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Detection of Anolis carolinensis using drone images and a deep neural network: an effective tool for controlling invasive species
Biological Invasions ( IF 2.9 ) Pub Date : 2021-03-12 , DOI: 10.1007/s10530-020-02434-y
Tomoki Aota , Koh Ashizawa , Hideaki Mori , Mitsuhiko Toda , Satoshi Chiba

Invasive species greatly disrupt island ecosystems, risk assessment and the conservation of native ecosystems have therefore become pressing concerns. However, the cost of monitoring invasive species by humans is often high. In this study, we developed a system to detect an invasive lizard species, Anolis carolinensis, that threatens the native insect ecosystem of the Ogasawara Islands in Japan. Surveying these forest lizards requires specialized field observers, a challenge that prevents the government of Japan from efficient conservation and management of this ecosystem. The proposed system detects these lizards in drone images using a type of machine learning called deep neural network. Data were collected using a drone on Ani-jima in the Ogasawara Islands, and the trained network shows approximately 70% precision of detecting A. carolinensis. This study shows the combination of remote sensing and machine learning have the potential to contribute to an efficient and effective approach to conserving ecosystems.



中文翻译:

使用无人机图像和深层神经网络检测Carolinensis Anolis:控制入侵物种的有效工具

外来入侵物种极大地破坏了岛屿生态系统,因此风险评估和原生生态系统的保护已成为迫切关注的问题。但是,人类监视入侵物种的成本通常很高。在这项研究中,我们开发了一种检测入侵蜥蜴物种Anolis carolinensis的系统威胁日本小gas原群岛的原生昆虫生态系统。对这些森林蜥蜴进行调查需要专业的现场观察员,这一挑战使日本政府无法有效地保护和管理该生态系统。拟议的系统使用一种称为深度神经网络的机器学习来检测无人机图像中的这些蜥蜴。使用小gas原群岛阿尼岛上的一架无人驾驶飞机收集数据,经过训练的网络显示出检测卡罗来纳州曲霉的准确率约为70%。这项研究表明,将遥感与机器学习相结合,有可能为保护生态系统的有效方法做出贡献。

更新日期:2021-03-12
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