{
  "abstract": "Invasive pests pose a significant threat to agricultural production, particularly maize crops, with severe implications for food security. Timely detection of pest development stages and accurate prediction of outbreak risks are essential for effective management. This study introduces a hybrid model combining Explainable Artificial Intelligence (XAI), a lightweight Convolutional Neural Network (CNN), and Fuzzy Logic (FL) for Fall Armyworm (FAW) detection and weather-based risk prediction. The model uses Tiny-MobileNet-SE for image classification, Grad-CAM for interpretability, and FL inference based on environmental parameters. Tiny-MobileNet-SE achieved 98.6% accuracy, 98.5% F1-score, 98.6% recall, a compact size of 0.72 MB, and 80 ms latency on Raspberry Pi 5, outperforming state-of-the-art lightweight models including EfficientNetB0, SqueezeNet, MobileNet-v2, MobileNet-v3, and ShuffleNet. The proposed system delivers a power-efficient, scalable, and user-friendly solution for precision agriculture, providing actionable insights for pest management and supporting sustainable crop protection strategies.",
  "abstract_shown": true,
  "affiliations": [
    "African Centre of Excellence in Internet of Things (ACEIoT), College of Science and Technology, University of Rwanda, Kigali, Rwanda. njpindian@yahoo.fr",
    "Department of Electrical and Electronics Engineering, Rwanda Polytechnics-Gishari College, Rwamagana, Rwanda. njpindian@yahoo.fr",
    "University of Rwanda",
    "African Centre of Excellence in Internet of Things (ACEIoT), College of Science and Technology, University of Rwanda, Kigali, Rwanda",
    "Department of Mechanical and Energy Engineering, University of Rwanda, Kigali, Rwanda",
    "Department of ICT, Rwanda Polytechnics-Gishari College, Rwamagana, Rwanda",
    "Rwanda Polytechnics-Gishari College"
  ],
  "article_type": null,
  "arxiv": null,
  "author_total": 5,
  "authors": [
    {
      "affs": [
        0,
        1,
        2
      ],
      "name": "Jean Pierre Nyakuri",
      "orcid": "0000-0002-0459-7225"
    },
    {
      "affs": [
        3,
        4,
        2
      ],
      "name": "Célestin Nkundineza",
      "orcid": "0000-0002-9133-3999"
    },
    {
      "affs": [
        3,
        2
      ],
      "name": "Omar Gatera",
      "orcid": "0000-0002-5001-377X"
    },
    {
      "affs": [
        3,
        2
      ],
      "name": "Kizito N. Nkurikiyeyezu",
      "orcid": "0000-0002-9128-7080"
    },
    {
      "affs": [
        5,
        6
      ],
      "name": "Gervais Mwitende"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/ee44cesq8an6y.bib",
  "cited_by": 5,
  "doi": "10.1038/s41598-026-54478-0",
  "fulltext_url": null,
  "id": "ee44cesq8an6y",
  "issns": [
    "2045-2322"
  ],
  "landing_url": "https://doi.org/10.1038/s41598-026-54478-0",
  "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": "W4412583938",
  "pdf_url": "https://www.nature.com/articles/s41598-026-54478-0_reference.pdf",
  "pmcid": "PMC13439462",
  "pmid": "42192193",
  "published": "2026-05-26",
  "publisher": "Nature Portfolio",
  "ris": "https://research.draconitic.com/paper/ee44cesq8an6y.ris",
  "sources": [
    {
      "first_seen": "2026-11-07T13:20:51Z",
      "native_id": "W4412583938",
      "source": "openalex",
      "url": "https://doi.org/10.1038/s41598-026-54478-0"
    }
  ],
  "subjects": [
    {
      "field": "biology",
      "origin": "openalex:1110|Smart Agriculture and AI",
      "subfield": "plant-biology"
    }
  ],
  "title": "AI-IoT driven system for agricultural pest outbreak risk prediction",
  "title_text": "AI-IoT driven system for agricultural pest outbreak risk prediction",
  "type": "peer-reviewed",
  "updated_at": "2026-11-08T13:20:45Z",
  "url": "https://research.draconitic.com/paper/ee44cesq8an6y",
  "venue": "Scientific Reports",
  "versions": []
}