Time series of ZOOSCAN images and results of zooplankton samples at Villefranche from 1966 to 2003

ZooScan with ZooProcess and Plankton Identifier (PkID) software is an integrated analysis system for acquisition and classification of digital zooplankton images from preserved zooplankton samples. Zooplankton samples are digitized by the ZooScan and processed by ZooProcess and PkID in order to detect, enumerate, measure and classify the digitized objects. Here we present a semi-automatic approach that entails automated classification of images followed by manual validation, which allows rapid and accurate classification of zooplankton and abiotic objects. We demonstrate this approach with a biweekly zooplankton time series from the Bay of Villefranche-sur-mer, France. The classification approach proposed here provides a practical compromise between a fully automatic method with varying degrees of bias and a manual but accurate classification of zooplankton. We also evaluate the appropriate number of images to include in digital learning sets and compare the accuracy of six classification algorithms. We evaluate the accuracy of the ZooScan for automated measurements of body size and present relationships between machine measures of size and C and N content of selected zooplankton taxa. We demonstrate that the ZooScan system can produce useful measures of zooplankton abundance, biomass and size spectra, for a variety of ecological studies.

搭载ZooProcess与浮游生物识别器（Plankton Identifier，简称PkID）的ZooScan系统，是一套用于从保存的浮游动物样本中采集并分类数字化浮游动物图像的集成分析系统。浮游动物样本经ZooScan完成数字化后，再由ZooProcess与PkID进行处理，以实现对数字化目标的检测、计数、测量与分类。本研究提出了一种半自动分析方案，其流程为先对图像进行自动化分类，再辅以人工验证，可实现浮游动物与非生物目标的快速精准分类。我们以法国滨海自由城湾（Bay of Villefranche-sur-mer）的双周采样浮游动物时间序列数据集验证了该方案的有效性。本研究提出的分类方案，在存在不同程度偏差的全自动化分类方法与耗时精准的人工浮游动物分类之间，实现了兼具实用性的平衡。本研究同时评估了数字化学习集所需的最优图像数量，并对比了六种分类算法的分类精度。我们还评估了ZooScan用于自动化测量个体体长的精度，并揭示了机器测量的体长与选定浮游动物类群的碳、氮含量之间的相关关系。研究证实，ZooScan系统可生成可靠的浮游动物丰度、生物量及体长谱数据，可应用于各类生态学研究。