{
  "abstract": "Reliable joint perception of crop rows, furrows, and obstacles is essential for autonomous inter-row operations in seedling-stage cotton fields. However, small and sparsely distributed plants, perspective-convergent rows, irregular furrow boundaries, and low obstacle pixel coverage jointly challenge fine-detail preservation, long-range structural modeling, and boundary discrimination under real-time constraints. This study formulated cotton-field perception as a four-class semantic segmentation task and constructed a dataset of 709 pixel-wise annotated images comprising background, crop rows, furrows, and obstacles. We developed Agri-PIDNet-S, a field-specific lightweight network based on PIDNet-S. A coordinate-aware multi-kernel inverted residual module (CA-MKIR) preserves shallow details and small-obstacle contours, a squeeze-and-excitation-enhanced deep-to-token module (SaE-D2T) models long-range row and furrow structures, and a boundary-guided window-attention refinement Bag (BWR-Bag) improves semantic–boundary fusion. The network was trained with a composite loss incorporating auxiliary boundary supervision. On the cotton-field test set, Agri-PIDNet-S achieved 92.37% mean intersection over union (mIoU) and 96.02% mean Dice coefficient (mDice) in the model-comparison experiment, while repeated training with three seeds yielded an mIoU of 92.35 ± 0.03%. Relative to PIDNet-S under the same comparison protocol, mIoU increased by 3.53 percentage points, while obstacle IoU improved from 79.60% to 88.67%. The model contained 8.048 million parameters, required 19.161 GFLOPs, and achieved 155.67 frames per second. Zero-shot external evaluation on tomato, watermelon, and maize fields yielded a structural mIoU of 86.29% for the background, crop-row, and furrow classes. Under the evaluated conditions, the task-oriented modifications achieved higher segmentation point estimates than PIDNet-S while maintaining computational efficiency; the external results provide preliminary evidence of cross-crop structural transfer.",
  "abstract_shown": true,
  "affiliations": [
    "College of Computer and Information Engineering, Xinjiang Agricultural University, Urumqi, China.",
    "Engineering Research Center of Intelligent Agriculture, Ministry of Education, Urumqi, China.",
    "Xinjiang Agricultural Informatization Engineering Technology Research Center, Urumqi, China."
  ],
  "article_type": "Research Article",
  "arxiv": null,
  "author_total": 6,
  "authors": [
    {
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        0
      ],
      "name": "Yanlin Xin"
    },
    {
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        0,
        1,
        2
      ],
      "name": "Yongke Li"
    },
    {
      "affs": [
        0
      ],
      "name": "Wuchen Wei"
    },
    {
      "affs": [
        0,
        1,
        2
      ],
      "name": "Xinyu Cao"
    },
    {
      "affs": [
        0,
        1,
        2
      ],
      "name": "Bowen Mao"
    },
    {
      "affs": [
        0,
        1,
        2
      ],
      "name": "Yanhong Chen",
      "orcid": "0009-0006-6741-4066"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/czfjg67qjx8xe.bib",
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  "doi": "10.1371/journal.pone.0361992",
  "fulltext_url": "https://europepmc.org/articles/PMC13726948",
  "id": "czfjg67qjx8xe",
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  "landing_url": "https://doi.org/10.1371/journal.pone.0361992",
  "language": "eng",
  "licence": "CC-BY",
  "licence_label": "CC BY",
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  "licence_url": "https://creativecommons.org/licenses/by/4.0/",
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  "native_subjects": [
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      "/Research and analysis methods/Animal studies/Experimental organism systems/Plant and algal models/Maize"
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  "pdf_url": "https://europepmc.org/articles/PMC13726948?pdf=render",
  "pmcid": "PMC13726948",
  "pmid": "42970580",
  "published": "2026-11-02",
  "publisher": "Public Library of Science",
  "ris": "https://research.draconitic.com/paper/czfjg67qjx8xe.ris",
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  "subjects": [
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      "origin": "openalex:1110|Smart Agriculture and AI",
      "subfield": "plant-biology"
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  ],
  "title": "Agri-PIDNet-S: Lightweight semantic segmentation for field-structure and obstacle perception in seedling-stage cotton fields",
  "title_text": "Agri-PIDNet-S: Lightweight semantic segmentation for field-structure and obstacle perception in seedling-stage cotton fields",
  "type": "peer-reviewed",
  "updated_at": "2026-11-06T12:22:46Z",
  "url": "https://research.draconitic.com/paper/czfjg67qjx8xe",
  "venue": "PLOS One",
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}