{
  "abstract": "Precise identification of chili flowers and buds is essential for enabling intelligent robotic pollination, continuous crop monitoring, and early yield prediction in protected horticulture. Reliable visual sensing remains challenging because chili targets are extremely small, densely distributed, and frequently occluded by foliage, while greenhouse environments introduce strong illumination variations and background reflections. These factors often lead to insufficient feature extraction and unstable detection accuracy in existing models, limiting their practical deployment in automated monitoring systems. To address these challenges, this study proposes GCP-YOLO, a lightweight yet high-performance detection framework built upon the YOLOv11n architecture. The model enhances small-target perception through three key improvements. First, a redesigned Generalized Feature Pyramid Network (GFPN) strengthens cross-scale feature interaction, improving the fusion of fine-grained texture cues and deep semantic information. Second, a C2CGA context-guided attention module is introduced to emphasize floral structural features while suppressing background noise caused by reflections and canopy clutter. Third, extended multi-scale detection heads (P2-P6) incorporate broader contextual information to reduce missed detections and false positives in dense planting scenarios. Experimental results on a custom chili flower dataset show that the proposed method achieves 92.8% precision, 83.7% recall, 90.8% mAP50, and 72.7% mAP50-95, improving upon the YOLOv11n baseline by 2.1, 1.3, 3.9, and 6.6 percentage points, respectively. Deployment on an NVIDIA Jetson AGX Orin edge platform demonstrates real-time inference at 97.9 FPS, confirming its suitability for on-device phenotyping. Overall, the proposed approach significantly improves detection robustness under complex greenhouse conditions, providing an effective visual sensing methodology for automated crop monitoring and data-driven yield estimation.",
  "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",
    "Department of Computer and Electrical Engineering, Mid Sweden University",
    "College of Intelligent Science and Engineering, Beijing University of Agriculture"
  ],
  "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": "0000-0003-3443-9786"
    },
    {
      "affs": [
        0,
        2
      ],
      "name": "Xiaojian Li"
    },
    {
      "affs": [
        1
      ],
      "name": "Xiangjun Zou"
    },
    {
      "affs": [
        0,
        2
      ],
      "name": "Dawei Liu"
    },
    {
      "affs": [
        0
      ],
      "name": "Yang Xiang"
    },
    {
      "affs": [
        3
      ],
      "name": "Sebastian Bader"
    },
    {
      "affs": [
        0,
        2
      ],
      "name": "Fangping XIE"
    },
    {
      "affs": [
        4,
        3
      ],
      "name": "Yuxuan Zhang"
    },
    {
      "affs": [
        0,
        2
      ],
      "name": "Xu Li"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/98sqdk80n7kat.bib",
  "cited_by": 5,
  "doi": "10.3389/fpls.2026.1879612",
  "fulltext_url": null,
  "id": "98sqdk80n7kat",
  "issns": [
    "1664-462X"
  ],
  "landing_url": "https://doi.org/10.3389/fpls.2026.1879612",
  "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": "W7167492024",
  "pdf_url": null,
  "pmcid": "PMC13381778",
  "pmid": "42519601",
  "published": "2026-07-06",
  "publisher": "Frontiers Media",
  "ris": "https://research.draconitic.com/paper/98sqdk80n7kat.ris",
  "sources": [
    {
      "first_seen": "2026-11-08T13:20:45Z",
      "native_id": "W7167492024",
      "source": "openalex",
      "url": "https://doi.org/10.3389/fpls.2026.1879612"
    }
  ],
  "subjects": [
    {
      "field": "biology",
      "origin": "openalex:1110|Smart Agriculture and AI",
      "subfield": "plant-biology"
    }
  ],
  "title": "GCP-YOLO: high-precision detection of tiny chili flowers in complex greenhouse scenes",
  "title_text": "GCP-YOLO: high-precision detection of tiny chili flowers in complex greenhouse scenes",
  "type": "peer-reviewed",
  "updated_at": "2026-11-09T13:21:09Z",
  "url": "https://research.draconitic.com/paper/98sqdk80n7kat",
  "venue": "Frontiers in Plant Science",
  "versions": []
}