{
  "abstract": "Crop and forest damage from sika deer, wild boar, and Japanese macaque is a serious economic problem in Japanese satoyama, where farmland and forest intermingle. Camera traps enable scalable monitoring, yet existing benchmarks evaluate recognition in-distribution, rarely prioritize night infrared imagery, and do not jointly address regional domain shift, novel-species detection, and uncertainty-based triage on a single dataset. We introduce the Satoyama Camera Trap Dataset (SatoyamaCT), 12,642 expert-verified crops from night-IR-dominant camera traps across three satoyama regions. An iterative annotation protocol combining BioCLIP embeddings with confidence-ordered confirmation reduced expert effort while all labels were verified by domain ecologists; species-level inter-annotator agreement reached Cohen's kappa = 0.900. A multi-axis protocol jointly evaluates domain generalization across region, camera placement, and illumination; open-set novel-species detection; and selective prediction, all under capture-event-based leakage control. Difficulty separates into distinct failure modes: a data-inherent regional gap of 16 to 27 percentage points persists in Wakayama across four backbones and domain-generalization methods including CORAL, DANN, and GroupDRO, and open-set detection reaches AUROC 0.93 to 0.96 overall yet degrades jointly with classification in Wakayama. Selective prediction recovers Wakayama accuracy from 0.593 to 0.815 at 50% coverage, supporting an uncertainty-aware triage workflow for practical pest monitoring in agroforestry.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": "2609.30278",
  "author_total": 4,
  "authors": [
    {
      "name": "Keito Inoshita"
    },
    {
      "name": "Kohei Hisayama"
    },
    {
      "name": "Haruto Sugeno"
    },
    {
      "name": "Kota Nojiri"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/5tkfvwz3474pw.bib",
  "cited_by": 0,
  "doi": "10.48550/arxiv.2609.30278",
  "fulltext_url": null,
  "id": "5tkfvwz3474pw",
  "issns": [],
  "landing_url": "https://arxiv.org/abs/2609.30278",
  "language": null,
  "licence": "LicenseRef-arXiv-nonexclusive-1.0",
  "licence_label": "arXiv licence",
  "licence_source": "article",
  "licence_url": "http://arxiv.org/licenses/nonexclusive-distrib/1.0/",
  "metadata_licence": "CC0-1.0",
  "native_subjects": [
    [
      "arxiv",
      "eess.IV"
    ],
    [
      "arxiv",
      "q-bio.QM"
    ],
    [
      "openalex",
      "1110|Smart Agriculture and AI"
    ]
  ],
  "openalex": "W7214675857",
  "pdf_url": "https://arxiv.org/pdf/2609.30278",
  "pmcid": null,
  "pmid": null,
  "published": "2026-09-01",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/5tkfvwz3474pw.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:24:58Z",
      "native_id": "2609.30278",
      "source": "arxiv",
      "url": "https://arxiv.org/abs/2609.30278"
    }
  ],
  "subjects": [
    {
      "field": "biology",
      "origin": "arxiv:q-bio.QM",
      "subfield": "bioinformatics"
    }
  ],
  "title": "SatoyamaCT: A Multi-Axis Night-IR Camera-Trap Benchmark for Monitoring Crop-Damaging Wildlife in Japanese Agroforestry",
  "title_text": "SatoyamaCT: A Multi-Axis Night-IR Camera-Trap Benchmark for Monitoring Crop-Damaging Wildlife in Japanese Agroforestry",
  "type": "preprint",
  "updated_at": "2026-10-30T12:24:58Z",
  "url": "https://research.draconitic.com/paper/5tkfvwz3474pw",
  "venue": "arXiv",
  "versions": []
}