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<p>Mosquito-borne diseases pose a major public health challenge and require effective, scalable surveillance to guide targeted interventions. Existing monitoring techniques, ranging from manual morphological identification to acoustic, optical, and spectroscopic sensing, remain constrained by environmental sensitivity, labor demands, and limited ground-truth validation. Here, we present a fully autonomous, field-deployable platform, called automated intelligent mosquito sentinel (AIMS), integrating distributed mosquito monitoring outposts (MMOs) and a centralized analysis center (AC) for scalable, non-invasive mosquito surveillance. AIMS employs an adaptive event-triggering mechanism, optimized through feature engineering of colour and texture pairs, to enable energy-efficient detection with zero missed events and a false-positive rate below 1%. At the analytical level, a hierarchical gated residual network performs multitask classification of taxonomy and sex with accuracies of 99.51% at species and 98.02% for sex, demonstrating interpretable, biologically meaningful attention patterns. The self-powered architecture, robust wireless data transmission, and large-scale field dataset underpin reliable operation across diverse ecological settings. Together, these results show that AIMS can support scalable and sustainable mosquito surveillance and may also be useful for broader entomological monitoring and public health applications.</p>

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An autonomous intelligent mosquito sentinel for field-deployed surveillance

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年5月14日

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  • Nuofei Lin,
  • Yixiang Qian,
  • Li Wei,
  • Bo Dai,
  • Heng Peng,
  • Yajun Ma,
  • Songlin Zhuang,
  • Dawei Zhang

摘要

Mosquito-borne diseases pose a major public health challenge and require effective, scalable surveillance to guide targeted interventions. Existing monitoring techniques, ranging from manual morphological identification to acoustic, optical, and spectroscopic sensing, remain constrained by environmental sensitivity, labor demands, and limited ground-truth validation. Here, we present a fully autonomous, field-deployable platform, called automated intelligent mosquito sentinel (AIMS), integrating distributed mosquito monitoring outposts (MMOs) and a centralized analysis center (AC) for scalable, non-invasive mosquito surveillance. AIMS employs an adaptive event-triggering mechanism, optimized through feature engineering of colour and texture pairs, to enable energy-efficient detection with zero missed events and a false-positive rate below 1%. At the analytical level, a hierarchical gated residual network performs multitask classification of taxonomy and sex with accuracies of 99.51% at species and 98.02% for sex, demonstrating interpretable, biologically meaningful attention patterns. The self-powered architecture, robust wireless data transmission, and large-scale field dataset underpin reliable operation across diverse ecological settings. Together, these results show that AIMS can support scalable and sustainable mosquito surveillance and may also be useful for broader entomological monitoring and public health applications.