Key message Our scalable two-step method estimates genotypic performance and genetic parameters from ranking data, producing reliable results comparable to quantitative analyses, enabling the integration of ranking data into breeding pipelines. Plant breeding research has chiefly relied on on-station experiments to evaluate varietal performance. Nevertheless, these trials often fail to represent on-farm growing conditions and farmers' preferences, potentially leading to poorly defined breeding targets. Recent work has demonstrated the potential of using on-farm verification trials combined with ranking data to support farmers in evaluating varieties while providing information that is representative of farmers' needs. Despite this potential, scalable methods for quantifying genetic differences and assessing the strength of the genetic signal in such trials remain limited. Here, we present a two-step procedure for analyzing trials based on ranking data, allowing the estimation of genetic parameters. The approach follows a common strategy in quantitative genetics, in which parameters are estimated from tables of genotypic means and their variances. In our framework, these estimates are obtained from Thurstonian and/or Plackett-Luce models, which treat rankings as observations of an underlying continuous trait associated with genotypic performance. Using simulated data, we showed that genotypic mean estimates derived from ranking analyses are linearly related to those obtained from quantitative trait analyses and that their variances adequately capture estimation uncertainty. We further demonstrated that incorporating these estimates and their variances into a second-step mixed-effects model yields accurate estimates of variance components. Analyses of groundnut, maize, and sweetpotato datasets confirmed the applicability of the approach and showed that ranking data can provide reliable estimates of genetic parameters. We argue that this framework can be scaled to obtain genotypic performance estimates from multi-trial on-farm data.
Why it matches plant phenotyping methods作物品種の遺伝型性能をランキングデータから推定する統計的方法そのものが研究の中心であり、育種に再利用可能な植物性能の推定手法を開発・検証している。
abstractHere, we present a two-step procedure for analyzing trials based on ranking data, allowing the estimation of genetic parameters.
Reproduction assets foundThe paper's Data availability statement provides public access to the observed groundnut and sweetpotato ranking/trial datasets (Zenodo 17112492), the authors' R functions and simulation workflow (GitHub hdorado/tricot-ranking-analysis, archived Zenodo 17942919), and supplementary material with methods and figures (ZenDataset · publicThe observed data for groundnut and sweetpotato used in this study are publicly available and can be accessed at: Global multi-crop agricultural trial data supported by citizen science, Zenodo [ https://doi.org/10.5281/zenodo.17112492 ]Open asset ↗Zenodo · 10.5281/zenodo.17112492lines:205-225Code · publicThe R functions and simulation workflow used in this study are publicly available at: - Source code available from: [ https://github.com/hdorado/tricot-ranking-analysis ]Open asset ↗GitHub · hdorado/tricot-ranking-analysislines:205-225Code · public- Archived software available from: [ https://doi.org/10.5281/zenodo.17942919 ] - License: [MIT License]Open asset ↗Zenodo · 10.5281/zenodo.17942919lines:205-225Code / dataset availability confirmedOpenAlex · Europe PMC · checked 5 Sept 2026
Peanut (Arachis hypogaea L.), a major legume crop valued for its high oil content, displays complex genotypic-phenotypic interactions shaped by environmental influences, yet these relationships remain poorly understood. We present a high-throughput phenotyping framework that captures the geometry of peanut pods using digital microscopy or smartphone imaging integrated with manifold-learning for large-scale analysis and visualization. Using over 6500 pods collected across China, we identify a geographically distinct morphological signature and demonstrate accurate cultivar discrimination. This scalable approach establishes the foundation for a Large Geometric Model capable of predicting phenotypic traits and accelerating precision agriculture. Our pipeline offers a transformative tool for peanut breeding and sustainable crop improvement.
Why it matches plant phenotyping methodsデジタル顕微鏡・スマートフォン画像と多様体学習を統合し、ピーナッツ莢の形態を大規模に取得・解析する高スループット表現型解析フレームワークが中心である。
abstractWe present a high-throughput phenotyping framework that captures the geometry of peanut pods using digital microscopy or smartphone imaging integrated with manifold-learning for large-scale analysis and visualization.
Reproduction assets foundThe authors state that the peanut pod image dataset, extracted phenotypic trait data, and the Orange Data Mining workflow (.ows) used for analysis are publicly available in their GitHub repository.Dataset · publicThe image dataset of peanut pods analyzed in this study and the extracted phenotypic trait data are publicly available in the GitHub repository: https://github.com/pengwengkung/Complex-geometry-peanut .Open asset ↗pengwengkung/Complex-geometry-peanutlines:169-192Code / dataset availability confirmedOpenAlex · Europe PMC · checked 15 Sept 2026
Unmanned aerial systems (UAS) are reliable tools for field phenotyping, enabling rapid, large-scale, and cost-effective data collection to support breeding programs. However, many UAS-based approaches rely on manual data processing, limiting scalability and efficiency. This study presents a fully automated pipeline for high-throughput phenotyping (HTP) of peanut crop architectural traits, including canopy height (CH), growth habit (GH), and mainstem prominence (MP) by integrating UAS imagery, a vision foundation model-Segment Anything Model (SAM), and convolutional neural networks (CNN). SAM auto-mask generator mode was used to identify field extent and orientation, while SAM interactive mode enabled individual plot segmentation using auto-generated point prompts. Terrain points automatically sampled near each plot were used to model the ground surface and compute the canopy height model, allowing CH estimations at the plot level. CH estimations showed strong agreement with manual measurements (R² = 0.78, RMSE = 3 cm, MAPE = 10 %). For MP and GH estimation, three pre-trained CNN models (AlexNet, ResNet18, and EfficientNet-B0) were evaluated, with AlexNet achieving the highest accuracy (89 % for GH, 83 % for MP). To assess the feasibility of using these HTP-derived estimations in plant breeding, quantitative trait loci (QTL) analysis was performed, identifying major-effect loci associated with these traits. The results were consistent with conventional QTL mapping methods, demonstrating that UAS-based phenotyping provides reliable trait data for genetic studies in peanut breeding. Overall, our deep learning-based data processing workflow minimizes manual efforts, providing an efficient and scalable approach that can accelerate genetic studies and trait selection in large-scale breeding programs.
Why it matches plant phenotyping methodsUAS画像、SAM、CNNを統合したピーナッツの草冠高・生育型・主茎優勢度の自動推定パイプラインを開発・検証しており、表現型取得と抽出手法が研究の中心である。
abstractThis study presents a fully automated pipeline for high-throughput phenotyping (HTP) of peanut crop architectural traits, including canopy height (CH), growth habit (GH), and mainstem prominence (MP) by integrating UAS imagery, a vision foundation model-Segment Anything Model (SAM), and convolutional neural networks (CNN).
Reproduction assets foundThe authors deposited the paper's phenotyping inputs (plot-level aerial RGB images and nDSM maps for GH/MP classification) publicly on Zenodo. The analysis source code is only available upon request, so it does not qualify as a public asset.Dataset · public0126 .
Contributor Information
Peggy Ozias-Akins, Email: pozias@uga.edu.
Changying Li, Email: cli2@ufl.edu.
Appendix A.
Supplementary data
The following is the supplementary data to this article:
Multimedia component 1
Multimedia component 1
Data availability
The datasets supporting this study are publicly available on Zenodo [ https://doi.org/10.5281/zenodo.17274012 ]. They include plot-level aerial RGB images and nDSM maps from peanut breeding fields for classification of Growth Habit and Mainstem Prominence. The source code used for data processing and analysis will be made available upon request.
References
1. U. S. Department of Agriculture . USDA National Agricultural Statistics ServiOpen asset ↗Zenodo · 10.5281/zenodo.17274012lines:327-356Code / dataset availability confirmedEurope PMC · checked 6 Sept 2026
Manual assessment of toxic fungal infection levels in crop seeds is important for developing antifungal-resistant cultivars, yet it has long been recognized as health-risking and inherently subjective. This study presents an edge computing-based computer vision approach for high-throughput on-site assessment and quantification of Aspergillus flavus infection in crop seeds. The edge computing-based computer vision approach, termed Edge CV, was developed using the Jetson Nano, embedded cameras, and deployed with the proposed Edge CV model to enable intelligent evaluation with constrained computing resources and GPU power. The Edge CV model: First, leveraging semantic segmentation in computer vision tasks to differentiate between A. flavus -infected and uninfected; Second, utilizing post-processing techniques to accurately separate connected peanut seeds while merging segments belonging to the same ones; Third, analyzing and quantifying infection indices, as well as results presentation. Finally, deep transfer learning was employed to validate the model's transferability for other crop seeds. As a result, Edge CV inference showed agreement with manual measurements (R 2 = 0.901, RMSE = 0.07) and superior consistency, with only a 0.01 % fluctuation compared to 4.2 % for human assessments. Moreover, Edge CV demonstrated its transferability to other crop seeds, such as maize (R 2 = 0.968, RMSE = 0.13) and rice (R 2 = 0.949, RMSE = 0.26). These results underscore the potential of Edge CV as a transferable solution for assessing toxic fungal infections. The approach developed also offers valuable insights for enhancing proximal machine vision, improving the distinction of adjacent seeds, and enabling more accurate calculation of the infection index.
Why it matches plant phenotyping methods種子の真菌感染状態を画像から定量化するエッジコンピューティング画像手法を開発し、手動測定との一致および他作物種への移 transfer 性を検証しており、植物状態の取得・抽出が研究の中心である。
abstractThis study presents an edge computing-based computer vision approach for high-throughput on-site assessment and quantification of Aspergillus flavus infection in crop seeds.
Reproduction assets foundThe paper's data availability statement points to a public GitHub repository containing the authors' data and code for the Edge CV peanut A. flavus infection assessment pipeline.Code · publicThe data and code will be made available on this URL: https://github.com/lililibin2022/Edge-CV-for-peanut-AF-infection-assessment.Open asset ↗https://github.com/lililibin2022/Edge-CV-for-peanut-AF-infection-assessmenthtml-lines:349-374Code / dataset availability confirmedOpenAlex · checked 14 Sept 2026
Abstract Leaf spot is a devastating disease in cultivated peanut ( Arachis hypogaea L.) that can lead to significant yield losses without chemical controls. Multiple disease symptoms, two causal organisms, inconsistent testing environments, and genotype by environment interactions are all components that make breeding for leaf spot‐resistant peanuts challenging. To better understand this disease, and make gains in breeding for disease resistance, an accurate and objective phenotyping strategy must be implemented. In this work, data derived from leaf scans, unoccupied aerial vehicle‐captured red, green, blue and multispectral imagery were evaluated as a replacement for the subjective visual rating scale used at present. Standard operating procedures are detailed for all digital methods evaluated in this paper, and all digital phenotypes are fully characterized with descriptive statistics. Feature importance and post hoc proof of concept studies are conducted to further evaluate the new digital methods. Ultimately, “visible atmospherically resistant index” was selected as the most appropriate proxy for visual ratings and should be deployed by researchers and plant breeders in the peanut community for the objective evaluation of leaf spot resistance.
Why it matches plant phenotyping methods落花生葉斑病の客観的表現型評価を目的に、葉スキャンおよびUAVのRGB・マルチスペクトル画像を用いるデジタル手法を評価・標準化しており、病害表現型の取得法が中心である。
abstractan accurate and objective phenotyping strategy must be implemented
Reproduction assets foundThe paper deposits its phenotyping datasets (visual ratings, leaf scans, UAV RGB/multispectral imagery) in Dryad and hosts analysis scripts and supporting information in a public GitHub repository, both explicitly linked by the authors.Dataset · publicUS
Department of Agriculture is an equal opportunity provider
and employer.
C O N F L I C T O F I N T E R E S T S TAT E M E N T
The authors declare no conflicts of interest.
DATA AVA I L A B I L I T Y S TAT E M E N T
The datasets generated during and/or analyzed during the cur-
rent study are available in the Dryad repository: https://doi.org/10.5061/dryad.rn8pk0pnm.O RC I D
RyanAndres https://orcid.org/0000-0001-8635-4077
JeffreyDunne https://orcid.org/0000-0003-0544-9889
R E F E R E N C E S
Anco, D. J., Thomas, J. S., Jordan, D. L., Shew, B. B., Monfort, W. S.,
Mehl, H. L., Small, I. M., Wright, D. L., Tillman, B. L., Dufault, N.
S., Hagan, A. K., & Campbell, H. L. (2020). Peanut yield losOpen asset ↗Dryad · 10.5061/dryad.rn8pk0pnm.Opdf-raw-page:15 lines:1-82Code / dataset availability confirmedEurope PMC · checked 7 Sept 2026
Abstract Background: Late and early leaf spot in peanuts is a foliar disease contributing to a significant amount of lost yield globally. Peanut breeding programs frequently focus on developing disease-resistant peanut genotypes. However, existing phenotyping protocols employ subjective rating scales, performed by human raters, who determine the severity of leaf spot infection. The objective of this study was to develop an objective end-to-end pipeline that can serve to replace an expert human scorer in the field. This was accomplished using image capture protocols and segmentation neural networks that extracted lesion areas from plot-level images to determine an appropriate rating for infection severity. Results: The pipeline incorporated a neural network that accurately determined the infected leaf surface area and identified dead leaves from plot-level cellphone imagery. Image processing algorithms then convert these labels into quality metrics that can efficiently score these images based on infected versus non-infected area. The pipeline was evaluated using field data from plots with varying leaf spot severity, creating a dataset of thousands of images that spanned conventional visual severity scores ranging from 1-9. These predictions were based on the amount of infected leaf area and the presence of defoliated leaves in the surrounding area. We were able to demonstrate automated scoring, as compared to exprt visual scoring, with a root mean square error of 0.996 visual scores, on individual images (one image per plot), and 0.800 visual scores when three images were captured of each plot. Conclusion: Results indicated that the model and image processing pipeline can serve as an alternative to human scoring. Eliminating human subjectivity for the scoring protocols will allow non-experts to collect scores and may enable drone-based data collection. This could reduce the time needed to obtain new lines or identify new genes responsible for leaf spot resistance in peanut.
Why it matches plant phenotyping methods落花生の葉斑病重症度という植物状態を、画像取得・セグメンテーション・画像処理で自動推定するパイプラインの開発と評価が中心である。
abstractThe objective of this study was to develop an objective end-to-end pipeline that can serve to replace an expert human scorer in the field.
Reproduction assets foundThe paper explicitly states that the complete dataset of RGB images and semantic segmentation labels, generated/analysed during the study, is publicly available in the authors' GitHub repository (Automated Leaf Spot Scoring), which directly reproduces this paper's peanut leaf spot phenotyping images and annotations.Dataset · publicThe datasets generated and/or analysed during the current study are available in the Automated
Leaf Spot Scoring GitHub repository:
https://github.ncsu.edu/jclarse2/AutomatedLeafSpotScoringOpen asset ↗AutomatedLeafSpotScoringpdf-page:19 lines:1-44Code / dataset availability confirmedEurope PMC · checked 15 Sept 2026
Introduction The emergence rate of crop seedlings is an important indicator for variety selection, evaluation, field management, and yield prediction. To address the low recognition accuracy caused by the uneven size and varying growth conditions of crop seedlings under salt-alkali stress, this research proposes a peanut seedling recognition model, MS-YOLOv8. Methods This research employs close-range remote sensing from unmanned aerial vehicles (UAVs) to rapidly recognize and count peanut seedlings. First, a lightweight adaptive feature fusion module (called MSModule) is constructed, which groups the channels of input feature maps and feeds them into different convolutional layers for multi-scale feature extraction. Additionally, the module automatically adjusts the channel weights of each group based on their contribution, improving the feature fusion effect. Second, the neck network structure is reconstructed to enhance recognition capabilities for small objects, and the MPDIoU loss function is introduced to effectively optimize the detection boxes for seedlings with scattered branch growth. Results Experimental results demonstrate that the proposed MS-YOLOv8 model achieves an AP50 of 97.5% for peanut seedling detection, which is 12.9%, 9.8%, 4.7%, 5.0%, 11.2%, 5.0%, and 3.6% higher than Faster R-CNN, EfficientDet, YOLOv5, YOLOv6, YOLOv7, YOLOv8, and RT-DETR, respectively. Discussion This research provides valuable insights for crop recognition under extreme environmental stress and lays a theoretical foundation for the development of intelligent production equipment.
Why it matches plant phenotyping methodsUAVリモートセンシング画像からピーナッツ幼苗を認識・計数するモデルを開発し、検出性能を比較検証している。幼苗数・出現率という植物状態の推定が研究の中心である。
abstractthis research proposes a peanut seedling recognition model, MS-YOLOv8
Reproduction assets foundThe paper's data availability statement explicitly deposits the peanut seedling UAV image dataset (and associated model resources) in a public GitHub repository, matching an allowed URL.Dataset · publicy close-range remote sensing. It provides a certain theoretical guidance for the development of an intelligent monitoring platform for peanut.
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://github.com/zfvincent1997/MS-YOLOV8 .
Author contributions
FZ: Investigation, Resources, Software, Writing – original draft. LZ: Conceptualization, Supervision, Writing – review & editing. DW: Investigation, Writing – review & editing. JW: Investigation, Writing – review & editing. IS: Software, Visualization, Writing – review & editing. JL: Conceptualization, Open asset ↗https://github.com/zfvincent1997/MS-YOLOV8lines:667-765Code / dataset availability confirmedEurope PMC · checked 14 Sept 2026
Groundnut (Arachis hypogaea) is a widely cultivated legume crop that plays a vital role in global agriculture and food security. It is a major source of vegetable oil and protein for human consumption, as well as a cash crop for farmers in many regions. Despite the importance of this crop to household food security and income, diseases, particularly Leaf spot (early and late), Alternaria leaf spot, Rust, and Rosette, have had a significant impact on its production. Deep learning (DL) techniques, especially convolutional neural networks (CNNs), have demonstrated significant ability for early diagnosis of the plant leaf diseases. However, the availability of groundnut-specific datasets for training and evaluation of DL models is limited, hindering the development and benchmarking of groundnut-related deep learning applications. Therefore, this study provides a dataset of groundnut leaf images, both diseased and healthy, captured in real cultivation fields at Ramchandrapur, Purba Medinipur, West Bengal, using a smartphone camera. The dataset contains a total of 1720 original images, that can be utilized to train DL models to detect groundnut leaf diseases at an early stage. Additionally, we provide baseline results of applying state-of-the-art CNN architectures on the dataset for groundnut disease classification, demonstrating the potential of the dataset for advancing groundnut-related research using deep learning. The aim of creating this dataset is to facilitate in the creation of sophisticated methods that will aid farmers accurately identify diseases and enhance groundnut yields.
Why it matches plant phenotyping methods落花生葉の健全・病葉画像データセットを提供し、植物病害状態の画像ベース判定を可能にすることが中心で、ベースライン評価も含むため。
abstractTherefore, this study provides a dataset of groundnut leaf images, both diseased and healthy, captured in real cultivation fields
Reproduction assets foundThe paper's own groundnut leaf image dataset (1720 images, diseased and healthy) is publicly deposited on Mendeley Data with an explicit DOI and direct URL, matching an allowed URL.Dataset · publiccategorised based on disease criteria with the assistance of a pathologist.
Data source location
Ramchandrapur, Purba Medinipur, West Bengal, India, Pin: 721429 Latitude 21.930146 and Longitude 87.556852
Data accessibility
Repository name: Mendeley Data.
Data identification number: DOI: 10.17632/x6x5jkk873.2
Direct URL to data: https://data.mendeley.com/datasets/x6x5jkk873/2
Instructions for accessing these data: All the image can be downloaded by the following link: https://data.mendeley.com/datasets/x6x5jkk873/2
1.
Value of the Data
•
We address four prominent diseases that specifically target groundnut leaves, causing significant damage to numerous groundnut fields. Researchers and practiOpen asset ↗Mendeley Data · 10.17632/x6x5jkk873.2lines:1-47Code / dataset availability confirmedOpenAlex · Europe PMC · checked 7 Sept 2026
High-throughput phenotyping brings new opportunities for detailed genebank accessions characterization based on image-processing techniques and data analysis using machine learning algorithms. Our work proposes to improve the characterization processes of bean and peanut accessions in the CIAT genebank through the identification of phenomic descriptors comparable to classical descriptors including methodology integration into the genebank workflow. To cope with these goals morphometrics and colorimetry traits of 14 bean and 16 forage peanut accessions were determined and compared to the classical International Board for Plant Genetic Resources (IBPGR) descriptors. Descriptors discriminating most accessions were identified using a random forest algorithm. The most-valuable classification descriptors for peanuts were 100-seed weight and days to flowering, and for beans, days to flowering and primary seed color. The combination of phenomic and classical descriptors increased the accuracy of the classification of Phaseolus and Arachis accessions. Functional diversity indices are recommended to genebank curators to evaluate phenotypic variability to identify accessions with unique traits or identify accessions that represent the greatest phenotypic variation of the species (functional agrobiodiversity collections). The artificial intelligence algorithms are capable of characterizing accessions which reduces costs generated by additional phenotyping. Even though deep analysis of data requires new skills, associating genetic, morphological and ecogeographic diversity is giving us an opportunity to establish unique functional agrobiodiversity collections with new potential traits.
Why it matches plant phenotyping methods画像処理・形態計測・色彩計測と機械学習を用いて遺伝資源の表現型記述子を開発・比較し、遺伝資源管理ワークフローへ統合することが中心であるため。
abstractHigh-throughput phenotyping brings new opportunities for detailed genebank accessions characterization based on image-processing techniques and data analysis using machine learning algorithms.
Reproduction assets foundThe paper's Data Availability statement explicitly points to a public GitHub repository containing the phenomics and traditional descriptor data underlying the study, which directly reproduces the paper's plant-phenotyping measurements. Figures and tables in the article are not treated as separate assets.Dataset · publicData Availability: The data underlying the results presented in the study are available from https://github.com/agrocompuepidemlab/Digital-descriptors-genebank The data of the phenomics and traditional descriptors of the evaluated accessions are associated to this one.Open asset ↗agrocompuepidemlab/Digital-descriptors-genebanklines:143-155Code / dataset availability confirmedEurope PMC · Crossref · checked 15 Sept 2026
The use of machine learning is rapidly expanding across many industries, including agriculture and the IT sector. However, data is essential for machine learning models, and a substantial amount of data is required prior to training a model. We have collected data of groundnut plant leaves in the form of digital photographs taken in the Koppal (Karnataka, India) area with the assistance of a pathologist in natural settings. Images of leaves are categorized into six distinct groups according to their condition. Collected images are pre-processed and the processed images of groundnut leaves are kept in 6 folders as: the "healthy leaves" folder with 1871 images, the "early leaf spot" folder with 1731 images, the "late leaf spot" folder with 1896 images, the "Nutrition deficiency" folder with 1665 images, the "rust" folder with 1724 images, and the "early rust" folder with 1474 images. The total number of images in the dataset is 10361. This dataset will be useful to train and validate deep learning and machine learning algorithms for groundnut leaf disease classification and recognition. Disease detection in plants is crucial for limiting crop losses and our dataset will help disease detection in groundnut plants. This dataset is freely accessible to public at https://data.mendeley.com/datasets/22p2vcbxfk/3 and at https://doi.org/10.17632/22p2vcbxfk.3.
Why it matches plant phenotyping methods落花生葉の画像データセット自体を構築・公開し、葉の健康状態や病徴分類に利用する研究であり、植物病害状態の画像ベース表現型取得が中心です。
titleDataset of groundnut plant leaf images for classification and detection
Reproduction assets foundThe paper is a data descriptor for a public groundnut leaf image dataset (10,361 annotated images across six disease/health classes) deposited on Mendeley Data, with explicit public URLs and DOI. This is a paper-specific plant image dataset directly used for the phenotyping/disease-classification analysis. No author's Dataset · publichis dataset will be useful to train and validate deep learning and machine learning algorithms for groundnut leaf disease classification and recognition. Disease detection in plants is crucial for limiting crop losses and our dataset will help disease detection in groundnut plants. This dataset is freely accessible to public at https://data.mendeley.com/datasets/22p2vcbxfk/3 and at https://doi.org/10.17632/22p2vcbxfk.3
Keywords: Classification of leaf diseases, Image dataset, Diagnosis of disease, Computer Vision
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 2023 Mar 10; Revised 2023 Apr 14; Accepted 2023Open asset ↗10.17632/22p2vcbxfk.3lines:1-56Code / dataset availability confirmedEurope PMC · checked 7 Sept 2026
Rapid nondestructive testing of peanut seed vigor is of great significance in current research. Before seeds are sown, effective screening of high-quality seeds for planting is crucial to improve the quality of crop yield, and seed vitality is one of the important indicators to evaluate seed quality, which can represent the potential ability of seeds to germinate quickly and whole and grow into normal seedlings or plants. Meanwhile, the advantage of nondestructive testing technology is that the seeds themselves will not be damaged. In this study, hyperspectral technology and superoxide dismutase activity were used to detect peanut seed vigor. To investigate peanut seed vigor and predict superoxide dismutase activity, spectral characteristics of peanut seeds in the wavelength range of 400-1000 nm were analyzed. The spectral data are processed by a variety of hot spot algorithms. Spectral data were preprocessed with Savitzky-Golay (SG), multivariate scatter correction (MSC), and median filtering (MF), which can effectively to reduce the effects of baseline drift and tilt. CatBoost and Gradient Boosted Decision Tree were used for feature band extraction, the top five weights of the characteristic bands of peanut seed vigor classification are 425.48nm, 930.8nm, 965.32nm, 984.0nm, and 994.7nm. XGBoost, LightGBM, Support Vector Machine and Random Forest were used for modeling of seed vitality classification. XGBoost and partial least squares regression were used to establish superoxide dismutase activity value regression model. The results indicated that MF-CatBoost-LightGBM was the best model for peanut seed vigor classification, and the accuracy result was 90.83%. MSC-CatBoost-PLSR was the optimal regression model of superoxide dismutase activity value. The results show that the R 2 was 0.9787 and the RMSE value was 0.0566. The results suggested that hyperspectral technology could correlate the external manifestation of effective peanut seed vigor.
Why it matches plant phenotyping methods落花生種子の活力という植物形質をハイパースペクトル画像と機械学習で非破壊推定する手法が研究の中心であり、分類・回帰性能も評価している。
abstractIn this study, hyperspectral technology and superoxide dismutase activity were used to detect peanut seed vigor.
Reproduction assets foundThe paper's data availability statement points to a public GitHub repository containing the authors' original contributions (hyperspectral seed vigor data and analysis). The repository URL in the text (https://github.com/cjkka/cjkka/tree/main) is under the allowed base URL https://github.com/cjkka/. No separate code orDataset · publicavailable. This data can be found here: https://github.com/cjkka/ absence of any commercial or financial relationships that could beOpen asset ↗cjkkapdf-page:12 lines:1-54Code / dataset availability confirmedEurope PMC · checked 8 Sept 2026
Moldy peanut seeds are damaged by mold, which seriously affects the germination rate of peanut seeds. At the same time, the quality and variety purity of peanut seeds profoundly affect the final yield of peanuts and the economic benefits of farmers. In this study, hyperspectral imaging technology was used to achieve variety classification and mold detection of peanut seeds. In addition, this paper proposed to use median filtering (MF) to preprocess hyperspectral data, use four variable selection methods to obtain characteristic wavelengths, and ensemble learning models (SEL) as a stable classification model. This paper compared the model performance of SEL and extreme gradient boosting algorithm (XGBoost), light gradient boosting algorithm (LightGBM), and type boosting algorithm (CatBoost). The results showed that the MF-LightGBM-SEL model based on hyperspectral data achieves the best performance. Its prediction accuracy on the data training and data testing reach 98.63% and 98.03%, respectively, and the modeling time was only 0.37s, which proved that the potential of the model to be used in practice. The approach of SEL combined with hyperspectral imaging techniques facilitates the development of a real-time detection system. It could perform fast and non-destructive high-precision classification of peanut seed varieties and moldy peanuts, which was of great significance for improving crop yields.
Why it matches plant phenotyping methodsピーナッツ種子の品種分類とカビ状態検出を目的に、ハイパースペクトル画像と前処理・機械学習モデルを中心的に開発・比較しており、植物の状態を推定するフェノタイピング手法に該当する。
abstracthyperspectral imaging technology was used to achieve variety classification and mold detection of peanut seeds.
Reproduction assets foundThe paper's data availability statement explicitly deposits the original study contributions (peanut seed hyperspectral data) in a public GitHub repository, which is listed among the allowed URLs.Dataset · publicThe original contributions presented in the study are publicly available. This data can be found here: https://github.com/wuqingsongwj/Peanut-seed .Open asset ↗wuqingsongwj/Peanut-seedlines:589-618Code / dataset availability confirmedOpenAlex · Crossref · Europe PMC · checked 14 Sept 2026
Traditional seed and fruit phenotyping are mainly accomplished by manual measurement or extraction of morphological properties from two-dimensional images. These methods are not only in low-throughput but also unable to collect their three-dimensional (3D) characteristics and internal morphology. X-ray computed tomography (CT) scanning, which provides a convenient means of non-destructively recording the external and internal 3D structures of seeds and fruits, offers a potential to overcome these limitations. However, the current CT equipment cannot be adopted to scan seeds and fruits with high throughput. And there is no specialized software for automatic extraction of phenotypes from CT images. Here, we introduced a high-throughput image acquisition approach by mounting a specially-designed seed-fruit container onto the scanning bed. The corresponding 3D image analysis software, 3DPheno-Seed&Fruit, was created for automatic segmentation and rapid quantification of eight morphological phenotypes of internal and external compartments of seeds and fruits. 3DPheno-Seed&Fruit is a graphical user interface designed and user-friendly software with an excellent phenotype result visualization function. We described the software in detail and benchmarked it based upon CT image analyses in seeds of soybean, wheat, peanut, pine nut, pistacia nut and dwarf Russian almond fruit. R2 values between the extracted and manual measurements of seed length, width, thickness, and radius ranged from 0.80 to 0.96 for soybean and wheat. High correlations were found between the 2D (length, width, thickness, and radius) and 3D (volume and surface area) phenotypes for soybean. Overall, our methods provide robust and novel tools for phenotyping the morphological seed and fruit traits of various plant species, which could benefit crop breeding and functional genomics.
Why it matches plant phenotyping methodsCT画像取得法と3D解析ソフトウェアを開発し、種子・果実形態形質の自動抽出をベンチマークしており、フェノタイピング手法が中心である。
abstractHere, we introduced a high-throughput image acquisition approach by mounting a specially-designed seed-fruit container onto the scanning bed.
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 · public3DPheno-Seed&Fruit software and CT image datasets used in this manuscript are free for academic purpose and can be downloaded from http://www.wutbiolab.com/resources/39/info/29 and https://github.com/whut-biolab-liuchang/projectOpen asset ↗lines:304-314Code / dataset availability confirmedEurope PMC · checked 14 Sept 2026
Peanut allergy is one of the serious health concern and affects more than 1% of the world's population mainly in Americas, Australia, and Europe. Peanut allergy is sometimes life-threatening and adversely affect the life quality of allergic individuals and their families. Consumption of hypoallergen peanuts is the best solution, however, not much effort has been made in this direction for identifying or developing hypoallergen peanut varieties. A highly diverse peanut germplasm panel was phenotyped using a recently developed monoclonal antibody-based ELISA protocol to quantify five major allergens. Results revealed a wide phenotypic variation for all the five allergens studied i.e. , Ara h 1 (4-36,833 µg/g), Ara h 2 (41-77,041 µg/g), Ara h 3 (22-106,765 µg/g), Ara h 6 (829-103,892 µg/g), and Ara h 8 (0.01-70.12 µg/g). The hypoallergen peanut genotypes with low levels of allergen proteins for Ara h 1 (4 µg/g), Ara h 2 (41 µg/g), Ara h 3 (22 µg/g), Ara h 6 (829 µg/g), and Ara h 8 (0.01 µg/g) have paved the way for their use in breeding and genomics studies. In addition, these hypoallergen peanut genotypes are available for use in cultivation and industry, thus opened up new vistas for fighting against peanut allergy problem across the world.
Why it matches plant phenotyping methods多様性パネルを対象に、モノクローナル抗体ELISAで落花生種子の主要アレルゲン量を大規模に定量し、低アレルゲン遺伝子型を同定する測定ワークフローが研究の中心である。
abstractA highly diverse peanut germplasm panel was phenotyped using a recently developed monoclonal antibody-based ELISA protocol to quantify five major allergens.
Reproduction assets found保存済みの本文根拠を更新済みルールで再検証し、公開資産1件を確認しました。Supplement · publicAll datasets generated for this study are included in the article/ Supplementary Material .Open asset ↗lines:1096-1135