[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-2542":3},{"id":4,"title":5,"url":6,"summary":7,"summary_zh":8,"content":9,"source_name":10,"source_url":6,"published_at":11,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":15,"score_detail":16,"sources":23,"tags":25,"view_count":31,"doi":32,"paper":33,"created_at":50},2542,"Discrete Element Method-Based Modeling and Application of Agricultural Materials: A Review","https:\u002F\u002Fdoi.org\u002F10.3390\u002Fagriculture16181958","Discrete element method (DEM) has become an essential tool for analyzing particle-scale behavior and complex interactions in agricultural materials and machinery. This review synthesizes recent advances in DEM-based modeling and applications in agricultural engineering. It systematically examines particle modeling strategies, contact model development, and parameter calibration frameworks for representative materials such as soil, seeds, fertilizers, stalks, and roots. The evolution from simple spherical approximations to multi-sphere, bonded, and 3D scan-based reconstructions is highlighted, along with the shift from classical Hertz–Mindlin models to cohesive, elasto-plastic, and fracture-capable formulations. Calibration methods are traced from empirical assignment to systematic frameworks that integrate design of experiments, response surface methodology, and machine-learning-assisted inverse analysis. These advances in fidelity and accuracy have enabled extensive DEM applications in tillage, seeding, fertilization, and harvesting, with emphasis on equipment optimization, mechanism analysis, and performance prediction. Despite the progress, challenges remain in model standardization, parameter transferability, computational cost, and multiphysics coupling. Future work points to unified modeling frameworks, standardized databases, real-time simulation, and integration with artificial intelligence to support digital agriculture and intelligent machinery. This review aims to serve as a reference for high-fidelity DEM modeling and for advancing the digital transformation of agricultural engineering.","离散元法（DEM）已成为分析农业物料与机械中颗粒尺度行为及复杂相互作用的重要工具。本文综述了基于DEM的建模与农业工程应用的最新进展，系统考察了土壤、种子、肥料、茎秆和根系等代表性物料的颗粒建模策略、接触模型开发及参数标定框架。重点阐述了从简单球形近似到多球体、粘结型及基于三维扫描的重构方法的演变，以及从经典Hertz–Mindlin模型向黏聚、弹塑性及可断裂本构模型的转变。标定方法从经验赋值发展为集成试验设计、响应面法和机器学习辅助反分析的系统框架。这些在保真度和精度方面的进步使得DEM在耕整、播种、施肥和收获等领域得到广泛应用，重点涉及装备优化、机理分析和性能预测。尽管取得了进展，但在模型标准化、参数可迁移性、计算成本和多物理场耦合方面仍存在挑战。未来工作指向统一建模框架、标准化数据库、实时仿真以及与人工智能的融合，以支撑数字农业和智能机械发展。本文旨在为高保真DEM建模及推进农业工程数字化转型提供参考。",null,"Agriculture","2026-09-12T00:00:00Z","论文",10,false,76,{"impact":17,"substance":18,"depth":17,"authority":19,"freshness":20,"relevant":21,"comment":22},18,21,13,6,1,"系统综述离散元法在农业物料建模与装备优化中的进展，方法学价值高，对智能农机与数字农业有参考意义。",[24],{"name":10,"url":6},[26,27,28,29,30],"数字农业","智慧农业","农业人工智能","农业机械","离散元仿真",0,"10.3390\u002Fagriculture16181958",{"doi":32,"openalex_id":34,"authors":35,"venue":10,"cited_by_count":31,"oa_url":6,"card":43,"direction":47,"ingested_from":49},"W7213152558",[36,38,40],{"name":37,"orcid":9},"Xingchi Zhou",{"name":39,"orcid":9},"Yanbin Liu",{"name":41,"orcid":42},"Zhenwei Liang","https:\u002F\u002Forcid.org\u002F0000-0001-6501-4364",{"tldr":44,"method":45,"finding":46,"direction":47,"opportunity":48},"综述农业物料离散元建模的粒子模型、接触模型与参数标定进展及应用。","综述离散元粒子建模、接触模型、标定方法及在耕播收中的应用。","建模从球形向多球\u002F键合\u002F3D重建演进，标定转向机器学习辅助反演。","农业人工智能与决策模型","可探索DEM与AI实时仿真、多物理场耦合及标准化参数数据库构建。","openalex","2026-09-15T23:30:36.643241Z"]