{
  "abstract": "Quantifying the variation in yield component traits of maize ( Zea mays L.), which collectively determine the overall productivity of this globally significant crop, is critical to plant genetics research, plant breeding, and the development of improved agronomic practices. Grain yield per acre is a function of the number of plants per acre ears per plant number of kernels per ear average kernel weight. Number of kernels per ear is the product of the number of kernel rows per ear ( kernel‐row‐number ) and the number of kernels per row ( kernels‐per‐row ). Traditional manual approaches to quantifying kernels‐per‐row, which exhibits strong environmental sensitivity, are time‐consuming, limiting the scale of data collected for either trait. Recent automation efforts using image processing and deep learning face challenges of high annotation costs and limited generalizability across diverse germplasm. We address these limitations by exploring large vision models for zero‐shot, annotation‐free maize kernel segmentation. Utilizing an open source large vision model—Segment Anything Model—we segment individual kernels in images of maize ears and implement a graph‐based algorithm to calculate kernels‐per‐row. Our approach effectively identifies kernels‐per‐row across diverse maize ears, demonstrating the potential of zero‐shot foundation vision models coupled with image processing routines to enhance automation and reduce subjectivity in agronomic data collection. All our codes are open‐sourced to democratize these frugal phenotyping approaches.",
  "abstract_shown": true,
  "affiliations": [
    "Department of Electrical and Computer Engineering Iowa State University Ames Iowa USA",
    "Department of Agronomy Iowa State University Ames Iowa USA",
    "Plant Science Institute Iowa State University Ames Iowa USA",
    "Translational AI Research and Education Center Iowa State University Ames Iowa USA",
    "Department of Agronomy University of Nebraska‐Lincoln Lincoln Nebraska USA",
    "Department of Mechanical Engineering Iowa State University Ames Iowa USA"
  ],
  "article_type": null,
  "arxiv": null,
  "author_total": 9,
  "authors": [
    {
      "affs": [
        0
      ],
      "name": "Hossein Zaremehrjerdi"
    },
    {
      "affs": [
        1,
        2
      ],
      "name": "Lisa Coffey",
      "orcid": "0009-0001-0968-5099"
    },
    {
      "affs": [
        3
      ],
      "name": "Talukder Zaki Jubery",
      "orcid": "0000-0003-4107-1617"
    },
    {
      "affs": [
        1
      ],
      "name": "Huyu Liu",
      "orcid": "0000-0002-0186-3401"
    },
    {
      "affs": [
        4
      ],
      "name": "Jonathan Turkus",
      "orcid": "0000-0002-2006-0137"
    },
    {
      "affs": [
        4
      ],
      "name": "Kyle Linders",
      "orcid": "0009-0006-9206-3608"
    },
    {
      "affs": [
        4
      ],
      "name": "James C. Schnable",
      "orcid": "0000-0001-6739-5527"
    },
    {
      "affs": [
        1,
        2
      ],
      "name": "Patrick S. Schnable",
      "orcid": "0000-0001-9169-5204"
    },
    {
      "affs": [
        0,
        5,
        2,
        3
      ],
      "name": "Baskar Ganapathysubramanian",
      "orcid": "0000-0002-8931-4852"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/5fx79xcxh7ase.bib",
  "cited_by": 5,
  "doi": "10.1002/ppj2.70102",
  "fulltext_url": null,
  "id": "5fx79xcxh7ase",
  "issns": [
    "2578-2703"
  ],
  "landing_url": "https://doi.org/10.1002/ppj2.70102",
  "language": "en",
  "licence": "CC-BY",
  "licence_label": "CC BY",
  "licence_source": "journal",
  "licence_url": "https://creativecommons.org/licenses/by/4.0/",
  "metadata_licence": null,
  "native_subjects": [
    [
      "openalex",
      "1110|Smart Agriculture and AI"
    ]
  ],
  "openalex": "W4407764192",
  "pdf_url": null,
  "pmcid": null,
  "pmid": null,
  "published": "2026-09-20",
  "publisher": "Wiley",
  "ris": "https://research.draconitic.com/paper/5fx79xcxh7ase.ris",
  "sources": [
    {
      "first_seen": "2026-11-08T13:20:45Z",
      "native_id": "W4407764192",
      "source": "openalex",
      "url": "https://doi.org/10.1002/ppj2.70102"
    }
  ],
  "subjects": [
    {
      "field": "biology",
      "origin": "openalex:1110|Smart Agriculture and AI",
      "subfield": "plant-biology"
    }
  ],
  "title": "MaizeEar‐SAM: Zero‐shot maize ear phenotyping",
  "title_text": "MaizeEar‐SAM: Zero‐shot maize ear phenotyping",
  "type": "peer-reviewed",
  "updated_at": "2026-11-10T13:21:07Z",
  "url": "https://research.draconitic.com/paper/5fx79xcxh7ase",
  "venue": "The Plant Phenome Journal",
  "versions": []
}