In situ digital inline holography is a technique which can be used to acquire high‐resolution imagery of plankton and examine their spatial and temporal distributions within the water column in a nonintrusive manner. However, for effective expert identification of an organism from digital holographic imagery, it is necessary to apply a computationally expensive numerical reconstruction algorithm. This lengthy process inhibits real‐time monitoring of plankton distributions. Deep learning methods, such as convolutional neural networks, applied to interference patterns of different organisms from minimally processed holograms can eliminate the need for reconstruction and accomplish real‐time computation. In this article, we integrate deep learning methods with digital inline holography to create a rapid and accurate plankton classification network for 10 classes of organisms that are commonly seen in our data sets. We describe the procedure from preprocessing to classification. Our network achieves 93.8% accuracy when applied to a manually classified testing data set. Upon further application of a probability filter to eliminate false classification, the average precision and recall are 96.8% and 95.0%, respectively. Furthermore, the network was applied to 7500 in situ holograms collected at East Sound in Washington during a vertical profile to characterize depth distribution of the local diatoms. The results are in agreement with simultaneously recorded independent chlorophyll concentration depth profiles. This lightweight network exemplifies its capability for real‐time, high‐accuracy plankton classification and it has the potential to be deployed on imaging instruments for long‐term in situ plankton monitoring.

中文翻译：

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深度卷积神经网络从全息图像自动进行浮游生物分类

原位数字在线全息技术可用于获取浮游生物的高分辨率图像，并以非侵入方式检查其在水柱内的时空分布。然而，为了从数字全息图像中有效地鉴定生物，有必要应用计算上昂贵的数值重建算法。这个漫长的过程阻碍了浮游生物分布的实时监测。深度学习方法（例如卷积神经网络）从最低限度处理的全息图应用于不同生物的干扰模式，可以消除重建的需要并完成实时计算。在这篇文章中，我们将深度学习方法与数字在线全息技术相结合，为我们的数据集中常见的10类生物体创建了快速，准确的浮游生物分类网络。我们描述了从预处理到分类的过程。当我们的网络应用于手动分类的测试数据集时，可达到93.8％的准确性。进一步应用概率过滤器消除错误分类后，平均精度和召回率分别为96.8％和95.0％。此外，该网络还应用于垂直剖面期间在华盛顿东声音收集的7500幅原位全息图，以表征当地硅藻的深度分布。结果与同时记录的独立叶绿素浓度深度曲线一致。