用机器人网络自动识别并清除入侵的灯蛾幼虫，提升防治效率。
LanternNet: A Hub-and-Spoke System to Seek and Suppress Spotted Lanternfly Populations
- 中心枢纽+三台机器人构成探测与防控网络，实现自主作业。
- 五周实地测试显示虫口数量显著下降（p<0.01），树健康指标改善。
- 比传统方法更省成本、易扩展，可推广至其他入侵物种治理。
入侵性物种斑点灯蛾对农业和生态系统构成严重威胁，造成广泛损害。当前防控手段如刮卵、喷洒农药和封锁措施存在人力密集、环境危害大、长期效果差等问题。本研究提出LanternNet——一种新型自主式中心辐射型机器人系统，用于规模化检测与抑制灯蛾种群。中央树形枢纽采用YOLOv8计算机视觉模型精准识别灯蛾；三个专用机器人分支分别执行灭虫、环境监测和导航建图任务。在多个受侵区域连续部署5周的实地测试表明，定量分析显示灯蛾种群数量显著减少（配对t检验，p<0.01），多数测试点树木健康指标明显改善。相比传统方法，LanternNet具备显著成本优势与更强可扩展性。此外，系统具备增强自主性及针对其他入侵物种的适配潜力，具有广泛的生态应用前景。LanternNet展示了融合机器人与AI技术在入侵物种管理中的变革性潜力，可实现更优环境治理效果。
原文摘要 · Abstract (English)
The invasive spotted lanternfly (SLF) poses a significant threat to agriculture and ecosystems, causing widespread damage. Current control methods, such as egg scraping, pesticides, and quarantines, prove labor-intensive, environmentally hazardous, and inadequate for long-term SLF suppression. This research introduces LanternNet, a novel autonomous robotic Hub-and-Spoke system designed for scalable detection and suppression of SLF populations. A central, tree-mimicking hub utilizes a YOLOv8 computer vision model for precise SLF identification. Three specialized robotic spokes perform targeted tasks: pest neutralization, environmental monitoring, and navigation/mapping. Field deployment across multiple infested sites over 5 weeks demonstrated LanternNet's efficacy. Quantitative analysis revealed significant reductions (p < 0.01, paired t-tests) in SLF populations and corresponding improvements in tree health indicators across the majority of test sites. Compared to conventional methods, LanternNet offers substantial cost advantages and improved scalability. Furthermore, the system's adaptability for enhanced autonomy and targeting of other invasive species presents significant potential for broader ecological impact. LanternNet demonstrates the transformative potential of integrating robotics and AI for advanced invasive species management and improved environmental outcomes.
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