Phytoplankton parasites are largely understudied microbial components with a potentially significant ecological impact on phytoplankton bloom dynamics. To better understand their impact, we need improved detection methods to integrate phytoplankton parasite interactions in monitoring aquatic ecosystems. Automated imaging devices usually produce high amount of phytoplankton image data, while the occurrence of anomalous phytoplankton data is rare. Thus, we propose an unsupervised anomaly detection system based on the similarity of the original and autoencoder-reconstructed samples. With this approach, we were able to reach an overall F1 score of 0.75 in nine phytoplankton species, which could be further improved by species-specific fine-tuning. The proposed unsupervised approach was further compared with the supervised Faster R-CNN based object detector. With this supervised approach and the model trained on plankton species and anomalies, we were able to reach the highest F1 score of 0.86. However, the unsupervised approach is expected to be more universal as it can detect also unknown anomalies and it does not require any annotated anomalous data that may not be always available in sufficient quantities. Although other studies have dealt with plankton anomaly detection in terms of non-plankton particles, or air bubble detection, our paper is according to our best knowledge the first one which focuses on automated anomaly detection considering putative phytoplankton parasites or infections.
翻译:浮游植物寄生虫大多是研究不足的微生物成分,对浮游植物的繁殖动态具有潜在的重大生态影响。为了更好地了解其影响,我们需要改进检测方法,将浮游植物寄生虫的相互作用纳入水生生态系统的监测中。自动成像装置通常产生大量的浮游植物图像数据,而浮游植物的异常数据很少出现。因此,我们建议基于原始和自成一体再造样本的相似性,建立一个不受监督的异常检测系统。采用这种方法,我们得以在9个浮游植物物种中达到0.75的F1总分,而通过物种的微调可以进一步改进。拟议的非监督方法与监督下的快速R-CN天体探测器相比,通常产生大量浮游生物物种和异常模型。因此,我们得以达到0.86这一最高F1分。然而,由于能够检测未知的异常现象,因此预计非监督方法会更加普遍,而且不需要在9个浮游生物物种物种物种物种中进行任何有注释的F1分的F1分,而不需要通过特定物种的微调改进加以进一步改进。拟议的非监督方法进一步比较了。拟议的非监督方法,但根据其他的浮游生生物的浮游生物的检测结果研究研究,也无法从某种地分析得出了一种对浮质进行。</s>