for understanding the intricate relationships between genotypes and the environment. Image-based plant phenomics has progressed rapidly, and three-dimensional (3D) phenotyping is a valuable extension of traditional 2D phenomics. However, the increased data dimensionality poses challenges to feature extraction and phenotyping. In recent decades, deep learning has led to remarkable progress in revolutionizing 3D phenotyping. Therefore, this review highlights the importance of using deep learning in 3D plant phenomics. It systematically overviews the capabilities of deep learning for 3D computer vision, covering 3D representation, classification, detection and tracking, semantic segmentation, instance segmentation, and generation. Additionally, deep learning techniques for 3D point preprocessing (e.g., annotation, downsampling, and dataset organization) and various plant phenotyping tasks are discussed. Finally, the challenges and perspectives associated with deep learning in 3D plant phenomics are summarized, including (1) benchmark dataset construction by using synthetic datasets and methods such as generative artificial intelligence and unsupervised or weakly supervised learning; (2) accurate and efficient 3D point cloud analysis by leveraging multitask learning, lightweight models, and self-supervised learning; and (3) deep learning for 3D plant phenomics by exploring interpretability, extensibility, and multimodal data utilization. The exploration of deep learning in 3D plant phenomics is poised to spur breakthroughs in a new dimension of plant science.
Why it matches plant phenotyping methods3D植物フェノミクスにおける深層学習手法を体系的にレビューしており、植物形質の抽出・推定手法が中心である。
abstractTherefore, this review highlights the importance of using deep learning in 3D plant phenomics.
Reproduction assets found保存済みの本文根拠を更新済みルールで再検証し、公開資産1件を確認しました。Dataset · publicThe dataset can be downloaded from https://github.com/Jinlab-AiPhenomics/Mazie3D.Open asset ↗Jinlab-AiPhenomics/Mazie3Dhtml-lines:332-336Code / dataset availability confirmedEurope PMC · checked 14 Sept 2026
Large-scale rainfed cropping systems (broadacre agriculture) face intensifying climate and resource stresses that undermine yield stability and farm livelihoods. Remote sensing (RS) offers critical tools for improving resilience by monitoring crop performance—productivity, phenology, and environmental stress—across large areas and timeframes. This review aims to synthesize methodological advances over the past two decades in applying RS for broadacre crop monitoring and to identify key challenges and integration opportunities. Peer-reviewed studies across diverse crops and regions were systematically examined to evaluate the strengths, limitations, and emerging trends across the three RS application themes. The review finds that (1) RS enables spatially explicit yield estimation from regional to paddock scales, with vegetation indices (VIs) and phenology-adjusted metrics closely correlated with yield. (2) Time-series analyses of RS data effectively capture phenological transitions critical for forecasting, supported by advances in curve fitting, sensor fusion, and machine learning. (3) Thermal and multispectral indices support early detection of abiotic (drought, heat, salinity) and biotic (pests, disease) stresses, though specificity remains limited. Across themes, methodological silos and sensor integration barriers hinder holistic application. Emerging approaches—such as multi-sensor/scale fusion, RS–crop model data assimilation, and operational and big data integration—provide promising pathways toward resilience-focused decision support. Future research should define quantifiable resilience metrics and cross-theme predictive integration to guide climate adaptation.
Why it matches plant phenotyping methods作物の生産性、フェノロジー、環境ストレスをリモートセンシングで測定・推定する方法論レビューであり、植物形質・状態の取得手法が中心である。
abstractThis review aims to synthesize methodological advances over the past two decades in applying RS for broadacre crop monitoring
Reproduction assets foundThis methodological review includes a case study (Figure 2) using MODIS NDVI composites, SILO gridded climate data, and ABARES historical winter crop yield data. The authors explicitly state the case-study datasets are publicly accessible via official portals; the SILO and ABARES portals are paper-specific public data-Dataset · publiclies, and observed productivity. Note: This figure is derived from
the authors’ ongoing study. The monthly NDVI composites (MOD13C2) were generated post-season, which limits their
utility for in-season forecasting. The gridded Climate data were obtained from the Australian Scientific Information for
Land Owners (SILO) database (https://www.longpaddock.qld.gov.au/silo/), and historical winter crop yield data were
sourced from the Australian Bureau of Agricultural and Resource Economics and Sciences (ABARES)
(https://www.agriculture.gov.au/abares/data).
However, Figure 2 also illustrates key limitations of NDVI-based monitoring. First, the complete
seasonal NDVI composite becomes available onlOpen asset ↗SILOpdf-layout-page:8 lines:1-53Dataset · publiclity for in-season forecasting. The gridded Climate data were obtained from the Australian Scientific Information for
Land Owners (SILO) database (https://www.longpaddock.qld.gov.au/silo/), and historical winter crop yield data were
sourced from the Australian Bureau of Agricultural and Resource Economics and Sciences (ABARES)
(https://www.agriculture.gov.au/abares/data).
However, Figure 2 also illustrates key limitations of NDVI-based monitoring. First, the complete
seasonal NDVI composite becomes available only after crop harvest, limiting its usefulness for in-
season yield forecasting or early drought warning. In other words, detailed phenological curves and
productivity metrics can onlyOpen asset ↗ABARESpdf-layout-page:8 lines:1-53Code / dataset availability confirmedOpenAlex · arXiv · checked 6 Sept 2026
The precise characterization of plant morphology provides valuable insights into plant environment interactions and genetic evolution. A key technology for extracting this information is 3D segmentation, which delineates individual plant organs from complex point clouds. Despite significant progress in general 3D computer vision domains, the adoption of 3D segmentation for plant phenotyping remains limited by three major challenges: i) the scarcity of large-scale annotated datasets, ii) technical difficulties in adapting advanced deep neural networks to plant point clouds, and iii) the lack of standardized benchmarks and evaluation protocols tailored to plant science. This review systematically addresses these barriers by: i) providing an overview of existing 3D plant datasets in the context of general 3D segmentation domains, ii) systematically summarizing deep learning-based methods for point cloud semantic and instance segmentation, iii) introducing Plant Segmentation Studio (PSS), an open-source framework for reproducible benchmarking, and iv) conducting extensive quantitative experiments to evaluate representative networks and sim-to-real learning strategies. Our findings highlight the efficacy of sparse convolutional backbones and transformer-based instance segmentation, while also emphasizing the complementary role of modeling-based and augmentation-based synthetic data generation for sim-to-real learning in reducing annotation demands. In general, this study bridges the gap between algorithmic advances and practical deployment, providing immediate tools for researchers and a roadmap for developing data-efficient and generalizable deep learning solutions in 3D plant phenotyping. Data and code are available at https://github.com/perrydoremi/PlantSegStudio.
Why it matches plant phenotyping methods植物3Dセグメンテーションを中心に、データセット、手法、ベンチマーク、再現可能なフレームワークを体系的に扱っており、植物形態フェノタイピングの取得・抽出法が中核である。
abstractThis review systematically addresses these barriers by: i) providing an overview of existing 3D plant datasets in the context of general 3D segmentation domains, ii) systematically summarizing deep learning-based methods for point cloud semantic and instance segmentation, iii) introducing Plant Segmentation Studio (PSS), an open-source framework for reproducible benchmarking, and iv) conducting extensive quantitative experiments to evaluate representative networks and sim-to-real learning str
Reproduction assets foundThe paper introduces Plant Segmentation Studio (PSS), an open-source benchmarking framework for 3D plant point cloud segmentation, with explicit public availability of data and code at the authors' GitHub repository, which matches an allowed URL.Code · publicData and code are available at: https://github.com/perrydoremi/PlantSegStudio.Open asset ↗perrydoremi/PlantSegStudiopdf-page:1 lines:1-66Code / dataset availability confirmedCrossref · checked 14 Sept 2026
The economy of Tanzania is mostly driven by agriculture. Disease is one of the reasons that contributes to the low production of staple foods like cassava and maize, alongside climate change. Loss of income and food security are the results. In order to detect the diseases early, preventative measures are required. A potential option for farmers could be the use of image processing tools to identify plant diseases on leaves. Implementing the existing method of disease detection, which involves an expert using their naked eyes, on a large farm is a laborious and time consuming process. This study provides a comprehensive overview of recent research in image processing by reviewing methods for identifying plant diseases in their leaves or fruits and the corresponding machine learning models for disease classification. This study examines issues in the identification of plant diseases, pertinent to agriculture-dependent nations like Tanzania and India. Presenting the present state of the art, elucidating the steps done during the image processing stage, and assessing the pros and cons of each technique as well as the effectiveness of the machine learning model used for disease classification are the primary goals of the work. Among the preprocessing and resampling techniques, the evaluation's results show that GIN-based approach for resampling, in conjunction with contrast limited adaptive histogram equalization (CLAHE), achieved the best results, with an average F1-score of 95.65% and a classification accuracy of 95.62%. The study concludes with a generic process for a disease detection system, which may be broken down into individual components as needed.
Why it matches plant phenotyping methods植物葉・果実画像から病害を推定する画像処理・機械学習手法をレビューし、手法性能も評価しており、植物病害状態の表現型取得が中心である。
abstractThis study provides a comprehensive overview of recent research in image processing by reviewing methods for identifying plant diseases in their leaves or fruits and the corresponding machine learning models for disease classification.
Reproduction assets foundThe paper's plant-phenotyping analysis uses the public PlantDoc dataset of leaf disease images, explicitly cited with a Kaggle URL; no author code or models are reported as available.Dataset · publicThe PlantDoc dataset (Uddin, 2024) shares similar
classes and illnesses with PlantVillage. It is also
publically available. However, the PlantDoc dataset is
substantially smaller. This study employed Images from
PlantDoc datasets.Open asset ↗pdf-raw-page:3 lines:1-127Code / dataset availability confirmedEurope PMC · checked 14 Sept 2026
Earth's polar regions are experiencing significant climate change, impacting global oceanographic and weather patterns. Arctic "greening" is well studied, but a debate has emerged about whether similar trends are occurring in Antarctica and whether and how remote sensing can assess them. Recent studies have introduced a concept of "greening" in Antarctica, framed primarily around moss cover expansion over bare ground. This interpretation differs from Arctic greening studies, which focus mainly on changes in vascular plant productivity and successional dynamics. This paper evaluates the Antarctic greening concept, focusing on how Normalized Difference Vegetation Index (NDVI)-based methods are applied and interpreted in this context, considering regional limitations in technology, data availability, and the unique Antarctic vegetation characteristics. Unlike the Arctic, Antarctic vegetation consists mainly of nonvascular organisms (algae, cyanobacteria, lichens, and bryophytes) that interact with slow-weathering soils with minimal organic inputs. These biological and environmental differences likely influence NDVI greening metrics and their ecological relevance, but remain poorly understood due to limited long-term data and validation. Despite advances in remote sensing, Antarctic vegetation mapping remains in its early stages. The small size and patchy distribution of vegetation complicate detection of presence and extent, and even with modern satellites, capturing subcentimeter annual growth rates remains challenging. The lack of historical high-resolution imagery hampers change detection, limiting our ability to track habitat expansion, vegetation dynamics, and community composition changes over time. Based on critical assessment, we identify serious concerns regarding the accuracy and interpretation of NDVI-based greening trends in Antarctica in recent studies, particularly in relation to technological constraints and biological realism. To address these issues, we propose a refined framework for interpreting NDVI data in Antarctica, aiming to prevent misleading conclusions about vegetation changes and trends. This framework suggests an urgent need for re-evaluation of how "greening" is both quantified and interpreted in Antarctica.
Why it matches plant phenotyping methods南極植生の状態・変化をNDVIで推定するリモートセンシング手法を中心に、その精度・解釈上の問題を批判的に評価し、改良フレームワークを提案する方法論的レビューである。
abstractThis paper evaluates the Antarctic greening concept, focusing on how Normalized Difference Vegetation Index (NDVI)-based methods are applied and interpreted in this context
Reproduction assets foundThe paper's Data Availability Statement states the supporting data are openly available in Edinburgh Data Share at DOI 10.7488/ds/7945. This is a paper-specific public dataset deposit (the study's vegetation/greening analysis data). The related spectral library dataset (10.7488/ds/7720) is cited prior work, not this论文.Dataset · publicfunding provided by grants from Dartmouth's
College of Arts and Sciences and the Clare Garber Goodman Fund for
Anthropological Research.
Conflicts of Interest
The authors declare no conflicts of interest.
Data Availability Statement
The data that support the findings of this study are openly available in
Edinburgh Data Share at https://doi.org/10.7488/ds/7945.References
Aartsma, P., J. Asplund, A. Odland, S. Reinhardt, and H. Renssen.
2021. “Microclimatic Comparison of Lichen Heaths and Shrubs:
Shrubification Generates Atmospheric Heating but Subsurface Cooling
During the Growing Season.” Biogeosciences 18: 1577–1599. https://doi.org/10.5194/bg-18-1577-2021.Allison, J. S., and R. I. SmithOpen asset ↗Edinburgh Data Share · 10.7488/ds/7945pdf-raw-page:14 lines:1-79Code / dataset availability confirmedEurope PMC · checked 14 Sept 2026
Global biodiversity is changing at unprecedented rates during the Anthropocene. Whereas current biodiversity patterns can be observed directly, information from the recent past is far less easily retrieved yet urgently needed to understand present observations and predict future developments. For plants, herbaria offer such a unique glimpse into the past. Evaluation of plant specimens allows determining a wide range of attributes like species identity, morphological and phenological traits and even signs of biotic interactions. Specimen’s labels convey data such as species identity (and identification history), date and locality of collection, as well as the surrounding biotic and abiotic environment. Current methodological developments in sensor technology and computer vision increasingly enable us to extract this information in a high throughput and automated way. Equally vast developments in data science allow to integrate data from other sources for much more comprehensive analyses than before. With millions of specimens already digitized and digitization schemes running in many institutions, we will be increasingly able to determine characteristics of species and link them via distribution records to large-scale climate change scenarios. This allows us to better predict species’ threat levels, and to develop scenarios on the consequences of biodiversity change for ecosystem functioning. The present contribution reviews recent herbaria research and describes potential avenues with respect to Museomics and the Extended Specimen concept, and we propose Collectomics as a new framework to unravel, understand, and cope with the Anthropocene biodiversity change.
Why it matches plant phenotyping methods植物標本から形態・季節形質をセンサー技術とコンピュータビジョンで高スループット抽出する方法を扱うレビューであり、植物形質取得手法が実質的に含まれる。
abstractEvaluation of plant specimens allows determining a wide range of attributes like species identity, morphological and phenological traits and even signs of biotic interactions.
Reproduction assets foundThe authors openly publish the paper-specific dataset underlying their phenotyping analysis (Figure 3): an RO-Crate containing 100 annotated, digitized herbarium specimens with processed images, organ segmentation annotations, and computed surface-area measurements, semantically mapped to the Flora Phenotype Ontology.Dataset · publicure.
274
Comments of two anonymous reviewers greatly helped us to sharpen our ideas.
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7. Data availability statement
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The dataset consisting of 100 annotated, digitized herbarium specimens, which is referenced in
278
section 4, is available as an RO-Crate under a Creative Commons license and openly published under
279
https://doi.org/10.12761/w2c1-x551.280Open asset ↗10.12761/w2c1-x551.280pdf-raw-page:10 lines:1-68Code / dataset availability confirmedCrossref · checked 14 Sept 2026
The identification and classification of medicinal plant leaf diseases using machine learning techniques have become essential in agricultural and pharmaceutical research. This paper extends previous studies by reviewing advanced machine learning classifiers—XGBoost, Naïve Bayes (NB), Logistic Regression (LR), and k-Nearest Neighbors (KNN). Using the medicinal plant dataset available at [https://data.mendeley.com/datasets/hb74ynkjcn/5], we analyze classification performance, computational efficiency, and practical applicability. Unlike earlier studies that focused on Support Vector Machine (SVM), Decision Tree (DT), and Random Forest (RF), this review highlights new approaches, including boosting methods and probabilistic classifiers.
Why it matches plant phenotyping methods薬用植物の葉病害を機械学習で識別・分類する手法をレビューし、複数分類器の性能と計算効率を比較しているため、植物病害状態のフェノタイピング手法が中心です。
titleAn Advanced Review of Machine Learning Methods for Identifying Medicinal Plant Leaf Diseases
Reproduction assets foundThe paper's medicinal plant leaf disease classification experiments use a public Mendeley Data leaf image dataset (hb74ynkjcn/5), explicitly cited as the dataset analyzed in the study.Dataset · publicdiseases using machine learning techniques have become
essential in agricultural and pharmaceutical research. This paper extends previous studies by reviewing advanced
machine learning classifiers—XGBoost, Naïve Bayes (NB), Logistic Regression (LR), and k-Nearest Neighbors (KNN).
Using the medicinal plant dataset available at [https://data.mendeley.com/datasets/hb74ynkjcn/5], we analyze
classification performance, computational efficiency, and practical applicability. Unlike earlier studies that focused on
Support Vector Machine (SVM), Decision Tree (DT), and Random Forest (RF), this review highlights new approaches,
including boosting methods and probabilistic classifiers.
*Author of corresOpen asset ↗hb74ynkjcn/5pdf-raw-page:1 lines:1-67Code / dataset availability confirmedCrossref · checked 7 Sept 2026
Abstract Enhancing rapid phenotyping for key plant traits, such as biomass and nitrogen content, is critical for effectively monitoring crop growth and maximizing yield. Studies have explored the relationship between vegetation indices (VIs) and plant traits using drone imagery. However, there is a gap in the literature regarding data availability, accessible datasets. Based on this context, we conducted a systematic review to retrieve relevant data worldwide on the state of the art in drone-based plant trait assessment. The final dataset consists of 41 peer-reviewed papers with 11,189 observations for 11 major crop species distributed across 13 countries. It focuses on the association of plant traits with VIs at different growth/phenological stages. This dataset provides foundational knowledge on the key VIs to focus for phenotyping key plant traits. In addition, future updates to this dataset may include new open datasets. Our goal is to continually update this dataset, encourage collaboration and data inclusion, and thereby facilitate a more rapid advance of phenotyping for critical plant traits to increase yield gains over time.
Why it matches plant phenotyping methodsドローン画像と植生指数に基づく作物形質評価研究を体系的に収集・統合したデータセットであり、植物フェノタイピングの再利用可能な資源が中心です。
titleA global dataset for assessing nitrogen-related plant traits using drone imagery in major field crop species
Reproduction assets foundThe paper's own dataset (Dataset.xlsx with UAV_dataset, sensor info, and quantitative analysis tabs) plus authors' analysis code (R scripts and Jupyter notebook for Figs. 2-4) are publicly deposited on figshare at https://doi.org/10.6084/m9.figshare.22938797.v4.Dataset · publicThe data are accessible on the figshare repository39, available at https://doi.org/10.6084/m9.figshare.22938797,
and includes the following files:
1. “Dataset.xlsx” includes the data. It contains three tabs: “UAV_dataset”, “Sensor and processing info”, and
“Quantitatively analysis”.Open asset ↗figsharepdf-page:3 lines:58-75Code / dataset availability confirmedOpenAlex · Europe PMC · checked 14 Sept 2026
Integrating imaging sensors and artificial intelligence (AI) have contributed to detecting plant stress symptoms, yet data analysis remains a key challenge. Data challenges include standardized data collection, analysis protocols, selection of imaging sensors and AI algorithms, and finally, data sharing. Here, we present a systematic literature review (SLR) scrutinizing plant imaging and AI for identifying stress responses. We performed a scoping review using specific keywords, namely abiotic and biotic stress, machine learning, plant imaging and deep learning. Next, we used programmable bots to retrieve relevant papers published since 2006. In total, 2,704 papers from 4 databases (Springer, ScienceDirect, PubMed, and Web of Science) were found, accomplished by using a second layer of keywords (e.g., hyperspectral imaging and supervised learning). To bypass the limitations of search engines, we selected OneSearch to unify keywords. We carefully reviewed 262 studies, summarizing key trends in AI algorithms and imaging sensors. We demonstrated that the increased availability of open-source imaging repositories such as PlantVillage or Kaggle has strongly contributed to a widespread shift to deep learning, requiring large datasets to train in stress symptom interpretation. Our review presents current trends in AI-applied algorithms to develop effective methods for plant stress detection using image-based phenotyping. For example, regression algorithms have seen substantial use since 2021. Ultimately, we offer an overview of the course ahead for AI and imaging technologies to predict stress responses. Altogether, this SLR highlights the potential of AI imaging in both biotic and abiotic stress detection to overcome challenges in plant data analysis.
Why it matches plant phenotyping methods植物ストレス検出のための画像センサーとAI手法を体系的にレビューしており、植物表現型取得・解析手法が中心である。
abstractOur review presents current trends in AI-applied algorithms to develop effective methods for plant stress detection using image-based phenotyping.
Reproduction assets foundThe paper's authors publicly released the programmable-bot Python code used to conduct the systematic literature review's database searches and data processing, with explicit availability language and a GitHub URL. The Zotero group library of 262 studies is public but has no URL in the allowed list; Kaggle/Zindi/SpectrCode · publicJ.J.W. and E.M.; data curation and visualisation: J.J.W. writing original draft: all authors; writing, review and editing: J.J.W. and S.N.; funding acquisition: S.N.
Competing interests: The authors declare no conflict of interest.
Data Availability
All code used to create and run the programmable bots is available on GitHub ( https://github.com/Walshj73/data-processing-bot.git ) and licensed under the MIT license. All 262 studies found during this SLR process are available in a publicly accessible Zotero group library (titled “Advancements in Imaging Sensors and AI for Plant Stress Detection”). The group library can be accessed on Zotero by using the “Search for groups” feature found under Open asset ↗Walshj73/data-processing-botlines:160-199Code / dataset availability confirmedCrossref · Europe PMC · checked 13 Sept 2026
Abstract Plant pathogens can decimate crops and render the local cultivation of a species unprofitable. In extreme cases this has caused famine and economic collapse. Timing is vital in treating crop diseases, and the use of computer vision for precise disease detection and timing of pesticide application is gaining popularity. Computer vision can reduce labour costs, prevent misdiagnosis of disease, and prevent misapplication of pesticides. Pesticide misapplication is both financially costly and can exacerbate pesticide resistance and pollution. Here, we review the application and development of computer vision and machine learning methods for the detection of plant disease. This review goes beyond the scope of previous works to discuss important technical concepts and considerations when applying computer vision to plant pathology. We present new case studies on adapting standard computer vision methods and review techniques for acquiring training data, the use of diagnostic tools from biology, and the inspection of informative features. In addition to an in‐depth discussion of convolutional neural networks (CNNs) and transformers, we also highlight the strengths of methods such as support vector machines and evolved neural networks. We discuss the benefits of carefully curating training data and consider situations where less computationally expensive techniques are advantageous. This includes a comparison of popular model architectures and a guide to their implementation.
Why it matches plant phenotyping methods植物病害を対象としたコンピュータビジョンによる症状・病害の検出手法を中心に扱うレビューであり、植物フェノタイピング手法の方法論的整理と評価が主題である。
abstractHere, we review the application and development of computer vision and machine learning methods for the detection of plant disease.
Reproduction assets foundThe paper's data availability statement provides public OSF deposits (view-only links) containing image data, annotations, training data, and semi-supervised model weights for the cocoa disease-detection case studies, plus public GitHub repositories with the authors' custom training/analysis code (CocoaReader, CocoaNetDataset · publicThe image data, annotations, and link to the accompanying GitHub repository for Case Study 1 can be found at: https://osf.io/79kx3/?view_only=4a2c1dccee1a4baeb85de5002c702f10 .Open asset ↗osflines:411-466Dataset · publicFor Case Study 2, the data used to train the initial supervised model, the .csv search terms file for the below web scraper, and the final semi‐supervised model weights can be found at: https://osf.io/h5gj7/?view_only=dbf9f245e21a41e185f5b73e718b4cad .Open asset ↗osflines:411-466Code · publicThe custom code used to train both the initial model and the final semi‐supervised model can be found at: https://github.com/jrsykes/CocoaReader/blob/main/PlantNotPlant .Open asset ↗github · jrsykes/CocoaReaderlines:411-466Code · publicThe custom code to run the sweep in Case Study 4 can be found in the following GitHub repository: https://github.com/jrsykes/CocoaReader/tree/main/CocoaNet .Open asset ↗github · jrsykes/CocoaReaderlines:411-466Dataset · publicThe data used to generate these results and the full wandb report can be found at: https://osf.io/2fw6g/?view_only=adc66ba66f83465a9e7b111515a60bf2 .Open asset ↗osflines:411-466Code · publicThe “contaminated” data used to train the semi‐supervised model were generated using the code at: https://github.com/jrsykes/Google-Image-Scraper .Open asset ↗github · jrsykes/Google-Image-Scraperlines:411-466Code / dataset availability confirmedOpenAlex · checked 13 Sept 2026
Expanded use of 3D imaging in organismal biology and paleontology has substantially enhanced the ability to visualize and analyze specimens. These techniques have improved our understanding of the anatomy of many taxa, and the integration of downstream computational tools applied to 3D datasets have broadened the range of analyses that can be performed (e.g., finite element analyses, geometric morphometrics, biomechanical modeling, physical modeling using 3D printing). However, morphological analyses inevitably present challenges, particularly in fossil taxa where taphonomic or preservational artifacts distort and reduce the fidelity of the original morphology through shearing, compression, and disarticulation, for example. Here, we present a compilation of techniques to build high-quality 3D digital models of extant and fossil taxa from 3D imaging data using freely available software for students and educators. Our case studies and associated step-by-step supplementary tutorials present instructions for working with reconstructions of plants and animals to directly address and resolve common issues with 3D imaging data. The strategies demonstrated here optimize scientific accuracy and computational efficiency and can be applied to a broad range of taxa.
Why it matches plant phenotyping methods植物を含む標本の3D画像データから高品質な形態モデルを構築する技術と手順が中心で、植物形態の取得・解析に再利用可能な方法論を提供している。
abstractHere, we present a compilation of techniques to build high-quality 3D digital models of extant and fossil taxa from 3D imaging data using freely available software for students and educators.
Reproduction assets foundThe paper's Data Availability statement points to a public Figshare deposit containing the authors' 3D object files and tutorial materials used in the case studies (including plant specimen reconstructions), making it a paper-specific, publicly actionable asset.Dataset · publicData is available as part of the Supplemental Materials and from Figshare: https://doi.org/10.6084/m9.figshare.21266568 .Open asset ↗Figshare · 10.6084/m9.figshare.21266568lines:72-101Code / dataset availability confirmedCrossref · checked 15 Sept 2026
In plant–insect interactions, calcium (Ca2+) variations are among the earliest events associated with the plant perception of biotic stress. Upon herbivory, Ca2+ waves travel long distances to transmit and convert the local signal to a systemic defense program. Reactive oxygen species (ROS), Ca2+ and electrical signaling are interlinked to form a network supporting rapid signal transmission, whereas the Ca2+ message is decoded and relayed by Ca2+-binding proteins (including calmodulin, Ca2+-dependent protein kinases, annexins and calcineurin B-like proteins). Monitoring the generation of Ca2+ signals at the whole plant or cell level and their long-distance propagation during biotic interactions requires innovative imaging techniques based on sensitive sensors and using genetically encoded indicators. This review summarizes the recent advances in Ca2+ signaling upon herbivory and reviews the most recent Ca2+ imaging techniques and methods.
Why it matches plant phenotyping methods植物のCa2+シグナルを測定するイメージング技術・手法をレビューしており、植物の生理状態の取得方法が中心的に扱われている。
abstractMonitoring the generation of Ca2+ signals at the whole plant or cell level and their long-distance propagation during biotic interactions requires innovative imaging techniques based on sensitive sensors and using genetically encoded indicators.
Reproduction assets foundThe paper's own supplementary material contains Video S1, the authors' calcium imaging time-course of Spodoptera littoralis feeding on R-GECO1-expressing Arabidopsis, publicly downloadable from the MDPI supplementary URL.Supplement · publiccknowledgments
The authors whish to thank A. Costa, M. Grenzi and NOLIMITS, an advanced imaging facility established by the University of Milan, for providing the video and images of the fast calcium imaging analyses upon S. littoralis herbivory.
Supplementary Materials
The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/plants11202689/s1 , Video S1: Rapid Ca 2+ signals generated when Spodoptera littoralis feeds on Arabidopsis thaliana expressing the genetically encoded R-GECO1 sensor.
Click here for additional data file.
Author Contributions
Conceptualization, M.E.M.; methodology, M.E.M.; investigation, A.S.P. and M.E.M.; resources, M.E.M.; writinOpen asset ↗lines:66-85Code / 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 confirmedOpenAlex · Europe PMC · checked 14 Sept 2026
Many biochemical and physiological properties of plants that are of interest to breeders and geneticists have extremely low throughput and/or can only be measured destructively. This has limited the use of information on natural variation in nutrient and metabolite abundance, as well as photosynthetic capacity in quantitative genetic contexts where it is necessary to collect data from hundreds or thousands of plants. A number of recent studies have demonstrated the potential to estimate many of these traits from hyperspectral reflectance data, primarily in ecophysiological contexts. Here, we summarize recent advances in the use of hyperspectral reflectance data for plant phenotyping, and discuss both the potential benefits and remaining challenges to its application in plant genetics contexts. The performances of previously published models in estimating six traits from hyperspectral reflectance data in maize were evaluated on new sample datasets, and the resulting predicted trait values shown to be heritable (e.g., explained by genetic factors) were estimated. The adoption of hyperspectral reflectance-based phenotyping beyond its current uses may accelerate the study of genes controlling natural variation in biochemical and physiological traits.
Why it matches plant phenotyping methods植物形質をハイパースペクトル反射データから推定する手法をレビューし、トウモロコシの新規サンプルで既存モデルを評価しており、表現型取得・推定法が中心である。
abstractHere, we summarize recent advances in the use of hyperspectral reflectance data for plant phenotyping, and discuss both the potential benefits and remaining challenges to its application in plant genetics contexts.
Reproduction assets foundThe paper's Data availability statement explicitly deposits the authors' spectral reflectance data and ground truth phenotyping measurements in a public repository (Zenodo-style DOI 10.21232/y5TTxY3N), which is an allowed URL. This is a paper-specific, publicly actionable hyperspectral phenotyping dataset.Dataset · publicd the potential for reusable genotypic datasets, that makes the potential of hyperspectral reflectance phenotyping to both expand our current genetic knowledge and address the challenges of breeding for the 21st century so exciting.
Data availability
Spectral reflectance data and ground truth measurements have been deposited in https://doi.org/10.21232/y5TTxY3N .
Funding
This research was supported by the Office of Science (BER), 10.13039/100000015 U.S. Department of Energy , grant no. DE-SC0020355 to J.C.S. and Y.G., the 10.13039/100000001 National Science Foundation under grant OIA-1557417 to Y.G. and J.C.S. and OIA-1826781 to J.C.S. This project was completed utilizing the HollandOpen asset ↗10.21232/y5TTxY3Nlines:311-337Code / dataset availability confirmedEurope PMC · checked 14 Sept 2026
Background With up to 200 published contributions, the GreenLab mathematical model of plant growth, developed since 2000 under Sino-French co-operation for agronomic applications, is descended from the structural models developed in the AMAP unit that characterize the development of plants and encompass them in a conceptual mathematical framework. The model also incorporates widely recognized crop model concepts (thermal time, light use efficiency and light interception), adapting them to the level of the individual plant. Scope Such long-term research work calls for an overview at some point. That is the objective of this review paper, which retraces the main history of the model's development and its current status, highlighting three aspects. (1) What are the key features of the GreenLab model? (2) How can the model be a guide for defining relevant measurement strategies and experimental protocols? (3) What kind of applications can such a model address? This last question is answered using case studies as illustrations, and through the Discussion. Conclusions The results obtained over several decades illustrate a key feature of the GreenLab model: owing to its concise mathematical formulation based on the factorization of plant structure, it comes along with dedicated methods and experimental protocols for its parameter estimation, in the deterministic or stochastic cases, at single-plant or population levels. Besides providing a reliable statistical framework, this intense and long-term research effort has provided new insights into the internal trophic regulations of many plant species and new guidelines for genetic improvement or optimization of crop systems.
Why it matches plant phenotyping methodsGreenLabモデルの開発史と、植物構造・成長の測定戦略およびパラメータ推定用実験プロトコルを中心にレビューしており、植物形質の取得・モデル化手法が主要テーマである。
abstractWhat are the key features of the GreenLab model? (2) How can the model be a guide for defining relevant measurement strategies and experimental protocols?
Reproduction assets foundThe review explicitly states that the data and source codes for its maize and coffee GreenLab calibration case studies are publicly available as Supplementary Data S1 and via the authors' URL http://greenlab.cirad.fr/StemGL/AoB_19945R_Codes.zip. The GLUVED eLearning site is a general course resource, not a paper-phenotCode · public06 ). Other heuristic methods have also been used, such as particle swarm optimization ( Qi et al. , 2010 ) and neural networks ( Fan et al. , 2015 ).
We illustrate here the parameter estimations on maize and coffee. Data and sources codes are available as Supplementary Data S1 . These are also available from the following link http://greenlab.cirad.fr/StemGL/AoB_19945R_Codes.zip .
These case studies are somewhat iconic. The study of maize is interesting as it has a simple non-branched deterministic structure but complex organ expansions, due to their duration and sink strength variation. The fruits are not numerous, but their biomass is significant due to their high sink strength. Thus, thiOpen asset ↗lines:306-320Code / 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 10 Sept 2026
Despite the availability of whole genome sequences of apple and peach, there has been a considerable gap between genomics and breeding. To bridge the gap, the European Union funded the FruitBreedomics project (March 2011 to August 2015) involving 28 research institutes and private companies. Three complementary approaches were pursued: (i) tool and software development, (ii) deciphering genetic control of main horticultural traits taking into account allelic diversity and (iii) developing plant materials, tools and methodologies for breeders. Decisive breakthroughs were made including the making available of ready-to-go DNA diagnostic tests for Marker Assisted Breeding, development of new, dense SNP arrays in apple and peach, new phenotypic methods for some complex traits, software for gene/QTL discovery on breeding germplasm via Pedigree Based Analysis (PBA). This resulted in the discovery of highly predictive molecular markers for traits of horticultural interest via PBA and via Genome Wide Association Studies (GWAS) on several European genebank collections. FruitBreedomics also developed pre-breeding plant materials in which multiple sources of resistance were pyramided and software that can support breeders in their selection activities. Through FruitBreedomics, significant progresses were made in the field of apple and peach breeding, genetics, genomics and bioinformatics of which advantage will be made by breeders, germplasm curators and scientists. A major part of the data collected during the project has been stored in the FruitBreedomics database and has been made available to the public. This review covers the scientific discoveries made in this major endeavour, and perspective in the apple and peach breeding and genomics in Europe and beyond.
Why it matches plant phenotyping methodsリンゴ・モモ育種プロジェクトのレビューで、複雑形質の新しい表現型測定法の開発とデータベース化を明示的に扱っており、フェノタイピング手法・データが中心的な構成要素の一つである。
abstractThis review covers the scientific discoveries made in this major endeavour, and perspective in the apple and peach breeding and genomics in Europe and beyond.
Reproduction assets foundThe paper describes the FruitBreedomics database storing the project's apple/peach phenotypic and genotypic data, publicly accessible at the tecnoparco URL, and the HapAg (HaploblockAggregator) software developed for the apple linkage map analysis, publicly available at the Wageningen URL. Both are paper-specific,公开,和可Dataset · publicAll data are accessible through a stable and well-maintained interface, available at the address http://bioinformatics.tecnoparco.org/fruitbreedomics .Open asset ↗lines:39-45Code · publicThe dedicated software HapAg was developed in support to this approach, which has been made public available at http://www.wageningenur.nl/en/show/HaploblockAggregator.htm .Open asset ↗lines:62-69Code / dataset availability confirmedEurope PMC · OpenAlex · checked 10 Sept 2026
Contemporary terrestrial laser scanning (TLS) is being used widely in forest ecology applications to examine ecosystem properties at increasing spatial and temporal scales. Harvard Forest (HF) in Petersham, MA, USA, is a long-term ecological research (LTER) site, a National Ecological Observatory Network (NEON) location and contains a 35 ha plot which is part of Smithsonian Institution's Forest Global Earth Observatory (ForestGEO). The combination of long-term field plots, eddy flux towers and the detailed past historical records has made HF very appealing for a variety of remote sensing studies. Terrestrial laser scanners, including three pioneering research instruments: the Echidna Validation Instrument, the Dual-Wavelength Echidna Lidar and the Compact Biomass Lidar, have already been used both independently and in conjunction with airborne laser scanning data and forest census data to characterize forest dynamics. TLS approaches include three-dimensional reconstructions of a plot over time, establishing the impact of ice storm damage on forest canopy structure, and characterizing eastern hemlock ( Tsuga canadensis ) canopy health affected by an invasive insect, the hemlock woolly adelgid ( Adelges tsugae ). Efforts such as those deployed at HF are demonstrating the power of TLS as a tool for monitoring ecological dynamics, identifying emerging forest health issues, measuring forest biomass and capturing ecological data relevant to other disciplines. This paper highlights various aspects of the ForestGEO plot that are important to current TLS work, the potential for exchange between forest ecology and TLS, and emphasizes the strength of combining TLS data with long-term ecological field data to create emerging opportunities for scientific study.
Why it matches plant phenotyping methodsTLSを用いた森林キャノピー構造、健康状態、バイオマスの測定・監視を中心に扱うレビューであり、植物状態の取得手法が主要テーマです。
abstractTerrestrial laser scanners, including three pioneering research instruments: the Echidna Validation Instrument, the Dual-Wavelength Echidna Lidar and the Compact Biomass Lidar, have already been used both independently and in conjunction with airborne laser scanning data and forest census data to characterize forest dynamics.
Reproduction assets foundThe paper's TLS work is grounded in the Harvard Forest ForestGEO plot census data (HF253), which the authors explicitly make available in the Data accessibility statement via the Harvard Forest Data Archive. This is a paper-specific, publicly accessible field/phenotype dataset (stem surveys, DBH, mortality assessments)Dataset · publicAdditional data are available from: http://harvardforest.fas.harvard.edu:8080/exist/apps/datasets/showData.html?id=hf253 .Open asset ↗hf253lines:111-224Code / dataset availability confirmedEurope PMC · checked 10 Sept 2026
Vibrational spectroscopy provides non-destructively the molecular fingerprint of plant cells in the native state. In combination with microscopy, the chemical composition can be followed in context with the microstructure, and due to the non-destructive application, in-situ studies of changes during, e.g., degradation or mechanical load are possible. The two complementary vibrational microspectroscopic approaches, Fourier-Transform Infrared (FT-IR) Microspectroscopy and Confocal Raman spectroscopy, are based on different physical principles and the resulting different drawbacks and advantages in plant applications are reviewed. Examples for FT-IR and Raman microscopy applications on plant cell walls, including imaging as well as in-situ studies, are shown to have high potential to get a deeper understanding of structure-function relationships as well as biological processes and technical treatments. Both probe numerous different molecular vibrations of all components at once and thus result in spectra with many overlapping bands, a challenge for assignment and interpretation. With the help of multivariate unmixing methods (e.g., vertex components analysis), the most pure components can be revealed and their distribution mapped, even tiny layers and structures (250 nm). Instrumental as well as data analysis progresses make both microspectroscopic methods more and more promising tools in plant cell wall research.
Why it matches plant phenotyping methods植物細胞壁の構造・化学組成を対象とする振動顕微分光法（FT-IRおよび共焦点ラマン）の植物への応用、画像化、データ解析を方法論としてレビューしており、植物状態の取得・抽出法が中心である。
abstractThe two complementary vibrational microspectroscopic approaches, Fourier-Transform Infrared (FT-IR) Microspectroscopy and Confocal Raman spectroscopy, are based on different physical principles and the resulting different drawbacks and advantages in plant applications are reviewed.
Reproduction assets foundThe review mentions an author-established public spectral database of plant cell wall reference components and spectra, hosted at bionami.at/spectra.html, which directly supports the paper's vibrational microspectroscopy measurements and band-assignment analysis. No code, models, or image datasets with explicit public-Dataset · publica spectral database, including reference components as well as different plant cell walls is currently established and made available to the scientific community ( http://bionami.at/spectra.html ).Open asset ↗bionami.atlines:98-107Code / dataset availability confirmedOpenAlex · checked 15 Sept 2026
Abstract Taxonomic descriptions contain valuable phenotypic data that is often not directly accessible for modern evolutionary, ecological, or biodiversity analyses. We describe a process for building a consensus‐based controlled vocabulary from taxonomic descriptions for plants, which also can be applied for building controlled vocabularies for other taxon groups. Controlled vocabularies are useful as lexicons for text mining algorithms, as source of candidate terms for ontologies, and as guides to help future authors use domain vocabulary more appropriately and consistently. We extracted phenotype‐ describing phrases terms from descriptions of 30 volumes of the Flora of North America and Flora of China and merged these with terms from the Categorical Glossary of the Flora of North America. Seven contributors placed the terms into a set of categories until there was an agreement among two or more categorizations per term. Term categorization makes the meaning of a term more explicit for the subsequent users of the glossary. The resulting “Plant Glossary” (terms and categorization of terms) contains 9228 terms grouped in 53 categories. Differences in term categorization represented 49% of the categorization effort, and the many differences among individual classifications can be attributed to individual interpretation of terms and to the fluid nature of descriptive language used in Floras. The difficulties experienced while classifying the terms allowed us to explore cases where the use of language can hinder the accurate and detailed annotation of taxonomic descriptions. The Plant Glossary represents a significant step towards creating and enriching formal ontologies for plant phenotypes as the semantic phenomena found through this exercise is useful background information for building ontologies. The glossary has been used by new software to parse and annotate plant taxonomic descriptions, and over 6000 new terms are available for creating ontologies.
Why it matches plant phenotyping methods植物の表現型記述語彙を構築し、注釈・オントロジー化やテキストマイニングに利用する基盤を開発した研究であり、植物表現型情報の抽出・表現に関する方法論が中心。
abstractWe describe a process for building a consensus‐based controlled vocabulary from taxonomic descriptions for plants
Reproduction assets foundThe paper's core output, the Plant Glossary (9228 terms with expert categorizations derived from Flora of North America and Flora of China descriptions), is explicitly stated to be publicly available on the TDG terms wiki (with source sentences) and as a .csv file in the authors' GitHub biosemantics/glossaries Plant reDataset · publicin Table 3. Disagreement in term
classification among two or more contributors represented
49% of the categorizations, and it was more prevalent in some
categories than others (Table 4). The Plant Glossary (terms
and their corresponding categories) is publically available at
Taxonomic Databases Working Group (TWDG) terms wiki:
http://terms.tdwg.org/wiki/FloraTerms (with source sentences)
and at https://github.com/biosemantics/glossaries/tree/master/Plant (not including source sentences). In both places, users
can continue commenting on term categorization. The Plant
Glossary has also been incorporated into the Text Capture Tool
of “Explorer of Taxon Concepts” online portal (Cui & al., 20Open asset ↗FloraTermspdf-raw-page:7 lines:86-145Dataset · publicuage. London: Continuum.
Beentje, H. 2012. The Kew plant glossary – An illustrated diction-
ary of plant terms. Richmond: Kew Publishing, Royal Botanic
Gardens Kew.
The terms contained in the Plant Glossary with their cor-
responding categories are available at http://terms.tdwg.org/wiki/FloraTerms and as a .csv file in Github (https://github.com/biosemantics/glossaries/tree/master/Plant). They also have
been incorporated into the Text Capture Tool of the Explorer of
Taxon Concepts (ETC) online portal. In the ETC environment,
the Plant Glossary is used to help extract character data from
taxonomic descriptions, and these data have been successfully
used to construct matrices for phylogeneticOpen asset ↗github.com/biosemantics/glossariespdf-raw-page:13 lines:1-87