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Journal of Tropical Oceanography

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Dark-Field Microscopic In Situ Observation Technique for Coral Reef Ecosystems

SUN Qiantai1,2, WANG Xinwei1,2,3,LEI Pingshun1, SUN Liang1, LIU Sheng4, CHEN Jianan1, ZHANG Yurun1, ZHOU Yan1,2,3, LIU Yuliang1,2   

  1. 1. Optoelectronic System Laboratory, Institute of Semiconductors, Chinese Academy of Sciences, Beijing 100083, China;

    2. Center of Materials Science and Optoelectronics Engineering, University of Chinese Academy of Sciences, Beijing 100049, China;

    3. School of Electronic, Electrical and Communication Engineering, University of Chinese Academy of Sciences, Beijing 100049, China;

    4. State Key Laboratory of Tropical Oceanography, Guangdong Provincial Key Laboratory of Applied Marine Biology, South China Sea Institute of Oceanology, Chinese Academy of Sciences, Guangzhou 510301, China;




  • Received:2026-05-03 Revised:2026-06-16 Accepted:2026-07-20
  • Supported by:

    Science & Technology Fundamental Resources Investigation Program (2022FY100605); Key Research Infrastructures in CAS Field Stations (KFJ-SW-YW047)

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Abstract: To address the limited capability for in situ observation of microscale targets in coral reef ecosystems, such as coral tissues, symbiotic zooxanthellae, and plankton, this study proposes and develops a dual-modal dark-field microscopic in situ observation system for coral reefs, named “Jinjing”. An artificial intelligence (AI)-based quantitative analysis workflow is also introduced to meet the underwater observation requirements for targets ranging from 20μm to 1mm at the coral reef benthic interface and in the overlying water column. The system adopts two differentiated illumination modes, namely light-sheet illumination and annular illumination, to accommodate different observation scenarios. Jinjing-Coral employs orthogonal sheet illumination for close-range microscopic observation of the coral benthic interface, while Jinjing-Plankton uses lateral annular illumination for microscopic observation of plankton and suspended particles in vertical profiles of the overlying water. Experimental results show that Jinjing maintains good target visibility under turbid conditions with a light attenuation coefficient of up to 25.63 m⁻¹, and remains capable of effective imaging even in highly turbid seawater with a light attenuation coefficient as high as 29.88 m⁻¹. In the AI-based quantitative analysis workflow, image enhancement, YOLOv8-based target detection and counting, and particle size statistics are integrated to enable quantitative analyses such as biomass abundance estimation and particle size distribution. The results demonstrate that the AI workflow can stably perform target detection, abundance estimation, and particle size analysis, achieving a relative error of less than 6% in abundance estimation for high-concentration algal samples. The developed system has been applied in ecological surveys of coral reef ecosystems in the Xisha Islands, and this study provides technical support for coral reef ecological monitoring and related ecological process research.

Key words: coral reef, microscopic imaging, dark-field imaging, artificial intelligence, quantitative analysis