The LanternPredator: A Machine-learning-based Robot for Controlling the Spread of Invasive Species

Wei Gou, David Banyamin, Mark Rezk, Wictor Fedorowait, Daniel Burbano, Sasan Haghani
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Abstract

Lanternflies are an invasive pest species that can cause significant economic damage to agriculture by affecting plants and crops and disrupting the balance of natural ecosystems. These insects have a fast reproduction cycle, can withstand high-temperature variations, and have no natural predators in the US, making it very difficult to control their spread. Motivated by this environmental issue, we designed the LanternPredator, an autonomous pest control robot to help control the population growth of lanternflies. The proposed solution integrates machine learning algorithms for detecting the right insect species, an acoustic stimulus to attract the insects to a zap trap, and sonar for autonomous navigation.
灯笼捕食者基于机器学习的控制入侵物种扩散的机器人
灯蝇是一种入侵害虫,会影响植物和农作物,破坏自然生态系统的平衡,从而对农业造成重大经济损失。这些昆虫繁殖周期快,能承受高温变化,在美国没有天敌,因此很难控制其蔓延。受这一环境问题的启发,我们设计了一种自主害虫控制机器人 LanternPredator,以帮助控制灯火蝇的数量增长。我们提出的解决方案整合了机器学习算法来检测正确的昆虫种类、声学刺激来吸引昆虫到捕虫夹前,以及用于自主导航的声纳。
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