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Peer-reviewedCC BYScientific Reports

AI-IoT driven system for agricultural pest outbreak risk prediction

  • Jean Pierre Nyakuri1,2,3
  • Célestin Nkundineza4,5,3
  • Omar Gatera4,3
  • Kizito N. Nkurikiyeyezu4,3
  • Gervais Mwitende6,7
7 affiliations
  1. 1African Centre of Excellence in Internet of Things (ACEIoT), College of Science and Technology, University of Rwanda, Kigali, Rwanda. [email protected]
  2. 2Department of Electrical and Electronics Engineering, Rwanda Polytechnics-Gishari College, Rwamagana, Rwanda. [email protected]
  3. 3University of Rwanda
  4. 4African Centre of Excellence in Internet of Things (ACEIoT), College of Science and Technology, University of Rwanda, Kigali, Rwanda
  5. 5Department of Mechanical and Energy Engineering, University of Rwanda, Kigali, Rwanda
  6. 6Department of ICT, Rwanda Polytechnics-Gishari College, Rwamagana, Rwanda
  7. 7Rwanda Polytechnics-Gishari College
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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.

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Cite

BibTeXRIS
BibTeX
@article{nyakuri2026aiiot,
  title = {{AI-IoT driven system for agricultural pest outbreak risk prediction}},
  author = {Jean Pierre Nyakuri and Célestin Nkundineza and Omar Gatera and Kizito N. Nkurikiyeyezu and Gervais Mwitende},
  journal = {Scientific Reports},
  year = {2026},
  month = may,
  doi = {10.1038/s41598-026-54478-0},
  url = {https://doi.org/10.1038/s41598-026-54478-0},
  note = {CC BY},
}
RIS
TY  - JOUR
TI  - AI-IoT driven system for agricultural pest outbreak risk prediction
AU  - Jean Pierre Nyakuri
AU  - Célestin Nkundineza
AU  - Omar Gatera
AU  - Kizito N. Nkurikiyeyezu
AU  - Gervais Mwitende
PY  - 2026
DA  - 2026/05/26
JO  - Scientific Reports
DO  - 10.1038/s41598-026-54478-0
UR  - https://doi.org/10.1038/s41598-026-54478-0
AB  - 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.
SN  - 2045-2322
ER  -