Department of Physiological Diversity, Helmholtz-Centre for Environmental Research-UFZ, Permoserstraße 15, 04318, Leipzig, Germany.
Department of Physiological Diversity, German Centre for Integrative Biodiversity Research-iDiv, Deutscher Platz 5a, 04103, Leipzig, Germany.
BMC Ecol. 2018 Dec 3;18(1):51. doi: 10.1186/s12898-018-0209-5.
Phytoplankton species identification and counting is a crucial step of water quality assessment. Especially drinking water reservoirs, bathing and ballast water need to be regularly monitored for harmful species. In times of multiple environmental threats like eutrophication, climate warming and introduction of invasive species more intensive monitoring would be helpful to develop adequate measures. However, traditional methods such as microscopic counting by experts or high throughput flow cytometry based on scattering and fluorescence signals are either too time-consuming or inaccurate for species identification tasks. The combination of high qualitative microscopy with high throughput and latest development in machine learning techniques can overcome this hurdle.
In this study, image based cytometry was used to collect ~ 47,000 images for brightfield and Chl a fluorescence at 60× magnification for nine common freshwater species of nano- and micro-phytoplankton. A deep neuronal network trained on these images was applied to identify the species and the corresponding life cycle stage during the batch cultivation. The results show the high potential of this approach, where species identity and their respective life cycle stage could be predicted with a high accuracy of 97%.
These findings could pave the way for reliable and fast phytoplankton species determination of indicator species as a crucial step in water quality assessment.
浮游植物物种鉴定和计数是水质评估的关键步骤。特别是饮用水水库、浴场和压载水需要定期监测有害物种。在富营养化、气候变暖以及入侵物种引入等多种环境威胁的时代，更密集的监测将有助于制定适当的措施。然而，传统的方法，如专家进行的显微镜计数或基于散射和荧光信号的高通量流式细胞术，要么耗时过长，要么在物种鉴定任务中不够准确。高质量显微镜与高通量的结合，以及机器学习技术的最新发展，可以克服这一障碍。
在这项研究中，基于图像的细胞计量法用于收集约 47,000 张图像，用于在 60×放大倍数下对纳米和微浮游植物的九个常见淡水物种进行明场和 Chl a 荧光。将这些图像训练的深度神经元网络应用于鉴定分批培养过程中的物种及其相应的生命周期阶段。结果表明了这种方法的巨大潜力，其中物种身份及其各自的生命周期阶段可以以 97%的高精度进行预测。
这些发现可以为水质评估中作为关键步骤的指示物种的可靠和快速浮游植物物种确定铺平道路。
