LanternNet: スポッテッドランタンフライ個体群を探索・抑制するハブアンドスポークシステム
LanternNet: A Hub-and-Spoke System to Seek and Suppress Spotted Lanternfly Populations
樹木型ハブと3種のロボットスポークで構成される自律システムを開発し、YOLOv8による侵入害虫スポッテッドランタンフライの検出と抑制を実現、野外実験で個体群減少と樹木健康改善を確認した。
著者: Vinil Polepalli
分類: cs.RO, cs.AI, cs.CV
原文アブストラクト
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.