{
  "abstract": "Multi-crop disease classification requires discrimination among visually similar symptoms. This study evaluates late feature fusion of Dilateformer and MobileNetV2 for 45-class image classification. The two pooled representations are concatenated before classification. The original experiment achieved 97.21% Top-1 accuracy and 96.63% macro F1 on a 59,905-image benchmark. During revision, PlantDoc-derived samples were added to the existing collection to broaden image diversity. On this expanded 68,682-sample dataset, the selected fusion checkpoint achieved 95.45% accuracy and 93.63% macro F1 on 6,550 test samples, compared with 95.48% accuracy for MobileNetV2. These results belong to distinct data and implementation versions. Dataset provenance, parameter accounting and branch-specific gradient-weighted class activation mapping (Grad-CAM) support interpretation of the revision-stage results. The findings demonstrate useful multi-crop classification performance, while showing that simple feature concatenation does not guarantee improvement over a single branch. Independent field generalization remains to be established.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": "Methods",
  "arxiv": null,
  "author_total": 10,
  "authors": [
    {
      "name": "Rongrong Gu"
    },
    {
      "name": "Zhigang Huang"
    },
    {
      "name": "Yang Zhang"
    },
    {
      "name": "Shengyi Zhao"
    },
    {
      "name": "Yunming Zhao"
    },
    {
      "name": "Jiasheng Jiang"
    },
    {
      "name": "Xi Kan"
    },
    {
      "name": "Qingan Da"
    },
    {
      "name": "Nan Fu"
    },
    {
      "name": "Jizhang Wang"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/7e8k7vjb101kd.bib",
  "cited_by": 0,
  "doi": "10.3389/fpls.2026.1961479",
  "fulltext_url": null,
  "id": "7e8k7vjb101kd",
  "issns": [
    "1664-462X"
  ],
  "landing_url": "https://www.frontiersin.org/articles/10.3389/fpls.2026.1961479",
  "language": null,
  "licence": "CC-BY-4.0",
  "licence_label": "CC BY 4.0",
  "licence_source": "article",
  "licence_url": "https://creativecommons.org/licenses/by/4.0/",
  "metadata_licence": null,
  "native_subjects": [
    [
      "feed-journal",
      "biology/plant-biology"
    ]
  ],
  "openalex": null,
  "pdf_url": null,
  "pmcid": null,
  "pmid": null,
  "published": "2026-11-09",
  "publisher": "Frontiers Media SA",
  "ris": "https://research.draconitic.com/paper/7e8k7vjb101kd.ris",
  "sources": [
    {
      "first_seen": "2026-11-09T12:20:32Z",
      "native_id": "10.3389/fpls.2026.1961479",
      "source": "feed-frontiers",
      "url": "https://www.frontiersin.org/articles/10.3389/fpls.2026.1961479"
    },
    {
      "first_seen": "2026-11-09T12:23:05Z",
      "native_id": "10.3389/fpls.2026.1961479",
      "source": "crossref",
      "url": "https://www.frontiersin.org/articles/10.3389/fpls.2026.1961479/full"
    }
  ],
  "subjects": [
    {
      "field": "biology",
      "origin": "feed-journal:biology/plant-biology",
      "subfield": "plant-biology"
    }
  ],
  "title": "Multi-crop disease classification using a Dilateformer-MobileNetV2 dual-branch fusion network",
  "title_text": "Multi-crop disease classification using a Dilateformer-MobileNetV2 dual-branch fusion network",
  "type": "peer-reviewed",
  "updated_at": "2026-11-10T00:21:34Z",
  "url": "https://research.draconitic.com/paper/7e8k7vjb101kd",
  "venue": "Frontiers in Plant Science",
  "versions": []
}