Takaya Kosuke, Sasaki Yu, Ise Takeshi
Graduate School of Agriculture, Kyoto University, Kitashirakawaoiwake-cho, Sakyo-ku, Kyoto 606-8502, Japan.
Center for the Promotion of Interdisciplinary Education and Research, Kyoto University, Kitashirakawaoiwake-cho, Sakyo-ku, Kyoto 606-8502, Japan.
Breed Sci. 2022 Mar;72(1):96-106. doi: 10.1270/jsbbs.21062. Epub 2022 Feb 5.
Monitoring and detection of invasive alien plant species are necessary for effective management and control measures. Although efforts have been made to detect alien trees using satellite images, the detection of alien herbaceous species has been difficult. In this study, we examined the possibility of detecting non-native plants using deep learning on images captured by two action cameras. We created a model for each camera using the chopped picture method. The models were able to detect the alien plant (tall goldenrod) and obtained an average accuracy of 89%. This study proved that it is possible to automatically detect exotic plants using inexpensive action cameras through deep learning. This advancement suggests that, in the future, citizen science may be useful for conducting distribution surveys of alien plants in a wide area at a low cost.
监测和检测外来入侵植物物种对于有效的管理和控制措施至关重要。尽管已经努力利用卫星图像检测外来树木，但检测外来草本物种一直很困难。在本研究中，我们探讨了使用深度学习对两台运动相机拍摄的图像进行非本地植物检测的可能性。我们使用裁剪图片法为每台相机创建了一个模型。这些模型能够检测外来植物（高一枝黄花），平均准确率达到89%。本研究证明，通过深度学习利用廉价的运动相机自动检测外来植物是可行的。这一进展表明，未来公民科学可能有助于以低成本在广大区域开展外来植物分布调查。
