[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-84835-en":3,"doc-seo-84835-105":29,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":13,"seo_description":14,"update_tm":27,"read_time":28},84835,8796095462418,"Noah","https://ap-avatar.wpscdn.com/avatar/80000253c1241d02b47?x-image-process=image/resize,m_fixed,w_180,h_180&k=1778826106357471780",8,"Research & Report","Procedural Volumetric Modeling of Plant Branching Structures for Finite Element Analysis","Precision agriculture and agricultural robotics depend on accurate, automated plant modeling with high-fidelity 3D plant architecture. Such geometry supports simulations for water and nutrient transport, light interception, structural loading, and crop lodging. Unlike static pipelines, procedural frameworks generate analysis-ready hexahedral meshes and enable generative crop diversity as well as dynamic growth. An automated volumetric method converts input skeletons or point clouds into rooted graphs, spline-based centerlines, joined blending volumes, and smooth conforming meshes.","Procedural Volumetric Modeling of Plant Branching Structures for Finite Element Analysis  \nAjith Moolaa , Prashant Kumar Guptaa , Baskar Ganapathysubramaniana and Aishwarya Pawara  \na Department of Mechanical Engineering, Iowa State University, United States  \nARTICLE INFO  \nKeywords:  \nProcedural Plant Modeling Computer-Aided Design Volumetric Mesh Generation Finite Element Analysis Dynamic Plant Modeling  \n.GR] 27 Jun 2026  \nAB STRACT  \nPrecision agriculture, smart breeding, and agricultural robotics require accurate and automated plant modeling. These models provide high-fidelity three-dimensional (3D) representations of plant architecture. They provide the geometric foundation for simulations of water and nutrient transport, light interception, structural loading, and crop lodging. Unlike static plant modeling pipelines, procedural modeling frameworks not only generate accurate 3D plant geometries but also support the generative modeling of crop diversity and the dynamic modeling of plant growth. While terrestrial laser scanning, LiDAR, photogrammetry, and neural reconstructionbased approaches have made 3D plant reconstruction possible, the resulting data are typically in the form of point clouds, which cannot be directly utilized for high-fidelity simulations. We present an automated volumetric procedural modeling framework for plant branching structures that generates analysis-suitable hexahedral meshes from input skeletons or 3D point clouds. The input skeleton is first converted into a rooted graph representation that captures the plant branching topology. Each graph edge is then represented by a smooth centerline B-spline curve, around which a cylindrical tensor-product B-spline volume is constructed. At each junction, incident B-spline volume control lattices are joined using blending operations. The resulting volumetric parameterization is evaluated to generate a smooth and conforming hexahedral mesh of the whole plant. We demonstrate the framework on three diverse plant datasets, namely mung bean, tomato, and walnut trees, generating meshes with both uniform and spatially varying branch radii. Mesh quality is evaluated using the element-wise scaled Jacobian metric, and solver readiness is demonstrated by solving a steady-state diffusion problem on the generated meshes. The framework also supports dynamic mesh generation suitable for modeling plant growth by locally updating newly added branches without reconstructing the full plant geometry. This procedural framework thus bridges the gap between skeleton-based plant geometry and analysis-suitable volumetric modeling for plant biomechanics, transport simulations, and digital twin applications.  \n[cs  \narXiv :2607 .05421v1  \n1. Introduction  \nAccurate geometric modeling of plant architecture is a fundamental requirement in computational agricultural applications, such as digital twins [1–4], functional-structural plant models [5–8], precision agriculture [9, 10], resource optimization [11, 12], genotype-to-phenotype modeling [13–15], bio-design [16–18], and crop breeding [19–22]. Highfidelity geometric models generated from advanced sensor datasets provide quantitative analysis of plant architecture traits under different genetic, environmental, and management conditions, improving agricultural productivity and resilience [8, 12, 23, 24] . By integrating real-time sensor data and environmental parameters, plant models can provide a realistic virtual representation of plants, enabling non-invasive, dynamic, and bi-directional decision-making. In precision agriculture, they allow for accurate morphological evaluations, canopy architecture design, efficient plant breeding, and resource usage [20, 25]. In simulation-driven crop design, high-fidelity crop models can be used in inverse design applications to generate idealized ideotypes for high productivity and improved resilience [26–30] . The goal isnot only to generate plant models for visualization but also to use the","cbCaimq38JU5KGke","https://ap.wps.com/l/cbCaimq38JU5KGke","pdf",16196300,1,31,"English","en",105,"# Introduction\n## Motivation and Applications\n## Sensor-based 3D Plant Reconstruction\n## Procedural Modeling Approaches","[{\"question\":\"What problem does the document address in plant modeling for simulations?\",\"answer\":\"Sensor-based 3D plant reconstructions (e.g., point clouds or surfaces) are not directly suitable for physics-based, high-fidelity simulations because converting them into analysis-ready volumetric meshes is computationally intensive and hard to scale.\"},{\"question\":\"How does the proposed framework construct volumetric meshes from the inputs?\",\"answer\":\"The framework converts an input skeleton into a rooted graph capturing branching topology, represents each graph edge with a smooth B-spline centerline, builds a cylindrical tensor-product B-spline volume around it, and joins junction volumes using blending operations to obtain a smooth, conforming hexahedral mesh.\"},{\"question\":\"How is solver readiness and dynamic growth support demonstrated?\",\"answer\":\"Mesh quality is evaluated using an element-wise scaled Jacobian metric, and solver readiness is shown by solving a steady-state diffusion problem on the generated meshes. Dynamic mesh generation is supported by locally updating newly added branches without reconstructing the full plant geometry.\"}]",1784198614,78,{"code":4,"msg":30,"data":31},"ok",{"site_id":24,"language":23,"slug":32,"title":13,"keywords":33,"description":14,"schema_data":34,"social_meta":86,"head_meta":88,"extra_data":90,"updated_unix":27},"procedural-volumetric-modeling-of-plant-branching-structures-for-finite-element-analysis","",{"@graph":35,"@context":85},[36,53,68],{"@type":37,"itemListElement":38},"BreadcrumbList",[39,43,47,50],{"item":40,"name":41,"@type":42,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":44,"name":45,"@type":42,"position":46},"https://docshare.wps.com/document/","Document",2,{"item":48,"name":12,"@type":42,"position":49},"https://docshare.wps.com/document/research-report/",3,{"item":51,"name":13,"@type":42,"position":52},"https://docshare.wps.com/document/procedural-volumetric-modeling-of-plant-branching-structures-for-finite-element-analysis/84835/",4,{"url":51,"name":13,"@type":54,"author":55,"headline":13,"publisher":57,"fileFormat":60,"inLanguage":23,"description":14,"dateModified":61,"datePublished":62,"encodingFormat":60,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":56},"Person",{"url":40,"name":58,"@type":59},"DocShare","Organization","application/pdf","2026-07-17","2026-07-16",true,{"@type":65,"interactionType":66,"userInteractionCount":20},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"What problem does the document address in plant modeling for simulations?","Question",{"text":75,"@type":76},"Sensor-based 3D plant reconstructions (e.g., point clouds or surfaces) are not directly suitable for physics-based, high-fidelity simulations because converting them into analysis-ready volumetric meshes is computationally intensive and hard to scale.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How does the proposed framework construct volumetric meshes from the inputs?",{"text":80,"@type":76},"The framework converts an input skeleton into a rooted graph capturing branching topology, represents each graph edge with a smooth B-spline centerline, builds a cylindrical tensor-product B-spline volume around it, and joins junction volumes using blending operations to obtain a smooth, conforming hexahedral mesh.",{"name":82,"@type":73,"acceptedAnswer":83},"How is solver readiness and dynamic growth support demonstrated?",{"text":84,"@type":76},"Mesh quality is evaluated using an element-wise scaled Jacobian metric, and solver readiness is shown by solving a steady-state diffusion problem on the generated meshes. 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