For geochemical analysis such as stable isotope ratio, radiocarbon dating and minor element analysis for a single species of microfossils, a large number of specimens, is required. Collecting specimens one by one under a microscope requires enormous time and effort. In this study, we developed a device that automates these efforts and can be used without expert knowledge. Microfossils can be accurately classified and identified to taxonomic species level using deep learning, which is one of the learning methods of artificial intelligence (AI), and picked up using a micromanipulator installed in the microscope with an automated motorized X-Y stage. A prototype of the classification model AI-PIC_20181024 showed the ability to classify microfossil species Cycladophora davisiana and Actinomma boreale (radiolarians) with accuracy exceeding 90% at a confidence level > 0.90. Using this method, it is possible to collect a large number of particles with speed and accuracy that cannot be achieved by a human technician. Although this technology can only be used for specific species of microfossils, it greatly reduces the hand work of picking and also enables chemical analysis, such as isotope ratio and minor element analysis, for small microfossil species for which it had been difficult to collect enough specimens. In addition to microfossils, this technology can be applied to other particles, with applications expected in various fields, such as medical, food, horticulture, and materials.

中文翻译：

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使用深度学习微操纵器系统自动收集单个种类的微化石

对于地球化学分析，例如稳定同位素比率，放射性碳测年和单个微化石物种的微量元素分析，需要大量标本。在显微镜下一个一个地收集标本需要大量的时间和精力。在这项研究中，我们开发了一种可以自动完成这些工作的设备，并且无需专业知识即可使用。可以使用深度学习（一种人工智能（AI）的学习方法之一）将微化石准确分类和分类到分类物种级别，然后使用安装在显微镜中的微操纵器（带有自动XY电动工作台）将微化石拾取。分类模型的原型AI-PIC_20181024显示到微体化石的物种分类能力Cycladophora davisiana和Actinomma boreale（放射性）在置信度> 0.90时准确度超过90％。使用这种方法，有可能以速度和精度收集人类技术人员无法实现的大量颗粒。尽管该技术只能用于特定种类的微化石，但它极大地减少了采集工作，并且还可以对难以收集足够标本的小型微化石进行化学分析，例如同位素比和微量元素分析。除微化石外，该技术还可以应用于其他颗粒，并且有望应用于医疗，食品，园艺和材料等各个领域。