State Key Laboratory of Grassland Agro-Ecosystems & College of Ecology, Lanzhou University, Lanzhou, China.
Guangdong Provincial Key Laboratory of Silviculture, Protection and Utilization, Guangdong Academy of Forestry, Guangzhou, China.
Pest Manag Sci. 2024 Oct;80(10):5277-5285. doi: 10.1002/ps.8254. Epub 2024 Jul 1.
The Red Imported Fire Ant (RIFA), scientifically known as Solenopsis invicta, is a destructive invasive species causing considerable harm to ecosystems and generating substantial economic costs globally. Traditional methods for RIFA nests detection are labor-intensive and may not be scalable to larger field areas. This study aimed to develop an innovative surveillance system that leverages artificial intelligence (AI) and robotic dogs to automate the detection and geolocation of RIFA nests, thereby improving monitoring and control strategies.
The designed surveillance system, through integrating the CyberDog robotic platform with a YOLOX AI model, demonstrated RIFA nest detection precision rates of >90%. The YOLOX model was trained on a dataset containing 1118 images and achieved a final precision rate of 0.95, with an inference time of 20.16 ms per image, indicating real-time operational suitability. Field tests revealed that the CyberDog system identified three times more nests than trained human inspectors, with significantly lower rates of missed detections and false positives.
The findings underscore the potential of AI-driven robotic systems in advancing pest management. The CyberDog/YOLOX system not only matched human inspectors in speed, but also exceeded them in accuracy and efficiency. This study's results are significant as they highlight how technology can be harnessed to address biological invasions, offering a more effective, ecologically friendly, and scalable solution for RIFA detection. The successful implementation of this system could pave the way for broader applications in environmental monitoring and pest control, ultimately contributing to the preservation of biodiversity and economic stability. © 2024 Society of Chemical Industry.
红火蚁（RIFA），学名 Solenopsis invicta，是一种具有破坏性的入侵物种，对全球生态系统造成了相当大的危害，并产生了巨大的经济成本。传统的红火蚁巢穴检测方法劳动强度大，可能无法扩展到更大的野外区域。本研究旨在开发一种创新的监测系统，利用人工智能（AI）和机器狗来自动检测和定位红火蚁巢穴，从而改进监测和控制策略。
设计的监测系统通过将 CyberDog 机器人平台与 YOLOX AI 模型集成，实现了 >90%的红火蚁巢穴检测精度。YOLOX 模型在包含 1118 张图像的数据集上进行了训练，最终精度达到 0.95，每张图像的推断时间为 20.16 毫秒，表明具有实时操作适用性。野外测试表明，CyberDog 系统比经过训练的人工检查员多识别出三倍的巢穴，漏检率和误报率显著降低。
研究结果突显了人工智能驱动的机器人系统在推进害虫管理方面的潜力。CyberDog/YOLOX 系统不仅在速度上与人工检查员相匹配，而且在准确性和效率上也超过了他们。本研究的结果意义重大，因为它们强调了如何利用技术来应对生物入侵，为红火蚁检测提供了更有效、更环保且更具可扩展性的解决方案。该系统的成功实施可以为环境监测和害虫控制的更广泛应用铺平道路，最终有助于保护生物多样性和经济稳定。
