Marine and freshwater microalgae belong to taxonomically and morphologically diverse groups of organisms spanning many phyla with thousands of species. These organisms play an important role as indicators of water ecosystem conditions since they react quickly and predictably to a broad range of environmental stressors, thus providing early signals of dangerous changes. Traditionally, microscopic analysis has been used to identify and enumerate different types of organisms present within a given environment at a given point in time. However, this approach is both time-consuming and labor intensive, as it relies on manual processing and classification of planktonic organisms present within collected water samples. Furthermore, it requires highly skilled specialists trained to recognize and distinguish one taxa from another on the basis of often subtle morphological differences. Given these restrictions, a considerable amount of effort has been recently funneled into automating different steps of both the sampling and classification processes, making it possible to generate previously unprecedented volumes of plankton image data and obtain an essential database to analyze the composition of plankton assemblages. In this review we report state-of-the-art methods used for automated plankton classification by means of digital microscopy. The computer-microscope system hardware and the image processing techniques used for recognition and classification of planktonic organisms (segmentation, shape feature extraction, pigment signature determination and neural network grouping) will be described. An introduction and overview of the topic, its current state and indications of future directions the field is expected to take will be provided, organizing the review for both experts and researchers new to the field.

中文翻译：

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微藻数码显微识别与分类的水质监测

海洋和淡水微藻属于分类学和形态学上不同的生物群，跨越许多门和数千种。这些生物作为水生态系统状况的指标发挥着重要作用，因为它们对广泛的环境压力因素做出快速且可预测的反应，从而提供危险变化的早期信号。传统上，微观分析已被用于识别和列举特定时间点特定环境中存在的不同类型的生物。然而，这种方法既费时又费力，因为它依赖于对收集到的水样中存在的浮游生物进行手动处理和分类。此外，它需要训练有素的高技能专家根据通常细微的形态差异来识别和区分一种分类群。鉴于这些限制，最近投入了大量精力来自动化采样和分类过程的不同步骤，从而可以生成前所未有的浮游生物图像数据量，并获得必要的数据库来分析浮游生物组合的组成。在这篇综述中，我们报告了通过数字显微镜进行自动浮游生物分类的最先进方法。用于浮游生物识别和分类的计算机显微镜系统硬件和图像处理技术（分割、形状特征提取、色素特征确定和神经网络分组）将被描述。将提供对该主题的介绍和概述、其现状以及该领域预计未来方向的指示，为该领域的新专家和研究人员组织审查。