{
  "abstract": "Rapid and precise identification of strawberry diseases is paramount for securing crop yield and quality. To address challenges arising from significant scale variations, diminutive early-stage lesions, and high inter-class visual similarity, this study proposes a parameter-efficient multi-scale feature fusion network derived from YOLO26, termed LMF-YOLO. The architecture incorporates three strategic enhancements: First, leveraging the end-to-end NMS-free framework of YOLO26, a Triple Feature Encoder (TFE) is developed to aggregate multi-scale features in a one-shot manner, preserving critical high resolution spatial details. Second, a lightweight Multi-scale Initialization Module (MSInit), utilizing separable dilated convolutions, captures contextual information across diverse receptive fields, enhancing the model’s discriminative power against visually similar diseases. Third, a P2 micro-detection head is added to provide explicit supervision for extremely early tiny lesions. Evaluations on a custom strawberry disease dataset (7 categories, 6,246 annotations) demonstrate that the proposed model LMF-YOLO (YOLO26n + LMF neck + P2 head) achieves an mAP50 of 82.0%, marking a 1.9 percentage point increase over the baseline YOLO26n. Notably, the model yields an 11.5% improvement in AP_small for early lesions. Following TensorRT acceleration on the Jetson Nano, the model achieves an inference latency of 33.6 ms (~30 FPS), meeting the real-time constraints for mobile-based recognition. Ablation and comparative studies confirm that LMF-YOLO outperforms mainstream YOLO variants in balancing computational efficiency with detection sensitivity, providing a robust technical solution for mobile-intelligent strawberry disease diagnostics.",
  "abstract_shown": true,
  "affiliations": [
    "College of Chemical Engineering and Environment, Weifang University of Science and Technology , ,"
  ],
  "article_type": "research article",
  "arxiv": null,
  "author_total": 2,
  "authors": [
    {
      "affs": [
        0
      ],
      "name": "Yuzhen Niu"
    },
    {
      "affs": [
        0
      ],
      "name": "Ping Lin"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/aa9ywtbetd6z2.bib",
  "cited_by": 0,
  "doi": "10.3389/fpls.2026.1884996",
  "fulltext_url": "https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13662136/?tool=EBI",
  "id": "aa9ywtbetd6z2",
  "issns": [
    "1664-462X"
  ],
  "landing_url": "https://europepmc.org/article/PMC/PMC13662136",
  "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": "W7214977555",
  "pdf_url": "https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13662136/pdf/?tool=EBI",
  "pmcid": "PMC13662136",
  "pmid": null,
  "published": "2026-09-30",
  "publisher": "Frontiers Media SA",
  "ris": "https://research.draconitic.com/paper/aa9ywtbetd6z2.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:17:20Z",
      "native_id": "PMC:PMC13662136",
      "source": "europepmc",
      "url": "https://europepmc.org/article/PMC/PMC13662136"
    },
    {
      "first_seen": "2026-10-30T12:19:25Z",
      "native_id": "10.3389/fpls.2026.1884996",
      "source": "crossref",
      "url": "https://www.frontiersin.org/articles/10.3389/fpls.2026.1884996/full"
    }
  ],
  "subjects": [
    {
      "field": "biology",
      "origin": "openalex:1110|Smart Agriculture and AI",
      "subfield": "plant-biology"
    }
  ],
  "title": "LMF-YOLO: a multi-scale inception model for edge-based strawberry disease detection",
  "title_text": "LMF-YOLO: a multi-scale inception model for edge-based strawberry disease detection",
  "type": "peer-reviewed",
  "updated_at": "2026-10-30T12:19:25Z",
  "url": "https://research.draconitic.com/paper/aa9ywtbetd6z2",
  "venue": "Frontiers in plant science",
  "versions": []
}