{
  "abstract": "Red crown rot (RCR), caused by Calonectria ilicicola, is an emerging soybean disease in the U.S. Midwest for which scalable approaches to characterize within-field disease distribution are lacking. This study evaluated high-resolution PlanetScope satellite imagery for mapping RCR-affected soybean canopies across 15 commercial fields in Illinois surveyed during the 2024 and 2025 growing seasons. A total of 2,921 georeferenced canopy plots were classified as asymptomatic or RCR-affected and paired with six multispectral bands and seven vegetation indices. Spectral differences between classes were evaluated using linear mixed-effects models, and seven machine-learning classifiers representing linear, tree-based, neural-network, kernel, and probabilistic approaches were compared using spatially independent leave-one-field-out cross-validation. RCR-affected canopies exhibited increased reflectance in the visible and red-edge regions, reduced near-infrared reflectance, and lower vegetation-index values relative to asymptomatic canopies. All classifiers showed strong discrimination, with ROC-AUC values ranging from 0.963 to 0.982. Regularized logistic regression achieved the highest overall performance, with an accuracy of 0.945, balanced accuracy of 0.945, F1-score of 0.948, and ROC-AUC of 0.982 at the optimized decision threshold. Permutation analysis identified EVI, NDVI, and red reflectance as the most influential predictors across representative model architectures. Satellite-derived probability and classification maps generally corresponded with symptomatic canopy patterns observed in high-resolution UAV imagery, although mixed pixels reduced precision near disease-patch boundaries. These results demonstrate the potential of high-resolution satellite imagery for within-field mapping of RCR-associated canopy symptoms across independent commercial soybean fields.",
  "abstract_shown": true,
  "affiliations": [
    "University of Illinois Urbana-Champaign"
  ],
  "article_type": "New results",
  "arxiv": null,
  "author_total": 7,
  "authors": [
    {
      "name": "B. D. Pugliese"
    },
    {
      "name": "J. A. Paredes"
    },
    {
      "name": "A. F. Ruiz"
    },
    {
      "name": "N. D. Bowman"
    },
    {
      "name": "E. Ersoz"
    },
    {
      "name": "N. F. Martin"
    },
    {
      "affs": [
        0
      ],
      "name": "B. X. Camiletti"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/876qg4jwwyaj9.bib",
  "cited_by": 0,
  "doi": "10.64898/2026.09.22.753594",
  "fulltext_url": "https://www.biorxiv.org/content/10.64898/2026.09.22.753594v1.full",
  "id": "876qg4jwwyaj9",
  "issns": [],
  "landing_url": "https://www.biorxiv.org/content/10.64898/2026.09.22.753594v1",
  "language": null,
  "licence": "CC-BY",
  "licence_label": "CC BY",
  "licence_source": "article",
  "licence_url": "https://creativecommons.org/licenses/by/4.0/",
  "metadata_licence": null,
  "native_subjects": [
    [
      "biorxiv",
      "plant biology"
    ],
    [
      "openalex",
      "2303|Remote Sensing in Agriculture"
    ]
  ],
  "openalex": "W7214126630",
  "pdf_url": "https://www.biorxiv.org/content/10.64898/2026.09.22.753594v1.full.pdf",
  "pmcid": null,
  "pmid": null,
  "published": "2026-09-22",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/876qg4jwwyaj9.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:24:06Z",
      "native_id": "10.64898/2026.09.22.753594",
      "source": "biorxiv",
      "url": "https://www.biorxiv.org/content/10.64898/2026.09.22.753594v1"
    }
  ],
  "subjects": [
    {
      "field": "biology",
      "origin": "biorxiv:plant biology",
      "subfield": "plant-biology"
    }
  ],
  "title": "Detection of Red Crown Rot of Soybean in Illinois Fields Using High-Resolution Satellite Imagery and Machine Learning",
  "title_text": "Detection of Red Crown Rot of Soybean in Illinois Fields Using High-Resolution Satellite Imagery and Machine Learning",
  "type": "preprint",
  "updated_at": "2026-10-30T12:24:06Z",
  "url": "https://research.draconitic.com/paper/876qg4jwwyaj9",
  "venue": "bioRxiv",
  "versions": []
}