{
  "abstract": "The discrete element method (DEM) is widely used in agricultural mechanization, but accurate parameter calibration for UAV seeding simulation of coated rice seeds remains challenging due to empirical parameter selection and single-stage optimization in existing studies. This study combined physical and simulation experiments to calibrate DEM parameters for coated rice seeds (CRS). Physical tests measured the intrinsic parameters and actual repose angle (18.84°). A three-stage progressive optimization strategy was then adopted: Plackett–Burman test to screen significant parameters, steepest ascent test to narrow the optimal range, and quadratic rotation orthogonal combination test to establish a regression model. Four significant parameters were identified: the CRS-CRS coefficient of restitution (C), the CRS-CRS coefficient of static friction (D), the CRS-CRS coefficient of rolling friction (E), and the CRS-stainless steel coefficient of static friction (G). The optimal combination was determined as 0.35, 0.22, 0.09, and 0.51, respectively. Verification using static repose angle, dynamic repose angle, and UAV total seeding rate showed average relative errors below 5% for all three indices. These results demonstrate that the calibrated parameters are reliable for DEM simulation of coated rice seeds, and the proposed multi-stage method effectively improves calibration accuracy and model applicability for UAV seeding operations.",
  "abstract_shown": true,
  "affiliations": [
    "Nanjing Institute of agricultural mechanization, Ministry of Agriculture and Rural Areas, Nanjing, China",
    "Key Laboratory of Modern Agricultural Equipment, Ministry of Agriculture and Rural Affairs, Nanjing, China",
    "Institute of Western Agriculture, Chinese Academy of Agricultural Science, Changji, China"
  ],
  "article_type": "Research Article",
  "arxiv": null,
  "author_total": 5,
  "authors": [
    {
      "affs": [
        0
      ],
      "name": "Yu Qingxu"
    },
    {
      "affs": [
        1
      ],
      "name": "Mengxiao Liu"
    },
    {
      "affs": [
        2
      ],
      "name": "Yan Gong"
    },
    {
      "name": "Xiao Chen"
    },
    {
      "name": "Jianling Hu"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/465gwcfkwsbdr.bib",
  "cited_by": 0,
  "doi": "10.1371/journal.pone.0361229",
  "fulltext_url": null,
  "id": "465gwcfkwsbdr",
  "issns": [
    "1932-6203"
  ],
  "landing_url": "https://doi.org/10.1371/journal.pone.0361229",
  "language": null,
  "licence": "CC-BY-4.0",
  "licence_label": "CC BY 4.0",
  "licence_source": "journal",
  "licence_url": "https://creativecommons.org/licenses/by/4.0/",
  "metadata_licence": null,
  "native_subjects": [
    [
      "openalex",
      "2210|Agricultural Engineering and Mechanization"
    ],
    [
      "plos",
      "/Biology and life sciences/Organisms/Eukaryota/Plants/Grasses/Rice"
    ],
    [
      "plos",
      "/Biology and life sciences/Physiology/Biological locomotion/Climbing"
    ],
    [
      "plos",
      "/Biology and life sciences/Plant science/Plant anatomy/Seeds"
    ],
    [
      "plos",
      "/Computer and information sciences/Software engineering/Computer software"
    ],
    [
      "plos",
      "/Engineering and technology/Mechanical engineering/Tribology/Friction"
    ],
    [
      "plos",
      "/Engineering and technology/Software engineering/Computer software"
    ],
    [
      "plos",
      "/Physical sciences/Materials science/Metallurgy/Alloys/Steel/Stainless steel"
    ],
    [
      "plos",
      "/Physical sciences/Materials science/Tribology/Friction"
    ],
    [
      "plos",
      "/Research and analysis methods/Animal studies/Experimental organism systems/Plant and algal models/Rice"
    ],
    [
      "plos",
      "/Research and analysis methods/Research design/Experimental design"
    ],
    [
      "plos",
      "/Research and analysis methods/Simulation and modeling"
    ]
  ],
  "openalex": "W7225412150",
  "pdf_url": null,
  "pmcid": null,
  "pmid": null,
  "published": "2026-11-06",
  "publisher": "Public Library of Science",
  "ris": "https://research.draconitic.com/paper/465gwcfkwsbdr.ris",
  "sources": [
    {
      "first_seen": "2026-11-07T12:20:24Z",
      "native_id": "10.1371/journal.pone.0361229",
      "source": "plos",
      "url": "https://doi.org/10.1371/journal.pone.0361229"
    }
  ],
  "subjects": [
    {
      "field": "biology",
      "origin": "plos:plant science",
      "subfield": "plant-biology"
    },
    {
      "field": "chemistry",
      "origin": "plos:materials science",
      "subfield": "materials-polymers"
    },
    {
      "field": "medicine",
      "origin": "plos:physiology",
      "subfield": "physiology"
    }
  ],
  "title": "Parameter calibration of the simulation model for coated rice seeds in UAV seeding based on discrete element methodology",
  "title_text": "Parameter calibration of the simulation model for coated rice seeds in UAV seeding based on discrete element methodology",
  "type": "peer-reviewed",
  "updated_at": "2026-11-08T12:20:48Z",
  "url": "https://research.draconitic.com/paper/465gwcfkwsbdr",
  "venue": "PLOS One",
  "versions": []
}