Quantitative pollen viability analysis is a critical but labor-intensive step in plant reproductive biology. Existing deep-learning Segment Anything Models (SAM) fail to reliably segment viable pollen in Alexander-stained anthers. To address this, we fine-tuned an existing Cellpose-SAM model for pollen segmentation. We integrated it into PAT (Pollen Analysis Tool), a cross-platform desktop application. PAT features instance segmentation with interactive quality control, an in-app model retraining module, and publication-ready statistical outputs. We deployed PAT in an EMS suppressor screen of semi-sterile Arabidopsis smg7-6 mutants, enabling efficient candidate prioritization for whole-genome sequencing and mapping of the candidate mutation. This screen led to the identification of a point mutation in CAP-D2 (capd2-2), a Condensin I subunit, that rescues the smg7-6 meiotic phenotype. Notably, mutation in a Condensin II subunits (CAP-D3 and CAP-H2) does not confer rescue. Further characterization suggests the capd2-2 allele is hypomorphic, showing no defects in vegetative growth, chromocenter compaction, or transposable element silencing. Collectively, we demonstrate that accessible AI tools have the potential to bridge gaps in plant phenotyping and accelerate the pace of biological discovery.
Why it matches plant phenotyping methods花粉生存性を画像から自動推定するセグメンテーション手法とソフトウェアPATの開発が研究の中心であり、植物表現型計測ツールとして明確に該当する。
titlePAT: An Image Analysis Tool for Automated Scoring of Pollen in Alexander-Stained Anthers
Reproduction assets foundThe paper's Data availability statement explicitly deposits the authors' PAT pollen-phenotyping tool (the software implementing the paper's computational analysis, including the fine-tuned CPSAM segmentation model support) as open source on GitHub. Note: the full repository URL in the text (https://github.com/Riha-429[Code · public17
Data availability
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Pollen Analysis tool (PAT) is available as open source tool at Github repository (https://github.com/Riha-429
Lab/Pollen-Analysis-Tool).
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Figure legends
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Fig. 1. Cellpose performance on Alexander-stained anther cross-sections across varying pollen
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densities.
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Representative cross-sections of Alexander-stained anthers showing a range of pollen densities, from
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low (top rows, light staining) to high (bottom rows, dense reOpen asset ↗Pollen-Analysis-Toolpdf-raw-page:17 lines:1-64Code / dataset availability confirmedEurope PMC · bioRxiv · checked 6 Sept 2026
0. Morphological traits such as floral area and body size are fundamental to ecological research, serving as inputs for studies of pollinator–plant interactions, habitat quality, and biodiversity monitoring. However, accurately measuring these traits from images remains challenging, particularly in complex field conditions where existing tools exhibit reduced accuracy and limited generalizability across taxa. We present EcoMorph, a modular morphological measurement system that leverages the Segment Anything Model 3 (SAM3) to quantify traits across diverse ecological contexts. Unlike task-specific segmentation models requiring domain-specific training data, SAM3’s prompt-based architecture enables segmentation of arbitrary biological structures from natural-language prompts, using the same underlying model across flowers, insects, and other targets without retraining. From the resulting segmentations, EcoMorph extracts three classes of measurement: area, linear dimensions, and object counts. We validated EcoMorph across two ecological scales. At the intermediate scale, EcoMorph-derived floral area agreed closely with manual ImageJ measurements (R 2 = 0.935, n = 74) under simple-background conditions and (R 2 = 0.928, n = 58) under complex-background conditions, with valid predictions for 95% of images. At the fine scale, EcoMorph-derived insect body area was strongly correlated with hand-measured intertegular distance (r = 0.810, n = 349), capturing body-size variation across species from the small Bombus impatiens to the large Xylocopa virginica . Object counts matched manual counts almost exactly for well-separated insects in an insect box (R 2 = 0.9997, n = 12). By combining prompt-based segmentation with modular measurement, EcoMorph enables high-throughput quantification of area, size, and abundance from heterogeneous image sources without taxon-specific training. This generality supports a broad range of ecological applications, including pollinator and plant trait research, biodiversity and abundance monitoring, and allometric biomass estimation.
Why it matches plant phenotyping methods画像から花の面積など植物形態形質を抽出する汎用システムを開発し、手動測定との一致で検証しており、植物フェノタイピング手法が中心である。
abstractWe present EcoMorph, a modular morphological measurement system that leverages the Segment Anything Model 3 (SAM3) to quantify traits across diverse ecological contexts.
Reproduction assets foundThe paper's Data and code availability statement provides a public Zenodo deposit containing the validation datasets and code used for the EcoMorph phenotyping measurements (floral area, insect morphometrics, counts), plus a public web deployment of the EcoMorph software itself.Code · publicValidation datasets and code are available here on Zenodo
https://zenodo.org/records/20980236.Open asset ↗Zenodo · 20980236pdf-page:2 lines:1-54Code / dataset availability confirmedEurope PMC · checked 5 Sept 2026
The robust Dutch rose, also known as the Rosa hybrida is distinguished by its vibrant colors, superior product quality, and extended vase life. These rose varieties, originating from Netherlands, have proven highly successful in Indian agricultural conditions and the international export industry. The dataset consists of a total of 1,995 high resolution petal image collected during this research, encompassing petal color categories, such as red, yellow, white, pink, purple, orange, bi-color, and multi-color, as well as health statuses including fresh, dry, and diseased petals. The primary purpose of this dataset is to support machine learning activities in agriculture and specifically for tasks such as automatic petal health evaluation and rose variety categorization. Although the rose flower is scientifically rich and has a wide range of industrial uses, it has not been given much attention in machine learning, especially when compared to other plant-based datasets. This study adds to the accuracy of quality assessment through the use of modern computer vision and machine learning methods, thus helping the agriculture sector, rose-based edible product making, and flavor development industries.
Why it matches plant phenotyping methodsバラ花弁画像データセットの構築と、花弁の健康状態・色分類による植物状態評価が研究の中心であり、画像ベースの表現型計測データセットに該当する。
abstractThe dataset consists of a total of 1,995 high resolution petal image collected during this research, encompassing petal color categories, such as red, yellow, white, pink, purple, orange, bi-color, and multi-color, as well as health statuses including fresh, dry, and diseased petals.
Reproduction assets foundThe paper's own rose petal image dataset is publicly deposited on Mendeley Data, and the authors' validation/metadata scripts are publicly available on GitHub. Both are paper-specific, public, and actionable.Dataset · publicThe RoseVisuals dataset is publicly available on Mendeley Data at Direct URL to data: https://data.mendeley.com/datasets/f44jwtbfjg/5. Data Identification Number: 10.17632/f44jwtbfjg.5. Repository Name: RoseVisuals.Open asset ↗Mendeley Data · 10.17632/f44jwtbfjg.5html-lines:246-284Code · publicThe RoseVisuals codebase, comprising all validation scripts, is publicly available on GitHub Repository at https://github.com/Arya-S14/RoseVisuals-Validation-Doc.Open asset ↗GitHubhtml-lines:246-284Code / dataset availability confirmedOpenAlex · Europe PMC · checked 15 Sept 2026
The number of petals in an inflorescence is an important phenotypic indicator for quality evaluation and cultivar identification of cut chrysanthemums ( Chrysanthemum morifolium Ramat.). Current manual measurement methods are time-consuming, error-prone, and poorly suited to the complex geometry of chrysanthemum flowers, which limits their utility for large-scale phenotyping and breeding programs. Although image-based phenotyping has advanced rapidly, automated and reliable methods for petal counting in densely packed or partially obscured inflorescences remain underdeveloped. Here, we developed a deep learning-based framework for automatic extraction of petal number in cut chrysanthemums. Images from multiple varieties were collected to construct a representative dataset, and petal density maps were generated through manual annotation with Gaussian kernel function. We employed a Congested Scene Recognition Network (CSRNet) enhanced with a Squeeze-and-Excitation (SE) channel attention mechanism (SE-CSRNet) for petal density estimation. Spearman correlation analysis revealed strong agreement between visible and actual petal counts (Spearman’s r=0.953, p<0.0001). Compared with the original CSRNet, SE-CSRNet reduced mean absolute error (MAE) and root mean squared error (RMSE) by 5.2% and 7.4%, respectively. Further optimization using regression fitting revealed that random forest achieved the best performance (MAE = 4.24, RMSE = 5.06, R 2 = 0.967), indicating reliable stability and satisfactory generalization under the conditions evaluated in this work. Application of the optimized model to two cut chrysanthemum varieties confirmed its practicality by successfully detecting reductions in petal number under high-temperature stress. Our results demonstrate that integrating dataset construction, deep learning–based density estimation, and machine learning optimization enables efficient and accurate prediction of petal number in cut chrysanthemums.
Why it matches plant phenotyping methods花弁数という植物形質を画像から自動抽出する深層学習手法を開発し、データセット構築、性能比較、検証、実用適用まで行っており、表現型取得手法が研究の中心である。
abstractHere, we developed a deep learning-based framework for automatic extraction of petal number in cut chrysanthemums.
Reproduction assets foundThe article states that some data (the chrysanthemum petal-counting dataset and related materials) will be available at the authors' public GitHub repository (qwsdfgz/petalscount), with other data available from the corresponding author upon reasonable request. The repository URL is explicitly provided by the authors,但Dataset · publicnctional components of bud-leaves and flowers in edible chrysanthemum (Chrysanthemum morifolium Ramat)
Horticulturae 11 5 2025 448
10.3390/horticulturae11050448
Appendix A
Supplementary data
The following is the Supplementary data to this article.
Multimedia component 1
Data availability
Some data will be available at this URL: https://github.com/qwsdfgz/petalscount . Other data are openly available from the corresponding author upon reasonable request.
Appendix A
Supplementary data to this article can be found online at https://doi.org/10.1016/j.plaphe.2026.100238 .Open asset ↗qwsdfgz/petalscountlines:602-636Code / dataset availability confirmedCrossref · checked 15 Sept 2026
Manual quantification of specialty crop traits, such as flowers and fruits, is often labor-intensive, time-consuming, and inconsistent, limiting scalability and precision. We present PhenoSnap, an artificial intelligence (AI)-powered web application that provides an intuitive and efficient interface for automated specialty crop trait extraction from images. PhenoSnap bridges the gap between advanced computer vision technologies and practical agricultural applications by eliminating the need for programming expertise. This ready-to-use solution can enable growers, breeders, and Extension faculty to accelerate field work and enhance decision-making related to strawberry and tomato yield estimation for breeding selections and strawberry runner management. Written by Santhi Daggubati, Xu Wang, Xue Zhou, Shubham Singh, and Jessica Chitwood-Brown, and published by the UF/IFAS Department of Agricultural and Biological Engineering, June 2026.
Why it matches plant phenotyping methods画像から花・果実などの植物形質を自動抽出するAIウェブアプリケーションの開発・提供が中心であり、植物フェノタイピング手法およびソフトウェアとして適格。
abstractWe present PhenoSnap, an artificial intelligence (AI)-powered web application that provides an intuitive and efficient interface for automated specialty crop trait extraction from images.
Reproduction assets foundThe article describes PhenoSnap, a publicly accessible AI web application for specialty crop trait extraction, and cites a publicly released Dryad imagery dataset (Zhou et al. 2025b) that is a subset of the training data for the Strawberry Runner model. Both are paper-specific, public, and actionable. No author code orDataset · publicDataset preparation
and the training process are detailed in Zhou et al. (2025a),
and a subset of the dataset has been publicly released on
Dryad (Zhou et al. 2025b).Open asset ↗Dryadpdf-raw-page:5 lines:1-55Code / dataset availability confirmedEurope PMC · checked 14 Sept 2026
Field / plotFlowerClassificationCountingFruit / seed / panicle traits
As a crucial agricultural crop in China, litchi exhibits a biennial bearing pattern with alternating high-yield and low-yield cycles, known as on-year and off-year respectively. Research has identified unstable floral initiation as the primary cause of irregular fruiting in mid-to-late maturing cultivars. Rapid and accurate quantification of female to male flower ratios during the flowering phase enables targeted management strategies to optimize floral development and enhance fruit-setting rates. This study proposes Flower Quantification and Gender Recognition Network (FQGR-Net), a three-branch neural network architecture for simultaneous classification and counting of female and male flowers. Through module-level optimization, FQGR-Net improves both counting accuracy and computational efficiency, achieving average MAE of 8.498 and RMSE of 13.209 across categories in experiments conducted on the self-constructed dataset. Comparative experiments with other deep neural network models on public datasets show the proposed method achieves optimal performance. A regression analysis between predictions and ground truth produces R2 values of 0.930 and 0.971 for female and male flower quantification respectively. A dedicated litchi flower phenotyping analyzer was developed to address the technological gap in automated floral census systems. Field trials demonstrated over 80% accuracy in female/male flower counting.
Why it matches plant phenotyping methods雌雄花の画像ベース計数・性別認識手法と専用フェノタイピング解析器を開発し、データセットおよび野外試験で性能評価しているため、植物形質取得法が中心である。
abstractThis study proposes Flower Quantification and Gender Recognition Network (FQGR-Net), a three-branch neural network architecture for simultaneous classification and counting of female and male flowers.
Reproduction assets foundThe authors explicitly state that the code and data for this litchi flower quantification/gender recognition study are publicly downloadable from their GitHub repository. The Roboflow datasets are cited third-party comparison datasets, not paper-specific assets, and the litchi dataset itself is only available upon (un)Code · publicThe code and data mentioned in the article can be downloaded from https://github.com/Mazhe-02/Morphology-based-Litchi-Flower-Quantification-and-Gender-RecognitionOpen asset ↗Mazhe-02/Morphology-based-Litchi-Flower-Quantification-and-Gender-Recognitionlines:583-591Code / dataset availability confirmedCrossref · Europe PMC · checked 14 Sept 2026
LiDAR / point cloudFlowerLeafStem / branchSegmentationGrowth / development / phenology
The segmentation of 3D point clouds of plant organs, such as leaves and stems, helps to monitor plant growth and is a key step in plant growth phenotype analysis. Compared to point cloud segmentation tasks in other fields, plant point cloud segmentation is more challenging due to the interwoven distribution of various parts such as stems, leaves, and flowers. In this paper, we propose a universal point cloud segmentation network PlantEFRSegnet that can be used for multi-species of plants. The proposed PlantEFRSegnet utilizes a newly designed edge point preservation downsampling module to identify and preserve the points at the edges of plant organs during the downsampling process, in order to assist the segmentation network in learning the contours of various plant organs. PlantEFRSegnet performs supervised feature repair on the point cloud features obtained through downsampling to mitigate the impact of feature loss on segmentation performance during feature embedding. The encoder of the segmentation network is composed of four local feature extraction modules. These four modules can not only extract features but also enhance the features corresponding to points with high contributions in local regions based on point attention mechanism. We evaluated the proposed PlantEFRSegnet on a laser-scanned plant point cloud dataset. Compared with the state-of-the-art approaches, the proposed PlantEFRSegnet achieved better segmentation results.
Why it matches plant phenotyping methods植物器官の3D点群を対象に、器官分割と植物成長フェノタイプ解析を行う新規ネットワークを開発・評価しており、フェノタイプ取得の計算手法が中心である。
abstractThe segmentation of 3D point clouds of plant organs, such as leaves and stems, helps to monitor plant growth and is a key step in plant growth phenotype analysis.
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 experimental dataset used in this paper can be obtained through the following link: https://github.com/dllab23/PlantPointCloud (accessed on 11 May 2026).Open asset ↗dllab23/PlantPointCloudhtml-lines:785-806Code / dataset availability confirmedEurope PMC · checked 5 Sept 2026
Rice ( Oryza sativa ) production underpins food security in many rice-consuming nations. As a critical developmental transition that directly determines yield and grain quality, flowering dates and timing are genetically complex and highly sensitive to environmental fluctuations. This complexity requires new methods to quantify diurnal floral characteristics, which are essential to hybrid breeding in cereals. Here, we present BloomSight, an ultra-high-frequency and deep-learning (DL) powered framework for phenotyping and measuring minute-level flowering dynamics in japonica and indica rice. After monitoring 172 rice accessions selected from the Chinese Rice Mini-Core Collection using cost-effective time-lapse imaging platforms for 16 days, we acquired over 530,000 accession-level images and established the Open Rice Flowering Training (ORFT) dataset, with over 39,000 panicles and 350,000 anthers annotated. Next, a two-stage customised DL model (i.e. YOLACT-Panicle for panicle segmentation and UNet-Anther for anther identification) was trained using the ORFT set, enabling ultra-high-frequency measures of anther extrusion at the minute level. Based on trait analysis, we further fitted curves to dynamically identify diurnal flowering patterns, including key timepoints such as the initial flowering timepoint ( T Ini. ), quickest flowering timepoint ( T Qck. ), and peak flowering time ( T Peak ), and novel traits such as the duration of rapid flowering phase ( P Rpd. ) and flowering density across key phases. After validating BloomSight-derived traits against manual observations, we classified the japonica and indica accessions into three patterns: Slow, Moderate, and Fast, all of which had distinct flowering windows. These analyses helped us integrate phenotypic variations into a genome-wide association study (GWAS), revealing many significant single nucleotide polymorphisms (SNPs) associated with known (e.g. EMF1 , OsMYB8 , and PME42 ) and several repeatedly identified unknown loci (one of these loci has been recently verified by other groups), demonstrating the value of the BloomSight framework. Taken together, we believe that BloomSight provides an ultra-high-frequency framework for diurnal flowering phenotyping, enabling the measurement of biological meaningful floral traits with minute-level resolution that can enable flowering-related developmental studies and hybrid-breeding applications in rice and more broadly benefit the plant and crop research community.
Why it matches plant phenotyping methodsイネの開花動態を高頻度画像と深層学習で抽出するフェノタイピング基盤を開発し、データセット構築と手動観測による検証も行っているため、方法が研究の中心である。
abstractwe present BloomSight, an ultra-high-frequency and deep-learning (DL) powered framework for phenotyping and measuring minute-level flowering dynamics in japonica and indica rice
Reproduction assets foundThe paper's Data and code availability statement explicitly provides public access to the ORFT annotated image dataset (BioStudies S-BSST2157), Python source code for floral trait analysis (GitHub The-Zhou-Lab/BloomSight), and trained DL models (GitHub releases). SRA accessions are molecular sequencing data, not phenotCode · publicPython-based source codes for automating floral trait analysis using the above data are accessible via our GitHub repository ( https://github.com/The-Zhou-Lab/BloomSight ).Open asset ↗The-Zhou-Lab/BloomSightlines:336-349Code / dataset availability confirmedEurope PMC · checked 5 Sept 2026
RGB / grayscaleFlowerClassificationGrowth / development / phenology
This study presents a comprehensive BDFlower growth stage dataset designed to support research in precision agriculture and floriculture. The dataset encompasses eight common flower species found in Bangladesh: Bush Allamanda, Red Hibiscus, Yellow Bell, Pinwheel Flower, Pink Periwinkle, White Madagascar Periwinkle, Marvel of Peru, and White Hibiscus. Each species is represented across three growth stages-Early, Mid, and Full-resulting in 24 distinct classes. A total of 23,334 colour images are included, comprising 3889 original photographs and 19,445 augmented samples generated with five augmentation techniques. Bush Allamanda contains 499 images, Red Hibiscus contains 489 images, Yellow Bell contains 483 images, Pinwheel Flower contains 497 images, Pink Periwinkle contains 452 images, White Madagascar Periwinkle contains 472 images, Marvel of Peru contains 468 images and White Hibiscus contains 529 images. Each image was collected using smartphone camera at three-time intervals per day, spaced eight hours apart, to capture natural variations in lighting and appearance. The dataset is further organized into training, validation, and testing splits, enabling direct application to machine learning workflows. This is a publicly available dataset specifically curated for flower growth stage classification. In addition to dataset collection, we also conducted a simple experiment using a CNN model to evaluate its performance on this dataset. It is intended to facilitate the development of robust computer vision models that can monitor flower development, with potential applications in automated plant phenotyping, crop monitoring, and digital floriculture systems.
Why it matches plant phenotyping methods花の生育段階を画像で分類する公開データセットを構築し、CNN評価も行っており、植物表現型取得・解析が研究の中心である。
abstractThis is a publicly available dataset specifically curated for flower growth stage classification.
Reproduction assets foundThe paper's own flower growth-stage image dataset (BDFlower) is publicly deposited on Mendeley Data with an explicit direct URL and DOI, directly reproducing the paper's phenotyping (flower growth stage) image measurements. No author analysis code or trained model checkpoints are explicitly deposited.Dataset · publicRepository name: Data Mendeley
Data identification number: 10.17632/m8g2wynwyr.2
Direct URL to data: https://data.mendeley.com/datasets/m8g2wynwyr/2Open asset ↗10.17632/m8g2wynwyr.2html-lines:94-129Code / dataset availability confirmedEurope PMC · bioRxiv · Crossref · checked 5 Sept 2026
Premise: Herbarium specimens are increasingly used to extract morphological traits for ecological and evolutionary studies, yet the effects of tissue desiccation on trait measurements remain poorly understood. Here, we tested whether higher tissue water content leads to greater measurement changes after herborization (H1) and whether fresh trait values can be reliably predicted from herbarium measurements (H2). Methods: We evaluated the reliability of herbarium-based measurements by comparing fresh and dried traits of leaves, flowers, fleshy fruits, and seeds across 262 individuals representing 133 Neotropical Myrtaceae species. Phylogenetic least square models and machine-learning regressions were used to test H1 and H2. Results: Leaves and flowers generally shrank after herborization, fruits size metrics tended to increase, and seeds were largely unaffected. Water content was significantly associated with the magnitude of herborization effects in flowers and some leaf and seed traits. Fresh trait values were accurately predicted from herbarium measurements. Prediction errors were lowest for leaf traits, followed by fruits, flowers, and seeds. Discussion: These results partially support H1 and support H2, indicating that herbarium specimens can be reliably used for trait analyses when organ-specific responses are considered, providing a practical framework to account for potential desiccation bias in functional trait research.
Why it matches plant phenotyping methodsハーバリウム標本による植物形態形質測定の信頼性評価と、生鮮形質の予測手法が研究の中心であり、植物フェノタイピング手法の検証に該当する。
abstractWe evaluated the reliability of herbarium-based measurements by comparing fresh and dried traits of leaves, flowers, fleshy fruits, and seeds across 262 individuals representing 133 Neotropical Myrtaceae species.
Reproduction assets foundThe authors explicitly state that the code used for the PGLS and machine-learning analyses is publicly available in a GitHub repository; raw phenotype data is promised only upon acceptance, so the code asset qualifies while the dataset is not yet actionable.Code · publicSupporting Information and the code used to perform the analyses are available at
https://github.com/ykilsztajn/fresh_dry_myrtaceae. All raw data will be made available in the
same repository upon acceptance for publication.Open asset ↗ykilsztajn/fresh_dry_myrtaceaepdf-page:9 lines:1-48Code / dataset availability confirmedEurope PMC · checked 5 Sept 2026
Doubled haploid (DH) technology can fast-track crop breeding. Haploid induction yields haploids with only one set of genomes, which are usually sterile. Haploid fertility (HF) is the ability of haploid plants to set seed, and it is a critical bottleneck in DH pipelines. Genetic mechanisms to restore HF hold immense potential in DH crop breeding, yet its phenotyping remains manual, destructive, and inconsistent. While recent advances in imaging and machine learning have improved throughput for general plant traits, no curated image dataset exists for Arabidopsis thaliana that explicitly represents HF. Here, we present AutoSiQ, a dataset and baseline deep learning pipeline for automated HF quantification. AutoSiQ includes high-resolution scanned inflorescences annotated with a seven-class ontology encompassing green siliques, green fertile siliques, mature siliques, fertile siliques, cracked fertile siliques, cracked siliques, and flowers. This multi-class annotation scheme preserves biologically meaningful information beyond binary fertile/non-fertile distinctions, enabling reliable fertility estimation and future phenotyping applications. We release baseline object detection models (YOLOv5), trained using the AutoSiQ dataset, and evaluate their performance across confidence thresholds. Model predictions strongly correlate with manual counts, achieving R² up to 0.94 for total silique number estimation. We further demonstrate AutoSiQ's utility for automated haploid fertility rate (HFR) estimation and genotype discrimination between two contrasting genotypes (WT and bmf2 mutant). A longitudinal analysis identifies ~60 days after sowing (DAS) as the optimal harvest time for maximizing mature silique counts by balancing between the number of immature buds and silique shattering. By releasing both the dataset and baseline code, AutoSiQ provides a reproducible and extensible foundation for high-throughput fertility phenotyping in haploid Arabidopsis .
Why it matches plant phenotyping methodsハプロイド稔性を画像から定量するデータセットと深層学習パイプラインを開発・評価しており、植物フェノタイピング手法が中心である。
abstractHere, we present AutoSiQ, a dataset and baseline deep learning pipeline for automated HF quantification.
Reproduction assets foundThe paper's AutoSiQ dataset (annotated scanned Arabidopsis inflorescence images with seven-class silique ontology and manual fertility counts) is publicly deposited on Zenodo per the data availability statement. The YOLOv5 GitHub repository is a generic third-party library, not an authors' code asset.Dataset · publicThe datasets presented in this study can be found in online repositories. The names of the repository/repositories and accession number(s) can be found below: https://zenodo.org/records/17905566 .Open asset ↗zenodo · 17905566lines:367-402Code / dataset availability confirmedEurope PMC · checked 5 Sept 2026
Accurate flower-load assessment is critical for informed thinning strategies in orchard management. UAV-based deep learning automated counting offers efficiency advantages, yet precise counting is heavily dependent on abundant annotated data, which is scarce and costly to obtain in agricultural settings. While semi-supervised learning alleviates dependency on manual annotation, its application to UAV-based orchard imagery faces challenges: complex backgrounds and small target sizes, which undermine pseudo-label reliability. To address these challenges, this study proposes a two-stage framework to achieve separate counting of apple flowers at different phenological stages. First, a color-SAM flower extractor (CSAM-FE) is proposed to preprocess images using a strategy combining color thresholding with the Segment Anything Model (SAM), suppressing background noise and extracting high-quality flower clusters, thereby providing purified inputs for the subsequent counting network. Second, an uncertainty-guided semi-supervised flower counting network (USCount-Net) is proposed for accurate stage-specific flower counting with limited labeled data. The USCount-Net incorporates two key components: an adaptive pseudo-label filtering (PLF) mechanism based on frequent forward uncertainty estimation (FFUE) is designed to dynamically suppress noisy gradient backpropagation, mitigating error propagation from unreliable pseudo-labels; and a noise-sensitive adaptive gated fusion (AGF) module is introduced to fuse cross-scale features without redundancy, addressing significant scale variations across phenological stages and observation angles. Comparative experiments on a self-built apple flower counting dataset demonstrate that USCount-Net achieves lower MAE and RMSE than state-of-the-art methods at 10%, 30%, and 50% labeling ratios. The results demonstrate that the proposed methodology serves as methodological support for rapid and precise apple flower counting in low-annotation agricultural scenarios.
Why it matches plant phenotyping methodsリンゴ花の画像抽出・計数手法と半教師あり解析ネットワークを開発し、データセット上で比較評価しているため、植物表現型取得が中心である。
abstractthis study proposes a two-stage framework to achieve separate counting of apple flowers at different phenological stages.
Reproduction assets foundThe paper's Data availability statement explicitly provides public access to the authors' USCount-Net source code on GitHub and the self-built apple flower counting dataset (UAV images, annotations, flower cluster images) on Google Drive.Code · publicThe source code is publicly available at https://github.com/haohuihui5019/USCount-Net . And the source dataset can be accessed at https://drive.google.com/drive/folders/1KP8H0qIuct56hWre5GV6ZJnzwOpen asset ↗USCount-Netlines:681-780Dataset · publicThe source code is publicly available at https://github.com/haohuihui5019/USCount-Net . And the source dataset can be accessed at https://drive.google.com/drive/folders/1KP8H0qIuct56hWre5GV6ZJnzwOpen asset ↗lines:681-780Code / dataset availability confirmedOpenAlex · Crossref · Europe PMC · checked 5 Sept 2026
Abstract Observer bias and inconsistencies in traditional plant phenotyping methods limit the accuracy and reproducibility of fine-grained plant analysis. To address these limitations, TomatoMAP is introduced as a comprehensive dataset for Solanum lycopersicum . The dataset contains 68,080 RGB images: 3,616 high-resolution macrophotographs (3648 × 5472) with semantic annotations, and 64,464 moderate-resolution images (1080 × 1440) captured from 12 plant poses at four camera elevations. Each image is accompanied by manually annotated bounding boxes for seven regions of interest (leaves, panicle, flower clusters, fruit clusters, axillary shoot, shoot, and whole-plant area) and by labels spanning 50 BBCH classes representing phenologically growth stages. A general cascading structure is proposed. For real-time applicability, models emphasizing the accuracy-efficiency trade-off (MobileNetv3, YOLOv11, and Mask R-CNN) are prioritized and benchmarked against multiple state-of-the-art models. Performance is assessed using accuracy, mAP, inference FPS, and normalized confusion matrices. In a study involving five domain experts, AI models trained on TomatoMAP achieves comparable accuracy levels. Reliability of automated fine-grained phenotyping is supported by Cohen’s Kappa statistics and inter-rater agreement heatmaps.
Why it matches plant phenotyping methodsトマトの多視点画像、器官領域・生育ステージ注釈を備えたデータセットを構築し、画像モデルの精度・効率・専門家一致度をベンチマークしており、植物フェノタイピング手法が中心である。
titleTomato Multi-Angle Multi-Pose Dataset for Fine-Grained Phenotyping.
Reproduction assets foundThe paper's authors publicly release their analysis code (dataset construction scripts for TomatoMAP-Cls/Det and model training/evaluation code) on GitHub. The TomatoMAP phenotype image dataset itself is deposited at e!DAL (10.5447/ipk/2025/14), but no matching URL is present in the allowed list, so only the code assetCode · publicThe scripts for constructing TomatoMAP-Cls and TomatoMAP-Det, as well as the code used for model evaluation, are available at: https://github.com/0YJ/TomatoMAP.Open asset ↗https://github.com/0YJ/TomatoMAPhtml-lines:423-479Code / dataset availability confirmedEurope PMC · checked 14 Sept 2026
Multispectral / hyperspectralFlowerObject detectionStress response / tolerancePlant / canopy temperature
Hyperspectral imaging (HSI) is a noncontact camera-based technique that enables deep learning models to learn various plant conditions by detecting light reflectance under illumination. In this study, we investigated the effects of four light sources-halogen (HAL), incandescent (INC), fluorescent (FLU), and light-emitting diodes (LED)-on the quality of spectral images and the vase life (VL) of cut roses, which are vulnerable to abiotic stresses. Cut roses 'All For Love' and 'White Beauty' were used to compare cultivar-specific visible reflectance characteristics associated with contrasting petal pigmentation. HSI was performed at four time points, yielding 640 images per light source from 40 cut roses. The results revealed that the light source strongly affected both the image quality (mAP@0.5 60-80%) and VL (0-3 d) of cut roses. The HAL lamp produced high-quality spectral images across wavelengths (WL) ranging from 480 to 900 nm and yielded the highest object detection performance (ODP), reaching mAP@0.5 of 85% in 'All For Love' and 83% in 'White Beauty' with the YOLOv11x models. However, it increased petal temperature by 2.7-3 °C, thereby stimulating leaf transpiration and consequently shortening the VL of the flowers by 1-2.5 d. In contrast, INC produced unclear images with low spectral signals throughout the WL and consequently resulted in lower ODP, with mAP@0.5 of 74% and 69% in 'All For Love' and 'White Beauty', respectively. The INC only slightly increased petal temperature (1.2-1.3 °C) and shortened the VL by 1 d in the both cultivars. Although FLU and LED had only minor effects on petal temperature and VL, these illuminations generated transient spectral peaks in the WL range of 480-620 nm, resulting in decreased ODP (mAP@0.5 60-75%). Our results revealed that HAL provided reliable, high-quality spectral image data and high object detection accuracy, but simultaneously had negative effects on flower quality. Our findings suggest an alternative two-phase approach for illumination applications that uses HAL during the initial exploration of spectra corresponding to specific symptoms of interest, followed by LED for routine plant monitoring. Optimizing illumination in HSI will improve the accuracy of deep learning-based prediction and thereby contribute to the development of an automated quality sorting system that is urgently required in the cut flower industry.
Why it matches plant phenotyping methods切り花の状態評価に用いるHSIについて、照明条件が画像品質と検出精度に及ぼす影響を比較・検証し、実運用向けの照明戦略を提案しているため、植物フェノタイピング手法が中心である。
abstractwe investigated the effects of four light sources-halogen (HAL), incandescent (INC), fluorescent (FLU), and light-emitting diodes (LED)-on the quality of spectral images and the vase life (VL) of cut roses
Reproduction assets found保存済みの本文根拠を更新済みルールで再検証し、公開資産1件を確認しました。Supplement · publicSupplementary Table S1: SNR of hyperspectral images acquired under different illumination sources in two cut rose cultivars (‘All For Love’ and ‘White Beauty’); Figure S1: Effect of light sources on hyperspectral image (HSi) quality in cut roses ‘All For Love’ and ‘White Beauty’.Open asset ↗lines:64-174Code / dataset availability confirmedOpenAlex · checked 14 Sept 2026
Genotype-to-phenotype prediction remains a fundamental challenge in current genetic research. In recent years, it has become possible to construct different predictive models based on genomic data. However, in many horticultural crops, it is difficult to accurately verify genomic variations because of the complexity of their genome, making the application of these genome-based methods challenging. Gene expression reflects both genetic regulatory mechanisms and environmental stimuli, offering potential for predicting phenotypes in plants with complex genomes. Thus, in this paper, we tested the possibility for predicting adult plant phenotypes using the gene expression data from seedlings. By applying the transcriptional-based deep learning methods on cut chrysanthemums (Chrysanthemum spp.), which exhibits a complex genetic background characterized by high repetitiveness, heterozygosity, and genome size and is recognized as a segmental allopolyploid, we found that the method is robust and accurate for predicting continuous variables such as leaf vase life, as well as categorical variables such as flower types on the basis of gene expression data. Moreover, the power and performance of transcriptional-based deep learning methods for prediction was validated in rice (Oryza sativa). Our research shows the good performance of phenotype prediction based on gene expression, with potential applications in future gene chip-based breeding practices.
Why it matches plant phenotyping methods遺伝子発現データから成体の植物形質を予測する深層学習手法を開発・検証しており、形質予測が研究の中心である。
titlePredicting adult phenotypes from seedling transcriptional data using deep learning: a case study in chrysanthemum
Reproduction assets foundThe paper deposits its authors' analysis code publicly on GitHub and its raw RNA-seq data (used for the seedling-transcriptome phenotype prediction) in the Genome Sequence Archive with accession CRA022074. Both are paper-specific, public, and actionable.Code · publicn for
multiclass classification. For compiling each model, the RMSprop
optimization algorithm was used with a default initial learning rate
of 0.001, and categorical cross-entropy was selected as the loss func-
tion. The model was trained for 100 epochs with a default batch size
of 32.
The source codes are publicly available at https://github.com/lkwwang-ui/Deep-model-for-predicting-adult-traits-using-seedling-data-study.git
We used Weka 3.9.7 data mining software[23] and performed
machine learning analysis as described in our previously published
paper[24]. In brief, all 101 samples were used for training and testing
with 10-fold cross-validation, and the 20 samples from BGZ were
used for mOpen asset ↗https://github.com/lkwwang-ui/Deep-model-for-predicting-adult-traits-using-seedling-data-study.gitpdf-raw-page:3 lines:1-80Code / dataset availability confirmedEurope PMC · bioRxiv · checked 15 Sept 2026
FlowerAnnotation / quality controlObject detectionGrowth / development / phenology
ABSTRACT Herbarium specimens represent critical historical records of plant phenology, yet automating annotation of reproductive structures remains challenging given the diversity of floral morphologies, specimen age and quality, and image quality. Here, we present a machine learning pipeline that uses an ensemble modeling approach to detect flowers on herbarium specimens and deliver these data to the phenology research community. After testing multiple strategies for generating training data, we found in-house expert-curated annotations were essential for producing reliable results. Expert validation found relatively strong accuracy for detecting present floral structures, but still had moderately high false negative rates. Applying the ensemble to our filtered final image dataset of 22 million records resulted in 11.1 million records labeled with flowers present. However, only 2.9 million of these contained complete metadata necessary for downstream phenology research, highlighting the need for full label digitization efforts. Still, this dataset represents a large compilation of historical herbarium-derived phenology records available as a resource for the phenology community. We end by demonstrating how integrating these machine-labeled records into Phenobase, a publicly-available phenology database, expands taxonomic and temporal coverage for large-scale phenological analyses, and discuss remaining challenges and next steps.
Why it matches plant phenotyping methods植物標本画像から花の存在を自動検出し、精度検証と大規模な phenology データセット化を行う機械学習手法が研究の中心であるため、植物フェノタイピング手法として適格です。
abstractwe present a machine learning pipeline that uses an ensemble modeling approach to detect flowers on herbarium specimens and deliver these data to the phenology research community.
Reproduction assets foundThe paper's Data Availability Statement explicitly deposits the ensemble models, training/validation/test images, training data and final ensemble output on Zenodo, and the analysis code on GitHub; machine-labeled records are also served via the public Phenobase portal. All are paper-specific, public, and actionable.Dataset · publicors contributed to drafts and gave final
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approval for publication.
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Data Availability Statement
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The ensemble data models and a corresponding JSON file with model metadata data are
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housed on Zenodo (https://doi.org/10.5281/zenodo.17079402). Images used in training,
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validation, and testing are located here: https://zenodo.org/records/17675089. Code used for
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this project can be found on github (https://github.com/rafelafrance/phenobase/tree/v1.0.0).461
Training data and final ensemble output can be found on Zenodo
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(https://doi.org/10.5281/zenodo.17675089).463
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Supporting Information
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Additional Supporting Information may be found online in the SupportinOpen asset ↗Zenodo · 17675089pdf-raw-page:19 lines:1-55Dataset · publictps://doi.org/10.5281/zenodo.17079402). Images used in training,
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validation, and testing are located here: https://zenodo.org/records/17675089. Code used for
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this project can be found on github (https://github.com/rafelafrance/phenobase/tree/v1.0.0).461
Training data and final ensemble output can be found on Zenodo
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(https://doi.org/10.5281/zenodo.17675089).463
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Supporting Information
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Additional Supporting Information may be found online in the Supporting Information section at
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the end of the article.
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Appendix S1. List of difficult-to-annotate genera and families removed from training and
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downstream data.
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Appendix S2. Table S1. Validation results for held-oOpen asset ↗Zenodo · 10.5281/zenodo.17675089pdf-raw-page:19 lines:1-55Code · publiclity Statement
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The ensemble data models and a corresponding JSON file with model metadata data are
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housed on Zenodo (https://doi.org/10.5281/zenodo.17079402). Images used in training,
459
validation, and testing are located here: https://zenodo.org/records/17675089. Code used for
460
this project can be found on github (https://github.com/rafelafrance/phenobase/tree/v1.0.0).461
Training data and final ensemble output can be found on Zenodo
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(https://doi.org/10.5281/zenodo.17675089).463
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Supporting Information
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Additional Supporting Information may be found online in the Supporting Information section at
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the end of the article.
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Appendix S1. List of difficult-to-annotaOpen asset ↗GitHub · rafelafrance/phenobasepdf-raw-page:19 lines:1-55Code / dataset availability confirmedEurope PMC · checked 6 Sept 2026
Tea flowers play a crucial role in taxonomic research and hybrid breeding of tea plants. As traditional methods of observing tea flower traits are labor-intensive and inaccurate, TflosYOLO and Tea Flowering Stage Classification (TFSC) models were proposed for tea flowering quantification, which enable the estimation of flower count and flowering period. In this study, a highly representative and diverse dataset was constructed by collecting flower images from 29 tea accessions in 2 years. Based on this dataset, the TflosYOLO model was built on the YOLOv5 architecture and enhanced with the Squeeze-and-Excitation (SE) network, Adaptive Rectangular Convolution, and Attention Free Transformer, which is the first model to offer a viable solution for detecting and counting tea flowers. The TflosYOLO model achieved a mean Average Precision at 50% IoU (mAP50) of 0.844, outperforming YOLOv5, YOLOv7, and YOLOv8. Furthermore, the TflosYOLO model was tested on 31 datasets encompassing 26 tea accessions and five flowering stages, demonstrating high generalization and robustness. The correlation coefficient (R 2 ) between the predicted and actual flower counts was 0.964. Additionally, the TFSC model-a seven-layer neural network-was designed for the automatic classification of the flowering period. The TFSC model was evaluated for 2 years and achieved an accuracy of 0.738 and 0.899. Using the TflosYOLO+TFSC model, the tea flowering dynamics were monitored, and the changes in flowering stages were tracked across various tea accessions. The framework provides crucial support for tea plant breeding programs and the phenotypic analysis of germplasm resources.
Why it matches plant phenotyping methods茶花画像から花数と開花期を推定するモデルを開発・検証しており、植物表現型の取得・抽出手法が研究の中心である。
abstractTflosYOLO and Tea Flowering Stage Classification (TFSC) models were proposed for tea flowering quantification, which enable the estimation of flower count and flowering period.
Reproduction assets foundThe paper's data availability statement explicitly deposits the tea flower datasets and models in a public GitHub repository (sufie-mi/tea-flower-model), which directly supports this paper's tea flower phenotyping measurements and models. The labelImg repository is a generic third-party annotation tool, not a paper-ownDataset · publicThe datasets presented in this study can be found in online repositories. The names of the repository/repositories and accession number(s) can be found below: https://github.com/sufie-mi/tea-flower-model .Open asset ↗https://github.com/sufie-mi/tea-flower-model · tea-flower-modellines:764-781Code / dataset availability confirmedEurope PMC · checked 6 Sept 2026
Double haploid (DH) technology can significantly shorten the breeding cycle and improve the breeding efficiency, and it is favored by breeders. The metrics for evaluating the effect of haploid genome doubling mainly include anther emergence and ear seed setting. The evaluation of fertility restoration ability is mainly conducted through visual inspection at present, which is time-consuming, and easy to be affected by fatigue, resulting in errors and inconsistencies. Therefore, it is urgent to develop efficient and accurate evaluation technology to reduce the field work burden of researchers. In this work, we propose a grading evaluation model (Maize-IRNet) of haploid anther emergence and ear seed setting based on Inception-ResNet. Firstly, the modules of Stem and Inception-ResNet are utilized for image feature extraction and multi-scale feature learning. Then, the Reduction module is used for spatial downsampling and feature compression, and the global attention mechanism (GAM) is used to enhance the recognition of key regions of the image. The experimental results show that the Maize-IRNet's classification accuracy of haploid ear seed setting and anther emergence is 84.2 % and 84.0 %, which is higher than six baseline methods (VGG11_bn, ResNet50, ResNet101, ViT-Base-16, gMLP, MLP-Mixer). In order to facilitate the practical application for breeding researchers, we have developed a mobile application that integrates the Maize-IRNet model. This study helps to achieve high-throughput collection of fertility restoration phenotypes, improves the evaluation efficiency of fertility restoration, reduces breeding costs, and provides technical support for the promotion of engineering breeding of DH technology.
Why it matches plant phenotyping methodsトウモロコシの葯出現と穂の種子着生という生殖形質を画像から自動評価する深層学習モデルを開発・比較し、モバイルアプリにも実装しており、表現型取得法が中心である。
abstractTherefore, it is urgent to develop efficient and accurate evaluation technology to reduce the field work burden of researchers.
Reproduction assets foundThe paper's data availability statement explicitly provides the maize haploid fertility image dataset (1897 ear images, 6443 tassel images), the Maize-IRNet source code, and the Android APK, all hosted on the authors' public GitHub repository.Dataset · publicThe maize haploid fertility image dataset collected by smartphones is available at https://github.com/wyzwyz666/maize-haploid-fertility/blob/main/datasetOpen asset ↗wyzwyz666/maize-haploid-fertilitylines:506-531Code · publicThe source code: https://github.com/wyzwyz666/maize-haploid-fertility/blob/main/sourcecodeOpen asset ↗wyzwyz666/maize-haploid-fertilitylines:506-531Code / dataset availability confirmedEurope PMC · checked 14 Sept 2026
Field / plotFlowerLeafRootStem / branchClassificationDisease symptoms / severity
This dataset comprises 5452 images of durian plant parts-including leaves, flowers, branches, stems, and roots-affected by ten common disease classes. The images were captured from one family-owned durian orchard and four nearby orchards in Vinh Long Province, Vietnam. Each class contains approximately 405-427 raw images, photographed using an iPhone 14 under natural field conditions. These conditions simulate typical farmer photography practices, featuring varied angles, inconsistent lighting, and complex environmental backgrounds, resulting in significant visual noise. All raw JPEG images were manually reviewed and cropped on macOS systems using MacBook devices equipped with Apple M4 chips to focus on disease-affected regions, reduce file size, and minimize background noise. The processed, cropped images are provided in PNG format with variable dimensions. Images were resized to 224×224 pixels only during model training for machine learning experiments. Disease symptoms were verified in collaboration with plant pathologists to ensure accurate classification. This dataset is publicly available on Mendeley Data and is suitable for developing and evaluating machine learning models in plant disease classification. It is particularly valuable for testing model performance under real-world, noisy conditions and for supporting the creation of mobile or edge-based diagnostic tools in agriculture.
Why it matches plant phenotyping methods植物病徴を画像で直接捉えた公開データセットで、植物病害状態の分類モデル開発・評価を主目的とするため、表現型計測データセットとして中心的です。
abstractThis dataset comprises 5452 images of durian plant parts-including leaves, flowers, branches, stems, and roots-affected by ten common disease classes.
Reproduction assets foundThe paper is a Data in Brief describing a public durian disease image dataset (5452 field images, ten classes) deposited on Mendeley Data with an explicit DOI and direct URL, matching an allowed URL. This is a paper-specific, publicly available image dataset directly reproducing the paper's phenotyping measurements. NoDataset · publicRepository name: Mendeley Data
Data identification number: 10.17632/mhjwyb5p48
Direct URL to data: https://data.mendeley.com/datasets/mhjwyb5p48/1Open asset ↗Mendeley Data · 10.17632/mhjwyb5p48lines:47-125Code / dataset availability confirmedarXiv · checked 15 Sept 2026
Cotton is one of the most important natural fiber crops worldwide, yet harvesting remains limited by labor-intensive manual picking, low efficiency, and yield losses from missing the optimal harvest window. Accurate recognition of cotton bolls and their maturity is therefore essential for automation, yield estimation, and breeding research. We propose Cott-ADNet, a lightweight real-time detector tailored to cotton boll and flower recognition under complex field conditions. Building on YOLOv11n, Cott-ADNet enhances spatial representation and robustness through improved convolutional designs, while introducing two new modules: a NeLU-enhanced Global Attention Mechanism to better capture weak and low-contrast features, and a Dilated Receptive Field SPPF to expand receptive fields for more effective multi-scale context modeling at low computational cost. We curate a labeled dataset of 4,966 images, and release an external validation set of 1,216 field images to support future research. Experiments show that Cott-ADNet achieves 91.5% Precision, 89.8% Recall, 93.3% mAP50, 71.3% mAP, and 90.6% F1-Score with only 7.5 GFLOPs, maintaining stable performance under multi-scale and rotational variations. These results demonstrate Cott-ADNet as an accurate and efficient solution for in-field deployment, and thus provide a reliable basis for automated cotton harvesting and high-throughput phenotypic analysis. Code and dataset is available at https://github.com/SweefongWong/Cott-ADNet.
Why it matches plant phenotyping methods綿花の花・ボール認識を対象とする画像解析手法を開発し、データセット作成、外部検証、性能評価まで行っており、植物器官の表現型取得が中心である。
abstractWe propose Cott-ADNet, a lightweight real-time detector tailored to cotton boll and flower recognition under complex field conditions.
Reproduction assets foundThe paper explicitly states that its code and curated cotton boll/flower detection dataset (4,966 labeled images plus a 1,216-image external validation set) are publicly released at the authors' GitHub repository. The ultralytics repository is a generic third-party library, not a paper-specific asset.Code · publicy 7.5 GFLOPs, maintaining stable performance under multi-scale and rotational variations. These results demonstrate Cott-ADNet as an accurate and efficient solution for in-field deployment, and thus provide a reliable basis for automated cotton harvesting and high-throughput phenotypic analysis. Code and dataset is available at https://github.com/SweefongWong/Cott-ADNet .
† † footnotetext: ∗ * Corresponding author: cuij@wfu.edu
Index Terms :
cotton, cotton boll detection, lightweight object detection, rotational convolution
1 Introduction
Cotton is one of the most critical economic crops worldwide, accounting for nearly 35% of global natural fiber production. It underpins industries such as Open asset ↗SweefongWong/Cott-ADNetlines:1-57Code / dataset availability confirmedCrossref · Europe PMC · checked 6 Sept 2026
Floral area is a major predictor of the attractiveness of a flowering plant for pollinators, yet the measurement of floral area is time-consuming and inconsistent across studies. Here, we developed an AI-powered algorithm, FloralArea, to automate floral area measurement from an image. The FloralArea algorithm has two main components: an object segmentation module and an area estimation module. The object segmentation module extracts the pixels of flowers and the reference object in an image. The area estimation module predicts floral area based on the ratio between flower and reference object pixels. We fine-tuned two YOLOv8 segmentation models for flower and reference object segmentation. The flower segmentation model achieved moderate precision, recall, mAP0.5, and mAP0.5-0.95 of 0.794, 0.68, 0.741, and 0.455 on the test dataset, while the reference object model achieved an impressive performance of 0.907, 0.940, 0.933, and 0.832. We evaluated FloralArea using 75 images of flowering plants. We used ImageJ to calculate the actual floral area for all the images and compared them with the predicted floral area from FloralArea. The predicted floral area correlated well with the measured floral area with a coefficient of determination (R 2 ) of 0.93 and a root mean square error of 20.58 cm 2 . The FloralArea algorithm reduced the time it takes to calculate floral area from an image by 99.24% compared with traditional methods with image processing tools like ImageJ. By streamlining floral area estimation, the FloralArea algorithm provides a scalable, efficient, consistent, and accessible tool for researchers, particularly to aid in assessing plant attractiveness to different pollinator groups.
Why it matches plant phenotyping methods花画像から花の面積という植物形質を自動抽出するAI手法を開発し、実測値との比較検証と処理時間評価を行っており、植物フェノタイピング手法が研究の中心です。
abstractHere, we developed an AI-powered algorithm, FloralArea, to automate floral area measurement from an image.
Reproduction assets foundThe paper's authors publicly released the FloralArea source code on GitHub and the flower image dataset (used for fine-tuning YOLOv8 models and evaluating the algorithm) on Penn State's ScholarSphere repository, as stated in the Data Availability statement.Code · publicThe source code for the FloralArea algorithm is available on GitHub ( https://github.com/eai6/FloralArea_Web.git ).Open asset ↗GitHub · eai6/FloralArea_Weblines:137-148Dataset · publicThe image dataset used to fine-tune the YOLOv8 models and evaluate the FloralArea algorithm is on the ScholarSphere repository of the Pennsylvania State University ( https://scholarsphere.psu.edu/resources/33452dff-b807-44b0-8783-71c8c47b5242 ).Open asset ↗ScholarSphere · 33452dff-b807-44b0-8783-71c8c47b5242lines:137-148Code / dataset availability confirmedarXiv · checked 15 Sept 2026
StrawberryField / plotLiDAR / point cloudFlowerObject detectionPose / keypoint estimation
The small scale of urban farms and the commercial availability of low-cost robots (such as the FarmBot) that automate simple tending tasks enable an accessible platform for plant phenotyping. We have used a FarmBot with a custom camera end-effector to estimate strawberry plant flower pose (for robotic pollination) from acquired 3D point cloud models. We describe a novel algorithm that translates individual occupancy grids along orthogonal axes of a point cloud to obtain 2D images corresponding to the six viewpoints. For each image, 2D object detection models for flowers are used to identify 2D bounding boxes which can be converted into the 3D space to extract flower point clouds. Pose estimation is performed by fitting three shapes (superellipsoids, paraboloids and planes) to the flower point clouds and compared with manually labeled ground truth. Our method successfully finds approximately 80% of flowers scanned using our customized FarmBot platform and has a mean flower pose error of 7.7 degrees, which is sufficient for robotic pollination and rivals previous results. All code will be made available at https://github.com/harshmuriki/flowerPose.git.
Why it matches plant phenotyping methodsカスタムカメラ付きロボットによる3D花姿勢推定アルゴリズムとプラットフォームを開発・評価しており、花の姿勢という植物形質の取得が中心である。
abstractenable an accessible platform for plant phenotyping
Reproduction assets foundThe paper's flower pose estimation pipeline (translating occupancy grid, 2D/3D conversion, shape fitting) has an explicit authors' code deposit statement with a public GitHub URL, phrased as future availability ('will be made available'), so actionability is likely but not fully confirmed. No public dataset of the FarmCode · publiclower point clouds and compared with manually labeled ground truth. Our method successfully finds approximately 80% of flowers scanned using our customized FarmBot platform and has a mean flower pose error of 7.7 degrees, which is sufficient for robotic pollination and rivals previous results. All code will be made available at https://github.com/harshmuriki/flowerPose.git .
I Introduction
Urban farms [ 1 ] provide healthy food to local communities and can serve as platforms for education and sustainability. Unlike their rural counterparts, urban farms are usually small in scale and commercially available robotic systems such as the FarmBot [ 2 ] have been developed to help automate basic cuOpen asset ↗harshmuriki/flowerPoselines:1-53Code / dataset availability confirmedEurope PMC · checked 6 Sept 2026
FlowerClassificationObject detectionGrowth / development / phenology
As an ornamental plant, Alstroemeria Genus Morado holds great significance in precision agriculture for the automatic detection and classification of its flower maturity. However, due to its diverse morphologies, complex growth environments, and factors such as occlusion and lighting changes, related tasks face numerous challenges, and research in this area is relatively scarce. This study proposes a deep - learning - based object detection framework, the Morado Flower Detection Network (MFDN), which consists of two parts: a backbone network and a head network. Novel modules such as C3k2_PPA are introduced. Through multi - branch fusion and the attention mechanism, the ability to detect small targets is enhanced. The head network uses the CARAFE module for upsampling, combines features through Concat, accelerates processing with the optimized C2f module, and finally achieves precise detection and classification through the Detect module. In the comparative experiment on the morado_5may dataset, MFDN performs outstandingly in indicators such as Precision, Recall, and F1 - score. The mean Average Precision (mAP) of MFDN is 1.3% - 5.8% higher than that of YOLO - series models. It has strong generalization ability and is expected to contribute to improving the efficiency and automation level of agricultural production.
Why it matches plant phenotyping methodsアルストロメリア花の成熟度を画像から検出・分類する深層学習手法を開発し、データセット上で比較評価しており、植物表現型取得が中心である。
abstractThis study proposes a deep - learning - based object detection framework, the Morado Flower Detection Network (MFDN)
Reproduction assets foundThe paper's core phenotyping measurements (flower maturity detection/classification) are performed on the publicly released morado_5may dataset of 414 annotated Alstroemeria Morado flower images (5,439 bounding-box labels, raw/ripe classes), which the authors state is publicly available on Kaggle. The YOLOv5/ultralytcsDataset · publicLentsch T. (2021). Available online at: https://www.kaggle.com/datasets/teddevrieslentsch/morado-5may (Accessed July 20, 2021)Open asset ↗Kaggle · teddevrieslentsch/morado-5mayhtml-lines:544-586Dataset · publicthis study selected the publicly available morado_5may dataset (Lentsch, 2021) for experimentsOpen asset ↗morado_5mayhtml-lines:97-149Code / dataset availability confirmedEurope PMC · checked 6 Sept 2026
Riparian zones in the United Kingdom have high species diversity but are prone to anthropogenic changes and alien plant invasions, like Impatiens glandulifera . However, identification can be challenging due to poor accessibility or visibility via tree canopies. UAVs provide a means to access previously inaccessible areas and capture imagery of the area. In this study, a method is introduced to identify the flowers of invasive species ( Impatiens glandulifera ) and map their locations using a computer vision framework and oblique image capture methods. The process includes thresholding images, image masking, blurring, ellipsoid shape search, noise reduction, and contour extraction. Locations are determined using camera parameters, EXIF data, and the average flower size, then converted into vector format for GIS software. This method is wrapped into a single executable program named the semi-automatic thresholding tool (SATT). A validation set of 312 UAV images from the River Elwy, North Wales, showed high precision (79%-96%) and mean average precision (mAP) scores of 73%-86%. This demonstrates that the SATT consistently and correctly identifies Impatiens glandulifera flowers from UAV imagery, making it effective for identifying hotspots and targeting management techniques along riparian corridors. The tool has been wrapped into a single-file executable program with a graphical user interface, enabling nonexperts to use the tool without the need of any software installation. Overall, the tool obtains consistent detection levels of abundance/or flower density across the study site. The tool also does not require an extensive amount of training data, and the intuitive design of the software enables nonexperts to utilize the tool and modify parameter values to adapt it to their needs.
Why it matches plant phenotyping methodsUAV画像から花を検出・抽出し、花の存在位置だけでなく個体群の abundance／flower density を推定する手法と実行可能なツールを開発・検証しており、植物器官形質の取得が中心です。
abstracta method is introduced to identify the flowers of invasive species ( Impatiens glandulifera ) and map their locations using a computer vision framework and oblique image capture methods.
Reproduction assets foundThe paper's Data Availability Statement explicitly deposits the raw UAV imagery dataset (312 Phantom 4 multispectral images of Impatiens glandulifera along the River Elwy) and the authors' SATT analysis code in a public GitHub repository with an actionable URL. Other URLs (Shapely, ExifTool, GeoPandas) are generic toolCode · publicThe raw data and code used in this study are available in the public repository on GitHub. The dataset includes images ofOpen asset ↗lines:303-335Code / dataset availability confirmedarXiv · checked 6 Sept 2026
Observer bias and inconsistencies in traditional plant phenotyping methods limit the accuracy and reproducibility of fine-grained plant analysis. To overcome these challenges, we developed TomatoMAP, a comprehensive dataset for Solanum lycopersicum using an Internet of Things (IoT) based imaging system with standardized data acquisition protocols. Our dataset contains 64,464 RGB images that capture 12 different plant poses from four camera elevation angles. Each image includes manually annotated bounding boxes for seven regions of interest (ROIs), including leaves, panicle, batch of flowers, batch of fruits, axillary shoot, shoot and whole plant area, along with 50 fine-grained growth stage classifications based on the BBCH scale. Additionally, we provide 3,616 high-resolution image subset with pixel-wise semantic and instance segmentation annotations for fine-grained phenotyping. We validated our dataset using a cascading model deep learning framework combining MobileNetv3 for classification, YOLOv11 for object detection, and MaskRCNN for segmentation. Through AI vs. Human analysis involving five domain experts, we demonstrate that the models trained on our dataset achieve accuracy and speed comparable to the experts. Cohen's Kappa and inter-rater agreement heatmap confirm the reliability of automated fine-grained phenotyping using our approach.
Why it matches plant phenotyping methods植物の多視点画像取得、アノテーション付きデータセット、深層学習による分類・検出・セグメンテーションを中心に開発・検証した、明確な植物フェノタイピング手法研究です。
abstractwe developed TomatoMAP, a comprehensive dataset for Solanum lycopersicum using an Internet of Things (IoT) based imaging system with standardized data acquisition protocols.
Reproduction assets foundThe paper's TomatoMAP dataset (images, annotations) is publicly deposited in e!DAL at IPK with an explicit DOI URL given in the Data Records section.Dataset · publicDataset is deposited in e!DAL (electronic data archive library) of IPK (Leibniz Institute of Plant Genetics
and Crop Plant Research): https://doi.ipk-gatersleben.de/DOI/10bb9f14-ce90-4747-836f-cf61dfb5eea1/Open asset ↗e!DAL · 10bb9f14-ce90-4747-836f-cf61dfb5eea1pdf-page:7 lines:1-73Code / dataset availability confirmedEurope PMC · checked 6 Sept 2026
RiceFlowerObject detectionGrowth / time-series analysisGrowth / development / phenology
Flower opening time (FOT) is a key trait for successful reproduction and reproductive isolation. In crop science, FOT is critical for stress avoidance and efficient breeding practices. This study developed a system for the automatic detection of rice flower openings and FOT estimation by integrating a low-cost time-lapse camera with machine learning technology. This approach enabled high-resolution monitoring of flowering dynamics in two cultivars: the japonica cultivar Taichung 65 (T65) and the indica cultivar IR24. The system accurately identified regions containing open flowers, and the estimated FOTs varied within a 3-h range, with a root mean square error of approximately 30 min compared to manual detection. A significant difference in estimated FOTs between IR24 and T65 demonstrated the system's potential for genetic screening applications. FOT of both cultivars exhibited a significant negative correlation with daily mean temperature. Notably, a temperature-sensitive period was identified in the morning, suggesting that temperature influences not only flower opening but also preceding physiological processes such as panicle and spikelet development. This study presents a novel approach to investigating FOT dynamics in rice and provides insights into the interaction between environmental factors and internal regulatory mechanisms governing this critical reproductive trait.
Why it matches plant phenotyping methods低コストタイムラプスカメラと機械学習によるイネの開花時刻という植物形質の自動検出・推定システムを開発し、手動検出との誤差で検証しているため、方法が中心的です。
abstractThis study developed a system for the automatic detection of rice flower openings and FOT estimation by integrating a low-cost time-lapse camera with machine learning technology.
Reproduction assets foundThe authors explicitly state that the Python scripts, training dataset (annotated time-lapse rice flower images), and trained YOLOX model used for flower-opening detection are publicly available on their GitHub repository (mwbotan/FLpanicle). This is a paper-specific, public, actionable asset directly reproducing the FCode · publicThe Python scripts, training dataset, and trained model used in this analysis are available on GitHub ( https://github.com/mwbotan/FLpanicle ).Open asset ↗mwbotan/FLpaniclelines:73-84Code / dataset availability confirmedEurope PMC · checked 6 Sept 2026
Characterizing patterns of trait variation across widespread species is a fundamental goal of natural history. Here we create a pipeline to analyze a large community science dataset and test hypothesized flower color divergence across the range of a widespread wildflower. Monarda fistulosa is a North American perennial that produces showy lavender inflorescences. Although previous literature suggests that the flowers of western M. fistulosa might display a deeper purple color than the eastern varieties, this divergence has not been assessed at scale. We process over 40,000 community science photographs of M. fistulosa to identify flowers and extract color. We demonstrate that the flowers of the montane western variety have lower lightness and higher chroma, corresponding to a deeper violet color, than those of eastern M. fistulosa . Our approach and validation provides a scalable framework for phenotyping community science images and enables analysis of geographic color variation in other widespread species.
Why it matches plant phenotyping methods市民科学画像から花を同定し、花色を抽出・検証するスケーラブルな表現型解析パイプラインが研究の中心であり、植物器官の形質を直接推定している。
abstractWe process over 40,000 community science photographs of M. fistulosa to identify flowers and extract color.
Reproduction assets foundThe paper's flower-color phenotyping pipeline has explicit public availability statements: the analysis code and produced datasets are on the authors' GitHub repository, the trained Roboflow segmentation model is publicly hosted, and the source iNaturalist/GBIF observation-image export is archived with a DOI. All are直接Code · publicAll code required for the analysis (referenced with brackets in text above), as well as datasets produced by the analysis, are available at: https://github.com/pmckenz1/monarda_fistulosa_colorOpen asset ↗pmckenz1/monarda_fistulosa_colorlines:147-182Code / dataset availability confirmedOpenAlex · Crossref · Europe PMC · checked 6 Sept 2026
Introduction Recent advancements in sensor technologies have enabled collection of many large, high-resolution plant images datasets that could be used to non-destructively explore the relationships between genetics, environment and management factors on phenotype or the physical traits exhibited by plants. The phenotype data captured in these datasets could then be integrated into models of plant development and crop yield to more accurately predict how plants may grow as a result of changing management practices and climate conditions, better ensuring future food security. However, automated methods capable of reliably and efficiently extracting meaningful measurements of individual plant components (e.g. leaves, flowers, pods) from imagery of whole plants are currently lacking. In this study, we explore interdisciplinary application of MapReader, a computer vision pipeline for annotating and classifying patches of larger images that was originally developed for semantic exploration of historical maps, to time-series images of whole oilseed rape (Brassica napus) plants. Methods Models were trained to classify five plant structures in patches derived from whole plant images (branches, leaves, pods, flower buds and flowers), as well as background patches. Three modelling methods are compared: (i) 6-label multi-class classification, (ii) a chain of binary classifiers approach, and (iii) an approach combining binary classification of plant and background patches, followed by 5-label multi-class classification of plant structures. Results A combined plant/background binarization and 5-label multi-class modelling approach using a ‘resnext50d_4s2x40d’ model architecture for both the binary classification and multi-class classification components was found to produce the most accurate patch classification for whole B. napus plant images (macro-averaged F1-score = 88.50, weighted average F1-score = 97.71). This combined binary and 5-label multi-class classification approach demonstrate similar performance to the top-performing MapReader ‘railspace’ classification model. Discussion This highlights the potential applicability of the MapReader model framework to images data from across scientific and humanities domains, and the flexibility it provides in creating pipelines with different modelling approaches. The pipeline for dynamic plant phenotyping from whole plant images developed in this study could potentially be applied to imagery from varied laboratory conditions, and to images datasets of other plants of both agricultural and conservation concern.
Why it matches plant phenotyping methods植物全体画像から葉・花・莢などの構造を自動抽出・分類する動的フェノタイピング手法を開発・評価しており、方法が研究の中心である。
abstractautomated methods capable of reliably and efficiently extracting meaningful measurements of individual plant components (e.g. leaves, flowers, pods) from imagery of whole plants are currently lacking.
Reproduction assets foundThe paper's phenotyping analysis is based on a public RGB image dataset of Brassica napus plants, explicitly deposited by the authors with a public URL. MapReader is a generic pre-existing library and the HuggingFace railspace models are cited prior work, not paper-specific assets.Dataset · publicThe datasets presented in this study can be found in online repositories. The names of the repository/repositories and accession number(s) can be found below: https://research.aber.ac.uk/en/datasets/collection-of-side-view-and-top-view-rgb-images-of-brassica-napus .Open asset ↗research.aber.ac.uklines:982-1027Code / dataset availability confirmedEurope PMC · checked 6 Sept 2026
How robust three-dimension (3D) organ shape emerges during morphogenesis is a fundamental question in biology. Addressing this question requires a comprehensive quantification of organ geometry in 3D. To tackle these issues, we considered the sepal of Arabidopsis as a model. Using a unique pipeline allowing to recover 3D sepal morphology, we analysed fifteen mutants affected in different pathways. The results of a Principal Component Analysis reveal sepal curvature as an important parameter accounting for variations in sepal morphology within genotypes. Unexpectedly, despite genetic homogeneity of the wild-type plants and reproducible culture conditions, we found a significant level of variability in sepal morphology. Our data also show that sepal shape from wild-type plants is more robust (less variable) than sepal size, hinting to a possible selective pressure on shape parameters.
Why it matches plant phenotyping methods3D萼片形態を復元・定量する独自パイプラインが研究の中心であり、器官形状・曲率という植物表現型を解析している。
abstractUsing a unique pipeline allowing to recover 3D sepal morphology, we analysed fifteen mutants affected in different pathways.
Reproduction assets foundThe paper's data availability statement explicitly deposits the phenotyping assets (original sepal images, segmented images, and sepal measurements) on recherche.data.gouv.fr under DOI 10.57745/LTTTBK. No custom code was developed; analysis used standard Python libraries, so no code asset qualifies.Dataset · publicAll the data that were used for the statistical analysis in this study (original images, segmented images, as well as sepal measurements) are openly available as a published dataset from the French national data platform recherche.data.gouv.fr at https://doi.org/10.57745/LTTTBK . No custom code or scripts were developed for the analysisOpen asset ↗recherche.data.gouv.fr · 10.57745/LTTTBKlines:128-315Code / dataset availability confirmedEurope PMC · checked 15 Sept 2026
Computer vision technology is widely used in smart agriculture, primarily because of its non-invasive nature, which avoids causing damage to delicate crops. Nevertheless, the deployment of computer vision algorithms on agricultural machinery with limited computing resources represents a significant challenge. Algorithm optimization with the aim of achieving an equilibrium between accuracy and computational power represents a pivotal research topic and is the core focus of our work. In this paper, we put forward a lightweight hybrid network, named VM-YOLO, for the purpose of detecting strawberry flowers. Firstly, a multi-branch architecture-based fast convolutional sampling module, designated as Light C2f, is proposed to replace the C2f module in the backbone of YOLOv8, in order to enhance the network's capacity to perceive multi-scale features. Secondly, a state space model-based lightweight neck with a global sensitivity field, designated as VMambaNeck, is proposed to replace the original neck of YOLOv8. After the training and testing of the improved algorithm on a self-constructed strawberry flower dataset, a series of experiments is conducted to evaluate the performance of the model, including ablation experiments, multi-dataset comparative experiments, and comparative experiments against state-of-the-art algorithms. The results show that the VM-YOLO network exhibits superior performance in object detection tasks across diverse datasets compared to the baseline. Furthermore, the results also demonstrate that VM-YOLO has better performances in the mAP, inference speed, and the number of parameters compared to the YOLOv6, Faster R-CNN, FCOS, and RetinaNet.
Why it matches plant phenotyping methodsイチゴ花の検出を目的とする画像解析モデルを開発し、データセット上でアブレーション、比較、性能評価を行っており、植物器官の取得・抽出法が研究の中心である。
abstractwe put forward a lightweight hybrid network, named VM-YOLO, for the purpose of detecting strawberry flowers.
Reproduction assets foundThe authors' self-constructed strawberry flower dataset (3388 labeled images used for all VM-YOLO experiments) is explicitly made publicly available via a Google Drive link in the Data Availability statement. The other listed datasets (Global Wheat Head 2020, CropAndWeed, Strawberry Disease) are cited prior public setsDataset · publicThe datasets can be found at the following: The strawberry flower dataset (Accessed on 2 February 2024): https://drive.google.com/drive/folders/1aT6ur3cLPp0xD0urIH6ex_mrFYkIAtm8 ; The Global Wheat Head Detection Dataset 2020 (Accessed on 10 February 2024): http://www.global-wheat.com/gwhd.html ; The CropAndWeed dataset (Accessed on 15 February 2024): https://github.com/cropandweed/cropandweed-dataset ; and The Strawberry Disease dataset (Accessed on 20 February 2024): www.kaggle.com/usmanafzaal/strawbeOpen asset ↗lines:145-326Code / dataset availability confirmedbioRxiv · Europe PMC · checked 7 Sept 2026
Pollen function is critical for successful plant reproduction and crop productivity and it is important to develop accessible methods to quantitatively analyze pollen performance to enhance reproductive resilience. Here we introduce TubeTracker as a method to quantify key parameters of pollen performance such as, time to pollen grain germination, pollen tube tip velocity and pollen tube survival. TubeTracker integrates manual and automatic image processing routines and the graphical user interface allows the user to interact with the software to make manual corrections of automated steps. TubeTracker does not depend on training data sets required to implement machine learning approaches and thus can be immediately implemented using readily available imaging systems. Furthermore, TubeTracker is an excellent tool to produce the pollen performance data sets necessary to take advantage of emerging AI-based methods to fully automate analysis. We tested TubeTracker and found it to be accurate in measuring pollen tube germination and pollen tube tip elongation across multiple cultivars of tomato. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=114 SRC="FIGDIR/small/624782v2_ufig1.gif" ALT="Figure 1"> View larger version (33K): org.highwire.dtl.DTLVardef@1fc2a63org.highwire.dtl.DTLVardef@42f3a2org.highwire.dtl.DTLVardef@18911d6org.highwire.dtl.DTLVardef@1f236f0_HPS_FORMAT_FIGEXP M_FIG Graphical Abstract Graphical user interface of TubeTracker showing all supported functionalities. C_FIG
Why it matches plant phenotyping methods植物の花粉管画像から発芽時間、伸長速度、生存性などの表現型を抽出するソフトウェア手法を開発し、複数トマト品種で精度検証しているため。
abstractHere we introduce TubeTracker as a method to quantify key parameters of pollen performance such as, time to pollen grain germination, pollen tube tip velocity and pollen tube survival.
Reproduction assets foundThe paper's authors publicly released TubeTracker, the Python software used to perform all automated pollen germination, elongation, and survival phenotyping measurements in this study, on GitHub with explicit availability language and a video sample for training.Code · publicWe further encourage users to independently
improve upon our tool and have provided the complete python code at
https://github.com/souonkap/TubeTracker, along with installation instructions and a video
sample for training purposes.Open asset ↗souonkap/TubeTrackerpdf-page:22 lines:1-44Code / dataset availability confirmedEurope PMC · checked 7 Sept 2026
Pollination is critical for crop development, especially those essential for subsistence. This study addresses the pollination challenges faced by Actinidia, a dioecious plant characterized by female and male flowers on separate plants. Despite the high protein content of pollen, the absence of nectar in kiwifruit flowers poses difficulties in attracting pollinators. Consequently, there is a growing interest in using artificial intelligence and robotic solutions to enable pollination even in unfavourable conditions. These robotic solutions must be able to accurately detect flowers and discern their genders for precise pollination operations. Specifically, upon identifying female Actinidia flowers, the robotic system should approach the stigma to release pollen, while male Actinidia flowers should target the anthers to collect pollen. We identified two primary research gaps: (1) the lack of gender-based flower detection methods and (2) the underutilisation of contemporary deep learning models in this domain. To address these gaps, we evaluated the performance of four pretrained models (YOLOv8, YOLOv5, RT-DETR and DETR) in detecting and determining the gender of Actinidia flowers. We outlined a comprehensive methodology and developed a dataset of manually annotated flowers categorized into two classes based on gender. Our evaluation utilised k-fold cross-validation to rigorously test model performance across diverse subsets of the dataset, addressing the limitations of conventional data splitting methods. DETR provided the most balanced overall performance, achieving precision, recall, F1 score and mAP of 89%, 97%, 93% and 94%, respectively, highlighting its robustness in managing complex detection tasks under varying conditions. These findings underscore the potential of deep learning models for effective gender-specific detection of Actinidia flowers, paving the way for advanced robotic pollination systems.
Why it matches plant phenotyping methodsキウイフルーツ花の画像検出と雌雄判定という植物器官の状態推定手法を開発・比較検証し、注釈付きデータセットと交差検証による性能評価も行っているため、フェノタイピング手法が中心である。
abstractwe evaluated the performance of four pretrained models (YOLOv8, YOLOv5, RT-DETR and DETR) in detecting and determining the gender of Actinidia flowers.
Reproduction assets foundThe paper's authors publicly deposited their gender-annotated Actinidia flower image dataset (augmented version) on Zenodo, explicitly linked in the Data availability statement. No author analysis code or trained model checkpoints are stated as publicly available.Dataset · publicThe data presented in this study are openly available in the digital repository Zenodo: Actinidia chinensis cv. ’Hayward’ Flower Dataset 2024 (augmented version)— https://doi.org/10.5281/zenodo.13692222Open asset ↗Zenodo · 10.5281/zenodo.13692222lines:226-255Code / dataset availability confirmedOpenAlex · Europe PMC · checked 15 Sept 2026
BACKGROUND: Phenotyping of plant traits presents a significant bottleneck in Quantitative Trait Loci (QTL) mapping and genome-wide association studies (GWAS). Computerized phenotyping using digital images promises rapid, robust, and reproducible measurements of dimension, shape, and color traits of plant organs, including grain, leaf, and floral traits. RESULTS: We introduce GRABSEEDS, which is specifically tailored to extract a comprehensive set of features from plant images based on state-of-the-art computer vision and deep learning methods. This command-line enabled tool, which is adept at managing varying light conditions, background disturbances, and overlapping objects, uses digital images to measure plant organ characteristics accurately and efficiently. GRABSEED has advanced features including label recognition and color correction in a batch setting. CONCLUSION: GRABSEEDS streamlines the plant phenotyping process and is effective in a variety of seed, floral and leaf trait studies for association with agronomic traits and stress conditions. Source code and documentations for GRABSEEDS are available at: https://github.com/tanghaibao/jcvi/wiki/GRABSEEDS .
Why it matches plant phenotyping methods植物器官画像から形状・寸法・色などの形質を抽出するソフトウェア手法の開発が中心であり、植物フェノタイピング手法として明確に該当する。
abstractWe introduce GRABSEEDS, which is specifically tailored to extract a comprehensive set of features from plant images based on state-of-the-art computer vision and deep learning methods.
Reproduction assets foundThe paper's authors publicly release the GRABSEEDS software (the computational phenotyping tool used for all measurements in this paper) along with the example images and datasets generated, at the GitHub wiki URL stated in the abstract, availability section, and data availability statement.Code · publicures including label recognition and color correction in a batch setting.
Conclusion
GRABSEEDS streamlines the plant phenotyping process and is effective in a variety of seed, floral and leaf trait studies for association with agronomic traits and stress conditions. Source code and documentations for GRABSEEDS are available at: https://github.com/tanghaibao/jcvi/wiki/GRABSEEDS .
Keywords: Image analysis, Phenotype, Seed traits, High throughput, QTL mapping
status released display-pdf yes is-olf no is-manuscript no is-preprint no is-journal-matter no is-scanned no is-retracted no
Received 2024 May 21; Accepted 2024 Sep 6; Collection date 2024.
IntroductionOpen asset ↗github.com/tanghaibao/jcvi · GRABSEEDSlines:1-28Code / dataset availability confirmedEurope PMC · checked 15 Sept 2026
Arabidopsis thaliana sepals are excellent models for analyzing growth of entire organs due to their relatively small size, which can be captured at a cellular resolution under a confocal microscope. To investigate how differential growth of connected cell layers generate unique organ morphologies, it is necessary to live-image deep into the tissue. However, imaging deep cell layers of the sepal (or plant tissues in general) is practically challenging. Image processing is also difficult due to the low signal-to-noise ratio of the deeper tissue layers, an issue mainly associated with live imaging datasets. Addressing some of these challenges, we provide an optimized methodology for live imaging sepals, and subsequent image processing. For live imaging early-stage sepals, we found that the use of a bright fluorescent membrane marker, coupled with increased laser intensity and an enhanced Z- resolution produces high-quality images suitable for downstream image processing. Our optimized parameters allowed us to image the bottommost cell layer of the sepal (inner epidermal layer) without compromising viability. We used a 'voxel removal' technique to visualize the inner epidermal layer in MorphoGraphX image processing software. We also describe the MorphoGraphX parameters for creating a 2.5D mesh surface for the inner epidermis. Our parameters allow for the segmentation and parent tracking of individual cells through multiple time points, despite the weak signal of the inner epidermal cells. While we have used sepals to illustrate our approach, the methodology will be useful for researchers intending to live-image and track growth of deeper cell layers in 2.5D for any plant tissue.
Why it matches plant phenotyping methods植物組織の深部をライブイメージングし、画像処理・細胞セグメンテーション・追跡によって成長を解析する方法自体が中心的に開発・最適化されているため。
abstractwe provide an optimized methodology for live imaging sepals, and subsequent image processing.
Reproduction assets foundThe paper's Data availability statement deposits the study's datasets (live-imaging/phenotyping data underlying the sepal growth analysis) in two public OSF repositories with explicit DOIs, making them paper-specific, public, and actionable.Dataset · publicg and Michelle Heeney for their comments on the manuscript. We thank Richard Smith (John Innes Centre, UK) for his advice on image processing.
Data availability statement
The datasets presented in this study can be found in online repositories. The names of the repository/repositories and accession number(s) can be found below: https://doi.org/10.17605/OSF.IO/UMW9B , https://doi.org/10.17605/OSF.IO/P5Q39 .
Author contributions
AS: Conceptualization, Data curation, Formal analysis, Investigation, Methodology, Validation, Visualization, Writing – original draft, Writing – review & editing. AR: Conceptualization, Data curation, Funding acquisition, Project administration, Supervision, WritinOpen asset ↗OSF · 10.17605/OSF.IO/UMW9Blines:234-260Dataset · publicon the manuscript. We thank Richard Smith (John Innes Centre, UK) for his advice on image processing.
Data availability statement
The datasets presented in this study can be found in online repositories. The names of the repository/repositories and accession number(s) can be found below: https://doi.org/10.17605/OSF.IO/UMW9B , https://doi.org/10.17605/OSF.IO/P5Q39 .
Author contributions
AS: Conceptualization, Data curation, Formal analysis, Investigation, Methodology, Validation, Visualization, Writing – original draft, Writing – review & editing. AR: Conceptualization, Data curation, Funding acquisition, Project administration, Supervision, Writing – review & editing.
Conflict of intereOpen asset ↗OSF · 10.17605/OSF.IO/P5Q39lines:234-260Code / dataset availability confirmedEurope PMC · checked 14 Sept 2026
The monitoring of plant diseases in nurseries, breeding farms and orchards is essential for maintaining plant health. Fire blight ( Erwinia amylovora ) is still one of the most dangerous diseases in fruit production, as it can spread epidemically and cause enormous economic damage. All measures are therefore aimed at preventing the spread of the pathogen in the orchard and containing an infection at an early stage [1-6]. Efficiency in plant disease control benefits from the development of a digital monitoring system if the spatial and temporal resolution of disease monitoring in orchards can be increased [7]. In this context, a digital disease monitoring system for fire blight based on RGB images was developed for orchards. Between 2021 and 2024, data was collected on nine dates under different weather conditions and with different cameras. The data source locations in Germany were the experimental orchard of the Julius Kühn Institute (JKI), Institute of Plant Protection in Fruit Crops and Viticulture in Dossenheim, the experimental greenhouse of the Julius Kühn Institute for Resistance Research and Stress Tolerance in Quedlinburg and the experimental orchard of the JKI for Breeding Research on Fruit Crops located in Dresden-Pillnitz. The RGB images were taken on different apple genotypes after artificial inoculation with Erwinia amylovora , including cultivars, wild species and progeny from breeding. The presented ERWIAM dataset contains manually labelled RGB images with a size of 1280 × 1280 pixels of fire blight infected shoots, flowers and leaves in different stages of development as well as background images without symptoms. In addition, symptoms of other plant diseases were acquired and integrated into the ERWIAM dataset as a separate class. Each fire blight symptom was annotated with the Computer Vision Annotation Tool (CVAT [8]) using 2-point annotations (bounding boxes) and presented in YOLO 1.1 format (.txt files). The dataset contains a total of 1611 annotated images and 87 background images. This dataset can be used as a resource for researchers and developers working on digital systems for plant disease monitoring.
Why it matches plant phenotyping methodsRGB画像から植物病徴を検出するための注釈付きデータセットを開発・提示しており、植物病害状態の画像ベース表現型評価が中心である。
abstracta digital disease monitoring system for fire blight based on RGB images was developed for orchards.
Reproduction assets foundThe paper is a data descriptor for the ERWIAM dataset of annotated RGB images of fire blight symptoms, publicly deposited on Mendeley Data with a direct URL and DOI given in the text.Dataset · publicTolerance located in Quedlinburg (Germany) [51°46ʹ22″N 11°08ʹ41″E] and at the experimental orchard of the JKI for Breeding Research on Fruit Crops located in Dresden-Pillnitz (Germany) [51°00ʹ01″N 13°53ʹ12"E].
Data accessibility
Repository name: Mendeley Data
Data identification number: 10.17632/fpmnncmg84.1
Direct URL to data: https://data.mendeley.com/datasets/fpmnncmg84/1
1
Value of the Data
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In the experimental greenhouse of the JKI-Quedlinburg Institute, around 2000 different genotypes of apple breeding material were artificially inoculated with Erwinia amylovora in 2021 and 2022, which could be used to record fire blight symptoms. The JKI-Dossenheim Institute has a heterogeneous appleOpen asset ↗Mendeley Data · 10.17632/fpmnncmg84.1lines:42-82Code / dataset availability confirmedEurope PMC · Crossref · checked 15 Sept 2026
To study plant organs, it is necessary to investigate the three-dimensional (3D) structures of plants. In recent years, non-destructive measurements through computed tomography (CT) have been used to understand the 3D structures of plants. In this study, we use the Chrysanthemum seticuspe capitulum inflorescence as an example and focus on contact points between the receptacles and florets within the 3D capitulum inflorescence bud structure to investigate the 3D arrangement of the florets on the receptacle. To determine the 3D order of the contact points, we constructed slice images from the CT volume data and detected the receptacles and florets in the image. However, because each CT sample comprises hundreds of slice images to be processed and each C. seticuspe capitulum inflorescence comprises several florets, manually detecting the receptacles and florets is labor-intensive. Therefore, we propose an automatic contact point detection method based on CT slice images using image recognition techniques. The proposed method improves the accuracy of contact point detection using prior knowledge that contact points exist only around the receptacle. In addition, the integration of the detection results enables the estimation of the 3D position of the contact points. According to the experimental results, we confirmed that the proposed method can detect contacts on slice images with high accuracy and estimate their 3D positions through clustering. Additionally, the sample-independent experiments showed that the proposed method achieved the same detection accuracy as sample-dependent experiments.
Why it matches plant phenotyping methodsCT画像から花序内の小花と花托の接触点を自動検出し、3D位置を推定する手法の開発・精度評価が研究の中心であるため、植物フェノタイピング手法に該当する。
abstractTherefore, we propose an automatic contact point detection method based on CT slice images using image recognition techniques.
Reproduction assets foundThe authors publicly deposited the labeled CT slice-image dataset (contact point annotations and receptacle segmentation labels) on Figshare, and a 3D visualization video of the contact point estimation results is available on YouTube. Raw CT volumes are only available on request. No author analysis code repository is.Dataset · publicre task is to automate the clustering parameters, which are currently determined manually. We also plan to develop a mathematical model of the position of the contact point between the receptacle and florets based on the estimation results.
Data availability statement
The labeled data for this study can be found in the Figshare https://doi.org/10.6084/m9.figshare.25388434 . The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.
Author contributionsOpen asset ↗Figshare · 10.6084/m9.figshare.25388434lines:513-540Code / dataset availability confirmedEurope PMC · checked 7 Sept 2026
Manual segmentation of the petals of flower computed tomography (CT) images is time-consuming and labor-intensive because the flower has many petals. In this study, we aim to obtain a three-dimensional (3D) structure of Camellia japonica flowers and propose a petal segmentation method using computer vision techniques. Petal segmentation on the slice images fails by simply applying the segmentation methods because the shape of the petals in CT images differs from that of the objects targeted by the latest instance segmentation methods. To overcome these challenges, we crop two-dimensional (2D) long rectangles from each slice image and apply the segmentation method to segment the petals on the images. Thanks to cropping, it is easier to segment the shape of the petals in the cropped images using the segmentation methods. We can also use the latest segmentation method for the task because the number of images used for training is augmented by cropping. Subsequently, the results are integrated into 3D to obtain 3D segmentation volume data. The experimental results show that the proposed method can segment petals on slice images with higher accuracy than the method without cropping. The 3D segmentation results were also obtained and visualized successfully.
Why it matches plant phenotyping methods花弁のCT画像から3D構造を抽出する画像セグメンテーション手法の開発と精度比較が中心であり、植物形態フェノタイピングに該当する。
abstractThe experimental results show that the proposed method can segment petals on slice images with higher accuracy than the method without cropping.
Reproduction assets foundThe paper's CT volume data of Camellia japonica flowers (with ground-truth annotations) is publicly deposited on Figshare, and the authors' segmentation/integration code is publicly available on GitHub, both with explicit availability statements.Dataset · publicThe datasets presented in this study can be found in online repositories. The names of the repository/repositories and accession number(s) can be found below: https://doi.org/10.6084/m9.figshare.25264774.v1Open asset ↗figshare · 10.6084/m9.figshare.25264774.v1lines:447-494Code · publicThe code implementing the proposed method is available at https://github.com/yu-NK/petal_ct_crop_seg.gitOpen asset ↗github · yu-NK/petal_ct_crop_seglines:447-494Code / dataset availability confirmedEurope PMC · bioRxiv · checked 14 Sept 2026
ArabidopsisMesh / voxelMicroscopyFlowerSegmentationGrowth / time-series analysisTrackingGrowth / development / phenology
Background Arabidopsis thaliana sepals are excellent models for analyzing growth of entire organs due to their relatively small size, which can be captured at a cellular resolution under a confocal microscope [1]. To investigate how growth of different tissue layers generates unique organ morphologies, it is necessary to live-image deep into the tissue. However, imaging deep cell layers of the sepal is practically challenging, as it is hindered by the presence of extracellular air spaces between mesophyll cells, among other factors which causes optical aberrations. Image processing is also difficult due to the low signal-to-noise ratio of the deeper tissue layers, an issue mainly associated with live imaging datasets. Addressing some of these challenges, we provide an optimized methodology for live imaging sepals and subsequent image processing. This helps us track the growth of individual cells on the outer and inner epidermal layers, which are the key drivers of sepal morphogenesis. Results For live imaging sepals across all tissue layers at early stages of development, we found that the use of a bright fluorescent membrane marker, coupled with increased laser intensity and an enhanced Z-resolution produces high-quality images suitable for downstream image processing. Our optimized parameters allowed us to image the bottommost cell layer of the sepal (inner epidermal layer) without compromising viability. We used a ‘voxel removal’ technique to visualize the inner epidermal layer in MorphoGraphX [2, 3] image processing software. Finally, we describe the process of optimizing the parameters for creating a 2.5D mesh surface for the inner epidermis. This allowed segmentation and parent tracking of individual cells through multiple time points, despite the weak signal of the inner epidermal cells. Conclusion We provide a robust pipeline for imaging and analyzing growth across inner and outer epidermal layers during early sepal development. Our approach can potentially be employed for analyzing growth of other internal cell layers of the sepals as well. For each of the steps, approaches, and parameters we used, we have provided in-depth explanations to help researchers understand the rationale and replicate our pipeline.
Why it matches plant phenotyping methodsライブイメージング、画像処理、細胞セグメンテーションと追跡を統合した、萼片の成長・形態解析パイプラインの最適化が中心であり、植物表現型取得法に該当する。
abstractwe provide an optimized methodology for live imaging sepals and subsequent image processing
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 images of the WT flowers, as well as the final edited images can be accessed at https://doi.org/10.17605/OSF.IO/UMW9B . The images shown in this manuscript correspond to WT replicate 2.Open asset ↗OSF.IO/UMW9Blines:137-166Code / dataset availability confirmedbioRxiv · checked 15 Sept 2026
FlowerClassificationGrowth / time-series analysisGrowth / development / phenology
O_LIPhenology -- the timing of recurring life history events--is strongly linked to climate. Shifts in phenology have important implications for trophic interactions, ecosystem functioning and community ecology. However, data on plant phenology can be time consuming to collect and current records are biased across space and taxonomy. C_LIO_LIHere, we explore the performance of Convolutional Neural Networks (CNN) for classifying flowering phenology on a very large and taxonomically diverse dataset of citizen science images. We analyse >1.8 million iNaturalist records for plants listed in the National Botanical Gardens within South Africa, a country famed for its floristic diversity ([~]21,000 species) but poorly represented in phenological databases. C_LIO_LIWe were able to correctly classify images with >90% accuracy. Using metadata associated with each image, we then reconstructed the timing of peak flower production and length of the flowering season for the 6,986 species with >5 iNaturalist records. C_LIO_LIOur analysis illustrates how machine learning tools can leverage the vast wealth of citizen science biodiversity data to describe large-scale phenological dynamics. We suggest such approaches may be particularly valuable where data on plant phenology is currently lacking. C_LI
Why it matches plant phenotyping methods植物画像にCNNを適用して開花フェノロジーを分類し、開花時期と開花期間を推定する手法が研究の中心であるため、植物フェノタイピング手法研究に該当します。
abstractHere, we explore the performance of Convolutional Neural Networks (CNN) for classifying flowering phenology on a very large and taxonomically diverse dataset of citizen science images.
Reproduction assets foundThe authors publicly release all data and R code needed to recreate the analyses on GitHub (ML-Phenology-Code), and the phenotyping input data are iNaturalist research-grade observation images (1,807,310 images) downloaded via the iNaturalist GBIF DarwinCore Archive, both publicly accessible.Code · publicl images; R.D.S., N.B., and T.J.D. constructed and built
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the models. R.D.S. analysed the data; R.D.S. and T.J.D. interpreted results; R.D.S. and T.J.D.
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wrote the manuscript with significant input from N.B. and M.vdB.
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Data availability
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All data and R code needed to recreate analyses are available on GitHub at
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CC-BY-NC-ND 4.0 International license
perpetuity. It is made available under a
preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in
The copyright holder for this
this version posted December 23, 2023.
;
https://doi.Open asset ↗rossdstewart/ML-Phenology-Codepdf-raw-page:14 lines:1-47Code / dataset availability confirmedEurope PMC · checked 14 Sept 2026
Conventional methods of crop yield estimation are costly, inefficient, and prone to error resulting in poor yield estimates. This affects the ability of farmers to appropriately plan and manage their crop production pipelines and market processes. There is therefore a need to develop automated methods of crop yield estimation. However, the development of accurate machine-learning methods for crop yield estimation depends on the availability of appropriate datasets. There is a lack of such datasets, especially in sub-Saharan Africa. We present curated image datasets of coffee and cashew nuts acquired in Uganda during two crop harvest seasons. The datasets were collected over nine months, from September 2022 to May 2023. The data was collected using a high-resolution camera mounted on an Unmanned Aerial Vehicle . The datasets contain 3000 coffee and 3086 cashew nut images, constituting 6086 images. Annotated objects of interest in the coffee dataset consist of five classes namely: unripe, ripening, ripe, spoilt, and coffee_tree. Annotated objects of interest in the cashew nut dataset consist of six classes namely: tree, flower, premature, unripe, ripe, and spoilt. The datasets may be used for various machine-learning tasks including flowering intensity estimation, fruit maturity stage analysis, disease diagnosis, crop variety identification, and yield estimation.
Why it matches plant phenotyping methodsコーヒーとカシューナッツの画像データセットを構築し、開花強度、成熟段階、収量などの植物形質・状態推定に利用する方法基盤を提供しており、表現型取得用データセットが研究の中心である。
abstractWe present curated image datasets of coffee and cashew nuts acquired in Uganda during two crop harvest seasons.
Reproduction assets foundThe paper's own UAV coffee and cashew image datasets with YOLO annotations are publicly deposited on Mendeley Data (DOI 10.17632/r46c6bpfpf.1), directly reproducing the paper's phenotyping measurements. Annotation tools (Makesense AI, VGG Image Annotator) are generic third-party tools, not paper-specific assets.Dataset · publicre of f/1.7 and focus range of 1 m to ∞, shutter speed of 2-1/8000s and ISO range of 100-6400 (Auto and Manual)
Data source location
Institution: Makerere University
City: Kampala
Country: Uganda
Data accessibility
Repository name: Mendely Data
Data identification number: http://doi.org/10.17632/r46c6bpfpf.1
Direct URL to data:
https://data.mendeley.com/datasets/r46c6bpfpf/1
1.
Value of the Data
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Flowering intensity estimation. Flowering represents an important stage in coffee and cashew farming since it affects crop yield. It has a significant impact on yield in that flowering intensity is positively correlated with the amount of crop yield. Therefore, flowering intensity could be an imporOpen asset ↗10.17632/r46c6bpfpf.1lines:1-51Code / dataset availability confirmedEurope PMC · bioRxiv · checked 15 Sept 2026
MicroscopyCell / cellular structureFlowerTissue2D/3D reconstructionGrowth / development / phenology
Floral spurs are invaginations borne by perianth organs (petals and/or sepals) that have evolved repeatedly in various angiosperm clades. They typically store nectar and can limit the access of pollinators to this reward, resulting in pollination specialization that can lead to speciation in both pollinator and plant lineages. Despite the ecological and evolutionary importance of nectar spurs, the cellular mechanisms involved during spur development have only been described in detail in a handful of species, primarily with respect to epidermal cells. These studies show that the mechanisms involved are taxon-specific. Using confocal microscopy and automated 3D image analysis, we studied spur morphogenesis in Staphisagria picta (Ranunculaceae) and showed that the process is marked by an early phase of dominant cell proliferation, followed by a phase of anisotropic (directional) cell expansion. The comparison with Aquilegia , another taxon of Ranunculaceae with spurred petals, revealed that the convergence in form between the spurs of both taxa is obtained by partially similar developmental processes. The analytical pipeline designed here is an efficient method to visualize in 3D each cell of a developing organ, paving the way for future comparative studies of organ morphogenesis in multicellular eukaryotes. Highlight A new method of 3D analysis of plant tissues at the cellular level revealed that spur morphogenesis in Staphisagria picta is marked by an early phase of dominant cell proliferation, followed by a phase of anisotropic cell expansion. Floral spur development is analysed for the first time quantitatively, taking into account all tissues composing the organ, namely epidermis and parenchyma.
Why it matches plant phenotyping methods共焦点顕微鏡と自動3D画像解析による発生器官の細胞形態・増殖・異方的伸長の定量化手法が研究の中心であり、植物器官の表現型取得・解析に該当する。
abstractUsing confocal microscopy and automated 3D image analysis, we studied spur morphogenesis in Staphisagria picta (Ranunculaceae)
Reproduction assets foundThe paper's 3D segmentation/visualization pipeline (PlantSeg + MorphoLibJ + homemade Python scripts) is the paper-specific computational analysis, and the authors explicitly state the automation and visualization code is publicly available on GitHub. No separate public phenotype dataset or image deposit is stated; dataCode · public”.
235 Cell outliers, i.e. the 5% largest and smallest cells in terms of volume, were filtered out. To
236 visualize the interior of the petals, we relied on the opacity of the dots or on virtual sections.
237 The code that allowed the automation of the segmentations and the visualization of the data is
238 available on github [https://github.com/paulinedlpch/morphogenesis].Open asset ↗paulinedlpch/morphogenesispdf-layout-page:6 lines:1-57Code / dataset availability confirmedEurope PMC · checked 13 Sept 2026
There is a growing body of literature that recognises the importance of UAVs in precision agriculture tasks. Currently, flowering thinning tasks in orchard management rely on the decisions derived from time-consuming manual flower cluster counting in the field by an agrotechnician. Yet it is hard to guarantee the counting accuracy due to numerous human factors. The present dataset contains UAV images during the full blooming period of an apple orchard for three consecutive years, 2018, 2019, and 2020. It is directly linked to a research article entitled "Feasibility assessment of tree-level flower intensity quantification from UAV RGB imagery: A triennial study in an apple orchard". The data collection site was an apple orchard located at Randwijk, Overbetuwe, The Netherlands (51.938, 5.7068 in WGS84 UTM 31U). Moreover, the flower cluster number and floridity ground truth are also provided in one row from the orchard. The UAV flights were conducted with different flying altitudes, camera resolutions, and lighting conditions. This dataset aims to support researchers focussing on remote sensing, machine vision, deep learning, and image classification, and the stakeholders interested in precision horticulture and orchard management. It can be used for flowering intensity estimation and prediction, and spatial and temporal flowering variability mapping by using digital photogrammetry and 3D reconstruction.
Why it matches plant phenotyping methodsリンゴ樹の開花強度という植物形質をUAV RGB画像から推定するための3年間の画像・地上真値データセットであり、再利用可能なフェノタイピング基盤として中心的です。
abstractThe present dataset contains UAV images during the full blooming period of an apple orchard for three consecutive years, 2018, 2019, and 2020.
Reproduction assets foundThis Data in Brief article describes the authors' own public Zenodo deposit containing the paper-specific UAV RGB images, flower cluster/floridity ground truth, and GCP files for the apple orchard flowering monitoring study, with direct download URL provided.Dataset · publicRepository name: Zenodo
Data identification number: https://doi.org/10.5281/zenodo.6802308
Direct URL to data: https://zenodo.org/record/6802308#.YvvMFuxBz0pOpen asset ↗Zenodo · 10.5281/zenodo.6802308lines:1-51Code / dataset availability confirmedEurope PMC · checked 15 Sept 2026
Plant species complexes represent a particularly interesting example of taxonomically complex groups (TCGs), linking hybridization, apomixis, and polyploidy with complex morphological patterns. In such TCGs, mosaic-like character combinations and conflicts of morphological data with molecular phylogenies present a major problem for species classification. Here, we used the large polyploid apomictic European Ranunculus auricomus complex to study relationships among five diploid sexual progenitor species and 75 polyploid apomictic derivate taxa, based on geometric morphometrics using 11,690 landmarked objects (basal and stem leaves, receptacles), genomic data (97,312 RAD-Seq loci, 48 phased target enrichment genes, 71 plastid regions) from 220 populations. We showed that (1) observed genomic clusters correspond to morphological groupings based on basal leaves and concatenated traits, and morphological groups were best resolved with RAD-Seq data; (2) described apomictic taxa usually overlap within trait morphospace except for those taxa at the space edges; (3) apomictic phenotypes are highly influenced by parental subgenome composition and to a lesser extent by climatic factors; and (4) allopolyploid apomictic taxa, compared to their sexual progenitor, resemble a mosaic of ecological and morphological intermediate to transgressive biotypes. The joint evaluation of phylogenomic, phenotypic, reproductive, and ecological data supports a revision of purely descriptive, subjective traditional morphological classifications.
Why it matches plant phenotyping methods幾何学的形態計測を用いて葉や花托の形態形質を大規模に取得・解析し、ゲノムパターンとの比較に研究の中心的役割を持たせているため、植物フェノタイピング手法の実質的な適用に該当する。
abstractbased on geometric morphometrics using 11,690 landmarked objects (basal and stem leaves, receptacles)
Reproduction assets foundThe paper's geometric morphometric phenotyping inputs (leaf/receptacle images and landmark files) are publicly deposited on FigShare, directly reproducing this paper's plant-phenotyping measurements. Flow cytometric ploidy/reproduction-mode data and the MDPI supplement with morphometric tables are also paper-specific; Dataset · public3), respectively. More detailed data, tables, and figures concerning (phylo)genomic analyses are deposited on FigShare ( https://doi.org/10.6084/m9.figshare.14046305 ) (accessed on 7 March 2023). Flow cytometric (FC) and flow cytometric seed screening (FCSS) data (ploidy levels, reproduction modes) are also stored in Figshare ( https://doi.org/10.6084/m9.figshare.13352429 ) (accessed on 7 March 2023). We deposited image data processed for geometric morphometric analyses on FigShare upon publication ( https://doi.org/10.6084/m9.figshare.21393375 ) (accessed on 31 January 2023).
Conflicts of Interest
The authors declare no conflict of interest.
Funding Statement
This research was funded by theOpen asset ↗FigShare · 10.6084/m9.figshare.13352429lines:135-158Supplement · publice thank Esther Philine Zieschang, Anne-Sophie Burmeister, and Jennifer Krüger for processing leaf scans for geometric morphometric analyses, and Michael Kloster for providing scripts to automatically cut leaf scans into basal and stem leaf parts.
Supplementary Materials
The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/biology12030418/s1 , Figure S1: Landmark digitization of the taxonomically most informative Ranunculus auricomus traits; Figure S2: Correlation plot concerning non-autocorrelated (r < 0.8), standardized (0 mean, unit variance) abiotic environmental factors; Figure S3: Correlation plot concerning standardized axis shape scores of alOpen asset ↗lines:122-134Code / dataset availability confirmedOpenAlex · Crossref · checked 8 Sept 2026
A phenotype is the composite of an observable expression of a genome for traits in a given environment. The trajectories of phenotypes computed from an image sequence and timing of important events in a plant’s life cycle can be viewed as temporal phenotypes and indicative of the plant’s growth pattern and vigor. In this paper, we introduce a novel method called FlowerPhenoNet, which uses deep neural networks for detecting flowers from multiview image sequences for high-throughput temporal plant phenotyping analysis. Following flower detection, a set of novel flower-based phenotypes are computed, e.g., the day of emergence of the first flower in a plant’s life cycle, the total number of flowers present in the plant at a given time, the highest number of flowers bloomed in the plant, growth trajectory of a flower, and the blooming trajectory of a plant. To develop a new algorithm and facilitate performance evaluation based on experimental analysis, a benchmark dataset is indispensable. Thus, we introduce a benchmark dataset called FlowerPheno, which comprises image sequences of three flowering plant species, e.g., sunflower, coleus, and canna, captured by a visible light camera in a high-throughput plant phenotyping platform from multiple view angles. The experimental analyses on the FlowerPheno dataset demonstrate the efficacy of the FlowerPhenoNet.
Why it matches plant phenotyping methods花の検出と時系列表現型の抽出を行う深層学習手法を開発し、ベンチマークデータセットと評価も提示しており、植物フェノタイピング手法が中心である。
abstractwe introduce a novel method called FlowerPhenoNet, which uses deep neural networks for detecting flowers from multiview image sequences for high-throughput temporal plant phenotyping analysis.
Reproduction assets foundThe paper publicly releases the FlowerPheno benchmark dataset (17,022 multiview RGB image sequences of sunflower, canna, and coleus with ground-truth flower bounding boxes) and the FlowerPhenoNet source code, both with explicit availability statements and URLs.Dataset · publicThe dataset can be freely downloaded
from https://plantvision.unl.edu/dataset, accessed on 15 February 2021.Open asset ↗plantvision.unl.edupdf-page:4 lines:1-41Code · publicThe source code is available at
https://github.com/localchocotaco/FlowerPhenoNet, accessed on 27 November 2022.Open asset ↗github.com/localchocotaco/FlowerPhenoNetpdf-page:18 lines:1-58Code / dataset availability confirmedEurope PMC · Crossref · checked 15 Sept 2026
Flower morphologies shape the accessibility to nectar and pollen, two major traits that determine plant-pollinator interactions and reproductive success. Melon is an economically important crop whose reproduction is completely pollinator-dependent and, as such, is a valuable model for studying crop-ecological functions. High-resolution imaging techniques, such as micro-computed tomography (micro-CT), have recently become popular for phenotyping in plant science. Here, we implemented micro-CT to study floral morphology and honey bees in the context of nectar-related traits without a sample preparation to improve the phenotyping precision and quality. We generated high-quality 3D models of melon male and female flowers and compared the geometric measures. Micro-CT allowed for a relatively easy and rapid generation of 3D volumetric data on nectar, nectary, flower, and honey bee body sizes. A comparative analysis of male and female flowers showed a strong positive correlation between the nectar gland volume and the volume of the secreted nectar. We modeled the nectar level inside the flower and reconstructed a 3D model of the accessibility by honey bees. By combining data on flower morphology, the honey bee size and nectar volume, this protocol can be used to assess the flower accessibility to pollinators in a high resolution, and can readily carry out genotypes comparative analysis to identify nectar-pollination-related traits.
Why it matches plant phenotyping methodsマイクロCTを用いて花、蜜腺、蜜、ハナバチの3D形態・体積を取得し、花粉媒介関連形質を高精度に評価するプロトコルを実装・提示しており、表現型取得法が中心である。
abstractHere, we implemented micro-CT to study floral morphology and honey bees in the context of nectar-related traits without a sample preparation to improve the phenotyping precision and quality.
Reproduction assets foundThe paper deposits its Python image-processing/phenotyping pipeline on GitHub and provides a supplement containing raw nectar/nectary measurement data (Tables S1–S4). Both are paper-specific, public, and actionable.Supplement · publicThe following supporting information can be downloaded at https://www.mdpi.com/article/10.3390/cells11213452/s1 . Figure S1: pollen on Stamens in ♂ and ⚥ flower types at different magnifications; Table S1: nectar-related traits in male and female flowers; Table S2: correlation analysis between nectary volume, nectary cross-section area, nectary surface, flower width and nectar volume in the respective male, female and pooled melon flowerOpen asset ↗lines:83-224Code / dataset availability confirmedEurope PMC · OpenAlex · Crossref · checked 8 Sept 2026
In the absence of pollination, female reproductive organs senesce, leading to an irrevocable loss in the reproductive potential of the flower, which directly affects seed set. In self-pollinating crops like wheat (Triticum aestivum), the post-anthesis viability of unpollinated carpels has been overlooked, despite its importance for hybrid seed production systems. To advance our knowledge of carpel development in the absence of pollination, we created a high-throughput phenotyping approach to quantify stigma and ovary morphology. We demonstrate the suitability of the approach, which uses light-microscopy imaging and machine learning, for the analysis of floral organ traits in field-grown plants using fresh and fixed samples. We show that the unpollinated carpel undergoes a well-defined initial growth phase, followed by a peak phase in which stigma area reaches its maximum and the radial expansion of the ovary slows, and a final deterioration phase. These developmental dynamics were consistent across years and could be used to classify male-sterile cultivars. This phenotyping approach provides a new tool for examining carpel development, which we hope will advance research into female fertility of wheat.
Why it matches plant phenotyping methodsコムギの柱頭・子房形態を定量化する高スループット画像・機械学習手法の開発と適用が研究の中心であり、植物表現型取得法として明確に該当する。
abstractwe created a high-throughput phenotyping approach to quantify stigma and ovary morphology
Reproduction assets foundThe paper's authors publicly deposited both the analysis code (CNN training/implementation scripts and R scripts) on GitHub and the carpel image/training/validation datasets on Earlham OpenData, directly reproducing this paper's wheat carpel phenotyping measurements and analysis.Code · publicTraining codes used for the development of the CNNs, adapted stigma and ovary CNNs, and R scripts used for data curation and visualisation can be found at https://github.com/Uauy-Lab/ML-carpel_traitsOpen asset ↗Uauy-Lab/ML-carpel_traitslines:122-188Dataset · publicDatasets for the training and validation of the models and raw images used for the different experimental analyses are freely available at https://opendata.earlham.ac.uk/wheat/under_license/toronto/Millan-Blanquez_etal_2022_machine-learning-carpel-traits/Open asset ↗lines:122-188Code / dataset availability confirmedEurope PMC · Crossref · checked 15 Sept 2026
X-ray micro-computed tomography (X-ray μCT) has enabled the characterization of the properties and processes that take place in plants and soils at the micron scale. Despite the widespread use of this advanced technique, major limitations in both hardware and software limit the speed and accuracy of image processing and data analysis. Recent advances in machine learning, specifically the application of convolutional neural networks to image analysis, have enabled rapid and accurate segmentation of image data. Yet, challenges remain in applying convolutional neural networks to the analysis of environmentally and agriculturally relevant images. Specifically, there is a disconnect between the computer scientists and engineers, who build these AI/ML tools, and the potential end users in agricultural research, who may be unsure of how to apply these tools in their work. Additionally, the computing resources required for training and applying deep learning models are unique, more common to computer gaming systems or graphics design work, than to traditional computational systems. To navigate these challenges, we developed a modular workflow for applying convolutional neural networks to X-ray μCT images, using low-cost resources in Google's Colaboratory web application. Here we present the results of the workflow, illustrating how parameters can be optimized to achieve best results using example scans from walnut leaves, almond flower buds, and a soil aggregate. We expect that this framework will accelerate the adoption and use of emerging deep learning techniques within the plant and soil sciences.
Why it matches plant phenotyping methods植物試料のX線μCT画像を対象に、CNNによるセグメンテーション workflow を開発・最適化しており、植物画像からの表現型情報抽出法が中心である。
abstractwe developed a modular workflow for applying convolutional neural networks to X-ray μCT images, using low-cost resources in Google's Colaboratory web application.
Reproduction assets foundThe paper's X-ray μCT training/annotation datasets (walnut leaf, almond flower bud, soil aggregate scans and annotations) are publicly deposited on USDA Ag Data Commons. The workflow code is stated to be on GitHub (Rippner et al., 2022b), but no authors' public URL for it appears in the supplied text or allowed URLs,soDataset · public; Théroux-Rancourt et al., 2020 ). This will allow researchers to gain novel insights into the role that 3d architecture of soil and plant samples plays in a variety of important processes.
Data availability statement
The datasets presented in this study can be found on the National Agricultural Library Ag Data Commons website https://doi.org/10.15482/USDA.ADC/1524793 .
Author contributions
AM, DR, JE, PR, EF, and DP contributed to the conception and design of the study. MM, FD, and KS annotated images. PR, DR, JE, JN, and AB wrote code for image segmentation and data extraction. DR wrote the first draft of the manuscript. MM helped write the “Materials and Methods” section of the manuscriptOpen asset ↗National Agricultural Library Ag Data Commons · 10.15482/USDA.ADC/1524793lines:106-157Code / dataset availability confirmedEurope PMC · checked 8 Sept 2026
The soybean flower and the pod drop are important factors in soybean yield, and the use of computer vision techniques to obtain the phenotypes of flowers and pods in bulk, as well as in a quick and accurate manner, is a key aspect of the study of the soybean flower and pod drop rate (PDR). This paper compared a variety of deep learning algorithms for identifying and counting soybean flowers and pods, and found that the Faster R-CNN model had the best performance. Furthermore, the Faster R-CNN model was further improved and optimized based on the characteristics of soybean flowers and pods. The accuracy of the final model for identifying flowers and pods was increased to 94.36 and 91%, respectively. Afterward, a fusion model for soybean flower and pod recognition and counting was proposed based on the Faster R-CNN model, where the coefficient of determination R 2 between counts of soybean flowers and pods by the fusion model and manual counts reached 0.965 and 0.98, respectively. The above results show that the fusion model is a robust recognition and counting algorithm that can reduce labor intensity and improve efficiency. Its application will greatly facilitate the study of the variable patterns of soybean flowers and pods during the reproductive period. Finally, based on the fusion model, we explored the variable patterns of soybean flowers and pods during the reproductive period, the spatial distribution patterns of soybean flowers and pods, and soybean flower and pod drop patterns.
Why it matches plant phenotyping methodsダイズの花・莢数という植物形質を画像から自動認識・計数する深層学習手法を比較、改良、検証しており、表現型取得法が研究の中心である。
abstractthe use of computer vision techniques to obtain the phenotypes of flowers and pods in bulk, as well as in a quick and accurate manner, is a key aspect of the study
Reproduction assets foundThe authors publicly deposited the soybean flower and pod image datasets (the phenotyping inputs used for detection/counting) in an online repository via a Baidu Netdisk link with password, stated in the Data availability statement. No author analysis code or trained model checkpoints are explicitly shared; LabelImg isDataset · publicThe datasets presented in this study can be found in online repositories. The names of the repository/repositories and accession number(s) can be found below: https://pan.baidu.com/s/1j ZE6BHlpVjGay_JqVmOew:password: ate8 .Open asset ↗pan.baidu.com · ZE6BHlpVjGay_JqVmOewlines:751-822Code / dataset availability confirmedOpenAlex · Crossref · Europe PMC · checked 8 Sept 2026
Confocal imaging is a well-established method for investigating plant phenotypes on the tissue and organ level. However, many differences are difficult to assess by visual inspection and researchers rely extensively on ad hoc manual quantification techniques and qualitative assessment. Here we present a method for quantitatively phenotyping large samples of plant tissue morphologies using triangulated isosurfaces. We successfully demonstrate the applicability of the approach using confocal imaging of aerial organs in Arabidopsis thaliana. Automatic identification of flower primordia using the surface curvature as an indication of outgrowth allows for high-throughput quantification of divergence angles and further analysis of individual flowers. We demonstrate the throughput of our method by quantifying geometric features of 1065 flower primordia from 172 plants, comparing auxin transport mutants to wild type. Additionally, we find that a paraboloid provides a simple geometric parameterisation of the shoot inflorescence domain with few parameters. We utilise parameterisation methods to provide a computational comparison of the shoot apex defined by a fluorescent reporter of the central zone marker gene CLAVATA3 with the apex defined by the paraboloid. Finally, we analyse the impact of mutations which alter mechanical properties on inflorescence dome curvature and compare the results with auxin transport mutants. Our results suggest that region-specific expression domains of genes regulating cell wall biosynthesis and local auxin transport can be important in maintaining the wildtype tissue shape. Altogether, our results indicate a general approach to parameterise and quantify plant development in 3D, which is applicable also in cases where data resolution is limited, and cell segmentation not possible. This enables researchers to address fundamental questions of plant development by quantitative phenotyping with high throughput, consistency and reproducibility.
Why it matches plant phenotyping methods植物組織の3D画像から形態形質を自動抽出・定量する手法を開発し、高スループット性と再現性を実証しているため、フェノタイピング手法が中心である。
abstractHere we present a method for quantitatively phenotyping large samples of plant tissue morphologies using triangulated isosurfaces.
Reproduction assets foundThe paper's data availability statement explicitly deposits all original source data (confocal phenotyping data of Arabidopsis shoot apical meristems) in the Cambridge Apollo repository and all analysis/segmentation/quantification scripts in a public Sainsbury Laboratory GitLab repository. Both are paper-specific,公开,直接Dataset · publicAll original source data files used in this study are available via the Cambridge University Apollo Repository ( https://doi.org/10.17863/CAM.82442 ).Open asset ↗Cambridge University Apollo Repository · 10.17863/CAM.82442lines:369-397Code · publicAll scripts and software for segmentation, quantification, analysis and visualisation are available via the Sainsbury Laboratory GitLab repository ( https://gitlab.com/slcu/teamHJ/publications/aahl_etal_2022 ).Open asset ↗Sainsbury Laboratory GitLab repositorylines:369-397Code / dataset availability confirmedOpenAlex · Europe PMC · bioRxiv · checked 8 Sept 2026
Abstract In the absence of pollination, female reproductive organs senesce leading to an irrevocable loss in the reproductive potential of the flower and directly affecting seed set. In self-pollinating crops like wheat ( Triticum aestivum ), the post-anthesis viability of the unpollinated carpel has been overlooked, despite its importance for hybrid seed production systems. To advance our knowledge of carpel development in the absence of pollination, we created a relatively high-throughput phenotyping approach to quantify stigma and ovary morphology. We demonstrate the suitability of the approach, which is based on light microscopy imaging and machine learning, for the detailed study of floral organ traits in field grown plants using both fresh and fixed samples. We show that the unpollinated carpel undergoes a well-defined initial growth phase, followed by a peak phase (in which stigma area reaches its maximum and the radial expansion of the ovary slows), and a final deterioration phase. These developmental dynamics were largely consistent across years and could be used to classify male sterile cultivars, however the absolute duration of each phase varied across years. This phenotyping approach provides a new tool for examining carpel morphology and development which we hope will help advance research into this field and increase our mechanistic understanding of female fertility in wheat.
Why it matches plant phenotyping methodsコムギの柱頭・子房形態を定量化する高スループット表現型解析法を、光学顕微鏡画像と機械学習で開発・適用しており、表現型取得手法が研究の中心である。
abstractwe created a relatively high-throughput phenotyping approach to quantify stigma and ovary morphology.
Reproduction assets foundThe paper explicitly states that implementation scripts, data, and the trained stigma/ovary CNNs are publicly available at the authors' GitHub repository, which is an allowed URL.Code · publicImplementation scripts and data are available at
https://github.com/marina-millan/ML-carpel_traits.Open asset ↗marina-millan/ML-carpel_traits · ML-carpel_traitspdf-page:4 lines:1-61Code / dataset availability confirmedEurope PMC · checked 14 Sept 2026
Sunflowers are agricultural seed crops that can be used for essential edible oils and ornamental purposes. This cash crop is primarily cultivated in North and South America. Sunflower crops are prone to various diseases, insects, and nematodes, resulting in a wide range of production losses. Digital image processing and computer vision approaches have been widely utilized to categorize and detect plant diseases including leaves, fruits, and flowers over the last few decades. Early diagnosis of infections in sunflowers helps to prevent them from spreading throughout the farm and reducing financial losses to the farmers. This article offers a resourceful dataset of sunflower leaves and flowers that will help the researchers in developing effective algorithms for the detection of diseases. The dataset contains healthy and affected sunflower leaves and flowers with downy mildew, gray mold, and leaf scars. The images were captured manually between 25 th to 29 th November 2021 from the demonstration farm of Bangladesh Agricultural Research Institute (BARI) at Gazipur in cooperation with its one domain expert when the sunflower plants were about to bloom and the maximum diseases can be found. The dataset is hosted by the Department of Computer Science and Engineering, National Institute of Textile Engineering and Research (NITER), Bangladesh and freely available at https://data.mendeley.com/datasets/b83hmrzth8/1.
Why it matches plant phenotyping methodsヒマワリ葉・花の画像データセットを提供し、病害状態の画像ベース推定・分類を可能にすることが中心であるため、植物フェノタイピング用データセットとして含める。
abstractThis article offers a resourceful dataset of sunflower leaves and flowers that will help the researchers in developing effective algorithms for the detection of diseases.
Reproduction assets foundThe paper's own sunflower disease image dataset (467 original + 1668 augmented images) is publicly hosted on Mendeley Data with an explicit direct link and DOI, directly reproducing the paper's phenotyping measurements.Dataset · publicnstitute (BARI) at Gazipur in cooperation with its one domain expert when the sunflower plants were about to bloom and the maximum diseases can be found. The dataset is hosted by the Department of Computer Science and Engineering, National Institute of Textile Engineering and Research (NITER), Bangladesh and freely available at https://data.mendeley.com/datasets/b83hmrzth8/1 .
Keywords: Agriculture, Sunflower dataset, Computer vision, Deep learning
status released display-pdf yes is-olf no is-manuscript no is-preprint no is-journal-matter no is-scanned no is-retracted no
Received 2022 Jan 30; Revised 2022 Mar 5; Accepted 2022 Mar 7; Collection date 2022 Jun.
Specification Table
Subject
CompuOpen asset ↗lines:1-55Code / dataset availability confirmedOpenAlex · Crossref · Europe PMC · checked 8 Sept 2026
Phenotyping is considered a significant bottleneck impeding fast and efficient crop improvement. Similar to many crops, Brassica napus, an internationally important oilseed crop, suffers from low genetic diversity, and will require exploitation of diverse genetic resources to develop locally adapted, high yielding and stress resistant cultivars. A pilot study was completed to assess the feasibility of using indoor high-throughput phenotyping (HTP), semi-automated image processing, and machine learning to capture the phenotypic diversity of agronomically important traits in a diverse B. napus breeding population, SKBnNAM, introduced here for the first time. The experiment comprised 50 spring-type B. napus lines, grown and phenotyped in six replicates under two treatment conditions (control and drought) over 38 days in a LemnaTec Scanalyzer 3D facility. Growth traits including plant height, width, projected leaf area, and estimated biovolume were extracted and derived through processing of RGB and NIR images. Anthesis was automatically and accurately scored (97% accuracy) and the number of flowers per plant and day was approximated alongside relevant canopy traits (width, angle). Further, supervised machine learning was used to predict the total number of raceme branches from flower attributes with 91% accuracy (linear regression and Huber regression algorithms) and to identify mild drought stress, a complex trait which typically has to be empirically scored (0.85 area under the receiver operating characteristic curve, random forest classifier algorithm). The study demonstrates the potential of HTP, image processing and computer vision for effective characterization of agronomic trait diversity in B. napus, although limitations of the platform did create significant variation that limited the utility of the data. However, the results underscore the value of machine learning for phenotyping studies, particularly for complex traits such as drought stress resistance.
Why it matches plant phenotyping methods屋内ハイスループット表現型解析、画像処理、機械学習を用いて作物形質を抽出・予測し、プラットフォーム性能も評価しているため、方法が研究の中心である。
abstractA pilot study was completed to assess the feasibility of using indoor high-throughput phenotyping (HTP), semi-automated image processing, and machine learning to capture the phenotypic diversity of agronomically important traits
Reproduction assets foundThe paper's full LemnaTec HTP image dataset (RGB, NIR, FLUOR, HYP images of 50 B. napus founder lines) is openly available at the authors' P2IRC USask repository, directly reproducing this paper's phenotyping measurements. The genomevis tool concerns SNP/genotype visualization, not phenotyping, and no analysis code is,Dataset · publicThe full image dataset is openly available at https://p2irc-data-dev.usask.ca/dataset/10.1109.SciDataManager.2020.7284788 (Dataset name: P2IRC Flagship 1 Data).Open asset ↗P2IRC Flagship 1 Data · 10.1109.SciDataManager.2020.7284788lines:323-329Code / dataset availability confirmedEurope PMC · OpenAlex · checked 15 Sept 2026
The three-dimensional (3D) arrangement of cells in tissues provides an anatomical basis for analyzing physiological and biochemical aspects of plant and animal cellular development and function. In this study, we established a protocol for tissue clearing and 3D imaging in rice. Our protocol is based on three improvements: clearing with iTOMEI (clearing solution suitable for plants), developing microscopic conditions in which the Z step is optimized for 3D reconstruction, and optimizing cell-wall staining. Our protocol successfully 3D imaged rice shoot apical meristems, florets, and root apical meristems at cellular resolution throughout whole tissues. Using fluorescent reporters of auxin signaling in rice root tips, we also revealed the 3D distribution of auxin signaling events that are activated in the columella, quiescent center, and multiple rows of cells in the stele of the root apical meristem. Examination of cells with higher levels of auxin signaling revealed that only the central row of cells was connected to the quiescent center. Our method provides opportunities to observe the 3D arrangement of cells in rice tissues.
Why it matches plant phenotyping methodsイネ組織を対象に、組織透明化・最適化した3D顕微鏡撮像・細胞壁染色による細胞配置の取得法を開発しており、植物表現型の画像取得が中心的な技術貢献である。
abstractIn this study, we established a protocol for tissue clearing and 3D imaging in rice.
Reproduction assets foundThe paper's 3D imaging datasets (supplementary videos S1–S6 of rice SAMs, florets, anthers, and root tips, plus figure data) are publicly available via the MDPI supplementary materials link. No separate analysis code repository is mentioned.Dataset · publiccquisition, which took approximately 2 h for 150 μm in depth, the images were processed using LASX software (Leica Microsystems, Tokyo, Japan).
Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Supplementary Materials
The following are available online at https://www.mdpi.com/article/10.3390/ijms23010040/s1 .
Click here for additional data file.
Author Contributions
M.S. and H.T. designed the research; M.S., H.A., Y.S. and S.M. performed the research; M.S. and H.T. analyzed the data; M.S. and H.T. wrote the paper. All authors have read and agreed to the published version of the manuscript.
Funding
This study was supporOpen asset ↗lines:70-202Code / dataset availability confirmedEurope PMC · checked 9 Sept 2026
Bat-pollinated flowers have to attract their pollinators in absence of light and therefore some species developed specialized echoic floral parts. These parts are usually concave shaped and act like acoustic retroreflectors making the flowers acoustically conspicuous to the bats. Acoustic plant specializations only have been described for two bat-pollinated species in the Neotropics and one other bat-dependent plant in South East Asia. However, it remains unclear whether other bat-pollinated plant species also show acoustic adaptations. Moreover, acoustic traits have never been compared between bat-pollinated flowers and flowers belonging to other pollination syndromes. To investigate acoustic traits of bat-pollinated flowers we recorded a dataset of 32320 flower echoes, collected from 168 individual flowers belonging to 12 different species. 6 of these species were pollinated by bats and 6 species were pollinated by insects or hummingbirds. We analyzed the spectral target strength of the flowers and trained a convolutional neural network (CNN) on the spectrograms of the flower echoes. We found that bat-pollinated flowers have a significantly higher echo target strength, independent of their size, and differ in their morphology, specifically in the lower variance of their morphological features. We found that a good classification accuracy by our CNN (up to 84%) can be achieved with only one echo/spectrogram to classify the 12 different plant species, both bat-pollinated and otherwise, with bat-pollinated flowers being easier to classify. The higher classification performance of bat-pollinated flowers can be explained by the lower variance of their morphology.
Why it matches plant phenotyping methods花のエコーを用いて音響形質と形態差を測定し、CNNによる種・受粉症候群の分類手法を評価しており、植物形質の取得・抽出が研究の中心である。
abstractTo investigate acoustic traits of bat-pollinated flowers we recorded a dataset of 32320 flower echoes, collected from 168 individual flowers belonging to 12 different species.
Reproduction assets foundThe article's Data Availability statement explicitly points to the authors' public GitHub repository containing the paper's analysis code (CNN classification of flower echoes). The figshare collection (10.6084/m9.figshare.c.5703052) also holds the data, but its URL is not in the allowed list, so only the GitHub code isCode · publicData Availability: All relevant data and code are available from figshare: 10.6084/m9.figshare.c.5703052 and https://github.com/bakunowski/listen_like_a_bat .Open asset ↗bakunowski/listen_like_a_batlines:180-192Code / dataset availability confirmedEurope PMC · checked 14 Sept 2026
The color of particular parts of a flower is often employed as one of the features to differentiate between flower types. Thus, color is also used in flower-image classification. Color labels, such as 'green', 'red', and 'yellow', are used by taxonomists and lay people alike to describe the color of plants. Flower image datasets usually only consist of images and do not contain flower descriptions. In this research, we have built a flower-image dataset, especially regarding orchid species, which consists of human-friendly textual descriptions of features of specific flowers, on the one hand, and digital photographs indicating how a flower looks like, on the other hand. Using this dataset, a new automated color detection model was developed. It is the first research of its kind using color labels and deep learning for color detection in flower recognition. As deep learning often excels in pattern recognition in digital images, we applied transfer learning with various amounts of unfreezing of layers with five different neural network architectures (VGG16, Inception, Resnet50, Xception, Nasnet) to determine which architecture and which scheme of transfer learning performs best. In addition, various color scheme scenarios were tested, including the use of primary and secondary color together, and, in addition, the effectiveness of dealing with multi-class classification using multi-class, combined binary, and, finally, ensemble classifiers were studied. The best overall performance was achieved by the ensemble classifier. The results show that the proposed method can detect the color of flower and labellum very well without having to perform image segmentation. The result of this study can act as a foundation for the development of an image-based plant recognition system that is able to offer an explanation of a provided classification.
Why it matches plant phenotyping methods花と唇弁の色という植物器官形質を画像から自動推定するモデルとデータセットを開発・評価しており、表現型取得手法が中心である。
abstractwe have built a flower-image dataset, especially regarding orchid species
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 used in this paper can be downloaded at https://doi.org/10.7910/DVN/0HNECY [ 24 ].Open asset ↗DVN · 10.7910/DVN/0HNECYlines:261-290Code / dataset availability confirmedEurope PMC · checked 9 Sept 2026
Summary Male fertility in maize involves complex genetic programming affected by environmental factors. Evaluating the presence and proportion of fertile anthers is crucial for agronomic purposes. Anthers in maize emerge from male-only florets, and quantifying anther exertion is a key indicator of male fertility; however, traditional manual scoring methods are subjective. To address this limitation, we developed an automated method, Tasselyzer , for large-scale analysis. This image-based program uses the PlantCV platform to provide a quantitative assessment of anther exertion, capturing regional differences within the tassel based on the distinct color of anthers. We successfully applied this method to diverse maize lines to demonstrate its utility for research and breeding programs. Significance Statement Tasselyzer is a novel image-based segmentation tool for automated, large-scale measurement of anther exertion and the impact of genetic and environmental variation on male fertility in maize.
Why it matches plant phenotyping methodsトウモロコシの葯突出を画像ベースで自動定量する手法とソフトウェアを開発し、複数系統への適用も実施しており、植物フェノタイピング手法が研究の中心です。
abstractwe developed an automated method, Tasselyzer , for large-scale analysis.
Reproduction assets foundThe paper explicitly states that Tasselyzer code, original and pseudo-colored tassel images, and the full image sets are publicly available on GitHub and Zenodo, directly supporting the paper's maize anther exertion phenotyping analysis.Dataset · publicThe
full image sets were used in this study are available within Zenodo at
https://doi.org/10.5281/zenodo.5525073 (Teng et al., 2021).Open asset ↗10.5281/zenodo.5525073pdf-page:15 lines:1-61Code / dataset availability confirmedEurope PMC · bioRxiv · checked 15 Sept 2026
This article describes a new methodology for detailed mapping of the lignification capacity of plant cell walls that we have called “REPRISAL” for REP orter R atiometrics I ntegrating S egmentation for A nalyzing L ignification. REPRISAL consists of the combination of three separate approaches. In the first approach, H*, G* and S* monolignol chemical reporters, corresponding to p -coumaryl alcohol, coniferyl alcohol and sinapyl alcohol, are used to label the growing lignin polymer in a fluorescent triple labelling strategy based on the sequential use of 3 main bioorthogonal chemical reactions. In the second step, an automatic parametric and/or artificial intelligence (AI) segmentation algorithm is developed that assigns fluorescent image pixels to 3 distinct cell wall zones corresponding to cell corners (CC), compound middle lamella (CML) and secondary cell walls (SCW). The last step corresponds to the exploitation of a ratiometric approach enabling statistical analyses of differences in monolignol reporter distribution (ratiometric method 1) and proportions (ratiometric method 2) within the different cell wall zones. In order to demonstrate the potential of REPRISAL for investigating lignin formation we firstly describe its use to map developmentally-related changes in the lignification capacity of WT Arabidopsis interfascicular fiber cells. We then show how it can be used to reveal subtle phenotypical differences in lignification by analyzing the Arabidopsis prx64 peroxidase mutant and provide further evidence for the implication of the AtPRX64 protein in floral stem lignification. Finally, we demonstrate the general applicability of REPRISAL by using it to map lignification capacity in poplar, flax and maize.
Why it matches plant phenotyping methodsREPRISALという蛍光画像・自動セグメンテーション・比率解析を統合した、細胞壁リグニン形成状態の植物フェノタイピング手法を開発し、複数種・変異体で適用している。
abstractThis article describes a new methodology for detailed mapping of the lignification capacity of plant cell walls that we have called “REPRISAL”
Reproduction assets foundThe authors publicly deposited their Fiji/ImageJ segmentation plugin (GUI, parametric macro, WEKA classifier and training data) plus representative confocal sample images in a Zenodo repository, explicitly referenced in the methods and supplementary data as containing the paper's lignification ratiometric analysis toolDataset · publicThe binary mask of each region was applied to each fluorescence channel and
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fluorescence mean values were extracted for the 9 newly-created images. A recapitulative
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montage image was then created to quickly estimate segmentation quality. The imageJ macro
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and sample images are available in the Zenodo repository,
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http://doi.org/10.5281/zenodo.4809980.573
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AI Segmentation
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The Machine learning approach is based on the “Waikato Environment for Knowledge
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Analysis” (WEKA) implemented in ImageJ (Witten et al., 2016). We first defined a
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classification based on four categories: i) secondary cell wall, ii) cell corners, iii) compound
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middle lamella and iv) backgroOpen asset ↗zenodo · 10.5281/zenodo.4809980pdf-raw-page:21 lines:1-63Code / dataset availability confirmedCrossref · checked 14 Sept 2026
Phenotyping wheat (Triticum aestivum L.) is time-consuming and new methods are necessary to decrease labor. To develop a heterotic pool of male wheat lines for hybrid breeding, there must be an efficient way to measure both anther extrusion and the size of anthers. Five hundred and ninety-four soft red winter wheat lines in two replications of randomized complete block design were phenotyped for anther extrusion, a key trait for hybrid wheat production. A device was constructed to capture images using a mobile device. Four heads were sampled per line when anthesis was evident for half the heads in the plot. The extruded anthers were scraped onto a surface, their image was captured, and the area of the anthers was taken via ImageJ. The number of anthers extruded was estimated by counting the number of anthers per image and dividing by the number of heads sampled. The area per anther was taken by dividing the area of anthers per spike by the number of anthers per spike. A significant correlation (R=0.9, p
Why it matches plant phenotyping methods小麦の葯突出数と葯サイズを画像取得・ImageJ解析で測定する手法を開発し、大規模材料で適用・評価しており、表現型取得法が中心である。
abstractTo develop a heterotic pool of male wheat lines for hybrid breeding, there must be an efficient way to measure both anther extrusion and the size of anthers.
Reproduction assets foundThe paper's Data Availability Statement points to a public GitHub repository containing the paper's anther extrusion phenotyping data (HD, AD, AOAPS, NOAPS, APA for the HGAWN population), alongside request-based access via the corresponding author. The ImageJ macro and R analysis code are described but no separate codeDataset · publicof 7
Funding: This research was funded by USDA National Institute of Food and Agriculture, grant
number 2017-67007-25939.
Institutional Review Board Statement: Not applicable.
Informed Consent Statement: Not applicable.
Data Availability Statement: Data is available upon request via contact with the corresponding
author and at <https://github.com/zjwinn/Phenotyping-Anther-Extrusion-of-Wheat-Using-Image-Analysis>.
Acknowledgments: This work is supported by the Agriculture and Food Research Initiative Competi-
tive Grant 2017-67007-25939 (Wheat-CAP) from the USDA National Institute of Food and Agriculture.
Conflicts of Interest: The author claims no conflict of interest.
Abbreviations
NOAPS NuOpen asset ↗https://github.com/zjwinn/Phenotyping-Anther-Extrusion-of-Wheat-Using-Image-Analysispdf-raw-page:7 lines:1-53Code / dataset availability confirmedEurope PMC · checked 15 Sept 2026
Japanese cedar ( Cryptomeria japonica ) is the most important timber species in Japan; however, its pollen is the primary cause of pollinosis in Japan. The total number of pollen grains produced by a single tree is determined by the number of male strobili (male flowers) and the number of pollen grains per male strobilus. While the number of male strobili is a visible and well-investigated trait, little is known about the number of pollen grains per male strobilus. We hypothesized that genetic and environmental factors affect the pollen number per male strobilus and explored the factors that affect pollen production and genetic variation among clones. We counted pollen numbers of 523 male strobili from 26 clones using a cell counter method that we recently developed. Piecewise Structural Equation Modeling (pSEM) revealed that the pollen number is mostly affected by genetic variation, male strobilus weight, and pollen size. Although we collected samples from locations with different environmental conditions, statistical modeling succeeded in predicting pollen numbers for different clones sampled from branches facing different directions. Comparison of predicted pollen numbers revealed that they varied >3-fold among the 26 clones. The determination of the factors affecting pollen number and a precise evaluation of genetic variation will contribute to breeding strategies to counter pollinosis. Furthermore, the combination of our efficient counting method and statistical modeling will provide a powerful tool not only for Japanese cedar but also for other plant species.
Why it matches plant phenotyping methods花粉数という植物形質を測定する新開発のセルカウンター法を適用し、統計モデルと組み合わせた再利用可能な計測手法として位置づけているため、方法論的役割が中心的です。
abstractWe counted pollen numbers of 523 male strobili from 26 clones using a cell counter method that we recently developed.
Reproduction assets foundThe paper's phenotype measurements (pollen number, pollen size, male strobilus weight, direction/height for all 523 samples from 26 clones) are published as Supplementary Table S1, publicly available at the MDPI supplementary URL. No author analysis code or trained models are deposited; the Data Availability Statement仅Supplement · publicions will also be useful for examining other traits in the field research.
Acknowledgments
We thank Yukiko Ito (Niigata Prefectural Forest Research Institute) for providing breeding materials, and Naoto-Benjamin Hamaya (University of Zurich) for valuable suggestions.
Supplementary Materials
The following are available online at https://www.mdpi.com/article/10.3390/plants10050856/s1 . Supplementary Figure S1. Scatterplot of male strobilus weight and area. All samples are shown in this figure. Weight and area are strongly correlated (r = 0.892). Different clones are represented by different symbols (see Table 1 ). Supplementary Table S1. Data for all samples.
Click here for additional data filOpen asset ↗lines:258-292Code / dataset availability confirmedbioRxiv · checked 15 Sept 2026
FlowerGrowth / time-series analysisGrowth / development / phenology
Phenology, the study of the timing of cyclical life history events and seasonal changes, is a fundamental aspect of how individual species, communities, and ecosystems will respond to climate change. Both biotic and abiotic phenological patterns are changing rapidly in response to changing seasonal temperatures and other climate-related drivers, and the consequences of these shifts for individual species and entire ecosystems are largely unknown. Landscape-scale simulations can address some of these needs for better predictions by demonstrating how phenology measures can vary with spatial and temporal grain of observations, and how phenological responses can vary with landscape heterogeneity and climate drivers. To explicitly examine the spatial and temporal scale-dependence of multiple phenology measures, we constructed simulated landscapes populated by virtual plant species with realistic phenologies and environmental sensitivities. This enabled us to examine phenology measures and environmental sensitivities along a continuum of spatial and temporal grains, while also controlling other aspects of sampling design. By relating measures of phenology calculated at a given spatiotemporal grain to average environmental conditions at that same grain size, we are able to determine observed environmental sensitivities for multiple phenological metrics at that spatial and temporal scale. We demonstrate that different phenological events change distinctly and predictably with spatial and temporal measurement scale, opening the way to incorporating scaling laws into predictions. Using plant flowering as our example, we identify that the timing of the beginnings or ends of an event (e.g., First Flower date, Last Flower date), can be especially sensitive to the spatial and temporal grain (or resolution) of observations. Our work provides an initial assessment of the role of observation scale in landscape phenology, and a general approach for incorporating scale-dependence into predictions of a variety of phenological time series.
Why it matches plant phenotyping methods植物の開花フェノロジー指標を空間・時間スケール別に算出・評価するシミュレーション手法を中心に扱い、観測スケール依存性を組み込む一般的アプローチを提示している。
abstractTo explicitly examine the spatial and temporal scale-dependence of multiple phenology measures, we constructed simulated landscapes populated by virtual plant species with realistic phenologies and environmental sensitivities.
Reproduction assets foundThe paper's simulation and analysis code is explicitly deposited on GitHub with an authors' public URL; no separate phenotype dataset deposit is stated (field datasets are cited prior work).Code · publicHarte and Newman 2014).For each phenology measure, we determined scaling effects
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by comparing the phenology measures computed at a given scale to the measures taken at the
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finest spatial and temporal scale available: 2m grain size and daily sampling. Code for the
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simulation and detailed methods can be found at:
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https://github.com/ibreckhe/phenoscaling_sims
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Our simulation approach also allowed us to examine the scale-dependence of observed
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environmental sensitivities. The phenology of the virtual species respond to two aspects of the
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environment: the timing of seasonal snowpack disappearance (snow disappearance day, SDD)
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and the accumulation of air temperatOpen asset ↗ibreckhe/phenoscaling_simspdf-raw-page:6 lines:1-61Code / dataset availability confirmedEurope PMC · checked 9 Sept 2026
As an esthetic trait, ray floret color has a high importance in the development of new sunflower genotypes and their market value. Standard methodology for the evaluation of sunflower ray florets is based on International Union for the Protection of New Varieties of Plants (UPOV) guidelines for sunflower. The major deficiency of this methodology is the necessity of high expertise from evaluators and its high subjectivity. To test the hypothesis that humans cannot distinguish colors equally, six commercial sunflower genotypes were evaluated by 100 agriculture experts, using UPOV guidelines. Moreover, the paper proposes a new methodology for sunflower ray floret color classification - digital UPOV (dUPOV), that relies on software image analysis but still leaves the final decision to the evaluator. For this purpose, we created a new Flower Color Image Analysis ( FloCIA ) software for sunflower ray floret digital image segmentation and automatic classification into one of the categories given by the UPOV guidelines. To assess the benefits and relevance of this method, accuracy of the newly developed software was studied by comparing 153 digital photographs of F 2 genotypes with expert evaluator answers which were used as the ground truth. The FloCIA enabled visualizations of segmentation of ray floret images of sunflower genotypes used in the study, as well as two dominant color clusters, percentages of pixels belonging to each UPOV color category with graphical representation in the CIE (International Commission on Illumination) L ∗ a ∗ b ∗ (or simply Lab) color space in relation to the mean vectors of the UPOV category. Precision (repeatability) of ray flower color determination was greater between dUPOV based expert color evaluation and software evaluation than between two UPOV based evaluations performed by the same expert. The accuracy of FloCIA software used for unsupervised (automatic) classification was 91.50% on the image dataset containing 153 photographs of F 2 genotypes. In this case, the software and the experts had classified 140 out of 153 of images in the same color categories. This visual presentation can serve as a guideline for evaluators to determine the dominant color and to conclude if more than one significant color exists in the examined genotype.
Why it matches plant phenotyping methodsヒマワリの花弁色という植物形質を対象に、画像分割・自動分類ソフトウェアFloCIAを開発し、専門家評価との比較で精度を検証しているため、方法が研究の中心である。
abstractthe paper proposes a new methodology for sunflower ray floret color classification - digital UPOV (dUPOV), that relies on software image analysis
Reproduction assets foundThe paper states that the data and code (FloCIA software analysis) supporting the study are publicly available on Zenodo, with the URL given in the Methods and as a footnote. This is a paper-specific, publicly actionable asset covering the sunflower ray floret image dataset and analysis code.Code · publicThe data and code that support the findings of this study are publicly available on https://zenodo.org/record/4068475#.X31utGgza71 .Open asset ↗zenodo · 4068475lines:329-339Code / dataset availability confirmedbioRxiv · checked 15 Sept 2026
LettuceAerial / UAVFlowerSegmentationGrowth / time-series analysisGrowth / development / phenology
Flower opening and closure are traits of reproductive importance in all angiosperms because they determine the success of self- and cross-pollination. The temporal nature of this phenotype rendered it a difficult target for genetic studies. Cultivated and wild lettuce, Lactuca spp., have composite inflorescences comprised of multiple florets that open only once. Different accessions were observed to flower at different times of day. An F6 recombinant inbred line population (RIL) had been derived from accessions of L. serriola x L. sativa that originated from different environments and differed markedly for daily floral opening time. This population was used to map the genetic determinants of this trait; the floral opening time of 236 RILs was scored over a seven-hour period using time-course image series obtained by drone-based remote phenotyping on two occasions, one week apart. Floral pixels were identified from the images using a support vector machine (SVM) machine learning algorithm with an accuracy above 99%. A Bayesian inference method was developed to extract the peak floral opening time for individual genotypes from the time-stamped image data. Two independent QTLs, qDFO2.1 (Daily Floral Opening 2.1) and qDFO8.1, were discovered. Together, they explained more than 30% of the phenotypic variation in floral opening time. Candidate genes with non-synonymous polymorphisms in coding sequences were identified within the QTLs. This study demonstrates the power of combining remote imaging, machine learning, Bayesian statistics, and genome-wide marker data for studying the genetics of recalcitrant phenotypes such as floral opening time. One sentence summaryMachine learning and Bayesian analyses of drone-mediated remote phenotyping data revealed two genetic loci regulating differential daily flowering time in lettuce (Lactuca spp.).
Why it matches plant phenotyping methodsドローン画像、機械学習、ベイズ推定を用いた花開花時刻の表現型取得・抽出が研究の中心であり、方法の精度も評価されている。
abstractthe floral opening time of 236 RILs was scored over a seven-hour period using time-course image series obtained by drone-based remote phenotyping
Reproduction assets foundThe paper's Data Availability statement provides two paper-specific public assets: authors' analysis scripts (machine learning and Bayesian inference) on GitHub, and the GPS-anchored drone aerial image data on HydroShare. Both are directly used for this paper's phenotyping and analysis.Code · public51 2015-51181-24283 to RWM.
452
453 Data Availability
454 GBS data of the RILs and WGS data of the parents are available on the NCBI SRA database under
455 BioProjects PRJNA642889, PRJNA510128, and PRJNA478460, respectively. Scripts used in the
456 study for machine learning and Bayesian inference are available on GitHub at
457 https://www.github.com/rkbhan/FloralOpening. GPS-anchored aerial image data are available on
458 HydroShare at https://www.hydroshare.org/resource/1c5855dbeb3c49a8b5779300550e08f1/.
20Open asset ↗rkbhan/FloralOpeningpdf-layout-page:20 lines:1-57Dataset · publicavailable on the NCBI SRA database under
455 BioProjects PRJNA642889, PRJNA510128, and PRJNA478460, respectively. Scripts used in the
456 study for machine learning and Bayesian inference are available on GitHub at
457 https://www.github.com/rkbhan/FloralOpening. GPS-anchored aerial image data are available on
458 HydroShare at https://www.hydroshare.org/resource/1c5855dbeb3c49a8b5779300550e08f1/.
20Open asset ↗pdf-layout-page:20 lines:1-57Code / dataset availability confirmedCrossref · OpenAlex · Europe PMC · checked 14 Sept 2026
In the past years, the diversity of Capsicum has been mainly investigated through genetics and genomics approaches, fewer efforts have been made in the field of plant phenomics. Assessment of crop traits with high-throughput methodologies could enhance the knowledge of the plant phenome, giving at the same time a key contribution to the understanding of the function of many genes. In this study, a wide germplasm collection of 307 accessions retrieved from 48 world regions, and belonging to nine Capsicum species was characterized for 54 plant, leaf, flower and fruit traits. Conventional descriptors and semi-automated tools based on image analysis and colour coordinate detection were used. Significant differences were found among accessions, between species and between sweet and spicy cultivated types, revealing a large diversity. The results highlighted how the domestication process and the continued selection have increased the variability of fruit shape and colour. Hierarchical clustering based on conventional and fruit morphological descriptors reflected the separation of species on the basis of their phylogenetic relationships. These observations suggested that the flow between distinct gene pools could have contributed to determine the similarity of the species on the basis of morphological plant and fruit parameters. The approach used represents the first high-throughput phenotyping effort in Capsicum spp. aimed at broadening the knowledge of the diversity of domesticated and wild peppers. The data could help to select best the candidates for breeding and provide new insight into the understanding of the genetic base of the fruit shape of pepper.
Why it matches plant phenotyping methods大規模な植物表現型解析を主題とし、半自動画像解析・色座標検出ツールを用いて植物、葉、花、果実の形質を高スループットに取得しているため、方法の実質的適用に該当する。
abstractAssessment of crop traits with high-throughput methodologies could enhance the knowledge of the plant phenome
Reproduction assets found保存済みの本文根拠を更新済みルールで再検証し、公開資産1件を確認しました。Supplement · publicSupplementary Materials
The following are available online at http://www.mdpi.com/2223-7747/7/4/103/s1 , Figure S1: Distribution of fruit traits in the 307 pepper genotypes under study, Figure S2a: Loading plot of the first and second component based on eight highly correlated fruit traits in all species under study, Figure S2b: Loading plot of the first and second component based on eight highly fruit correlated traits in domesticated and wild species, Figure S3: Hierarchical clustering based on eight highly correlated fruit traits and two most significant plant traits, Table S1: Mean, range, significance of the means within each species and among the 9 Capsicum species for plant traits (BOpen asset ↗lines:943-957Code / dataset availability confirmedEurope PMC · checked 15 Sept 2026
MilletFlowerClassificationTrackingGrowth / development / phenology
In many plant species, the time of day at which flowers open to permit pollination is tightly regulated. Proper time of flower opening, or Time of Day of Anther Appearance ( TAA ), may coordinate flowering opening with pollinator activity or may shift temperature sensitive developmental processes to cooler times of the day. The genetic mechanisms that regulate the timing of this process in cereal crops are unknown. To address this knowledge gap, it is necessary to establish a monocot model system that exhibits variation in TAA. Here, we examine the suitability of Setaria viridis , the model for C4 photosynthesis, for such a role. We developed an imaging system to monitor the temporal regulation of growth, flower opening time, and other physiological characteristics in Setaria. This system enabled us to compare Setaria varieties Ames 32254, Ames 32276, and PI 669942 variation in growth and daily flower opening time. We observed that TAA occurs primarily at night in these three Setaria accessions. However, significant variation between the accessions was observed for both the ratio of flowers that open in the day vs. night and the specific time of day where the rate is maximal. Characterizing this physiological variation is a requisite step toward uncovering the molecular mechanisms regulating TAA. Leveraging the regulation of TAA could provide researchers with a genetic tool to improve crop productivity in new environments.
Why it matches plant phenotyping methodsSetariaの花開時刻や成長を時系列画像とニューラルネットワーク・運動解析で取得するイメージングシステムを開発しており、植物形質の取得手法が研究の中心です。
titleNeural Net Classification Combined With Movement Analysis to Evaluate Setaria viridis as a Model System for Time of Day of Anther Appearance.
Reproduction assets foundThe paper's phenotyping analysis code (camera optimization loop, day-time classification function and trained model, bristle density and growth scripts) is publicly available in the authors' DohertyLab GitHub repository, and a sample A10 day/night image dataset is deposited on Harvard Dataverse. Supplementary material,Code · publiclines never intersect, then the row of the first white pixel not classified as a bristle from the top of the image was used. The bristle density was then calculated for each image in a set. All day time images after panicle emergence were averaged to determine the final bristle density for that panicle. Scripts are available at https://github.com/DohertyLab/Setaria-Flower-Opening-Time .
Quantification of growth
ImageJ Fiji ( https://imagej.net/Fiji/Downloads ) was used to analyze the daily growth pattern of seedlings from 3 to 10 days after emergence. Dawn and dusk images at 8 a.m. and 8 p.m. were imported as an image sequence. In three replicates of each variety the second and/or third leavOpen asset ↗DohertyLab/Setaria-Flower-Opening-Timelines:43-49