{
  "abstract": "Introduction: The recognition of leaf segments and species in nature is a difficult process due to the changes in brightness, too much clutter in the background, and the overlapping of leaves due to which the accuracy falls down for 20-40% when using laboratory scans instead of field-ready applications.\n\nMethods: This research presents a unified framework of deep learning that includes Mask R-CNN -based instance segmentation and deep feature fusion in order to overcome this problem. The process of extracting a number of leaves took place through Detectron2 and the excessive stream extractor that included ResNet-50 and VGG-16 used in conjunction with other descriptive methods allowed providing a joint representation of 6,148 dimensions. The classifier was trained according to three methods: Baseline, PSO (particle swarm optimization), and Grid search.\n\nResults: The masked fusion system cut off the noise in the background while retaining the important botanical features with the help of achieving the mean Intersection-over-Union equal to 0.752, detection precision of 98.39%, and mAP@0.5 of 0.8291. The ResNet-50 model enhanced with PSO techniques has the best performance among other technologies based on ResNet-50 (90% accuracy, F1 score 0.9002), while the technology of Grid search revealed a little higher result for the special techniques such as ResNet-50 plus traditional features (F1 score = 0.9135) which means that the efficiency of the methodology is contingent to the type of the features not the fact if the technology is better than the other.\n\nDiscussion: Comparative and ablation data analysis proved the efficiency of the instance segmentation used in combination with multi-mode fusion especially when used in occlusion-prone conditions. The system offers a scalable application for smart agriculture and biodiversity monitoring while being a good example of bridging the gap in automated environmental intelligence.",
  "abstract_shown": true,
  "affiliations": [
    "Research Scholar, School of Computer Science & Engineering, Vellore Institute of Technology (VIT) Chennai",
    "Faculty, School of Computer Science & Engineering, Vellore Institute of Technology (VIT) Chennai"
  ],
  "article_type": "research article",
  "arxiv": null,
  "author_total": 2,
  "authors": [
    {
      "affs": [
        0
      ],
      "name": "K. Arunkumar"
    },
    {
      "affs": [
        1
      ],
      "name": "S. Leninisha"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/e8cdz07waa6m1.bib",
  "cited_by": 0,
  "doi": "10.3389/fpls.2026.1893779",
  "fulltext_url": "https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13716794/?tool=EBI",
  "id": "e8cdz07waa6m1",
  "issns": [
    "1664-462X"
  ],
  "landing_url": "https://www.frontiersin.org/articles/10.3389/fpls.2026.1893779/full",
  "language": "eng",
  "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": [
    [
      "heading",
      "Original Research"
    ],
    [
      "openalex",
      "1707|Advanced Neural Network Applications"
    ]
  ],
  "openalex": "W7222005311",
  "pdf_url": "https://www.frontiersin.org/journals/plant-science/articles/10.3389/fpls.2026.1893779/pdf",
  "pmcid": "PMC13716794",
  "pmid": "42959139",
  "published": "2026-10-15",
  "publisher": "Frontiers Media SA",
  "ris": "https://research.draconitic.com/paper/e8cdz07waa6m1.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:19:25Z",
      "native_id": "10.3389/fpls.2026.1893779",
      "source": "crossref",
      "url": "https://www.frontiersin.org/articles/10.3389/fpls.2026.1893779/full"
    },
    {
      "first_seen": "2026-10-31T00:20:37Z",
      "native_id": "PMC:PMC13716794",
      "source": "europepmc",
      "url": "https://europepmc.org/article/PMC/PMC13716794"
    },
    {
      "first_seen": "2026-11-01T00:20:52Z",
      "native_id": "MED:42959139",
      "source": "europepmc",
      "url": "https://europepmc.org/article/MED/42959139"
    },
    {
      "first_seen": "2026-11-01T12:24:00Z",
      "native_id": "PMC13716794",
      "source": "pmc",
      "url": "https://doi.org/10.3389/fpls.2026.1893779"
    }
  ],
  "subjects": [
    {
      "field": "biology",
      "origin": "issn:1664-462X",
      "subfield": "plant-biology"
    }
  ],
  "title": "An integrated instance segmentation and deep feature fusion model for robust leaf species classification in natural environments",
  "title_text": "An integrated instance segmentation and deep feature fusion model for robust leaf species classification in natural environments",
  "type": "peer-reviewed",
  "updated_at": "2026-11-02T12:21:14Z",
  "url": "https://research.draconitic.com/paper/e8cdz07waa6m1",
  "venue": "Frontiers in Plant Science",
  "versions": []
}