Research progress on marine biological observation technology based on acoustic and optics

Background The observation of marine biodiversity is fundamental for studying the structure and function of marine ecosystems. However, traditional observation methods are often inefficient, costly, and challenging for continuous monitoring.

Objective This study aims to promote the application of new technologies and enhance the observation capabilities of marine organisms.

Progress This article introduces innovative marine biodiversity observation technologies developed based on acoustic and optical principles. These new technologies include passive acoustic observation sensors, optical phytoplankton detectors utilizing spectral absorption, and plankton analyzers through optical imaging. The latter are further classified into flow imaging, silhouette imaging, dark field imaging, and holographic imaging based on differing imaging principles. Passive acoustic observation sensors are instrumental in revealing critical fish habitats. They offer advantages such as non-invasiveness, high spatiotemporal resolution, cost-effectiveness, and low operational costs. Optical phytoplankton detectors effectively identify bloom species within the water column and monitor phytoplankton communities. Flow imaging instruments collect fluorescence signals, such as scattering and chlorophyll, to observe nanoplankton and microplankton. Silhouette imaging employs backlighting to produce high-contrast images, characterized by low resolution but high observational capacity, making it suitable for studying planktonic communities. Dark field imaging relies on the scattering, reflection, and refraction of targets, enabling the observation of mesoplankton. Holographic imaging utilizes coherent light illumination to capture interferometric images of targets, followed by image reconstruction. This method boasts high resolution and a large depth of field, allowing for the observation of microplankton. By integrating multiple observation techniques, image data is classified using convolutional neural networks, achieving an accuracy rate exceeding 90%.

Prospect With the rapid advancement of artificial intelligence technology, these new methodologies are expected to find broader applications, significantly enhancing the efficiency of marine biological observation.