Remote 3D Imaging and Classification of Pelagic Microorganisms with A Short-Range Multispectral Confocal LiDAR

Plankton is essential to maintain healthy aquatic ecosystems since it influences the biological carbon pump globally. However, climate change-induced alterations to oceanic properties threaten planktonic communities. It is therefore crucial to monitor their abundance to assess the health status of marine ecosystems. In situ optical tools unlock high-resolution measurements of sub-millimeter specimens, but state-of-the-art underwater imaging techniques are limited to fixed and small close-range volumes, requiring the instruments to be vertically dived. Here, a novel scanning multispectral confocal light detection and ranging (LiDAR) system for short-range volumetric sensing in aquatic media is introduced. The system expands the inelastic confocal principle to multiple wavelength channels, allowing the acquisition of 4D point clouds combining near-diffraction limited morphological and spectroscopic data that is used to train artificial intelligence (AI) models. Volumetric mapping and classification of microplastics is demonstrated to sort them by color and size. Furthermore, in vivo autofluorescence is resolved from a community of free-swimming zooplankton and microalgae, and accurate spectral identification of different genera is accomplished. The deployment of this photonic platform alongside AI models overcomes the complex and subjective task of manual plankton identification and enables non-intrusive sensing from fixed vantage points, thus constituting a unique tool for underwater environmental monitoring.