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This article reports the development of an underwater imaging system and its trial on a moored surface buoy forin situplankton monitoring of coastal waters. The imager features shadowless white light illumination by an orthogonal lamellar lighting design, resulting in high-quality underwater darkfield color imaging of planktonic particles in the size range of ∼200μm to 40 mm and effective reduction of zooplankton phototaxis. Through raft and buoy trials, 46 804 plankton and suspending particle images have been annotated through a human–machine mutually assisted effort into a data set with 90 categories. In the meanwhile, a deep learning model based on a triclassification VGGNet-11 and multiclassification ResNet-18 convolutional neuron networks in a two-staged hierarchy has also been trained and developed. The model has been applied with human supervision to semiautomatically analyze a total of 1 545 187 images obtained from a buoy trial for six months from late spring to early winter of 2020. The high temporal resolution results well documented the variation of the mesoplankton community structure in two time series of 38 days in summer and 54 days in autumn of the target sea region. In addition, the dominant species in the trial period and a zooplankton outbreak that had threatened the safety of the nearby nuclear power plants were quantitatively analyzed. The system is expected to become a new entry into the toolkit for marine surface buoy platforms to upscale their capabilities for more comprehensivein situplankton monitoring.
Li et al. (Wed,) studied this question.