Traditional approaches to biomonitoring in aquatic systems, such as sample collection, sorting, and identification, require significant time and effort, thereby limiting the spatiotemporal resolution of sample collection. Additionally, collection and preservation of samples for subsequent taxonomic identification and enumeration leads to mortality of organisms. Recent advances in technologies that utilize optical imaging and machine learning have provided new opportunities to expedite biomonitoring and lead to significant cost savings. These technologies can be advantageous to scientists or managers that conduct routine biomonitoring to inform operations, as in the case of aquaculture facilities. The Small Aquatic Organism optical imaging system (SAO) is a high-throughput optical imaging and classification prototype system that relies on computer vision and machine learning (Support Vector Machines, or SVMs) to autonomously identify and enumerate aquatic organisms. The SAO provides a more sustainable method of collecting large volumes of data and has the benefit of being used in situ. In this study, we tested the performance of the SAO in providing comparable results to manual zooplankton community monitoring in ten ponds at an aquaculture facility. We performed a side-by-side study comparing the sampling methods of plankton tow nets, where major zooplankton taxonomic classes were manually identified and enumerated, to sampling with the SAO. Vouchered samples were used to develop a training library for the SAO, where classes consisted of water boatman and zooplankton groups: cladocerans, copepod adults, copepod nauplii, and rotifers. SAO imagery was manually classified and compared with predicted results for validation. Accuracy for the SVM classifier of the SAO was 37.4 %. Convolutional Neural Networks (CNN) and Random Forest classifiers were also applied to SAO imagery and image features for comparison. The best CNN model and our Random Forest model had accuracies of 80.4 % and 46.6 % respectively. Challenges faced included the small size of copepod nauplii and rotifers and the limited resolution of the imaging camera, although there are tradeoffs between imaging resolution and the sample processing rate. Our comparison shows that advancement in both optical imaging and ML are needed in order for the SAO prototype to yield comparable results to manual community monitoring in an aquaculture facility.

水生系统中生物监测的传统方法，例如样品采集、分类和识别，需要大量的时间和精力，从而限制了样品采集的时空分辨率。此外，用于随后的分类学鉴定和计数的样品的收集和保存会导致生物体的死亡。利用光学成像和机器学习的技术的最新进展为加快生物监测并显着节省成本提供了新的机会。这些技术对于进行常规生物监测以告知运营情况的科学家或管理人员来说是有利的，例如水产养殖设施。小型水生生物光学成像系统（SAO）是一种高通量光学成像和分类原型系统，依靠计算机视觉和机器学习（支持向量机或SVM）来自主识别和枚举水生生物。SAO 提供了一种更可持续的方法来收集大量数据，并具有就地使用的优点。在这项研究中，我们测试了 SAO 的性能，以提供与水产养殖设施的 10 个池塘中的手动浮游动物群落监测可比较的结果。我们进行了一项并行研究，比较了浮游生物拖网的采样方法（其中主要浮游动物分类类别是手动识别和枚举的）与 SAO 的采样方法。凭单样本被用来为 SAO 开发一个培训库，其中的课程包括水上船夫和浮游动物群：枝角类、桡足类成虫、桡足类无节幼体和轮虫。SAO 图像被手动分类并与预测结果进行比较以进行验证。SAO 的 SVM 分类器的准确度为 37.4%。卷积神经网络 (CNN) 和随机森林分类器也被应用于 SAO 图像和图像特征以进行比较。最好的 CNN 模型和我们的随机森林模型的准确率分别为 80.4% 和 46.6%。面临的挑战包括桡足类无节幼体和轮虫的小尺寸以及成像相机的有限分辨率，尽管成像分辨率和样本处理速率之间存在权衡。我们的比较表明，为了使 SAO 原型能够产生与水产养殖设施中的手动群落监测相当的结果，需要光学成像和机器学习方面的进步。