Expansion into offshore waters has become an important issue in the aquaculture industry, as the global blue economy seeks to expand seafood production. While offshore aquaculture has significant potential to increase total capacity the concept of shifting aquaculture further from the coastline has brought with it several environmental concerns, which in turn has created a challenging permitting landscape.
To fully bring the promise of offshore aquaculture to bear, tools to help facility operators effectively prevent and mitigate entanglement events and reassure federal stakeholders are critical. E ffective monitoring systems can help 1) increase understanding of population dynamics and animal behavior to better characterize entanglement risk, and 2) alert operators of imminent entanglement events.
Current solutions for monitoring protected species near offshore facilities can be time intensive and critically, do not provide real-time information that can be used to trigger rescue or other mitigation measures. There is then a need in the current monitoring landscape for tools which are both automated, to reduce manpower needs of wildlife observation, and real-time, and therefore able to prompt timely intervention and rescue efforts. The evolution of two major technological focus areas, machine learning (ML) and the internet of things (IoT) provide potential solutions . Machine learning uses large amounts of annotated (i.e., preprocessed) data to train software algorithms to perform tasks previously reserved for human operators, and can automate the detection of species of interest from video and audio sensors. The Internet of Things refers to a range of technologies facilitating deployment of embedded sensors and processors, allowing real-time communication between offshore unmanned systems and onshore users, via cellular networks, satellites, or other wireless communication protocols.
Case studies are underway to harness these technologies to address the challenge of marine entanglement. A prototype system developed by Synthetik Applied Technologies as part of a NOAA funded research program, provides an integrated hardware and software platform for the real-time automated detection of marine mammals at offshore facilities and is currently being demonstrated at the University of New Hampshire . The integrated hardware platform consists of embedded processors, communication hardware, and visual and audio sensors. Collected data is processed onboard the device to detect signatures of species of interest from cameras and hydrophones using machine learning models. Detection events are uploaded to an online dashboard where users can view paired video and audio recordings.