Abstract 3D models are used in plant phenotyping for non-destructive quantification and analysis of morphological characteristics. Analyzing plant structure allows breeders to select for desirable traits, associated with e.g. drought tolerance or increased productivity. In sugar beet, morphological parameters depict an essential element of the variety approval for distinguishing between genotypes. However, only a limited number of measured or scored parameters are considered at a single time point. In contrast, 4D data adds a temporal component and can depict the dynamic development of 3D parameters. To explore the potential of spatio-temporal 4D phenotyping for automated crop genotype differentiation, a greenhouse experiment was conducted by us covering twelve sugar beet genotypes. High-resolution 3D models were generated twice a week over the course of two months and both common and novel 3D morphological parameters were extracted. The importance of these parameters was assessed by us, and the dataset was analyzed using unsupervised pointwise clustering and time series clustering. Varying importance of parameters depending on the time point and significantly higher importance of plant parameters compared to leaf parameters are demonstrated by our results. Moreover, increased and more stable genotype differentiation is archived using time series clustering compared to pointwise clustering. Furthermore, taproot formation of sugar beet was found to have a crucial impact on morphological development. Substantial variations in the dynamic development of 3D morphological parameters underline the importance of 4D data for plant genotype differentiation. Thus, a novel foundation for genotype differentiation in plant phenotyping is provided by our findings.
Why it matches plant phenotyping methods3Dモデルから植物形態形質を抽出し、時系列クラスタリングで遺伝型識別を評価する4Dフェノタイピング手法が研究の中心である。
titleSpatio-temporal 4D phenotyping for automated morphological genotype differentiation of sugar beet
Reproduction assets foundThe paper publicly deposits its generated sugar beet point cloud dataset under CC BY 4.0 at a Dataverse DOI, directly reproducing the paper's phenotyping measurements. Supplementary Python codes and extracted parameter values are stated to be included with the article, but no authors' public URL for the code is presentDataset · publicThe generated point cloud dataset is available at https://doi.org/10.60507/FK2/IS8YBZ under CC BY 4.0 license.Open asset ↗10.60507/FK2/IS8YBZlines:277-363Code / dataset availability confirmedEurope PMC · OpenAlex · checked 14 Sept 2026
Sun-induced fluorescence (SIF) has emerged as a promising tool for tracking photosynthetic dynamics, yet its application in monitoring biotic stress remains underexplored in field conditions. In this study, we investigated the effects of Cercospora leaf spot (CLS), a destructive foliar disease of sugar beet (Beta vulgaris L.), for which traditional monitoring methods often fail to capture subtle disease effects or distinguish between structural and physiological stress responses. CLS infection was induced through artificial inoculation and manually scored. Canopy-level reflectance indices were acquired along with red and far-red passive SIF signals and active PSII efficiency traits using FloX and LIFT sensors mounted on an automated high-throughput phenotyping platform. The results demonstrate that SIF effectively detects CLS in sugar beet, with responses comparable with structural and disease- specific indices. Despite visible symptoms, PSII efficiency (Fq'/Fm') remained stable across treatments, indicating limited impairment of leaf photosynthetic efficiency at early stages. However, the canopy-level electron transport rate varied significantly and showed a strong relationship with red and far-red SIF, suggesting that CLS primarily affects canopy light absorption and utilization. After structural normalization, SIF yield remained largely unchanged, confirming that observed SIF reductions were mainly driven by canopy structural alterations. Overall the study demonstrates the effectiveness of SIF for large-scale disease monitoring and integration into high-throughput phenotyping, while also revealing structural and physiological factors influencing the SIF signal under disease stress.
Why it matches plant phenotyping methodsSIFおよびPSIIセンサーを搭載したハイスループット表現型解析プラットフォームで、サトウダイコンの病害状態と構造・生理応答を評価する手法の実質的な適用・検証が中心である。
abstractCanopy-level reflectance indices were acquired along with red and far-red passive SIF signals and active PSII efficiency traits using FloX and LIFT sensors mounted on an automated high-throughput phenotyping platform.
Reproduction assets foundThe paper's phenotyping dataset (SIF, reflectance indices, LIFT PSII traits, disease scores from the CLS sugar beet field trial) is deposited in the open access Jülich DATA repository under DOI 10.26165/JUELICH-DATA/FOQOFI. No separate author analysis code repository with explicit availability language is stated; R/lmeDataset · publicThe dataset has been deposited in the open access Jülich DATA reposi ease using UAV-supported image data and deep learning. Sugar Industry
tory: https://doi.org/10.26165/JUELICH-DATA/FOQOFI. 147, 79–86.
Ispizua Yamati FR, Bömer J, Noack N, Linkugel T, Paulus S, Mahlein
A-K. 2025. Configuration of a multisensor platform for advanced plant phe
References notyping and disease detection: case study on cercospora leaf spot in sugar
Ač A, Malenovský Z, Olejníč ková J, Gallé A, Rascher U, Mohammed beet. Smart AgricultOpen asset ↗Jülich DATA · 10.26165/JUELICH-DATA/FOQOFIpdf-layout-page:14 lines:52-72Code / dataset availability confirmedEurope PMC · checked 14 Sept 2026
Results: proved that synchronized use of inexpensive RGB images, image processing, and machine learning (ML) can accurately identify crop stress. Four Machine Learning Image Modules (MLIMs) were developed to enable rapid and cost-effective identification of sugar beet stresses caused by water and/or nitrogen deficiencies. RGB images representing stressed and non-stressed crops were used in the analysis. Each MLIM was trained and tested using 54 combinations derived from nine canopy and RGB-based input features and six ML algorithms. The most accurate MLIM used RGB bands as input to a Multi-Layer Perceptron, achieving 100% accuracy for overall stress detection, and 95.6% and 86.7% for water and nitrogen stress identification, respectively. A Stochastic Gradient Descent model, using only the green band, achieved 97.78% accuracy for stress detection while requiring only one-fourth the computation time. For specific stresses, a Random Forest (RF) model using RGB bands and canopy cover achieved 86.7% for water stress, while RF with the excess green index reached 75.6% for nitrogen stress. To address the trade-off between accuracy and computational cost, a bargaining theory-based framework was applied. This approach identified optimal MLIMs that balance performance and execution efficiency.
Why it matches plant phenotyping methodsRGB画像・画像処理・機械学習を用いてテンサイの水・窒素ストレスを識別する画像ベースの表現型推定手法を開発・比較しており、ストレス状態の取得・抽出が研究の中心です。
abstractsynchronized use of inexpensive RGB images, image processing, and machine learning (ML) can accurately identify crop stress
Reproduction assets foundThe paper's Data Availability Statement explicitly states the supporting data (the sugar beet RGB image dataset and derived inputs used for stress-detection ML) are openly available in a HydroShare repository, matching an allowed URL. No code or model deposit is stated.Dataset · publicualization, SRH, MH, RCP.; supervision, SR MH, RCP,
MS.; project administration, SRH, MH, RCP, MS. All authors have read and agreed to the published version of
the manuscript.
Funding: This research received no external funding
Data Availability Statement: The data that support the findings of this study are openly available in
http://www.hydroshare.org/resource/02b0a248417c4dd6b1b2d7a3c24bc5b6
Acknowledgments: We acknowledge the Writing Centre at Utah State University, USA, for assisting us in
improving the English in this paper, Imam Khomeini International University, Iran, for providing the supporting
resources, and Tehran Municipality, Iran, for their collaboration and support during thiOpen asset ↗hydroshare.org · 02b0a248417c4dd6b1b2d7a3c24bc5b6pdf-raw-page:15 lines:1-61Code / dataset availability confirmedEurope PMC · checked 6 Sept 2026
Background Agricultural production is crucial for nutrition, but it frequently faces challenges such as decreased yield, quality, and overall output due to the adverse effects of diseases and pests. Remote sensing technologies have emerged as valuable tools for diagnosing and monitoring these issues. They offer significant advantages over traditional methods, which are often time-consuming and limited in sampling. High-resolution images from drones and satellites provide fast and accurate solutions for detecting and diagnosing crops' health and identifying pests and diseases affecting them. Methods The research focused on the early detection of Cercospora leaf spot ( Cercospora beticola Sacc .) and powdery mildew ( Erysiphe betae (Vaňha) Weltzien ), which cause significant economic losses in sugar beet before visible symptoms emerge. The study was accomplished by capturing images of uncrewed aerial vehicle (UAV) in field conditions. To effectively evaluate different detection methods in agricultural contexts, the study targeted two key areas: (1) monitoring Cercospora in fields without pesticide application, utilizing the Metos climate station early warning system alongside UAV-based image analysis, and (2) monitoring powdery mildew, which involved visual disease detection and targeted spraying based on UAV image processing. Trial plots were established for this purpose, with six replications for each method. Results UAV-based images show that Normalized Difference Vegetation Index values in leaves decreased before disease onset. This change is an important warning sign for the emergence of the disease. Additionally, the study demonstrated that early detection of diseases is possible using K-nearest neighbors and logistic regression algorithms, exhibiting high discrimination and predictive accuracy.
Why it matches plant phenotyping methodsUAV画像と機械学習により、砂糖大根の病害状態を発症前に推定する方法が研究の中心であり、植物の病害表現型を直接評価している。
titleEarly detection of Cercospora beticola and powdery mildew diseases in sugar beet using uncrewed aerial vehicle-based remote sensing and machine learning.
Reproduction assets foundThe article's Data Availability section explicitly deposits the authors' classification code on GitHub and the paper-specific Sugar Beet Dataset (UAV/phenotyping measurements) on Zenodo, both with public URLs.Code · publicThe data and code are available at GitHub and Zenodo:Open asset ↗lines:960-1125Code / dataset availability confirmedOpenAlex · Crossref · Europe PMC · checked 6 Sept 2026
Digital phenotyping is a fast-growing area of hardware and software research and development. Phenotypic studies usually require determining whether there is a difference in some trait between plants with different genotypes or under different conditions. We developed StatFaRmer, a user-friendly tool tailored for analyzing time series of plant phenotypic parameters, ensuring seamless integration with common tasks in phenotypic studies. For maximum versatility across phenotypic methods and platforms, it uses data in the form of a set of spreadsheets (XLSX and CSV files). StatFaRmer is designed to handle measurements that have variation in timestamps between plants and the presence of outliers, which is common in digital phenotyping. Data preparation is automated and well-documented, leading to customizable ANOVA tests that include diagnostics and significance estimation for effects between user-defined groups. Users can download the results from each stage and reproduce their analysis. It was tested and shown to work reliably for large datasets across various experimental designs with a wide range of plants, including bread wheat (Triticum aestivum), durum wheat (Triticum durum), and triticale (× Triticosecale); sugar beet (Beta vulgaris), cocklebur (Xanthium strumarium) and lettuce (Lactuca sativa), corn (Zea mays) and sunflower (Helianthus annuus), and soybean (Glycine max). StatFaRmer is created as an open-source Shiny dashboard, and simple instructions on installation and operation on Windows and Linux are provided.
Why it matches plant phenotyping methods植物フェノタイピングの時系列データ解析を目的とするオープンソースShinyダッシュボードを開発し、データ準備・統計解析・再現可能なワークフローを提供しており、方法・ソフトウェアが中心である。
abstractWe developed StatFaRmer, a user-friendly tool tailored for analyzing time series of plant phenotypic parameters
Reproduction assets foundThe paper's authors publicly release StatFaRmer, an open-source R Shiny dashboard for phenotyping data analysis, via GitHub with installation instructions and a sample phenotypic dataset, and host a live deployment on shinyapps.io.Code · publicThe resulting tool can be accessed at 9 https://github.com/Stathmin/StatFaRmer ), with the instructions on installation and the sample dataset provided.Open asset ↗Stathmin/StatFaRmerlines:521-528Dataset · publicA sample dataset of different plant species (bread wheat ( Triticum aestivum ), durum wheat ( Triticum durum ), and triticale (× Triticosecale )), cultivars (35 variants) and plant genotypes (allelic state of 3 genes), with different treatments (3 variants), and the time series of morphological and spectral parameters of these plants is loaded in this tool as an example and available on GitHub.Open asset ↗lines:340-350Code / dataset availability confirmedEurope PMC · checked 6 Sept 2026
Remote sensing and artificial intelligence are pivotal technologies of precision agriculture nowadays. The efficient retrieval of large-scale field imagery combined with machine learning techniques shows success in various tasks like phenotyping, weeding, cropping, and disease control. This work will introduce a machine learning framework for automatized large-scale plant-specific trait annotation for the use case of disease severity scoring for CLS in sugar beet. With concepts of DLDL, special loss functions, and a tailored model architecture, we develop an efficient Vision Transformer based model for disease severity scoring called SugarViT. One novelty in this work is the combination of remote sensing data with environmental parameters of the experimental sites for disease severity prediction. Although the model is evaluated on this special use case, it is held as generic as possible to also be applicable to various image-based classification and regression tasks. With our framework, it is even possible to learn models on multi-objective problems, as we show by a pretraining on environmental metadata. Furthermore, we perform several comparison experiments with state-of-the-art methods and models to constitute our modeling and preprocessing choices.
Why it matches plant phenotyping methods植物病害重症度をUAV画像から自動推定するVision Transformerベースの手法を開発・比較評価しており、植物表現型取得が中心である。
abstractThis work will introduce a machine learning framework for automatized large-scale plant-specific trait annotation for the use case of disease severity scoring for CLS in sugar beet.
Reproduction assets foundThe paper's Data Availability statement explicitly says the data and code supporting the findings are publicly available on GitHub at the authors' repository URL, which is an allowed URL. This qualifies as a paper-specific public asset containing the authors' analysis code and the UAV multispectral plant image dataset.Code · publicData Availability: The data and code supporting the findings in this paper are available at GitHub ( https://github.com/mrcgndr/disease_severity_prediction/ ).Open asset ↗https://github.com/mrcgndr/disease_severity_prediction/lines:154-190Code / dataset availability confirmedCrossref · checked 13 Sept 2026
Field robots are an important tool when improving the efficiency and decreasing the climatic impact of food production. Although several commercial field robots are available, the advantages, limitations, and optimal utilization methods of this technology are still not well understood due to its novelty. This study aims to evaluate the performance of a commercial field robot for seeding and weeding tasks. The evaluation was carried out in a 2-hectare sugar beet field. The robot’s performance was assessed by counting plants and weeds using image processing. The YOLOv8 model was trained to detect sugar beets and weeds. The plant and weed densities were compared on a robotically weeded area of the field, a chemically weeded control area, and an untreated control area. The average weed density on the robotically treated area was about two times lower than that on the untreated area and about three times higher than on the chemically treated area. The testing robot in the specific testing environment and mode showed intermediate results, weeding a majority of the weeds between the rows; however, it left the most harmful weeds close to the plants. Software for robot performance assessment can be used for monitoring robot performance and plant conditions several times during plant growth according to the weeding frequency.
Why it matches plant phenotyping methodsYOLOv8画像処理で作物・雑草を検出し、植物密度を定量化する手法とロボット性能評価ソフトが研究の中心であり、植物状態の反復モニタリングに用いるため。
abstractThe robot’s performance was assessed by counting plants and weeds using image processing.
Reproduction assets foundThe paper's field image dataset (2272 sugar beet/weed images) is openly available on Zenodo, and the authors' Matlab robot-performance analysis software is publicly hosted on GitHub. Both are paper-specific, public, and actionable.Dataset · publicThe dataset consisting of 2272 images collected in this study is available in open
access (https://zenodo.org/records/10716274, accessed 18 September 2024).Open asset ↗zenodo · 10716274pdf-page:3 lines:1-146Code / 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 confirmedEurope PMC · checked 8 Sept 2026
Image processing and analysis based on deep learning are becoming mainstream and increasingly accessible for solving various scientific problems in diverse fields. However, it requires advanced computer programming skills and a basic familiarity with character user interfaces (CUIs). Consequently, programming beginners face a considerable technical hurdle. Because potential users of image analysis are experimentalists, who often use graphical user interfaces (GUIs) in their daily work, there is a need to develop GUI-based easy-to-use deep learning software to support their work. Here, we introduce JustDeepIt, a software written in Python, to simplify object detection and instance segmentation using deep learning. JustDeepIt provides both a GUI and a CUI. It contains various functional modules for model building and inference, and it is built upon the popular PyTorch, MMDetection, and Detectron2 libraries. The GUI is implemented using the Python library FastAPI, simplifying model building for various deep learning approaches for beginners. As practical examples of JustDeepIt, we prepared four case studies that cover critical issues in plant science: (1) wheat head detection with Faster R-CNN, YOLOv3, SSD, and RetinaNet; (2) sugar beet and weed segmentation with Mask R-CNN; (3) plant segmentation with U 2 -Net; and (4) leaf segmentation with U 2 -Net. The results support the wide applicability of JustDeepIt in plant science applications. In addition, we believe that JustDeepIt has the potential to be applied to deep learning-based image analysis in various fields beyond plant science.
Why it matches plant phenotyping methods植物画像の物体検出・インスタンスセグメンテーションを行うソフトウェア自体が中心で、植物科学での検証例も含むため、植物フェノタイピング手法として含める。
abstractHere, we introduce JustDeepIt, a software written in Python, to simplify object detection and instance segmentation using deep learning.
Reproduction assets foundThe paper's authors publicly deposited their analysis software JustDeepIt (the tool used for all four plant phenotyping case studies) on GitHub under an MIT License, and the data availability statement confirms the original contributions are available there.Code · publicThe source code is deposited in GitHub at https://github.com/biunit/JustDeepIt under an MIT License.Open asset ↗biunit/JustDeepItlines:277-285Code / dataset availability confirmedEurope PMC · Crossref · checked 15 Sept 2026
Background Unmanned aerial vehicle (UAV)-based image retrieval in modern agriculture enables gathering large amounts of spatially referenced crop image data. In large-scale experiments, however, UAV images suffer from containing a multitudinous amount of crops in a complex canopy architecture. Especially for the observation of temporal effects, this complicates the recognition of individual plants over several images and the extraction of relevant information tremendously. Results In this work, we present a hands-on workflow for the automatized temporal and spatial identification and individualization of crop images from UAVs abbreviated as "cataloging" based on comprehensible computer vision methods. We evaluate the workflow on 2 real-world datasets. One dataset is recorded for observation of Cercospora leaf spot-a fungal disease-in sugar beet over an entire growing cycle. The other one deals with harvest prediction of cauliflower plants. The plant catalog is utilized for the extraction of single plant images seen over multiple time points. This gathers a large-scale spatiotemporal image dataset that in turn can be applied to train further machine learning models including various data layers. Conclusion The presented approach improves analysis and interpretation of UAV data in agriculture significantly. By validation with some reference data, our method shows an accuracy that is similar to more complex deep learning-based recognition techniques. Our workflow is able to automatize plant cataloging and training image extraction, especially for large datasets.
Why it matches plant phenotyping methodsUAV画像から個体を時空間的に同定・個別化し、植物画像データセットを抽出するコンピュータビジョン手法が研究の中心であり、精度検証も行っている。
abstractwe present a hands-on workflow for the automatized temporal and spatial identification and individualization of crop images from UAVs
Reproduction assets foundThe paper's authors publicly released their plant cataloging workflow code on GitHub and deposited a supporting subset of the sugar beet UAV image data with code snapshots in GigaDB (10.5524/102225). The GitHub repository URL is in the allowed list; the GigaDB DOI is not, so only the code asset is listed with an exact-Code · publicponding data. By automatizing the plant cataloging and providing a data framework, our work helps to exploit the full potential of UAV imaging in agricultural contexts.
Availability of Source Code
The source code of our workflow is available in the following repository:
Project name: Plant Cataloging Workflow
GitHub repository: https://github.com/mrcgndr/plant_cataloging_workflow
RRID: SCR_022276
Operating system(s): Platform independent (with conda), Linux (with Docker)
Programming language: Python (3.9 or higher)
License: Apache License 2.0
Data Availability
A subset of the sugar beet data is available in order to run the workflow and reproduce our results. The data have been uploaded to tOpen asset ↗https://github.com/mrcgndr/plant_cataloging_workflowlines:172-190Code / dataset availability confirmedbioRxiv · Europe PMC · Crossref · checked 8 Sept 2026
Progresses in agronomy rely on accurate measurement of the experimentations conducted to improve the yield component. Measurement of the plant density is required for a number of applications since it drives part of the crop fate. The standard manual measurements in the field could be efficiently replaced by high-throughput techniques based on high-spatial resolution images taken from UAVs. This study compares several automated detection of individual plants in the images from which the plant density can be estimated. It is based on a large dataset of high resolution Red/Green/Blue (RGB) images acquired from Unmanned Aerial Vehicules (UAVs) during several years and experiments over maize, sugar beet and sunflower crops at early stages. A total of 16247 plants have been labelled interactively on the images. Performances of handcrafted method (HC) were compared to those of deep learning (DL). The HC method consists in segmenting the image into green and background pixels, identifying rows, then objects corresponding to plants thanks to knowledge of the sowing pattern as prior information. The DL method is based on the Faster Region with Convolutional Neural Network (Faster RCNN) model trained over 2/3 of the images selected to represent a good balance between plant development stage and sessions. One model is trained for each crop. Results show that simple DL methods generally outperforms simple HC, particularly for maize and sunflower crops. A significant level of variability of plant detection performances is observed between the several experiments. This was explained by the variability of image acquisition conditions including illumination, plant development stage, background complexity and weed infestation. The image quality determines part of the performances for HC methods which makes the segmentation step more difficult. Performances of DL methods are limited mainly by the presence of weeds. A hybrid method (HY) was proposed to eliminate weeds between the rows using the rules developed for the HC method. HY improves slightly DL performances in the case of high weed infestation. When few images corresponding to the conditions of the testing dataset were complementing the training dataset for DL, a drastic increase of performances for all the crops is observed, with relative RMSE below 5% for the estimation of the plant density.
Why it matches plant phenotyping methodsUAV画像から個体を検出・計数し、作物密度を推定する画像解析手法を比較・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。
abstractThis study compares several automated detection of individual plants in the images from which the plant density can be estimated.
Reproduction assets foundThe paper's authors explicitly state that the deep-learning model architecture and data augmentation details are given in their public code repository on GitHub, which is an authors' public URL implementing the paper's plant detection/counting analysis.Code · public258 architectural details are given in the code (https://github.com/EtienneDavid/plants-counting-detection)Open asset ↗EtienneDavid/plants-counting-detectionpdf-page:9 lines:1-52Code / dataset availability confirmedOpenAlex · Europe PMC · checked 8 Sept 2026
SoybeanSugar beetLiDAR / point cloudLeafMorphology / geometry measurement2D/3D reconstructionLeaf traits
The automation of plant phenotyping using 3D imaging techniques is indispensable. However, conventional methods for reconstructing the leaf surface from 3D point clouds have a trade-off between the accuracy of leaf surface reconstruction and the method's robustness against noise and missing points. To mitigate this trade-off, we developed a leaf surface reconstruction method that reduces the effects of noise and missing points while maintaining surface reconstruction accuracy by capturing two components of the leaf (the shape and distortion of that shape) separately using leaf-specific properties. This separation simplifies leaf surface reconstruction compared with conventional methods while increasing the robustness against noise and missing points. To evaluate the proposed method, we reconstructed the leaf surfaces from 3D point clouds of leaves acquired from two crop species (soybean and sugar beet) and compared the results with those of conventional methods. The result showed that the proposed method robustly reconstructed the leaf surfaces, despite the noise and missing points for two different leaf shapes. To evaluate the stability of the leaf surface reconstructions, we also calculated the leaf surface areas for 14 consecutive days of the target leaves. The result derived from the proposed method showed less variation of values and fewer outliers compared with the conventional methods.
Why it matches plant phenotyping methods3D点群から植物葉面を再構成し、ノイズ耐性と葉面積推定の安定性を従来法と比較検証する手法開発研究であり、植物フェノタイピング手法が中心です。
abstractwe developed a leaf surface reconstruction method that reduces the effects of noise and missing points while maintaining surface reconstruction accuracy
Reproduction assets foundThe paper's authors explicitly state that the Python implementation of their proposed leaf surface reconstruction method is publicly available on GitHub. No public deposit of the 3D point cloud phenotype data (soybean/sugar beet scans) is mentioned, so only the code qualifies as a paper-specific public asset.Code · publicWe implemented the algorithm for the proposed method in Python ( http://www.python.org/ ). The source code is at https://github.com/oceam/LeafSurfaceReconstruction .Open asset ↗oceam/LeafSurfaceReconstructionlines:46-55Code / dataset availability confirmedEurope PMC · checked 14 Sept 2026
Several seed and seedling traits are measured to evaluate germination and emergence potential in relation with environmental conditions. More generally, these traits are also measured in the field of ecology as simple traits that can be correlated to other adaptative traits more difficult to measure on adult plants, as for example traits of the rooting system. Methods were developed for deep high throughput phenotyping of hundreds of genotypes from dry seed to the end of heterotrophic growth. The present dataset comes from a project on genotyping and phenotyping of populations of genotypes, with different geographic and genetic origins so as to increase genotypic diversity of sugar beet in terms of germination and early growth traits, evaluated at low temperatures. Data were collected in relation to the creation of the first sugar beet crop ontology. This dataset corresponds to the first automated phenotyping of a population of 198 genotypes and 4 commercial control varieties and is hosted on INRAE public depository under the reference number doi.org/10.15,454/AKNF4Q. The equipment and methods presented here are available on a phenotyping platform opened to collaborative research and adaptable for specific services for characterizing thousands of genotypes on different crops or other species. The phenotyping values can also be linked to genomic information to study the genetic determinism of the trait values.
Why it matches plant phenotyping methods種子から幼苗までの形質を対象とした自動・高スループット表現型解析手法、データセット、公開プラットフォームが研究の中心であるため。
abstractMethods were developed for deep high throughput phenotyping of hundreds of genotypes from dry seed to the end of heterotrophic growth.
Reproduction assets foundThe paper is a data descriptor whose sugar beet seed/seedling phenotyping dataset (28 traits for 202 genotypes) is publicly deposited in the URGI Plant and Fungi Dataverse with DOI 10.15454/AKNF4Q. No author analysis code is publicly released (scripts in Avizo/TCL/MATLAB and Fiji are described but no deposit URL is给定).Dataset · publiculgaris L.) grown area and an exotic accession of Beta vulgaris maritima from Denmark. Institution: Florimond Desprez; City/Town/Region: Cappelle-en Pévèle; Country: France. Latitude and longitude for collected samples 50.5167; 3.1667
Data accessibility
Repository name: URGI Plant and Fungi Dataverse
Data identification number: https://doi.org/10.15454/AKNF4Q
Direct URL to data: https://doi.org/10.15454/AKNF4Q
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Value of the Data
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Seed and seedling traits are increasingly measured in the field of ecology as simple traits that can be used to describe species diversity. A deeper phenotyping of genetic diversity in crops is also necessary to better understand their tolOpen asset ↗URGI Plant and Fungi Dataverse · 10.15454/AKNF4Qlines:129-170Code / dataset availability confirmedCrossref · checked 10 Sept 2026
Abstract. Ground reference data are a prerequisite for the calibration, update, and validation of retrieval models facilitating the monitoring of land parameters based on Earth Observation data. Here, we describe the acquisition of a comprehensive ground reference database which was created to test and validate the recently developed Earth Observation Land Data Assimilation System (EO-LDAS) and products derived from remote sensing observations in the visible and infrared range. In situ data were collected for seven crop types (winter barley, winter wheat, spring wheat, durum, winter rape, potato, and sugar beet) cultivated on the agricultural Gebesee test site, central Germany, in 2013 and 2014. The database contains information on hyperspectral surface reflectance factors, the evolution of biophysical and biochemical plant parameters, phenology, surface conditions, atmospheric states, and a set of ground control points. Ground reference data were gathered at an approximately weekly resolution and on different spatial scales to investigate variations within and between acreages. In situ data collected less than 1 day apart from satellite acquisitions (RapidEye, SPOT 5, Landsat-7 and -8) with a cloud coverage ≤ 25 % are available for 10 and 15 days in 2013 and 2014, respectively. The measurements show that the investigated growing seasons were characterized by distinct meteorological conditions causing interannual variations in the parameter evolution. Here, the experimental design of the field campaigns, and methods employed in the determination of all parameters, are described in detail. Insights into the database are provided and potential fields of application are discussed. The data will contribute to a further development of crop monitoring methods based on remote sensing techniques. The database is freely available at PANGAEA (https://doi.org/10.1594/PANGAEA.874251).
Why it matches plant phenotyping methods複数作物の植物パラメータ、表現型、ハイパースペクトル反射を体系的に取得した地上基準データベースであり、取得設計と各パラメータの測定法を詳細に記述して、リモートセンシングモデルの校正・検証に用いる点が中心的です。
abstractwe describe the acquisition of a comprehensive ground reference database which was created to test and validate the recently developed Earth Observation Land Data Assimilation System (EO-LDAS) and products derived from remote sensing observations
Reproduction assets foundThis is a data descriptor paper whose plant-phenotyping measurements (biophysical/biochemical plant parameters, phenology, hyperspectral reflectance, FVC/PSM, soil moisture, photos, survey data) are explicitly deposited as public PANGAEA datasets with DOIs listed in the text. Multiple paper-specific public assets are直接Dataset · publicThe database is freely available at PANGAEA
(https://doi.org/10.1594/PANGAEA.874251).Open asset ↗PANGAEA · 10.1594/PANGAEA.874251pdf-page:1 lines:1-54