{
  "abstract": "Soybean growth habit is generally classified into determinate, indeterminate, and semi-determinate types, and its accurate recognition is important for breeding selection, high-density cultivation, and agronomic management. However, the semi-determinate type is intermediate between determinate and indeterminate types and shows continuous transitional characteristics with the indeterminate type in stem elongation, upper internode length, and pod distribution, making fine-grained discrimination between these two types difficult. Practical deployment also requires lightweight, efficient models. This study developed a lightweight fine-grained recognition framework for soybean growth habit from standardized plant images. The framework used RepViT-M1 as an efficient feature extraction backbone and integrated a multi-scale efficient channel attention module (MS-ECA) to enhance discriminative channel features, a Fine-grained Disambiguation Adapter (FDA) to recalibrate non-determinate features using class prototypes and reduce indeterminate/semi-determinate confusion, and a hierarchical classification head to incorporate the agronomic hierarchy between determinate and non-determinate types. The dataset comprised 944 original soybean plant images and was expanded to 4,709 images through offline augmentation. The model was evaluated using five-fold cross-validation and an independent test set. The proposed model achieved 97.45% accuracy on the independent test set, improving the RepViT-M1 baseline by approximately 3 percentage points. Accuracy for the indeterminate type increased from 91.30% to 97.83%, indicating that the model effectively reduced confusion between indeterminate and semi-determinate types. The model had 4.94 M parameters, required 0.82 GFLOPs, and reached 108.75 FPS, demonstrating a favorable balance between accuracy and efficiency. The proposed framework improves fine-grained soybean growth habit recognition at low computational cost and demonstrates potential for standardized, high-throughput phenotypic identification through on-device Android inference.",
  "abstract_shown": true,
  "affiliations": [
    "College of Intelligence Science and Engineering, Northeast Agricultural University, Harbin , ,",
    "State Key Laboratory of Smart Farm Technologies and Systems , ,"
  ],
  "article_type": "research article",
  "arxiv": null,
  "author_total": 8,
  "authors": [
    {
      "affs": [
        0
      ],
      "name": "Dongbo Liu"
    },
    {
      "affs": [
        1
      ],
      "name": "Daohan Cui"
    },
    {
      "affs": [
        1
      ],
      "name": "Hang Liu"
    },
    {
      "affs": [
        1
      ],
      "name": "Tianyuan Song"
    },
    {
      "affs": [
        1
      ],
      "name": "Zhanguo Zhang"
    },
    {
      "affs": [
        1
      ],
      "name": "Qingshan Chen"
    },
    {
      "affs": [
        1
      ],
      "name": "Fanchuang Li"
    },
    {
      "affs": [
        1
      ],
      "name": "Rongsheng Zhu"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/bmz0s17km4tbt.bib",
  "cited_by": 0,
  "doi": "10.3389/fpls.2026.1954156",
  "fulltext_url": "https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13661940/?tool=EBI",
  "id": "bmz0s17km4tbt",
  "issns": [
    "1664-462X"
  ],
  "landing_url": "https://europepmc.org/article/PMC/PMC13661940",
  "language": "eng",
  "licence": "CC-BY",
  "licence_label": "CC BY",
  "licence_source": "article",
  "licence_url": "https://creativecommons.org/licenses/by/4.0/",
  "metadata_licence": null,
  "native_subjects": [
    [
      "openalex",
      "1110|Smart Agriculture and AI"
    ]
  ],
  "openalex": "W7214978021",
  "pdf_url": "https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13661940/pdf/?tool=EBI",
  "pmcid": "PMC13661940",
  "pmid": null,
  "published": "2026-09-30",
  "publisher": "Frontiers Media SA",
  "ris": "https://research.draconitic.com/paper/bmz0s17km4tbt.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:17:20Z",
      "native_id": "PMC:PMC13661940",
      "source": "europepmc",
      "url": "https://europepmc.org/article/PMC/PMC13661940"
    },
    {
      "first_seen": "2026-10-30T12:19:25Z",
      "native_id": "10.3389/fpls.2026.1954156",
      "source": "crossref",
      "url": "https://www.frontiersin.org/articles/10.3389/fpls.2026.1954156/full"
    }
  ],
  "subjects": [
    {
      "field": "biology",
      "origin": "openalex:1110|Smart Agriculture and AI",
      "subfield": "plant-biology"
    }
  ],
  "title": "Soy-RepViT: a lightweight deep learning framework for fine-grained recognition of soybean growth habits",
  "title_text": "Soy-RepViT: a lightweight deep learning framework for fine-grained recognition of soybean growth habits",
  "type": "peer-reviewed",
  "updated_at": "2026-10-30T12:19:25Z",
  "url": "https://research.draconitic.com/paper/bmz0s17km4tbt",
  "venue": "Frontiers in plant science",
  "versions": []
}