{
  "abstract": "Agricultural engineering informatics is playing an increasingly important role in enabling intelligent perception, decision-making, and automated operations in modern horticultural production systems. Within this context, accurate visual perception of reproductive structures is essential for agricultural informatization tasks such as flowering-stage monitoring, precision pollination, and information-driven fruit-set management in chili cultivation. However, reliable detection and pose-aware recognition of chili flowers remain challenging because of small target size, dense distribution, foliage occlusion, and illumination variability in natural or semi-controlled environments. To address these challenges, this study proposes a lightweight and robust edge vision framework, termed CFPR-YOLO, for chili flower detection and pose-aware perception under complex agricultural conditions. Built upon an improved YOLOv11n architecture, the proposed framework incorporates EfficientFormerV2 to strengthen global-context feature extraction, a C3k2_EMA module to enhance localization of small and occluded targets, and Poly-Scale Convolution (PSConv) to preserve structural details while reducing computational redundancy. In addition, a lightweight attention mechanism is introduced to improve feature discrimination in cluttered backgrounds. Experimental results on both self-constructed and generalization datasets show that the proposed method achieves a precision of 92.6%, a recall of 86.8%, and an mAP50 of 92.1% with only 7.26 M parameters. The framework also demonstrates strong robustness and generalization across different chili varieties. When deployed on an edge computing platform (NVIDIA Jetson AGX Orin), the model achieves real-time inference at 39.5 FPS. Furthermore, validation experiments under controlled indoor conditions show that the proposed framework can effectively support simulated pollination tasks, achieving a success rate of 90.0% for upwardfacing flowers. These results indicate that CFPR-YOLO provides an effective visual perception solution for agricultural engineering informatics-oriented pollination systems and offers practical potential for precision pollination and intelligent fruit-set management in horticultural production.",
  "abstract_shown": true,
  "affiliations": [
    "College of Electrical and Mechanical Engineering, Hunan Agricultural University",
    "Foshan-Zhongke Innovation Research Institute of Intelligent Agriculture and Robotics",
    "Hunan Provincial Key Laboratory of Intelligent Agricultural Machinery Equipment",
    "College of Horticulture, Hunan Agricultural University",
    "College of Intelligent Science and Engineering, Beijing University of Agriculture",
    "Department of Computer and Electrical Engineering, Mid Sweden University"
  ],
  "article_type": null,
  "arxiv": null,
  "author_total": 10,
  "authors": [
    {
      "affs": [
        0,
        1
      ],
      "name": "Minqiu Kuang",
      "orcid": "0009-0004-9021-1756"
    },
    {
      "affs": [
        0
      ],
      "name": "Yushi Wang",
      "orcid": "0000-0001-9883-5329"
    },
    {
      "affs": [
        0,
        2
      ],
      "name": "Xiaojian Li"
    },
    {
      "affs": [
        0,
        2
      ],
      "name": "Dawei Liu"
    },
    {
      "affs": [
        0
      ],
      "name": "Yang Xiang"
    },
    {
      "affs": [
        3
      ],
      "name": "Feng Liu"
    },
    {
      "affs": [
        1
      ],
      "name": "Xiangjun Zou"
    },
    {
      "affs": [
        0,
        2
      ],
      "name": "Fangping XIE"
    },
    {
      "affs": [
        4,
        5
      ],
      "name": "Yuxuan Zhang"
    },
    {
      "affs": [
        0,
        2
      ],
      "name": "Xu Li"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/29qdzmscdqj9t.bib",
  "cited_by": 8,
  "doi": "10.3389/fpls.2026.1861155",
  "fulltext_url": null,
  "id": "29qdzmscdqj9t",
  "issns": [
    "1664-462X"
  ],
  "landing_url": "https://doi.org/10.3389/fpls.2026.1861155",
  "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": "W7163646912",
  "pdf_url": null,
  "pmcid": "PMC13279318",
  "pmid": "42328072",
  "published": "2026-06-05",
  "publisher": "Frontiers Media",
  "ris": "https://research.draconitic.com/paper/29qdzmscdqj9t.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T13:26:50Z",
      "native_id": "W7163646912",
      "source": "openalex",
      "url": "https://doi.org/10.3389/fpls.2026.1861155"
    }
  ],
  "subjects": [
    {
      "field": "biology",
      "origin": "openalex:1110|Smart Agriculture and AI",
      "subfield": "plant-biology"
    }
  ],
  "title": "CFPR-YOLO: chili flower pose estimation for robotic pollination in unstructured environments",
  "title_text": "CFPR-YOLO: chili flower pose estimation for robotic pollination in unstructured environments",
  "type": "peer-reviewed",
  "updated_at": "2026-11-08T13:20:44Z",
  "url": "https://research.draconitic.com/paper/29qdzmscdqj9t",
  "venue": "Frontiers in Plant Science",
  "versions": []
}