Plant Methods最新文献

{"title":"OpenEar: an ultra-affordable, high-throughput, and accurate maize ear phenotyping system.","authors":"Shaoqi Fan, Guoji Li, Revocatus Bahitwa, Zhiguo Jia, Hongwei Zhang, Jinghong Shao, Qiuying Yu, Xiaoran Chen, Yiheng Qian, Mingchi Xu, Linlin Zhu, Hai Wang","doi":"10.1186/s13007-026-01504-x","DOIUrl":"10.1186/s13007-026-01504-x","url":null,"abstract":"<p><p>Crop phenotyping of important agronomic traits in field conditions at single-plant resolution has long been a major bottleneck in both genetic analysis (e.g. large-scale association/linkage analysis) and breeding applications (e.g. genomic prediction/selection). Despite growing interest, ultra-affordable, high-throughput and accurate phenotyping tools for maize ears remain limited. Here, we developed OpenEar, an open source, low-cost phenotyping system that combines a DIY maize ear imaging platform with a deep learning-based end-to-end phenotypic data extraction pipeline. The imaging platform is composed of 3D-printed parts and electronics components easily available from local retailers to perform high-quality 360° surface scanning of maize ears. Our pipeline first employs CNN-based models to identify normally-developed ears suitable for phenotyping, followed by reliable segmentation of ears and ear surface projection images by YOLOv11-based models, from which ten key traits are subsequently extracted. OpenEar demonstrates reliable agreement with manual measurements across a diverse set of ear- and kernel-related traits, including ear length (R² = 0.972), ear diameter (R² = 0.905), ear volume (R² = 0.976), ear weight (R² = 0.878), kernel number (R² = 0.98), kernel row number (R² = 0.888), kernel number per row (R² = 0.852), kernel thickness (R² = 0.705), kernel width (R² = 0.515), and thousand kernel weight (R² = 0.605). A user-friendly graphical interface is developed for manual inspection of ears after computer annotation. Manually annotated ear videos and images are publicly released as a resource for the crop phenomics community. Our study highlights the potential of DIY-based low-cost solutions to make phenotyping more accessible in crop genetic analysis and breeding.</p>","PeriodicalId":20100,"journal":{"name":"Plant Methods","volume":" ","pages":""},"PeriodicalIF":5.2,"publicationDate":"2026-02-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12977455/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146143101","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Organ-level 3D phenotyping of saffron using a low-cost dual-camera workflow.","authors":"Xulong Huang, Huajuan Jiang, Xuanting Wan, Cuiping Chen, Wei Nie, Tao Zhou, Jin Pei, Cheng Peng","doi":"10.1186/s13007-026-01505-w","DOIUrl":"10.1186/s13007-026-01505-w","url":null,"abstract":"<p><strong>Background: </strong>Precise, non-destructive phenotyping of saffron during vegetative growth is critical for optimizing corm yield and accelerating breeding programs, yet systematic 3D measurements have remained elusive due to extreme morphological challenges: ultra-narrow leaves, severe mutual occlusion, and prostrate growth architecture. Traditional single-view imaging systems fail to resolve individual leaves under such conditions, limiting phenotypic analysis to whole-canopy descriptors. Here, we developed a specialized organ-level 3D phenotyping workflow specifically designed for narrow, overlapping leaves using a low-cost dual-camera rotary acquisition system integrated with open-source Structure-from-Motion Multi-View Stereo (SfM-MVS) reconstruction.</p><p><strong>Results: </strong>The dual-perspective strategy reduces occlusion-induced errors by 75% compared to single-view approaches, enabling robust organ-level segmentation via a multi-constraint clustering strategy. Automated measurements of leaf length and width across five developmental stages demonstrate exceptional agreement with manual references (R² > 0.94, MAPE < 6%), achieving accuracy benchmarks established for broad-leaved crops using commercial-grade hardware at 100 × lower cost. Systematic voxel sensitivity analysis across nine scales identified optimal preprocessing parameters (2 cm voxel size) balancing measurement precision with computational efficiency, addressing a critical reproducibility gap in plant phenotyping. Exploratory longitudinal tracking revealed that above-ground biomass was correlated with final corm yield (r = 0.68, P < 0.001), with mid-vegetative canopy volume also showing strong correlation (r = 0.52, P < 0.01), suggesting potential resource allocation trade-offs between vegetative expansion and storage organ development.</p><p><strong>Conclusions: </strong>This work demonstrates that organ-level 3D phenotyping of narrow, overlapping leaves is achievable using low-cost imaging hardware and transparent methodological workflows. Complete documentation of algorithmic parameters and hardware specifications enables direct replication and adaptation to other narrow-leaved crops (wheat, rice, onion, leek), democratizing access to high-throughput phenotyping in resource-limited settings. The workflow advances plant phenomics by demonstrating that methodological transparency and cost-effectiveness need not compromise measurement precision, opening new avenues for phenotype-to-genotype mapping and predictive breeding in underutilized crops.</p>","PeriodicalId":20100,"journal":{"name":"Plant Methods","volume":" ","pages":""},"PeriodicalIF":5.2,"publicationDate":"2026-02-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12977561/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146137798","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"MoEGP: an efficient crop genomic prediction approach based on the mixture of experts network.","authors":"Ruiqing Pan, Yaolong Yang, Yuanyuan Zhang, Qun Xu, Yue Feng, Junyu Chen, Wei Li, Xinghua Wei, Mengchen Zhang","doi":"10.1186/s13007-026-01500-1","DOIUrl":"10.1186/s13007-026-01500-1","url":null,"abstract":"<p><p>BACKGROUND: Genomic prediction plays a pivotal role in assessing crop germplasm resources, accelerating crop breeding, and enhancing genetic improvement. The mixture of experts network (MoE), has been successfully applied in the fields of natural language processing and image recognition. RESULTS: In this study, we proposed a novel, flexible, and robust approach for genomic prediction based on MoE, named Mixture of Experts for Genomic Prediction (MoEGP). This method incorporates a set of experts that process data features and a gating network that distributes samples and assigns weight score to each expert. MoEGP combines the predictions of the selected top-k experts, weighted by their corresponding weight score as final outputs. We evaluated the performance of MoEGP using 36 trait cases from five public datasets across three major cereal crops in comparison to five well-known genomic prediction methods. The results indicated that MoEGP exhibited superior performance in terms of both pearson correlation coefficient (PCC) and mean absolute error (MAE) in all 36 trait experiments, encompassing morphological, developmental, and yield traits. MoEGP achieved an average PCC improvement of 33.75%, and an average reduction of 19.55% in MAE compared with the other five reported methods. With its automated hyper-parameter search system, MoEGP offers flexibility in supporting traits governed by distinct quantitative trait locus (QTL) architectures, facilitating its broad application in crop genomic prediction. The corresponding code is available as open-source on GitHub ( https://github.com/CNRRI-RGRT/MoEGP ). CONCLUSIONS: We proposed a novel deep-learning network designed for genomic prediction, named MoEGP, which outperformed five previously reported methods and demonstrated strong potential for application in crop genomic selection. </p>","PeriodicalId":20100,"journal":{"name":"Plant Methods","volume":" ","pages":""},"PeriodicalIF":5.2,"publicationDate":"2026-02-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12958669/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146113862","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Establishment of fluorescent protein-tagged lines for investigating dynamic localization of organelle and other cell structures in rice pollen tube.","authors":"Su-Kyoung Lee, Woo-Jong Hong, Eui-Jung Kim, Eun Young Kim, Ki-Hong Jung","doi":"10.1186/s13007-026-01501-0","DOIUrl":"10.1186/s13007-026-01501-0","url":null,"abstract":"<p><p>Eukaryotic cells consist of various organelles, each responsible for specific biological processes, making the understanding of protein subcellular localization essential for determining their potential functions. However, the rapid polar growth of pollen tubes requires careful consideration of organelle trafficking when analyzing subcellular localization in related studies. Fluorescence-tagged organelle markers have shown limited utility for studying pollen during the reproductive stage. In this study, we developed pollen-specific fluorescent marker sets for organelles and other cell structures using the promoter of the OsTAPE gene, which is highly expressed in mature pollen and pollen tubes of rice (Oryza sativa L.). These marker sets enable the visualization of cell membranes, nuclei, endoplasmic reticulum, Golgi apparatus, prevacuolar compartments, and filamentous actin by tagging fluorescent proteins (FP) at the amino N-terminal end. Specifically designed to accommodate the rapid tube elongation of rice pollen, this system offers a valuable resource for gene function research and colocalization analysis, helping to elucidate the pollen tube elongation process. This study expands the potential for using fluorescent labeling in monocotyledonous plants like rice during reproductive stages, facilitating gene function studies under varying environmental conditions through subcellular localization analysis in growing pollen tubes.</p>","PeriodicalId":20100,"journal":{"name":"Plant Methods","volume":" ","pages":""},"PeriodicalIF":5.2,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12952121/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146100548","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The Tonoplast Topology Index-a new metric for describing vacuole organization.","authors":"Helena Kočová, George Alexandru Caldarescu, Radek Bezvoda, Fatima Cvrčková","doi":"10.1186/s13007-025-01493-3","DOIUrl":"10.1186/s13007-025-01493-3","url":null,"abstract":"<p><strong>Background: </strong>The plant vacuole arises by orchestrated interplay of membrane trafficking, cytoskeletal rearrangements and a variety of signaling pathways. In the root, the characteristic large central vacuole develops by endomembrane reorganization occurring mainly in the transition zone. The vacuole's bounding membrane-the tonoplast-can be visualized in vivo using fluorescent protein markers, allowing for quantitative analysis of confocal microscopy images. Tonoplast organization can thus serve as a sensitive indicator of changes to any of the processes involved in vacuole biogenesis. The Vacuolar Morphology Index (VMI) is widely accepted as a quantitative measure of vacuole structure. However, this metric has two drawbacks-it only reflects the size of the largest vacuolar compartment (missing therefore possible differences in the organization of smaller compartments), and its determination is labor intensive, limiting its use on large datasets.</p><p><strong>Results: </strong>We developed an alternative metric for describing vacuole organization, named the Tonoplast Topology Index (TTI), which overcomes the above-mentioned shortcomings of the VMI. We compared the performance of our protocol with VMI on a simulated dataset and on real data. To validate the methods´ performance, we used it to confirm the previously reported differences in vacuole shape and size between Arabidopsis thaliana roots grown on the surface of an agar medium compared to those embedded inside the agar. Both VMI and TTI could efficiently detect the relatively subtle changes in vacuole organization depending on the position of the root in the agar, and provided correlated results. However, only TTI produced data with close to normal value distribution, simplifying subsequent statistical evaluation.</p><p><strong>Conclusions: </strong>We present the protocol for TTI determination as a two-stage semi-automated procedure involving microscopic image analysis employing an ImageJ macro and subsequent processing of numeric data in the Jupyter Notebook environment, together with benchmarking image data. Since this implementation is freeware-based, platform-independent and (relatively) user-friendly, we hope it will find its use as a high throughput, added value alternative to the VMI metric.</p>","PeriodicalId":20100,"journal":{"name":"Plant Methods","volume":"22 1","pages":"5"},"PeriodicalIF":4.4,"publicationDate":"2026-01-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12821817/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146019200","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Correction: Research on the classification model of rubber leaf powdery mildew disease severity based on hyperspectral multi-dimensional feature fusion.","authors":"Donghua Wang, Huichun Ye, Yanan You, Chaojia Nie, Jingjing Wang, Bingsun Wu, Fengzheng Cai, Lixia Shen, Jiajian Deng","doi":"10.1186/s13007-025-01483-5","DOIUrl":"10.1186/s13007-025-01483-5","url":null,"abstract":"","PeriodicalId":20100,"journal":{"name":"Plant Methods","volume":"22 1","pages":"3"},"PeriodicalIF":4.4,"publicationDate":"2026-01-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12817413/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146003815","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"High-density field-based 3D reconstruction of rice architecture across diverse cultivars for genome-wide association studies.","authors":"Jiexiong Xu, Jiyoung Lee, Xiangchao Gan","doi":"10.1186/s13007-026-01499-5","DOIUrl":"10.1186/s13007-026-01499-5","url":null,"abstract":"&lt;p&gt;&lt;strong&gt;Background: &lt;/strong&gt;Rice plant architecture underpins yield and grain quality, yet two obstacles impede accurate field characterization in dense paddies. First, single-plant reconstruction is constrained by severe inter-plant occlusion, cluttered backgrounds, and limited viewpoints. These factors obscure culms, leaves, basal tillers, and the true physical scale of the plant. Active ranging devices are cumbersome in outdoor plots and can lose accuracy, whereas conventional passive photogrammetry performs poorly under such conditions. Second, delineating panicles within a 3D rice model is intrinsically difficult. Panicles are slender, highly branched, and visually similar to surrounding foliage, often interwoven and partially hidden. These factors result in fragmented boundaries and missing details. Direct point-cloud segmentation struggles with such discontinuous geometry and requires costly 3D annotation, whereas generic image segmentation models trained on natural scenes transfer poorly to paddy imagery. These challenges motivate a field-ready workflow that both reconstructs whole plants at high resolution in dense plantings and reliably segments panicles to enable trait extraction.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Results: &lt;/strong&gt;A low-cost, in-field, multi-view pipeline for whole-plant three-dimensional reconstruction, termed One Stop 3D Target Reconstruction And segmentation (OSTRA), operates on color images with a reference-board setup. The pipeline builds detailed three-dimensional models of individual rice plants and automatically segments key organs (in this case, panicles), despite dense surrounding vegetation. When applied to 231 diverse rice landraces grown in a crowded field setting, the method produced high-fidelity plant models with clearly delineated panicle structures. From these reconstructions, three architectural traits were derived: plant height, leaf area, and panicle length. Genome-wide association analysis of the measured traits identified strong genotype-phenotype associations tagging known candidate genes. Natural variants at D2 and RFL/APO2 were associated with plant height variation, variants at FLW7 were linked to differences in leaf area, and allelic variation at AAI1 corresponded to panicle length variation. These loci are established regulators of plant growth and morphology, indicating that this three-dimensional phenotyping pipeline attains accuracy sufficient to rediscover meaningful genetic signals.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Conclusions: &lt;/strong&gt;This study provides a practical tool for precise rice phenotyping even under dense field planting conditions, overcoming occlusion and structural complexity. By enabling non-destructive, field-based measurement of complete plant architecture and linking these phenotypes to specific genes, the pipeline bridges field phenomics and genomics. The integrated reconstruction and analysis framework advances the study of rice architecture and offers a general route to connect complex traits with","PeriodicalId":20100,"journal":{"name":"Plant Methods","volume":" ","pages":""},"PeriodicalIF":5.2,"publicationDate":"2026-01-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12924431/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145994484","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Cross-species optimization of nuclei isolation in ten plant species.","authors":"Yun Luo, Jiali Yan, Thuy La, Edward S Buckler, Jianbing Yan, M Cinta Romay","doi":"10.1186/s13007-025-01491-5","DOIUrl":"10.1186/s13007-025-01491-5","url":null,"abstract":"<p><p>Single-cell technologies are transforming plant biology, yet broadly transferable nuclei isolation remains a key bottleneck for snRNA-seq. We developed a reproducible, cost-efficient Percoll-based workflow that is applicable to multiple maize tissues and nine additional plant species. In maize, nuclei from root, shoot, leaf, and embryo consistently concentrated at the 80% Percoll interface and exhibited high integrity, with typical recoveries > 50,000 nuclei per sample. For other species, gradient compositions were tuned according to genome size to achieve efficient enrichment and clean suspensions, and yields ranged from 17,000 to 40,000 nuclei per sample. Downstream validation showed that nuclei from special interest maize and Tripsacum generated high-quality snRNA-seq libraries, as supported by cDNA quality profiles. These results demonstrate the versatility and robustness of the method across species and tissues.</p>","PeriodicalId":20100,"journal":{"name":"Plant Methods","volume":" ","pages":"19"},"PeriodicalIF":4.4,"publicationDate":"2026-01-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12892638/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145990242","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Introduction of the Ribo-BiFC method to plants using a split mVenus approach.","authors":"Karel Raabe, Alena Náprstková, Janto Pieters, Elnura Torutaeva, Veronika Jirásková, Zahra Kahrizi, Palash Chandra Mondol, Christos Michailidis, David Honys","doi":"10.1186/s13007-025-01494-2","DOIUrl":"10.1186/s13007-025-01494-2","url":null,"abstract":"<p><strong>Background: </strong>Translation is a fundamental process for every living organism. In plants, the rate of translation is tightly modulated during development and in responses to environmental cues. However, it is challenging to measure the actual translation state of the tissues in vivo.</p><p><strong>Results: </strong>Here, we report the introduction of an in vivo translation marker based on bimolecular fluorescence complementation, the Ribo-BiFC. We combined a method originally developed for the fruitflies with an improved low background split-mVenus BiFC system previously described in plants. We labelled small subunit ribosomal proteins (RPS) and large subunit ribosomal proteins (RPL) of Arabidopsis thaliana with fragments of the mVenus fluorescent protein (FP). We tested the Ribo-BiFC method using transiently expressed recombinant ribosomal proteins in epidermal cells of Nicotiana benthamiana. The BiFC-tagged ribosomal proteins complemented the mVenus molecule and were detected by fluorescence microscopy, potentially visualizing the close proximity of translating assembled 80S ribosomal subunits. Although the resulting signal is less intense than that of known interactors, its detection points to the functionality of the system.</p><p><strong>Conclusions: </strong>This Ribo-BiFC approach has further potential for use in stable transgenic lines in enabling the visualisation of translational rate in plant tissues and changing translation dynamics during plant development, under abiotic stress or in different genetic backgrounds.</p>","PeriodicalId":20100,"journal":{"name":"Plant Methods","volume":" ","pages":"17"},"PeriodicalIF":4.4,"publicationDate":"2026-01-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12888511/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145960022","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Evaluation of one-image 3D reconstruction for plant model generation.","authors":"Zihe Gao, Zane K J Hartley, Andrew P French","doi":"10.1186/s13007-025-01482-6","DOIUrl":"10.1186/s13007-025-01482-6","url":null,"abstract":"<p><p>Generating accurate and visually realistic 3D models of plants from single-view images is crucial yet remains challenging due to plants' intricate geometry and frequent occlusions. This capability matters because it supplements current plant datasets and enables non-destructive, high-throughput phenotyping for crop breeding and precision agriculture. More broadly, 3D reconstruction is particularly important because plant morphology is inherently three-dimensional, while 2D representations miss occluded leaves, branching geometry, and volumetric traits. However, plants present unique challenges compared to common rigid objects, and most current generative methods have not been systematically tested in this domain, leaving a gap in understanding their reliability for realistic plant reconstruction. This study systematically evaluates six advanced generative techniques-Hunyuan3D 2.0, Trellis (Structured 3D Latents), One2345++, InstantMesh, Direct3D and Unique3D-using the existing PlantDreamer dataset. Specifically, this research reconstructs mesh models from images of Bean plants and quantitatively assesses each method's performance against ground-truth models using Chamfer Distance, Normal Consistency, F-Score, PSNR, LPIPS, and CLIP Score. The paper also presents qualitative results of Kale and Mint plants. The results indicate that Hunyuan3D 2.0 achieves superior performance overall, suggesting its effectiveness in capturing complex plant structures. This work provides valuable insights into strengths and limitations of contemporary 3D generative approaches, guiding future improvements in realistic plant digitisation.</p>","PeriodicalId":20100,"journal":{"name":"Plant Methods","volume":" ","pages":"18"},"PeriodicalIF":4.4,"publicationDate":"2026-01-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12888700/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145966691","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}