Plankton imaging systems supported by automated classification and analysis have improved ecologists' ability to observe aquatic ecosystems. Today, we are on the cusp of reliably tracking plankton populations with a suite of lab-based and in situ tools, collecting imaging data at unprecedentedly fine spatial and temporal scales. But these data have potential well beyond examining the abundances of different taxa; the individual images themselves contain a wealth of information on functional traits. Here, we outline traits that could be measured from image data, suggest machine learning and computer vision approaches to extract functional trait information from the images, and discuss promising avenues for novel studies. The approaches we discuss are data agnostic and are broadly applicable to imagery of other aquatic or terrestrial organisms.

中文翻译：

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从图像数据中表征浮游生物功能特征的机器学习技术


由自动分类和分析支持的浮游生物成像系统提高了生态学家观察水生生态系统的能力。今天，我们正处于利用一套基于实验室的现场工具可靠地跟踪浮游生物种群的风口浪尖，以前所未有的精细空间和时间尺度收集成像数据。但这些数据的潜力远远超出了检查不同类群的丰度；各个图像本身包含丰富的功能特征信息。在这里，我们概述了可以从图像数据中测量的特征，建议机器学习和计算机视觉方法从图像中提取功能特征信息，并讨论新颖研究的有希望的途径。我们讨论的方法与数据无关，并且广泛适用于其他水生或陆地生物的图像。