865 / 2024-09-19 15:44:55

ChinaPlanktonNet: Towards an AIoT-based in situ Imaging Network for Marine Plankton Observation

Real-time processing,In situ observation,Ocean observations,Intelligence of Things,Artificial

In situ observation of plankton based on underwater imaging should theoretically have real-time and continuous advantages. However, current methods rely almost entirely on post-processing to convert image data into observational information. This results in significant delays in information acquisition and exerts enormous pressure on transmission network bandwidth and real-time processing capabilities required for the massive amounts of data collected from remote instruments.

To address these issues, we are developing an Artificial Intelligence of Things (AIoT) marine plankton imaging system as a proof-of-concept to combine edge-computing and cloud-computing to network multiple underwater dark-field imagers (Imaging Plankton Probe, IPP) distributed across various geograpically different locations, aiming to leverage the high temporal resolution advantage of in situ observations while expanding the spatial coverage of ocean observations. At the edge, a client IPP imager first captures in situ images, then processes them through advanced computer vision operations such as object detection, DOF extension, feature extraction, etc. On the cloud side, the server supports complex plankton data management and AI retrieval functions, with an intelligent application layer providing user management, device control and monitoring, and real-time data analysis and visualization functions.

Compared to traditional methods, this AIoT-based multi-location underwater imaging approach has significant advantages. The system optimizes computing and storage resources, enhances real-time data processing and security, improves transmission reliability, and reduces deployment and maintenance costs. By deploying multiple IPPs in various locations in China for periods ranging from one to eight months, we have preliminarily demonstrated the capability of this innovative AIoT-based paradigm to monitor complex marine planktonic dynamics, such as the bloom and decay of Noctiluca scintillans. This offers a more cost-effective solution for expanding global marine plankton observation capabilities in the future.