Automated plankton classification from holographic imagery with deep convolutional neural networks. (3rd December 2020)

• Record Type: Journal Article
• Authors: Guo, Buyu; Nyman, Lisa; Nayak, Aditya R.; Milmore, David; McFarland, Malcolm; Twardowski, Michael S.; Sullivan, James M.; Yu, Jia; Hong, Jiarong
• Is Part Of: Limnology and oceanography, methods. Volume 19:Number 1(2021)
• Page Start: 21
• Page End: 36
• Publication Date: 2020-12-03
• Subjects: Limnology -- Methodology -- Periodicals; Oceanography -- Methodology -- Periodicals; 551.48
• DOI: 10.1002/lom3.10402
• Languages: English
• ISSNs: 1541-5856

Abstract

Abstract: 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.