渔业研究2024，Vol.46Issue(6)：685-694,10.DOI:10.14012/j.jfr.2024105
基于声学和光学的海洋生物观测技术研究进展
Research progress on marine biological observation technology based on acoustic and optics
摘要
Abstract
[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 chal-lenging for continuous monitoring.[Objective]This study aims to promote the application of new technolo-gies and enhance the observation capabilities of marine organisms.[Progress]This article introduces innovat-ive marine biodiversity observation technologies developed based on acoustic and optical principles.These new technologies include passive acoustic observation sensors,optical phytoplankton detectors utilizing spectral ab-sorption,and plankton analyzers through optical imaging.The latter are further classified into flow imaging,sil-houette 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 phyto-plankton detectors effectively identify bloom species within the water column and monitor phytoplankton com-munities.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 com-munities.Dark field imaging relies on the scattering,reflection,and refraction of targets,enabling the observa-tion of mesoplankton.Holographic imaging utilizes coherent light illumination to capture interferometric im-ages 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.关键词
浮游生物/机器学习/声学观测/光谱观测/图像识别Key words
plankton/machine learning/acoustic observing/optical observing/image recognition分类
农业科技引用本文复制引用
李荣茂..基于声学和光学的海洋生物观测技术研究进展[J].渔业研究,2024,46(6):685-694,10.基金项目
福建省科技计划项目(2023N0028) （2023N0028）
福建省海洋服务与渔业高质量发展专项(2024) （2024）
