BACKGROUNDThe 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.RESULTSThe 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.CONCLUSIONThe 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.

中文翻译：

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机器狗在红火蚁巢穴自动检测监测中的应用


背景红火蚁（RIFA），科学上称为红火蚁（Solenopsis invicta），是一种破坏性的入侵物种，对生态系统造成相当大的危害，并在全球范围内产生巨大的经济成本。红火蚁巢穴检测的传统方法是劳动密集型的，并且可能无法扩展到更大的田间区域。本研究旨在开发一种创新的监控系统，利用人工智能 (AI) 和机器狗对红火蚁巢穴进行自动检测和地理定位，从而改进监控和控制策略。 结果 通过将 Cyber​​Dog 机器人平台与 YOLOX 集成，设计的监控系统AI模型，证明红火蚁巢检测准确率>90%。 YOLOX模型在包含1118张图像的数据集上进行训练，最终精度达到0.95，每张图像的推理时间为20.16毫秒，表明实时操作适用性。现场测试显示，Cyber​​Dog 系统识别的巢穴数量是训练有素的人类检查员的三倍，漏检率和误报率显着降低。结论研究结果强调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。 Cyber​​Dog/YOLOX系统不仅在速度上与人类检查员相匹配，而且在准确性和效率上也超过了他们。这项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，为红火蚁检测提供更有效、生态友好和可扩展的解决方案。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。 © 2024 化学工业协会。